The rates and patterns of social mixing differ across cultures. But, there has been no standardized multi-site study of social mixing patterns in low and middle-income countries. Data are needed from these regions so that realistic models can be developed. Using two methods, we are studying contact patterns in rural and urban areas in Guatemala, Pakistan, India and Mozambique. Participants in the GlobalMix are keeping diaries in which they write down their social contacts. A group of participants are also wearing proximity sensors which collect highly detailed data on contacts made in households. This study will lead to more realistic models infectious diseases and for evaluating interventions like vaccination.

We often think about the health impacts of influenza. But, pandemics also have the potential to disrupt business operations and can even affect the global economy. In the CorporateMix project, we are studying the social contact patterns of participating employees from a number of large companies in the United States. Like in GlobalMix, contacts are being recorded using diaries and wearable proximity sensors. We will use these data to develop infectious disease models and to assess the effectiveness of strategies such as teleworking to slow the spread of a influenza pandemic.

The spread of disease is normally discuss within the context of the general population but communal living spaces and healthcare facilities can facilitate rapid disease transmission. In the Nursing Home Mix project, we are studying the social contact patterns of participating employees from several nursing homes in Georgia. Like in GlobalMix and CorporateMix, contacts are being recorded using diaries and wearable proximity sensors. We will use these data to develop infectious disease models to assess the effectiveness of control strategies to slow the spread of COVID-19 in nursing homes.

Patterns of human contact in households, in communities, and across regions determine how infectious diseases spread and modulate the impact of control measures. However, our understanding of how human contact and mobility patterns shape disease risk is limited. This knowledge gap is most acute in low-and middle-income country populations, where the burden of infectious diseases is highest. The purpose of this project is to develop a new platform that will integrate human contact and mobility data with infection history to build tractable, realistic models of disease transmission. We will demonstrate the range and utility of this platform by modeling (1) geographic variation in rotavirus incidence post-vaccine introduction and (2) social distancing and travel restrictions to reduce spread of SARS-CoV-2.