Public policy and economic dynamics of COVID-19 spread: A mathematical modeling study

Abstract

With the COVID-19 pandemic infecting millions of people, large-scale isolation policies have been enacted across the globe. To assess the impact of isolation measures on deaths, hospitalizations, and economic output, we create a mathematical model to simulate the spread of COVID-19, incorporating effects of restrictive measures and segmenting the population based on health risk and economic vulnerability. Policymakers make isolation policy decisions based on current levels of disease spread and economic damage. For 76 weeks in a population of 330 million, we simulate a baseline scenario leaving strong isolation restrictions in place, rapidly reducing isolation restrictions for non-seniors shortly after outbreak containment, and gradually relaxing isolation restrictions for non-seniors. We use 76 weeks as an approximation of the time at which a vaccine will be available. In the baseline scenario, there are 235,724 deaths and the economy shrinks by 34.0%. With a rapid relaxation, a second outbreak takes place, with 525,558 deaths, and the economy shrinks by 32.3%. With a gradual relaxation, there are 262,917 deaths, and the economy shrinks by 29.8%. We also show that hospitalizations, deaths, and economic output are quite sensitive to disease spread by asymptomatic people. Strict restrictions on seniors with very gradual lifting of isolation for non-seniors results in a limited number of deaths and lesser economic damage. Therefore, we recommend this strategy and measures that reduce non-isolated disease spread to control the pandemic while making isolation economically viable.

Designing the Safe Reopening of US Towns Through High‐Resolution Agent‐Based Modeling

“…[5][6][7] In the first phase of the pandemic, models have been largely adopted to conduct what-if analyses on the effect of nonpharmaceutical interventions (NPIs) for the containment of the spread, [8][9][10][11][12] also considering their socio-economic and psychological impact. [13][14][15] More recently, models are gaining traction as decision support systems to design efficient vaccination campaigns. [16][17][18][19][20][21][22][23][24] Effective vaccine rollout strategies are the solution of complex optimization problems, due to limited availability of vaccines, differential effectiveness and adverse effects across age strata and fragility profiles, time constraints on double-dose administration, and distribution issues.…”

Effects of social-distancing on infectious disease dynamics: an evolutionary game theory and economic perspective

“…The lack of economic perspective has been identified as a serious drawback of the multiple COVID-19 mathematical models that have emerged since the beginning of the pandemic [46]. The question is so important that it has driven some preliminary studies with limited mathematical discussion [12]. Our goal here is to develop mathematical model frameworks, as well as the mathematical techniques to study these types of frameworks (an aspect that has been hitherto omitted in some of the earlier models that considered coupled diseaseeconomic dynamics), and then use the developed framework and tools to understand the interplay between infectious diseases, human response to disease control measures, and the associated economic impact.…”

“…In this section, we want to understand the dynamical behaviour of model (12). For analytical tractability, we set α = 0.5.…”

“…Figure 9. Simulations of the model(12) showing the dynamics of (a) the proportion of infectious individuals (i(t)), (b) the proportion of individuals who are strictly social-distancing (x(t)), and (c) the capital per effective worker (k(t)) as a function of time for different initial conditions. The parameter values used for the simulations are δ 0 = 0.01 per day, r s = 0.2 per day, c = 2.7, μ = 0.01 per day, γ = 0.5 per day, β = 2.7 per day, ρ = 1 per day, s = 0.5 per day, d = 0.4 per day.…”

Weekly Bayesian Modelling Strategy to Predict Deaths by COVID-19: a Model and Case Study for the State of Santa Catarina, Brazil

“…With no drug treatments or vaccines in sight at that time and in the face of a lack of national measures to prevent the spread of the disease 4,5 , governors and mayors had to decide independently on the implementation of non-pharmacological measures (NPI) 6 . In the midst of this scenario and despite the harsh inherent challenges of epidemic modeling, mathematical models offered a timely approach to help understand the regional dynamics of contagion of the disease and to predict how this health crisis could unfold in the weeks and months that followed [7][8][9][10][11][12][13][14][15][16] .…”