Prediction of invasion from the early stage of an epidemic

TL;DR

This paper develops and tests simple, robust methods to predict the invasion probability of epidemics, demonstrated through experiments and models, emphasizing early-stage assessment and the predictive value of small-scale epidemic data.

Contribution

It introduces parsimonious methodologies for early epidemic invasion prediction, validated on experimental and simulated data, highlighting their reliability and practical utility.

Findings

Simple methods can reliably predict invasion probability.

Small-scale epidemic data have significant predictive power.

Framework enables quick early-stage epidemic threat assessment.

Abstract

Predictability of undesired events is a question of great interest in many scientific disciplines including seismology, economy, and epidemiology. Here, we focus on the predictability of invasion of a broad class of epidemics caused by diseases that lead to permanent immunity of infected hosts after recovery or death. We approach the problem from the perspective of the science of complexity by proposing and testing several strategies for the estimation of important characteristics of epidemics, such as the probability of invasion. Our results suggest that parsimonious approximate methodologies may lead to the most reliable and robust predictions. The proposed methodologies are first applied to analysis of experimentally observed epidemics: invasion of the fungal plant pathogen \emph{Rhizoctonia solani} in replicated host microcosms. We then consider numerical experiments of the SIR (susceptible-infected-removed) model to investigate the performance of the proposed methods in further detail. The suggested framework can be used as a valuable tool for quick assessment of epidemic threat at the stage when epidemics only start developing. Moreover, our work amplifies the significance of the small-scale and finite-time microcosm realizations of epidemics revealing their predictive power.