Data Challenge - Quantifying the Impact of Extreme Weather on Antimicrobial Resistance using Synthetic Control Methods

Extreme weather events (EWE) and antimicrobial resistance (AMR) are among the most pressing global health threats. While both have increased in frequency, severity, and burden over the past decade, the impact of extreme weather on AMR remains poorly quantified. Understanding this relationship is essential for informing adaptive public health responses.
Our objectives are to (1) quantify the causal effects of EWE on AMR across pathogens and regions, (2) identify organisms most sensitive to disruptions due to EWE, and (3) develop an interactive, user-friendly dashboard to visualize AMR-weather dynamics across space and time, supporting exploration by researchers and policymakers.
We will integrate global AMR surveillance data from Pfizer’s ATLAS dataset, Shionogi’s SIDERO-WT, and Venatorx’s GEARS alongside high-resolution climate data from, e.g., NOAA’s Storm Events Database, NASA's Disasters Database, and International Best Track Archive for Climate Stewardship database. We focus on countries and U.S. states repeatedly affected by heavy precipitation, flooding, and tropical storms, e.g., Puerto Rico, Costa Rica, the Dominican Republic, Vietnam, and the Philippines.
We propose a quasi-experimental design based on generalized synthetic control methods. Our outcomes include proportions, incidence, and distributions of MICs among phenotypically resistant isolates. To address non-linearities and biases, outcomes will undergo appropriate transformations such as logarithmic scaling. Our primary exposure variable will be the occurrence of EWE. Time-varying covariates such as antimicrobial consumption, healthcare infrastructure, population density, economic indicators, and climate-based variables (e.g., El Niño and the Southern Oscillation index, sea surface temperatures, and atmospheric pressure anomalies) will inform the construction of synthetic controls from unaffected regions, allowing robust estimation of counterfactual AMR trajectories. This project addresses a critical gap at the intersection of climate change and infectious disease. By combining causal methods, global surveillance data, and interactive tools, we aim to generate policy-relevant insights for AMR preparedness in a changing climate.