ArticlePloS one2018
Modeling the spread of the Zika virus using topological data analysis.
Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
8 citing papers in PubMed.
- Article
- Nonlinear time series analysis of state-wise COVID-19 in Malaysia using wavelet and persistent homology.Scientific reports · 2024Article
- Topological data analysis in biomedicine: A review.Journal of biomedical informatics · 2022Review
- Noise robustness of persistent homology on greyscale images, across filtrations and signatures.PloS one · 2021Article
- Article
- Article
- Vector distribution and transmission risk of the Zika virus in South and Central America.PeerJ · 2019Article
- A roadmap for the computation of persistent homology.EPJ data science · 2017Article
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Zika virus (ZIKV), a disease spread primarily through the Aedes aegypti mosquito, was identified in Brazil in 2015 and was declared a global health emergency by the World Health Organization (WHO). Epidemiologists often use common state-level attributes such as population density and temperature to determine the spread of disease. By applying techniques from topological data analysis, we believe that epidemiologists will be able to better predict how ZIKV will spread. We use the Vietoris-Rips filtration on high-density mosquito locations in Brazil to create simplicial complexes, from which we extract homology group generators. Previously epidemiologists have not relied on topological data analysis to model disease spread. Evaluating our model on ZIKV case data in the states of Brazil demonstrates the value of these techniques for the improved assessment of vector-borne diseases.
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