Human mobility models to forecast disease dynamics and the effectiveness of public health interventions (MIDAS)
Collaborating with the Johns Hopkins University and the University of Florida, our team are performing systematic analyses of existing mobility data and models to identify which models perform best in specific circumstances.
Using a range of simulations and data from historic outbreaks, we are working with investigators at the collaborating institutions and other researchers at the University of Southampton, to assemble and ensure the accuracy of datasets as well as undertaking key study analyses. We are also refining and developing methods for analysing and integrating health and mobility data from different sources, including mobile network operators, statistical offices, satellite data providers and social media companies.
While there is a growing use of these data sources to analyse mobility patterns, there is a considerable variability in data density and availability. Gaps in important global datasets mean that certain populations and geographies are consistently being missed in analyses – which lead to the generation of biased insights. To quantify the features of these data gaps, we are combining mobility datasets with geospatial covariates to explore different global mobility datasets and potential drivers at varying spatial/temporal scales.
The novel SARS-CoV-2 variants of concern (VOCs) have raised serious concerns about the effectiveness of vaccines and the potential for a new wave of the pandemic. Gaining a better understanding of international travel networks and connectivity will help to inform the potential range of transmission across regions. Thus we are also exploring the communities of population movements between countries and at administrative level-1 units (e.g., provinces or states) across the globe, using de-identified and aggregated mobility data obtained from the movements between countries and at administrative level-1 units (e.g., provinces or states) across the globe, using de-identified and aggregated mobility data obtained from different sources.
- Exploring international travel patterns and connected communities for understanding the spreading risk of VOC Omicron.
- Untangling the changing impact of non-pharmaceutical interventions and vaccination on European COVID-19 trajectories. (Code and data are available at: Vaccine-NPIs-in-EuropeV2)
Related WorldPop projects
- Exploring the Seasonality of COVID-19
- Monitoring outbreak events for disease surveillance in a data science context (MOOD)