Real-time forecasting of infectious disease dynamics with a stochastic semi-mechanistic model

TLDR

Real‑time infectious disease forecasts aid public health planning, yet parameterising models in real time is challenging because behavioural changes, interventions, and transmission routes are often unknown. The study presents a semi‑mechanistic model applied in real time to the 2013–2016 West African Ebola epidemic and demonstrates its fit to a 2015 Ebola Forecasting Challenge using simulated data. The authors developed this semi‑mechanistic model for real‑time forecasting, evaluated its performance across scenarios, and identified its strengths and shortcomings. The results suggest that combining mechanistic structure with flexible uncertainty handling can provide valuable tools for future outbreak response.

Abstract

Real-time forecasts of infectious diseases can help public health planning, especially during outbreaks. If forecasts are generated from mechanistic models, they can be further used to target resources or to compare the impact of possible interventions. However, paremeterising such models is often difficult in real time, when information on behavioural changes, interventions and routes of transmission are not readily available. Here, we present a semi-mechanistic model of infectious disease dynamics that was used in real time during the 2013–2016 West African Ebola epidemic, and show fits to a Ebola Forecasting Challenge conducted in late 2015 with simulated data mimicking the true epidemic. We assess the performance of the model in different situations and identify strengths and shortcomings of our approach. Models such as the one presented here which combine the power of mechanistic models with the flexibility to include uncertainty about the precise outbreak dynamics may be an important tool in combating future outbreaks.