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Free Course by Fiocruz Bahia Offers Intensive Training in Mathematical Modeling and Statistics for Health Professionals, with International Partnership and Limited Slots.
Fiocruz Bahia has opened 36 slots for the Course on Mathematical Modeling and Statistics Applied to Health Surveillance, a free activity that will take place from November 10 to 12, 2025, at the institution’s campus in Salvador.
The training is aimed at professionals in Mathematics, Physics, Statistics, Epidemiology, and Public Health and requires, as a prerequisite, basic programming skills and familiarity with the R environment.
In addition to the limited number of slots, the course brings together researchers from Brazilian institutions and the United Kingdom.
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The initiative is organized by Fiocruz Bahia, in partnership with Federal University of Bahia (UFBA), Federal Fluminense University (UFF), London School of Hygiene & Tropical Medicine (LSHTM), and London School of Economics (LSE).
The event is supported by the Graduate Program in Clinical and Translational Research, the graduate programs in Biotechnology in Health and Investigative Medicine, and Physics, and the Institute of Collective Health.
Target Audience and Selection Criteria
Applications are directed to professionals and advanced students in the indicated fields, especially those working with surveillance, data analysis in health, and evaluation of interventions.
As a technical criterion, participants must demonstrate basic programming knowledge and the ability to work in R.
According to the organizers, the goal is to level the class for practical activities and optimize the use of computational tools during the exercises.
Course Structure and Workload
The program focuses on three days of immersion, totaling 24 hours of theoretical and practical activities.
The schedule combines conceptual presentations, case studies, and exercises in R and Python, using Google Colab in the modules aimed at machine learning and data analysis.
On November 10, in the morning, the opening and Theoretical Module I will take place, introducing the use of modeling in health surveillance.
Concepts such as natural history of disease, parameter estimation using SIR models, and applications of these approaches in surveillance scenarios will be presented.
In the afternoon, Practical Module I proposes mathematical modeling exercises in R and Python, reinforcing the connection between theory and application.
The schedule for November 11 deepens the topic with Theoretical Module II, focused on chronic infectious diseases.
The focus will be on disease burden estimation and the impact assessment of the introduction of the tuberculosis vaccine.
In the afternoon, Practical Module II guides the construction of a transmission model to estimate incidence and risk of tuberculosis infection, further exploring scenarios for improving diagnosis and cost-effectiveness analyses in R.
On November 12, the course advances to statistical and computational approaches.
In the morning, Theoretical-Practical Module III covers statistical modeling and machine learning, with techniques for classification, prediction, and anomaly detection implemented in Python in Colab.
In the afternoon, the emphasis shifts to acute infectious diseases, including image-based classification for lung diseases and time series analysis related to severe acute respiratory syndromes, also in a Python/Colab environment.
Contents and Skills Developed
The training track is designed to articulate mathematical and statistical foundations with real public health problems.
Throughout the activities, participants will engage with concepts of compartmental models and their variations, techniques for parameter estimation, and model fitting for surveillance data.
In parallel, the practical modules prioritize translating theoretical references into reproducible analysis pipelines, from data organization to result interpretation.
While the blocks on tuberculosis address transmission dynamics and the evaluation of interventions, the sessions dedicated to machine learning explore applications of supervised classification in medical imaging and pattern detection in time series.
The selection of themes reflects the daily operations of surveillance teams, which deal with incidence indicators, test sensitivity, vaccine impact, and early warnings for anomalous events.
Academic Partnerships and Institutional Support
The presence of Brazilian and British universities signals the intention to promote scientific exchange and standardization of international methods.
The LSHTM and LSE contribute with their tradition in epidemiology, biostatistics, and applied social sciences, while UFBA and UFF strengthen the integration with national graduate programs and research groups.
From Fiocruz’s side, the support from different programs highlights the transversality of the theme, which dialogues with biotechnology, investigative medicine, and physics applied to health.
Methodology and Tools
The combined use of R and Python facilitates the transition of concepts between widely adopted languages in the scientific community.
In R, participants work with packages aimed at compartmental modeling, Bayesian fitting, and visualization, while in Python, activities include machine learning libraries, medical image classification, and time series exploration in Colab.
This strategy seeks to enhance the technical autonomy of those involved in surveillance data analysis.
Priority Audience and Prerequisites
To keep pace with the activities, candidates must have programming knowledge and experience with the R environment.
The course is intended for profiles that navigate between quantitative methods and public health issues, such as data analysts, researchers, and professionals from health departments, university hospitals, and research centers.
Although there will be introductory sessions, familiarity with applied statistics and basic concepts of epidemiology contributes to better participation.
Service and Registrations
- Course: Mathematical Modeling and Statistics Applied to Health Surveillance.
- Period: November 10 to 12, 2025.
- Location: Fiocruz Bahia, Salvador.
- Slots: 36.
- Prerequisite: basic programming knowledge and familiarity with R.
- Total workload: 24 hours.
Participating institutions: Oswaldo Cruz Foundation (BA), UFBA, UFF, London School of Hygiene & Tropical Medicine, and London School of Economics.
Support: Graduate Program in Clinical and Translational Research, Graduate Program in Biotechnology in Health and Investigative Medicine, Graduate Program in Physics, and Institute of Collective Health.
Registrations are made through the institutional form. Interested parties must fill out the requested information and observe the limit of 36 slots. Details about the program, schedules, and logistical guidance are available on the event page.
Boa iniciativa!