Urban Vector-Borne Pathogens in Tropical Cities Under Climate Change

A special issue of Tropical Medicine and Infectious Disease (ISSN 2414-6366). This special issue belongs to the section "Vector-Borne Diseases".

Deadline for manuscript submissions: 30 September 2026 | Viewed by 1928

Special Issue Editor

Special Issue Information

Dear Colleagues,

There has been a substantial effort focusing on how climate change will impact vector-borne diseases globally and regionally, most especially emphasizing the changing exposure and disease burden distributions. One neglected aspect is how climate change will impact the burden at the urban scale and that specifically in the tropics and subtropics and what mitigation and adaptation strategies are being performed and should be developed to alleviate this burden. Cities generate their own climate to an extent, and it is unclear how global climate change and urban-specific climate, especially in the tropics, interact to impact VBDs. Crucial aspects to this concern urban development and what can be done not only to alleviate the recognized problems of urban heat and pollution islands but also how to simultaneously plan for mitigating VBDs. This Special Issue therefore aims to fill this gap by addressing (i) how climate change is anticipated to impact subtropical and tropical cities, most notably from a socio-demographic viewpoint in the context of projections under the different Shared Socioeconomic Pathways, with specific respect to the VBD burden; (ii) what the consequences are for the intra-urban VBD burden that can already be seen and what is expected to occur in the near future; and (iii) what mitigation and adaptation strategies can be envisaged, including those that focus on the sprawling peri-urban development we are currently witnessing. Papers addressing case studies on specific cities with respect to the VBD burden as well as mitigation and adaptation strategies will be most welcome, as will more general papers addressing the relative contributions of climate change vs. the urban climate on VBDs. Whilst the focus is for the most part on mosquito-borne pathogens, including both human-specific pathogens and zoonoses, other arthropod vectors, including other dipteran vectors and ticks, will be especially welcome.

Dr. Rick E. Paul
Guest Editor

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Keywords

  • vector-borne diseases
  • urban climate
  • climate change
  • mitigation and adaptation strategies

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Published Papers (1 paper)

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19 pages, 7373 KB  
Article
District-Level Dengue Early Warning Prediction System in Bangladesh Using Hybrid Explainable AI and Bayesian Deep Learning
by Md. Abu Bokkor Shiddik, Farzana Zannat Toshi, Sadia Yesmin and Md. Siddikur Rahman
Trop. Med. Infect. Dis. 2026, 11(3), 73; https://doi.org/10.3390/tropicalmed11030073 - 5 Mar 2026
Viewed by 1534
Abstract
Dengue is a mosquito-borne viral disease which is predominantly endemic in tropical and subtropical countries. In Bangladesh, 321,179 dengue cases were reported in 2023, followed by 101,214 cases in 2024, which highlights a severe and ongoing public health challenge. Dengue transmission risks are [...] Read more.
Dengue is a mosquito-borne viral disease which is predominantly endemic in tropical and subtropical countries. In Bangladesh, 321,179 dengue cases were reported in 2023, followed by 101,214 cases in 2024, which highlights a severe and ongoing public health challenge. Dengue transmission risks are shaped by climatic variability, rapid urbanization, socio-economic vulnerability, and healthcare strain. But existing dengue surveillance models remain limited in their ability to capture district-level disparities in Bangladesh. This study aimed to develop a district-level dengue early warning system that integrates climatic, socio-demographic, economic, healthcare, and environmental determinants to generate accurate and interpretable predictions. We examined dengue cases across all 64 districts in Bangladesh from 2017 to 2024, integrating Directorate General of Health Services (DGHS) case records with climate, socio-demographic, economic, and healthcare indicators. Machine learning and deep learning approaches, including Multi-Layer Perceptron (MLP) and Convolutional Long Short-Term Memory (ConvLSTM), were combined with SHAP (Shapley Additive Explanations)-based explainable artificial intelligence. We also used Bayesian spatio-temporal models to capture spatial clustering, temporal dependence, and the lagged transmission effects of dengue. Dengue outbreaks peaked in September 2023, with Dhaka recording 113,233 cases. DENV-4 (Dengue Virus type 4) emerged in 2022, accounting for 27% of infections in 2023. Climate was the strongest predictor of dengue transmission (humidity SHAP = 0.314; minimum temperature SHAP = 0.146; rainfall RR = 1.303). Poverty (SHAP = 0.193) and healthcare capacity (nursing/midwifery density SHAP = 0.073) mostly contributed to dengue prediction. The MLP model achieved the best yearly performance (accuracy = 0.93; ROC-AUC = 0.99), ConvLSTM was the best model in monthly prediction (recall = 0.88; ROC-AUC = 0.81), and Bayesian BYM2_RW2 with lagged effects improved predictive fit (DIC = 3671.055). Our integrated framework delivers transparent, interpretable predictions and district-level early warnings, supporting adaptive dengue outbreak preparedness and resource allocation in Bangladesh. Full article
(This article belongs to the Special Issue Urban Vector-Borne Pathogens in Tropical Cities Under Climate Change)
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