Controlling Epidemic Spread: Reducing Economic Losses with Targeted Closures

Abstract

Data on population movements can be helpful in designing targeted policy responses to curb epidemic spread. However, it is not clear how to exactly leverage such data and how valuable they might be for the control of epidemics. To explore these questions, we study a spatial epidemic model that explicitly accounts for population movements and propose an optimization framework for obtaining targeted policies that restrict economic activity in different neighborhoods of a city at different levels. We focus on COVID-19 and calibrate our model using the mobile phone data that capture individuals’ movements within New York City (NYC). We use these data to illustrate that targeting can allow for substantially higher employment levels than uniform (city-wide) policies when applied to reduce infections across a region of focus. In our NYC example (which focuses on the control of the disease in April 2020), our main model illustrates that appropriate targeting achieves a reduction in infections in all neighborhoods while resuming 23.1%–42.4% of the baseline nonteleworkable employment level. By contrast, uniform restriction policies that achieve the same policy goal permit 3.92–6.25 times less nonteleworkable employment. Our optimization framework demonstrates the potential of targeting to limit the economic costs of unemployment while curbing the spread of an epidemic. This paper was accepted by Carri Chan, healthcare management.

Optimal Vaccine Allocation for Pandemic Stabilization

“…. All the epidemic spread models considered in this work are compartmental or network models [8] with "locations" corresponding to neighborhoods, counties, or other geographic subdivisions. Our framework can be applied to general epidemic spread models.…”

Optimizing timetable and network reopen plans for public transportation networks during a COVID19-like pandemic

“…Based on these models, operations management and economics researchers have studied the optimal lockdown or reopen decision making aspects during the pandemic. Birge et al [11] proposed a spatial compartmental model of epidemics (SCME) that was an extension of the works of [3][12] [37].They optimized the neighborhood travel ban/reopen plan policy in New York City. [31] proposed a decision-making model implemented at Yale University.…”

“…(2) Novel formulation of a pandemic in public transit systems. We use a space-time dynamic transit network to capture complicated behaviors such as transfers and waiting at platforms, and we capture the risk of pandemic transmission using a simplified spatial compartmental model of epidemics (SCME) [11] (3) Efficient algorithms for similar problems. Although the full formulation is difficult to solve because of a huge number of variables and constraints, we show that with Lagrangian relaxation, the problem can be decomposed into two subproblems.…”

“…According to pandemic models like SCME [11] models. We know that pandemic like COVID-19 transmission happens when susceptible passengers stay with patients in the same location for a certain period of time.…”

“…If we ignore the walking time inside stations (see assumption 4 in section 3.1), the pandemic transmission happens only when susceptible passengers stay with patients in the same vehicle or on the same platform for a period of time. Using the dynamics from SCME [11], we treat each origin node o ∈ O as the place where population lived, and each in-vehicle travel link and onplatform waiting link as place where transmission happens. We ignore the dead population since our time horizon is short.…”