SARS-CoV-2 Surveillance Through China Influenza Surveillance Information System - China, December 1, 2022 to February 12, 2023.

Introduction: The World Health Organization (WHO) proposed using influenza surveillance systems to carry out coronavirus disease 2019 (COVID-19) surveillance due to the similarity between the two diseases in some respiratory symptoms. To assess the prevalence of COVID-19, we analyzed the influenza-like illness (ILI) and positive rate of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) detections in ILI patients reported to the influenza Surveillance Information System (CNISIS) since late 2022. Methods: Data related to ILI were reported by national surveillance sentinel hospitals. Positive testing for SARS-CoV-2 and influenza viruses was conducted using real-time reverse transcription polymerase chain reaction (rRT-PCR) detection by the national influenza surveillance network laboratories. Surveillance data were reported to CNISIS. Results: Beginning on December 12, 2022 (Week 50), the ILI percentage increased dramatically, peaking in Week 51 at 12.1%. Subsequently, the ILI percentage began to decline rapidly from Week 52, 2022, and by Week 6, 2023 (February 6-12), the ILI and ILI percentage had returned to the levels observed at the beginning of December 2022. From December 1, 2022 to February 12, 2023, 115,844 specimens were tested for both SARS-CoV-2 and influenza virus. Of these, 30,381 (26.2%) were positive for SARS-CoV-2 and 1,763 (1.5%) were positive for influenza virus. The positive rate of SARS-CoV-2 tests peaked at 74.1% around December 23 and 25. Conclusions: Sentinel-based surveillance, previously established for influenza, is an effective way to track the circulation trend of SARS-CoV-2 during community-level epidemics. There was no co-prevalence of SARS-CoV-2 and influenza virus during the outbreak of SARS-CoV-2, even during the winter influenza season. However, it is important to remain vigilant for the potential rise of influenza activities following the COVID-19 epidemic.

Comparative efficacy of Chinese herbal injections for treating severe pneumonia: A protocol for systematic review and Bayesian network meta-analysis of randomized controlled trials.

BACKGROUND: Severe pneumonia (SP) has a high mortality and is responsible for significant healthcare cost. Chinese herbal injections (CHIs) have been widely used in China as a novel and promising treatment option for SP. Therefore, this study will assess and rank the effectiveness of CHIs to provide more sights for the selection of SP treatment. METHOD: Seven databases will be searched, including PubMed, the Cochrane Library, Embase, Web of Science, China National Knowledge Infrastructure (CNKI), Wanfang Database, and the Chinese Scientific Journal Database (VIP) from their inception up to October, 2021. The literatures screening, data extraction and the quality assessment of included studies will be conducted independently by two reviewers. Then Bayesian network meta-analysis (NMA) will be performed by WinBUGS 14.0 and STATA 14.0 software. Surface under the cumulative ranking curve (SUCRA) probability values will be applied to rank the examined treatments. The risk of bias of each included study will be evaluated using the Revised Cochrane risk-of-bias tool for randomized trials (ROB 2). Publication bias will be reflected by a funnel plot. RESULTS: The results of this NMA will be disseminated through a peer-reviewed journal publication. CONCLUSION: Our study findings maybe reveal which CHI or CHIs will be better in the treatment of SP and provide more therapy strategies for clinical practitioners and patients. PROSPERO REGISTRATION NUMBER: CRD42021244587. STRENGTHS AND LIMITATIONS OF THIS STUDY: Bayesian network meta-analysis (NMA) can integrate direct evidence with indirect evidence of severe pneumonia treated by Chinese herbal injections to generate a clinically useful ranking of these regimens. This NMA will address Chinese herbal injections for SP and its findings may help to provide more sights for selection of SP treatment. Evidence drawn from an NMA is limited and should be interpreted with caution. We only included studies in Chinese and English languages, which may increase the publication bias.

Did patients with COVID-19 receive timely treatment in the early epidemic?-a systematic review and meta-analysis.

BACKGROUND: Corona virus disease 2019 (COVID-19) showed a significant difference in case fatality rate between different regions at the early stage of the epidemic. In addition to the well-known factors such as age structure, detection efficiency, and race, there was also a possibility that medical resource shortage caused the increase of the case fatality rate in some regions. METHODS: Medline, Cochrane Library, Embase, Web of Science, CBM, CNKI, and Wanfang of identified articles were searched through 29 June 2020. Cohort studies and case series with duration information on COVID-19 patients were included. Two independent reviewers extracted the data using a standardized data collection form and assessed the risk of bias. Data were synthesized through description and analysis methods including a meta-analysis. RESULTS: A total of 109 articles were retrieved. The time interval from onset to the first medical visit of COVID-19 patients in China was 3.38±1.55 days (corresponding intervals in Hubei province, non-Hubei provinces, Wuhan, Hubei provinces without Wuhan were 4.22±1.13, 3.10±1.57, 4.20±0.97, and 4.34±1.72 days, respectively). The time interval from onset to the hospitalization of COVID-19 patients in China was 8.35±6.83 days (same corresponding intervals were 12.94±7.43, 4.17±1.45, 14.86±7.12, and 5.36±1.19 days, respectively), and when it was outside China, this interval was 5.27±1.19 days. DISCUSSION: In the early stage of the COVID-19 epidemic, patients with COVID-19 did not receive timely treatment, resulting in a higher case fatality rate in Hubei province, partly due to the relatively insufficient and unequal medical resources. This research suggested that additional deaths caused by the out-of-control epidemic can be avoided if prevention and control work is carried out at the early stage of the epidemic. TRIAL REGISTRATION: CRD42020195606.

Assessing Asymptomatic, Presymptomatic, and Symptomatic Transmission Risk of Severe Acute Respiratory Syndrome Coronavirus 2.

BACKGROUND: The relative contributions of asymptomatic, presymptomatic, and symptomatic transmission of severe acute respiratory syndrome coronavirus 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 4 provinces and 1 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 in the study. The secondary attack rates among close contacts of symptomatic and asymptomatic index cases were 4.1% (128 of 3136) and 1.1% (12 of 1078), respectively, corresponding to a higher transmission risk from symptomatic cases than from asymptomatic cases (odds ratio, 3.79; 95% confidence interval, 2.06-6.95). Approximately 25% (32 of 128) and 50% (6 of 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. CONCLUSIONS: Asymptomatic and presymptomatic transmissions 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.

Transmission of Severe Acute Respiratory Syndrome Coronavirus 2 to Close Contacts, China, January-February 2020.

We estimated the symptomatic, PCR-confirmed secondary attack rate (SAR) for 2,382 close contacts of 476 symptomatic persons with coronavirus disease in Yichang, Hubei Province, China, identified during January 23-February 25, 2020. The SAR among all close contacts was 6.5%; among close contacts who lived with an index case-patient, the SAR was 10.8%; among close-contact spouses of index case-patients, the SAR was 15.9%. The SAR varied by close contact age, from 3.0% for those <18 years of age to 12.5% for those >60 years of age. Multilevel logistic regression showed that factors significantly associated with increased SAR were living together, being a spouse, and being >60 years of age. Multilevel regression did not support SAR differing significantly by whether the most recent contact occurred before or after the index case-patient's onset of illness (p = 0.66). The relatively high SAR for coronavirus disease suggests relatively high virus transmissibility.

Risk factors for developing severe COVID-19 in China: an analysis of disease surveillance data.

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.

Evidence for pre-symptomatic transmission of coronavirus disease 2019 (COVID-19) in China.

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.

Active case finding with case management: the key to tackling the COVID-19 pandemic.

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.