Adoption of Preventive Measures During the Very Early Phase of the COVID-19 Outbreak in China: National Cross-sectional Survey Study

BackgroundThe outbreak of COVID-19 in China occurred around the Chinese New Year (January 25, 2020), and infections decreased continuously afterward. General adoption of preventive measures during the Chinese New Year period was crucial in driving the decline. It is imperative to investigate preventive behaviors among Chinese university students, who could have spread COVID-19 when travelling home during the Chinese New Year break.ObjectiveIn this study, we investigated levels of COVID-19–related personal measures undertaken during the 7-day Chinese New Year holidays by university students in China, and associated COVID-19–related cognitive factors.MethodsA cross-sectional anonymous web-based survey was conducted during the period from February 1 to 10, 2020. Data from 23,863 students (from 26 universities, 16 cities, 13 provincial-level regions) about personal measures (frequent face-mask wearing, frequent handwashing, frequent home staying, and an indicator that combined the 3 behaviors) were analyzed (overall response rate 70%). Multilevel multiple logistic regression analysis was performed.ResultsOnly 28.0% of respondents (6684/23,863) had left home for >4 hours, and 49.3% (11,757/23,863) had never left home during the 7-day Chinese New Year period; 79.7% (19,026/23,863) always used face-masks in public areas. The frequency of handwashing with soap was relatively low (6424/23,863, 26.9% for >5 times/day); 72.4% (17,282/23,863) had frequently undertaken ≥2 of these 3 measures. COVID-19–related cognitive factors (perceptions on modes of transmission, permanent bodily damage, efficacy of personal or governmental preventive measures, nonavailability of vaccines and treatments) were significantly associated with preventive measures. Associations with frequent face-mask wearing were stronger than those with frequent home staying.ConclusionsUniversity students had strong behavioral responses during the very early phase of the COVID-19 outbreak. Levels of personal prevention, especially frequent home staying and face-mask wearing, were high. Health promotion may modify cognitive factors. Some structural factors (eg, social distancing policy) might explain why the frequency of home staying was higher than that of handwashing. Other populations might have behaved similarly; however, such data were not available to us.     

Self-reported handwashing and surface disinfection behaviors by U.S. adults with disabilities to prevent COVID-19, Spring 2020

BackgroundHandwashing and surface cleaning and disinfection are two hygiene behaviors promoted to prevent the spread of COVID-19. Persons with disabilities may be at increased risk for severe COVID-19 illness due to underlying medical conditions that have been associated with COVID-19.ObjectiveThis study aims to describe self-reported hygiene behaviors among U.S. adults with disabilities to prevent transmission of COVID-19.MethodsData were obtained from the March 2020 Porter Novelli ConsumerStyles survey. This study includes 6463 U.S. adults (≥18 years) who participated in the survey (58.2% response rate). Participants were asked about frequent handwashing and surface disinfection. Participants were also asked six questions to assess disability status and disability type. Prevalence estimates with 95% confidence intervals were calculated; chi-square tests were conducted.ResultsA total of 1295 (20.3%) of survey participants reported at least one disability and their hygiene-related behavior. Overall, 91.3% of respondents with disabilities reported frequent handwashing; only 72% reported frequent surface disinfection. Those with hearing, vision, cognition, mobility, self-care, and independent living disabilities (range: 77.9%–90.6%) were significantly less likely than those without any disability (94.0%) to report frequent handwashing. People with vision (62.2%) and independent living (66.8%) disabilities were less likely to report frequent surface disinfection than those without any disability (74.6%).ConclusionsPractices such as handwashing and disinfecting surfaces are effective for reducing and preventing the spread of COVID-19. Promotion of hygiene-related practices among people with disabilities is essential. Tailored communications and implementation of evidence-based strategies are needed to address hygiene-related behaviors among the subgroups of people with disabilities most affected.     

The COVID-19 pandemic in Ireland: An overview of the health service and economic policy response

ObjectivesTo outline the situation in Ireland with regard to the COVID-19 pandemic.MethodsAnalyse the evolution of the COVID-19 pandemic in Ireland. Review the key public health and health system responses.ResultsOver 1700 people have died with COVID-19 by July 19th while almost 3000 people had been admitted to hospital with COVID-19. A high proportion of the deaths occurred in nursing homes and other residential centres who did not receive sufficient attention during the early phase of the pandemic.ConclusionsIreland's response to the COVID-19 crisis has been comprehensive and timely. Transparency, a commitment to a relatively open data policy, the use of traditional and social media to inform the population, and the frequency of updates from the Department of Health and the Health Services Executive are all commendable and have led to a high level of compliance among the general public with the various non-medical measures introduced by the government.     

