Epidemic Forecasting on Networks: Bridging Local Samples with Global Outcomes

Yeganeh Alimohammadi; Christian Borgs; Remco van der Hofstad; Amin Saberi

doi:10.1287/opre.2023.0524

Epidemic Forecasting on Networks: Bridging Local Samples with Global Outcomes

Yeganeh Alimohammadi¹; Christian Borgs²; Remco van der Hofstad³; Amin Saberi⁴

¹ Marshall School of Business, University of Southern California, Los Angeles, California 90089 · ² Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, California 94720 · ³ Department of Mathematics and Computer Science, Eindhoven University of Technology, 5612 AZ Eindhoven, Netherlands · ⁴ Department of Management Science and Engineering, Stanford University, Stanford, California 94305

Operations Research 2026

Epidemic Forecasting on Networks: Bridging Local Samples with Global Outcomes Forecasting how an epidemic will unfold, its trajectory and eventual size, usually presumes access to the full contact network. In practice, no one ever has the full mapping of networks. New research shows that full mapping is unnecessary. By observing only the local neighborhoods around a small number of individuals, the kind of partial data that can actually be collected during an epidemic, the authors prove that global outcomes, such as the eventual size of an epidemic, can be predicted with rigorous accuracy guarantees. The work formally connects what public health planners can observe locally—for example, connections revealed by contact tracing and similar methods—to how an epidemic behaves globally, giving them a principled way to turn limited, realistic data into reliable forecasts.

DOI: 10.1287/opre.2023.0524
Language: en
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