Social distancing and testing as optimal strategies against the spread of COVID-19 in the Rio Grande Valley of Texas

Infectious Disease Modelling

Tamer Oraby¹  Josef Sifuentes²  María Cristina Villalobos³  Jose Campo Maldonado⁴  Timothy Huber⁵  Kristina P. Vatcheva⁵ 

[1] Corresponding author. School of Mathematical &Statistical Sciences, University of Texas Rio Grande Valley, Brownsville, TX, 78520, USA;Statistical Sciences, University of Texas Rio Grande Valley, Edinburg, TX, 78539, USA;Statistical Sciences, University of Texas Rio Grande Valley, One West University Blvd., Brownsville, TX, 78520, USA.;;School of Mathematical &

关键词: COVID-19;    Mathematical modeling;    Optimal control;    Rio Grande Valley (RGV);    Testing and social distancing;    School reopening;   

DOI  :  

来源: DOAJ

【 摘 要 】

At the beginning of August 2020, the Rio Grande Valley (RGV) of Texas experienced a rapid increase of coronavirus disease 2019 (abbreviated as COVID-19) cases and deaths. This study aims to determine the optimal levels of effective social distancing and testing to slow the virus spread at the outset of the pandemic. We use an age-stratified eight compartment epidemiological model to depict COVID-19 transmission in the community and within households. With a simulated 120-day outbreak period data we obtain a post 180-days period optimal control strategy solution. Our results show that easing social distancing between adults by the end of the 180-day period requires very strict testing a month later and then daily testing rates of 5% followed by isolation of positive cases. Relaxing social distancing rates in adults from 50% to 25% requires both children and seniors to maintain social distancing rates of 50% for nearly the entire period while maintaining maximum testing rates of children and seniors for 150 of the 180 days considered in this model. Children have higher contact rates which leads to transmission based on our model, emphasizing the need for caution when considering school reopenings.

【 授权许可】

Unknown