Lead supervisor: Hua Lu, British Antarctic Survey
Co-supervisor: Matt Osman, Geography; Paul Kattuman, Judge Business School; Andrew Orr, British Antarctic Survey, Philip Bett, MetOffice
Brief summary:
This project aims to develop a dynamic statistical model to improve understanding of the impact of extreme weather events on health service demand in two targeted regions of India and the UK, which will be used to develop an early warning system to enable the timely scheduling of preventive strategies to alleviate weather-induced healthcare strain.
Importance of the area of research concerned:
Extreme weather events are increasingly being observed around the globe. For example, England issued its first ever Red extreme heat warning in 2022, with temperatures exceeding 40°C. India has also seen an increase in extreme temperature and rainfall. For example, extreme rainfall in Northern India during the 2023 monsoon season resulted in flash floods and landslides, with multiple loss of lives and catastrophic damage to infrastructure. These events pose acute challenges to healthcare systems in both the UK and India. A recent risk assessment report identified 11 climate-linked health risks, seven of which are considered as high-priority (Betts et al., 2021). It’s estimated that weather-triggered illnesses in the UK, including respiratory and cardiovascular diseases, could increase five to tenfold in the next 40 years (Rizmie et al., 2022), leading to increased demands on healthcare services. Given this, it's essential to better understand how extreme weather events impact the operation of health services. It is also vital to develop reliable early warning systems that minimize the impacts of an extreme weather event to ensure that health services are better prepared.
Project summary :
This project will develop an early warning system to forecast healthcare needs arising due to extreme weather in two target regions in northwest India and eastern England. These regions are chosen as they differ in terms of the extreme events and the associated weather patterns, as well as their health system infrastructure/responsiveness. Using climate data and weather forecasts, the project will investigate the characteristics of temperature and precipitation extremes, including their frequency, intensity, and causes. Using a dynamic statistical model that was previous used in COVID-19 planning (Harvey and Kattuman, 2021), this information will be combined with healthcare data (e.g., hospital admissions, mortality rates) to develop understanding between extreme weather and health impacts. This model will be used to forecast the impact of extreme weather on health service demand.
What will the student do?:
You will analyse observed and simulated weather data to investigate extreme temperature and precipitation events in two target regions in northwest India and eastern England. You will refine an existing dynamic statistical model that links these events to disease outbreaks. Specifically, you will:
- Apply extreme value distributions to pinpoint weather extremes and use atmosphere reanalysis datasets and output from high-resolution regional climate model simulations to understand their underlying causes.
- Examine the relationships between weather extremes and health metrics like GP visits and number of hospitalizations.
- Improve the accuracy of the dynamic statistical model by employing relevant weather variables as lead indicators for predicting weather-induced health crises, such as surges in cardiovascular or respiratory admissions.
- Utilize Met Office ensemble hindcasts/forecasts to set confidence intervals in the prediction of extreme weather events.
- Conduct sensitivity tests to validate the robustness of the impacts estimated by the dynamic statistical model.
- Create dashboards / trackers based on these forecasts, broken down by disease type, age, and region.
References - references should provide further reading about the project:
Betts, R. A., Haward, A. B. and Pearson, K. V. The Third UK Climate Change Risk Assessment Technical Report, prepared for the Climate Change Committee, London, 2021.
Rizmie, D., de Preux, L., Miraldo, M., and Atun, R. 2022. Impact of extreme temperatures on emergency hospital admissions by age and socio-economic deprivation in England, Social Science & Medicine, 308, 115193.
Harvey, A., and P. Kattuman. 2021. A Farewell to R: Time series models for tracking and forecasting epidemics. Journal of the Royal Society Interface. 18, 20210179.
Applying
You can find out about applying for this project on the British Antarctic Survey (BAS) page.