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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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Spatiotemporal Tracking of SARS-CoV-2 Variants using informative subtype markers and association graphs
Viral subtyping can facilitate visualization and modeling of the geographic distribution and temporal dynamics of disease spread. Understanding the virus's evolution spatiotemporally can help forensic strategies. We have identified mutation variation within SARS-CoV-2 sequences via an entropy measure followed by frequency analysis. These signatures, Informative Subtype Markers (ISMs), define a compact set of nucleotide sites that characterize the most variable (and thus most informative) positions in the viral genomes sequenced from different individuals. Using these ISMs, we show that we can use them for a variety of downstream analyses, such as comparing countries' subtype compositions. We present association graphs as a visualization tool to connect different ISMs based on their co-occurrence across different individuals. In particular, we investigate dominant ISMs for different locations, across different factors such as gender and age.
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- Award ID(s):
- 1936791
- PAR ID:
- 10291896
- Date Published:
- Journal Name:
- Spatiotemporal Tracking of SARS-CoV-2 Variants using informative subtype markers and association graphs
- Page Range / eLocation ID:
- 516 to 519
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/IEEECONF51394.2020.9443496
