期刊
SCIENTIFIC REPORTS
卷 11, 期 1, 页码 -出版社
NATURE PORTFOLIO
DOI: 10.1038/s41598-021-98999-2
关键词
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资金
- National Institutes of Health (NIH) [1R01GM109718]
- NSF BIG DATA Grant [IIS-1633028]
- NSF [OAC-1916805]
- NSF Expeditions in Computing Grant [CCF-1918656, CCF-1917819]
- NSF RAPID [CNS-2028004, OAC-2027541]
- US Centers for Disease Control and Prevention [75D30119C05935]
- University of Virginia Strategic Investment Fund [SIF160]
- Defense Threat Reduction Agency (DTRA) [HDTRA1-19-D-0007]
Infections caused by non-symptomatic individuals can either increase or reduce the final epidemic size depending on individuals' risk misperception. The impact of non-symptomatic infections is modulated by symptomatic individuals' behavior under behavioral response. There exists an optimal planning horizon that minimizes the final epidemic size.
Infections produced by non-symptomatic (pre-symptomatic and asymptomatic) individuals have been identified as major drivers of COVID-19 transmission. Non-symptomatic individuals, unaware of the infection risk they pose to others, may perceive themselves-and be perceived by others-as not presenting a risk of infection. Yet, many epidemiological models currently in use do not include a behavioral component, and do not address the potential consequences of risk misperception. To study the impact of behavioral adaptations to the perceived infection risk, we use a mathematical model that incorporates the behavioral decisions of individuals, based on a projection of the system's future state over a finite planning horizon. We found that individuals' risk misperception in the presence of non-symptomatic individuals may increase or reduce the final epidemic size. Moreover, under behavioral response the impact of non-symptomatic infections is modulated by symptomatic individuals' behavior. Finally, we found that there is an optimal planning horizon that minimizes the final epidemic size.
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