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http://conacyt.repositorioinstitucional.mx/jspui/handle/1000/862| Analysis of COVID-19 epidemic traced data and stochastic discrete transmission dynamic model | |
| Tang Sanyi Tang Biao Nicola Luigi Bragazzi Xia Fan Li Tangjuan He Sha Ren Pengyu Wang Xia Changcheng Xiang Peng Zhihang Wu Jianhong Xiao Yanni | |
| Acceso Abierto | |
| Atribución-NoComercial-SinDerivadas | |
| The epidemic of novel coronavirus pneumonia has spread throughout the country. The early epidemic cases in many provinces, including Shaanxi, are mainly imported cases. The latest epidemic situation has been decreasing under restrict prevention and control strategies. Accessing the efficacy of control measures, analyzing the impact of population flow on the epidemic situation are of great significance for the study of the epidemic situation in Shaanxi (or other areas with imported cases as the main cases) and the future response to emergent infectious diseases. According to the detailed data published by Shaanxi, we can obtain the transmission chains (infection tree), and the median durations from the illness onset to the first medical visit, to the admission, and then to the final confirmation. We can obtain the daily number of latent, infectious and hospitalized individuals and the spatial distribution of their state evolution. The control reproduction number of COVID-19 epidemic was determined (1.48–1.69). We develop the statistical inference method to calculate the effective regeneration number under the strict control measures in Shaanxi province. Furthermore, a novel stochastic discrete transmission model for COVID-19 was proposed, which integrates possible interventions and import cases. The parameterization of the formulated model was realized through multiple source data. Our main conclusion shows that intermittent population flow, close attention and effective isolation of the floating populationcan effectively reduce the risk of secondary outbreak, which consequently provides decision support for the orderly organization of returning to work/school. | |
| Scientia Sinica Mathematica | |
| 2020 | |
| Preimpreso | |
| https://coronavirus.1science.com/item/9a631edf1c374daace5e94e78178075d92a78329 | |
| Inglés | |
| VIRUS RESPIRATORIOS | |
| Aparece en las colecciones: | Artículos científicos |
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