Climate-sensitive infectious diseases (CSIDs) represent a significant and growing challenge to global public health. This field is critically shaped by the interplay of climatic factors, pathogen dynamics, and human and ecological systems. As climate change accelerates, the incidence and geographic distribution of many diseases, particularly vector-borne infections like dengue and malaria, are increasingly influenced by shifts in temperature, precipitation, and extreme weather events. 

The central theme uniting CSID research is the profound impact of environmental variability on disease transmission. These dynamics introduce unique challenges for modelling, particularly in capturing spatiotemporal heterogeneities and integrating the effects of climate change into projections. The complex interplay between disease systems and environmental change necessitates tailored modelling approaches that go beyond traditional frameworks. 

Our research addresses a range of modelling approaches and applications: 

• Understanding climatic drivers of diseases using statistical models: We develop and apply time series regression models to investigate how climatic factors drive the transmission of diseases such as malaria and dengue. These models enhance our understanding of temporal and spatial variations in disease risk which can inform early warning and forecasting systems.
• Assessing the health impacts of climate change: Our work evaluates how climate change affects the distribution and burden of vector-borne and other climate-sensitive diseases, with a particular focus on Southeast Asia and other vulnerable regions.
• Integrating health considerations into climate action plans: We emphasize the importance of incorporating infectious disease impacts into climate adaptation and mitigation strategies, addressing diseases such as chikungunya, Rift Valley fever, and yellow fever.
• Advancing dynamical modelling approaches: We develop and apply dynamic compartmental models to understand the mechanism through which climate affects disease dynamics and suggest how interventions could be used to adapt to changing risk with climate change." 

• Gibb R, et al. Interactions between climate change, urban infrastructure and mobility are driving dengue emergence in Vietnam. Nature communications 14.1 (2023): 8179. https://doi.org/10.1038/s41467-023-43954-0
• Tiley K, et al. Using models and maps to inform Target Product Profiles and Preferred Product Characteristics: the example of Wolbachia replacement. Gates Open Research 7.68 (2024): 68. https://doi.org/10.12688/gatesopenres.14300.3
• Kang H, et al. Chikungunya seroprevalence, force of infection, and prevalence of chronic disability after infection in endemic and epidemic settings: a systematic review, meta-analysis, and modelling study. The Lancet Infectious Diseases (2024). https://doi.org/10.1016/S1473-3099(23)00810-1
• Finch E, et al. Climate variation and serotype competition drive dengue outbreak dynamics in Singapore. medRxiv (2024): 2024-09. https://doi.org/10.1101/2024.09.17.24313793