One of the most effective ways to understand the spread of disease is through a spatially-enabled surveillance system. Public health surveillance systems have been used to effectively provide locational awareness and response to hazardous events and disease outbreak around the world. The purpose of this project is to develop and implement a surveillance system that will allow us to detect clusters and monitor the transmission of diseases in real-time across London-Middlesex, while accounting for how clustering of cases changes over time.
This project aims to address the following research questions:
- How does the spread and clustering of COVID-19 cases change over time?
- What individual, social, and physical environmental factors are associated with clusters of COVID-19cases?
- Can we detect emerging clusters of COVID-19 in real-time using a geospatial portal? If yes, does this detection offer value in guiding local public health policy?How can this information be effectively reported to healthcare partners (e.g.,primary care)?