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斐济新冠疫情的时间离散SIR模型。

Time-discrete SIR model for COVID-19 in Fiji.

作者信息

Singh Rishal Amar, Lal Rajnesh, Kotti Ramanuja Rao

机构信息

School of Mathematical and Computing Sciences, Fiji National University, Lautoka, Fiji.

出版信息

Epidemiol Infect. 2022 Apr 7;150:1-17. doi: 10.1017/S0950268822000590.

DOI:10.1017/S0950268822000590
PMID:35387697
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9043634/
Abstract

Using the data provided by Fiji's ministry of health and medical services, we apply an implicit time-discrete SIR (susceptible people–infectious people–removed people) model that tracks the transmission and recovering rate at time, to predict the trend of the coronavirus disease 2019 (COVID-19) pandemic in Fiji. The model implied time-varying transmission and recovery rates were calculated from 4 May 2021 to 9 October 2021. The estimator functions for these rates were determined, and a short-term (30 days) forecast was done. The model was validated with observed values of the active and recovered cases from 11 October 2021 to 9 December 2021. Statistical results reveal a good fit of profiles between model simulated and the reported COVID-19 data. The gradual decrease of the time-varying basic reproduction number with values below one towards the end of the study period suggest the government's success in controlling the epidemic. The mean reproduction number for the second wave of COVID-19 in Fiji was estimated as 2.7818. The results from this study can be used by researchers, the Fijian government, and the relevant health policy makers in making informed decisions should a third COVID-19 wave occur.

摘要

利用斐济卫生与医疗服务部提供的数据，我们应用了一种隐式时间离散的SIR（易感人群 - 感染人群 - 康复人群）模型，该模型跟踪某一时刻的传播率和康复率，以预测斐济2019年冠状病毒病（COVID - 19）大流行的趋势。模型隐含的随时间变化的传播率和康复率是在2021年5月4日至2021年10月9日期间计算得出的。确定了这些比率的估计函数，并进行了短期（30天）预测。该模型用2021年10月11日至2021年12月9日的活跃病例和康复病例的观测值进行了验证。统计结果表明，模型模拟结果与报告的COVID - 19数据之间的曲线拟合良好。在研究期结束时，随时间变化的基本再生数逐渐下降且值低于1，这表明政府在控制疫情方面取得了成功。斐济第二波COVID - 19的平均再生数估计为2.7818。如果出现第三波COVID - 19疫情，本研究结果可供研究人员、斐济政府及相关卫生政策制定者用于做出明智决策。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d798/9043634/a93d1cfc82e8/S0950268822000590_fig9.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d798/9043634/f9420c28019d/S0950268822000590_fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d798/9043634/26b446d29264/S0950268822000590_fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d798/9043634/292ce0dfada7/S0950268822000590_fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d798/9043634/95957588eaa2/S0950268822000590_fig4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d798/9043634/1fcb7d11ea59/S0950268822000590_fig5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d798/9043634/ca7f95e802d9/S0950268822000590_fig6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d798/9043634/7d5ea180060c/S0950268822000590_fig7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d798/9043634/283be3f188f0/S0950268822000590_fig8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d798/9043634/a93d1cfc82e8/S0950268822000590_fig9.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d798/9043634/f9420c28019d/S0950268822000590_fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d798/9043634/26b446d29264/S0950268822000590_fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d798/9043634/292ce0dfada7/S0950268822000590_fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d798/9043634/95957588eaa2/S0950268822000590_fig4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d798/9043634/1fcb7d11ea59/S0950268822000590_fig5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d798/9043634/ca7f95e802d9/S0950268822000590_fig6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d798/9043634/7d5ea180060c/S0950268822000590_fig7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d798/9043634/283be3f188f0/S0950268822000590_fig8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d798/9043634/a93d1cfc82e8/S0950268822000590_fig9.jpg

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