PMC:7039910 / 20489-22841
Annnotations
{"target":"https://pubannotation.org/docs/sourcedb/PMC/sourceid/7039910","sourcedb":"PMC","sourceid":"7039910","source_url":"https://www.ncbi.nlm.nih.gov/pmc/7039910","text":"Model\nWe employed an infectious disease dynamics model (SEIR model) for the purpose of modeling and predicting the number of COVID-19 cases in Wuhan, China. The model is a classic epidemic method to analyze the infectious disease, which has a definite latent period, and has proved to be predictive for a variety of acute infectious diseases in the past such as Ebola and SARS22,26–31. Application of the mathematical model is of great guiding significance to assess the impact of isolation of symptomatic cases as well as observation of asymptomatic contact cases and to promote evidence-based decisions and policy.\nWe assumed no new transmissions from animals, no differences in individual immunity, the time-scale of the epidemic is much faster than characteristic times for demographic processes (natural birth and death), and no differences in natural births and deaths. In this model, individuals are classified into four types: susceptible (S; at risk of contracting the disease), exposed (E; infected but not yet infectious), infectious (I; capable of transmitting the disease), and removed (R; those who recover or die from the disease). The total population size (N) is given by N = S + E + I + R. It is assumed that susceptible individuals who have been infected first enter a latent (exposed) stage, during which they may have a low level of infectivity. The differential equations of the SEIR model are given as:32,33\\documentclass[12pt]{minimal} \t\t\t\t\\usepackage{amsmath} \t\t\t\t\\usepackage{wasysym} \t\t\t\t\\usepackage{amsfonts} \t\t\t\t\\usepackage{amssymb} \t\t\t\t\\usepackage{amsbsy} \t\t\t\t\\usepackage{mathrsfs} \t\t\t\t\\usepackage{upgreek} \t\t\t\t\\setlength{\\oddsidemargin}{-69pt} \t\t\t\t\\begin{document}$$\\begin{array}{l}{\\mathrm{d}}S/{\\mathrm{d}}t = - {\\beta}\\,{S}\\,{I}/{N},\\\\ {\\mathrm{d}}E/{\\mathrm{d}}t = {\\beta}\\,{S}\\,{I}/{N} - {\\sigma}\\,{E},\\\\ {\\mathrm{d}}I/{\\mathrm{d}}t = {\\sigma}\\,{E} - {\\gamma}\\,{I},\\\\ {\\mathrm{d}}R/{\\mathrm{d}}t = {\\gamma}\\,{I},\\\\ {\\beta} = {R}_{\\mathrm{0}}{\\gamma},\\end{array}$$\\end{document}dS∕dt=−βSI∕N,dE∕dt=βSI∕N−σE,dI∕dt=σE−γI,dR∕dt=γI,β=R0γ,where β is the transmission rate, σ is the infection rate calculated by the inverse of the mean latent period, and γ is the recovery rate calculated by the inverse of infectious period.\nR software (version 3.6.2) was applied for all the calculations and estimates in the current 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