天津市新型冠状病毒肺炎确诊病例流行病学特征分析

目的 探讨天津市135例新型冠状病毒肺炎（COVID-19）确诊病例临床及流行病学特征。方法 收集天津市135例COVID-19确诊病例的临床及流行病学资料,对数据进行描述性分析,并对病情严重程度影响因素进行分析。结果 135例病例中,男性72例,女性63例,年龄（48.62±16.83）岁,病死率为2.22%。74.81%的病例感染来源为本地传播。共发生33起聚集性疫情,涉及的病例占全部病例的85.92%。疾病的中位潜伏期为6.50 d,代间距平均为5.00 d,家庭内续发率为20.46%。发热的病例占比78.63%,其次为咳嗽56.48%;多因素回归分析显示年龄（OR=1.038,95% CI：1.010~1.167）、慢性基础性疾病病种数（OR=1.709,95% CI：1.052~2.777）是重症的危险因素。结论 天津市COVID-19病例早期以发热为主,本地聚集性疫情为确诊病例的主要构成,高年龄、有多种基础性疾病的人群容易转为重症,对密切接触者严格隔离及加强高危人群的救治是降低发病率和病死率的主要措施。     

Policies and innovations to battle Covid-19 – A case study of South Korea

ObjectivesTo describe how health care crisis resulting from the COVID-19 pandemic in South Korea has led to innovation and changes to government policy. This paper presents the significant cluster events, relevant developments of innovation, and economical impact in Korea that could inform policy makers on how to respond to health crises in the future.MethodsHealth care, economy, epidemiological data are collected from various sources including the Korea Centers for Disease Control and Prevention (KCDC) or other government sources.ResultsThe KCDC jointly with medical professionals developed a series of innovations such as 1) Full contact tracing and rapid testing with a 12 h turnaround and 10 min movement tracking systems, 2) transparent disclosure of all contract tracing data to the public through a central database, 3) Drive-Through and Walk-Through testing methods, and 4) a 4 tier patient severity index and community treatment isolation centers. Korea moved from the 4th in the world for total confirmed cases in March down to 76th in August.ConclusionsExpedited enforcement of amended legislation acts to protect the healthcare workforce resulted in only 10 healthcare professionals contracting the virus while caring for Covid-19 patients. This has resulted in minimal human capital loss and the government was able to re-direct existing medical workforce to areas in need. The quarantine strategies implemented resulted in little need to lock down the whole economy but also limited the cost spent to gain a year of life to 193,848 Won (US$163).     

新型冠状病毒肺炎两种不同流行模式及其防控效果比较：基于广州和温州市的分析

目的 比较广州、温州市两个城市新型冠状病毒肺炎（COVID-19）的流行模式,并评估两个城市疫情的防控效果。方法 获取截至2020年2月29日广州和温州市COVID-19确诊病例的个案数据,绘制两个城市疫情的流行曲线,收集不同时间的防控措施,计算在两个城市的实时再生数。结果 广州和温州市分别纳入确诊病例346例和465例,两个城市病例均集中在30～59岁（广州市：54.9%;温州市：70.3%）。流行曲线显示广州和温州市的每日发病数分别在1月27日与1月26日到达峰值,随后出现下降趋势。两个城市的发病高峰均出现在湖北省输入病例的抵达高峰后,且温州市的湖北省输入病例的抵达高峰早于广州市。广州市一直以输入病例为主,温州市从前期的以输入病例为主转变为后期以本地病例为主。虽然两个城市流行模式存在差异,在采取了有力的防控措施后,两个城市均取得了较好的防控效果。结论 COVID-19输入疫情可导致两种不同的流行模式,但采取强有力的防控措施,均能有效控制疫情蔓延。     

