An explainable covariate compartmental model for predicting the spatio-temporal patterns of dengue in Sri Lanka
(2025) In PLoS Computational Biology 21(9).- Abstract
A majority of all infectious diseases manifest some climate-sensitivity. However, many of those sensitivities are not well understood as meteorological drivers of infectious diseases co-occur with other drivers exhibiting complex non-linear influences and feedback. This makes it hard to dissect their individual contributions. Here we apply a novel deep learning Explainable AI (XAI) compartment model with covariate drivers and dynamic feedback to predict and explain the dengue incidence across Sri Lanka. We compare the compartmental Susceptible-Exposed-Infected-Recovered (SEIR) model to a deep learning model without a compartmental structure. We find that the covariate compartmental hybrid model performs better and can describe drivers... (More)
A majority of all infectious diseases manifest some climate-sensitivity. However, many of those sensitivities are not well understood as meteorological drivers of infectious diseases co-occur with other drivers exhibiting complex non-linear influences and feedback. This makes it hard to dissect their individual contributions. Here we apply a novel deep learning Explainable AI (XAI) compartment model with covariate drivers and dynamic feedback to predict and explain the dengue incidence across Sri Lanka. We compare the compartmental Susceptible-Exposed-Infected-Recovered (SEIR) model to a deep learning model without a compartmental structure. We find that the covariate compartmental hybrid model performs better and can describe drivers of the dengue spatiotemporal incidence over time. The strongest drivers in our model in order of importance are precipitation, socio-demographics, and normalized vegetation index. The novel method demonstrated can be used to leverage known infectious disease dynamics while accounting for the influence of other drivers and different population immunity contexts. While allowing for interpretation of the covariate driver influences, the approach bridges the gap between dynamical compartmental and data driven dynamical models.
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- author
- Liu, Yichao ; Fransson, Peter ; Heidecke, Julian ; Liyanage, Prasad ; Wallin, Jonas LU and Rocklöv, Joacim
- organization
- publishing date
- 2025-09
- type
- Contribution to journal
- publication status
- published
- subject
- in
- PLoS Computational Biology
- volume
- 21
- issue
- 9
- article number
- e1013540
- publisher
- Public Library of Science (PLoS)
- external identifiers
-
- pmid:41004532
- scopus:105017993103
- ISSN
- 1553-7358
- DOI
- 10.1371/journal.pcbi.1013540
- language
- English
- LU publication?
- yes
- id
- 00d7387e-3c95-4d98-abc6-5d6a8dfad01a
- date added to LUP
- 2025-11-27 11:41:06
- date last changed
- 2025-12-11 13:08:35
@article{00d7387e-3c95-4d98-abc6-5d6a8dfad01a,
abstract = {{<p>A majority of all infectious diseases manifest some climate-sensitivity. However, many of those sensitivities are not well understood as meteorological drivers of infectious diseases co-occur with other drivers exhibiting complex non-linear influences and feedback. This makes it hard to dissect their individual contributions. Here we apply a novel deep learning Explainable AI (XAI) compartment model with covariate drivers and dynamic feedback to predict and explain the dengue incidence across Sri Lanka. We compare the compartmental Susceptible-Exposed-Infected-Recovered (SEIR) model to a deep learning model without a compartmental structure. We find that the covariate compartmental hybrid model performs better and can describe drivers of the dengue spatiotemporal incidence over time. The strongest drivers in our model in order of importance are precipitation, socio-demographics, and normalized vegetation index. The novel method demonstrated can be used to leverage known infectious disease dynamics while accounting for the influence of other drivers and different population immunity contexts. While allowing for interpretation of the covariate driver influences, the approach bridges the gap between dynamical compartmental and data driven dynamical models.</p>}},
author = {{Liu, Yichao and Fransson, Peter and Heidecke, Julian and Liyanage, Prasad and Wallin, Jonas and Rocklöv, Joacim}},
issn = {{1553-7358}},
language = {{eng}},
number = {{9}},
publisher = {{Public Library of Science (PLoS)}},
series = {{PLoS Computational Biology}},
title = {{An explainable covariate compartmental model for predicting the spatio-temporal patterns of dengue in Sri Lanka}},
url = {{http://dx.doi.org/10.1371/journal.pcbi.1013540}},
doi = {{10.1371/journal.pcbi.1013540}},
volume = {{21}},
year = {{2025}},
}