2020, Volume 6, Issue 10
Engineering >> 2020, Volume 6, Issue 10 doi: 10.1016/j.eng.2020.04.008
Implementation of Clinical Diagnostic Criteria and Universal Symptom Survey Contributed to Lower Magnitude and Faster Resolution of the COVID-19 Epidemic in Wuhan
a Departments of Epidemiology and Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China
b Department of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
c Guangzhou Women and Children’s Medical Center, Guangzhou 510623, China
d Department of Epidemiology and Biostatistics, School of Public Health, Fudan University, Shanghai 200032, China
e Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China
# These authors contributed equally to this work.
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Abstract
The majority of cases infected with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in China centered in the city of Wuhan. Despite a rapid increase in the number of cases and deaths due to the coronavirus disease 2019 (COVID-19), the epidemic was stemmed via a combination of epidemic mitigation and control measures. This study evaluates how the implementation of clinical diagnostics and universal symptom surveys contributed to epidemic control in Wuhan. We extended the susceptibles-exposed-infectious-removed (SEIR) transmission dynamics model by considering three quarantined compartments (SEIR+Q). The SEIR+Q dynamics model was fitted using the daily reported number of confirmed infections and unconfirmed cases by clinical diagnostic criteria up to February 14, 2020, in Wuhan. Applying the model to carry forward the pre-February 14 trend in Wuhan, the number of daily new diagnosed cases would be expected to drop below 100 by March 25, below 10 by April 29, and reach 0 by May 31, 2020. The observed case counts after February 14 demonstrated that the daily new cases fell below 100 by March 6, below 10 by March 11, and reached 0 by March 18, respectively, 19, 49, and 74 d earlier than model predictions. By March 30, the observed number of cumulative confirmed cases was 50 006, which was 19 951 cases fewer than the predicted count. Effective reproductive number R(t) analysis using observed frequencies showed a remarkable decline after the implementation of clinical diagnostic criteria and universal symptom surveys, which was significantly below the R(t) curve estimated by the model assuming that the pre-February 14 trend was carried forward. In conclusion, the proposed SEIR+Q dynamics model was a good fit for the epidemic data in Wuhan and explained the large increase in the number of infections during February 12–14, 2020. The implementation of clinical diagnostic criteria and universal symptom surveys contributed to a contraction in both the magnitude and the duration of the epidemic in Wuhan.
Keywords
COVID-19 ; Extended SEIR+Q dynamics model ; Clinical diagnostic criteria ; Universal symptom survey ; Evaluation of the intervention effect
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References
[ 1 ] Gorbalenya AE, Baker SC, Baric RS, de Groot RJ, Drosten C, Gulyaeva AA, et al. The species severe acute respiratory syndrome-related coronavirus: classifying 2019-nCoV and naming it SARS-CoV-2. Nat Microbiol 2020;5:536–44.
[ 2 ] National Health Commission of the People’s Republic of China; National Administration of Traditional Chinese Medicine of the People’s Republic of Chin. Guidance for coronavirus disease 2019: prevention, control, diagnosis and management. Beijing: People’s Medical Publishing House; 2020. link1
[ 3 ] Remuzzi A, Remuzzi G. COVID-19 and Italy: what next? Lancet 2020;395:1225–8. link1
[ 4 ] Dong E, Du H, Gardner L. An interactive web-based dashboard to track COVID19 in real time. Lancet Infect Dis 2020;20(5):533–4. link1
[ 5 ] National Health Commission of the People’s Republic of China; National Administration of Traditional Chinese Medicine. Interpretation of ‘‘Guidelines for diagnosis and treatment of novel coronavirus pneumonia (trial version 5) [Internet]. Beijing: The State Council of the People’s Republic of China; 2020 [cited 2020 Mar 31]. Available from: http://www.nhc.gov.cn/yzygj/s7652m/ 202002/e84bd30142ab4d8982326326e4db22ea.shtml. Chinese. link1
[ 6 ] Chinazzi M, Davis JT, Ajelli M, Gioannini C, Litvinova M, Merler S, et al. The effect of travel restrictions on the spread of the 2019 novel coronavirus (COVID-19) outbreak. Science 2020;368(6489):395–400.
[ 7 ] Pan A, Liu L, Wang C, Guo H, Hao X, Wang Q, et al. Association of public health interventions with the epidemiology of the COVID-19 outbreak in Wuhan. China. JAMA 2020;323(19):1915–23. link1
[ 8 ] Hao X, Cheng S, Wu D, Wu T, Lin X, Wang C. Reconstruction of the full transmission dynamics of COVID-19 in Wuhan. Nature 2020;584 (7821):420–4. link1
[ 9 ] Wallinga J, Teunis P. Different epidemic curves for severe acute respiratory syndrome reveal similar impacts of control measures. Am J Epidemiol 2004;160(6):509–16. link1
[10] National Health Commission of the People’s Republic of China; National Administration of Traditional Chinese Medcine. Guideline for diagnosis and treatment of novel coronavirus pneumonia (trial version 6) [Internet]. Beijing: The State Council of the Peopel’s Republic of China; 2020 Feb 19 [cited 2020 Mar 31]. Available from: http://www.nhc.gov.cn/xcs/zhengcwj/202002/ 8334a8326dd94d329df351d7da8aefc2.shtml. Chinese. link1
[11] Kucharski AJ, Russell TW, Diamond C, Liu Y, Edmunds J, Funk S, et al. Early dynamics of transmission and control of COVID-19: a mathematical modelling study. Lancet Infect Dis 2020;20(5):553–8. link1
[12] Li Q, Guan X, Wu P, Wang X, Zhou L, Tong Y, et al. Early transmission dynamics in Wuhan, China, of novel coronavirus-infected pneumonia. N Engl J Med 2020;382:1199–207. link1
[13] Tang B, Wang X, Li Q, Bragazzi BL, Tang S, Xiao Y, et al. Estimation of the transmission risk of the 2019-nCoV and its implication for public health interventions. J Clin Med 2020;9(2):462. link1
[14] Wei Y, Lu Z, Du Z, Zhang Z, Zhao Y, Shen S, et al. Fitting and forecasting the trend of COVID-19 by SEIR+CAQ dynamic model. Chin J Epidemiol 2020;41 (4):470–5. Chinese. link1
[15] Song PX, Wang L, Zhou Y, He J, Zhu B, Wang F, et al. An epidemiological forecast model and software assessing interventions on COVID-19 epidemic in China. MedRxiv: 2020.02.29.20029421v1.
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