A sociotechnical approach to predictive modeling of climate health

Climate change impacts health through increased extreme weather events, higher risks of heat-related illnesses, respiratory disorders, water and vector-borne diseases, food and water insecurity, and population displacement. This project will develop knowledge on how to work across disciplines such as data science, public health, meteorology, and information systems to develop predictive models that anticipate the impact of climate change on health. Such predictive models are an important part of future information systems for monitoring and addressing the impacts of climate change on human health and its implications for public health systems and policies. Developing these systems requires sensitivity to the sociotechnical context, knowing what questions to ask, and mapping the needs of stakeholders. The postdoc will be part of the DHIS2 for Climate and Health project (https://dhis2.org/climate/why-dhis2/), engage in action research, and develop skills and knowledge of how data science researchers can ensure that models and systems meet user needs.    

The candidate applying for this postdoc position should have a background in the social sciences or information systems (IS), be a qualitative researcher, and have experience working design-oriented in trying to understand informants and their needs as users of information systems. A background in or knowledge of sociotechnical design or design thinking is considered an advantageous starting point for working in a user-centric manner with the development of ML-based models and systems. It is preferable if the candidate has experience with or is comfortable with stakeholder management. Climate health solutions require the involvement of stakeholders from many different areas, including - but not limited to - public health, epidemiology, ecology, medicine, meteorology and climatology, and various governmental agencies. Climate health is an interdisciplinary field, and innovative solutions based on predictive models and ML more broadly require an integrated approach that involves stakeholders from these disciplines to understand, predict, prevent, and respond to the health challenges posed by the changing climate.

The topic of climate health and the development of solutions to address the health challenges posed by climate change will be approached from a socio-technical perspective through qualitative research.