Sociodemographic Variables Can Guide Prioritized Testing Strategies for Epidemic Control in Resource-Limited Contexts

• Published in: The Journal of infectious diseases Vol. 228; no. 9; pp. 1189 - 1197
• Main Authors: Evans, Michelle V, Ramiadantsoa, Tanjona, Kauffman, Kayla, Moody, James, Nunn, Charles L, Rabezara, Jean Yves, Raharimalala, Prisca, Randriamoria, Toky M, Soarimalala, Voahangy, Titcomb, Georgia, Garchitorena, Andres, Roche, Benjamin
• Format: Journal Article
• Language: English
• Published: United States Oxford University Press 02.11.2023
• Subjects: Coronaviruses; COVID-19 - diagnosis; COVID-19 - epidemiology; Disease Susceptibility; Epidemics; Humans; Major; Network topologies; Public Health; SARS-CoV-2; Severe acute respiratory syndrome coronavirus 2; Social networks; Social organization; Sociodemographics; COVID-19; social network; Madagascar; epidemic control
• ISSN: 0022-1899; 1537-6613
• DOI: 10.1093/infdis/jiad076

Abstract Background Targeted surveillance allows public health authorities to implement testing and isolation strategies when diagnostic resources are limited, and can be implemented via the consideration of social network topologies. However, it remains unclear how to implement such surveillance and control when network data are unavailable. Methods We evaluated the ability of sociodemographic proxies of degree centrality to guide prioritized testing of infected individuals compared to known degree centrality. Proxies were estimated via readily available sociodemographic variables (age, gender, marital status, educational attainment, household size). We simulated severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) epidemics via a susceptible-exposed-infected-recovered individual-based model on 2 contact networks from rural Madagascar to test applicability of these findings to low-resource contexts. Results Targeted testing using sociodemographic proxies performed similarly to targeted testing using known degree centralities. At low testing capacity, using proxies reduced infection burden by 22%–33% while using 20% fewer tests, compared to random testing. By comparison, using known degree centrality reduced the infection burden by 31%–44% while using 26%–29% fewer tests. Conclusions We demonstrate that incorporating social network information into epidemic control strategies is an effective countermeasure to low testing capacity and can be implemented via sociodemographic proxies when social network data are unavailable. In empirical social networks from rural Madagascar, epidemic simulations show that targeted testing guided by sociodemographic characteristics controls epidemics as effectively as targeted testing guided by known social network characteristics.