The advantage of self-protecting interventions in mitigating epidemic circulation at the community level
arXiv:2204.13965 · doi:10.1038/s41598-022-20152-4
Abstract
Protecting interventions of many types (both pharmaceutical and non-pharmaceutical) can be deployed against the spreading of a communicable disease, as the worldwide COVID-19 pandemic has dramatically shown. Here we investigate in detail the effects at the population level of interventions that provide an asymmetric protection between the people involved in a single interaction. Masks of different filtration types, either protecting mainly the wearer or the contacts of the wearer, are a prominent example of these interventions. By means of analytical calculations and extensive simulations of simple epidemic models on networks, we show that interventions protecting more efficiently the adopter (e.g the mask wearer) are more effective than interventions protecting primarily the contacts of the adopter in reducing the prevalence of the disease and the number of concurrently infected individuals ("flattening the curve"). This observation is backed up by the study of a more realistic epidemic model on an empirical network representing the patterns of contacts in the city of Portland. Our results point out that promoting wearer-protecting face masks and other self-protecting interventions, though deemed selfish and inefficient, can actually be a better strategy to efficiently curtail pandemic spreading.
18 pages, 9 figures
References in corpus (5)
- To mask or not to mask: Modeling the potential for face mask use by the general public to curtail the COVID-19 pandemic
- Non-pharmaceutical interventions during the COVID-19 pandemic: a rapid review
- Predicting the size and probability of epidemics in a population with heterogeneous infectiousness and susceptibility
- Modeling the Impact of Social Distancing and Targeted Vaccination on the Spread of COVID-19 through a Real City-Scale Contact Network
- Impact of assortative mixing by mask-wearing on the propagation of epidemics in networks