Mathematical Modelling of Infectious Diseases in Epidemiology using R
Schedule
Mon Jan 23 2023 at 09:00 am to Fri Feb 03 2023 at 04:00 am
Location
Fahari Palace Serviced Apartments | Nairobi, NA
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Workshop Title: Mathematical Modeling of Infectious Diseases in Epidemiology using RDates and registration links
08/05/2023 to 19/05/2023 -https://bit.ly/3yYYDZK
14/08/2023 to 25/08/2023 -https://bit.ly/3Tr2yXr
06/11/2023 to 17/11/2023 -https://bit.ly/3eQHryK
04/12/2023 to 15/12/2023 -https://bit.ly/3Siw2pa
INTRODUCTION
Infectious diseases are disorders caused by organisms such as bacteria, viruses, fungi, protozoa, helminthes, prions or parasites and they include SARS-CoV-2, Zika, Ebola, HIV/AIDS, swine flu, MERS CoV, ringworm, trichinosis, influenza, rabies, measles, rubella, tuberculosis and malaria among others. With the increased emergence and re-emergence of these diseases, there has been equally increased use of mathematical modeling to support relevant infectious diseases stakeholders (public health, pharmaceutical industry professionals, policy makers, infectious diseases researchers) in understanding the transmission and control of these diseases. This is possible when professionals are capable of interpreting and effectively evaluating both epidemiological data and the findings of mathematical modeling studies. This 10 days course will equip participants with knowledge on infectious diseases and hands on skills on use of R studio software in mathematical modeling of infectious diseases.
Duration
10 days
Who should attend?
Public health, medical, pharmaceutical industry professionals, policy makers, veterinary scientists, medical statisticians and infectious disease researchers and anybody who is looking forward to analyzing and interpreting epidemiological data on infectious diseases and predicting the potential impact of infectious diseases control programmers.
What you will learn
By the end of the training participants will be able to:
• Understand fundamental statistical concepts
• Analyze data by applying appropriate statistical techniques
• Write a simple mathematical model that is appropriate for a specific infectious disease and related research question
• Learn R Programming
• Analyze the dynamics of the model
• Use the model to consider varying cost and intervention scenarios
• Construct valid mathematical models capturing the natural history of a given infectious disease.
• Use a calibrated model to create model projections for different intervention scenarios
• Implement a mathematical model in R, calibrating it against epidemiological data in order to estimate key model parameters
• Explain the strengths and limitations of a mathematical model in relation to given research and policy questions
Course outline
Module 1: R Programming
• Introduction to the R Statistical Software & R Studio
• Different Data Structures in R
• Reading in Data from Different Sources
• Indexing and Subletting of Data
• Data Cleaning: managing missing values, recoding to string variables to numeric variables
• Exploratory Data Analysis in R
Module 2: Understanding type of data and type of data analysis
• Descriptive Statistics
• Inferential statistics
• Test statistics- Test for normality
Module 3: Test statistics
• Test for independence for parametric data (one sample t test, independent sample t test, paired sample t-test, one way analysis of variance, repeated measure anova)
• Test for independence of non-parametric data (Wilcoxon signed rank, Wilcoxon signed rank, Man Whitney, Friedman test and Kruskal Wallis test )
• Test for independence of dichotomous data- MCNemar test, Chi-square test/ Fischer’s’ exact test, Cochran’s Q test
Module 4: Test of associations
• Tests of associations- Chis-square test of association, Pearson correlation, Speraman correlation
• Regression analysis
• Data reduction methods
Module 5: Developing infectious disease models
• Introduction to the major concepts used for studying the epidemiology of infectious diseases:
• Basic reproduction number
• Incubation periods
• Serial intervals, herd immunity
• Seasonal transmission
Module 6: Introduction to the main types of models that can be employed
• Application of model to determine optimal control strategies for outbreaks involving new pathogens as well as for endemic infection
• Learn methods for setting up deterministic models (difference and differential equations)
Module 7: Analysis of data and applications of modeling of seroprevalence data
• How to analyses and interpret seroprevalence data,
• Different fitting methodologies.
• estimate (age-dependent) infection incidences (“forces of infection”) for high and low infection transmission settings
• Determine how seroprevalence data can be used to estimate mixing patterns of subgroups in given populations and how different contact patterns between individuals affect the impact of control.
Module 8: Additional methods and dynamics - stochastic and network modeling, health economics and sensitivity analyses
• Stochastic and network models,
• Health economics
• Sensitivity analyses
Module 9: Applications of modeling
• Applications of mathematical models
• The extent of transmission of diseases such as malaria
Module 10: Case study of data analysis and modeling of infectious disease of participants’ choice
• Interpretation of results and presentation of results using tables, charts and figures
• Discussion of findings from data analysis
• Review of articles (Past papers) that have used methods and critique them
• Exercise to plan and develop a model
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Where is it happening?
Fahari Palace Serviced Apartments, Westlands Church road,Nairobi, KenyaEvent Location & Nearby Stays: