Master thesis | Statistical Science for the Life and Behavioural Sciences (MSc)
open access
Reliable forecasting of infectious disease epidemics is critical for decision-making regarding the allocation of public health resources. Until now, efforts have mostly been devoted to...Show moreReliable forecasting of infectious disease epidemics is critical for decision-making regarding the allocation of public health resources. Until now, efforts have mostly been devoted to understanding disease transmission rather than forecasting. The present thesis took a Bayesian approach to forecasting epidemics, focusing on modelling the time between successive observed infection events using a Gamma generalised linear model (GLM). Specifically, a tailored sampler was introduced to solve common convergence problems associated with the use of the inverse link function. The posterior distribution obtained from the Bayesian Gamma GLM was then used to forecast stochastically. This approach was extended to diseases with different transmission dynamics, as described by traditional compartmental epidemiological models: susceptible-infectious (SI), susceptible-infectioussusceptible (SIS) and susceptible-infectious-recovered (SIR). The calibration of the forecasting technique was evaluated using probability integral transform (PIT) histograms in a large simulation study. Results showed that forecasts of SI and SIS-type epidemics in an early growth phase underestimated true future values. Across epidemic types, there was evidence of overdispersion in the forecasts. Furthermore, the method was applied to data from the Meningococcal disease, serogroup W outbreak in the Netherlands between 2012 and 2018. Forecasts suggested the outbreak has reached an equilibrium of approximately 50-55 new observed cases per 6 months. Avenues for future research are provided, with a focus on how we could improve the Bayesian approach and adapt the method to account for covariates of interest.Show less
Master thesis | Statistical Science for the Life and Behavioural Sciences (MSc)
open access
In this project a new approach to forecasting infectious disease epidemics was tested in a simulation and applied to data of the 2014 - 2016 Ebola epidemic. GLMs were applied to the (simulated)...Show moreIn this project a new approach to forecasting infectious disease epidemics was tested in a simulation and applied to data of the 2014 - 2016 Ebola epidemic. GLMs were applied to the (simulated) data, from which the key quantities contact rate and epidemic size could be obtained. With (non-)parametric bootstrapping, the GLM results could be assessed, and the key quantities were obtained and subsequently used to produce forecasts. Forecasting intervals were made to show the accuracy of the forecasts in terms of epidemic size and duration. Simulation results suggested that the method underestimated the eventual epidemic size, and overestimated the contact rate. However, applying the method to a real-life data set resulted in overestimation of the eventual epidemic size. The results of the contact rate for the application on real-life data should be compared to estimates from literature, before a significant meaning can be given to the results. Both simulation and application results gave variable estimates for the epidemic duration, although a positive relation was seen between epidemic size and epidemic length. Estimates for the contact rate could be improved. The major issues with prediction were accountable to exact collinearity introducted by the systematic model; the major issues with forecasting were accountable to extreme estimates of the epidemic size. The cause of both issues lies in the GLMs that were fit to the data.Show less