Suppression of Groups Intermingling as an Appealing Option for Flattening and Delaying the Epidemiological Curve While Allowing Economic and Social Life at a Bearable Level during the COVID‐19 Pandemic

Abstract

The COVID-19 pandemic in a population modelled as a network wherein infection can propagate both via intra-and inter-group interactions is simulated. The results emphasize the importance of diminishing the inter-group infections in the effort of substantial flattening/delaying of the epi(demiologic) curve with concomitant mitigation of disastrous economy and social consequences. To exemplify, splitting a population into m (say, 5 or 10) noninteracting groups while keeping intra-group interaction unchanged yields a stretched epidemiological curve having the maximum number of daily infections reduced and postponed in time by the same factor m (5 or 10). More generally, the 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 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 lockdown, the proposed approach prevents economic ruin and keeps social life at a more bearable level than distancing everyone from anyone. The declaration for the first time in Europe that COVID-19 epidemic ended in the two-million population Slovenia may be taken as support for the strategy proposed here.

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

“…The latter aspect is of paramount importance [17] also because, if not adequately considered by governments currently challenged to DOI: 10.1002/adts.202000225 deciding possibly under dramatic circumstances and formidable tight schedule, it can jeopardize the health care system itself. As an effort in this direction, we drew recently attention [18] 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.…”

“…issues. [18,22] Logistic functions (see Equation (2) below) were utilized for studying various problems. [29][30][31][32][33][34] Studies on population dynamics of epidemic populations [35][36][37][38][39][40][41] were also frequently based on the logistic function.…”

Analysis and Simulation of Epidemic COVID-19 Curves with the Verhulst Model Applied to Statistical Inhomogeneous Age Groups

“…3 of 16 or more precisely: R = e 1d/τ , (4) which is equivalent to a doubling time, T 2 , expressed as [3]:…”

“…The characteristic time, τ, is an effective value and depends on the intrinsic effectiveness of the virus transmission, the risk to become ill and its incubation time. In addition, τ depends on extrinsic factors, such as contact frequency, exposure time, distancing, quality of ventilation and contact tracing [3], intermingling [4], differences in culture (handshaking and kissing or polite greeting), the health care system (stocks of basic protective gear, face protection and testing kits), sanitation, food habits and immune status (BMI-range) [8]. Increasing contact time and frequency will increase the risk, 1/τ, and reduce the τ-value.…”

“…The aim of the simulation is to enlighten some main ideas in a broad-brush picture. Therefore, the above flexible analytical phenomenological model V n2 (logistic with n = 2) is chosen [4,5]. The refined discrete models are at the expense of too many unknown parameters and a complex set of equations with less insight.…”