Leveraging social network topology could improve the efficiency of SARS-CoV-2 epidemic control strategies in resource-limited contexts

Abstract

BackgroundSuperspreading infections play an important role in the SARS-CoV-2 pandemic. Superspreading is caused primarily by heterogeneity in social contact rates, and therefore represents an opportunity for targeting surveillance and control via consideration of social network topologies, particularly in resource-limited settings. Yet, it remains unclear how to implement such surveillance and control, espeically when network data is unavailable.MethodsWe evaluated the efficiency of a testing strategy that targeted potential superspreading 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.FindingsTargeted testing of potential superspreaders reduced the infection burden by 40-63% at low testing capacities, while requiring between 45-78% fewer tests compared to random testing. Further, targeted testing remained more efficient when the true network topology was unknown and prioritization was based on socio-demographic characteristics.InterpretationIncorporating 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.FundingThis research was funded by the Agence Nationale de la Recherche, a NIH-SSF-NIFA Ecology and Evolution of Infectious Disease Award (No. 1R01-TW011493-01), and a Duke University Provost’s Collaboratory grant.

Morbidity and mortality burden of COVID-19 in rural Madagascar: results from a longitudinal cohort and nested seroprevalence study

“…It is well known that individuals with high social connectivity are at higher risk of infection and can contribute disproportionately to the spread of diseases such as COVID-19 [63][64][65]. While research on at-risk occupational activities in Africa has mostly focused on healthcare workers [25], a better understanding of the role played by other socioeconomic groups with high mobility and social connections on COVID-19 spread could open new possibilities for disease control [66].…”

A blueprint for a new commercial driving epidemiology: An emerging paradigm grounded in integrative exposome and network epistemologies

“…94 [95][96][97][98] These meganodes interact among themselves in various ways, while similar processes and connections exist within nodes themselves-especially within the WLE-CD. The arrangement and connections (edges) of nodes, their spatial patterns, and especially their emergent topological properties (or network topology 99,100 ) are of foremost importance in unravelling relational patterns and causal structures of drivers' health and safety risks. Figure 1 represents a heuristic portrayal of commercial drivers' health and safety risks as CANoNs.…”