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TropicalMed
Special Issues
Surveillance, Modelling, and Risk Mapping of Tropical Infectious Diseases
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Surveillance, Modelling, and Risk Mapping of Tropical Infectious Diseases

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

Deadline for manuscript submissions: 30 June 2026 | Viewed by 2741

Special Issue Editors

Dr. Jinxin Zheng

E-Mail Website
Guest Editor
School of Global Health, Chinese Center for Tropical Diseases Research, Shanghai Jiao Tong University School of Medicine, 280 South Chongqing Rd., Huangpu District, Shanghai 200025, China
Interests: global health; tropical medicine; infectious disease epidemiology; spatial modelling; machine learning; surveillance systems
Dr. Zhaoyu Guo

E-Mail Website
Guest Editor
Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, UK
Interests: global health; One Health; infectious disease; machine learning; public health
Dr. Yeromin Mlacha

E-Mail Website
Guest Editor
Ifakara Health Institute, VM4M+359, Off Mlabani Passage, P.O. Box 53, Ifakara, Tanzania
Interests: vector biology; malaria control; arboviruses (dengue, chikungunya, zika); machine learning; entomological surveillance; insecticide resistance

Special Issue Information

Dear Colleagues,

Tropical infectious diseases—such as malaria, dengue, leishmaniasis, and schistosomiasis—represent significant contributors to global morbidity and mortality, particularly in low- and middle-income countries. In recent years, the integration of surveillance data into spatial and predictive modelling techniques has led to powerful tools for better understanding, forecasting, and responding to these diseases.

This Special Issue will bring together interdisciplinary research focused on innovations in disease surveillance, modelling methodologies (e.g., geostatistics, machine learning, Bayesian frameworks), and risk mapping for tropical infectious diseases. We welcome original research, systematic reviews, and methodological papers that address disease transmission dynamics, spatiotemporal trends, environmental or climate drivers, and applications in public health decision-making.

By advancing the science of surveillance and predictive analytics, this Special Issue will support more effective and equitable disease control strategies and policy interventions.

We warmly invite contributions from researchers, practitioners, and policymakers working across global health, epidemiology, data science, and tropical medicine.

Dr. Jinxin Zheng
Dr. Zhaoyu Guo
Dr. Yeromin Mlacha
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Tropical Medicine and Infectious Disease is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • surveillance systems
  • spatial epidemiology
  • tropical diseases
  • risk mapping
  • predictive modelling
  • machine learning
  • public health decision support
  • infectious disease forecasting
  • One Health

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Published Papers (2 papers)

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Research

14 pages, 1011 KB  
Article
AI-Assisted Differentiation of Dengue and Chikungunya Using Big, Imbalanced Epidemiological Data
by Thanh Huy Nguyen and Nguyen Quoc Khanh Le
Trop. Med. Infect. Dis. 2026, 11(2), 40; https://doi.org/10.3390/tropicalmed11020040 - 30 Jan 2026
Viewed by 1196
Abstract
Dengue and chikungunya are endemic arboviral diseases in many low- and middle-income countries, often co-circulating and presenting with overlapping symptoms that hinder early diagnosis. Timely differentiation is critical, especially in resource-limited settings where laboratory testing is unavailable. We developed and evaluated machine-learning (ML)- [...] Read more.
Dengue and chikungunya are endemic arboviral diseases in many low- and middle-income countries, often co-circulating and presenting with overlapping symptoms that hinder early diagnosis. Timely differentiation is critical, especially in resource-limited settings where laboratory testing is unavailable. We developed and evaluated machine-learning (ML)- and deep-learning (DL) models to classify dengue, chikungunya, and discarded cases using a large-scale, real-world dataset of over 6.7 million entries from Brazil (2013–2020). After applying the Synthetic Minority Oversampling Technique (SMOTE) to address class imbalance, we trained six ML models and one artificial neural network (ANN) using only demographic, clinical, and comorbidity features. The Random Forest model achieved strong multi-class classification performance (Recall: 0.9288, the Area Under the Curve (AUC): 0.9865). The ANN model excelled in identifying chikungunya cases (Recall: 0.9986, AUC: 0.9283), suggesting its suitability for rapid screening. External validation confirmed the generalizability of our models, particularly for distinguishing discarded cases. Our models demonstrate high-accuracy in differentiating dengue and chikungunya using routinely collected clinical and epidemiological data. This work supports the development of Artificial Intelligence-powered decision-support tools to assist frontline healthcare workers in under-resourced settings and aligns with the One Health approach to improving surveillance and diagnosis of neglected tropical diseases. Full article
(This article belongs to the Special Issue Surveillance, Modelling, and Risk Mapping of Tropical Infectious Diseases)
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12 pages, 932 KB  
Article
Spatial Analysis of Drug-Resistant Tuberculosis in Colombia (2020–2023): Departmental Rates, Clusters, and Associated Factors
by Brayan Patiño-Palma, Sandra Chacon-Bambague, Farlhyn Bermudez-Moreno, Carmencita Peña-Briceño, Juan Bustos-Carvajal and Florencio Arias-Coronel
Trop. Med. Infect. Dis. 2025, 10(12), 351; https://doi.org/10.3390/tropicalmed10120351 - 15 Dec 2025
Viewed by 1119
Abstract
Background: Drug-resistant tuberculosis (DR-TB) constitutes a serious threat to global public health due to the increase in strains resistant to multiple drugs, especially isoniazid and rifampicin. This resistance increases mortality, estimated at 25.6% globally, and complicates treatments due to its high toxicity and [...] Read more.
Background: Drug-resistant tuberculosis (DR-TB) constitutes a serious threat to global public health due to the increase in strains resistant to multiple drugs, especially isoniazid and rifampicin. This resistance increases mortality, estimated at 25.6% globally, and complicates treatments due to its high toxicity and cost. Materials and Methods: A quantitative ecological study was carried out with data on drug-resistant tuberculosis reported in Sivigila in the years (2020–2023) SIVIGILA database. 1694 cases were analyzed, considering sociodemographic variables such as age, sex, nationality and prioritized population groups. Departmental rates per 100,000 inhabitants were calculated with DANE projection, from these choropleth maps were developed. Applying a Kulldorff spatial scan under a Poisson model using the SMERC package of R (version 4.5.1), with windows centered on each department and Monte Carlo simulation contrast to identify high-risk clusters (RR > 1). Results: (DR-TB) Predominantly in men aged 30–44 years, with a progressive increase until 2023 (IRR = 2.11). Three high-risk clusters were detected in the southwest and center of the country. Discussion: Drug-resistant tuberculosis in Colombia showed a sustained increase in the years of study, with a cumulative increase of 110% compared to 2020, associated with economically active people more exposed due to occupational and social factors. The greatest burden was observed in the general population. Cases also increased in groups with social and health vulnerability conditions. Conclusions: The departments of Risaralda, Meta, and Valle del Cauca presented the highest drug resistance rates in Colombia. Full article
(This article belongs to the Special Issue Surveillance, Modelling, and Risk Mapping of Tropical Infectious Diseases)
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