COVID-19 pandemic in the United Kingdom

ObjectivesTo describe epidemiological data on cases of COVID-19 and the spread of Severe Acute Respiratory Syndrome Coronavirus 2 in the United Kingdom (UK), and the subsequent policy and technological response to the pandemic, including impact on healthcare, business and the economy.MethodsEpidemiological, business and economic data were extracted from official government sources covering the period 31st January to 13th August 2020; healthcare system data up to end of June 2019.ResultsUK-wide COVID-19 cases and deaths were 313,798 and 46,706 respectively (472 cases and 70 deaths per 100,000 population) by 12th August. There were regional variations in England, with London and North West (756 and 666 cases per 100,000 population respectively) disproportionately affected compared with other regions. As of 11th August, 13,618,470 tests had been conducted in the UK. Increased risk of mortality was associated with age (≥60 years), gender (male) and BAME groups. Since onset of the pandemic, emergency department attendance, primary care utilisation and cancer referrals and inpatient/outpatient referrals have declined; emergency ambulance and NHS111 calls increased. Business sectors most impacted are the arts, entertainment and recreation, followed by accommodation and food services. Government interventions aimed at curtailing the business and economic impact have been implemented, but applications for state benefits have increased.ConclusionsThe impact of COVID-19 on the UK population, health system and economy has been profound. More data are needed to implement the optimal policy and technological responses to preventing further spikes in COVID-19 cases, and to inform strategic planning to manage future pandemics.     

The Influence of Average Temperature and Relative Humidity on New Cases of COVID-19: Time-Series Analysis

BackgroundThe influence of meteorological factors on the transmission and spread of COVID-19 is of interest and has not been investigated.ObjectiveThis study aimed to investigate the associations between meteorological factors and the daily number of new cases of COVID-19 in 9 Asian cities.MethodsPearson correlation and generalized additive modeling (GAM) were performed to assess the relationships between daily new COVID-19 cases and meteorological factors (daily average temperature and relative humidity) with the most updated data currently available.ResultsThe Pearson correlation showed that daily new confirmed cases of COVID-19 were more correlated with the average temperature than with relative humidity. Daily new confirmed cases were negatively correlated with the average temperature in Beijing (r=–0.565, P<.001), Shanghai (r=–0.47, P<.001), and Guangzhou (r=–0.53, P<.001). In Japan, however, a positive correlation was observed (r=0.416, P<.001). In most of the cities (Shanghai, Guangzhou, Hong Kong, Seoul, Tokyo, and Kuala Lumpur), GAM analysis showed the number of daily new confirmed cases to be positively associated with both average temperature and relative humidity, especially using lagged 3D modeling where the positive influence of temperature on daily new confirmed cases was discerned in 5 cities (exceptions: Beijing, Wuhan, Korea, and Malaysia). Moreover, the sensitivity analysis showed, by incorporating the city grade and public health measures into the model, that higher temperatures can increase daily new case numbers (beta=0.073, Z=11.594, P<.001) in the lagged 3-day model.ConclusionsThe findings suggest that increased temperature yield increases in daily new cases of COVID-19. Hence, large-scale public health measures and expanded regional research are still required until a vaccine becomes widely available and herd immunity is established.     

COVID-19 pandemic in the United States

ObjectivesThe paper highlights US health policy and technology responses to the COVID-19 pandemic from January 1, 2020 – August 9, 2020.MethodsA review of primary data sources in the US was conducted. The data were summarized to describe national and state-level trends in the spread of COVID-19 and in policy and technology solutions.ResultsCOVID-19 cases and deaths initially peaked in late March and April, but after a brief reduction in June cases and deaths began rising again during July and continued to climb into early August. The US policy response is best characterized by its federalist, decentralized nature. The national government has led in terms of economic and fiscal response, increasing funding for scientific research into testing, treatment, and vaccines, and in creating more favorable regulations for the use of telemedicine. State governments have been responsible for many of the containment, testing, and treatment responses, often with little federal government support. Policies that favor economic re-opening are often followed by increases in state-level case numbers, which are then followed by stricter containment measures, such as mask wearing or pausing re-opening plans.ConclusionsWhile all US states have begun to “re-open” economic activities, this trend appears to be largely driven by social tensions and economic motivations rather than an ability to effectively test and surveil populations.     

