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Modularity Maximization to Design Contiguous Policy Zones for Pandemic Response.

ABSTRACT: The health and economic devastation caused by the COVID-19 pandemic has created a significant global humanitarian disaster. Pandemic response policies guided by geospatial approaches are appropriate additions to traditional epidemiological responses when addressing this disaster. However, little is known about finding the optimal set of locations or jurisdictions to create policy coordination zones. In this study, we propose optimization models and algorithms to identify coordination communities based on the natural movement of people. To do so, we develop a mixed-integer quadratic-programming model to maximize the modularity of detected communities while ensuring that the jurisdictions within each community are contiguous. To solve the problem, we present a heuristic and a column-generation algorithm. Our computational experiments highlight the effectiveness of the models and algorithms in various instances. We also apply the proposed optimization-based solutions to identify coordination zones within North Carolina and South Carolina, two highly interconnected states in the U.S. Results of our case study show that the proposed model detects communities that are significantly better for coordinating pandemic related policies than the existing geopolitical boundaries.

SUBMITTER: Baghersad M

PROVIDER: S-EPMC8755430 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature

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Modularity maximization to design contiguous policy zones for pandemic response.

Baghersad Milad M Emadikhiav Mohsen M Huang C Derrick CD Behara Ravi S RS

European journal of operational research 20220113 1

The health and economic devastation caused by the COVID-19 pandemic has created a significant global humanitarian disaster. Pandemic response policies guided by geospatial approaches are appropriate additions to traditional epidemiological responses when addressing this disaster. However, little is known about finding the optimal set of locations or jurisdictions to create policy coordination zones. In this study, we propose optimization models and algorithms to identify coordination communities ...[more]

PMID: 35039709

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Project description:The COVID-19 pandemic has had an enormous toll on human health and well-being and led to major social and economic disruptions. Public health interventions in response to burgeoning case numbers and hospitalizations have repeatedly bent down the epidemic curve, effectively creating a feedback control system. Worst case scenarios have been avoided in many places through this responsive feedback. We aim to formalize and illustrate how to incorporate principles of feedback control into pandemic projections and decision-making, and ultimately shift the focus from prediction to the design of interventions. Starting with an epidemiological model for COVID-19, we illustrate how feedback control can be incorporated into pandemic management using a simple design that couples recent changes in case numbers or hospital occupancy with explicit policy restrictions. We demonstrate robust ability to control a pandemic using a design that responds to hospital cases, despite simulating large uncertainty in reproduction number R0 (range: 1.04-5.18) and average time to hospital admission (range: 4-28 days). We show that shorter delays, responding to case counts versus hospital measured infections, reduce both the cumulative case count and the average level of interventions. Finally, we show that feedback is robust to changing compliance to public health directives and to systemic changes associated with variants of concern and with the introduction of a vaccination program. The negative impact of a pandemic on human health and societal disruption can be reduced by coupling models of disease propagation with models of the decision-making process. In contrast to highly varying open-loop projections, incorporating feedback explicitly in the decision-making process is more reflective of the real-world challenge facing public health decision makers. Using feedback principles, effective control strategies can be designed even if the pandemic characteristics are highly uncertain, encouraging earlier and smaller actions that reduce both case counts and the extent of interventions.

Project description:BackgroundRegional National Weather Service (NWS) heat advisory criteria in New York State (NYS) were based on frequency of heat events estimated by sparse monitoring data. These may not accurately reflect temperatures at which specific health risks occur in large geographic regions. The objectives of the study were to use spatially resolved temperature data to characterize health risks related to summertime heat exposure and estimate the temperatures at which excessive risk of heat-related adverse health occurs in NYS. We also evaluated the need to adjust current heat advisory threshold and messaging based on threshold temperatures of multiple health outcomes.MethodsWe assessed the effect of multi-day lag exposure for maximum near-surface air temperature (Tmax) and maximum Heat Index derived from the gridded National Land Data Assimilation System (NLDAS) reanalysis dataset on emergency department (ED) visits/ hospitalizations for heat stress, dehydration, acute kidney failure (AKF) and cardiovascular diseases (CVD) using a case-crossover analysis during summers of 2008-2012. We assessed effect modification using interaction terms and stratified analysis. Thresholds were estimated using piecewise spline regression.ResultsWe observed an increased risk of heat stress (Risk ratio (RR) = 1.366, 95% confidence interval (CI): 1.347, 1.386) and dehydration (RR = 1.024, 95% CI: 1.021, 1.028) for every 1 °C increase in Tmax on the day of exposure. The highest risk for AKF (RR = 1.017, 95% CI: 1.014, 1.021) and CVD (RR = 1.001, 95% CI: 1.000, 1.002) were at lag 1 and 4 respectively. The increased risk of heat-health effects persists up to 6 days. Rural areas of NYS are at as high a risk of heat-health effects as urban areas. Heat-health risks start increasing at temperatures much lower than the current NWS criteria.ConclusionReanalysis data provide refined exposure-response functions for health research, in areas with sparse monitor observations. Based on this research, rural areas in NYS had similar risk for health effects of heat. Heat advisories in New York City (NYC) had been reviewed and lowered previously. As such, the current NWS heat advisory threshold was lowered for the upstate region of New York and surrounding areas. Enhanced outreach materials were also developed and disseminated to local health departments and the public.

Dataset Information

Modularity Maximization to Design Contiguous Policy Zones for Pandemic Response.

Publications

Modularity maximization to design contiguous policy zones for pandemic response.

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