ABSTRACT
Targeted surveillance allows public health authorities to implement testing and isolation strategies when diagnostic resources are limited. When transmission patterns are determined by social contact rates, the consideration of social network topologies in testing schemes is one avenue for targeted surveillance, specifically by prioritizing those individuals likely to contribute disproportionately to onward transmission. Yet, it remains unclear how to implement such surveillance and control when network data is unavailable, as is often the case in resource-limited settings. We evaluated the efficiency of a testing strategy that targeted individuals based on their degree centrality on a social network compared to a random testing strategy in the context of low testing capacity. We simulated SARS-CoV-2 dynamics on two contact networks from rural Madagascar and measured the epidemic duration, infection burden, and tests needed to end the epidemics. In addition, we examined the robustness of this approach when individuals true degree centralities were unknown and were instead estimated via readily-available socio-demographic variables (age, gender, marital status, educational attainment, and household size). Targeted testing reduced the infection burden by between 5 - 50% at low testing capacities, while requiring up to 28% fewer tests than random testing. Further, targeted tested remained more efficient when the true network topology was unknown and prioritization was based on socio-demographic characteristics, demonstrating the feasibility of this approach under realistic conditions. Incorporating social network topology into epidemic control strategies is an effective public health strategy for health systems suffering from low testing capacity and can be implemented via socio-demographic proxies when social networks are unknown. *French abstract available in Supplemental Materials
