Epidemiological Modelling & Disease Forecasting Training Workshop/ Seminar
10 Days5 sessions available Certificate on completion
The Epidemiological Modelling & Disease Forecasting Training Workshop equips public health professionals, epidemiologists, biostatisticians, researchers and disease surveillance officers with practical skills to model disease transmission, analyze epidemic patterns, forecast disease trends and support evidence-based public health decisions.
The training explores mathematical and statistical approaches for understanding infectious disease dynamics, estimating transmission rates, evaluating intervention strategies and forecasting future disease burden. Participants learn to work with surveillance datasets, assess model assumptions, compare modelling approaches, interpret uncertainty and communicate forecasts to public health decision-makers.
The Epidemiological Modelling & Disease Forecasting Training Workshop develops practical skills in disease transmission modelling, epidemic forecasting, statistical analysis, model validation and public health scenario planning. Designed for epidemiologists, public health professionals, biostatisticians, researchers and surveillance officers, the workshop covers SIR and SEIR models, reproduction numbers, time-series forecasting, uncertainty analysis and intervention simulations to support evidence-based disease prevention, outbreak preparedness and public health decision-making.
Explain the mathematical and statistical foundations of epidemiological modelling.
Develop a practical epidemiological modelling project using a sample disease dataset.
Communicate forecasts and model assumptions through reports, visualizations and dashboards.
Interpret modelling results for outbreak preparedness, health planning and resource allocation.
Evaluate model performance, uncertainty, sensitivity and limitations.
Conduct scenario analysis to assess the potential effects of public health interventions.
Compare alternative modelling approaches and assess their suitability for different diseases.
Develop and evaluate short-term disease forecasts using historical data.
pply statistical methods to analyze epidemic trends and estimate disease patterns.
Develop and interpret compartmental disease transmission models. A
Calculate and interpret key epidemiological indicators, including reproduction numbers and growth rates.
Prepare and analyze disease surveillance data for modelling applications.
Who should attend
Epidemiologists, biostatisticians, public health researchers, disease surveillance officers, infectious disease specialists, mathematical modellers, health data analysts, monitoring and evaluation professionals, health information officers, public health emergency response teams, research scientists, university lecturers, postgraduate students in epidemiology and public health, ministry of health personnel, county health department staff, NGO health programme managers and professionals involved in epidemic preparedness and response.
Course content
Module 1: Foundations of Epidemiological Modelling Definition and scope of epidemiological modelling, mathematical versus statistical models, deterministic and stochastic approaches, mechanistic and data-driven models, purposes of modelling in public heal
Module 2: Epidemiological Data Preparation and Exploration Sources of disease surveillance data, case counts and population denominators, data collection and validation, missing values and duplicate records, data cleaning and transformation, daily and wee
Module 3: Mathematical and Statistical Foundations Variables and parameters, functions and rates of change, exponential growth and decay, differential equations, probability distributions, descriptive statistics, sampling variability, parameter estimation
Module 4: Disease Transmission Dynamics Susceptible, exposed, infectious and recovered populations, transmission pathways, incubation and infectious periods, contact rates, transmission probabilities, epidemic growth and decline, generation intervals, sus
Module 5: Compartmental Epidemiological Models The SIR model, SEIR model, SIS model, extensions for asymptomatic and hospitalized populations, births and deaths in transmission models, vaccination compartments, age-structured models, model equations and p
Module 6: Reproduction Numbers and Transmission Parameter Estimation Basic reproduction number \(R_0\), effective reproduction number \(R_t\), epidemic growth rates, transmission rates, generation and serial intervals, estimating transmission parameters f
Module 7: Statistical Models for Disease Incidence Linear and generalized linear modelling concepts, Poisson regression, negative binomial regression, logistic regression applications, modelling disease counts and rates, incorporating population offsets,
Module 8: Time-Series Analysis and Disease Forecasting Temporal patterns in disease incidence, trends and seasonality, moving averages, autoregressive concepts, ARIMA and related forecasting approaches, weekly and monthly disease patterns, short-term fore
Module 9: Parameter Estimation and Model Calibration Model parameters and initial conditions, fitting models to observed data, least-squares estimation, likelihood-based estimation concepts, Bayesian estimation principles, selecting calibration periods, p
Module 10: Model Validation and Performance Assessment Training and validation periods, out-of-sample forecasting, rolling-origin evaluation, residual analysis, forecast error measures, comparison of competing models, sensitivity to data quality, assessin
