The Huanan Seafood Wholesale Market in Wuhan was the early epicenter of the COVID-19 pandemic
武汉华南海鲜批发市场是 COVID-19 大流行的早期震中
Pandemic epicenter 新冠疫情震中
As 2019 turned into 2020, a coronavirus spilled over from wild animals into people, sparking what has become one of the best documented pandemics to afflict humans. However, the origins of the pandemic in December 2019 are controversial. Worobey et al. amassed the variety of evidence from the City of Wuhan, China, where the first human infections were reported. These reports confirm that most of the earliest human cases centered around the Huanan Seafood Wholesale Market. Within the market, the data statistically located the earliest human cases to one section where vendors of live wild animals congregated and where virus-positive environmental samples concentrated. In a related report, Pekar et al. found that genomic diversity before February 2020 comprised two distinct viral lineages, A and B, which were the result of at least two separate cross-species transmission events into humans (see the Perspective by Jiang and Wang). The precise events surrounding virus spillover will always be clouded, but all of the circumstantial evidence so far points to more than one zoonotic event occurring in Huanan market in Wuhan, China, likely during November–December 2019. —CA
随着 2019 年进入 2020 年，一种冠状病毒从野生动物传播到人类，引发了一场成为人类历史上记录最详尽的疫情之一。然而，2019 年 12 月疫情起源存在争议。Worobey 等人收集了来自中国武汉市（首次报告人类感染病例的城市）的各种证据。这些报告证实，大多数最早的人类病例集中在华南海鲜批发市场。在市场内，数据显示最早的人类病例集中在活野生动物摊贩聚集、病毒阳性环境样本集中的区域。在相关报告中，Pekar 等人发现，2020 年 2 月之前的基因组多样性由两个不同的病毒谱系 A 和 B 组成，这是至少两次跨物种传播事件进入人类的结果（参见江和王的观点）。病毒溢出的确切事件将始终笼罩在迷雾中，但迄今为止的所有间接证据都指向中国武汉市华南海鲜市场在 2019 年 11 月至 12 月期间发生了不止一次的动物源性事件。—CA
随着 2019 年进入 2020 年，一种冠状病毒从野生动物传播到人类，引发了一场成为人类历史上记录最详尽的疫情之一。然而，2019 年 12 月疫情起源存在争议。Worobey 等人收集了来自中国武汉市（首次报告人类感染病例的城市）的各种证据。这些报告证实，大多数最早的人类病例集中在华南海鲜批发市场。在市场内，数据显示最早的人类病例集中在活野生动物摊贩聚集、病毒阳性环境样本集中的区域。在相关报告中，Pekar 等人发现，2020 年 2 月之前的基因组多样性由两个不同的病毒谱系 A 和 B 组成，这是至少两次跨物种传播事件进入人类的结果（参见江和王的观点）。病毒溢出的确切事件将始终笼罩在迷雾中，但迄今为止的所有间接证据都指向中国武汉市华南海鲜市场在 2019 年 11 月至 12 月期间发生了不止一次的动物源性事件。—CA
Abstract 摘要
Understanding how severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged in 2019 is critical to preventing future zoonotic outbreaks before they become the next pandemic. The Huanan Seafood Wholesale Market in Wuhan, China, was identified as a likely source of cases in early reports, but later this conclusion became controversial. We show here that the earliest known COVID-19 cases from December 2019, including those without reported direct links, were geographically centered on this market. We report that live SARS-CoV-2–susceptible mammals were sold at the market in late 2019 and that within the market, SARS-CoV-2–positive environmental samples were spatially associated with vendors selling live mammals. Although there is insufficient evidence to define upstream events, and exact circumstances remain obscure, our analyses indicate that the emergence of SARS-CoV-2 occurred through the live wildlife trade in China and show that the Huanan market was the epicenter of the COVID-19 pandemic.
了解 2019 年严重急性呼吸综合征冠状病毒 2（SARS-CoV-2）是如何出现的，对于在疫情成为下一场大流行之前预防未来的动物源性疫情至关重要。中国武汉的华南海鲜批发市场在早期报告中被确定为病例的可能来源，但后来这一结论变得有争议。我们在此表明，2019 年 12 月最早知的 COVID-19 病例，包括那些没有报告直接联系的案件，在地理上集中在该市场。我们报告称，2019 年底市场上有活 SARS-CoV-2 易感哺乳动物出售，并且在该市场中，SARS-CoV-2 阳性的环境样本在空间上与出售活哺乳动物的摊位相关联。尽管缺乏定义上游事件的确凿证据，确切情况仍然不明朗，但我们的分析表明，SARS-CoV-2 的出现是通过中国的活野生动物贸易，并显示华南海鲜市场是 COVID-19 大流行的震中。
了解 2019 年严重急性呼吸综合征冠状病毒 2（SARS-CoV-2）是如何出现的，对于在疫情成为下一场大流行之前预防未来的动物源性疫情至关重要。中国武汉的华南海鲜批发市场在早期报告中被确定为病例的可能来源，但后来这一结论变得有争议。我们在此表明，2019 年 12 月最早知的 COVID-19 病例，包括那些没有报告直接联系的案件，在地理上集中在该市场。我们报告称，2019 年底市场上有活 SARS-CoV-2 易感哺乳动物出售，并且在该市场中，SARS-CoV-2 阳性的环境样本在空间上与出售活哺乳动物的摊位相关联。尽管缺乏定义上游事件的确凿证据，确切情况仍然不明朗，但我们的分析表明，SARS-CoV-2 的出现是通过中国的活野生动物贸易，并显示华南海鲜市场是 COVID-19 大流行的震中。
On 31 December 2019, the World Health Organization (WHO) first learned of an outbreak of severe pneumonia of unknown etiology in Wuhan, Hubei Province, China (1–4), a city of ~11 million people. Of the initial 41 people hospitalized with unknown pneumonia by 2 January 2020, 27 (66%) had direct exposure to the Huanan Wholesale Seafood Market (hereafter, “Huanan market”) (2, 5, 6). These first cases were confirmed to be infected with a novel coronavirus, subsequently named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and were suffering from a disease later named coronavirus disease 2019 (COVID-19). The initial diagnoses of COVID-19 were made in several hospitals independently between 18 and 29 December 2019 (5). These early reports were free from ascertainment bias because they were based on signs and symptoms before the Huanan market was identified as a shared risk factor (5). A subsequent systematic review of all cases reported to China’s National Notifiable Disease Reporting System by hospitals in Wuhan as part of the joint WHO-Chinese “WHO-convened global study of origins of SARS-CoV-2: China Part” (hereafter, “WHO mission report”) (7) showed that 55 of 168 of the earliest known COVID-19 cases were associated with this market. However, the observation that the preponderance of early cases were linked to the Huanan market, alone, does not establish that the pandemic originated there.
2019 年 12 月 31 日，世界卫生组织（WHO）首次了解到中国湖北省武汉市发生了一种原因不明的严重肺炎疫情（1-4），该市人口约为 1100 万。截至 2020 年 1 月 2 日，共有 41 人因不明肺炎住院，其中 27 人（66%）曾直接接触华南海鲜市场（以下简称“华南海鲜市场”）（2，5，6）。这些最初病例被证实感染了一种新型冠状病毒，后来命名为严重急性呼吸综合征冠状病毒 2（SARS-CoV-2），并患有后来命名为 2019 冠状病毒病（COVID-19）的疾病。COVID-19 的初步诊断是在 2019 年 12 月 18 日至 29 日期间由几家医院独立做出的（5）。这些早期报告没有确定偏差，因为它们基于华南海鲜市场被确定为共同风险因素之前的症状和体征（5）。 对武汉医院报告给中国国家传染病报告系统的所有病例进行的后续系统回顾显示，在最早知的 168 例 COVID-19 病例中，有 55 例与该市场有关。然而，仅观察到大多数早期病例与华南海鲜市场有关，并不能证明大流行起源于那里。
2019 年 12 月 31 日，世界卫生组织（WHO）首次了解到中国湖北省武汉市发生了一种原因不明的严重肺炎疫情（1-4），该市人口约为 1100 万。截至 2020 年 1 月 2 日，共有 41 人因不明肺炎住院，其中 27 人（66%）曾直接接触华南海鲜市场（以下简称“华南海鲜市场”）（2，5，6）。这些最初病例被证实感染了一种新型冠状病毒，后来命名为严重急性呼吸综合征冠状病毒 2（SARS-CoV-2），并患有后来命名为 2019 冠状病毒病（COVID-19）的疾病。COVID-19 的初步诊断是在 2019 年 12 月 18 日至 29 日期间由几家医院独立做出的（5）。这些早期报告没有确定偏差，因为它们基于华南海鲜市场被确定为共同风险因素之前的症状和体征（5）。 对武汉医院报告给中国国家传染病报告系统的所有病例进行的后续系统回顾显示，在最早知的 168 例 COVID-19 病例中，有 55 例与该市场有关。然而，仅观察到大多数早期病例与华南海鲜市场有关，并不能证明大流行起源于那里。
Sustained live mammal sales during 2019 occurred at the Huanan market and three other markets in Wuhan, and included wild and farmed wildlife (8). Several of these species are known to be experimentally susceptible to SARS-related coronaviruses (SARSr-CoVs) such as SARS-CoV (hereafter, “SARS-CoV-1”) and SARS-CoV-2 (9–11). During the early stages of the COVID-19 pandemic, animals sold at the Huanan market were hypothesized to be the source of the unexplained pneumonia cases (12–19) (data S1), consistent with the emergence of SARS-CoV-1 from 2002 to 2004 (20), as well as other viral zoonoses (21–23). This led to the decision to close and sanitize the Huanan market on 1 January 2020, with environmental samples also being collected from vendors’ stalls (7, 12, 24) (data S1).
2019 年，在武汉华南海鲜市场及另外三个市场持续销售活体哺乳动物，包括野生和养殖野生动物（8）。其中一些物种已知对 SARS 相关冠状病毒（SARSr-CoVs）如 SARS-CoV（以下简称“SARS-CoV-1”）和 SARS-CoV-2（9-11）具有实验性易感性。在 COVID-19 大流行早期阶段，华南海鲜市场销售的动物被推测是未解释肺炎病例（12-19）的来源（数据 S1），这与 2002 年至 2004 年 SARS-CoV-1 的出现以及其他病毒性人畜共患病（21-23）一致。这导致决定于 2020 年 1 月 1 日关闭并消毒华南海鲜市场，同时从摊位收集环境样本（7，12，24）（数据 S1）。
2019 年，在武汉华南海鲜市场及另外三个市场持续销售活体哺乳动物，包括野生和养殖野生动物（8）。其中一些物种已知对 SARS 相关冠状病毒（SARSr-CoVs）如 SARS-CoV（以下简称“SARS-CoV-1”）和 SARS-CoV-2（9-11）具有实验性易感性。在 COVID-19 大流行早期阶段，华南海鲜市场销售的动物被推测是未解释肺炎病例（12-19）的来源（数据 S1），这与 2002 年至 2004 年 SARS-CoV-1 的出现以及其他病毒性人畜共患病（21-23）一致。这导致决定于 2020 年 1 月 1 日关闭并消毒华南海鲜市场，同时从摊位收集环境样本（7，12，24）（数据 S1）。
Determining the epicenter of the COVID-19 pandemic at the neighborhood level rather than at the city level could help to resolve whether SARS-CoV-2 had a zoonotic origin, similar to SARS-CoV-1 (20). In this study, we obtained data from a range of sources to test the hypothesis that the COVID-19 pandemic began at the Huanan market. Despite limited testing of live wildlife sold at the market, collectively, our results provide evidence that the Huanan market was the early epicenter of the COVID-19 pandemic and suggest that SARS-CoV-2 likely emerged from the live wildlife trade in China. However, events upstream of the market, as well as exact circumstances at the market, remain obscure, highlighting the need for further studies to understand and lower the risk of future pandemics.
确定 COVID-19 大流行在社区层面的震中，而不是在城市层面，有助于解决 SARS-CoV-2 是否具有与 SARS-CoV-1 类似的动物源性。在本研究中，我们收集了来自多个来源的数据来测试 COVID-19 大流行始于华南海鲜市场的假设。尽管对市场上销售的活野生动物进行了有限的检测，但我们的结果总体上提供了证据，表明华南海鲜市场是 COVID-19 大流行的早期震中，并表明 SARS-CoV-2 很可能起源于中国的活野生动物交易。然而，市场上的上游事件以及市场的确切情况仍然模糊不清，突出了进一步研究以理解和降低未来大流行风险的需求。
确定 COVID-19 大流行在社区层面的震中，而不是在城市层面，有助于解决 SARS-CoV-2 是否具有与 SARS-CoV-1 类似的动物源性。在本研究中，我们收集了来自多个来源的数据来测试 COVID-19 大流行始于华南海鲜市场的假设。尽管对市场上销售的活野生动物进行了有限的检测，但我们的结果总体上提供了证据，表明华南海鲜市场是 COVID-19 大流行的早期震中，并表明 SARS-CoV-2 很可能起源于中国的活野生动物交易。然而，市场上的上游事件以及市场的确切情况仍然模糊不清，突出了进一步研究以理解和降低未来大流行风险的需求。
Results 结果
Early cases lived near to and centered on the Huanan market
早期病例生活在华南市场附近，并围绕该市场集中
The 2021 WHO mission report identified 174 COVID-19 cases in Hubei Province in December 2019 after careful examination of reported case histories (7). Although geographical coordinates of the residential locations of the 164 cases who lived within Wuhan were unavailable, we were able to reliably extract the latitude and longitude coordinates of 155 cases from maps in the report (figs. S1 to S8).
2021 年世界卫生组织任务报告在仔细审查报告病例史后，在 2019 年 12 月确定了湖北省 174 例 COVID-19 病例。尽管居住在武汉的 164 例病例的地理位置坐标不可用，但我们能够从报告中的地图（图 S1 至 S8）中可靠地提取出 155 例病例的经纬度坐标。
2021 年世界卫生组织任务报告在仔细审查报告病例史后，在 2019 年 12 月确定了湖北省 174 例 COVID-19 病例。尽管居住在武汉的 164 例病例的地理位置坐标不可用，但我们能够从报告中的地图（图 S1 至 S8）中可靠地提取出 155 例病例的经纬度坐标。
Although early COVID-19 cases occurred across Wuhan, most clustered in central Wuhan near the west bank of the Yangtze River, with a high density of cases near to, and surrounding, the Huanan market (Fig. 1AOpens in image viewer). We used a kernel density estimate (KDE) to reconstruct an underlying probability density function from which the home locations for each case were drawn (25). Using all 155 of the December 2019 cases, the location of the Huanan market lies within the highest density contour that contains 1% of the probability mass (Fig. 1BOpens in image viewer). For a KDE estimated using the 120 cases with no known linkage to the market, the market remains within the highest density 1% contour (Fig. 1COpens in image viewer). The clustering of COVID-19 cases in December around the Huanan market (Fig. 1, B and COpens in image viewer, insets) contrasts with the pattern of widely dispersed cases across Wuhan by early January through mid-February 2020 (Fig. 1, D and EOpens in image viewer), which we mapped using location data from individuals who had used a COVID-19 assistance channel on Sina Weibo, a Chinese social media platform (26). Weibo-based data analyses showed that, unlike early COVID-19 cases, by January and February, many of the sick individuals who sought help resided in highly populated areas of the city, particularly in areas with a high density of older people (Fig. 1EOpens in image viewer and figs. S9 and S10).
尽管早期 COVID-19 病例出现在武汉全市，但大多数病例集中在武汉市中心，靠近长江西岸，病例高密度区域靠近并环绕着华南海鲜市场（ Fig. 1AOpens in image viewer ）。我们使用核密度估计（KDE）从潜在的概率密度函数中重建每个病例的居住地（25）。使用 2019 年 12 月的所有 155 个病例，华南海鲜市场的位置位于包含 1%概率质量的最高密度轮廓内（ Fig. 1BOpens in image viewer ）。对于使用没有与市场已知联系的 120 个病例估计的 KDE，市场仍然位于最高密度 1%轮廓内（ Fig. 1COpens in image viewer ）。2020 年 12 月围绕华南海鲜市场的 COVID-19 病例聚集（ Fig. 1, B and COpens in image viewer ，插图）与 2020 年 1 月初至 2 月中旬武汉广泛分散的病例模式形成对比（ Fig. 1, D and EOpens in image viewer ），我们使用来自使用新浪微博上 COVID-19 援助渠道的个人位置数据绘制了这一模式（26）。 微博数据分析显示，与早期 COVID-19 病例不同，到一月份和二月份，许多寻求帮助的患病个体居住在城市人口密集地区，尤其是在老年人高密度居住的地区（ Fig. 1EOpens in image viewer 和图 S9 和 S10）。
尽管早期 COVID-19 病例出现在武汉全市，但大多数病例集中在武汉市中心，靠近长江西岸，病例高密度区域靠近并环绕着华南海鲜市场（ Fig. 1AOpens in image viewer ）。我们使用核密度估计（KDE）从潜在的概率密度函数中重建每个病例的居住地（25）。使用 2019 年 12 月的所有 155 个病例，华南海鲜市场的位置位于包含 1%概率质量的最高密度轮廓内（ Fig. 1BOpens in image viewer ）。对于使用没有与市场已知联系的 120 个病例估计的 KDE，市场仍然位于最高密度 1%轮廓内（ Fig. 1COpens in image viewer ）。2020 年 12 月围绕华南海鲜市场的 COVID-19 病例聚集（ Fig. 1, B and COpens in image viewer ，插图）与 2020 年 1 月初至 2 月中旬武汉广泛分散的病例模式形成对比（ Fig. 1, D and EOpens in image viewer ），我们使用来自使用新浪微博上 COVID-19 援助渠道的个人位置数据绘制了这一模式（26）。 微博数据分析显示，与早期 COVID-19 病例不同，到一月份和二月份，许多寻求帮助的患病个体居住在城市人口密集地区，尤其是在老年人高密度居住的地区（ Fig. 1EOpens in image viewer 和图 S9 和 S10）。
Fig. 1. Spatial patterns of COVID-19 cases in Wuhan in December 2019 and January–February 2020.
图 1. 2019 年 12 月和 2020 年 1 月至 2 月武汉 COVID-19 病例的空间分布模式。
图 1. 2019 年 12 月和 2020 年 1 月至 2 月武汉 COVID-19 病例的空间分布模式。
(A) Locations of the 155 cases that we extracted from the WHO mission report (7). Inset: map of Wuhan with the December 2019 cases indicated with gray dots (no cases are obscured by the inset). In both the inset and the main panel, the location of the Huanan market is indicated with a red square. (B) Probability density contours reconstructed by a KDE using all 155 COVID-19 cases locations from December 2019. The highest density 50% contour marked is the area for which cases drawn from the probability distribution are as likely to lie inside as outside. Also shown are the highest density 25%, 10%, 5%, and 1% contours. Inset: expanded view and the highest density 1% probability density contour. (C) Probability density contours reconstructed using the 120 COVID-19 cases locations from December 2019 that were unlinked to the Huanan market. (D) Locations of 737 COVID-19 cases from Weibo data dating to January–February 2020. (E) The same highest probability density contours (50% through 1%) as shown in (B) and (C) for 737 COVID-19 case locations from Weibo data.
