Reconstructing the course of the COVID-19 epidemic over 2020 for US states and counties: Results of a Bayesian evidence synthesis model.
Reconstructing the course of the COVID-19 epidemic over 2020 for US states and counties: Results of a Bayesian evidence synthesis model.
复制标题
重建 2020 年美国各州和县的 COVID-19 流行病程：贝叶斯证据综合模型的结果。
DOI：
10.1371/journal.pcbi.1010465
复制
发表时间：
2022-08
影响因子：
4.3
通讯作者：
中科院分区：
文献类型：
--
作者：
报告的 COVID-19 病例和死亡提供了美国 (US) SARS-CoV-2 感染的延迟且不完整的情况。需要准确估计感染的时间和程度，以描述病毒传播动态并更好地了解 COVID-19 疾病负担。我们估计了从 2020 年 1 月 13 日报告的首例 COVID-19 病例到 2021 年 1 月 1 日期间美国每个县的 SARS-CoV-2 传播和其他 COVID-19 结果的时间趋势。为此，我们采用了贝叶斯建模方法，该方法明确考虑了报告延迟和病例确定的变化，并根据报告的 COVID-19 病例和死亡情况生成每日 SARS-CoV-2 感染事件估计。该模型可作为 covidestim R 包免费提供。我们估计，到 2020 年底，全国范围内已有 4,900 万有症状的 COVID-19 病例和 404,214 例 COVID-19 死亡病例，28% 的美国人口已被感染。各县级的发病时间和严重程度存在差异，当地流行病学趋势与州或地区平均水平存在很大差异，导致到 2020 年底感染人口的估计比例存在巨大差异。我们对与 COVID-19 相关的真实死亡人数的估计与超额死亡率的独立估计一致，并且我们对 SARS-CoV-2 感染累积发病率的估计趋势与现有抗体检测研究的血清阳性率估计趋势一致。重建美国各县 SARS-CoV-2 感染的潜在发病率可以更详细地了解疾病趋势和流行病学驱动因素的潜在影响。由于许多 COVID-19 感染未被发现，因此报告的 COVID-19 病例和死亡人数低估了该流行病的真实规模。为了解决这个问题，我们建立了一个模型来估计美国各州和县随时间推移新增 SARS-CoV-2 感染的数量。在本文中，我们介绍了从美国首次报告病例到 2021 年 1 月 1 日期间每个州和县的感染和其他疾病结果的时间趋势。感染估计的时间序列表明，美国的流行病最好被描述为一系列相关的流行病，其时间和强度各不相同。我们估计，2020 年，超过四分之一的美国人口感染了 SARS-CoV-2，到 2020 年底，有 0.12% 的人口死于 COVID-19。州一级的结果与疾病负担的外部衡量标准一致，包括 SARS-CoV-2 血清流行率和超额死亡率的估计。我们的研究结果有助于更好地了解前疫苗时代的流行病，并证明使用常规报告的病例和死亡数据估计地方水平的 SARS-CoV-2 感染的可行性。
Reported COVID-19 cases and deaths provide a delayed and incomplete picture of SARS-CoV-2 infections in the United States (US). Accurate estimates of both the timing and magnitude of infections are needed to characterize viral transmission dynamics and better understand COVID-19 disease burden. We estimated time trends in SARS-CoV-2 transmission and other COVID-19 outcomes for every county in the US, from the first reported COVID-19 case in January 13, 2020 through January 1, 2021. To do so we employed a Bayesian modeling approach that explicitly accounts for reporting delays and variation in case ascertainment, and generates daily estimates of incident SARS-CoV-2 infections on the basis of reported COVID-19 cases and deaths. The model is freely available as the covidestim R package. Nationally, we estimated there had been 49 million symptomatic COVID-19 cases and 404,214 COVID-19 deaths by the end of 2020, and that 28% of the US population had been infected. There was county-level variability in the timing and magnitude of incidence, with local epidemiological trends differing substantially from state or regional averages, leading to large differences in the estimated proportion of the population infected by the end of 2020. Our estimates of true COVID-19 related deaths are consistent with independent estimates of excess mortality, and our estimated trends in cumulative incidence of SARS-CoV-2 infection are consistent with trends in seroprevalence estimates from available antibody testing studies. Reconstructing the underlying incidence of SARS-CoV-2 infections across US counties allows for a more granular understanding of disease trends and the potential impact of epidemiological drivers. Because many COVID-19 infections go undetected, reported numbers of COVID-19 cases and deaths underestimate the true size of the epidemic. To address this problem, we built a model to estimate the number of new SARS-CoV-2 infections over time in each U.S. state and county. In this paper, we present time trends of infections and other disease outcomes from the first reported case in the U.S. until January 1, 2021, for each state and county. The time series of infection estimates suggest that the US epidemic is best described a series of related epidemics, varying in their timing and intensity. We estimate that over a quarter of the US population was infected with SARS-CoV-2 in 2020 and by the end of 2020 0.12% of the population had died from COVID-19. State-level results were consistent with external measures of disease burden, including estimates of SARS-CoV-2 seroprevalence and excess mortality. Our findings help better understand the epidemic in the pre-vaccine era and demonstrate the feasibility of estimating SARS-CoV-2 infections at local levels using routinely reported case and death data.
登录
查看更多内容
影响因子：
16.6
作者：
Leung K;Wu JT;Leung GM
通讯作者：
Leung GM
DOI：
10.3138/jammi-2020-0030
发表时间：
2020-12-01
期刊：
Journal of the Association of Medical Microbiology and Infectious Disease Canada = Journal officiel de l'Association pour la microbiologie medicale et l'infectiologie Canada
影响因子：
--
作者：
Byambasuren, Oyungerel;Cardona, Magnolia;Glasziou, Paul
通讯作者：
Glasziou, Paul
影响因子：
16.6
作者：
Nouvellet P;Bhatia S;Cori A;Ainslie KEC;Baguelin M;Bhatt S;Boonyasiri A;Brazeau NF;Cattarino L;Cooper LV;Coupland H;Cucunuba ZM;Cuomo-Dannenburg G;Dighe A;Djaafara BA;Dorigatti I;Eales OD;van Elsland SL;Nascimento FF;FitzJohn RG;Gaythorpe KAM;Geidelberg L;Green WD;Hamlet A;Hauck K;Hinsley W;Imai N;Jeffrey B;Knock E;Laydon DJ;Lees JA;Mangal T;Mellan TA;Nedjati-Gilani G;Parag KV;Pons-Salort M;Ragonnet-Cronin M;Riley S;Unwin HJT;Verity R;Vollmer MAC;Volz E;Walker PGT;Walters CE;Wang H;Watson OJ;Whittaker C;Whittles LK;Xi X;Ferguson NM;Donnelly CA
通讯作者：
Donnelly CA
影响因子：
5
作者：
Hitchings MDT;Dean NE;García-Carreras B;Hladish TJ;Huang AT;Yang B;Cummings DAT
通讯作者：
Cummings DAT
影响因子：
13.6
作者：
Levin AT;Hanage WP;Owusu-Boaitey N;Cochran KB;Walsh SP;Meyerowitz-Katz G
通讯作者：
Meyerowitz-Katz G