Initial Model for the Impact of Social Distancing on COVID-19 Spread
Year: 2025
Type: article
Abstract: The initial stages of the CoVID-19 coronavirus pandemic all around the world exhibit a nearly exponential rise in the number of infections with time. Planners, governments, and agencies are scrambling to figure out “ How much? How bad? ” and how to effectively treat the potentially large numbers of simultaneously sick people. Modeling the CoVID-19 pandemic using an exponential rise implicitly assumes a nearly unlimited population of uninfected persons ( “dilute pandemic” ). Once a significant fraction of the population is infected ( “saturated pandemic” ), an exponential growth no longer applies. A new model is developed here, which modifies the standard exponential growth function to account for factors such as Social Distancing . A Social Mitigation Parameter [SMP] α S is introduced to account for these types of society-wide changes, which can modify the standard exponential growth function, as follows: The doubling-time t dbl = (In 2)/ K o can also be used to substitute for K o , giving a { t dbl , α S } parameter pair for comparing to actual CoVID-19 data. This model shows how the number of CoVID-19 infections can self-limit before reaching a saturated pandemic level. It also provides estimates for: (a) the timing of the pandemic peak , (b) the maximum number of new daily cases that would be expected, and (c) the expected total number of CoVID-19 cases. This model shows fairly good agreement with the presently available CoVID-19 pandemic data for several individual States, and the for the USA as a whole ( 6 Figures ), as well as for various countries around the World ( 9 Figures ). An augmented model with two Mitigation Parameters, α S and β S , is also developed, which can give better agreement with the daily new CoVID-19 data. Data-to-model comparisons also indicate that using α S by itself likely provides a worst-case estimate, while using both α S and β S likely provides a best-case estimate for the CoVID-19 spread.
Source: Open Journal of Applied Sciences
Author: Genghmun Eng
Institution: University of Illinois Urbana-Champaign
Language: English
Subfield: Modeling and Simulation
Field: Mathematics
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4410944439