（A）我们从世界卫生组织任务报告（7）中提取的 155 个病例的位置。插图：武汉地图，用灰色点标出 2019 年 12 月的病例（插图内没有病例被遮挡）。在插图和主面板中，用红色方块标出华南海鲜市场的位置。（B）使用 2019 年 12 月所有 155 个 COVID-19 病例位置重建的概率密度等高线。标记的最高密度 50%等高线是抽取的病例在概率分布内部和外部一样可能存在的区域。还显示了最高密度 25%、10%、5%和 1%的等高线。插图：放大视图和最高密度 1%概率密度等高线。（C）使用 2019 年 12 月与华南海鲜市场未关联的 120 个 COVID-19 病例位置重建的概率密度等高线。（D）来自 2020 年 1 月至 2 月微博数据的 737 个 COVID-19 病例的位置。（E）与（B）和（C）中显示的相同最高概率密度等高线（从 50%到 1%）用于微博数据中的 737 个 COVID-19 病例位置。
（A）我们从世界卫生组织任务报告（7）中提取的 155 个病例的位置。插图：武汉地图，用灰色点标出 2019 年 12 月的病例（插图内没有病例被遮挡）。在插图和主面板中，用红色方块标出华南海鲜市场的位置。（B）使用 2019 年 12 月所有 155 个 COVID-19 病例位置重建的概率密度等高线。标记的最高密度 50%等高线是抽取的病例在概率分布内部和外部一样可能存在的区域。还显示了最高密度 25%、10%、5%和 1%的等高线。插图：放大视图和最高密度 1%概率密度等高线。（C）使用 2019 年 12 月与华南海鲜市场未关联的 120 个 COVID-19 病例位置重建的概率密度等高线。（D）来自 2020 年 1 月至 2 月微博数据的 737 个 COVID-19 病例的位置。（E）与（B）和（C）中显示的相同最高概率密度等高线（从 50%到 1%）用于微博数据中的 737 个 COVID-19 病例位置。
We also investigated whether the December COVID-19 cases were closer to the market than expected based on an empirical null distribution of Wuhan’s population density [data from WorldPop.org (27, 28)], with a median distance to the Huanan market of 16.11 km (25). To account for older individuals being more likely to be hospitalized and sick with COVID-19 (29), we age-matched the population data to the December 2019 COVID-19 case data. We considered three categories of cases, which were all significantly closer to the Huanan market than expected: (i) all cases (median distance 4.28 km; P < 0.001), (ii) cases linked directly to the Huanan market (median distance 5.74 km; P < 0.001), and (iii) cases with no evidence of a direct link to the Huanan market (median distance 4.00 km; P < 0.001) (Fig. 2AOpens in image viewer). The cases with no known link to the market on average resided closer to the market than the cases with links to the market (P = 0.029). Furthermore, the distances between the center points (Fig. 2BOpens in image viewer) and the Huanan market were shorter than expected for all categories of December cases compared with the empirical null distribution of Wuhan’s population density (Fig. 2AOpens in image viewer). For all December cases, the center point was located 1.02 km away (P = 0.007); for cases with market links, it was 2.28 km away (P = 0.034); and for the cases with no reported link to the market, it was 0.91 km away (P = 0.006). By comparison, the center point of age-matched samples drawn from the empirical null distribution was 4.65 km away from the market (Fig. 2AOpens in image viewer).
我们也调查了 12 月份的 COVID-19 病例是否比预期的更接近市场，这是基于武汉人口密度的经验性零分布[数据来自 WorldPop.org(27, 28)]，与华南海鲜市场的中位距离为 16.11 公里(25)。为了考虑到老年人更有可能因 COVID-19 而住院和患病(29)，我们将人口数据与 2019 年 12 月的 COVID-19 病例数据进行了年龄匹配。我们考虑了三种病例类别，它们都比预期的更接近华南海鲜市场：(i)所有病例(中位距离 4.28 公里；P < 0.001)，(ii)直接与华南海鲜市场相关的病例(中位距离 5.74 公里；P < 0.001)，以及(iii)没有直接联系到华南海鲜市场的病例(中位距离 4.00 公里；P < 0.001)( Fig. 2AOpens in image viewer )。没有已知市场联系的病例平均居住距离比有市场联系的病例更近(P = 0.029)。此外，与武汉人口密度的经验性零分布( Fig. 2AOpens in image viewer )相比，所有 12 月份病例类别的中心点( Fig. 2BOpens in image viewer )与华南海鲜市场的距离都短于预期。 对于所有 12 月病例，中心点位于 1.02 公里处（P = 0.007）；对于有市场联系的病例，它位于 2.28 公里处（P = 0.034）；而对于没有报告与市场联系的病例，它位于 0.91 公里处（P = 0.006）。相比之下，从经验零分布中抽取的年龄匹配样本的中心点距离市场 4.65 公里（ Fig. 2AOpens in image viewer ）。
我们也调查了 12 月份的 COVID-19 病例是否比预期的更接近市场，这是基于武汉人口密度的经验性零分布[数据来自 WorldPop.org(27, 28)]，与华南海鲜市场的中位距离为 16.11 公里(25)。为了考虑到老年人更有可能因 COVID-19 而住院和患病(29)，我们将人口数据与 2019 年 12 月的 COVID-19 病例数据进行了年龄匹配。我们考虑了三种病例类别，它们都比预期的更接近华南海鲜市场：(i)所有病例(中位距离 4.28 公里；P < 0.001)，(ii)直接与华南海鲜市场相关的病例(中位距离 5.74 公里；P < 0.001)，以及(iii)没有直接联系到华南海鲜市场的病例(中位距离 4.00 公里；P < 0.001)( Fig. 2AOpens in image viewer )。没有已知市场联系的病例平均居住距离比有市场联系的病例更近(P = 0.029)。此外，与武汉人口密度的经验性零分布( Fig. 2AOpens in image viewer )相比，所有 12 月份病例类别的中心点( Fig. 2BOpens in image viewer )与华南海鲜市场的距离都短于预期。 对于所有 12 月病例，中心点位于 1.02 公里处（P = 0.007）；对于有市场联系的病例，它位于 2.28 公里处（P = 0.034）；而对于没有报告与市场联系的病例，它位于 0.91 公里处（P = 0.006）。相比之下，从经验零分布中抽取的年龄匹配样本的中心点距离市场 4.65 公里（ Fig. 2AOpens in image viewer ）。
Fig. 2. Spatial analyses.
图 2. 空间分析
图 2. 空间分析
(A) Inset: map of Wuhan, with gray dots indicating the 1000 random samples from the WorldPop.com null distribution. In the main panel, the median distance between Huanan market and the WorldPop.org null distribution is indicated by the outer black circle. December 2019 cases are indicated by concentric red circles (distances to Huanan market are described in the purple boxes). The center point of Wuhan population density data is indicated by a blue dot. Center points of December 2019 case locations are shown as follows: red dots indicate “all,” “linked,” and “unlinked” cases, and the yellow dot indicates lineage B cases. Distance from center points to Huanan market are described in orange boxes. (B) Schematic showing how cases can be near to, but not centered on, a specific location. We hypothesized that if the Huanan market were the epicenter of the pandemic, then early cases should fall not just unexpectedly near to it but should also be unexpectedly centered on it (see the materials and methods). The blue dots show how hypothetical cases quite near the Huanan market could nevertheless not be centered on it. (C) Tolerance contours based on relative risk of COVID-19 cases in December 2019 versus data from January–February 2020. The gray dots show the December case locations. The contours represent the probability of observing that density of December cases within the bounds of the given contour if the December cases had been drawn from the same spatial distribution as the January–February data.
(A) 插图：武汉地图，灰色点表示来自 WorldPop.com 空分布的 1000 个随机样本。在主面板中，用外圈黑色圆圈表示华南海鲜市场和 WorldPop.org 空分布之间的中位距离。2019 年 12 月的病例用同心红色圆圈表示（距离华南海鲜市场的距离在紫色方框中描述）。武汉人口密度数据的中心点用蓝色点表示。2019 年 12 月病例位置的中心点如下所示：红色点表示“所有”、“关联”和“非关联”病例，黄色点表示 B 系病例。中心点到华南海鲜市场的距离在橙色方框中描述。(B) 示意图显示病例可以靠近但不在特定位置的中心。我们假设如果华南海鲜市场是疫情的震中，那么早期病例不仅应该意外地靠近它，还应该意外地集中在它周围（见材料和方法）。蓝色点显示假设的病例虽然非常靠近华南海鲜市场，但仍然没有集中在它周围。 (基于 2019 年 12 月 COVID-19 病例相对风险与 2020 年 1 月至 2 月数据的容差等值线。灰色点表示 12 月病例位置。等值线代表在给定等值线范围内观察到 12 月病例密度的概率，前提是 12 月病例是从与 1 月至 2 月数据相同的空间分布中抽取的。)
(A) 插图：武汉地图，灰色点表示来自 WorldPop.com 空分布的 1000 个随机样本。在主面板中，用外圈黑色圆圈表示华南海鲜市场和 WorldPop.org 空分布之间的中位距离。2019 年 12 月的病例用同心红色圆圈表示（距离华南海鲜市场的距离在紫色方框中描述）。武汉人口密度数据的中心点用蓝色点表示。2019 年 12 月病例位置的中心点如下所示：红色点表示“所有”、“关联”和“非关联”病例，黄色点表示 B 系病例。中心点到华南海鲜市场的距离在橙色方框中描述。(B) 示意图显示病例可以靠近但不在特定位置的中心。我们假设如果华南海鲜市场是疫情的震中，那么早期病例不仅应该意外地靠近它，还应该意外地集中在它周围（见材料和方法）。蓝色点显示假设的病例虽然非常靠近华南海鲜市场，但仍然没有集中在它周围。 (基于 2019 年 12 月 COVID-19 病例相对风险与 2020 年 1 月至 2 月数据的容差等值线。灰色点表示 12 月病例位置。等值线代表在给定等值线范围内观察到 12 月病例密度的概率，前提是 12 月病例是从与 1 月至 2 月数据相同的空间分布中抽取的。)
We tested the robustness of our results for the possibility of ascertainment bias (25). For all mapped cases (n = 155), under the “center-point distance to the Huanan market” test, the 38 cases residing closest to the market (within a radius of 1.6 km) could be removed from the dataset before losing significance at the α = 0.05 level (fig. S12). For the “median distance to Huanan market” test, we could remove 98 cases (63%) (r = 5.8 km). For cases not directly linked to the Huanan market (n = 120), we could remove 36 (30%) (r = 1.5 km) and 81 (68%) (r = 4.3 km) cases for the two tests, respectively, before losing significance at the α = 0.05 level (fig. S12).
我们测试了结果的稳健性，以排除确定偏差的可能性（25）。对于所有已映射的案例（n = 155），在“中心点距离华南海鲜市场”测试中，距离市场最近（1.6 公里范围内）的 38 个案例在α = 0.05 水平上失去显著性之前可以从数据集中移除（图 S12）。对于“中位数距离华南海鲜市场”测试，我们可以移除 98 个案例（63%）（r = 5.8 公里）。对于与华南海鲜市场没有直接联系的案例（n = 120），在α = 0.05 水平上失去显著性之前，两个测试分别可以移除 36 个（30%）（r = 1.5 公里）和 81 个（68%）（r = 4.3 公里）案例（图 S12）。
我们测试了结果的稳健性，以排除确定偏差的可能性（25）。对于所有已映射的案例（n = 155），在“中心点距离华南海鲜市场”测试中，距离市场最近（1.6 公里范围内）的 38 个案例在α = 0.05 水平上失去显著性之前可以从数据集中移除（图 S12）。对于“中位数距离华南海鲜市场”测试，我们可以移除 98 个案例（63%）（r = 5.8 公里）。对于与华南海鲜市场没有直接联系的案例（n = 120），在α = 0.05 水平上失去显著性之前，两个测试分别可以移除 36 个（30%）（r = 1.5 公里）和 81 个（68%）（r = 4.3 公里）案例（图 S12）。
We performed a spatial relative risk analysis (25) to compare December 2019 COVID-19 cases with January–February 2020 cases reported through Weibo (Fig. 2COpens in image viewer). The Huanan market is located within a well-defined area with high case density that would be expected to be observed in <1 in 100,000 samplings of the Weibo data empirical distribution (the relative risk analysis is shown in Fig. 2COpens in image viewer and the control distribution in Fig. 1DOpens in image viewer). No other regions in Wuhan showed a comparable case density.
我们进行了一项空间相对风险分析（25），以比较 2019 年 12 月与 2020 年 1 月至 2 月通过微博（ Fig. 2COpens in image viewer ）报告的 COVID-19 病例。华南海鲜市场位于一个定义明确的区域，病例密度高，预计在微博数据经验分布的<1/100,000 样本中观察到。相对风险分析显示在 Fig. 2COpens in image viewer ，控制分布显示在 Fig. 1DOpens in image viewer 。武汉其他地区没有显示出可比的病例密度。
我们进行了一项空间相对风险分析（25），以比较 2019 年 12 月与 2020 年 1 月至 2 月通过微博（ Fig. 2COpens in image viewer ）报告的 COVID-19 病例。华南海鲜市场位于一个定义明确的区域，病例密度高，预计在微博数据经验分布的<1/100,000 样本中观察到。相对风险分析显示在 Fig. 2COpens in image viewer ，控制分布显示在 Fig. 1DOpens in image viewer 。武汉其他地区没有显示出可比的病例密度。
Both early lineages of SARS-CoV-2 were geographically associated with the market
SARS-CoV-2 的早期谱系与市场在地理上相关
Two lineages of SARS-CoV-2 designated A and B (30) have co-circulated globally since early in the COVID-19 pandemic (31). Until a report in a recent preprint (24), only lineage B sequences had been sampled at the Huanan market. The 11 lineage B cases from December 2019 for which we have location information resided closer than expected to the Huanan market compared with the age-matched Wuhan population distribution (median distance 8.30 km; P = 0.017) (25). The center point of the 11 lineage B cases was 1.95 km from the Huanan market, also closer than expected (P = 0.026). The two lineage A cases for which we have location information involved the two earliest lineage A genomes known to date. Neither case reported any contact with the Huanan market (7). The first case was detected before any knowledge of a possible association of the unexplained pneumonia in Wuhan with the Huanan market (5), and therefore could not have been a product of ascertainment bias in favor of cases residing near the market. The second case had stayed in a hotel near the market (32) for the 5 days preceding symptom onset (25). Relative to the age-matched Wuhan population distribution, the first individual resided closer to the Huanan market (2.31 km) than expected (P = 0.034). Although the exact location of the hotel near the market was not reported (32), there are at least 20 hotels within 500 m (table S1). Under the conservative assumption that the hotel could have been located as far as 2.31 km from the Huanan market (as was the residence of the other lineage A case), and assuming that this location is comparable to a residential location given the timing of the stay before symptom onset (25), it would be unlikely to observe both of the earliest lineage A cases this near to the Huanan market (P = 0.001 or less). The finding that both identified lineage A cases had a geographical connection to the market, in combination with the detection of lineage A within the market (24), support the likelihood that during the early epidemic, lineage A was, like lineage B, disseminating outward from the Huanan market into the surrounding neighborhoods.
两种 SARS-CoV-2 谱系 A 和 B（30）自 COVID-19 大流行初期以来在全球范围内共循环（31）。在最近一篇预印本报告（24）之前，只有 B 谱系序列在华南市场进行了采样。我们拥有的 2019 年 12 月 11 个 B 谱系病例的位置信息显示，与年龄匹配的武汉人口分布相比，这些病例居住地距离华南市场比预期更近（中位距离 8.30 公里；P = 0.017）（25）。11 个 B 谱系病例的中心点距离华南市场 1.95 公里，也比预期更近（P = 0.026）。我们拥有的两个 A 谱系病例的位置信息涉及迄今为止已知的两个最早的 A 谱系基因组。这两个病例都没有报告与华南市场的接触（7）。第一个病例是在任何关于武汉不明肺炎可能与华南市场有关的知识之前被检测到的（5），因此不可能是对居住在市场附近病例的确认偏差的结果。第二个病例在症状出现前 5 天住在市场附近的一家酒店（32）。 与年龄匹配的武汉人口分布相比，第一个个体居住地距离华南海鲜市场更近（2.31 公里），超出预期（P = 0.034）。尽管未报告市场附近酒店的准确位置（32），但至少有 20 家酒店在 500 米范围内（表 S1）。在保守假设下，酒店可能位于距离华南海鲜市场 2.31 公里处（正如其他 A 系病例的居住地），并且考虑到症状出现前停留的时间（25），观察到的最早的两个 A 系病例都如此接近华南海鲜市场是不太可能的（P = 0.001 或更低）。发现两个确定的 A 系病例都与市场有地理联系，结合市场内检测到 A 系（24），支持了在早期疫情期间，A 系像 B 系一样，可能从华南海鲜市场向外传播到周边社区的假设。
两种 SARS-CoV-2 谱系 A 和 B（30）自 COVID-19 大流行初期以来在全球范围内共循环（31）。在最近一篇预印本报告（24）之前，只有 B 谱系序列在华南市场进行了采样。我们拥有的 2019 年 12 月 11 个 B 谱系病例的位置信息显示，与年龄匹配的武汉人口分布相比，这些病例居住地距离华南市场比预期更近（中位距离 8.30 公里；P = 0.017）（25）。11 个 B 谱系病例的中心点距离华南市场 1.95 公里，也比预期更近（P = 0.026）。我们拥有的两个 A 谱系病例的位置信息涉及迄今为止已知的两个最早的 A 谱系基因组。这两个病例都没有报告与华南市场的接触（7）。第一个病例是在任何关于武汉不明肺炎可能与华南市场有关的知识之前被检测到的（5），因此不可能是对居住在市场附近病例的确认偏差的结果。第二个病例在症状出现前 5 天住在市场附近的一家酒店（32）。 与年龄匹配的武汉人口分布相比，第一个个体居住地距离华南海鲜市场更近（2.31 公里），超出预期（P = 0.034）。尽管未报告市场附近酒店的准确位置（32），但至少有 20 家酒店在 500 米范围内（表 S1）。在保守假设下，酒店可能位于距离华南海鲜市场 2.31 公里处（正如其他 A 系病例的居住地），并且考虑到症状出现前停留的时间（25），观察到的最早的两个 A 系病例都如此接近华南海鲜市场是不太可能的（P = 0.001 或更低）。发现两个确定的 A 系病例都与市场有地理联系，结合市场内检测到 A 系（24），支持了在早期疫情期间，A 系像 B 系一样，可能从华南海鲜市场向外传播到周边社区的假设。
Our statistical results were robust to a range of factors, for example, the use of an empirical control distribution that was based on presumptive COVID-19 cases locations later in the Wuhan epidemic (Weibo data); laboratory-confirmed versus clinically diagnosed cases; and uncertainty in case location or missing data (figs. S13 to S15) (25). For instance, we artificially introduced location uncertainty (“noise”) in each case location in our dataset by randomly resampling each point within a circle of radius 1000 m centered on its original center point, and the conclusions were unaffected (fig. S13). The extraction method that we used actually introduced only up to ~50 m of noise in each case location estimate (fig. S7), ruling out the possibility that our overall results were affected by this source of error. The results were also robust when corrected for multiple-hypothesis testing (table S4).
