Targeting interventions for the control of infectious diseases relies on accurate information about the location and dynamics of human populations. Surprisingly, accurate estimates of these basic measures are almost completely lacking, particularly in low-income settings. In this context, even identifying at-risk populations and measuring changes in disease incidence are challenging, which means that allocating scarce resources effectively is difficult for control programs.
Digital data is increasingly ubiquitous, however, offering a scalable solution to this critical information gap. Data from mobile phones and other electronic devices provide – for the first time – scalable, real-time estimates of the distribution, density, and movement patterns of entire populations. We are exploring the use of mobile phone data to quantify human population dynamics in relation to the spread and control of infectious diseases, working in partnership with mobile operators world-wide and control programs to create sustainable analytical pipelines for disease control. We have previously shown, for example, that mobile phone data can be combined with epidemiological models to:
- Identify sources and sinks of imported malaria (Wesolowski et al. 2012)
- Quantify seasonal drivers of rubella in Kenya (Wesolowski et al. 2015)
- Forecast dengue outbreaks in Pakistan (Wesolowski et al. 2015)
- Locate areas with low vaccine and ante-natal care uptake (Wesolowski et al. 2015)
- Estimate population displacement following an earthquake in Nepal (Wilson et al. 2016)
Our group focuses on combining mobility estimates with other metadata, particularly pathogen genomics, to measure the spatial spread of diseases. We are also interested in how city structure impacts human travel patterns, and how this determines epidemic risks and containment strategies. We have published multiple studies on this topic, including on methodological challenges and the sustainability of these approaches.
Funding: R35 NIGMS