Modeling the Dynamic Effects of Human Mobility and Airborne Particulate Matter on the Spread of COVID-19

IF 1.9 Q2 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS

Computation Pub Date : 2023-10-30 DOI:10.3390/computation11110211

Klot Patanarapeelert, Rossanan Chandumrong, Nichaphat Patanarapeelert

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引用次数: 0

Abstract

Identifying the relationship between human mobility, air pollution, and communicable disease poses a challenge for impact evaluation and public health planning. Specifically, Coronavirus disease 2019 (COVID-19) and air pollution from fine particulates (PM2.5), by which human mobility is mediated in a public health emergency. To describe the interplay between human mobility and PM2.5 during the spread of COVID-19, we proposed a nonlinear model of the time-dependent transmission rate as a function of these factors. A compartmental epidemic model, together with daily confirmed case data in Bangkok, Thailand during 2020–2021, was used to estimate the intrinsic parameters that can determine the impact on the transmission dynamic of the two earlier outbreaks. The results suggested a positive association between mobility and transmission, but this was strongly dependent on the context and the temporal characteristics of the data. For the ascending phase of an epidemic, the estimated coefficient of mobility variable in the second wave was greater than in the first wave, but the value of the mobility component in the transmission rate was smaller. Due to the influence of the baseline value and PM2.5, the estimated basic reproduction number of the second wave was higher than that of the first wave, even though mobility had a greater influence. For the descending phase, the value of the mobility component in the second wave was greater, due to the negative value of the estimated mobility coefficient. Despite this scaling effect, the results suggest a negative association between PM2.5 and the transmission rates. Although this conclusion agrees with some previous studies, the true effect of PM2.5 remains inconclusive and requires further investigation.

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人类流动性和空气颗粒物对COVID-19传播的动态影响建模

确定人类流动性、空气污染和传染病之间的关系对影响评估和公共卫生规划提出了挑战。特别是2019冠状病毒病(COVID-19)和细颗粒物(PM2.5)造成的空气污染，它们在突发公共卫生事件中调节了人类的流动性。为了描述COVID-19传播期间人类流动性与PM2.5之间的相互作用，我们提出了一个随时间变化的传播率的非线性模型，作为这些因素的函数。使用区隔流行病模型以及2020-2021年期间泰国曼谷每日确诊病例数据来估计可确定对早期两次疫情传播动态影响的内在参数。结果表明，流动性和传播之间存在正相关关系，但这在很大程度上取决于背景和数据的时间特征。在上升阶段，第二波的迁移率变量估计系数大于第一波，但在传播率中迁移率分量的估计值较小。由于基线值和PM2.5的影响，尽管流动性的影响更大，但第二次浪潮的估计基本再现数高于第一次浪潮。在下降阶段，由于估计的迁移系数为负值，第二波的迁移率分量值更大。尽管存在这种比例效应，但研究结果表明PM2.5与传播率之间存在负相关。虽然这一结论与之前的一些研究相一致，但PM2.5的真正影响仍然没有定论，需要进一步调查。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Computation Mathematics-Applied Mathematics

CiteScore

3.50

自引率

4.50%

发文量

201

审稿时长

8 weeks

期刊介绍： Computation a journal of computational science and engineering. Topics: computational biology, including, but not limited to: bioinformatics mathematical modeling, simulation and prediction of nucleic acid (DNA/RNA) and protein sequences, structure and functions mathematical modeling of pathways and genetic interactions neuroscience computation including neural modeling, brain theory and neural networks computational chemistry, including, but not limited to: new theories and methodology including their applications in molecular dynamics computation of electronic structure density functional theory designing and characterization of materials with computation method computation in engineering, including, but not limited to: new theories, methodology and the application of computational fluid dynamics (CFD) optimisation techniques and/or application of optimisation to multidisciplinary systems system identification and reduced order modelling of engineering systems parallel algorithms and high performance computing in engineering.