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

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.

Comparing transmission potential networks based on social network surveys, close contacts and environmental overlap in rural Madagascar.

Social and spatial network analysis is an important approach for investigating infectious disease transmission, especially for pathogens transmitted directly between individuals or via environmental reservoirs. Given the diversity of ways to construct networks, however, it remains unclear how well networks constructed from different data types effectively capture transmission potential. We used empirical networks from a population in rural Madagascar to compare social network survey and spatial data-based networks of the same individuals. Close contact and environmental pathogen transmission pathways were modelled with the spatial data. We found that naming social partners during the surveys predicted higher close-contact rates and the proportion of environmental overlap on the spatial data-based networks. The spatial networks captured many strong and weak connections that were missed using social network surveys alone. Across networks, we found weak correlations among centrality measures (a proxy for superspreading potential). We conclude that social network surveys provide important scaffolding for understanding disease transmission pathways but miss contact-specific heterogeneities revealed by spatial data. Our analyses also highlight that the superspreading potential of individuals may vary across transmission modes. We provide detailed methods to construct networks for close-contact transmission pathogens when not all individuals simultaneously wear GPS trackers.

Food insecurity related to agricultural practices and household characteristics in rural communities of northeast Madagascar.

Ending hunger and alleviating poverty are key goals for a sustainable future. Food security is a constant challenge for agrarian communities in low-income countries, especially in Madagascar. We investigated agricultural practices, household characteristics, and food security in northeast Madagascar. We tested whether agricultural practices, demographics, and socioeconomics in rural populations were related to food security. Over 70% of respondents reported times during the last three years during which food for the household was insufficient, and the most frequently reported cause was small land size (57%). The probability of food insecurity decreased with increasing vanilla yield, rice yield, and land size. There was an interaction effect between land size and household size; larger families with smaller land holdings had higher food insecurity, while larger families with larger land had lower food insecurity. Other socioeconomic and agricultural variables were not significantly related to food insecurity, including material wealth, education, crop diversity, and livestock ownership. Our results highlight the high levels of food insecurity in these communities and point to interventions that would alleviate food stress. In particular, because current crop and livestock diversity were low, agricultural diversification could improve outputs and mitigate food insecurity. Development of sustainable agricultural intensification, including improving rice and vanilla cultivation to raise yields on small land areas, would likely have positive impacts on food security and alleviating poverty. Increasing market access and off-farm income, as well as improving policies related to land tenure could also play valuable roles in mitigating challenges in food security. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s12571-021-01179-3.