Repository containing series of simulation files to generate set of heterogeneous environments for paper evaluating the role of clustering of non-vaccination on measles outbreak potential and how aggregating such data can lead to bias in predicting outbreaks (both probability and size). Paper can be found at: www.pnas.org/cgi/doi/10.1073/pnas.2011529117

Abstract: The US experienced historically high numbers of measles cases in 2019, despite achieving national measles vaccination coverage above the WHO recommendation of 95%. Since the arrival of the COVID-19 pandemic earlier this year resulting in suspension of many clinical preventive services, pediatric vaccination rates in the US have fallen precipitously, dramatically increasing the risk of national measles resurgence. Previous research has shown that measles outbreaks in high-coverage contexts are driven by spatial clustering of non-vaccination, which decreases local immunity below the herd immunity threshold. Nationwide drops in coverage suggest that more areas are likely to experience outbreaks in the aftermath of the COVID-19 pandemic. However, little is known about how to best conduct surveillance and target interventions to detect and address these high-risk areas, and most vaccination data is reported at the state level – a resolution too coarse to detect the community-level clustering of non-vaccination characteristic of many recent outbreaks. In this paper, we perform a series of computational experiments to assess the impact of clustering of non-vaccination on outbreak potential and the magnitude of bias in predicting disease risk posed by measuring vaccination rates at coarse spatial scales. We find that when non-vaccination is locally clustered, reporting data aggregated to the state- or county-level can result in substantial underestimates of the risk of large outbreaks. The COVID-19 pandemic has shone a bright light on the weaknesses in U.S. infectious disease surveillance, as well as a broader gap in our understanding of how to best use detailed spatial data to interrupt and control infectious disease transmission. Our research clearly outlines the types of data that should be collected to prevent a return to endemic measles transmission in the U.S.