Nonparametric survival analysis of epidemic data

TL;DR

This paper introduces nonparametric methods for analyzing epidemic contact interval data, utilizing the Nelson-Aalen estimator and EM algorithm to estimate transmission hazards without assuming specific parametric forms.

Contribution

It develops a novel nonparametric approach for survival analysis in epidemics, extending chain-binomial models to continuous time and handling unobserved transmission pathways.

Findings

Estimator is unbiased when who-infects-whom is known

EM algorithm converges to a nonparametric MLE

Methods effectively analyze influenza transmission data

Abstract

This paper develops nonparametric methods for the survival analysis of epidemic data based on contact intervals. The contact interval from person i to person j is the time between the onset of infectiousness in i and infectious contact from i to j, where we define infectious contact as a contact sufficient to infect a susceptible individual. We show that the Nelson-Aalen estimator produces an unbiased estimate of the contact interval cumulative hazard function when who-infects-whom is observed. When who-infects-whom is not observed, we average the Nelson-Aalen estimates from all transmission networks consistent with the observed data using an EM algorithm. This converges to a nonparametric MLE of the contact interval cumulative hazard function that we call the marginal Nelson-Aalen estimate. We study the behavior of these methods in simulations and use them to analyze household surveillance data from the 2009 influenza A(H1N1) pandemic. In an appendix, we show that these methods extend chain-binomial models to continuous time.