title: Real-time forecasting of infectious disease dynamics with a stochastic semi-mechanistic model.
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.
authors.affiliation: Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, United Kingdom. Electronic address: sebastian.funk@lshtm.ac.uk.
authors.forename: Sebastian
authors.initials: S
authors.lastname: Funk
authors.affiliation: Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, United Kingdom.
authors.forename: Anton
authors.initials: A
authors.lastname: Camacho
authors.affiliation: Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, United Kingdom.
authors.forename: Adam J
authors.initials: AJ
authors.lastname: Kucharski
authors.affiliation: Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, United Kingdom.
authors.forename: Rosalind M
authors.initials: RM
authors.lastname: Eggo
authors.affiliation: Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, United Kingdom.
authors.forename: W John
authors.initials: WJ
authors.lastname: Edmunds
mesh_terms: D001499:Bayes Theorem; D058872:Epidemics / Q000706:statistics & numerical data*; D005544:Forecasting; D019142:Hemorrhagic Fever, Ebola / Q000453:epidemiology*; D006801:Humans; D015233:Models, Statistical; D015203:Reproducibility of Results; D013995:Time
keywords: Forecasting; Infectious disease dynamics; Outbreak; Real-time modelling