Suppression of Groups Intermingling as Appealing Option For Flattening and Delaying the Epidemiologic Curve While Allowing Economic and Social Life at Bearable Level During COVID-19 Pandemic

Abstract

In this work, we simulate the COVID-19 pandemic dynamics in a population modeled as a network of groups wherein infection can propagate both via intra-group and via inter-group interactions. Our results emphasize the importance of diminishing the inter-group infections in the effort of substantial flattening and delaying of the epi(demiologic) curve with concomitant mitigation of disastrous economy and social consequences. To exemplify with a limiting case, splitting a population into m (say, 5 or 10) noninteracting groups while keeping intra-group interaction unchanged yields a stretched epidemiologic curve having the maximum number of daily infections reduced and postponed in time by the same factor m (5 or 10). More generally, our study suggests a practical approach to fight against SARS-CoV-2 virus spread based on population splitting into groups and minimizing intermingling between them. This strategy 1 . CC-BY-NC 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) : medRxiv preprint can be pursued by large-scale infrastructure reorganization of activity at different levels in big logistic units (e.g., large productive networks, factories, enterprises, warehouses, schools, (seasonal) harvest work). Importantly, unlike total lockdwon strategy, the proposed approach prevents economic ruin and keeps social life at a more bearable level than distancing everyone from anyone.

Quantifying Inaccuracies in Modeling COVID-19 Pandemic within a Continuous Time Picture

“…[12][13][14][15] Along with numerous applications in various other fields, [16][17][18][19][20][21] this model turned out to be also useful in previous studies on epidemics, [22][23][24][25] also including COVID-19 pandemic. [26][27][28] As compared to various SIR-based approaches, the logistic growth model possesses a great advantage: it can be directly validated against ongoing epidemiological reports. 28 In the continuous time description, logistic growth in time of an infected population obeys a first-order differential equation…”

What Can We Learn from the Time Evolution of COVID-19 Epidemic in Slovenia?

“…The latter aspect is of paramount importance 8 also because, if not adequately considered by governments currently challenged to deciding possibly under dramatic circumstances and formidable tight schedule, it can jeopardize the healthcare system itself. As an effort in this direction, we drew recently attention 9 to the general fact that the spread of the SARS-CoV-2 virus in smaller groups can be substantially slowed down as compared to the case of larger populations. In this vein, the time evolution of COVID-19 disease in the two million people Slovenia certainly deserves special consideration, as on 15 May 2020, concluding that this country has the best epidemic situation in Europe, Prime Minister Janez Janska declared the end of the COVID-19 epidemic within Slovenian borders.…”

“…This model is considerably simpler than SIR flavors, and already turned out to be an appealing framework in dealing with current COVID-19 pandemic issues. 9,13 Logistic functions (see equation (2) below) were utilized for studying various problems. [20][21][22][23][24][25] Studies on population dynamics of epidemic populations [26][27][28][29][30][31][32] were also frequently based on the logistic function.…”

What Can We Learn from the Time Evolution of COVID-19 Epidemic in Slovenia?

“…The latter aspect is of paramount importance 8 also because, if not adequately considered by governments currently challenged to deciding possibly under dramatic circumstances and formidable tight schedule, it can jeopardize the healthcare system itself. As an effort in this direction, we drew recently attention 9 to the general fact that the spread of the SARS-CoV-2 virus in smaller groups can be substantially slowed down as compared to the case of larger populations. In this vein, the time evolution of COVID-19 disease in the two million people Slovenia certainly deserves special consideration, as on 15 May 2020, concluding that this country has the best epidemic situation in Europe, Prime Minister Janez Janška declared the end of the COVID-19 epidemic within Slovenian borders.…”

“…This model is considerably simpler than SIR flavors, and already turned out to be an appealing framework in dealing with current COVID-19 pandemic issues. 9,13 Logistic functions (see equation (2) below) were utilized for studying various problems. [20][21][22][23][24][25] Studies on population dynamics of epidemic populations [26][27][28][29][30][31][32] were also frequently based on the logistic function.…”