Spatial accessibility of fever clinics for multi-tiered prevention and control on COVID-19 in Beijing / 北京大学学报(医学版)

OBJECTIVE@#To simulate the different prevalence of corona virus disease 2019 (COVID-19) in Beijing as the spreading and the outbreak city and analyze the response capacity of its medical resources of fever clinics, and to provide a scientific basis for optimizing the spatial layout in Beijing under severe epidemics.@*METHODS@#The study obtained epidemiological indicators for COVID-19, factors about medical resources and population movement as parameters for the SEIR model and utilized the model to predict the maximum number of infections on a single day at different control levels in Beijing, simulated as an epidemic spreading city and an epidemic outbreak city respectively. The modified two-step floating catchment area method under ArcGIS 10.6 environment was used to analyze spatial accessibility to fever clinics services for the patients in Beijing.@*RESULTS@#According to the results of the SEIR model, the highest number of infections in a single day in Beijing simulated as an epidemic spreading city at low, medium and high levels of prevention and control were 8 514, 183, and 68 cases, the highest number of infections in a single day in Beijing simulated as an outbreak city was 22 803, 10 868 and 3 725 cases, respectively. The following result showed that Beijing was simulated as an epidemic spreading city: among the 585 communities in Beijing, under the low level of prevention and control, there were 17 communities (2.91%) with excellent accessibility to fever clinics, and that of 41 communities (7.01%) with fever clinics was good. Spatial accessibility of fever clinics in 56 communities (9.57%) was ranked average, and 62 communities' (10.60%) accessibility was fair and 409 communities (69.91%) had poor accessibility; at the medium level of prevention and control, only the west region of Fangshan District and Mentougou District, the north region of Yanqing District, Huairou District and Miyun District had poor accessibility; under the high level of prevention and control, 559 communities' (95.56%) had excellent accessibility. The accessibility in 24 communities (4.10%) was good and in 2 communities (0.34%) was average. In brief, the existing fever clinics could meet the common demand. Beijing was simulated as an outbreak city: under the low level of prevention and control, only 1 community (0.17%) had excellent accessibility to fever clinics, and 5 communities (0.86%) had good accessibility. The accessibility of fever clinics in 10 communities (1.71%) was average and in 12 communities (2.05%) was fair. The accessibility of fever clinics in 557 communities (95.21%), nearly all areas of Beijing, was poor; under the middle and high level of prevention and control, the accessibility of ecological conservation areas was also relatively poor.@*CONCLUSION@#The distribution of fever clinic resources in Beijing is uneven. When Beijing is simulated as an epidemic spreading city: under the high level of prevention and control, the number of fever clinics can be appropriately reduced to avoid cross-infection; at the medium level of prevention and control, the fever clinics can basically meet the needs of patients with fever in Beijing, but the accessibility of fever clinics in ecological conservation areas is insufficient, and priority should be given to the construction of fever clinics in public hospitals above the second level in the ecological conservation areas. When the level of prevention and control is low, the accessibility of fever clinics in ecological conservation areas is poor. Priority should be given to the construction of fever clinics in ecological conservation areas, and temporary fever sentinels can be established to relieve the pressure of fever clinics. When Beijing is simulated as an outbreak city and has low prevention and control, due to a large number of infections, it is necessary to upgrade the prevention and control level to reduce the flow of people to curb the development of the epidemic.

Modeling of a coronavirus disease outbreak at the beginning of 2021 in Shijiazhuang， Hebei Province， China / 上海预防医学

Objective:To model an outbreak of coronavirus disease 2019 （COVID-19） in Shijiazhuang and forecast its spread trend. Method:We collected confirmed COVID-19 cases from the Health Commission of Hebei Province during the period of January 2 to January 27， 2021. We built a new model （SEIaIcRK）， including the contribution of asymptomatic cases， based on the traditional SEIR model to explore and analyze the transmission of COVID-19. Results:A total of 863 confirmed cases were reported during the study period （ended on January 27， 2021）. Our model fitted well with the daily cumulative incidence data and showed that the effective reproductive number decreased sharply from 3.80 on January 2 to 1.54 on January 4， then further decreased to <1 afterwards. Our model also predicted that number of COVID-19 cases would not increase after Feb 16， 2021. Conclusion:The SEIaIcRK model can be used to predict the spread trend of COVID-19 in Shijiazhuang. The current COVID-19 countermeasures effectively contain the disease spread.

Assessment and prediction of the oversea imported risk of COVID-19 in Shanghai / 上海预防医学

Objective To determine the association between global epidemic of COVID-19 and local situation of imported cases from abroad to Shanghai, and then to predict the risk of imported COVID-19 epidemic from December 2020 through March 2021. Methods A retrospective analysis on the imported COVID-19 cases from abroad to Shanghai was conducted. The correlation between global and country-specific confirmed COVID-19 cases(weekly confirmed cases per 100 000 population)and imported cases(weekly reported)in Shanghai was determined. Compared to the risk in November 2020, country-specific risk of imported cases to Shanghai was assessed to calculate the possible number of imported case in the near future using SEIR model. Results The number of imported case of COVID-19 from abroad to Shanghai increased along with the global epidemic, with several peaks accordingly. However, the imported cases did not accumulate, as potential epidemic has been always effectively contained through timely implementation of prevention and control measures. The number of weekly imported cases in Shanghai was significantly correlated with the number of global weekly confirmed cases per 100 000 population(rSpearman = 0.349, P = 0.029), and also correlated with weekly reported cases in certain countries(P < 0.05), such as the UK and France. Using the number of imported cases from abroad to Shanghai in November as baseline, the estimated monthly number of imported cases in Shanghai might increase in the following four months. Conclusion The risk of imported COVID-19 cases from abroad to Shanghai may increase in the near future. Prediction of imported case would provide scientific evidence for optimizing prevention and control measures and reserving medical resources for the imported epidemic.

Modeling transmission dynamics of severe acute respiratory syndrome coronavirus 2 in São Paulo, Brazil

Abstract INTRODUCTION Severe acute respiratory syndrome coronavirus 2 has been transmitted to more than 200 countries, with 92.5 million cases and 1,981,678 deaths. METHODS This study applied a mathematical model to estimate the increase in the number of cases in São Paulo state, Brazil during four epidemic periods and the subsequent 300 days. We used different types of dynamic transmission models to measure the effects of social distancing interventions, based on local contact patterns. Specifically, we used a model that incorporated multiple transmission pathways and an environmental class that represented the pathogen concentration in the environmental reservoir and also considered the time that an individual may sustain a latent infection before becoming actively infectious. Thus, this model allowed us to show how the individual quarantine and active monitoring of contacts can influence the model parameters and change the rate of exposure of susceptible individuals to those who are infected. RESULTS The estimated basic reproductive number, R o , was 3.59 (95% confidence interval [CI]: 3.48 - 3.72). The mathematical model data prediction coincided with the real data mainly when the social distancing measures were respected. However, a lack of social distancing measures caused a significant increase in the number of infected individuals. Thus, if social distancing measures are not respected, we estimated a difference of at least 100,000 cases over the next 300 days. CONCLUSIONS: Although the predictive capacity of this model was limited by the accuracy of the available data, our results showed that social distancing is currently the best non-pharmacological measure.