COVID-19 was declared a pandemic by WHO on March 11, 2020, the first non-influenza pandemic, affecting more than 200 countries and areas, with more than 5•9 million cases by May 31, 2020. Countries have developed strategies to deal with the COVID-19 pandemic that fit their epidemiological situations, capacities, and values. We describe China's strategies for prevention and control of COVID-19 (containment and suppression) and their application, from the perspective of the COVID-19 experience to date in China. Although China has contained severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and nearly stopped indigenous transmission, a strong suppression effort must continue to prevent re-establishment of community transmission from importation-related cases. We believe that case finding and management, with identification and quarantine of close contacts, are vitally important containment measures and are essential in China's pathway forward. We describe the next steps planned in China that follow the containment effort. We believe that sharing countries' experiences will help the global community manage the COVID-19 pandemic by identifying what works in the struggle against SARS-CoV-2.

Background Between mid‐January and early February, provinces of mainland China outside the epicentre in Hubei province were on high alert for importations and transmission of COVID‐19. Many properties of COVID‐19 infection and transmission were still not yet established. Methods We collated and analysed data on 449 of the earliest COVID‐19 cases detected outside Hubei province to make inferences about transmission dynamics and severity of infection. We analysed 64 clusters to make inferences on serial interval and potential role of pre‐symptomatic transmission. Results We estimated an epidemic doubling time of 5.3 days (95% confidence interval (CI): 4.3, 6.7) and a median incubation period of 4.6 days (95% CI: 4.0, 5.2). We estimated a serial interval distribution with mean 5.7 days (95% CI: 4.7, 6.8) and standard deviation 3.5 days, and effective reproductive number was 1.98 (95% CI: 1.68, 2.35). We estimated that 32/80 (40%) of transmission events were likely to have occurred prior to symptoms onset in primary cases. Secondary cases in clusters had less severe illness on average than cluster primary cases. Conclusions The majority of transmissions are occurring around illness onset in an infected person, and pre‐symptomatic transmission does play a role. Detection of milder infections among the secondary cases may be more reflective of true disease severity.

Background The relative contributions of asymptomatic, pre-symptomatic and symptomatic transmission of SARS-CoV-2 have not been clearly measured although control measures may differ in response to the risk of spread posed by different types of cases. Methods We collected detailed information on transmission events and symptom status based on laboratory-confirmed patient data and contact tracing data from four provinces and one municipality in China. We estimated the variation in risk of transmission over time, and the severity of secondary infections, by symptomatic status of the infector. Results There were 393 symptomatic index cases with 3136 close contacts and 185 asymptomatic index cases with 1078 close contacts included into the study. The secondary attack rate among close contacts of symptomatic and asymptomatic index cases were 4.1% (128/3136) and 1.1% (12/1078), respectively, corresponding to a higher transmission risk from symptomatic cases than from asymptomatic cases (OR: 3.79, 95% CI: 2.06, 6.95). Approximately 25% (32/128) and 50% (6/12) of the infected close contacts were asymptomatic from symptomatic and asymptomatic index cases, respectively, while more than one third (38%) of the infections in the close contacts of symptomatic cases were attributable to exposure to the index cases before symptom onset. Infected contacts of asymptomatic index cases were more likely to be asymptomatic and less likely to be severe. Conclusions Asymptomatic and pre-symptomatic transmission play an important role in spreading infection, although asymptomatic cases pose a lower risk of transmission than symptomatic cases. Early case detection and effective test-and-trace measures are important to reduce transmission.

Background COVID-19 has posed an enormous threat to public health around the world. Some severe and critical cases have bad prognoses and high case fatality rates, unraveling risk factors for severe COVID-19 are of significance for predicting and preventing illness progression, and reducing case fatality rates. Our study focused on analyzing characteristics of COVID-19 cases and exploring risk factors for developing severe COVID-19. Methods The data for this study was disease surveillance data on symptomatic cases of COVID-19 reported from 30 provinces in China between January 19 and March 9, 2020, which included demographics, dates of symptom onset, clinical manifestations at the time of diagnosis, laboratory findings, radiographic findings, underlying disease history, and exposure history. We grouped mild and moderate cases together as non-severe cases and categorized severe and critical cases together as severe cases. We compared characteristics of severe cases and non-severe cases of COVID-19 and explored risk factors for severity. Results The total number of cases were 12 647 with age from less than 1 year old to 99 years old. The severe cases were 1662 (13.1%), the median age of severe cases was 57 years [Inter-quartile range(IQR): 46–68] and the median age of non-severe cases was 43 years (IQR: 32–54). The risk factors for severe COVID-19 were being male [adjusted odds ratio (aOR) = 1.3, 95% CI: 1.2–1.5]; fever (aOR = 2.3, 95% CI: 2.0–2.7), cough (aOR = 1.4, 95% CI: 1.2–1.6), fatigue (aOR = 1.3, 95% CI: 1.2–1.5), and chronic kidney disease (aOR = 2.5, 95% CI: 1.4–4.6), hypertension (aOR = 1.5, 95% CI: 1.2–1.8) and diabetes (aOR = 1.96, 95% CI: 1.6–2.4). With the increase of age, risk for the severity was gradually higher [20–39 years (aOR = 3.9, 95% CI: 1.8–8.4), 40–59 years (aOR = 7.6, 95% CI: 3.6–16.3), ≥ 60 years (aOR = 20.4, 95% CI: 9.5–43.7)], and longer time from symtem onset to diagnosis [3–5 days (aOR = 1.4, 95% CI: 1.2–1.7), 6–8 days (aOR = 1.8, 95% CI: 1.5–2.1), ≥ 9 days(aOR = 1.9, 95% CI: 1.6–2.3)]. Conclusions Our study showed the risk factors for developing severe COVID-19 with large sample size, which included being male, older age, fever, cough, fatigue, delayed diagnosis, hypertension, diabetes, chronic kidney diasease, early case identification and prompt medical care. Based on these factors, the severity of COVID-19 cases can be predicted. So cases with these risk factors should be paid more attention to prevent severity.