Module 11: Uncertainty Quantification and Sensitivity Analysis Parameter uncertainty, observation error, structural uncertainty, confidence and prediction intervals, sensitivity analysis, scenario assumptions, Monte Carlo simulation concepts, probabilisti
Module 12: Intervention Modelling and Scenario Analysis Vaccination and immunization scenarios, social and behavioral interventions, isolation and quarantine strategies, contact reduction, testing and treatment interventions, combining multiple control me
Module 13: Spatial and Regional Disease Modelling Geographical variation in disease incidence, spatial distribution of cases, regional transmission patterns, mapping disease burden, population movement and connectivity, spatial clustering, regional foreca
Module 14: Advanced Modelling Applications and Public Health Decision Support Stochastic transmission models, agent-based modelling concepts, network-based transmission, age-structured modelling, ensemble forecasting, nowcasting and reporting-delay adjust
Module 15: Practical Modelling Project and Forecasting Capstone Importing and cleaning a sample disease dataset, exploring epidemic trends, calculating growth rates and reproduction indicators, selecting an appropriate modelling approach, fitting or calib
General notes
Recommended Software and Tools Python, R, Excel, SQL, Power BI, QGIS, Jupyter Notebook, SPSS, Stata, DHIS2, Epi Info, WHO disease surveillance resources, enterprise AI tools.
. Practical Exercises and Expected Outputs Clean a historical disease surveillance dataset — validated modelling dataset, calculate epidemic growth rates — transmission trend summary, estimate reproduction indicators from supplied data — reproduction number analysis, develop an SIR or SEIR model — compartmental model specification and simulation, model weekly disease counts — statistical incidence model, forecast future case counts — forecast chart with prediction intervals, compare forecasting approaches — model performance comparison report, evaluate model predictions against observed cases — validation summary, conduct sensitivity analysis — parameter sensitivity report, simulate vaccination and intervention scenarios — scenario comparison charts, analyze regional disease patterns — epidemiological map, estimate potential hospital demand from a supplied scenario — healthcare demand projection, develop a public health forecasting dashboard — interactive dashboard, complete the capstone project — disease forecasting report with limitations and recommendations.
Training Methodology The workshop combines expert-led presentations, mathematical modelling demonstrations, guided data analysis, epidemiological case studies, computer-based practical exercises, group modelling assignments, forecasting simulations and a capstone project. Participants work with sample or appropriately anonymized surveillance datasets. Exercises progress from basic disease trend analysis to model development, forecasting, validation and scenario interpretation. The emphasis is on understanding modelling assumptions and applying results responsibly, rather than treating forecasts as exact predictions.
. Expected Learning Outcomes Participants will be able to prepare disease datasets for analysis, select suitable epidemiological models, interpret transmission parameters, estimate and evaluate disease forecasts, assess uncertainty, compare intervention scenarios and present modelling results to public health decision-makers. They will also develop a practical understanding of when models are appropriate, what data they require and how limitations in surveillance completeness, reporting delays and changing disease dynamics can affect forecast reliability.
An applied professional training programme combining mathematical epidemiology, statistical forecasting, disease transmission modelling and public health decision support for professionals involved in surveillance, research, preparedness and outbreak response.
Frequently asked questions
The workshop is suitable for epidemiologists, public health analysts, biostatisticians, researchers, surveillance officers, health data scientists and professionals involved in epidemic preparedness, response and planning.
A basic understanding of statistics is recommended. Familiarity with algebra and epidemiological indicators is helpful, particularly for participants interested in the mathematical modelling modules.
Yes. Participants explore time-series forecasting, compartmental models, model calibration, forecast validation and prediction intervals using sample epidemiological data.
Yes. The workshop introduces SIR and SEIR models, explains their assumptions and parameters, and demonstrates how they can be used to simulate disease transmission and assess intervention scenarios.
Yes. Practical exercises can use Python, R, Excel and other appropriate analytical tools to explore data, estimate parameters, develop forecasts and visualize results.
Yes. Participants learn to evaluate parameter uncertainty, prediction intervals, sensitivity, data limitations and forecast performance. These concepts are essential for responsible public health interpretation.
Expected outputs include a cleaned surveillance dataset, an epidemiological trend analysis, a transmission model, a disease forecast, a model validation summary, intervention scenario comparisons and a final modelling report.