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Abstract ObjectivesUnderstanding disease transmission is a fundamental challenge in ecology. We used transmission potential networks to investigate whether a gastrointestinal protozoan (Blastocystisspp.) is spread through social, environmental, and/or zoonotic pathways in rural northeast Madagascar. Materials and MethodsWe obtained survey data, household GPS coordinates, and fecal samples from 804 participants. Surveys inquired about social contacts, agricultural activity, and sociodemographic characteristics. Fecal samples were screened forBlastocystisusing DNA metabarcoding. We also tested 133 domesticated animals forBlastocystis. We used network autocorrelation models and permutation tests (networkk‐test) to determine whether networks reflecting different transmission pathways predicted infection. ResultsWe identified six distinctBlastocystissubtypes among study participants and their domesticated animals. Among the 804 human participants, 74% (n = 598) were positive for at least oneBlastocystissubtype. Close proximity to infected households was the most informative predictor of infection with any subtype (model averaged OR [95% CI]: 1.56 [1.33–1.82]), and spending free time with infected participants was not an informative predictor of infection (model averaged OR [95% CI]: 0.95 [0.82–1.10]). No human participant was infected with the same subtype as the domesticated animals they owned. DiscussionOur findings suggest thatBlastocystisis most likely spread through environmental pathways within villages, rather than through social or animal contact. The most likely mechanisms involve fecal contamination of the environment by infected individuals or shared food and water sources. These findings shed new light on human‐pathogen ecology and mechanisms for reducing disease transmission in rural, low‐income settings.
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Embodied Participatory Simulations of Disease as an Entry Point for Network Analysis
Participatory simulations of disease spread were conducted using wearable computers (badges). Participants interacted (simulating various forms of social network exchange) without knowing whether exchange partners were infected. Afterwards, the NetLogo modeling environment was used to visualize the network. In class discussion, the impact on the social group of different members being infected was explored. This balanced network growth dynamics with disease spread dynamics.
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- Award ID(s):
- 1652372
- PAR ID:
- 10311983
- Date Published:
- Journal Name:
- Constructionism
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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