Dynamic transmission models of infectious diseases are increasingly influential for developing interventions and informing policy. Infectious disease transmissibility and hence, the effectiveness of control strategies, is strongly influenced by social interactions. Consequently, accurate data on social contact rates and mixing patterns are fundamental parameters in the calculation of the force of infection (i.e. the rate of susceptible individuals becoming infected). Despite the strong role social mixing patterns play in the accurate parameterization of mathematical models, these data remain limited, particularly in low and middle-income countries (LMICs). There are also limited data on the social interactions of young infants that are too young to be vaccinated or the diversity in patterns between rural and urban populations at the community level, which are important factors for understanding infectious disease transmission. We propose the first multi-site study with the overall goal to use standardized methods to collect social contact data from urban and rural populations in LMICs. Special focus will be given to study the social interactions of infants less than six months of age. Data will be rigorously collected from four different LMICs: Guatemala, Pakistan, India and Mozambique. We will use standardized social contact diaries to characterize the patterns of social contacts and mixing across the age range in urban and rural LMIC settings. We will also comprehensively profile the social contacts of infants with their household members in LMICs by analyzing high resolution measurements collected using wearable proximity-sensing devices. Moreover, through this project, we will create a database of social mixing data on LMIC populations. We will make this database publicly available using contemporary standards in Open Access data sharing and documentation. These data can be used by infectious disease modelers and other researchers in the biomedical and social science communities.
Mathematical models of infectious diseases are increasingly influential in informing health policy and investment strategies globally. Accurate data on social mixing patterns are critical for the development of valid mathematical models that simulate disease transmission dynamics. Specifically, social contact rates and mixing patterns are a fundamental parameter in the calculation of the force of infection (i.e. the rate of susceptible individuals becoming infected) in disease transmission models and are informed by social mixing studies. However, there has been no standardized multi-site social mixing study conducted in low and middle-income countries that can be used to broadly inform policy in these regions. We will collect and analyze contact data, in both rural and urban settings, in Guatemala, Pakistan, India and Mozambique in order to better parameterize infectious disease models, and thus evaluate infectious disease interventions.