我们的统计结果对一系列因素具有稳健性，例如，使用基于武汉疫情后期疑似 COVID-19 病例位置的实证控制分布（微博数据）；实验室确诊病例与临床诊断病例；以及病例位置的不确定性或数据缺失（图 S13 至 S15）（25）。例如，我们通过在以原始中心点为中心、半径为 1000 米的圆内随机重采样每个点，在我们的数据集中对每个病例位置人工引入位置不确定性（“噪声”），结论并未受到影响（图 S13）。我们使用的提取方法实际上在每个病例位置估计中只引入了最多约 50 米的噪声（图 S7），排除了我们的总体结果可能受到这种错误来源的影响的可能性。在纠正了多重假设检验后，结果也具有稳健性（表 S4）。
我们的统计结果对一系列因素具有稳健性，例如，使用基于武汉疫情后期疑似 COVID-19 病例位置的实证控制分布（微博数据）；实验室确诊病例与临床诊断病例；以及病例位置的不确定性或数据缺失（图 S13 至 S15）（25）。例如，我们通过在以原始中心点为中心、半径为 1000 米的圆内随机重采样每个点，在我们的数据集中对每个病例位置人工引入位置不确定性（“噪声”），结论并未受到影响（图 S13）。我们使用的提取方法实际上在每个病例位置估计中只引入了最多约 50 米的噪声（图 S7），排除了我们的总体结果可能受到这种错误来源的影响的可能性。在纠正了多重假设检验后，结果也具有稳健性（表 S4）。
Wild animal trading in Wuhan markets
武汉市场上的野生动物交易
In addition to selling seafood, poultry, and other commodities, the Huanan market was among four markets in Wuhan reported to consistently sell a variety of live wild-captured or farmed mammal species in the years and months leading up to the COVID-19 pandemic (8). There are, however, no prior reports of which species, if any, were sold at the Huanan market in the months leading up to the pandemic. Here, we report that multiple plausible intermediate wildlife hosts of SARS-CoV-2 progenitor viruses, including red foxes (Vulpes vulpes), hog badgers (Arctonyx albogularis), and common raccoon dogs (Nyctereutes procyonoides), were sold live at the Huanan market up until at least November 2019 (Table 1Opens in image viewer and table S5). No reports are known to be available for SARS-CoV-2 test results from these mammals at the Huanan market. Despite a general slowdown in live animal sales during the winter months, we report that raccoon dogs, which are sold for both meat and fur, were consistently available for sale throughout the year, including at the Huanan market in November 2019 (Table 1Opens in image viewer and table S5).
除了销售海鲜、家禽和其他商品外，华南海鲜市场是武汉四个报告称持续销售各种活野生捕获或养殖哺乳动物种类的市场之一，在 COVID-19 大流行前几年和几个月内（8）。然而，没有关于大流行前几个月华南海鲜市场销售哪些物种（如果有的话）的先前报道。在这里，我们报告说，包括赤狐（Vulpes vulpes）、猪獾（Arctonyx albogularis）和普通貉（Nyctereutes procyonoides）在内的多种可能的 SARS-CoV-2 祖病毒中间野生动物宿主，在至少 2019 年 11 月之前在华南海鲜市场活体销售（ Table 1Opens in image viewer 和表 S5）。据知，没有关于这些哺乳动物在华南海鲜市场的 SARS-CoV-2 检测结果的相关报告。尽管在冬季月份活体动物销售普遍放缓，但我们报告说，既用于肉食也用于毛皮销售的貉，在整个年份都持续有售，包括 2019 年 11 月的华南海鲜市场（ Table 1Opens in image viewer 和表 S5）。
除了销售海鲜、家禽和其他商品外，华南海鲜市场是武汉四个报告称持续销售各种活野生捕获或养殖哺乳动物种类的市场之一，在 COVID-19 大流行前几年和几个月内（8）。然而，没有关于大流行前几个月华南海鲜市场销售哪些物种（如果有的话）的先前报道。在这里，我们报告说，包括赤狐（Vulpes vulpes）、猪獾（Arctonyx albogularis）和普通貉（Nyctereutes procyonoides）在内的多种可能的 SARS-CoV-2 祖病毒中间野生动物宿主，在至少 2019 年 11 月之前在华南海鲜市场活体销售（ Table 1Opens in image viewer 和表 S5）。据知，没有关于这些哺乳动物在华南海鲜市场的 SARS-CoV-2 检测结果的相关报告。尽管在冬季月份活体动物销售普遍放缓，但我们报告说，既用于肉食也用于毛皮销售的貉，在整个年份都持续有售，包括 2019 年 11 月的华南海鲜市场（ Table 1Opens in image viewer 和表 S5）。
| Species (susceptibility*) 物种（易感性*） | Family 家庭 (susceptibility*) 敏感性* | Order (susceptibility*) 订单（敏感性*） | Observed at Huanan market November 2019 观测于 2020 年 11 月华南市场 |
|---|---|---|---|
| Raccoon dog (Nyctereutes procyonoides) (Y) 浣熊犬（Nyctereutes procyonoides） | Canidae (Y) 犬科（Y） | Carnivora (Y) 肉食目（Y） | Y |
| Amur hedgehog (Erinaceus amurensis) 东北刺猬（Erinaceus amurensis） | Erinaceidae 刺猬科 | Eulipotyphla 欧脂齿目 | Y |
| Hog badger (Arctonyx albogularis) (Y) | Mustelidae (Y) | Carnivora (Y) | Y |
| Asian badger (Meles leucurus) | Mustelidae (Y) | Carnivora (Y) | Y |
| Chinese hare (Lepus sinensis) | Leporidae (Y) | Lagomorpha (Y) | Y |
| Chinese bamboo rat (Rhizomys sinensis) (Y) | Spalacidae (Y) | Rodentia (Y) | Y |
| Malayan porcupine (Hystrix brachyura) | Hystricidae | Rodentia (Y) | Y |
| Chinese muntjac (Muntiacus reevesi) | Cervidae (Y) | Artiodactyla (Y) | Y |
| Marmot (Marmota himalayana) | Sciuridae | Rodentia (Y) | Y |
| Red fox (Vulpes vulpes) (Y) | Canidae (Y) | Carnivora (Y) | Y |
| Siberian weasel (Mustela sibirica) | Mustelidae (Y) | Carnivora (Y) | N† |
| Pallas’s squirrel (Callosciurus erythraeus) | Sciuridae | Rodentia (Y) | N |
| Masked palm civet (Paguma larvata) (Y) | Viverridae (Y) | Carnivora (Y) | N |
| Coypu (Myocastor coypus) | Echimyidae | Rodentia (Y) | N |
| Mink (Neovison vison) (Y) | Mustelidae (Y) | Carnivora (Y) | N |
| Red squirrel (Sciurus vulgaris) | Sciuridae | Rodentia (Y) | N |
| Wild boar (Sus scrofa) (Y) | Suidae (Y) | Artiodactyla (Y) | N |
| Complex-toothed flying squirrel (Trogopterus xanthipes) | Sciuridae | Rodentia (Y) | N |
Table 1. Live mammals traded at the Huanan market in November and December 2019.
表 1. 2019 年 11 月和 12 月在华南海鲜市场交易的活体哺乳动物。
表 1. 2019 年 11 月和 12 月在华南海鲜市场交易的活体哺乳动物。
*Based on live susceptibility findings, serological findings, or ACE2-binding assays. See table S5 for details and associated references.
†Animals listed as “N” (no) were, however, present at Wuhan market during the 2017–2019 study period (8).
基于实时敏感性发现、血清学发现或 ACE2 结合实验。见表 S5 获取详细信息及相关参考文献。†虽然被列为“N”（无）的动物在 2017-2019 研究期间出现在武汉市场（8）。
There were potentially many locations in Wuhan, a city of 11 million, that would have been equally or more likely than the Huanan market to sustain the first recognized cluster of a new respiratory pathogen had its introduction not been linked to a live animal market, including other shopping venues, hospitals, elder care facilities, workplaces, universities, and places of worship. To investigate possible sites, we compared the relative extent of intra-urban human traffic to the Huanan market versus other locations within the city of Wuhan using a location-specific dataset of social media check-ins in the Sina Visitor System (25, 33). We found at least 70 other markets throughout the city of Wuhan that received more social media check-ins than the Huanan market (Fig. 3Opens in image viewer). To extend this analysis beyond only markets, we also used a subsequently published list of known SARS-CoV-2 superspreader locations (34) to identify 430 locations in Wuhan that may have been at high risk for superspreader events and which received more check-ins than the Huanan market (Fig. 3Opens in image viewer, inset). The Huanan market accounted for 0.12% (120 of 98,146) of social media check-ins to markets in the dataset that received at least as many check-ins as the Huanan market. The market accounted for 0.04% (120 of 262,233) of all social media check-ins to the >400 sites in Wuhan identified as especially likely to be potential superspreader locations and which received at least as many social media visits as the Huanan market. Considering the number of check-ins to all four markets selling live wild animals in Wuhan (combined), they accounted for 0.21% (206 of 98,146) of market visits and 0.079% (206 of 262,233) of visits to the 430 potential superspreader sites, where a new respiratory disease might first be noticed in a large city.
武汉，一个 1100 万人口的城市，可能存在许多地点，其发生新的呼吸道病原体首次集群的可能性与华南海鲜市场相当甚至更高，如果不是其引入与活动物市场相关联，包括其他购物场所、医院、养老设施、工作场所、大学和宗教场所。为了调查可能的地点，我们使用新浪访客系统（25，33）中的特定位置社交媒体签到数据集，比较了城市内部人流量相对于华南海鲜市场与其他武汉城市内地点的相对程度。我们发现至少有 70 个其他市场在华南海鲜市场接收了更多的社交媒体签到（ Fig. 3Opens in image viewer ）。为了将这一分析扩展到仅限于市场之外，我们还使用了一篇随后发表的已知 SARS-CoV-2 超级传播者地点列表（34），以确定 430 个武汉地点，这些地点可能面临超级传播事件的高风险，并且接收到的签到比华南海鲜市场更多（ Fig. 3Opens in image viewer ，插图）。华南海鲜市场占 0.12%（120 个中的 98,146 个）的社交媒体签到发生在数据集中市场，这些市场的签到次数至少与华南海鲜市场一样多。该市场占所有社交媒体签到在武汉识别为特别可能成为潜在超级传播者地点的>400 个地点中的 0.04%（120 个中的 262,233 个）。考虑到武汉所有四个销售活野生动物市场的签到总数（合并），它们占市场访问的 0.21%（98,146 个中的 206 个）和 430 个潜在超级传播者地点访问的 0.079%（262,233 个中的 206 个），在这些地点，一种新的呼吸道疾病可能在大型城市首次被发现。
武汉，一个 1100 万人口的城市，可能存在许多地点，其发生新的呼吸道病原体首次集群的可能性与华南海鲜市场相当甚至更高，如果不是其引入与活动物市场相关联，包括其他购物场所、医院、养老设施、工作场所、大学和宗教场所。为了调查可能的地点，我们使用新浪访客系统（25，33）中的特定位置社交媒体签到数据集，比较了城市内部人流量相对于华南海鲜市场与其他武汉城市内地点的相对程度。我们发现至少有 70 个其他市场在华南海鲜市场接收了更多的社交媒体签到（ Fig. 3Opens in image viewer ）。为了将这一分析扩展到仅限于市场之外，我们还使用了一篇随后发表的已知 SARS-CoV-2 超级传播者地点列表（34），以确定 430 个武汉地点，这些地点可能面临超级传播事件的高风险，并且接收到的签到比华南海鲜市场更多（ Fig. 3Opens in image viewer ，插图）。华南海鲜市场占 0.12%（120 个中的 98,146 个）的社交媒体签到发生在数据集中市场，这些市场的签到次数至少与华南海鲜市场一样多。该市场占所有社交媒体签到在武汉识别为特别可能成为潜在超级传播者地点的>400 个地点中的 0.04%（120 个中的 262,233 个）。考虑到武汉所有四个销售活野生动物市场的签到总数（合并），它们占市场访问的 0.21%（98,146 个中的 206 个）和 430 个潜在超级传播者地点访问的 0.079%（262,233 个中的 206 个），在这些地点，一种新的呼吸道疾病可能在大型城市首次被发现。
Fig. 3. Visitors to locations throughout Wuhan.
图 3. 武汉各地游客
图 3. 武汉各地游客
Shown is the number of social media check-ins in the Sina Visitor System from 2013 to 2014 as shared by (33). The numbers of check-ins to individual markets throughout the city are shown in comparison with check-ins at the Huanan market. Inset: the total number of check-ins to all individual locations across the city of Wuhan grouped by category. Locations with >50 visitor check-ins are shown, and the locations that received more check-ins than the Huanan market in the same period are shown in red.
显示的是 2013 年至 2014 年新浪访客系统中社交媒体签到次数，由（33）分享。整个城市各个市场的签到次数与华南海鲜市场的签到次数进行比较。插图：按类别分组统计武汉市所有单个地点的签到总数。显示签到次数超过 50 次的地点，同一时期签到次数超过华南海鲜市场的地点用红色标出。
显示的是 2013 年至 2014 年新浪访客系统中社交媒体签到次数，由（33）分享。整个城市各个市场的签到次数与华南海鲜市场的签到次数进行比较。插图：按类别分组统计武汉市所有单个地点的签到总数。显示签到次数超过 50 次的地点，同一时期签到次数超过华南海鲜市场的地点用红色标出。
A dataset from the Chinese Center for Disease Prevention and Control (CCDC) report dated 22 January 2020 (data S1) (12, 13, 15, 16) was made publicly available in June 2020 (24, 35). A total of 585 environmental samples were initially taken from various surfaces in the Huanan market on 1 and 12 January 2020 by the CCDC (tables S6 and S7 and data S1) (12, 13, 15, 16, 24, 35), with further samples taken throughout the market during January and February (24). We extended the analysis in the WHO mission report (7) by integrating public online maps and photographic evidence, data from public business registries (table S8 and data S2), information about which live mammal species were sold at the Huanan market in late 2019 (Table 1Opens in image viewer and table S5), and the CCDC report (data S1). We reconstructed the floor plan of the market and integrated information from business registries of vendors at the market (fig. S16 and table S8), as well as an official report (36) recording fines to three business owners for illegal sales of live mammals (data S2) (36). From this, we identified an additional five stalls that were likely selling live or freshly butchered mammals or other unspecified meat products in the southwest corner of the western section of the market (Fig. 4AOpens in image viewer, figs. S16 and S17, and table S6).
中国疾病预防控制中心（CCDC）2020 年 1 月 22 日报告的数据集（数据 S1）（12，13，15，16）于 2020 年 6 月公开（24，35）。CCDC 于 2020 年 1 月 1 日和 12 日从华南海鲜市场各种表面采集了 585 个环境样本（表格 S6 和 S7 以及数据 S1）（12，13，15，16，24，35），并在 1 月和 2 月期间在整个市场采集了更多样本（24）。我们通过整合公共在线地图和照片证据、公共商业登记数据（表格 S8 和数据 S2）、关于 2019 年底在华南海鲜市场销售的哺乳动物物种的信息（ Table 1Opens in image viewer 和表格 S5）以及 CCDC 报告（数据 S1）来扩展了世界卫生组织任务报告（7）中的分析。我们重建了市场的平面图，并整合了市场摊贩的商业登记信息（图 S16 和表格 S8），以及一份记录对三名非法销售活体哺乳动物的商业主罚款的官方报告（数据 S2）（36）。 从这，我们确定了另外五个摊位，这些摊位可能位于市场西部区域的西南角，出售活体或新鲜屠宰的哺乳动物或其他未指定的肉类产品（ Fig. 4AOpens in image viewer ，图 S16 和 S17，以及表 S6）。
中国疾病预防控制中心（CCDC）2020 年 1 月 22 日报告的数据集（数据 S1）（12，13，15，16）于 2020 年 6 月公开（24，35）。CCDC 于 2020 年 1 月 1 日和 12 日从华南海鲜市场各种表面采集了 585 个环境样本（表格 S6 和 S7 以及数据 S1）（12，13，15，16，24，35），并在 1 月和 2 月期间在整个市场采集了更多样本（24）。我们通过整合公共在线地图和照片证据、公共商业登记数据（表格 S8 和数据 S2）、关于 2019 年底在华南海鲜市场销售的哺乳动物物种的信息（ Table 1Opens in image viewer 和表格 S5）以及 CCDC 报告（数据 S1）来扩展了世界卫生组织任务报告（7）中的分析。我们重建了市场的平面图，并整合了市场摊贩的商业登记信息（图 S16 和表格 S8），以及一份记录对三名非法销售活体哺乳动物的商业主罚款的官方报告（数据 S2）（36）。 从这，我们确定了另外五个摊位，这些摊位可能位于市场西部区域的西南角，出售活体或新鲜屠宰的哺乳动物或其他未指定的肉类产品（ Fig. 4AOpens in image viewer ，图 S16 和 S17，以及表 S6）。
Fig. 4. Map of the Huanan market.
图 4. 华南市场地图。
图 4. 华南市场地图。
(A) Aggregated environmental sampling and human case data from the Huanan market. Captions describe the types of SARS-CoV-2–positive environmental samples obtained from known live animal vendors (left) and from stalls with samples with known virus lineage (center). Lineage is unknown unless noted; sequencing data have not been released for some samples, and many samples were PCR-positive but not sequenced. Image at left shows raccoon dogs in a metal cage on top of caged birds from a business with five positive environmental samples (photo by E.C.H.). Center: Rectangle with dashed outline indicates the “wildlife” section of the market. (B) Relative risk analysis of positive environmental samples. Tolerance contours enclose regions with statistically significant elevation in density of positive environmental samples relative to the distribution of sampled stalls. (C) Distribution of positive environmental samples. Sample locations (centroid of corresponding business) and quantity are shown as black circles. (D) Control distribution for relative risk analysis. All businesses investigated with environmental sampling are shown as black circles (there is one circle per business regardless of whether a positive sample was found). See table S12 for details on stalls that were SARS-CoV-2–negative.
（A）从华南海鲜市场收集的环境样本和人类病例数据。图注描述了从已知活体动物供应商（左侧）和已知病毒谱系的摊位（中间）获得 SARS-CoV-2 阳性环境样本的类型。除非注明，否则谱系未知；一些样本的测序数据尚未发布，许多样本 PCR 检测呈阳性但未测序。左侧图像显示在金属笼子上的浣熊犬和来自有五个阳性环境样本的企业的笼中鸟类（照片由 E.C.H.拍摄）。中间：带有虚线轮廓的矩形表示市场的“野生动物”区域。（B）阳性环境样本的相对风险分析。容差轮廓包围了相对于采样摊位分布，阳性环境样本密度显著升高的区域。（C）阳性环境样本的分布。样本位置（对应企业的质心）和数量以黑色圆圈表示。（D）相对风险分析的对照分布。 所有通过环境采样调查的企业均以黑色圆圈表示（无论是否发现阳性样本，每个企业都有一个圆圈）。有关 SARS-CoV-2 阴性摊位的详细信息，请参阅表 S12。
（A）从华南海鲜市场收集的环境样本和人类病例数据。图注描述了从已知活体动物供应商（左侧）和已知病毒谱系的摊位（中间）获得 SARS-CoV-2 阳性环境样本的类型。除非注明，否则谱系未知；一些样本的测序数据尚未发布，许多样本 PCR 检测呈阳性但未测序。左侧图像显示在金属笼子上的浣熊犬和来自有五个阳性环境样本的企业的笼中鸟类（照片由 E.C.H.拍摄）。中间：带有虚线轮廓的矩形表示市场的“野生动物”区域。（B）阳性环境样本的相对风险分析。容差轮廓包围了相对于采样摊位分布，阳性环境样本密度显著升高的区域。（C）阳性环境样本的分布。样本位置（对应企业的质心）和数量以黑色圆圈表示。（D）相对风险分析的对照分布。 所有通过环境采样调查的企业均以黑色圆圈表示（无论是否发现阳性样本，每个企业都有一个圆圈）。有关 SARS-CoV-2 阴性摊位的详细信息，请参阅表 S12。
Five of the SARS-CoV-2–positive environmental samples were taken from a single stall selling live mammals in late 2019 (table S6). Further, all five objects sampled showed an association with animal sales, including a metal cage, two carts (of the kind frequently used to transport mobile animal cages), and a hair and feather remover (table S6). No human COVID-19 cases were reported there (7, 12). The same stall was visited by one of us (E.C.H.) in 2014, and live raccoon dogs were observed housed in a metal cage stacked on top of a cage with live birds (Fig. 4AOpens in image viewer) (37). A recent report (24) identified that the grates outside of this stall, upon which animal cages were stacked (37), were positive for SARS-CoV-2.
