Global Environmental Change and Infectious Diseases

This axis involves designing and conducting primary research through cohort or case control studies and analyzing secondary data sources such as surveillance and clinical data to better understand the epidemiological profile of certain emerging and epidemic infectious diseases. Multi-level approaches are often used to consider both individual-level and area-level risk factors such as neighbourhood and broader scale environmental/ climatic risk factors. An important part of this research is to understand the significant drivers of disease at the population level and to identify vulnerable communities, which are often marginalized and poor. 

Mosquito-borne diseases are thriving and expanding globally despite decades of large-scale control efforts. This axis aims to understand the patterns of infectious disease spread over time and space in order to help inform targeted intervention programs including education campaigns or vector elimination interventions. An accurate disease forecasting method would allow clinical and public health services to proactively plan and implement control and prevention measures. We often work with high resolution disease surveillance data in conjunction with other data sources such as national surveys, censuses, and environmental/climate data. Various statistical methods are used ranging from time series forecasting, machine learning algorithms such as random forests, to Bayesian spatio-temporal regression models.

Improving the evidence for effective interventions for infectious diseases is an urgent priority in endemic and at-risk regions, particularly environmentally sustainable approaches to control. Mixed method evaluations that include impact evaluation and different evaluations such as process, acceptability, and fidelity, provide essential information that is required for scaling up or out an intervention. For impact evaluations, we use secondary data such as cohort or surveillance data to measure the effectiveness of an intervention.