Short-term forecasting of wildfire spread: A network epidemiology approach

Abstract

Wildfire spread poses substantial environmental and public-health risks, motivating interpretable models for short-term forecasting. We develop a statistical framework that combines cellular automata with ideas from network epidemiology to model wildfire evolution across a spatial lattice. Each grid cell is classified as available, burning, or consumed. State transitions distinguish spread from burning neighbors, intrinsic ignition, and cessation of burning, with transition rates linked to meteorological and environmental covariates. A likelihood-based estimation procedure yields transition-specific covariate effects and probabilistic forecasts of cell states. We assess forecasting performance in a simulation study and in applications to the 2018 California wildfires and the 2019-2020 Australian wildfires. We also compare the method with a published forecasting approach using the 2017 Haypress fire. The results show strong short-term discrimination in many settings, with reduced accuracy at longer forecast horizons and during abrupt fire expansion. The framework provides an interpretable basis for studying wildfire dynamics and identifies opportunities to improve ignition forecasts through richer spatial and observation models.