基于指数平滑模型的湖北省新冠肺炎疫情预测分析

目的分析和预测湖北省新冠肺炎(COVID-19)疫情变化趋势。方法采用平滑指数模型对累计确诊病例数、累计治愈出院病例数、累计死亡病例数、重症病例数及危重症病例数进行拟合和预测。结果湖北省COVID-19疫情逐渐得到缓解,在2月18日进入快速“缓解期”后,3月21日进入慢速“缓解期”。采用指数平滑模型获得的拟合值与实际值的趋势基本吻合,模型拟合较好,预测结果表明在4月2日现存确诊病例数将减少至1000例以内,且主要为重症和危重症病例。结论湖北省COVID-19疫情的防控措施是有效的,指数平滑法拟合效果较好,可用于COVID-19的疫情预测。     

基于健康大数据平台的鄞州区新型冠状病毒肺炎监测病例流行病学特征分析

目的 分析鄞州区基于健康大数据平台的新型冠状病毒肺炎（COVID-19）监测病例流行特征,为COVID-19监测网络体系建设提供依据。方法 收集鄞州区新型冠状病毒肺炎监测与预警信息系统每日COVID-19监测病例数据,分析COVID-19监测病例人群构成、流行病学史比例、核酸检测率、核酸阳性检出率和确诊病例监测发现率。结果 2020年1月1日至3月30日共报告COVID-19监测病例1 595例,其中社区人群和重点人群分别占79.94%和20.06%。监测病例现场调查核实率100.00%,有武汉市或湖北省接触流行病学史占6.27%,社区和重点人群中有流行病学史者占比分别为2.12%和22.81%（P<0.001）。COVID-19核酸总检测率18.24%（291/1 595）,有、无流行病学史者核酸检测率分别为53.00%和15.92%（P<0.001）,COVID-19核酸阳性检出率1.72%（5/291）。监测确诊病例发现率0.31%（5/1 595）,监测确诊病例和其他确诊病例初次就诊至初次核酸检测时间间隔差异无统计学意义（P>0.05）。结论 基于健康大数据平台的COVID-19监测工作运转良好,但确诊病例监测发现率有待提高。     

陕西省新型冠状病毒肺炎流行病学特征分析

目的 了解陕西省新型冠状病毒肺炎（COVID-19）的发病趋势及流行特征。方法 收集陕西省截至2020年2月22日COVID-19的疫情数据,进行流行病学描述性分析。结果 陕西省共报告245例COVID-19确诊病例,全省累积报告的确诊病例以轻型、普通型为主（87.76%）,随着时间的推移,出现确诊病例的地区不断扩大,以西安市报告病例数最多（占全省报告总病例数近一半）。陕西省由输入型病例逐渐转为本地病例为主,而本地病例的传播途径主要以家庭聚集性传播为主。陕西省不同来源确诊病例均造成了二代病例传播。2月7日之后报告病例数开始波动下降并逐渐趋于平稳,目前陕西省疫情发展处于归零期。结论 陕西省的总体疫情形势已趋向缓和,但考虑到复工返学的到来,同时随着境外输入病例的不断增加,防控工作将面临新的挑战。     

COVID-19 Incidence and hospitalization during the delta surge were inversely related to vaccination coverage among the most populous U.S. Counties

ObjectiveWe tested whether COVID-19 incidence and hospitalization rates during the Delta surge were inversely related to vaccination coverage among the 112 most populous counties in the United States, comprising 44 percent of the country's total population.MethodsWe measured vaccination coverage as the percent of the county population fully vaccinated as of July 15, 2021. We measured COVID-19 incidence as the number of confirmed cases per 100,000 population during the 14-day period ending August 12, 2021 and hospitalization rates as the number of confirmed COVID-19 admissions per 100,000 population during the same 14-day period.ResultsIn log-linear regression models, a 10-percentage-point increase in vaccination coverage was associated with a 28.3% decrease in COVID-19 incidence (95% confidence interval, 16.8 - 39.7%), a 44.9 percent decrease in the rate of COVID-19 hospitalization (95% CI, 28.8 - 61.0%), and a 16.6% decrease in COVID-19 hospitalizations per 100 cases (95% CI, 8.4 - 24.8%). Inclusion of demographic covariables, as well as county-specific diabetes prevalence, did not weaken the observed inverse relationship with vaccination coverage. A significant inverse relationship between vaccination coverage and COVID-19 deaths per 100,000 during August 20 – September 16 was also observed. The cumulative incidence of COVID-19 through June 30, 2021, a potential indicator of acquired immunity due to past infection, had no significant relation to subsequent case incidence or hospitalization rates in August.ConclusionHigher vaccination coverage was associated not only with significantly lower COVID-19 incidence during the Delta surge, but also significantly less severe cases of the disease.Public Interest SummaryWe tested whether COVID-19 incidence and hospitalization rates during the Delta variant-related surge were inversely related to vaccination coverage among the 112 most populous counties in the United States, together comprising 44 percent of the country's total population. A 10-percentage-point increase in vaccination coverage was associated with a 28.3% decrease in COVID-19 incidence, a 44.9 percent decrease in the rate of COVID-19 hospitalization, and a 16.6% decrease in COVID-19 hospitalizations per 100 cases. Inclusion of demographic covariables, as well as county-specific diabetes prevalence, did not weaken the observed inverse relationship with vaccination coverage. A significant inverse relationship between vaccination coverage and COVID-19 deaths per 100,000 during August 20 – September 16 was also observed. Higher vaccination coverage was associated not only with significantly lower COVID-19 incidence during the Delta surge, but also significantly less severe cases of the disease.     

