In infectious disease transmission, regression and variable selection for time-to-event outcomes are complicated by transmission-induced dependence that violates standard independence assumptions. Pairwise survival analysis addresses this dependence by modeling contact intervals in ordered pairs, defined as the time fr...
Jeong-Min Lee, Patrick M. Schnell, G. Rempała et al.· 0 citations
Mathematical biology has long relied on mechanistic models, including ordinary and partial differential equations, stochastic systems, and agent-based models, to study biological processes across scales. These approaches remain central because they provide structure, interpretability, and biological insight. However, m...
Kobra Rabiei, G. Rempała, R. Laubenbacher et al.· Bulletin of Mathematical Bio...· 0 citations
Horvitz--Thompson (HT) estimators can provide unbiased daily estimates of infectious disease prevalence under repeated surveillance by correcting for nonrandom testing induced by scheduled, symptom-based, and contact-tracing components. However, because each HT estimate is based on the testing data available for that d...
Jeong-Min Lee, G. Rempała, Patrick M. Schnell· 0 citations
A counterfactual framework is developed that links the observation process to a hypothetical process in which infection is prevented, and enables unbiased estimation of disease prevalence by modeling the testing process, possibly nonparametrically, without requiring explicit modeling of transmission dynamics.
Jeong-Min Lee, Junke Yang, G. Rempała et al.· 1 citation
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