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.

Fig 1. Compartmental model.

Each box represents an individual compartment and the arrows represent transitions between the compartments. The individuals in every compartment are divided by age, as represented by the cyan bars, their pre-pandemic productivity, as represented by the dotted lines, and by changes to their pandemic productivity, as represented by the background color of the boxes. White boxes represent normal pre-pandemic productivity, dead individuals have no productivity, which is represented in red, isolated individuals have decreased productivity, which is represented in yellow, and hospitalized individuals have no productivity and incur treatment costs, which are also represented in red. As individuals recover, they receive a small boost in productivity due to their acquired immunity if they are in isolation, which is represented in green.

Fig 2. Outcomes in the baseline scenario.

(A)Number of infected non-senior individuals. (B) Number of non-senior deaths. (C) Total hospitalizations. The gray dotted line marks the hospital capacity strain level and the black dotted line marks the saturation level. (D) Number of infected senior individuals. (E) Number of senior deaths. (F) Change in economic output compared to the pre-pandemic state.

Fig 3. Sudden release of isolation measures after outbreak containment.

Completely relaxing the isolation measures after the outbreak is controlled but before completely eradicating the virus leads to a second outbreak. Isolation measures are re-enforced after the second outbreak takes place and hospitals become saturated. (A) Number of infected non-seniors. (B) Number of non-senior deaths. (C) Total hospitalizations. The gray dotted line marks the hospital capacity strain level and the black dotted line marks the saturation level. (D) Number of infected seniors. (E) Number of senior deaths. (F) Change in economic output compared to the pre-pandemic state. The vertical blue dotted lines mark the time at which isolation policies are lifted and the vertical red lines mark the time at which they are re-enforced.

Fig 4. Progressive release of the non-senior population with extremely strict isolation measures for the seniors.

(A) Number of infected non-seniors. (B) Non-senior deaths. (C) Total hospitalizations. The gray dotted line marks the hospital capacity strain level and the black dotted line marks the saturation level. (D) Number of infected seniors. (E) Number of senior deaths. (F) Change in economic output compared to the pre-pandemic state. (A-F) The black curve represents the scenario where the isolation policies for the seniors are extreme. The red curve represents the scenario where the policy is weakly implemented and the isolation measures for the senior population are not extreme. The vertical blue dotted lines mark the time at which isolation policies are lifted.

Fig 5. Comparison of all scenarios.

(A) Peak number of hospitalizations. (B) Non-senior deaths. (C) Senior deaths. (D) Net change in productivity.

Fig 6. Sensitivity analysis.

a-c. Total hospitalizations, deaths, and net change in economic productivity for one and a half years depending on the control of contagiousness of asymptomatic (β) and hospitalized (ϵ) individuals. d-f. Total hospitalizations, deaths, and net change in economic productivity for one and a half years depending on the strictness of enforcement of isolation policies. g-i. Total hospitalizations, deaths, and net change in economic productivity for one and a half years depending on the relative importance of the economic situation in the public policies. The blue dotted vertical lines mark the values used in the baseline scenario.