Modeling the spread of the Zika virus using topological data analysis

Area of Science:

• Epidemiology
• Computational Biology
• Topology

Background:

• Zika virus (ZIKV) emerged in Brazil in 2015, prompting a global health emergency declaration by the WHO.
• Traditional epidemiological models rely on state-level data like population density and temperature to predict disease transmission.
• The Aedes aegypti mosquito is the primary vector for ZIKV transmission.

Purpose of the Study:

• To introduce and evaluate topological data analysis (TDA) as a novel method for predicting ZIKV spread.
• To demonstrate the potential of TDA in enhancing epidemiological assessments of vector-borne diseases.
• To bridge the gap between advanced mathematical techniques and public health disease modeling.

Main Methods:

• Application of the Vietoris-Rips filtration on high-density Aedes aegypti mosquito location data in Brazil.
• Construction of simplicial complexes from filtered data to represent spatial relationships.
• Extraction of homology group generators to identify significant topological features.
• Evaluation of the TDA model using ZIKV case data across Brazilian states.

Main Results:

• The study successfully generated topological features from mosquito location data.
• The developed TDA model showed promise in predicting ZIKV transmission patterns.
• Results indicate that TDA can provide valuable insights beyond traditional epidemiological metrics.
• The approach demonstrated improved assessment capabilities for vector-borne disease spread.

Conclusions:

• Topological data analysis presents a powerful, previously underutilized tool for epidemiological modeling.
• This methodology offers a more nuanced understanding of disease dynamics by analyzing spatial data structure.
• The findings support the integration of TDA into future strategies for monitoring and predicting vector-borne diseases like ZIKV.