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With the coronavirus disease 2019 (COVID-19) pandemic, one of the major concerns is the direct medical cost and resource use burden imposed on the US health care system. We developed a Monte Carlo simulation model that represented the US population and what could happen to each person who got infected. We estimated resource use and direct medical costs per symptomatic infection and at the national level, with various “attack rates” (infection rates), to understand the potential economic benefits of reducing the burden of the disease. A single symptomatic COVID-19 case could incur a median direct medical cost of $3,045 during the course of the infection alone. If 80 percent of the US population were to get infected, the result could be a median of 44.6 million hospitalizations, 10.7 million intensive care unit (ICU) admissions, 6.5 million patients requiring a ventilator, 249.5...

Covid-19 induced job losses occurred predominantly in industries with intensive worker-client interaction as well as in pink-collar and blue-collar occupations. We study the ability of fiscal policy to stabilize employment by occupation and industry during the Covid-19 crisis. We use a multi-sector, multi-occupation macroeconomic model and investigate different fiscal policy instruments that help the economy recover faster. We show that fiscal stimuli foster job growth for hard-hit pink-collar workers, whereas stimulating blue-collar job creation is more challenging. A cut in labor taxes performs best in stabilizing total employment and the employment composition.

The Covid-19 pandemic has motivated a myriad of studies and proposals on how economic policy should respond to this colossal shock. But participants in this debate seldom recognize that the health shock is not entirely exogenous. Its magnitude and dynamics themselves depend on economic policies, and the explicit or implicit incentives those policies provide. To illuminate the feedback loops between medical and economic factors we develop a minimal economic model of pandemics. In the model, as in reality, individual decisions to comply (or not) with virus-related public health directives depend on economic variables and incentives, which themselves respond to current economic policy and expectations of future policies. The analysis yields several practical lessons: because policies affect the speed of virus transmission via incentives, public health measures and economic policies can...

Background The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) was first reported in China, which caused a respiratory disease known as Coronavirus Disease 2019 (COVID-19). Since its discovery, the virus has spread to over 100 countries and claimed more than 4000 deaths. This study aimed to assess the effectiveness and cost-effectiveness of various response public health measures. Method The stochastic agent-based model was used to simulate the process of COVID-19 outbreak in scenario I (imported one case) and II (imported four cases) with a series of public health measures, involving the personal protection, isolation-and-quarantine, gathering restriction, and community containment. The virtual community was constructed following the susceptible-latent-infectious-recovered framework. The epidemiological and economic parameters derived from the previous literature and...