Calculation of Epidemic First Passage and Peak Time Probability Distributions

TL;DR

This paper develops analytical and numerical methods to estimate the distribution of epidemic peak times, combining stochastic modeling with deterministic ODEs to improve accuracy and computational efficiency in outbreak forecasting.

Contribution

It introduces a novel approach that integrates stochastic first passage analysis with ODE models to predict epidemic peak timing distributions efficiently.

Findings

Accurately approximates the distribution of epidemic peak times.

Reduces computational cost compared to full Monte Carlo simulations.

Provides a practical tool for epidemic forecasting and public health planning.

Abstract

Understanding the timing of the peak of a disease outbreak forms an important part of epidemic forecasting. In many cases, such information is essential for planning increased hospital bed demand and for designing of public health interventions. The time taken for an outbreak to become large is inherently stochastic, and therefore uncertain, but after a sufficient number of infections has been reached the subsequent dynamics can be modelled accurately using ordinary differential equations. Here, we present analytical and numerical methods for approximating the time at which a stochastic model of a disease outbreak reaches a large number of cases and for quantifying the uncertainty arising from demographic stochasticity around that time. We then project this uncertainty forwards in time using an ordinary differential equation model in order to obtain a distribution for the peak timing of the epidemic that agrees closely with large simulations but that, for error tolerances relevant to most realistic applications, requires a fraction of the computational cost of full Monte Carlo approaches.