Ian Macadam

Dengue is a major public health concern in tropical and sub-tropical areas of the world. Dengue virus is transmitted to humans primarily by female Aedes aegypti mosquitoes and its epidemiology is heavily influenced by the climate. We developed a mechanistic compartmental model to characterize how climate and the evolution of population-level immunity influence changes in the epidemiology of dengue. The model explicitly incorporates aquatic and adult stages of the mosquito vector and cross-immunity (to another serotype) to better represent the transmission of dengue compared to previous models. It incorporates parameters related to mosquito development, behaviour, and mortality that are dependent on temperature, rainfall, and humidity. We calibrated the model to available dengue seroprevalence estimates for Dhaka, Bangladesh as it is a well-observed densely populated endemic area for dengue and will be strongly impacted by climate change. We then simulated dengue epidemiology for the period of 1995-2014 and compared the simulation output with available epidemiologic data. The model produced repeating annual outbreaks of dengue with strong seasonality, as observed in the dengue case reports for Dhaka. The median number of annual dengue infections produced by the model was 1.6 million cases (IQR: 0.6-2.3 million) and the median of the annual daily peak size of dengue infections was 24,121 (IQR: 7,807-39,308) across the simulations. These figures while exceeding reported cases in Dhaka, which do not include asymptomatic infections or cases not seeking healthcare, align with previous estimates. The modelled seroprevalence of 62% (IQR: 58– 75%) aligned with survey results from Dhaka. In most model simulations, dengue infections occurred during August to December with the median peak occurring in October. The model outputs demonstrated a high degree of conformity to the reported data in terms of seasonality, and seroprevalence, indicating that the model provides a good representation of overall dengue epidemiology in Dhaka. By accounting for climate variables, host immunity, and their interplay, our model is well-suited for evaluating the long-term impact of climate change on dengue transmission in Dhaka and other settings where climate and epidemiological data are available.

To help meet increasing demands for high‐resolution climate change projections in the Philippines, this study provides the results of multiple dynamically downscaled climate model simulations for projected changes in rainfall and temperature over the country by the mid‐21st century (2036–2065) relative to the baseline period (1971–2000), under the RCP8.5 scenario. The model‐simulated seasonal means of temperature, rainfall, and low‐level wind patterns were first compared with observations during the baseline period. Comparisons made between the model‐derived and APHRODITE observation‐based gridded temperature and rainfall data indicate that the dynamically downscaled simulations provide an overall improvement from their driving global climate models in capturing the spatial patterns of rainfall over the country, and the spatial and temporal characteristics of the country's mean temperature. Future climate projections show that the country's climate is expected to become warmer by the mid‐21st century, with a multi‐model ensemble mean increase of 1.2 to 1.9°C, relative to the baseline period, projected for many parts of the country and across most seasons. Slightly higher increases are projected during the country's hottest season, March–April–May. However, there are large differences in the models' projected rainfall changes by the mid‐21st century across seasons and regions. For most parts of the country, the multi‐model ensemble includes simulations that show increases and simulations that show decreases in rainfall. Nevertheless, there is a tendency of model projections towards wetter conditions over northern and central sections of the country (particularly in the December–January–February season) and drier conditions in the southern region of the country in almost all seasons. The results demonstrate the need for communities in the Philippines to adapt to a future warmer climate and prepare for a range of possible future changes in rainfall and temperature.

The Philippines is one of the most exposed countries in the world to tropical cyclones. In order to provide information to help the country build resilience and plan for a future under a warmer climate, we build on previous research to investigate implications of future climate change on tropical cyclone activity in the Philippines. Experiments were conducted using three regional climate models with horizontal resolutions of approximately 12 km (HadGEM3‐RA) and 25 km (HadRM3P and RegCM4). The simulations are driven by boundary data from a subset of global climate model simulations from the CMIP5 ensemble. Here we present the experimental design, the methodology for selecting CMIP5 models, the results of the model validation, and future projections of changes to tropical cyclone frequency and intensity by the mid‐21st century. The models used are shown to represent the key climatological features of tropical cyclones across the domain, including the seasonality and general distribution of intensities, but issues remain in resolving very intense tropical cyclones and simulating realistic trajectories across their life‐cycles. Acknowledging model inadequacies and uncertainties associated with future climate model projections, the results show a range of plausible changes with a tendency for fewer but slightly more intense tropical cyclones. These results are consistent with the basin‐wide results reported in the IPCC AR5 and provide clear evidence that the findings from these previous studies are applicable in the Philippines region.