Identifying outbreak risk factors through case-controls comparisons

Nina H Fefferman^{1

2

3

4}, Michael J Blum⁵, Lydia Bourouiba⁶, Nathaniel L Gibson⁵, Qiang He^{7

8}, Debra L Miller⁹, Monica Papeș⁵, Dana K Pasquale^{10

11}, Connor Verheyen⁶, Sadie J Ryan^{12

13

14}

Affiliations

¹ Department of Ecology and Evolutionary Biology, University of Tennessee, Knoxville, TN, USA. nfefferm@utk.edu.
² National Institute for Mathematical and Biological Synthesis, University of Tennessee, Knoxville, TN, USA. nfefferm@utk.edu.
³ Department of Mathematics, University of Tennessee, Knoxville, TN, USA. nfefferm@utk.edu.
⁴ One Health Initiative, School of Natural Resources and College of Veterinary Medicine, University of Tennessee, Knoxville, TN, USA. nfefferm@utk.edu.
⁵ Department of Ecology and Evolutionary Biology, University of Tennessee, Knoxville, TN, USA.
⁶ The Fluid Dynamics of Disease Transmission Laboratory, Fluids and Health Network, Massachusetts Institute of Technology, Cambridge, MA, USA.
⁷ Department of Civil and Environmental Engineering, University of Tennessee, Knoxville, TN, USA.
⁸ Institute for a Secure and Sustainable Environment, University of Tennessee, Knoxville, TN, USA.
⁹ One Health Initiative, School of Natural Resources and College of Veterinary Medicine, University of Tennessee, Knoxville, TN, USA.
¹⁰ Duke Department of Population Health Sciences, Duke University School of Medicine, Durham, NC, USA.
¹¹ Duke Network Analysis Center, Duke University, Durham, NC, USA.
¹² Department of Geography, Quantitative Disease Ecology and Conservation (QDEC) Lab, University of Florida, Gainesville, FL, USA. sjryan@ufl.edu.
¹³ Emerging Pathogens Institute, University of Florida, Gainesville, FL, USA. sjryan@ufl.edu.
¹⁴ College of Life Sciences, University of KwaZulu-Natal, Durban, South Africa. sjryan@ufl.edu.

PMID: 40447926
PMCID: PMC12125396
DOI: 10.1038/s43856-025-00916-5

Review

Identifying outbreak risk factors through case-controls comparisons

Nina H Fefferman et al. Commun Med (Lond). 2025.

. 2025 May 30;5(1):210.

doi: 10.1038/s43856-025-00916-5.

Authors

Affiliations

¹ Department of Ecology and Evolutionary Biology, University of Tennessee, Knoxville, TN, USA. nfefferm@utk.edu.
² National Institute for Mathematical and Biological Synthesis, University of Tennessee, Knoxville, TN, USA. nfefferm@utk.edu.
³ Department of Mathematics, University of Tennessee, Knoxville, TN, USA. nfefferm@utk.edu.
⁴ One Health Initiative, School of Natural Resources and College of Veterinary Medicine, University of Tennessee, Knoxville, TN, USA. nfefferm@utk.edu.
⁵ Department of Ecology and Evolutionary Biology, University of Tennessee, Knoxville, TN, USA.
⁶ The Fluid Dynamics of Disease Transmission Laboratory, Fluids and Health Network, Massachusetts Institute of Technology, Cambridge, MA, USA.
⁷ Department of Civil and Environmental Engineering, University of Tennessee, Knoxville, TN, USA.
⁸ Institute for a Secure and Sustainable Environment, University of Tennessee, Knoxville, TN, USA.
⁹ One Health Initiative, School of Natural Resources and College of Veterinary Medicine, University of Tennessee, Knoxville, TN, USA.
¹⁰ Duke Department of Population Health Sciences, Duke University School of Medicine, Durham, NC, USA.
¹¹ Duke Network Analysis Center, Duke University, Durham, NC, USA.
¹² Department of Geography, Quantitative Disease Ecology and Conservation (QDEC) Lab, University of Florida, Gainesville, FL, USA. sjryan@ufl.edu.
¹³ Emerging Pathogens Institute, University of Florida, Gainesville, FL, USA. sjryan@ufl.edu.
¹⁴ College of Life Sciences, University of KwaZulu-Natal, Durban, South Africa. sjryan@ufl.edu.

PMID: 40447926
PMCID: PMC12125396
DOI: 10.1038/s43856-025-00916-5

Abstract

Outbreaks are typically investigated using approaches that aim to identify place- and context-dependent causative factors. As the focus is on understanding the basis of a specific outbreak, the resulting narratives are rarely suitable for forecasting risk or developing generalizable predictive and preventative measures. This Perspective article proposes applying a case-control framework as an outbreak epidemiological study design to promote evidence-based decision-making for prevention and response to outbreaks. The approach involves identifying counterfactuals, with case-control comparisons drawn to test hypotheses about conditions that manifest outbreaks. First, a framework is described for iterative multidisciplinary interrogation to elucidate and identify minimally sufficient sets of factors that lead to disease outbreaks. Next, example case-control comparison frameworks are discussed, centered on pathogen(s), influential contributor(s), or landscape(s), illustrated with examples focused on pathogen transmission.

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Conflict of interest statement

Competing interests: The authors declare no competing interests.

Figures

**Box Fig. 1**
The global dataset described in established to understand the role of indoor climate in COVID-19 deaths. The aggregated global dataset was stratified in color-coded regions (map) to investigate the effects of low indoor relative humidity (RH) in contrast to moderate and high RH.

See this image and copyright information in PMC

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Identifying outbreak risk factors through case-controls comparisons

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Identifying outbreak risk factors through case-controls comparisons

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Conflict of interest statement

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