Mathematical Assessment of the Role of Human Behavior Changes on SARS-CoV-2 Transmission Dynamics in the United States.

IF 4.6 Q2 MATERIALS SCIENCE, BIOMATERIALS
Binod Pant, Salman Safdar, Mauricio Santillana, Abba B Gumel
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Abstract

The COVID-19 pandemic has not only presented a major global public health and socio-economic crisis, but has also significantly impacted human behavior towards adherence (or lack thereof) to public health intervention and mitigation measures implemented in communities worldwide. This study is based on the use of mathematical modeling approaches to assess the extent to which SARS-CoV-2 transmission dynamics is impacted by population-level changes of human behavior due to factors such as (a) the severity of transmission (such as disease-induced mortality and level of symptomatic transmission), (b) fatigue due to the implementation of mitigation interventions measures (e.g., lockdowns) over a long (extended) period of time, (c) social peer-pressure, among others. A novel behavior-epidemiology model, which takes the form of a deterministic system of nonlinear differential equations, is developed and fitted using observed cumulative SARS-CoV-2 mortality data during the first wave in the United States. The model fits the observed data, as well as makes a more accurate prediction of the observed daily SARS-CoV-2 mortality during the first wave (March 2020-June 2020), in comparison to the equivalent model which does not explicitly account for changes in human behavior. This study suggests that, as more newly-infected individuals become asymptomatically-infectious, the overall level of positive behavior change can be expected to significantly decrease (while new cases may rise, particularly if asymptomatic individuals have higher contact rate, in comparison to symptomatic individuals).

Abstract Image

人类行为变化对美国 SARS-CoV-2 传播动态影响的数学评估。
COVID-19 大流行不仅带来了重大的全球公共卫生和社会经济危机,而且还对全球各社区实施的公共卫生干预和缓解措施的遵守(或不遵守)行为产生了重大影响。本研究采用数学建模方法,评估 SARS-CoV-2 传播动态受人群行为变化影响的程度,这些因素包括:(a) 传播的严重程度(如疾病引起的死亡率和无症状传播的程度);(b) 因长期(延长)实施缓解干预措施(如封锁)而产生的疲劳;(c) 社会同伴压力等。利用美国第一波非典-CoV-2 期间的累积死亡率数据,开发并拟合了一个新的行为-流行病学模型,该模型采用确定性非线性微分方程系统的形式。与未明确考虑人类行为变化的等效模型相比,该模型不仅拟合了观察到的数据,而且对第一波(2020 年 3 月至 2020 年 6 月)期间观察到的每日 SARS-CoV-2 死亡率做出了更准确的预测。这项研究表明,随着越来越多的新感染者成为无症状感染者,积极行为变化的总体水平预计会显著下降(而新病例可能会上升,特别是如果无症状者的接触率高于有症状者)。
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
ACS Applied Bio Materials
ACS Applied Bio Materials Chemistry-Chemistry (all)
CiteScore
9.40
自引率
2.10%
发文量
464
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