四川省新型冠状病毒肺炎流行现况与防控策略探讨

  目的   了解和探究四川省新型冠状病毒肺炎(coronavirus disease 2019, COVID-19)流行现状, 为现阶段防控策略与措施的制定提供参考依据。   方法   采用描述性流行病学方法深入剖析疫情变化, 明晰现状。   结果   四川省COVID-19病例主要集中于20~59岁人群, 男性占比略高于女性, 四个时期中有武汉/湖北暴露史病例者占比分别达到52.91%、21.97%、16.67%和0.00%。截至3月16日, 新增确诊病例与现有疑似病例均为0, 累计确诊病例达539例, 累计治愈病例为520例, 累计死亡病例3例。累计确诊病例在全省21个市州均有所分布, 成都居首位(144例, 26.72%)。除成都市武侯区外, 余下182个县(市、区)已无现症病例。   结论   当前四川省COVID-19疫情形势逐渐趋好, 但输入型感染风险仍然存在, 应持续加强防控力度, 深化分区分类防控, 精准科学施策。     

黑龙江省新型冠状病毒肺炎本土相关疫情流行特征

目的 描述黑龙江省新型冠状病毒肺炎（新冠肺炎）本土相关疫情的流行状况及特点,为黑龙江省新冠肺炎防控策略提供参考依据。方法 新冠肺炎个案调查、聚集性疫情调查资料来源于中国CDC全国传染病报告信息管理系统和突发公共卫生事件报告管理信息系统。采用Excel 2010和SPSS 23.0软件整理数据与统计学分析,描述人群、时间及地区分布特征。结果 2020年1月22日黑龙江省报告首例新冠肺炎确诊病例,截至2020年3月11日,13个城市累计报告境内病例482例,发病率为1.28/10万,病死率为2.70%（13/482）。其中聚集性疫情81起,确诊病例数占总确诊病例的79.25%（382/482）,死亡12例;家庭聚集70起（86.42%,70/81）。与散发病例相比,聚集性疫情尤其是家庭聚集性病例中,病死率、≥60岁老年人与重/危重型的比例均较高,差异均无统计学意义（均P>0.05）。涉及病例≥5例的聚集性疫情占41.98%（34/81）,涉及病例数占聚集性病例的68.31%（261/382）,涉及6~9例病例数的聚集性疫情中,年龄≥65岁者的比例较多（26.53%,39/147）。结论 黑龙江省新冠肺炎的发病率较高,前期疫情较为严重;聚集性疫情尤其是家庭聚集涉及病例数较多。     

基于传染病动力学模型的宁波市新型冠状病毒肺炎防控措施效果评估

目的 基于传染病动力学模型评估宁波市新型冠状病毒肺炎（COVID-19）防控措施的效果。方法 收集截至2020年3月9日宁波市COVID-19疫情个案数据、疾病进程等信息。根据防控策略落实情况,建立SEIR传染病动力学模型,计算基本再生数（R₀）和实时再生数（R_t）,评估防控效果。结果 宁波市累计确诊COVID-19病例157例,无死亡病例,重症病例比例为12.1%。从暴露到发病（潜伏期）平均（5.7±2.9）d,发病到确诊平均（5.4±3.7）d,从确诊到出院平均（16.6±6.5）d。累计医学观察105 339人,其中居家医学观察者COVID-19感染率为0.1%,集中医学观察者感染率为0.3%,确诊病例在就诊前处于医学观察期者占63.1%。估算R₀为4.8。随着防控措施的加强,R_t呈逐渐下降趋势,到2月4日下降至1.0以下,之后持续下降到2月中旬的0.2。结论 通过建立传染病动力学模型,能够有效评估宁波市COVID-19防控措施的效果,为防控策略的制定提供科学依据。     

