Improving estimates of social contact patterns for the airborne transmission of respiratory pathogens

Abstract

AbstractBackgroundData on social contact patterns are widely used to parameterise age-mixing matrices in mathematical models of infectious diseases designed to help understand transmission patterns or estimate intervention impacts. Despite this, little attention is given to how social contact data are collected and analysed, or how the types of contact most relevant for transmission may vary between different infections. In particular, the majority of studies focus on close contacts only – people spoken to face-to-face. This may be appropriate for infections spread primarily by droplet transmission, but it neglects the larger numbers of ‘shared air’ casual contacts who may be at risk from airborne transmission of pathogens such as Mycobacterium tuberculosis, measles, and SARS-CoV-2.MethodsWe conducted social contact surveys in communities in two provinces of South Africa in 2019 (KwaZulu-Natal and Western Cape). In line with most studies, we collected data on people spoken to (close contacts). We also collected data on places visited and people present, allowing casual contact patterns to be estimated. Using these data, we estimated age mixing patterns relevant for i) droplet and ii) non-saturating airborne transmission. We also estimated a third category of pattern relevant for the transmission of iii) Mycobacterium tuberculosis (Mtb), an airborne infection where saturation of household contacts plays an important role in transmission dynamics.ResultsEstimated contact patterns by age did not vary greatly between the three transmission routes/infections, in either setting. In both communities, relative to other adult age groups, overall contact intensities were lower in 50+ year olds when considering contact relevant for non-saturating airborne transmission or the transmission of Mycobacterium tuberculosis than when considering contact relevant for droplet transmission.ConclusionsOur findings provide some reassurance that the widespread use of close contact data to parameterise age-mixing matrices for transmission models of airborne infections may not be resulting in major inaccuracies. The contribution of older age groups to transmission may be over-estimated, however. There is a need for future social contact surveys to collect data on casual contacts, to investigate whether our findings can be generalised to a wider range of settings, and to improve model predictions for infections with substantial airborne transmission.