Epidemiological Data Analysis, Biostatistics & Public Health Statistics Training Course/Seminar/Workshop
10 Days5 sessions available Certificate on completion
The Epidemiological Data Analysis, Biostatistics & Public Health Statistics Training Course is a professional training programme designed to develop practical competencies in epidemiological research, statistical analysis, disease surveillance and evidence-based public health decision-making.
The course addresses the full analytical process, including translating public health questions into measurable research objectives, selecting appropriate study designs, assessing data quality, preparing datasets, calculating epidemiological indicators, conducting statistical inference, estimating associations, building multivariable models and communicating findings responsibly.
The Epidemiological Data Analysis, Biostatistics & Public Health Statistics Training Course develops practical competencies in epidemiological research, statistical analysis and evidence-based public health decision-making. Participants learn to clean health datasets, calculate incidence and prevalence, conduct statistical tests, estimate measures of association, build regression models, analyse survival data and interpret diagnostic accuracy. The course supports public health research, disease surveillance, programme evaluation and health policy development in Kenya, Africa and internationally.
Course objectives
Design epidemiological analyses by translating research questions into appropriate study designs, estimands, measurable variables, sampling strategies and statistical analysis plans. Prepare high-quality epidemiological datasets through systematic data cleaning, validation, coding, missing-data assessment, outlier investigation and documentation of analytical decisions.
Produce defensible public health evidence through statistical reporting, data visualisation, interpretation of limitations, reproducible analysis and evidence-based recommendations.
Analyse complex public health outcomes using suitable methods for survival data, diagnostic accuracy, stratified populations, disease trends and other relevant epidemiological applications.
Estimate and interpret epidemiological associations using measures of effect, regression modelling, confounding control, effect-modification assessment and sensitivity analysis.
Apply descriptive and inferential biostatistical methods to calculate disease frequency, assess population differences, test hypotheses and quantify uncertainty using appropriate statistical procedures.
Who should attend
Epidemiologists, public health officers, biostatisticians, disease surveillance officers, medical doctors, nurses involved in research, clinical officers, health information management professionals, monitoring and evaluation officers, public health researchers, research assistants, university lecturers, postgraduate students, Ministry of Health personnel, county health officials, NGO health programme managers and professionals involved in disease prevention and control.
Course content
Module 1: Foundations of Epidemiological Data Analysis and Biostatistics Principles of epidemiology and biostatistics, descriptive and analytical epidemiology, epidemiological research questions, population health indicators, types of epidemiological data
Module 2: Epidemiological Study Designs, Sampling and Statistical Analysis Planning Cross-sectional studies, cohort studies, case-control studies, ecological studies, randomised controlled trials, quasi-experimental designs, prospective and retrospective
Module 3: Epidemiological Data Collection, Management and Quality Assurance Primary and secondary epidemiological data sources, household surveys and questionnaires, clinical records and laboratory data, disease surveillance databases, routine health info
Module 4: Data Cleaning, Transformation and Exploratory Analysis Importing and inspecting epidemiological datasets, reviewing variable names and data types, handling missing and inconsistent values, detecting duplicate records, identifying outliers and im
Module 5: Descriptive Statistics and Descriptive Epidemiology Frequency distributions and cross-tabulations, counts and proportions, percentages and rates, mean median and mode, variance and standard deviation, range and interquartile range, distribution
Module 6: Measures of Disease Frequency, Mortality and Disease Burden Incidence proportion and cumulative incidence, incidence rates and person-time calculations, point prevalence and period prevalence, crude mortality rates, cause-specific mortality, age
Module 7: Measures of Association, Effect and Public Health Impact Two-by-two contingency tables, risk ratios and relative risk, odds ratios in case-control studies, prevalence ratios, incidence rate ratios, risk differences and absolute effects, attribut
Module 8: Probability, Statistical Inference and Hypothesis Testing Fundamentals of probability, Bernoulli and binomial distributions, Poisson and normal distributions, sampling distributions, standard errors and confidence intervals, point and interval e
