Fine-scale spatial clustering of measles nonvaccination that increases outbreak potential is obscured by aggregated reporting data

Nina B. Masters (), Marisa C. Eisenberg, Paul L. Delamater, Matthew Kay, Matthew L. Boulton, Matthew L. Boulton and Jon Zelner ()
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Nina B. Masters: Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, MI 48109
Marisa C. Eisenberg: Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, MI 48109
Paul L. Delamater: Department of Geography, University of North Carolina, Chapel Hill, NC 27514
Matthew Kay: School of Information, University of Michigan, Ann Arbor, MI 48104
Matthew L. Boulton: Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, MI 48109; Department of Internal Medicine, Division of Infectious Disease, University of Michigan Medical School, Ann Arbor, MI 48109
Matthew L. Boulton: Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, MI 48109; Department of Internal Medicine, Division of Infectious Disease, University of Michigan Medical School, Ann Arbor, MI 48109
Jon Zelner: Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, MI 48109; Center for Social Epidemiology and Population Health, University of Michigan School of Public Health, Ann Arbor, MI 48109

Proceedings of the National Academy of Sciences, 2020, vol. 117, issue 45, 28506-28514

Abstract: The United States experienced historically high numbers of measles cases in 2019, despite achieving national measles vaccination rates above the World Health Organization recommendation of 95% coverage with two doses. Since the COVID-19 pandemic began, resulting in suspension of many clinical preventive services, pediatric vaccination rates in the United States have fallen precipitously, dramatically increasing risk of measles resurgence. Previous research has shown that measles outbreaks in high-coverage contexts are driven by spatial clustering of nonvaccination, which decreases local immunity below the herd immunity threshold. 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 are reported at the state-level—a resolution too coarse to detect community-level clustering of nonvaccination characteristic of recent outbreaks. In this paper, we perform a series of computational experiments to assess the impact of clustered nonvaccination on outbreak potential and magnitude of bias in predicting disease risk posed by measuring vaccination rates at coarse spatial scales. We find that, when nonvaccination is locally clustered, reporting aggregate data at the state- or county-level can result in substantial underestimates of outbreak risk. The COVID-19 pandemic has shone a bright light on the weaknesses in US infectious disease surveillance and 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 that finer-scale vaccination data should be collected to prevent a return to endemic measles transmission in the United States.

Keywords: measles; epidemiology; simulation model; disease dynamics; vaccination clustering (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:nas:journl:v:117:y:2020:p:28506-28514

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