Assessing the association between area deprivation index on COVID-19 prevalence: a contrast between rural and urban U.S. jurisdictions

BackgroundThe COVID-19 pandemic has impacted communities differentially, with poorer and minority populations being more adversely affected. Prior rural health research suggests such disparities may be exacerbated during the pandemic and in remote parts of the U.S.ObjectivesTo understand the spread and impact of COVID-19 across the U.S., county level data for confirmed cases of COVID-19 were examined by Area Deprivation Index (ADI) and Metropolitan vs. Nonmetropolitan designations from the National Center for Health Statistics (NCHS). These designations were the basis for making comparisons between Urban and Rural jurisdictions.MethodKendall''s Tau-B was used to compare effect sizes between jurisdictions on select ADI composites and well researched social determinants of health (SDH). Spearman coefficients and stratified Poisson modeling was used to explore the association between ADI and COVID-19 prevalence in the context of county designation.ResultsResults show that the relationship between area deprivation and COVID-19 prevalence was positive and higher for rural counties, when compared to urban ones. Family income, property value and educational attainment were among the ADI component measures most correlated with prevalence, but this too differed between county type.ConclusionsThough most Americans live in Metropolitan Areas, rural communities were found to be associated with a stronger relationship between deprivation and COVID-19 prevalence. Models predicting COVID-19 prevalence by ADI and county type reinforced this observation and may inform health policy decisions.     

1 665名新型冠状病毒肺炎密切接触者医学观察情况分析

目的 了解某市新型冠状病毒肺炎（COVID-19）病例密切接触者的确定及医学观察情况,为疫情防控策略调整提供科学依据。方法 采用描述性流行病学方法,对所有密切接触者一般情况进行分类分析。对密切接触者采集咽拭子进行实时荧光定量RT-PCR法检测,按照新型冠状病毒感染的肺炎诊疗方案（试行第五版）确定确诊病例。结果 1 665名密切接触者中在医学观察期发病的有10例,占全市所有确诊病例的30.30%,10例密切接触者确诊病例多为确诊病例近亲属,与确诊病例最晚接触时间最长为8 d,最短为0 d。10例密切接触者病例构成了6起家族聚集性疫情。结论 在COVID-19防控工作中,某市积极追踪确诊病例传播证据链,及时确定密切接触者,将确诊病例发病前14 d接触人群纳入密切接触者管理,在控制疫情中起到了非常关键的作用,全市近1/3的病例在医学观察期发病,做到了传染源有效管控和切断传播途径,及时阻止了聚集性疫情的扩散传播。     

Economic burden of COVID-19, China,January–March, 2020: a cost-of-illness study

ObjectiveTo estimate the economic cost of coronavirus disease 19 (COVID-19) in 31 provincial-level administrative regions and in total, in China.MethodsWe used data from government reports, clinical guidelines and other publications to estimate the main cost components of COVID-19 during 1 January–31 March 2020. These components were: identification and diagnosis of close contacts; suspected cases and confirmed cases of COVID-19; treatment of COVID-19 cases; compulsory quarantine of close contacts and suspected cases; and productivity losses for all affected residents. Primary outcomes were total health-care and societal costs.FindingsThe total estimated health-care and societal costs associated with COVID-19 were 4.26 billion Chinese yuan (¥; 0.62 billion United States dollars, US$) and ¥ 2646.70 billion (US$ 383.02 billion), respectively. Inpatient care accounted for 44.2% (¥ 0.95 billion/¥ 2.15 billion) of routine health-care costs followed by medicines, accounting for 32.5% (¥ 0.70 billion/¥ 2.15 billion). Productivity losses accounted for 99.8% (¥ 2641.61 billion/¥ 2646.70 billion) of societal costs, which were mostly attributable to the effect of movement-restriction policies on people who did not have COVID-19. Societal costs were most sensitive to salary costs and number of working days lost due to movement-restriction policies. Hubei province had the highest health-care cost while Guangdong province had the highest societal cost.ConclusionOur results highlight the high economic burden of the COVID-19 outbreak in China. The control measures to prevent the spread of disease resulted in substantial costs from productivity losses amounting to 2.7% (US$ 382.29 billion/US$ 14.14 trillion) of China’s annual gross domestic product.