Module 9: Regression Analysis for Epidemiological Research Principles and applications of regression analysis, simple and multiple linear regression, binary logistic regression, multinomial and ordinal logistic regression, Poisson regression for count out
Module 10: Confounding, Effect Modification, Bias and Causal Interpretation Confounding and mediation, effect modification and interaction, directed acyclic graphs and causal reasoning, identification of potential confounders, selection of adjustment vari
Module 11: Missing Data, Complex Sampling and Advanced Analytical Considerations Patterns and mechanisms of missing data, missing completely at random, missing at random and missing not at random, complete-case analysis, single imputation and multiple imp
Module 12: Survival Analysis and Time-to-Event Data Introduction to survival analysis, definition of event time and follow-up period, right censoring and incomplete observation, survival functions and hazard functions, Kaplan–Meier survival estimation,
Module 13: Diagnostic Test Evaluation and Screening Statistics Diagnostic accuracy study designs, index tests and reference standards, construction of diagnostic two-by-two tables, sensitivity and specificity, positive and negative predictive values, posi
Module 14: Epidemiological Data Visualisation, Reproducible Analysis and Scientific Reporting Principles of epidemiological data visualisation, selection of charts according to data type, epidemic curves and temporal trend plots, bar charts and histograms
Module 15: Integrated Epidemiological Data Analysis Capstone Project Selection of a public health research problem, formulation of research questions and analytical objectives, definition of study population and estimands, development of a statistical ana
General notes
epidemiology training course, biostatistics training, public health data analysis, epidemiological statistics, disease surveillance data analysis, statistical analysis for public health, epidemiological research methods, SPSS epidemiology training, R for epidemiology, regression analysis in public health.
The Epidemiological Data Analysis, Biostatistics & Public Health Statistics Training Course develops practical competencies in epidemiological research, statistical analysis and evidence-based public health decision-making. Participants learn to clean health datasets, calculate incidence and prevalence, conduct statistical tests, estimate measures of association, build regression models, analyse survival data and interpret diagnostic accuracy. The course supports public health research, disease surveillance, programme evaluation and health policy development in Kenya, Africa and internationally.
The Epidemiological Data Analysis, Biostatistics and Public Health Statistics Training Course equips public health professionals, researchers and disease surveillance officers with practical skills in statistical analysis and epidemiological research. Participants learn data cleaning, descriptive statistics, incidence and prevalence calculations, hypothesis testing, measures of association, regression modelling, survival analysis and epidemiological reporting. The training supports evidence-based disease surveillance, public health research, programme evaluation and health policy development in Kenya, Africa and internationally.
Frequently asked questions
It is a professional training programme that develops practical skills in epidemiological statistics, data preparation, disease frequency measurement, hypothesis testing, regression modelling, survival analysis and public health reporting.
The course is suitable for epidemiologists, public health practitioners, surveillance officers, biostatisticians, health researchers, monitoring and evaluation professionals, healthcare practitioners and postgraduate students.
Basic familiarity with statistics is helpful but not essential for introductory participation. The training progresses from foundational concepts to more advanced analytical methods.
Depending on the training arrangement, practical sessions can use Excel, R, SPSS, Stata, Epi Info or Python. The specific software can be selected to suit participants’ institutional requirements.
Yes. Participants learn to calculate and interpret incidence proportion, incidence rates, point and period prevalence, mortality rates, case fatality ratios and other important epidemiological indicato
Yes. The programme covers linear regression, logistic regression, multivariable analysis, interpretation of coefficients and confidence intervals, assessment of model assumptions and adjustment for confounding.
Yes. Participants undertake hands-on exercises using sample datasets to clean data, calculate epidemiological indicators, conduct statistical tests, fit regression models and prepare analytical reports.
The methods are applicable to disease surveillance, outbreak analysis, health research, county health reporting, programme evaluation and population health planning. Examples can be adapted to regional health priorities and local data systems.
Expected outputs include a cleaned dataset, descriptive statistical tables, epidemiological indicators, hypothesis-testing results, regression outputs, data visualisations and a final epidemiological analysis report.