Combining immunity and climate date streams to forecast infectious disease transmission

Infectious disease transmission is driven by a complex interplay between population immunity, population behaviour and, in some instances, climate variation. Forecasting models aiming to predict future infectious disease transmission can be highly useful in public health planning and decision-making, and inform early warning and response systems. Forecasting model frameworks are increasingly incorporating multiple data streams to better capture the drivers of infectious disease transmission, and to improve the accuracy and reliability of forecasts. The aim of this PhD is to integrate serological and climate data within statistical and mathematical modelling frameworks to investigate the role of population immunity, climate and control interventions in driving infectious disease dynamics. As a case study, I will focus on two pressing health threats in the Dominican Republic: SARS-CoV-2 and dengue virus. In my first two chapters, I will quantify the risk of SARS-CoV-2 reinfection using longitudinal serological data from the United States. This will then inform the parameterisation of a compartmental model to investigate the drivers of SARS-CoV-2 transmission in the Dominican Republic and simulate possible future epidemic scenarios as vaccination coverage increases. In my third chapter, I will quantify the effect of climate variation on dengue risk in the Dominican Republic using a spatiotemporal Bayesian modelling framework and evaluate the viability of a climate-driven dengue early warning system. Finally, I will develop a transmission dynamic model integrating surveillance data, serological data, and climate data to investigate the drivers of dengue transmission in the Dominican Republic. I will develop and evaluate forecasts for early warning of dengue outbreaks and compare the success of these forecasts with the statistical climate-based approach in the previous chapter. Overall, I aim to integrate novel data streams within modelling frameworks to improve infectious disease forecasting, in an approach that may be generalisable to other Caribbean islands and Small Island Developing States. This research will be developed in close collaboration with the Dirección General de Epidemiologia (DIGEPI) in the Dominican Republic in order to ensure that project outputs align with the public health needs in the Dominican Republic, as well as the needs of key partners such as the US CDC and PAHO/WHO. During this project I am furthering my quantitative analysis skills, in particular my knowledge of mechanistic and statistical modelling, as well as Bayesian inference techniques. Additionally, I am looking forward to enhancing my interdisciplinary skills by integrating insights from climate-driven modelling approaches with mechanistic transmission dynamic modelling methods to improve forecasting of infectious diseases.