Optimization of COVID-19 prevention and control measures during the Beijing 2022 Winter Olympics: a model-based study.

BACKGROUND: The continuous mutation of severe acute respiratory syndrome coronavirus 2 has made the coronavirus disease 2019 (COVID-19) pandemic complicated to predict and posed a severe challenge to the Beijing 2022 Winter Olympics and Winter Paralympics held in February and March 2022. METHODS: During the preparations for the Beijing 2022 Winter Olympics, we established a dynamic model with pulse detection and isolation effect to evaluate the effect of epidemic prevention and control measures such as entry policies, contact reduction, nucleic acid testing, tracking, isolation, and health monitoring in a closed-loop management environment, by simulating the transmission dynamics in assumed scenarios. We also compared the importance of each parameter in the combination of intervention measures through sensitivity analysis. RESULTS: At the assumed baseline levels, the peak of the epidemic reached on the 57th day. During the simulation period (100 days), 13,382 people infected COVID-19. The mean and peak values of hospitalized cases were 2650 and 6746, respectively. The simulation and sensitivity analysis showed that: (1) the most important measures to stop COVID-19 transmission during the event were daily nucleic acid testing, reducing contact among people, and daily health monitoring, with cumulative infections at 0.04%, 0.14%, and 14.92% of baseline levels, respectively (2) strictly implementing the entry policy and reducing the number of cases entering the closed-loop system could delay the peak of the epidemic by 9 days and provide time for medical resources to be mobilized; (3) the risk of environmental transmission was low. CONCLUSIONS: Comprehensive measures under certain scenarios such as reducing contact, nucleic acid testing, health monitoring, and timely tracking and isolation could effectively prevent virus transmission. Our research results provided an important reference for formulating prevention and control measures during the Winter Olympics, and no epidemic spread in the closed-loop during the games indirectly proved the rationality of our research results.

Compartmental structures used in modeling COVID-19: a scoping review.

BACKGROUND: The coronavirus disease 2019 (COVID-19) epidemic, considered as the worst global public health event in nearly a century, has severely affected more than 200 countries and regions around the world. To effectively prevent and control the epidemic, researchers have widely employed dynamic models to predict and simulate the epidemic's development, understand the spread rule, evaluate the effects of intervention measures, inform vaccination strategies, and assist in the formulation of prevention and control measures. In this review, we aimed to sort out the compartmental structures used in COVID-19 dynamic models and provide reference for the dynamic modeling for COVID-19 and other infectious diseases in the future. MAIN TEXT: A scoping review on the compartmental structures used in modeling COVID-19 was conducted. In this scoping review, 241 research articles published before May 14, 2021 were analyzed to better understand the model types and compartmental structures used in modeling COVID-19. Three types of dynamics models were analyzed: compartment models expanded based on susceptible-exposed-infected-recovered (SEIR) model, meta-population models, and agent-based models. The expanded compartments based on SEIR model are mainly according to the COVID-19 transmission characteristics, public health interventions, and age structure. The meta-population models and the agent-based models, as a trade-off for more complex model structures, basic susceptible-exposed-infected-recovered or simply expanded compartmental structures were generally adopted. CONCLUSION: There has been a great deal of models to understand the spread of COVID-19, and to help prevention and control strategies. Researchers build compartments according to actual situation, research objectives and complexity of models used. As the COVID-19 epidemic remains uncertain and poses a major challenge to humans, researchers still need dynamic models as the main tool to predict dynamics, evaluate intervention effects, and provide scientific evidence for the development of prevention and control strategies. The compartmental structures reviewed in this study provide guidance for future modeling for COVID-19, and also offer recommendations for the dynamic modeling of other infectious diseases.

Impact of the coronavirus disease 2019 interventions on the incidence of hand, foot, and mouth disease in mainland China.

BACKGROUND: In early 2020, non-pharmaceutical interventions (NPIs) were implemented in China to reduce and contain the coronavirus disease 2019 (COVID-19) transmission. These NPIs might have also reduced the incidence of hand, foot, and mouth disease (HFMD). METHODS: The weekly numbers of HFMD cases and meteorological factors in 31 provincial capital cities and municipalities in mainland China were obtained from Chinese Center for Disease Control and Prevention (CCDC) and National Meteorological Information Center of China from 2016 to 2020. The NPI data were collected from local CDCs. The incidence rate ratios (IRRs) were calculated for the entire year of 2020, and for January-July 2020 and August-December 2020. The expected case numbers were estimated using seasonal autoregressive integrated moving average models. The relationships between kindergarten closures and incidence of HFMD were quantified using a generalized additive model. The estimated associations from all cities were pooled using a multivariate meta-regression model. FINDINGS: Stringent NPIs were widely implemented for COVID-19 control from January to July 2020, and the IRRs for HFMD were less than 1 in all 31 cities, and less than 0·1 for 23 cities. Overall, the proportion of HFMD cases reduced by 52·9% (95% CI: 49·3-55·5%) after the implementation of kindergarten closures in 2020, and this effect was generally consistent across subgroups. INTERPRETATION: The decrease in HFMD incidence was strongly associated with the NPIs for COVID-19. HFMD epidemic peaks were either absent or delayed, and the final epidemic size was reduced. Kindergarten closure is an intervention to prevent HFMD outbreaks. FUNDING: This research was supported by the National Natural Science Foundation of China (81973102 & 81773487), Public Health Talents Training Program of Shanghai Municipality (GWV-10.2-XD21), the Shanghai New Three-year Action Plan for Public Health (GWV-10.1-XK16), the Major Project of Scientific and Technical Winter Olympics from National Key Research and Development Program of China (2021YFF0306000), 13th Five-Year National Science and Technology Major Project for Infectious Diseases (2018ZX10725-509) and Key projects of the PLA logistics Scientific research Program (BHJ17J013).

Risk assessment of public health emergencies concerned in the mainland of China, September 2021

Objective: To assess the risk of public health emergencies, both the indigenous ones and the imported ones, which might occur in the mainland of China in September 2021.

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.