Containing the Transmission of COVID-19: A Modeling Study in 160 Countries

Yan, Ning; Rui, Jia; Wang, Qiupeng; Zhang, Wei; Chen, Zhiwei; Xie, Fang; Zhao, Zeyu; Lin, Shengnan; Zhu, Yuanzhao; Wang, Yao; Xu, Jingwen; Liu, Xingchun; Yang, Meng; Zheng, Wei; Chen, Kaixin; Xia, Yilan; Xu, Lijuan; Shi, Z.; Ji, Rongrong; Jin, Taisong; Chen, Yong; Zhao, Bin; Su, Yanhua; Song, Tie; Chen, Tianmu; Hu, Guoqing

doi:10.3389/fmed.2021.701836

Cited by 17 publications

(10 citation statements)

References 26 publications

(32 reference statements)

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“…Coronavirus disease 2019 (COVID-2019) (caused by SARS-CoV-2) invaded the world unexpectedly in 2019 and changed human life tremendously ( 1 ). The SARS-CoV-2 virus is transmitted via respiratory droplets with an incubation period of 2–14 days ( 2 ).…”

Section: Introductionmentioning

confidence: 99%

The Second Wave of COVID-19 in South and Southeast Asia and the Effects of Vaccination

et al. 2021

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Background: By February 2021, the overall impact of coronavirus disease 2019 (COVID-19) in South and Southeast Asia was relatively mild. Surprisingly, in early April 2021, the second wave significantly impacted the population and garnered widespread international attention.Methods: This study focused on the nine countries with the highest cumulative deaths from the disease as of August 17, 2021. We look at COVID-19 transmission dynamics in South and Southeast Asia using the reported death data, which fits a mathematical model with a time-varying transmission rate.Results: We estimated the transmission rate, infection fatality rate (IFR), infection attack rate (IAR), and the effects of vaccination in the nine countries in South and Southeast Asia. Our study suggested that the IAR is still low in most countries, and increased vaccination is required to prevent future waves.Conclusion: Implementing non-pharmacological interventions (NPIs) could have helped South and Southeast Asia keep COVID-19 under control in 2020, as demonstrated in our estimated low-transmission rate. We believe that the emergence of the new Delta variant, social unrest, and migrant workers could have triggered the second wave of COVID-19.

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Section: Introductionmentioning

confidence: 99%

The Second Wave of COVID-19 in South and Southeast Asia and the Effects of Vaccination

et al. 2021

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“…We considered pre-symptomatic infections based on the basic susceptible-exposed-symptomatic-asymptomatic-recovered/removed (SEIAR) transmission dynamics model according to the previous researches [ 25 – 29 ]. In our model, the whole population were first divided into two groups, completed booster vaccination population and uncompleted booster vaccination population.…”

Section: Methodsmentioning

confidence: 99%

Optimal control strategies of SARS-CoV-2 Omicron supported by invasive and dynamic models

et al. 2022

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Background There is a raising concern of a higher infectious Omicron BA.2 variant and the latest BA.4, BA.5 variant, made it more difficult in the mitigation process against COVID-19 pandemic. Our study aimed to find optimal control strategies by transmission of dynamic model from novel invasion theory. Methods Based on the public data sources from January 31 to May 31, 2022, in four cities (Nanjing, Shanghai, Shenzhen and Suzhou) of China. We segmented the theoretical curves into five phases based on the concept of biological invasion. Then, a spatial autocorrelation analysis was carried out by detecting the clustering of the studied areas. After that, we choose a mathematical model of COVID-19 based on system dynamics methodology to simulate numerous intervention measures scenarios. Finally, we have used publicly available migration data to calculate spillover risk. Results Epidemics in Shanghai and Shenzhen has gone through the entire invasion phases, whereas Nanjing and Suzhou were all ended in the establishment phase. The results indicated that Rt value and public health and social measures (PHSM)-index of the epidemics were a negative correlation in all cities, except Shenzhen. The intervention has come into effect in different phases of invasion in all studied cities. Until the May 31, most of the spillover risk in Shanghai remained above the spillover risk threshold (18.81–303.84) and the actual number of the spillovers (0.94–74.98) was also increasing along with the time. Shenzhen reported Omicron cases that was only above the spillover risk threshold (17.92) at the phase of outbreak, consistent with an actual partial spillover. In Nanjing and Suzhou, the actual number of reported cases did not exceed the spillover alert value. Conclusions Biological invasion is positioned to contribute substantively to understanding the drivers and mechanisms of the COVID-19 spread and outbreaks. After evaluating the spillover risk of cities at each invasion phase, we found the dynamic zero-COVID strategy implemented in four cities successfully curb the disease epidemic peak of the Omicron variant, which was highly correlated to the way to perform public health and social measures in the early phases right after the invasion of the virus. Graphical Abstract

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“…Compared with SIR and SEIR models, the susceptible-exposed-infectious-asymptomatic-removed (SEIAR) model is more applicable for COVID-19 because of its asymptomatic compartment. This model is more suitable for early outbreak predictions ( 47 - 48 ). Developing models for different age groups, genders, and occupations helps scholars understand important differences in the spread of COVID-19 under several scenarios ( 49 ).…”

Section: Status Of Covid-19 Modelsmentioning

confidence: 99%