Modeling of the Long-Term Epidemic Dynamics of COVID-19 in the United States.
Modeling of the Long-Term Epidemic Dynamics of COVID-19 in the United States.
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DOI：
10.3390/ijerph18147594
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发表时间：
2021-07-16
影响因子：
--
通讯作者：
Xiao Y
中科院分区：
文献类型：
--
作者：
Huang D;Tao H;Wu Q;Huang SY;Xiao Y
2019冠状病毒（COVID-19）正在引发一场严重的大流行，导致全球数百万确诊病例和死亡。在缺乏有效治疗药物的情况下，非药物干预是控制这一疾病的最有效方法。尽管部分国家已控制疫情，但全球所有国家（包括美国）仍处于控制COVID-19的过程中，这需要一个有效的流行病模型来描述COVID-19的传播动态。为满足这一需求，我们广泛调查了2020年1月22日至2021年2月14日期间美国50个州的COVID-19传播动态，从干预措施和人口特征方面揭示了病毒传播的一般原则。我们进一步提出了一个时间依赖的流行病模型，称为T-SIR，以模拟COVID-19在美国的长期传播动态。本文表明，我们的T-SIR模型可以有效地模拟所有50个州的COVID-19流行动态，这为COVID-19在美国的传播动态提供了见解。本研究将有助于了解COVID-19的流行动态，从而帮助政府确定和实施有效的干预措施或疫苗优先次序，以控制大流行。
Coronavirus 2019 (COVID-19) is causing a severe pandemic that has resulted in millions of confirmed cases and deaths around the world. In the absence of effective drugs for treatment, non-pharmaceutical interventions are the most effective approaches to control the disease. Although some countries have the pandemic under control, all countries around the world, including the United States (US), are still in the process of controlling COVID-19, which calls for an effective epidemic model to describe the transmission dynamics of COVID-19. Meeting this need, we have extensively investigated the transmission dynamics of COVID-19 from 22 January 2020 to 14 February 2021 for the 50 states of the United States, which revealed the general principles underlying the spread of the virus in terms of intervention measures and demographic properties. We further proposed a time-dependent epidemic model, named T-SIR, to model the long-term transmission dynamics of COVID-19 in the US. It was shown in this paper that our T-SIR model could effectively model the epidemic dynamics of COVID-19 for all 50 states, which provided insights into the transmission dynamics of COVID-19 in the US. The present study will be valuable to help understand the epidemic dynamics of COVID-19 and thus help governments determine and implement effective intervention measures or vaccine prioritization to control the pandemic.
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影响因子：
11.7
作者：
Chowell G;Sattenspiel L;Bansal S;Viboud C
通讯作者：
Viboud C
影响因子：
4.6
作者：
Cabrera M;Córdova-Lepe F;Gutiérrez-Jara JP;Vogt-Geisse K
通讯作者：
Vogt-Geisse K
影响因子：
7.4
作者：
Cao Z;Tang F;Chen C;Zhang C;Guo Y;Lin R;Huang Z;Teng Y;Xie T;Xu Y;Song Y;Wu F;Dong P;Luo G;Jiang Y;Zou H;Chen YQ;Sun L;Shu Y;Du X
通讯作者：
Du X
DOI：
10.3390/ijerph17144964
发表时间：
2020-07-01
影响因子：
--
作者：
Maugeri, Andrea;Barchitta, Martina;Agodi, Antonella
通讯作者：
Agodi, Antonella
影响因子：
8.1
作者：
Aguas R;White L;Hupert N;Shretta R;Pan-Ngum W;Celhay O;Moldokmatova A;Arifi F;Mirzazadeh A;Sharifi H;Adib K;Sahak MN;Franco C;Coutinho R;CoMo Consortium
通讯作者：
CoMo Consortium