2019 年底，从一家出售活体哺乳动物的摊位上采集了 5 份 SARS-CoV-2 阳性环境样本（表 S6）。此外，所采集的 5 个样本都与动物销售有关，包括一个金属笼子、两个手推车（常用于运输活动笼子的类型）和一个毛发和羽毛去除器（表 S6）。那里没有报告人类 COVID-19 病例（7，12）。我们中的一人在 2014 年访问了同一个摊位，并观察到活体浣熊狗被关在一个金属笼子里，笼子堆叠在装有活鸟的笼子上（ Fig. 4AOpens in image viewer ）（37）。最近的一份报告（24）指出，这个摊位外面的格栅，上面堆放着动物笼子（37），SARS-CoV-2 检测结果呈阳性。
2019 年底，从一家出售活体哺乳动物的摊位上采集了 5 份 SARS-CoV-2 阳性环境样本（表 S6）。此外，所采集的 5 个样本都与动物销售有关，包括一个金属笼子、两个手推车（常用于运输活动笼子的类型）和一个毛发和羽毛去除器（表 S6）。那里没有报告人类 COVID-19 病例（7，12）。我们中的一人在 2014 年访问了同一个摊位，并观察到活体浣熊狗被关在一个金属笼子里，笼子堆叠在装有活鸟的笼子上（ Fig. 4AOpens in image viewer ）（37）。最近的一份报告（24）指出，这个摊位外面的格栅，上面堆放着动物笼子（37），SARS-CoV-2 检测结果呈阳性。
Positive environmental samples linked both to live mammal sales and to human cases at the Huanan market
环境样本与活体哺乳动物销售以及华南海鲜市场的人类病例均有关联
We used a spatial relative risk analysis to identify potential regions of the market with an increased density of positive environmental samples (25). We found evidence (P < 0.05) of a region in the southwest area of the market where live mammals were for sale (Fig. 4BOpens in image viewer). Although environmental sampling of the market was incomplete and spatially heterogeneous (data S1 and table S6), our analysis accounts for the empirical environmental sampling distribution, which was biased toward “stalls related to December cases,” as well as “stalls that sold livestock, poultry, farmed wildlife” (7) (Fig. 4, C and DOpens in image viewer). The “distance to the nearest vendor selling live mammals” and the “distance to the nearest human case” were independently predictive of environmental sample positivity (P = 0.004 and 0.014, respectively, for n = 6; table S9). To further investigate the robustness of these findings to possible sampling biases, we considered three scenarios: (i) oversampling of live mammal and unknown meat stalls, (ii) overcounting of positive samples, and (iii) exclusion of the seafood stand near the wildlife area of the market (with five positive samples) from our analysis (table S10). In each case, the distance to live mammal vendors remained predictive of environmental sample positivity, and the region of increased positive sample density in the southwest corner of the western section of the market remained consistent (fig. S18).
我们使用空间相对风险分析来识别市场密度增加的阳性环境样本潜在区域（25）。我们发现（P < 0.05）市场西南区域有活体哺乳动物出售的证据（ Fig. 4BOpens in image viewer ）。尽管市场环境采样不完整且空间异质（数据 S1 和表 S6），我们的分析考虑了经验环境采样分布，该分布偏向“与 12 月病例相关的摊位”以及“出售家畜、家禽、养殖野生动物的摊位”（7）（ Fig. 4, C and DOpens in image viewer ）。“距离最近出售活体哺乳动物的摊位”和“距离最近的人类病例”独立预测环境样本阳性（对于 n = 6，分别为 P = 0.004 和 0.014；表 S9）。为了进一步研究这些发现对可能的采样偏差的稳健性，我们考虑了三种情况：（i）对活体哺乳动物和未知肉类摊位的过度采样，（ii）对阳性样本的过度计数，以及（iii）排除市场野生动物区域附近的海鲜摊位（有五个阳性样本）的分析（表 S10）。 在每种情况下，与活体哺乳动物供应商的距离仍然是环境样本阳性的预测因素，市场西部区域西南角增加的阳性样本密度区域保持一致（图 S18）。
我们使用空间相对风险分析来识别市场密度增加的阳性环境样本潜在区域（25）。我们发现（P < 0.05）市场西南区域有活体哺乳动物出售的证据（ Fig. 4BOpens in image viewer ）。尽管市场环境采样不完整且空间异质（数据 S1 和表 S6），我们的分析考虑了经验环境采样分布，该分布偏向“与 12 月病例相关的摊位”以及“出售家畜、家禽、养殖野生动物的摊位”（7）（ Fig. 4, C and DOpens in image viewer ）。“距离最近出售活体哺乳动物的摊位”和“距离最近的人类病例”独立预测环境样本阳性（对于 n = 6，分别为 P = 0.004 和 0.014；表 S9）。为了进一步研究这些发现对可能的采样偏差的稳健性，我们考虑了三种情况：（i）对活体哺乳动物和未知肉类摊位的过度采样，（ii）对阳性样本的过度计数，以及（iii）排除市场野生动物区域附近的海鲜摊位（有五个阳性样本）的分析（表 S10）。 在每种情况下，与活体哺乳动物供应商的距离仍然是环境样本阳性的预测因素，市场西部区域西南角增加的阳性样本密度区域保持一致（图 S18）。
Finally, to analyze the spatial patterning of human cases within the Huanan market, we plotted cases as a function of symptom onset from the WHO mission report (7) (Fig. 5AOpens in image viewer and table S11) (25). All eight COVID-19 cases detected before 20 December 2019 were from the western side of the market, where mammal species were also sold (Fig. 5, B and COpens in image viewer). Unlike SARS-CoV-2–positive environmental samples (Fig. 4, A and COpens in image viewer), we found that COVID-19 cases were more diffuse throughout the building (Fig. 5Opens in image viewer).
最后，为了分析华南海鲜市场内人类病例的空间分布，我们将病例作为 WHO 任务报告（7）（ Fig. 5AOpens in image viewer ）和表 S11 的函数绘制出来（25）。2019 年 12 月 20 日之前检测到的所有 8 例 COVID-19 病例均来自市场的西侧，那里也出售哺乳动物（ Fig. 5, B and COpens in image viewer ）。与 SARS-CoV-2 阳性环境样本（ Fig. 4, A and COpens in image viewer ）不同，我们发现 COVID-19 病例在整个建筑中分布更广（ Fig. 5Opens in image viewer ）。
最后，为了分析华南海鲜市场内人类病例的空间分布，我们将病例作为 WHO 任务报告（7）（ Fig. 5AOpens in image viewer ）和表 S11 的函数绘制出来（25）。2019 年 12 月 20 日之前检测到的所有 8 例 COVID-19 病例均来自市场的西侧，那里也出售哺乳动物（ Fig. 5, B and COpens in image viewer ）。与 SARS-CoV-2 阳性环境样本（ Fig. 4, A and COpens in image viewer ）不同，我们发现 COVID-19 病例在整个建筑中分布更广（ Fig. 5Opens in image viewer ）。
Fig. 5. Location and timing of human cases in Huanan market.
图 5. 华南市场人类病例的位置和时间。
图 5. 华南市场人类病例的位置和时间。
(A) Outline colors correspond to the timing of the first known case in each business. Individual case timing is denoted by marker color and shown within the outlined business. (B) Distribution of known cases on or before 20 December 2019. Case locations are shown as black circles. (C) Distribution of all known human cases in Huanan market. See table S11 for details on SARS-CoV-2–positive human cases with the Huanan market.
（A）轮廓颜色对应每个业务中已知第一例病例的时间。单个病例的时间由标记颜色表示，并在轮廓业务内显示。（B）截至 2019 年 12 月 20 日或之前的已知病例分布。病例位置以黑色圆圈表示。（C）华南海鲜市场所有已知人类病例的分布。有关 SARS-CoV-2 阳性人类病例的详细信息，请参阅表 S11。
（A）轮廓颜色对应每个业务中已知第一例病例的时间。单个病例的时间由标记颜色表示，并在轮廓业务内显示。（B）截至 2019 年 12 月 20 日或之前的已知病例分布。病例位置以黑色圆圈表示。（C）华南海鲜市场所有已知人类病例的分布。有关 SARS-CoV-2 阳性人类病例的详细信息，请参阅表 S11。
Study limitations 研究局限性
There are several limitations to our study. We have been able to recover location data for most of the December-onset COVID-19 cases identified by the WHO mission (7) with sufficient precision to support our conclusions. However, we do not have access to the precise latitude and longitude coordinates of all of these cases. Should such data exist, they may be accompanied by additional metadata, some of which we have reconstructed, but some of which, including the date of onset of each case, would be valuable for ongoing studies. We also lack direct evidence of an intermediate animal infected with a SARS-CoV-2 progenitor virus either at the Huanan market or at a location connected to its supply chain, such as a farm. Additionally, no line list of early COVID-19 cases is available, and we do not have complete details of environmental sampling. However, compared with many other outbreaks, we have more comprehensive information on early cases, hospitalizations, and environmental sampling (7).
本研究存在一些局限性。我们已能够以足够的精度恢复 WHO 任务（7）确定的多数 12 月份出现的 COVID-19 病例的位置数据，以支持我们的结论。然而，我们无法获取所有这些病例的精确经纬度坐标。如果此类数据存在，它们可能伴随有额外的元数据，其中一些我们已经重建，但一些，包括每个病例的发病日期，对于正在进行的研究将是有价值的。我们也没有直接证据表明在华南市场或与其供应链相连的地点（如农场）感染了 SARS-CoV-2 祖病毒的中介动物。此外，没有早期 COVID-19 病例的行列表，我们也没有环境采样的完整细节。然而，与许多其他疫情相比，我们在早期病例、住院和环境采样方面拥有更全面的信息（7）。
本研究存在一些局限性。我们已能够以足够的精度恢复 WHO 任务（7）确定的多数 12 月份出现的 COVID-19 病例的位置数据，以支持我们的结论。然而，我们无法获取所有这些病例的精确经纬度坐标。如果此类数据存在，它们可能伴随有额外的元数据，其中一些我们已经重建，但一些，包括每个病例的发病日期，对于正在进行的研究将是有价值的。我们也没有直接证据表明在华南市场或与其供应链相连的地点（如农场）感染了 SARS-CoV-2 祖病毒的中介动物。此外，没有早期 COVID-19 病例的行列表，我们也没有环境采样的完整细节。然而，与许多其他疫情相比，我们在早期病例、住院和环境采样方面拥有更全面的信息（7）。
Discussion 讨论
Several lines of evidence support the hypothesis that the Huanan market was the epicenter of the COVID-19 pandemic and that SARS-CoV-2 emerged from activities associated with the live wildlife trade there. Spatial analyses within the market show that SARS-CoV-2–positive environmental samples, including cages, carts, and freezers, were associated with activities concentrated in the southwest corner of the market. This is the same section where vendors were selling live mammals, including raccoon dogs, hog badgers, and red foxes, immediately before the COVID-19 pandemic. Multiple positive samples were taken from one stall known to have sold live mammals, and the water drain proximal to this stall, as well as other sewerages and a nearby wildlife stall on the southwest side of the market, tested positive for SARS-CoV-2 (24). These findings suggest that infected animals were present at the Huanan market at the beginning of the COVID-19 pandemic; however, we do not have access to any live animal samples from relevant species. Additional information, including sequencing data and detailed sampling strategy, would be invaluable to test this hypothesis comprehensively.
多行证据支持假设，华南市场是 COVID-19 大流行的震中，SARS-CoV-2 病毒起源于与该市场活野生动物贸易相关的活动。市场内的空间分析显示，SARS-CoV-2 阳性的环境样本，包括笼子、手推车和冰箱，与市场西南角的活动集中相关。这就是在 COVID-19 大流行前，卖家立即出售活体哺乳动物，包括浣熊犬、猪獾和红狐的同一部分。从一家已知出售活体哺乳动物的摊位取出了多个阳性样本，以及该摊位附近的水槽，以及其他市场西南侧的污水和野生动物摊位，均检测出 SARS-CoV-2 阳性（24）。这些发现表明，在 COVID-19 大流行初期，华南市场存在感染动物；然而，我们没有获取到相关物种的任何活体动物样本。包括测序数据和详细的采样策略在内的更多信息将非常有价值，以全面检验这一假设。
多行证据支持假设，华南市场是 COVID-19 大流行的震中，SARS-CoV-2 病毒起源于与该市场活野生动物贸易相关的活动。市场内的空间分析显示，SARS-CoV-2 阳性的环境样本，包括笼子、手推车和冰箱，与市场西南角的活动集中相关。这就是在 COVID-19 大流行前，卖家立即出售活体哺乳动物，包括浣熊犬、猪獾和红狐的同一部分。从一家已知出售活体哺乳动物的摊位取出了多个阳性样本，以及该摊位附近的水槽，以及其他市场西南侧的污水和野生动物摊位，均检测出 SARS-CoV-2 阳性（24）。这些发现表明，在 COVID-19 大流行初期，华南市场存在感染动物；然而，我们没有获取到相关物种的任何活体动物样本。包括测序数据和详细的采样策略在内的更多信息将非常有价值，以全面检验这一假设。
In a related study, we inferred separate introductions of SARS-CoV-2 lineages A and B into humans from likely infected animals at the Huanan market (38). We estimated the first COVID-19 case to have occurred in November 2019, with few human cases and hospitalizations occurring through mid-December (38). A recent preprint (24) confirms the authenticity of the CCDC report (data S1) and records additional positive environmental samples in the southwestern area of the market selling live animals. This report also documents the early presence of the A lineage of SARS-CoV-2 in a Huanan market environmental sample. This, along with the lineage A cases that we report in close geographical proximity to the market in December 2019, challenges the suggestion that the market was simply a superspreading event, which would be lineage specific. Rather, it adds to the evidence presented here that lineage A, like lineage B, may have originated at the Huanan market and then spread from this epicenter into the neighborhoods surrounding the market and beyond.
在一项相关研究中，我们从可能感染动物的华南海鲜市场推断出 SARS-CoV-2 的 A 和 B 谱系分别进入人类（38）。我们估计第一例 COVID-19 病例发生在 2019 年 11 月，到 12 月中旬只有少数病例和住院（38）。最近的一篇预印本（24）证实了 CCDC 报告的真实性（数据 S1），并记录了市场西南部销售活动物的环境样本中额外的阳性样本。这份报告还记录了 SARS-CoV-2 的 A 谱系在华南海鲜市场环境样本中的早期存在。这与我们在 2019 年 12 月报告的与市场邻近地区的谱系 A 病例一起，挑战了市场仅仅是超级传播事件的假设，这将具有谱系特异性。相反，它增加了在此处提出的证据，即 A 谱系，就像 B 谱系一样，可能起源于华南海鲜市场，然后从这个震中传播到市场周围的社区以及更远的地方。
在一项相关研究中，我们从可能感染动物的华南海鲜市场推断出 SARS-CoV-2 的 A 和 B 谱系分别进入人类（38）。我们估计第一例 COVID-19 病例发生在 2019 年 11 月，到 12 月中旬只有少数病例和住院（38）。最近的一篇预印本（24）证实了 CCDC 报告的真实性（数据 S1），并记录了市场西南部销售活动物的环境样本中额外的阳性样本。这份报告还记录了 SARS-CoV-2 的 A 谱系在华南海鲜市场环境样本中的早期存在。这与我们在 2019 年 12 月报告的与市场邻近地区的谱系 A 病例一起，挑战了市场仅仅是超级传播事件的假设，这将具有谱系特异性。相反，它增加了在此处提出的证据，即 A 谱系，就像 B 谱系一样，可能起源于华南海鲜市场，然后从这个震中传播到市场周围的社区以及更远的地方。
Several observations suggest that the geographic association of early COVID-19 cases with the Huanan market is unlikely to have been the result of ascertainment bias (see the supplementary text and tables S2 and S3) (39). These include that (i) few, if any, cases among Huanan market–unlinked individuals are likely to have been detected by active searching in the neighborhoods around the market, only in hospitals, because all of the cases analyzed here were hospitalized (7); (ii) public health officials simultaneously became aware of Huanan-linked cases both near and far from the Huanan market, not just the ones near it (fig. S11) (5); (iii) Huanan market–unlinked cases would not be expected to live significantly closer to the market than linked cases if they had been ascertained as contacts traced from those market-linked cases; and (iv) seroprevalence in Wuhan was highest in the districts around the market (40, 41). It is also noteworthy that the December 2019 COVID-19 cases that we consider here were identified based on reviews of clinical signs and symptoms, not epidemiological factors such as where they resided or links to the Huanan market (7), and that excess deaths from pneumonia rose first in the districts surrounding the market (42). Moreover, the spatial relationship with the Huanan market remains after removing the two-thirds of the unlinked cases residing nearest the market.
几项观察表明，早期 COVID-19 病例与华南海鲜市场的地理关联不太可能是确定偏差的结果（见补充文本和表 S2 和 S3）（39）。这包括：（i）在市场周边社区中，很少有或没有病例是通过主动搜索被发现的，只有在医院中，因为所有分析的病例都曾住院（7）；（ii）公共卫生官员同时意识到华南海鲜市场附近和远处的华南海鲜市场相关病例，而不仅仅是附近的那些（图 S11）（5）；（iii）如果这些与华南海鲜市场无关的病例是作为从那些市场相关病例追踪到的接触者确定的，那么它们不太可能比相关病例更靠近市场居住；以及（iv）武汉的血清流行率在市场周边地区最高（40，41）。 值得注意的是，我们在这里考虑的 2019 年 12 月的 COVID-19 病例是根据对临床体征和症状的审查确定的，而不是基于他们居住地或与华南海鲜市场的联系等流行病学因素（7），并且肺炎死亡人数首先在市场周边地区增加（42）。此外，在移除最靠近市场的三分之二未关联病例后，与华南海鲜市场的空间关系仍然存在。
几项观察表明，早期 COVID-19 病例与华南海鲜市场的地理关联不太可能是确定偏差的结果（见补充文本和表 S2 和 S3）（39）。这包括：（i）在市场周边社区中，很少有或没有病例是通过主动搜索被发现的，只有在医院中，因为所有分析的病例都曾住院（7）；（ii）公共卫生官员同时意识到华南海鲜市场附近和远处的华南海鲜市场相关病例，而不仅仅是附近的那些（图 S11）（5）；（iii）如果这些与华南海鲜市场无关的病例是作为从那些市场相关病例追踪到的接触者确定的，那么它们不太可能比相关病例更靠近市场居住；以及（iv）武汉的血清流行率在市场周边地区最高（40，41）。 值得注意的是，我们在这里考虑的 2019 年 12 月的 COVID-19 病例是根据对临床体征和症状的审查确定的，而不是基于他们居住地或与华南海鲜市场的联系等流行病学因素（7），并且肺炎死亡人数首先在市场周边地区增加（42）。此外，在移除最靠近市场的三分之二未关联病例后，与华南海鲜市场的空间关系仍然存在。
One of the key findings of our study is that “unlinked’ early COVID-19 patients, i.e., those who did not work at the market, did not know someone who did, and had not recently visited the market, resided significantly closer to the market than patients with a direct link to it. The observation that a substantial proportion of early cases had no known epidemiological link had previously been used as an argument against the Huanan market being the epicenter of the pandemic. However, this group of cases resided significantly closer to the market than those who worked there, indicating that they had been exposed to the virus at or near the Huanan market. For market workers, the exposure risk was their place of work, not their residential locations, which were significantly farther afield than those cases not formally linked to the market.
