Respiratory and other close-contact infectious diseases, such as TB, measles and pneumonia, are major killers in much of the developing world. Understanding how the diseases spread and for identifying how best to control it can be tackled by modelling the spread diseases. Although central to the models, few quantitative data are available on relevant contact patterns, and no study to measure these factors has yet been attempted in developing countries.

We have originally exploited device connectivity traces from the real world for modelling social network structure. The initial motivation was providing delay tolerant networks among smart phones formed by people. The empirical study of contact networks shares many issues with network-based epidemiology, and our work has been extended towards understanding the epidemic spread of infectious diseases. Capturing human interactions will provide an empirical, quantitative measurement of social mixing patterns to underpin mathematical models of the spread of close-contact diseases.

I will describe remote sensing platform to collect human mobility data using RFID sensors, Raspberry Pis and mobile phones, recording proximity, to gather information on human interactions in rural and urban communities in developing countries.