Endogenous social distancing and its underappreciated impact on the epidemic curve

Marko Gosak (Author), Moritz U. G. Kraemer (Author), Heinrich H. Nax (Author), Matjaž Perc (Author), Bary S. R. Pradelski (Author)

Abstract

Social distancing is an efective strategy to mitigate the impact of infectious diseases. If sick or healthy, or both, predominantly socially distance, the epidemic curve fattens. Contact reductions may occur for diferent reasons during a pandemic including health-related mobility loss (severity of symptoms), duty of care for a member of a high-risk group, and forced quarantine. Other decisions to reduce contacts are of a more voluntary nature. In particular, sick people reduce contacts consciously to avoid infecting others, and healthy individuals reduce contacts in order to stay healthy. We use game theory to formalize the interaction of voluntary social distancing in a partially infected population. This improves the behavioral micro-foundations of epidemiological models, and predicts diferential social distancing rates dependent on health status. The model's key predictions in terms of comparative statics are derived, which concern changes and interactions between social distancing behaviors of sick and healthy. We ft the relevant parameters for endogenous social distancing to an epidemiological model with evidence from infuenza waves to provide a benchmark for an epidemic curve with endogenous social distancing. Our results suggest that spreading similar in peak and case numbers to what partial immobilization of the population produces, yet quicker to pass, could occur endogenously. Going forward, eventual social distancing orders and lockdown policies should be benchmarked against more realistic epidemic models that take endogenous social distancing into account, rather than be driven by static, and therefore unrealistic, estimates for social mixing that intrinsically overestimate spreading.

Keywords

COVID-19;pandemija;dinamika bolezni;eksponentna rast;viralnost;pandemic;disease dynamics;exponential growth;virality;

Data

Language:	English
Year of publishing:	2021
Typology:	1.01 - Original Scientific Article
Organization:	UM FNM - Faculty of Natural Sciences and Mathematics
Publisher:	Nature Publishing Group
UDC:	616-036.21
COBISS:	50588675
ISSN:	2045-2322
Views:	29
Downloads:	0
Average score:	0 (0 votes)
Metadata:

Other data

Secondary language:	Slovenian
Secondary keywords:	COVID-19;pandemija;dinamika bolezni;eksponentna rast;viralnost;
Type (COBISS):	Scientific work
Pages:	str. 1-10
Issue:	ǂVol. ǂ11
Chronology:	2021
DOI:	10.1038/s41598-021-82770-8
ID:	24720604