T ransmissibility of an emerging infectious disease is a key factor for determining transmission dynamics in a population. The basic reproductive number, R 0 , indicates the average number of new cases resulting from 1 infected person in a completely susceptible population (1). In December 2019, an outbreak of coronavirus disease (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was identifi ed in Wuhan, Hubei Province, China (2). The mean R 0 of COVID-19 was estimated to be in the range of 1.90-6.49 (3), indicating a high contagiousness that led to its rapid spread across the world (4). Another indicator of infectiousness is secondary attack rate (SAR), which is the probability that infection occurs among susceptible persons within a reasonable incubation period after known contact with an infectious person or an infectious source (5,6). Few estimates are available for the SAR for COVID-19 and its variation by type of contact, characteristics of index case-patients and contacts, and other factors. Information about factors associated with variation in SAR could help identify persons at high risk of transmitting the virus or acquiring COVID-19. Studies have reported transmission during the incubation period of COV-ID-19 (7-10) but with unclear quantifi cation of risk. We estimated the SAR for COVID-19 and factors associated with risk for transmission. MethodsWe conducted this study from January 23 through February 25, 2020, in Yichang, Hubei Province, China; the city has a population of ≈4 million. In accordance with National Health Commission guidelines for prevention and control of COVID-19 (http://www. gov.cn/xinwen/2020-01/23/content_5471768.htm), close contacts of COVID-19 case-patients were placed under 14-day quarantine for medical observation, during which time they would be tested by PCR for SARS-CoV-2 one time if illness symptoms developed

Influenza endangers human health but can be prevented in part by vaccination. Assessing influenza vaccine effectiveness (VE) provides scientific evidence for developing influenza vaccination policy. We conducted a systematic review and meta-analysis of studies that evaluated influenza VE in mainland China. We searched six relevant databases as of 30 August 2019 to identify studies and used Review Manager 5.3 software to analyze the included studies. The Newcastle–Ottawa scale was used to assess the risk of publication bias. We identified 1408 publications, and after removing duplicates and screening full texts, we included 21 studies in the analyses. Studies were conducted in Beijing, Guangzhou, Suzhou, and Zhejiang province from the 2010/11 influenza season through the 2017/18 influenza season. Overall influenza VE for laboratory confirmed influenza was 36% (95% CI: 25–46%). In the subgroup analysis, VE was 45% (95% CI: 18–64%) for children 6–35 months who received one dose of influenza vaccine, and 57% (95% CI: 50–64%) who received two doses. VE was 47% (95% CI: 39–54%) for children 6 months to 8 years, and 18% (95% CI: 0–33%) for adults ≥60 years. For inpatients, VE was 21% (95% CI: −11–44%). We conclude that influenza vaccines that were used in mainland China had a moderate effectiveness, with VE being higher among children than the elderly. Influenza VE should be continuously monitored in mainland China to provide evidence for policy making and improving uptake of the influenza vaccine.

An amendment to this paper has been published and can be accessed via the original article.

Background Optimizing the timing of influenza vaccination based on regional temporal seasonal influenza illness patterns may make seasonal influenza vaccination more effective in China. Methods We obtained provincial weekly influenza surveillance data for 30 of 31 provinces in mainland China from the Chinese Center for Disease Control and Prevention for the years 2010–2018. Influenza epidemiological regions were constructed by clustering analysis. For each region, we calculated onset date, end date, and duration of seasonal influenza epidemics by the modified mean threshold method. To help identify initial vaccination target populations, we acquired weekly influenza surveillance data for four age groups (0–4, 5–18, 19–59, and ≥60 years) in each region and in 171 cities of wide‐ranging size. We used linear regression analyses to explore the association of epidemic onset dates by age group, city, and epidemiological region and provide evidence for initial target populations for seasonal influenza vaccination. Results We determined that northern, mid, southwestern, southeast regions of mainland China have distinct seasonal influenza epidemic patterns. We found significant regional, temporal, and spatial heterogeneity of seasonal influenza epidemics. There were significant differences by age group and city size in the interval between epidemic onset in the city or age group and regional spread (epidemic lead time), with longer epidemic lead times for 5‐ to 18‐year‐old children and larger cities. Conclusions Knowledge of influenza epidemic characteristics may help optimize local influenza vaccination timing and identify initial target groups for seasonal influenza vaccination in mainland China. Similar analyses may help inform seasonal influenza vaccination strategies in other regions and countries.