| Issue |
ITM Web Conf.
Volume 75, 2025
The Second International Conference on Mathematical Analysis and Its Applications (ICONMAA 2024)
|
|
|---|---|---|
| Article Number | 04001 | |
| Number of page(s) | 8 | |
| Section | Statistics and Stochastic Analysis | |
| DOI | https://doi.org/10.1051/itmconf/20257504001 | |
| Published online | 21 February 2025 | |
Implementation of Persistent Homology in Survival Analysis for Correlated and Longitudinal Data: A case study of COVID-19 Spread in Indonesia
Innopolis University, Russia
* e-mail: u.amartiwi@innopolis.university
Most survival models assume no autocorrelation between objects and time-independence. However, in many cases of disease spread, an object can potentially affect its neighbors, and the feature may change over time. To address these issues, we implemented persistent homology, a topological data analysis (TDA) approach, to handle the problems of correlated and longitudinal data. We applied this to the case of the first infection time of COVID-19 in each province in Indonesia, using the human mobility index as a feature. The results show that survival models with persistent homology achieved a higher C-Index than those without persistent homology.
© The Authors, published by EDP Sciences, 2025
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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