我们的研究发现的关键之一是，“未直接关联”的早期 COVID-19 患者，即那些没有在市场工作、不认识在市场工作的人且近期未访问过市场的人，他们居住在市场比有直接关联的患者更近。此前，大量早期病例没有已知的流行病学联系，这曾被用作反对华南市场是疫情震中的论据。然而，这一组病例的居住地比在市场工作的人更靠近市场，这表明他们在华南市场或其附近接触到了病毒。对于市场工作者来说，他们的暴露风险是工作场所，而不是他们的居住地，这些居住地比那些没有正式关联市场的人要远得多。
我们的研究发现的关键之一是，“未直接关联”的早期 COVID-19 患者，即那些没有在市场工作、不认识在市场工作的人且近期未访问过市场的人，他们居住在市场比有直接关联的患者更近。此前，大量早期病例没有已知的流行病学联系，这曾被用作反对华南市场是疫情震中的论据。然而，这一组病例的居住地比在市场工作的人更靠近市场，这表明他们在华南市场或其附近接触到了病毒。对于市场工作者来说，他们的暴露风险是工作场所，而不是他们的居住地，这些居住地比那些没有正式关联市场的人要远得多。
Our spatial analyses show how patterns of COVID-19 cases shifted between late 2019, when the outbreak began (43), and early 2020, as the epidemic spread widely across Wuhan. COVID-19 cases in December 2019 were associated with the Huanan market in a manner unrelated to Wuhan population density or demographic patterns, unlike the wide spatial distribution of cases observed during later stages of the epidemic in January–February 2020. This observation fits with the evidence from other sources that SARS-CoV-2 was not widespread in Wuhan at the end of 2019. For example, no SARS-CoV-2–positive sera or influenza-like illness reports were recorded among more 40,000 blood donor samples collected up to December 2019 (44, 45), and none of thousands of samples taken from patients with influenza-like illness at Wuhan hospitals in October to December 2019 tested for SARS-CoV-2 RNA was positive (7).
我们的空间分析显示了 2019 年底疫情开始时（43）和 2020 年初疫情广泛传播到武汉期间，COVID-19 病例模式的变化。2019 年 12 月的 COVID-19 病例与武汉人口密度或人口模式无关，与 2020 年 1 月至 2 月疫情后期观察到的病例广泛空间分布不同。这一观察结果与来自其他来源的证据相符，即 SARS-CoV-2 在 2019 年底并未在武汉广泛传播。例如，在截至 2019 年 12 月收集的超过 40,000 份献血者样本中，没有记录到 SARS-CoV-2 阳性血清或流感样疾病报告（44，45），并且在 2019 年 10 月至 12 月期间从武汉医院流感样疾病患者中采集的数千份样本中，没有检测到 SARS-CoV-2 RNA 阳性的（7）。
我们的空间分析显示了 2019 年底疫情开始时（43）和 2020 年初疫情广泛传播到武汉期间，COVID-19 病例模式的变化。2019 年 12 月的 COVID-19 病例与武汉人口密度或人口模式无关，与 2020 年 1 月至 2 月疫情后期观察到的病例广泛空间分布不同。这一观察结果与来自其他来源的证据相符，即 SARS-CoV-2 在 2019 年底并未在武汉广泛传播。例如，在截至 2019 年 12 月收集的超过 40,000 份献血者样本中，没有记录到 SARS-CoV-2 阳性血清或流感样疾病报告（44，45），并且在 2019 年 10 月至 12 月期间从武汉医院流感样疾病患者中采集的数千份样本中，没有检测到 SARS-CoV-2 RNA 阳性的（7）。
The sustained presence of a potential source of virus transmission into the human population in late 2019, plausibly from infected live mammals sold at the Huanan market, offers an explanation of our findings and the origins of SARS-CoV-2. The pattern of COVID-19 cases reported for the Huanan market, with the earliest cases in the same part of the market as the wildlife sales and evidence of at least two introductions (38), resembles the multiple cross-species transmissions of SARS-CoV-2 subsequently observed during the pandemic from animals to humans on mink farms (46) and from infected hamsters to humans in the pet trade (47). There was an extensive network of wildlife farms in western Hubei Province, with hundreds of thousands of wild mammals including civets, ferret badgers, and raccoon dogs on farms in Enshi Prefecture, which supplied the Huanan market (48). This region of Hubei contains extensive cave complexes housing Rhinolophus bats, which carry SARSr-CoVs (49). SARS-CoV-1 was recovered from farmed masked palm civets (Paguma larvata) from Hubei in 2003 and 2004 (20). The animals on these farms (nearly 1 million) were rapidly released, sold, or killed in early 2020 (48), apparently without testing for SARS-CoV-2 (7). Live animals sold at the market (Table 1Opens in image viewer) were apparently not sampled either. By contrast, during the SARS-CoV-1 outbreaks, farms and markets remained open for more than a year after the first human cases occurred, allowing sampling of viruses from infected animals (20).
2019 年底，一种可能的人畜共患病源持续存在于人群中，很可能来自华南海鲜市场销售的感染活体哺乳动物，这为我们发现和 SARS-CoV-2 的起源提供了解释。华南海鲜市场报告的 COVID-19 病例模式，最早病例出现在野生动物销售区域，并有至少两次引入的证据（38），与随后在大流行期间观察到的 SARS-CoV-2 在养殖场从动物到人类（46）以及从感染仓鼠到人类的跨物种传播相似（47）。湖北省西部有一个庞大的野生动物养殖网络，恩施州有数十万只野生哺乳动物，包括果子狸、黄鼬和浣熊犬，为华南海鲜市场提供供应（48）。湖北的这个地区有大量的洞穴群，栖息着携带 SARSr-CoVs 的菊头蝠（49）。2003 年和 2004 年，从湖北的养殖场中回收了 SARS-CoV-1，来自家养的大狐猴（Paguma larvata）（20）。 这些农场（近 100 万头）的动物在 2020 年初迅速被释放、出售或宰杀（48），显然没有对 SARS-CoV-2 进行检测（7）。市场上出售的活动物（ Table 1Opens in image viewer ）似乎也没有进行采样。相比之下，在 SARS-CoV-1 疫情爆发期间，农场和市场在首例人类病例发生后的超过一年里仍然开放，允许从感染动物中采集病毒（20）。
2019 年底，一种可能的人畜共患病源持续存在于人群中，很可能来自华南海鲜市场销售的感染活体哺乳动物，这为我们发现和 SARS-CoV-2 的起源提供了解释。华南海鲜市场报告的 COVID-19 病例模式，最早病例出现在野生动物销售区域，并有至少两次引入的证据（38），与随后在大流行期间观察到的 SARS-CoV-2 在养殖场从动物到人类（46）以及从感染仓鼠到人类的跨物种传播相似（47）。湖北省西部有一个庞大的野生动物养殖网络，恩施州有数十万只野生哺乳动物，包括果子狸、黄鼬和浣熊犬，为华南海鲜市场提供供应（48）。湖北的这个地区有大量的洞穴群，栖息着携带 SARSr-CoVs 的菊头蝠（49）。2003 年和 2004 年，从湖北的养殖场中回收了 SARS-CoV-1，来自家养的大狐猴（Paguma larvata）（20）。 这些农场（近 100 万头）的动物在 2020 年初迅速被释放、出售或宰杀（48），显然没有对 SARS-CoV-2 进行检测（7）。市场上出售的活动物（ Table 1Opens in image viewer ）似乎也没有进行采样。相比之下，在 SARS-CoV-1 疫情爆发期间，农场和市场在首例人类病例发生后的超过一年里仍然开放，允许从感染动物中采集病毒（20）。
The live animal trade and live animal markets are a common theme in virus spillover events (21–23, 50), with markets such as the Huanan market selling live mammals being in the highest risk category (51). The events leading up to the COVID-19 pandemic mirror the SARS-CoV-1 outbreaks from 2002 to 2004, which were traced to infected animals in the Guangdong, Jiangxi, Henan, Hunan, and Hubei provinces in China (20). Maximum effort must now be applied to elucidate the upstream events that might have brought SARS-CoV-2 into the Huanan market, culminating in the COVID-19 pandemic. To reduce the risk of future pandemics, we must understand, and then limit, the routes and opportunities for virus spillover.
活体动物贸易和活体动物市场是病毒跨物种传播事件的常见主题（21-23，50），其中像华南市场这样的活体哺乳动物市场属于最高风险类别（51）。导致 COVID-19 大流行的前因后果与 2002 年至 2004 年的 SARS-CoV-1 疫情相似，这些疫情被追溯到中国广东、江西、河南、湖南和湖北等省份的感染动物（20）。现在必须付出最大努力，阐明可能导致 SARS-CoV-2 进入华南市场的上游事件，最终导致 COVID-19 大流行。为了降低未来大流行的风险，我们必须了解并限制病毒跨物种传播的途径和机会。
活体动物贸易和活体动物市场是病毒跨物种传播事件的常见主题（21-23，50），其中像华南市场这样的活体哺乳动物市场属于最高风险类别（51）。导致 COVID-19 大流行的前因后果与 2002 年至 2004 年的 SARS-CoV-1 疫情相似，这些疫情被追溯到中国广东、江西、河南、湖南和湖北等省份的感染动物（20）。现在必须付出最大努力，阐明可能导致 SARS-CoV-2 进入华南市场的上游事件，最终导致 COVID-19 大流行。为了降低未来大流行的风险，我们必须了解并限制病毒跨物种传播的途径和机会。
Methods summary 方法摘要
Ethics statement 伦理声明
This research was reviewed by the Human Subject Protection Program at the University of Arizona and the Institutional Review Board (IRB) at The Scripps Research Institute and determined to be exempt from IRB approval because it constitutes secondary research for which consent is not required.
这项研究已由亚利桑那大学的人体受试者保护计划以及斯克里普斯研究所的机构审查委员会（IRB）审查，并决定无需 IRB 批准，因为它构成无需同意的二级研究。
这项研究已由亚利桑那大学的人体受试者保护计划以及斯克里普斯研究所的机构审查委员会（IRB）审查，并决定无需 IRB 批准，因为它构成无需同意的二级研究。
Data sources 数据来源
COVID-19 case data from December 2019 were obtained from the WHO mission report (7) and from our previous analyses (5). Location information was extracted and sensitivity analyses performed to confirm accuracy and assess potential ascertainment bias. Geotagged January–February 2020 data from Weibo COVID-19 help seekers was obtained from the authors (26). Population density data were obtained from WorldPop.org (27). Sequencing- or quantitative polymerase chain reaction (PCR)–based environmental sample SARS-CoV-2 positivity from the Huanan market was obtained from a January 2020 CCDC report (data S1) (24).
2019 年 12 月的 COVID-19 病例数据来自世界卫生组织任务报告（7）和我们的先前分析（5）。提取了位置信息，并进行了敏感性分析，以确认准确性和评估潜在的确定偏差。从作者处获得了 2020 年 1 月至 2 月微博 COVID-19 求助者的地理标记数据（26）。人口密度数据来自 WorldPop.org（27）。从 2020 年 1 月 CCDC 报告中获得了华南海鲜市场基于测序或定量聚合酶链反应（PCR）的环境样本 SARS-CoV-2 阳性数据（数据 S1）（24）。
2019 年 12 月的 COVID-19 病例数据来自世界卫生组织任务报告（7）和我们的先前分析（5）。提取了位置信息，并进行了敏感性分析，以确认准确性和评估潜在的确定偏差。从作者处获得了 2020 年 1 月至 2 月微博 COVID-19 求助者的地理标记数据（26）。人口密度数据来自 WorldPop.org（27）。从 2020 年 1 月 CCDC 报告中获得了华南海鲜市场基于测序或定量聚合酶链反应（PCR）的环境样本 SARS-CoV-2 阳性数据（数据 S1）（24）。
Wildlife trading at the Huanan market
华南市场野生动物交易
Animal sales from Wuhan wet markets immediately before the COVID-19 pandemic were previously reported (8), and in this study we report details about animals for sale at the Huanan market up until November 2019.
武汉湿市场在 COVID-19 大流行前的动物销售此前已有报道（8），本研究我们报告了截至 2019 年 11 月前在华南市场销售的动物详情。
武汉湿市场在 COVID-19 大流行前的动物销售此前已有报道（8），本研究我们报告了截至 2019 年 11 月前在华南市场销售的动物详情。
Spatial analyses of COVID-19 cases
COVID-19 病例的空间分析
Haversine distances to the Huanan market were calculated for each of the geolocated December 2019 cases. Center points and median distances from cases to the Huanan market were calculated separately for (i) all 155 cases, (ii) the 35 cases epidemiologically linked to the Huanan market, (iii) the 120 cases not epidemiologically linked to the market, (iv) the 11 lineage B cases, and (v) the earliest lineage A case. These distances were also calculated for the 737 Weibo help seekers from 8 January to 10 February 2020 (26). Empirical null distributions were generated from the population density data and the Weibo data. The population density–null distributions were age-matched to the December 2019 cases. KDEs were also generated for the market-linked cases, unlinked cases, and all cases to infer a probability density function from which the cases could have been drawn. Highest-density contours representing specific probability masses (0.5, 0.25, 0.1, 0.05, and 0.01) were inferred, and the location of the market was compared with these.
汉南市场到各地理定位 2019 年 12 月病例的哈弗辛距离已计算。分别计算了（i）所有 155 个病例，（ii）与汉南市场有流行病学联系的 35 个病例，（iii）与市场无流行病学联系的 120 个病例，（iv）11 个 B 系病例和（v）最早的 A 系病例的中心点和到汉南市场的中位数距离。这些距离也计算了 2020 年 1 月 8 日至 2 月 10 日（26）的 737 名微博求助者的距离。从人口密度数据和微博数据中生成了经验零分布。人口密度-零分布与 2019 年 12 月的病例年龄匹配。还生成了与市场相关病例、未相关病例和所有病例的 KDE，以推断一个概率密度函数，从中可以抽取病例。推断出代表特定概率质量（0.5、0.25、0.1、0.05 和 0.01）的最高密度轮廓，并将市场位置与这些轮廓进行比较。
汉南市场到各地理定位 2019 年 12 月病例的哈弗辛距离已计算。分别计算了（i）所有 155 个病例，（ii）与汉南市场有流行病学联系的 35 个病例，（iii）与市场无流行病学联系的 120 个病例，（iv）11 个 B 系病例和（v）最早的 A 系病例的中心点和到汉南市场的中位数距离。这些距离也计算了 2020 年 1 月 8 日至 2 月 10 日（26）的 737 名微博求助者的距离。从人口密度数据和微博数据中生成了经验零分布。人口密度-零分布与 2019 年 12 月的病例年龄匹配。还生成了与市场相关病例、未相关病例和所有病例的 KDE，以推断一个概率密度函数，从中可以抽取病例。推断出代表特定概率质量（0.5、0.25、0.1、0.05 和 0.01）的最高密度轮廓，并将市场位置与这些轮廓进行比较。
Mobility analyses 移动分析
To estimate the relative amount of intra-urban human traffic to the Huanan market compared with other locations within the city of Wuhan, we used a location-specific dataset of social media check-ins in the Sina Visitor System as shared by Li et al. (33). This dataset is based on 1,491,499 individual check-in events across the city of Wuhan from the years 2013–2014 (5 to 6 years before the start of the COVID-19 pandemic), and 770,521 visits were associated with 312,190 unique user identifiers. Location names and categories were translated using a Python API for Google Translate.
为了估算与武汉市其他地区相比，城市内部前往华南海鲜市场的相对人流量，我们使用了李等人（33）共享的基于新浪访客系统社交媒体签到数据的特定位置数据集。该数据集基于 2013-2014 年（在 COVID-19 大流行开始前 5 至 6 年）武汉市 1,491,499 个个人签到事件，其中 770,521 次访问与 312,190 个唯一用户标识符相关。地点名称和类别使用 Python API 进行谷歌翻译。
为了估算与武汉市其他地区相比，城市内部前往华南海鲜市场的相对人流量，我们使用了李等人（33）共享的基于新浪访客系统社交媒体签到数据的特定位置数据集。该数据集基于 2013-2014 年（在 COVID-19 大流行开始前 5 至 6 年）武汉市 1,491,499 个个人签到事件，其中 770,521 次访问与 312,190 个唯一用户标识符相关。地点名称和类别使用 Python API 进行谷歌翻译。
Spatial analyses of environmental samples at the Huanan market
环境样本在华南市场的空间分析
We used the official maps from the CCDC (12) (data S1) and the WHO map (7), as well as satellite photographs (Google Maps, Google Earth, Baidu Maps), aerial photographs, and images of the market in the public domain to reconstruct the floorplan of the market. Market stalls were assigned by categories of the types of goods sold using official reports and data from the TianYanCha.com business directory (this company has since gone out of business; for screenshots, see table S8 and data S2). Final maps of the Huanan market were converted into geoJSON format for spatial analyses. Significance testing of live animal vendors and/or human SARS-CoV-2 cases on the number of positive environmental samples was performed using a binomial general linear model. Distances between businesses were defined as the distance between their respective center points, and spatial relative risk analysis was performed using the ‘sparr’ package in R, with linear boundary kernels for edge correction (52) and bandwidth selection performed using least-squares cross-validation.
我们使用了 CCDC（12）（数据 S1）的官方地图和世界卫生组织地图（7），以及卫星照片（谷歌地图、谷歌地球、百度地图）、航空照片和公共领域的市场图像来重建市场平面图。市场摊位根据销售商品类型分为类别，使用官方报告和天眼查.com 商业目录（该公司现已停业；请参阅表 S8 和数据 S2）进行分配。最终将华南市场的地图转换为 geoJSON 格式进行空间分析。使用二项式广义线性模型对活体动物摊贩和/或人类 SARS-CoV-2 病例对阳性环境样本数量的显著性进行了检验。商业之间的距离定义为它们各自中心点之间的距离，并使用 R 中的'sparr'包进行空间相对风险分析，边缘校正使用线性边界核，带宽选择使用最小二乘交叉验证。
我们使用了 CCDC（12）（数据 S1）的官方地图和世界卫生组织地图（7），以及卫星照片（谷歌地图、谷歌地球、百度地图）、航空照片和公共领域的市场图像来重建市场平面图。市场摊位根据销售商品类型分为类别，使用官方报告和天眼查.com 商业目录（该公司现已停业；请参阅表 S8 和数据 S2）进行分配。最终将华南市场的地图转换为 geoJSON 格式进行空间分析。使用二项式广义线性模型对活体动物摊贩和/或人类 SARS-CoV-2 病例对阳性环境样本数量的显著性进行了检验。商业之间的距离定义为它们各自中心点之间的距离，并使用 R 中的'sparr'包进行空间相对风险分析，边缘校正使用线性边界核，带宽选择使用最小二乘交叉验证。
Acknowledgments 致谢
We thank the researchers who generated the geospatial and environmental sample data and the members of the China team involved in producing the WHO mission report for the maps that made this work possible; M. Standaert, B. LaFleur, @babarlelephant, M. Boni, F. Débarre, and B. Pierce for comments and assistance; WorldPop.org for making population density and demographic data from Wuhan freely available; the patients, clinicians, and researchers whose data made this research possible; and the five reviewers for insightful comments and feedback.
