As extreme weather events become more frequent, understanding their impact on human health becomes increasingly crucial. However, the utilization of Earth Observation to effectively analyze the environmental context in relation to health remains limited. This limitation is primarily due to the lack of fine-grained spatial and temporal data in public and population health studies, hindering a comprehensive understanding of health outcomes. For the years 2019 (pre-COVID) and 2020 (COVID), we collected spatio-temporal datasets for all Lower Layer Super Output Areas in England. These datasets included indicators such as prescriptions associated with seven medical conditions (metabolic, respiratory, and mental health issues), as well as environmental factors (totaling 42 point features and 4 seasonal satellite images, resulting in 44 composite satellite image bands) and sociodemographic features (over 60) linked to these health conditions in existing literature. The availability of these datasets presents an opportunity for the machine learning community to develop new techniques specific to public health challenges.     «

As extreme weather events become more frequent, understanding their impact on human health becomes increasingly crucial. However, the utilization of Earth Observation to effectively analyze the environmental context in relation to health remains limited. This limitation is primarily due to the lack of fine-grained spatial and temporal data in public and population health studies, hindering a comprehensive understanding of health outcomes. For the years 2019 (pre-COVID) and 2020 (COVID), we colle...     »