Advances in Infectious Disease Surveillance: Climate-Sensitive, Biostatistical, and Simulation Modeling Approaches
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: 31 December 2026
Special Issue Editor
Interests: infectious disease modeling; climate- and environment-sensitive diseases; spatiotemporal epidemiology; machine learning and time-series forecasting
Special Issue Information
Dear Colleagues,
In many health systems, large amounts of data are collected every day, but only a small part of these data is used to guide policy and practice. Routine infectious disease surveillance reports, hospital and clinic records, and cross-sectional surveys already contain important information about how infections spread, which populations are at risk, and how well interventions work.
This Special Issue, “Advances in Infectious Disease Surveillance: Climate-Sensitive Biostatistical and Simulation Modeling Approaches”, invites studies that turn such real-world data into evidence for action. We are particularly interested in work that applies spatial-temporal modeling and time-series forecasting to understand disease patterns over time and space, identify hotspots, or generate short- and medium-term predictions.
We welcome contributions that explore climate- and environment-sensitive transmission, including the roles of weather, climate variability, or environmental conditions in shaping infectious disease dynamics. Studies that focus on early outbreak detection, anomaly or signal detection in surveillance data, and simulation and scenario modeling to test alternative control strategies, estimate potential impacts, or reduce cases and mortality are especially encouraged.
Methodologically, the scope includes classical biostatistical models, spatial and spatial-temporal models, machine learning and AI approaches adapted for surveillance and survey data, and compartmental or agent-based simulations. Applied work from diverse settings, particularly low- and middle-income and tropical countries, is highly welcome.
The overall purpose of this Special Issue is to showcase practical, transparent modeling studies that start from routinely collected data and end with clear messages for public health decision-making.
Dr. Sudarat Chadsuthi
Guest Editor
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
- infectious disease surveillance
- spatial-temporal modeling
- early outbreak detection
- time-series forecasting
- climate- and environment-sensitive transmission
- machine learning and AI
- simulation and scenario modeling
- public health decision-making
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