我们感谢生成地理空间和环境样本数据的科研人员以及参与制作世界卫生组织任务报告的中国团队成员，使他们能够制作出使这项工作成为可能的地形图；感谢 M. Standaert、B. LaFleur、@babarlelephant、M. Boni、F. Débarre 和 B. Pierce 的评论和帮助；感谢 WorldPop.org 免费提供武汉的人口密度和人口统计数据；感谢那些使这项研究成为可能的患者、临床医生和研究人员；以及五位审稿人提供的深刻评论和反馈。
我们感谢生成地理空间和环境样本数据的科研人员以及参与制作世界卫生组织任务报告的中国团队成员，使他们能够制作出使这项工作成为可能的地形图；感谢 M. Standaert、B. LaFleur、@babarlelephant、M. Boni、F. Débarre 和 B. Pierce 的评论和帮助；感谢 WorldPop.org 免费提供武汉的人口密度和人口统计数据；感谢那些使这项研究成为可能的患者、临床医生和研究人员；以及五位审稿人提供的深刻评论和反馈。
Funding: This project has been funded in part with federal funds from the National Institute of Allergy and Infectious Diseases, National Institutes of Health (NIH), Department of Health and Human Services (contract no. 75N93021C00015 to M.W.). J.I.L. acknowledges support from the NIH (grant 5T32AI007244-38). S.A.G. acknowledges support from the NIH (grant F32AI152341). J.E.P. acknowledges support from the NIH (grant T15LM011271). J.O.W. acknowledges support from NIH (grants AI135992 and AI136056). D.L.R. acknowledges support from the Medical Research Council (grant MC_UU_12014/12) and the Wellcome Trust (grant 220977/Z/20/Z). M.A.S., P.L., and A.R. acknowledge support from the Wellcome Trust (collaborators award 206298/Z/17/Z – ARTIC network), the European Research Council (grant no. 725422 – ReservoirDOCS), and the NIH (grant R01AI153044). A.L.R. is supported by the Canadian Institutes of Health Research as part of the Coronavirus Variants Rapid Response Network (CoVaRR-Net; CIHR FRN#175622) and acknowledges that VIDO receives operational funding from the Canada Foundation for Innovation – Major Science Initiatives Fund and from the Government of Saskatchewan through Innovation Saskatchewan and the Ministry of Agriculture. M.K. receives funding from the European Union’s Horizon 2020 research and innovation program (grant no. 874735, VEO, Versatile Emerging Infectious Disease Observatory). R.F.G. acknowledges support from the NIH (grants R01AI132223, R01AI132244, U19AI142790, U54CA260581, U54HG007480, and OT2HL158260), the Coalition for Epidemic Preparedness Innovation, the Wellcome Trust Foundation, Gilead Sciences, and the European and Developing Countries Clinical Trials Partnership Programme. E.C.H. is supported by an Australian Research Council Laureate Fellowship (FL170100022). K.G.A. acknowledges support from the NIH (grants U19AI135995, U01AI151812, and UL1TR002550).
资助：本项目的部分资金来自美国国立卫生研究院过敏和传染病研究所（NIH）的联邦资金，卫生与公众服务部（合同编号 75N93021C00015 至 M.W.）。J.I.L.感谢 NIH 的支持（资助号 5T32AI007244-38）。S.A.G.感谢 NIH 的支持（资助号 F32AI152341）。J.E.P.感谢 NIH 的支持（资助号 T15LM011271）。J.O.W.感谢 NIH 的支持（资助号 AI135992 和 AI136056）。D.L.R.感谢医学研究委员会（资助号 MC_UU_12014/12）和惠康基金会（资助号 220977/Z/20/Z）的支持。M.A.S.、P.L.和 A.R.感谢惠康基金会（合作伙伴奖 206298/Z/17/Z – ARTIC 网络）、欧洲研究委员会（资助号 725422 – ReservoirDOCS）和 NIH（资助号 R01AI153044）的支持。A.L.R. 加拿大卫生研究院作为冠状病毒变种快速响应网络（CoVaRR-Net；CIHR FRN#175622）的一部分提供支持，并承认 VIDO 从加拿大创新基金会——重大科学计划基金以及萨斯喀彻温省政府通过创新萨斯喀彻温和创新部获得运营资金。M.K.获得欧盟“地平线 2020”研究和创新计划（项目号 874735，VEO，多用途新兴传染病观测站）的资助。R.F.G.感谢 NIH（资助号 R01AI132223、R01AI132244、U19AI142790、U54CA260581、U54HG007480 和 OT2HL158260）、传染病准备创新联盟、威康信托基金会、吉利德科学公司和欧洲及发展中国家临床试验伙伴计划的支持。E.C.H.获得澳大利亚研究理事会 Laureate 奖学金（FL170100022）的支持。K.G.A.感谢 NIH（资助号 U19AI135995、U01AI151812 和 UL1TR002550）的支持。
资助：本项目的部分资金来自美国国立卫生研究院过敏和传染病研究所（NIH）的联邦资金，卫生与公众服务部（合同编号 75N93021C00015 至 M.W.）。J.I.L.感谢 NIH 的支持（资助号 5T32AI007244-38）。S.A.G.感谢 NIH 的支持（资助号 F32AI152341）。J.E.P.感谢 NIH 的支持（资助号 T15LM011271）。J.O.W.感谢 NIH 的支持（资助号 AI135992 和 AI136056）。D.L.R.感谢医学研究委员会（资助号 MC_UU_12014/12）和惠康基金会（资助号 220977/Z/20/Z）的支持。M.A.S.、P.L.和 A.R.感谢惠康基金会（合作伙伴奖 206298/Z/17/Z – ARTIC 网络）、欧洲研究委员会（资助号 725422 – ReservoirDOCS）和 NIH（资助号 R01AI153044）的支持。A.L.R. 加拿大卫生研究院作为冠状病毒变种快速响应网络（CoVaRR-Net；CIHR FRN#175622）的一部分提供支持，并承认 VIDO 从加拿大创新基金会——重大科学计划基金以及萨斯喀彻温省政府通过创新萨斯喀彻温和创新部获得运营资金。M.K.获得欧盟“地平线 2020”研究和创新计划（项目号 874735，VEO，多用途新兴传染病观测站）的资助。R.F.G.感谢 NIH（资助号 R01AI132223、R01AI132244、U19AI142790、U54CA260581、U54HG007480 和 OT2HL158260）、传染病准备创新联盟、威康信托基金会、吉利德科学公司和欧洲及发展中国家临床试验伙伴计划的支持。E.C.H.获得澳大利亚研究理事会 Laureate 奖学金（FL170100022）的支持。K.G.A.感谢 NIH（资助号 U19AI135995、U01AI151812 和 UL1TR002550）的支持。
Author contributions: Conceptualization: M.W., K.G.A.; Data curation: M.W., A.R., K.G.A.; Formal analysis: M.W., J.I.L., A.C.-C., L.M., J.E.P., M.U.G.K., M.A.S., A.L.R., D.L.R., S.A.G., A.R., J.O.W., R.F.G., P.L., E.C.H., K.G.A.; Funding acquisition: M.W., J.I.L., A.C.-C., L.M., J.E.P., M.U.G.K., M.A.S., A.L.R., D.L.R., S.A.G., A.R., J.O.W., R.F.G., P.L., E.C.H., K.G.A.; Investigation: M.W., J.I.L., A.C.-C., L.M., J.E.P., M.U.G.K., M.A.S., M.K., A.L.R., D.L.R., C.N., S.A.G., A.R., J.O.W., R.F.G., P.L., E.C.H., K.G.A.; Methodology: M.W., J.I.L., A.C.-C., L.M., J.E.P., M.U.G.K., M.A.S., A.L.R., D.L.R., S.A.G., A.R., J.O.W., R.F.G., P.L., E.C.H., K.G.A.; Project administration: M.W., K.G.A.; Resources: M.W., J.O.W., K.G.A.; Software: L.M., J.I.L., J.E.P., J.O.W., P.L., A.R.; Supervision: M.W., J.O.W., K.G.A.; Validation: M.W., L.M., J.I.L., J.E.P., P.L., J.O.W., K.G.A.; Visualization: M.W., J.I.L., L.M., J.E.P., A.L.R., A.R., J.O.W., R.F.G., P.L., E.C.H., K.G.A.; Writing – original draft preparation: M.W., R.F.G.; Writing – review and editing: M.W., J.I.L., A.C.-C., L.M., J.E.P., M.U.G.K., M.A.S., M.K., A.L.R., C.N., D.L.R., S.A.G., A.R., J.O.W., R.F.G., P.L., E.C.H., K.G.A.
作者贡献：概念构思：M.W.，K.G.A.；数据整理：M.W.，A.R.，K.G.A.；形式分析：M.W.，J.I.L.，A.C.-C.，L.M.，J.E.P.，M.U.G.K.，M.A.S.，A.L.R.，D.L.R.，S.A.G.，A.R.，J.O.W.，R.F.G.，P.L.，E.C.H.，K.G.A.；资金获取：M.W.，J.I.L.，A.C.-C.，L.M.，J.E.P.，M.U.G.K.，M.A.S.，A.L.R.，D.L.R.，S.A.G.，A.R.，J.O.W.，R.F.G.，P.L.，E.C.H.，K.G.A.；调查：M.W.，J.I.L.，A.C.-C.，L.M.，J.E.P.，M.U.G.K.，M.A.S.，M.K.，A.L.R.，D.L.R.，C.N.，S.A.G.，A.R.，J.O.W.，R.F.G.，P.L.，E.C.H.，K.G.A.；方法论：M.W.，J.I.L.，A.C.-C.，L.M.，J.E.P.，M.U.G.K.，M.A.S.，A.L.R.，D.L.R.，S.A.G.，A.R.，J.O.W.，R.F.G.，P.L.，E.C.H.，K.G.A.；项目管理：M.W.，K.G.A.；资源：M.W.，J.O.W.，K.G.A.；软件：L.M.，J.I.L.，J.E.P.，J.O.W.，P.L.，A.R.；监督：M.W.，J.O.W.，K.G.A.；验证：M.W.，L.M.，J.I.L.，J.E.P.，P.L.，J.O.W.，K.G.A.；可视化：M.W.，J.I.L.，L.M.，J.E.P.，A.L.R.，A.R.，J.O.W.，R.F.G.，P.L.，E.C.H.，K.G.A.；写作——初稿：M.W.，R.F.G.；写作——审阅和编辑：M.W.，J.I.L.，A.C.-C.，L.M.，J.E.P.，M.U.G.K.，M.A.S.，M.K.，A.L.R.，C.N.，D.L.R.，S.A.G.，A.R.，J.O.W.，R.F.G.，P.L.，E.C.H.，K.G.A.
作者贡献：概念构思：M.W.，K.G.A.；数据整理：M.W.，A.R.，K.G.A.；形式分析：M.W.，J.I.L.，A.C.-C.，L.M.，J.E.P.，M.U.G.K.，M.A.S.，A.L.R.，D.L.R.，S.A.G.，A.R.，J.O.W.，R.F.G.，P.L.，E.C.H.，K.G.A.；资金获取：M.W.，J.I.L.，A.C.-C.，L.M.，J.E.P.，M.U.G.K.，M.A.S.，A.L.R.，D.L.R.，S.A.G.，A.R.，J.O.W.，R.F.G.，P.L.，E.C.H.，K.G.A.；调查：M.W.，J.I.L.，A.C.-C.，L.M.，J.E.P.，M.U.G.K.，M.A.S.，M.K.，A.L.R.，D.L.R.，C.N.，S.A.G.，A.R.，J.O.W.，R.F.G.，P.L.，E.C.H.，K.G.A.；方法论：M.W.，J.I.L.，A.C.-C.，L.M.，J.E.P.，M.U.G.K.，M.A.S.，A.L.R.，D.L.R.，S.A.G.，A.R.，J.O.W.，R.F.G.，P.L.，E.C.H.，K.G.A.；项目管理：M.W.，K.G.A.；资源：M.W.，J.O.W.，K.G.A.；软件：L.M.，J.I.L.，J.E.P.，J.O.W.，P.L.，A.R.；监督：M.W.，J.O.W.，K.G.A.；验证：M.W.，L.M.，J.I.L.，J.E.P.，P.L.，J.O.W.，K.G.A.；可视化：M.W.，J.I.L.，L.M.，J.E.P.，A.L.R.，A.R.，J.O.W.，R.F.G.，P.L.，E.C.H.，K.G.A.；写作——初稿：M.W.，R.F.G.；写作——审阅和编辑：M.W.，J.I.L.，A.C.-C.，L.M.，J.E.P.，M.U.G.K.，M.A.S.，M.K.，A.L.R.，C.N.，D.L.R.，S.A.G.，A.R.，J.O.W.，R.F.G.，P.L.，E.C.H.，K.G.A.
Competing interests: J.O.W. receives funding from the Centers for Disease Control and Prevention (CDC) through contracts to his institution unrelated to this research. M.A.S. receives funding from Janssen Research & Development, the US Food & Drug Administration, and the US Department of Veterans Affairs through contracts and grants unrelated to this research. R.F.G. is a cofounder of Zalgen Labs, a biotechnology company developing countermeasures for emerging viruses. M.W., A.L.R., A.R., M.A.S., E.C.H., S.A.G., J.O.W., and K.G.A. have received consulting fees and/or provided compensated expert testimony on SARS-CoV-2 and the COVID-19 pandemic. M.K. has participated in the second WHO mission to China to study the origins of the pandemic and has served as scientific adviser on emerging disease preparedness to the Guangdong CDC before 2020.
利益冲突：J.O.W. 通过与其机构无关的合同从疾病控制与预防中心（CDC）获得资金。M.A.S. 通过合同和与本研究无关的拨款从强生研究与发展、美国食品药品监督管理局和美国退伍军人事务部获得资金。R.F.G. 是 Zalgen Labs 的联合创始人，该公司是一家开发针对新兴病毒对策的生物技术公司。M.W.、A.L.R.、A.R.、M.A.S.、E.C.H.、S.A.G.、J.O.W. 和 K.G.A. 已收到关于 SARS-CoV-2 和 COVID-19 大流行的咨询费，并/或提供了有偿专家证词。M.K. 参加了世界卫生组织第二次赴中国调查大流行起源的任务，并在 2020 年之前担任广东省疾病预防控制中心新兴疾病预防的科学顾问。
利益冲突：J.O.W. 通过与其机构无关的合同从疾病控制与预防中心（CDC）获得资金。M.A.S. 通过合同和与本研究无关的拨款从强生研究与发展、美国食品药品监督管理局和美国退伍军人事务部获得资金。R.F.G. 是 Zalgen Labs 的联合创始人，该公司是一家开发针对新兴病毒对策的生物技术公司。M.W.、A.L.R.、A.R.、M.A.S.、E.C.H.、S.A.G.、J.O.W. 和 K.G.A. 已收到关于 SARS-CoV-2 和 COVID-19 大流行的咨询费，并/或提供了有偿专家证词。M.K. 参加了世界卫生组织第二次赴中国调查大流行起源的任务，并在 2020 年之前担任广东省疾病预防控制中心新兴疾病预防的科学顾问。
Data and materials availability: Data and code for this manuscript are available from (53). We acquired the Weibo dataset from (26).
数据和材料可用性：本文的数据和代码可从（53）获取。我们获取了微博数据集来自（26）。
数据和材料可用性：本文的数据和代码可从（53）获取。我们获取了微博数据集来自（26）。
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许可信息：本作品采用知识共享署名 4.0 国际许可协议（CC BY 4.0）许可，允许在任何媒介下无限制地使用、分发和复制，前提是适当引用原始作品。要查看此许可的副本，请访问 https://creativecommons.org/licenses/by/4.0/。本许可不适用于文章中归功于第三方的图像/照片/艺术品或其他内容；在使用此类材料之前，请从权利持有人处获得授权。
Supplementary Materials 补充材料
This PDF file includes: 此 PDF 文件包含：
Materials and Methods 材料和方法的
Supplementary Text 补充文本
Figs. S1 to S18 图 S1 至 S18
Tables S1 to S12 表 S1 至 S12
Data S1 and S2 数据 S1 和 S2
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- 13.49 MB
Other Supplementary Material for this manuscript includes the following:
其他补充材料包括以下内容：
MDAR Reproducibility Checklist
MDAR 可复现性清单
MDAR 可复现性清单
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A correction was posted to the supplementary materials of the research article “The Huanan Seafood Wholesale Market in Wuhan was the early epicenter of the COVID-19 pandemic” on 8 May 2023. However, because this correction involved changes to the data files posted to Github, Science should have indexed this as a formal erratum, which is now in place as of 14 March 2024. The details of the erratum do not differ from that of the correction made on 8 May 2023 and are as follows:
2023 年 5 月 8 日，对研究文章《武汉华南海鲜批发市场是 COVID-19 大流行的早期震中》的补充材料进行了更正。然而，由于这次更正涉及对发布到 Github 的数据文件进行更改，科学应该将其作为正式的勘误表索引，现在已于 2024 年 3 月 14 日实施。勘误表的细节与 2023 年 5 月 8 日进行的更正没有区别，具体如下：
2023 年 5 月 8 日，对研究文章《武汉华南海鲜批发市场是 COVID-19 大流行的早期震中》的补充材料进行了更正。然而，由于这次更正涉及对发布到 Github 的数据文件进行更改，科学应该将其作为正式的勘误表索引，现在已于 2024 年 3 月 14 日实施。勘误表的细节与 2023 年 5 月 8 日进行的更正没有区别，具体如下：
Correction (8 May 2023): 修正（2023 年 5 月 8 日）：
It has been brought to our attention that two files in the GitHub repository associated with our paper located at: https://www.science.org/doi/10.1126/science.abp8715 (https://github.com/sars-cov-2-origins/huanan-market/tree/main/data and http://doi.org/10.5281/zenodo.6786454) were identical:
已引起我们注意，与我们的论文相关的 GitHub 仓库中的两个文件相同：https://www.science.org/doi/10.1126/science.abp8715（https://github.com/sars-cov-2-origins/huanan-market/tree/main/data 和 http://doi.org/10.5281/zenodo.6786454）
已引起我们注意，与我们的论文相关的 GitHub 仓库中的两个文件相同：https://www.science.org/doi/10.1126/science.abp8715（https://github.com/sars-cov-2-origins/huanan-market/tree/main/data 和 http://doi.org/10.5281/zenodo.6786454）
‘distance_popdensityagegroups_null_35.csv’ and ‘distance_popdensityagegroups_null_120.csv’. These files included median distances to the Huanan market of locations drawn from a Wuhan population density map, as described in our paper. Upon inspection of these files, we further noticed that the number of pseudoreplicates contained in them, and in one additional file (‘distance_popdensityagegroups_null_155.csv’), was 10,000 rather than the n = 1000 used for all related analyses. We therefore generated new, n = 1000 versions of these files and re-ran all statistical tests involving them. All results remained the same as previously reported: p < 0.001 and p-Adjusted (BH) = 0.003 for each of the corrected 'distance_popdensityagegroups_null_35. csv', 'distance_popdensityagegroups_null_120.csv', and distance_popdensityagegroups_null_155.csv' (see table S4). We have uploaded the three corrected files to our GitHub repository and archived the updated repository on Zenodo (https://doi.org/10.5281/zenodo.7887816).
‘distance_popdensityagegroups_null_35.csv’和‘distance_popdensityagegroups_null_120.csv’文件包含了从武汉人口密度图中抽取的位置到华南海鲜市场的中位距离，如我们论文中所述。检查这些文件后，我们进一步注意到其中包含的伪重复数量，以及一个额外的文件（‘distance_popdensityagegroups_null_155.csv’）中的数量，均为 10,000，而不是所有相关分析中使用的 n = 1000。因此，我们生成了新的、n = 1000 版本的这些文件，并重新运行了所有涉及这些文件的统计测试。所有结果与之前报道的相同：校正后的每个'distance_popdensityagegroups_null_35.csv'、'distance_popdensityagegroups_null_120.csv'和'distance_popdensityagegroups_null_155.csv'的 p < 0.001 和 p-调整（BH）= 0.003（见表 S4）。我们已经将这三个校正文件上传到我们的 GitHub 仓库，并在 Zenodo 上存档了更新后的仓库（https://doi.org/10.5281/zenodo.7887816）。
‘distance_popdensityagegroups_null_35.csv’和‘distance_popdensityagegroups_null_120.csv’文件包含了从武汉人口密度图中抽取的位置到华南海鲜市场的中位距离，如我们论文中所述。检查这些文件后，我们进一步注意到其中包含的伪重复数量，以及一个额外的文件（‘distance_popdensityagegroups_null_155.csv’）中的数量，均为 10,000，而不是所有相关分析中使用的 n = 1000。因此，我们生成了新的、n = 1000 版本的这些文件，并重新运行了所有涉及这些文件的统计测试。所有结果与之前报道的相同：校正后的每个'distance_popdensityagegroups_null_35.csv'、'distance_popdensityagegroups_null_120.csv'和'distance_popdensityagegroups_null_155.csv'的 p < 0.001 和 p-调整（BH）= 0.003（见表 S4）。我们已经将这三个校正文件上传到我们的 GitHub 仓库，并在 Zenodo 上存档了更新后的仓库（https://doi.org/10.5281/zenodo.7887816）。
The original version is available here:
原文版本在此处提供：
原文版本在此处提供：
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- 13.45 MB
Correction (13 October 2023): The first sentence of the main text has been updated to indicate WHO learned of the outbreak on 31 December 2019, not that they were notified by the Chinese government on that date. In the Discussion section, the description of the network of wildlife farms in Hubei province has been updated to indicate that the hundreds of thousands of animals on these farms included wild mammals such as civets and ferret badgers rather than only raccoon dogs.
更正（2023 年 10 月 13 日）：主文的第一句话已更新，表明世界卫生组织于 2019 年 12 月 31 日得知疫情，而不是他们当天收到中国政府的通知。在讨论部分，关于湖北省野生动物养殖场的描述已更新，指出这些养殖场上的数十万动物包括果子狸和黄鼠狼等野生动物，而不仅仅是浣熊狗。
更正（2023 年 10 月 13 日）：主文的第一句话已更新，表明世界卫生组织于 2019 年 12 月 31 日得知疫情，而不是他们当天收到中国政府的通知。在讨论部分，关于湖北省野生动物养殖场的描述已更新，指出这些养殖场上的数十万动物包括果子狸和黄鼠狼等野生动物，而不仅仅是浣熊狗。
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Volume 377 | Issue 6609
26 August 2022
26 August 2022
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Copyright © 2022 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution License 4.0 (CC BY).
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Received: 2 March 2022
Accepted: 18 July 2022
Published in print: 26 August 2022
Acknowledgments
We thank the researchers who generated the geospatial and environmental sample data and the members of the China team involved in producing the WHO mission report for the maps that made this work possible; M. Standaert, B. LaFleur, @babarlelephant, M. Boni, F. Débarre, and B. Pierce for comments and assistance; WorldPop.org for making population density and demographic data from Wuhan freely available; the patients, clinicians, and researchers whose data made this research possible; and the five reviewers for insightful comments and feedback.
Funding: This project has been funded in part with federal funds from the National Institute of Allergy and Infectious Diseases, National Institutes of Health (NIH), Department of Health and Human Services (contract no. 75N93021C00015 to M.W.). J.I.L. acknowledges support from the NIH (grant 5T32AI007244-38). S.A.G. acknowledges support from the NIH (grant F32AI152341). J.E.P. acknowledges support from the NIH (grant T15LM011271). J.O.W. acknowledges support from NIH (grants AI135992 and AI136056). D.L.R. acknowledges support from the Medical Research Council (grant MC_UU_12014/12) and the Wellcome Trust (grant 220977/Z/20/Z). M.A.S., P.L., and A.R. acknowledge support from the Wellcome Trust (collaborators award 206298/Z/17/Z – ARTIC network), the European Research Council (grant no. 725422 – ReservoirDOCS), and the NIH (grant R01AI153044). A.L.R. is supported by the Canadian Institutes of Health Research as part of the Coronavirus Variants Rapid Response Network (CoVaRR-Net; CIHR FRN#175622) and acknowledges that VIDO receives operational funding from the Canada Foundation for Innovation – Major Science Initiatives Fund and from the Government of Saskatchewan through Innovation Saskatchewan and the Ministry of Agriculture. M.K. receives funding from the European Union’s Horizon 2020 research and innovation program (grant no. 874735, VEO, Versatile Emerging Infectious Disease Observatory). R.F.G. acknowledges support from the NIH (grants R01AI132223, R01AI132244, U19AI142790, U54CA260581, U54HG007480, and OT2HL158260), the Coalition for Epidemic Preparedness Innovation, the Wellcome Trust Foundation, Gilead Sciences, and the European and Developing Countries Clinical Trials Partnership Programme. E.C.H. is supported by an Australian Research Council Laureate Fellowship (FL170100022). K.G.A. acknowledges support from the NIH (grants U19AI135995, U01AI151812, and UL1TR002550).
Author contributions: Conceptualization: M.W., K.G.A.; Data curation: M.W., A.R., K.G.A.; Formal analysis: M.W., J.I.L., A.C.-C., L.M., J.E.P., M.U.G.K., M.A.S., A.L.R., D.L.R., S.A.G., A.R., J.O.W., R.F.G., P.L., E.C.H., K.G.A.; Funding acquisition: M.W., J.I.L., A.C.-C., L.M., J.E.P., M.U.G.K., M.A.S., A.L.R., D.L.R., S.A.G., A.R., J.O.W., R.F.G., P.L., E.C.H., K.G.A.; Investigation: M.W., J.I.L., A.C.-C., L.M., J.E.P., M.U.G.K., M.A.S., M.K., A.L.R., D.L.R., C.N., S.A.G., A.R., J.O.W., R.F.G., P.L., E.C.H., K.G.A.; Methodology: M.W., J.I.L., A.C.-C., L.M., J.E.P., M.U.G.K., M.A.S., A.L.R., D.L.R., S.A.G., A.R., J.O.W., R.F.G., P.L., E.C.H., K.G.A.; Project administration: M.W., K.G.A.; Resources: M.W., J.O.W., K.G.A.; Software: L.M., J.I.L., J.E.P., J.O.W., P.L., A.R.; Supervision: M.W., J.O.W., K.G.A.; Validation: M.W., L.M., J.I.L., J.E.P., P.L., J.O.W., K.G.A.; Visualization: M.W., J.I.L., L.M., J.E.P., A.L.R., A.R., J.O.W., R.F.G., P.L., E.C.H., K.G.A.; Writing – original draft preparation: M.W., R.F.G.; Writing – review and editing: M.W., J.I.L., A.C.-C., L.M., J.E.P., M.U.G.K., M.A.S., M.K., A.L.R., C.N., D.L.R., S.A.G., A.R., J.O.W., R.F.G., P.L., E.C.H., K.G.A.
Competing interests: J.O.W. receives funding from the Centers for Disease Control and Prevention (CDC) through contracts to his institution unrelated to this research. M.A.S. receives funding from Janssen Research & Development, the US Food & Drug Administration, and the US Department of Veterans Affairs through contracts and grants unrelated to this research. R.F.G. is a cofounder of Zalgen Labs, a biotechnology company developing countermeasures for emerging viruses. M.W., A.L.R., A.R., M.A.S., E.C.H., S.A.G., J.O.W., and K.G.A. have received consulting fees and/or provided compensated expert testimony on SARS-CoV-2 and the COVID-19 pandemic. M.K. has participated in the second WHO mission to China to study the origins of the pandemic and has served as scientific adviser on emerging disease preparedness to the Guangdong CDC before 2020.
Data and materials availability: Data and code for this manuscript are available from (53). We acquired the Weibo dataset from (26).
License information: This work is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/. This license does not apply to figures/photos/artwork or other content included in the article that is credited to a third party; obtain authorization from the rights holder before using such material.
Authors
Funding Information
National Institutes of Health: 5T32AI007244-38
National Institutes of Health: F32AI152341
National Institutes of Health: T15LM011271
National Institutes of Health: AI135992
National Institutes of Health: AI136056
National Institutes of Health: R01AI153044
National Institutes of Health: R01AI132223
National Institutes of Health: R01AI132244
National Institutes of Health: U19AI142790
National Institutes of Health: U54CA260581
National Institutes of Health: U54HG007480
National Institutes of Health: OT2HL158260
National Institutes of Health: U19AI135995
National Institutes of Health: U01AI151812
National Institutes of Health: UL1TR002550
Wellcome: 220977/Z/20/Z
Wellcome: 206298/Z/17/Z
Horizon 2020 Framework Programme: 874735
H2020 European Research Council: 725422
Laureate Education: FL170100022
Laureate Education: 75N93021C00015
Laureate Education: 5T32AI007244-38
Laureate Education: F32AI152341
Laureate Education: T15LM011271
Laureate Education: AI135992
Laureate Education: AI136056
Laureate Education: 12014/12
Laureate Education: 220977/Z/20/Z
Laureate Education: 206298/Z/17/Z
Laureate Education: 725422
Laureate Education: R01AI153044
Laureate Education: 175622
Laureate Education: 874735
Laureate Education: R01AI132223
Laureate Education: R01AI132244
Laureate Education: U19AI142790
Laureate Education: U54CA260581
Laureate Education: U54HG007480
Laureate Education: OT2HL158260
Laureate Education: U19AI135995
Laureate Education: U01AI151812
Laureate Education: UL1TR002550
Medical Research Council Canada: 12014/12
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Figures
Fig. 1. Spatial patterns of COVID-19 cases in Wuhan in December 2019 and January–February 2020.
(A) Locations of the 155 cases that we extracted from the WHO mission report (7). Inset: map of Wuhan with the December 2019 cases indicated with gray dots (no cases are obscured by the inset). In both the inset and the main panel, the location of the Huanan market is indicated with a red square. (B) Probability density contours reconstructed by a KDE using all 155 COVID-19 cases locations from December 2019. The highest density 50% contour marked is the area for which cases drawn from the probability distribution are as likely to lie inside as outside. Also shown are the highest density 25%, 10%, 5%, and 1% contours. Inset: expanded view and the highest density 1% probability density contour. (C) Probability density contours reconstructed using the 120 COVID-19 cases locations from December 2019 that were unlinked to the Huanan market. (D) Locations of 737 COVID-19 cases from Weibo data dating to January–February 2020. (E) The same highest probability density contours (50% through 1%) as shown in (B) and (C) for 737 COVID-19 case locations from Weibo data.
Fig. 2. Spatial analyses.
(A) Inset: map of Wuhan, with gray dots indicating the 1000 random samples from the WorldPop.com null distribution. In the main panel, the median distance between Huanan market and the WorldPop.org null distribution is indicated by the outer black circle. December 2019 cases are indicated by concentric red circles (distances to Huanan market are described in the purple boxes). The center point of Wuhan population density data is indicated by a blue dot. Center points of December 2019 case locations are shown as follows: red dots indicate “all,” “linked,” and “unlinked” cases, and the yellow dot indicates lineage B cases. Distance from center points to Huanan market are described in orange boxes. (B) Schematic showing how cases can be near to, but not centered on, a specific location. We hypothesized that if the Huanan market were the epicenter of the pandemic, then early cases should fall not just unexpectedly near to it but should also be unexpectedly centered on it (see the materials and methods). The blue dots show how hypothetical cases quite near the Huanan market could nevertheless not be centered on it. (C) Tolerance contours based on relative risk of COVID-19 cases in December 2019 versus data from January–February 2020. The gray dots show the December case locations. The contours represent the probability of observing that density of December cases within the bounds of the given contour if the December cases had been drawn from the same spatial distribution as the January–February data.
Fig. 3. Visitors to locations throughout Wuhan.
Shown is the number of social media check-ins in the Sina Visitor System from 2013 to 2014 as shared by (33). The numbers of check-ins to individual markets throughout the city are shown in comparison with check-ins at the Huanan market. Inset: the total number of check-ins to all individual locations across the city of Wuhan grouped by category. Locations with >50 visitor check-ins are shown, and the locations that received more check-ins than the Huanan market in the same period are shown in red.
Fig. 4. Map of the Huanan market.
(A) Aggregated environmental sampling and human case data from the Huanan market. Captions describe the types of SARS-CoV-2–positive environmental samples obtained from known live animal vendors (left) and from stalls with samples with known virus lineage (center). Lineage is unknown unless noted; sequencing data have not been released for some samples, and many samples were PCR-positive but not sequenced. Image at left shows raccoon dogs in a metal cage on top of caged birds from a business with five positive environmental samples (photo by E.C.H.). Center: Rectangle with dashed outline indicates the “wildlife” section of the market. (B) Relative risk analysis of positive environmental samples. Tolerance contours enclose regions with statistically significant elevation in density of positive environmental samples relative to the distribution of sampled stalls. (C) Distribution of positive environmental samples. Sample locations (centroid of corresponding business) and quantity are shown as black circles. (D) Control distribution for relative risk analysis. All businesses investigated with environmental sampling are shown as black circles (there is one circle per business regardless of whether a positive sample was found). See table S12 for details on stalls that were SARS-CoV-2–negative.
Fig. 5. Location and timing of human cases in Huanan market.
(A) Outline colors correspond to the timing of the first known case in each business. Individual case timing is denoted by marker color and shown within the outlined business. (B) Distribution of known cases on or before 20 December 2019. Case locations are shown as black circles. (C) Distribution of all known human cases in Huanan market. See table S11 for details on SARS-CoV-2–positive human cases with the Huanan market.
Multimedia
Tables
Table 1. Live mammals traded at the Huanan market in November and December 2019.
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Duplicate, missing, and biased data in the Worobey et al. study undermine their main result
重复、缺失和有偏的数据削弱了 Worobey 等人研究的主要结果
A claimed main result from the study of Worobey et al. [1] is that the 35 mapped COVID-19 cases with a documented epidemiological link to the Huanan Seafood Wholesale Market (the Huanan market) were significantly further away from the market location than the 120 cases which had no such link (“The cases with no known link to the market on average resided closer to the market than the cases with links to the market (p = 0.029)” and “[o]ne of the key findings of our study is that ‘unlinked’ early COVID-19 patients, i.e., those who did not work at the market, did not know someone who did, and had not recently visited the market, resided significantly closer to the market than patients with a direct link to it. The observation that a substantial proportion of early cases had no known epidemiological link had previously been used as an argument against the Huanan market being the epicenter of the pandemic. However, this group of cases resided significantly closer to the market than those who worked there, indicating that they had been exposed to the virus at or near the Huanan market.”)
研究 Worobey 等人[1]的一个声称的主要结果是，与华南海鲜批发市场（华南市场）有记录的流行病学联系的 35 个已映射的 COVID-19 病例，与没有这种联系的 120 个病例相比，距离市场位置明显更远（“没有市场联系病例的平均居住地比有市场联系病例更靠近市场（p = 0.029）”以及“我们研究的一个关键发现是，‘无联系’的早期 COVID-19 患者，即那些没有在市场工作、不认识在市场工作的人且最近没有访问过市场的人，比有直接联系的患者居住地明显更靠近市场。之前曾将大量早期病例没有已知的流行病学联系作为华南市场不是大流行的中心的论据。然而，这些病例的居住地明显比在那里工作的人更靠近市场，这表明他们在华南市场或其附近接触到了病毒。”）
The above result followed from the observed difference between the two corresponding samples of cases, with median distances to the Huanan market of 5.7 km and 4.0 km, respectively, which Worobey et al. report to be borderline statistically significant (p = 0.029 at α = 0.05 level; Wilcoxon rank sum test). However, as is directly shown below with their own data, this result is problematic for two reasons:
1. Their sample data file for the n=35 statistical background (“distance_popdensityagegroups_null_35.csv” retrieved from the study’s data collection at https://github.com/sars-cov-2-origins/huanan-market/tree/main/data) is a duplicate of the n=120 data file (“distance_popdensityagegroups_null_120.csv”), which also means that the n=35 sampling data itself is missing in their study. [Of note, this problem was already pointed out to Worobey et al. in a Technical Comment (manuscript ade3852 submitted to Science magazine on 12 August 2022; editorially rejected on 4 October 2022), but it has not been addressed in their first written reply (email letter attached to the 4 October 2022 editorial decision email) nor following their second written reply (email letter on the 17 November 2022).]
2. Worobey et al. did not conduct the necessary negative control, which specifically entails the rejection of the main hypothesis (i.e, the n=35 sample median distance to the Huanan market is greater than the n=120 sample median distance) on their chosen statistical background. Only if this negative control were passed their main claim would be specifically supported by their statistical data.
Given this data duplication in the Worobey et al. study, the Wilcoxon rank sum test for the n=35 and n=120 background samples trivially passes this negative control (due to the duplication, both samples are identical with zero shift) and therefore cannot be used to support their claim.
As the n=35 background distribution itself is missing in their study's data, the closest two background distributions available in their data set (in both directions of the sample size, n=36 and n=11) can be used instead for the negative control (files “distance_popdensityagegroups_null_36.csv” and “distance_popdensityagegroups_null_11.csv”). Yet this necessary negative control failed: the Wilcoxon rank sum test did reject the hypothesis that the median distances from n=36 and n=11 distributions were drawn without a positive shift when compared with the n=120 distances (p = 0.019 and p = 0.030, respectively). Thus the main result of Worobey et al. is directly undermined by their own statistical background, which for n=36 and n=11 produces biased median distances that are already significantly greater than the n=120 medians.
Therefore, in contradiction to the key claim by Worobey et al., it cannot be inferred that the observed closer proximity of the 120 cases not epidemiologically linked to the Huanan market is due to their origin being at that market. Together with a previous technical comment [2], which directly questioned the exclusive Huanan market origin of the earliest SARS-CoV-2 lineage B cases, this evidence further invalidates the market origin hypothesis of SARS-CoV-2.
To date (10 April 2023), Worobey et al. and Science editors have, after their official and documented acknowledgements of these duplicate and missing data problem on the 17th and on the 21st November 2022, respectively, failed (a) to address and to resolve said points 1 and 2 above as well as the previous point in the Science eLetter [2] from 16 October 2022, and (b) to correct the scientific record in question despite their own official announcements to do so.
References
[1] Worobey M, Levy JI, Malpica Serrano L, et al. The Huanan Seafood Wholesale Market in Wuhan was the early epicenter of the COVID-19 pandemic. Science. 2022;377(6609):951-959. doi:10.1126/science.abp8715
[2] Lisewski AM The geospatial data of Worobey et al. statistically links the Wuhan Institute of Virology with the Huanan Seafood Wholesale Market (16 October 2022 electronic eLetter response to Worobey et al. (2022) The Huanan Seafood Wholesale Market in Wuhan was the early epicenter of the COVID-19 pandemic Science, Vol 377, Issue 6609, pp. 951-959 URL:https://www.science.org/doi/10.1126/science.abp8715#elettersSection)
This article 这篇文章
退休生物化学教授
曾在新墨西哥州立大学
Did the investigators have access to Covid testing data on staff working in labs in Wuhan during late 2019? If any positives (RT-PCR or seropositivity) were found, where did these individuals live and how does their spatial location compare to what is shown in this paper?
调查员是否能够获取 2019 年底武汉实验室工作人员的 Covid 检测数据？如果发现任何阳性（RT-PCR 或血清阳性）病例，这些个体居住在哪里，他们的空间位置与论文中展示的相比如何？
SARS-CoV-2 origin is still unknown
SARS-CoV-2 的起源仍然未知
维吉妮·科蒂耶-奥戈戈佐
生物学家，法国国家科学研究中心研究主任
法国巴黎大学城市学院，雅克·莫诺研究所，法国国家科学研究中心，巴黎，法国
植物遗传学和分类学专家
BAIF 发展研究基金会，印度，浦那
In their article, Worobey et al. [1] confirm that the Huanan market served as an early superspreading event for COVID-19, but provide no definitive evidence that SARS-CoV-2 was first transmitted to humans from wildlife sold there. Out of the 457 animals (18 species) tested from the market, all were negative [2].
Although Worobey et al. date the first potential market-associated case to November 2019, wastewater surveillance and retrospective analysis of human samples raise the possibility that the virus may have been spreading in France, Brazil and Italy in September-November 2019 [3]. Details about the first official human cases unconnected to the market are still unclear [4–6].
Worobey et al. examine SARS-CoV-2 positive environmental samples at the market, collected in January 2020 [2]. These samples are probably of human origin because the corresponding published sequences are identical to the ones found in patients [2]. Since the earliest detected cases at the market occupied stalls too dispersed for their direct contamination from 1-2 animal sources [7] and appear to be due to human-to-human transmissions outside of stalls, analysis of spatial distribution of positive samples is not relevant to infer the place of the first animal contamination. In fact, what the Worobey et al. density risk map may locate is the epicenter of a superspreading event, an area in the market’s southwest where public toilets and a closed Mahjong room are found.
The distribution of human positive cases at the market is consistent with both a zoonotic introduction that would have occurred several weeks before the first cases were detected, and an introduction of the virus to the market by an externally infected person.
Worobey et al. situate the epicenter of earliest cases to a district that also includes the Wuhan Center for Disease Control laboratory, which conducts field and laboratory research on bat viruses [8] and which moved into a new location only 500 meters from the market on 2 December 2019, something they do not mention. The authors do not consider the possibility that this laboratory could be the site of the initial human case, but acknowledge that “upstream events” and “exact circumstances” remain “obscure”.
Although it may be challenging to distinguish an accidental infection during laboratory or field work from one that occurred between an animal and a market vendor, retrospective analyses of 2019 human samples available inside and outside China could prove informative to uncover upstream events.
In summary, the Huanan market can be considered as the epicenter of an early superspreader event but it is not possible to conclude that it was the entry point of SARS-CoV-2 into the human population. It is not yet clear how exactly SARS-CoV-2 originated. All hypotheses need to be investigated, including the possibility of a lab accident, as recently summarized in the SAGO committee report [9] and in the Lancet commission report [10].
References
1. Worobey M, Levy JI, Serrano LM, et al. The Huanan Seafood Wholesale Market in Wuhan was the early epicenter of the COVID-19 pandemic. Science;0:abp8715. doi:10.1126/science.abp8715
2. Gao G, Liu W, Liu P, et al. Surveillance of SARS-CoV-2 in the environment and animal samples of the Huanan Seafood Market. In Review 2022. doi:10.21203/rs.3.rs-1370392/v1
3. Canuti M, Bianchi S, Kolbl O, et al. Waiting for the truth: is reluctance in accepting an early origin hypothesis for SARS-CoV-2 delaying our understanding of viral emergence? BMJ Glob Health 2022;7:e008386.
4. Huang C, Wang Y, Li X, et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. The Lancet 2020;395:497–506. doi:10.1016/S0140-6736(20)30183-5
5. Zhou P, Yang X-L, Wang X-G, et al. A pneumonia outbreak associated with a new coronavirus of probable bat origin. Nature 2020;579:270–3.
doi:10.1038/s41586-020-2012-7
6. Joint WHO-China Study Team. WHO-convened Global Study of Origins of SARS-CoV-2: China Part. World Health Organisation 2021. https://www.who.int/health-topics/coronavirus/origins-of-the-virus
7. Courtier-Orgogozo V, de Ribera FA. SARS-CoV-2 infection at the Huanan seafood market. Environ Res 2022;214:113702.
8. Guo W-P, Lin X-D, Wang W, et al. Phylogeny and origins of hantaviruses harbored by bats, insectivores, and rodents. PLoS Pathog 2013;9:e1003159.
9. Scientific Advisory Group for the Origins of Novel Pathogens (SAGO). Preliminary Report. 2022.https://www.who.int/publications/m/item/scientific-advisory-group-on-the-origins-of-novel-pathogens-report (accessed 22 Sep 2022).
10. Sachs JD, Karim SSA, Aknin L, et al. The Lancet Commission on lessons for the future from the COVID-19 pandemic. The Lancet 2022;0. doi:10.1016/S0140-6736(22)01585-9.
Note: The views expressed here are those of the authors and not necessarily those of their respective institutions.
VCO and FG received funding from the "Who I am?" Labex to elucidate the proximal origins of the SARS-CoV-2 virus.
CDB has received a writer's fee from the United Nations Environment Programme for the forthcoming report "Covid-19: a warning".
RHE has received consulting fees on SARS-CoV-2 and the COVID-19 pandemic and receives funding from the National Institutes of Health and Janssen under grants and contracts unrelated to SARS-CoV-2 and the COVID-19 pandemic.
NP receives funding from the National Institutes of Health for vaccine research including COVID-19.
Competing interests for RAB, JMC, ED, JPD, HK, BK, ML, SM, MR, GT, NP: None declared.
Selective and cross-reactive SARS-CoV-2 T-cell epitopes in unexposed humans
The eLetter of Edward Parr cites the failure to include the study by Basaveraju et al. in this assessment. Basaveraju et al. did not include an appropriate control in their study; for example, analysis of the same number of samples from the same blood banks taken prior to the likely first identification of SARS-Cov-2 in Western countries, assuming availability. There are multiple papers describing the presence of cross-reactive antibodies in individuals who had (nominally) never been exposed to SARS-Cov-2; for example: Mateus et al., Selective and cross-reactive SARS-CoV-2 T-cell epitopes in unexposed humans, SCIENCE 4 Aug 2020 Vol 370, Issue 6512, pp. 89-94, DOI: 10.1126/science.abd3871. The source of these cross-reactive antibodies is proposed to be through exposure to the human coronaviruses associated with the "common cold". Unfortunately, the politicization of responsibility for the pandemic has precluded access to all the relevant data available in China to determine the most likely source and transmission routes of SARS-Cov-2. Based on the data presented in this paper, the Huanan market would appear to be a likely epicentre for the initial transmission of the virus in Wuhan.
The geospatial data of Worobey et al. statistically links the Wuhan Institute of Virology with the Huanan Seafood Wholesale Market
Worobey et al. [1] present a geometrical distance analysis of early COVID-19 cases within the city of Wuhan between December 2019 and January 2020. The results of this analysis lead them to conclude that during the earliest stages of the outbreak SARS-CoV-2 “lineage A, like lineage B, may have originated at the Seafood Wholesale Market then spread from this epicenter into the neighborhoods surrounding the market and then beyond”. This conclusion was already assumed to be valid in a companion study [2] about these two suggested root SARS-CoV-2 lineages in humans. A key methodological input in [1] was the choice of the median as the geometrical distance measure from the reference location of the Huanan Seafood Wholesale Market (abbreviated here as Huanan market). These medians were calculated from samples of mapped COVID-19 cases and from expected random null locations in Wuhan sampled from the city’s overall population density and matched for age. However, with respect to its possible statistical effects and epidemiological implications, the selective use of “medians rather than means” ([1], Supplementary Materials) was not sufficiently justified by the authors, as they simply discarded the difference between means and medians altogether as “outliers”.
In the context of COVID-19 outbreak epidemiology and the origin question, medians of geometrical distances are a questionable measure because any geometrical change (e.g., clustering) in the upper half of the sampled set of COVID-19 cases would not change the median distance to the Huanan market. One can therefore expect that the distance analysis of Worobey et al. would give different results if means were used, but this was not considered in their study.
In the following it is shown, by the authors’ own data, that means, but not medians, introduce the location of the Wuhan Institute of Virology (WIV) as geographically associated with the Huanan market (“the early epicenter of the COVID-19 pandemic”) at the same statistical significance level as the earliest known lineage B cases. Thus the authors’ choice against the use of means resulted in a selective bias against an important alternative hypothesis that is supported by their own data.
Specifically, the mean distance of the n=11 lineage B cases to the Huanan market is 12.2 km (p < 0.025; p-values, with 0.05 significance level, were calculated by direct numerical sampling of the expected distances in their data file “distance_popdensityagegroups_null_1.csv” retrieved from https://github.com/sars-cov-2-origins/huanan-market/tree/main/data). Yet the distance between the Huanan market and the Wuhan Institute of Virology is nearly as far, 12.1 km (the WIV location [30.53930206N, 114.35085239E] corresponds to the institute’s main research and administrative building, see Figure 1A-C; distances measured with both the Google Maps and the Baidu map software tools at 100 m resolution.) This value is equally significant, p < 0.025, when the WIV location is geometrically represented by any tight cluster of n=11 locations in or around the WIV such that their mean distance to the Hunan market is 12.1 km. This outcome is robust: a similar result for n=10 is obtained when the one lineage B case is removed which previously had not been linked epidemiologically with the Huanan market (12.9 km mean distance from Huanan market, p < 0.05). In contrast, when tested against expected n=11 or n=10 medians, the 12.1 km distance is not statistically significant (p < 0.184). Thus an important alternative hypothesis (i.e., WIV is geographically associated with the Huanan market at higher statistical significance than the 11 lineage B cases) is rejected when medians are used, and not rejected when means are tested.
The fact that the mean distance between lineage B cases and the Huanan market is greater or equal than the (mean) distance between the latter and the Wuhan Institute of Virology is relevant because only from this numerical order statistical significance of the former necessitates statistical significance of the latter. In contrast, the 35 cases linked to Huanan market as well as the 120 not epidemiologically linked to it [1] have both statistically significant mean distances (11.7 km and 6.7 km, respectively) that are markedly smaller than 12.1 km; thus for each of these two sets of cases one cannot strictly infer geographical association with the WIV even though the 12.1 km distance is also statistically significant on the n=35 and on the n=120 statistical backgrounds (both p < 0.0001).
In summary, based on the data of Worobey et al., any tight cluster of ten early COVID-19 cases in or around the Wuhan Institute of Virology would be geographically associated with the Huanan market at a higher level of statistical significance than the earliest lineage B cases that already had been linked epidemiologically with the Huanan market. Three main possibilities can then be considered: 1.) Such cluster did not exist in late 2019; 2.) it did exist and was documented but the epidemiological information has not been published yet; 3.) since the WIV is not a residential location the entire cluster or a part of it corresponds to a group of individuals among the 155 cases analysed by Worobey et al. Regarding this third possibility, it is remarkable that the four nearest cases to the WIV form a localized cluster—over roughly one square kilometer at the Shahuxincun neighborhood— with an average distance of 2.7 km to the Wuhan Institute of Virology (Figure 1A).
The critical question if and how many human SARS-CoV-2 infections had actually occurred at the WIV in late 2019 was entirely ignored by Worobey et al. This selective omission is not understandable as reports have existed since at least early 2021 which indicate that it might had been the case. For example, widely circulated official publications by the US government report “with moderate confidence that the first human infection with SARS-CoV-2 most likely was the result of a laboratory-associated incident, probably involving experimentation, animal handling, or sampling by the Wuhan Institute of Virology” [3], and that “[t]he U.S. government has reason to believe that several researchers inside the WIV became sick in autumn 2019, before the first identified case of the outbreak, with symptoms consistent with both COVID-19 and common seasonal illnesses.” [4] Thus the geospatial association of the Wuhan Institute of Virology with the suggested early epicenter of the COVID-19 pandemic, as detected here, might be epidemiologically relevant and therefore deserves further scientific attention.
As no acceptable scientific conclusion to the COVID-19 origins problem should evidently be the product of a selective bias (i.e., the deliberate choice of medians over means with the resulting omission of statistical data associated with the Wuhan Institute of Virology), it is suggested that Worobey et al. change their main interpretations and conclusions accordingly.
Figures
Figure 1A, B, C deposited at Harvard Dataverse (dataverse.harvard.edu):
Lisewski, Andreas Martin, 2022, "Figure 1A-C", https://doi.org/10.7910/DVN/OPRUXQ, Harvard Dataverse, V1
References
[1] Worobey M, Levy JI, Malpica Serrano L, et al. The Huanan Seafood Wholesale Market in Wuhan was the early epicenter of the COVID-19 pandemic. Science. 2022;377(6609):951-959. doi:10.1126/science.abp8715
[2] Pekar JE, Magee A, Parker E, et al. The molecular epidemiology of multiple zoonotic origins of SARS-CoV-2. Science. 2022;377(6609):960-966. doi:10.1126/science.abp8337
[3] The Office of the Director of National Intelligence (ODNI). Unclassified Summary of Assessment on COVID-19 Origins. 27 August 2021. URL (accessed 7 October 2022): https://www.dni.gov/index.php/newsroom/reports-publications/reports-publications-2021/item/2236-unclassified-summary-of-assessment-on-covid-19-origins
[4] The US Department of State - Office of the Spokesperson. Fact Sheet: Activity at the Wuhan Institute of Virology. 15 January 2021. URL (accessed 7 October 2022): https://2017-2021.state.gov/fact-sheet-activity-at-the-wuhan-institute-of-virology/index.html
Response to "The Huanan Seafood Wholesale Market in Wuhan was the early epicenter of the COVID-19 pandemic" published on 26 July 2022
eLetter
I read the study of Michael Worobey (1) with interest and appreciation. The authors wrote that environmental samples were taken from vendors’ stalls. Considering the SARS-CoV to be transmitted through respiratory droplets and contact routes, what is the justification for taking the samples from the vendors’ stall only and not from other places in the year 2020 (and in the year 2014 from grates outside vendors’ stall upon which metal cage was stacked)? Another data that the authors used were the social media check-ins in the Sina Visitor System. I do not think that social media check-ins should be considered synonymous with buying some items from the market, and in that case, the proxy of social media check-ins for buying some disease- spreading item from the market is not correct. Lastly, since most of the clustering of early COVID-19 cases occurred near the west bank of Yangtze River, and were detected from the western side of the market where mammal species were sold; I welcome some comments from authors in the light of these epidemiological facts.
References
1. Worobey et al., Science10.1126/science.abp8715 (2022).
Re. this article and the accompanying article
It notable that both this article and the companion article (Pekar JE et al) make no mention of the study published in CID in 2021 in which SARS- CoV-2-reactive antibodies were found in 106 of 7389 serum samples from blood donated to the American Red Cross between mid-December 2019 and mid-January 2020 (1). That study implied the possibility of substantial spread within the US by mid- to late-December, which may seem at odds with the idea that the origin was the Wuhan wet market in late-November to mid-December. Thus, the failure to mention and address that seeming inconsistency may fuel rather than resolve the controversy regarding the origin(s) of the pandemic.
1. Basavaraju SV, et al. Serologic Testing of US Blood Donations to Identify Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2)-Reactive Antibodies: December 2019-January 2020. Clin Infect Dis. 2021 Jun 15;72(12):e1004-e1009. doi: 10.1093/cid/ciaa1785. PMID: 33252659; PMCID: PMC7799215.
Proves animals at the market were the source? Falls dreadfully short, giving what's more like a movie treatment.
The Huanan Seafood Wholesale Market in Wuhan was the early epicenter of the COVID-19 pandemic
gives a description of the start of the pandemic that's been compared to the work of John Snow. Unfortunately, the paper also that led to a global conflagration. Here, the paper falls dreadfully short -- giving what's more like a movie treatment than anything resembling observation, calculation or interpretation.
"They say it was transmitted into people who were working or shopping there in two separate "spillover events", where a human contracted the virus from an animal."
Covid origin studies say evidence points to Wuhan market
https://www.bbc.com/news/science-environment-62307383
The staggeringly sad reality is that reporters won't look at the cites, but will simply accept the authors words as having been proven.
# # #
"The animals on these farms (nearly 1 million) were rapidly released, sold, or killed in early 2020,..."
The Huanan Seafood Wholesale Market in Wuhan was the early epicenter of the COVID-19 pandemic
"Released back into the woods" ... "Visits to nearly a dozen other former wildlife farms in the area yielded similar stories. Owners were either not home, denied raising animals listed on business registrations, or said they stopped farming before the outbreak."
M. Standaert, E. Dou, “In search for coronavirus origins, Hubei caves and wildlife farms draw new scrutiny,” The Washington Post, 11 October 2021; https://www.washingtonpost.com/world/asia_pacific/china-covid-bats-caves-hubei/2021/10/10/082eb8b6-1c32-11ec-bea8-308ea134594f_story.html.
From the Washington Post article, I can't see where the total of animals on farms is provided. Actually, learning anything about the farms is now an intractable problem for science. Information about the farms is either suppressed or questionable. The WP reporters only state the single answer of "Released back into the woods." Exactly what "similar stories" are is not explained. For the most part, what the WP relays is that information was not available or suspec,
# # #
"There was an extensive network of wildlife farms in western Hubei province, including hundreds of thousands of raccoon dogs on farms in Enshi prefecture, which supplied the Huanan market (48)."
M. Standaert, E. Dou, “In search for coronavirus origins, Hubei caves and wildlife farms draw new scrutiny,” The Washington Post, 11 October 2021; https://www.washingtonpost.com/world/asia_pacific/china-covid-bats-caves-hubei/2021/10/10/082eb8b6-1c32-11ec-bea8-308ea134594f_story.html.
"This region of Hubei contains extensive cave complexes housing Rhinolophus bats, which carry SARSr-CoVs (49). "
X.-D. Lin, W. Wang, Z.-Y. Hao, Z.-X. Wang, W.-P. Guo, X.-Q. Guan, M.-R. Wang, H.-W. Wang, R.-H. Zhou, M.-H. Li, G.-P. Tang, J. Wu, E. C. Holmes, Y.-Z. Zhang, Extensive diversity of coronaviruses in bats from China. Virology507, 1–10 (2017).
In the WP article, Dr. Holmes says that "to his knowledge, bat sampling had been done by scientists near but not inside Enshi, and that no coronaviruses were detected, but he added that the sample sizes were too small.
"I'm certain that SARS-CoV-2-like viruses will be found in China in places where you find Rhinolophus bats," Holmes said."
Certainly coronaviruses in the region's bats is a topic for further investigation -- that's not now politically possible. Still, speculation does not replace observation.
# # #
"Defunct wildlife farms sat as close as one mile from the entrances."
M. Standaert, E. Dou, “In search for coronavirus origins, Hubei caves and wildlife farms draw new scrutiny,” The Washington Post, 11 October 2021; https://www.washingtonpost.com/world/asia_pacific/china-covid-bats-caves-hubei/2021/10/10/082eb8b6-1c32-11ec-bea8-308ea134594f_story.html.
Has it been established how far the Horseshoe bats fly when foraging at night? Has any
means of the bats interacting with the farmed animals been established or speculated? Contaminated fruit is the supposed source of Hendra in Australia and Nipah in other countries. I believe all the suspect bats in China are insectivorous and most of the farmed animals are carnivores. Is there any reason to believe that bats had been collected to be fed to the farmed animals? Are free roaming domestic cats kept on farms in the area for rodent control? If there were infected bats a mile away, as in more standard communication theory, the Last Mile is all important.
On a separate— but related— topic, in China there were no CoViD 19 outbreaks on fur farms as there were in the US, the Netherlands, and Denmark?
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"... a potential source of virus transmission into the human population in late 2019, plausibly from infected live mammals sold at the Huanan market, "
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The China research cited (24) is much more circumspect. They could not link animals or even animal related activity at the market as the source.
"All the four sewerage wells in the market tested positive. This suggested that either contaminated sewage may have played a role in the cluster of cases in the market or that the infected people in the market contaminated the sewage."
"The 457 animal samples mainly collected between January 1st and March 2nd, 2020 included 188 individuals belonging to 18 species (with some stray animals sampled until March 30th) (Table 2). The sources of the samples include unsold goods kept in refrigerators and freezers in the stalls of HSM, and goods kept in warehouses and refrigerators related to the HSM. Samples from stray animals in the market were also collected, i.e. swab samples from 10 stray cats, 27 cat feces, one dog, one weasel, and 10 rats. All the 457 animal samples tested negative for SARS-CoV-2 nucleic acid, suggesting that the animal infections with SARS-CoV-2 might be rare in the market."
Surveillance of SARS-CoV-2 in the environment and animal samples of the Huanan Seafood Market
George Gao et al