New Year Offer End Date: 30th April 2024
Program

Mathematical Modelling and Analysis of Infectious Disease using R

Predicting and Preventing Disease Outbreaks with R: A Hands-on Workshop

Skills you will gain:

About Program:

A model serves as a simplified depiction of a more intricate system or process. Infectious disease models can aid in outbreak responses by offering insights into the spread of diseases within populations, estimating the magnitude of outbreaks, and assessing the potential effects of interventions. Mathematical models can forecast the progression of infectious diseases to illustrate the probable outcomes of an epidemic and assist in shaping public health measures and interventions. In recent years, mathematical modelling has emerged as a crucial Instrument in analysing the dynamics of infectious diseases and supporting the creation of control strategies.
R is a robust tool for modelling infectious diseases mathematically due to its flexible and opensource programming environment. It offers a broad array of statistical and numerical analysis functionalities, enabling researchers to construct intricate models, simulate disease behaviours, and evaluate the effects of various interventions, all within an intuitive interface.

Aim: The aim is to explore mathematical modelling and assessment of infectious diseases utilizing R.

Program Objectives:

  • To forecast the transmission of illness, determine interventions, and guide public health.
  • To aid in making choices regarding the limitation of disease transmission and the implementation of vaccination strategies.

What you will learn?

Day 1: Advanced Theoretical Foundations & Epidemiological Dynamics

  • Defining the “Compartment” philosophy: State variables, transition flows, and system balance.
  • The Transmission Engine: Frequency-dependent vs. Density-dependent transmission.
  • Force of Infection (λlambdaλ): Understanding the per capita rate at which susceptible individuals acquire infection.
  • Threshold Concepts: R0R_0R0​, RtR_tRt​, epidemic growth rate, and doubling time.
  • Mathematical derivation of the Basic Reproductive Ratio (R0R_0R0​).
  • Understanding the “Critical Vaccination Threshold” (1−1/R0)(1 – 1/R_0)(1−1/R0​).
  • Introduction to identifiability, uncertainty, and assumptions in infectious disease models.
  • Comparing deterministic vs. stochastic epidemic thinking.
  • Hands-on Tool/Activity:
    Manual parametrization and group exercise in calculating R0R_0R0​, growth rate, and vaccination threshold from historical outbreak datasets (e.g., Measles, Influenza, COVID-like incidence patterns).

Day 2: The R Ecosystem & Building the Infectious Disease Modelling Engine

  • Setting up the Laboratory.
  • Installation of RStudio and essential libraries: deSolve, ggplot2, magrittr, dplyr, and tidyr.
  • Writing the Ordinary Differential Equation (ODE) function in R.
  • Defining initial states (S0,I0,R0S_0, I_0, R_0S0​,I0​,R0​) and configuring time solvers.
  • Model visualization with ggplot2: Epidemic curves, peak infection, and comparative scenario plots.
  • Sensitivity analysis: Exploring the influence of transmission rate (βbetaβ) and recovery rate (γgammaγ).
  • Intervention simulation: Coding social distancing, vaccination, and time-varying transmission rate β(t)beta(t)β(t).
  • Reproducible epidemic reporting using Quarto for model summaries and outputs.
  • Hands-on Tool/Activity:
    Scripting the base SIR model in R, visualizing epidemic trajectories, and simulating how varying γgammaγ and intervention timing can flatten the curve.

Day 3: Advanced Variations, Demographic Complexity & Modern Epidemic Analytics

  • SIR with Population Demographics.
  • Incorporating vital dynamics: Birth rates (μmuμ) and non-disease-related death rates.
  • Analyzing endemic equilibrium and why some diseases persist in populations.
  • Structural variations of the model: SIS, SIRS, and SEIR frameworks.
  • Time-varying reproduction number (RtR_tRt​) and real-time epidemic tracking.
  • Introduction to stochastic epidemic modelling and uncertainty analysis.
  • Parameter estimation and model calibration using outbreak data.
  • Modern tools for infectious disease analytics: EpiEstim, pomp, Shiny, and introductory Bayesian workflows in R.
  • Hands-on Tool/Activity:
    Extending the basic SIR script to SEIR or SIR with demographics, estimating RtR_tRt​ from incidence data, and building a simple interactive epidemic dashboard or comparative simulation report.

Mentor Profile

Professor and Head Shalom New Life College, Bengaluru, Karnataka
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Fee Plan

INR 1999 /- OR USD 50

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Intended For :

  • Undergraduate/Postgraduate students in Epidemiology, Biostatistics, Public Health, or related fields.
  • Researchers and faculty in disease modelling and public health.
  • Data analysts and professionals in healthcare and epidemiology.
  • Individuals with a background in R programming and an interest in infectious disease modelling.

Career Supporting Skills

Epidemiology R Programming Data Analysis Mathematical Modelling Public Health Research

Program Outcomes

  • Understanding of infectious disease modelling concepts
  • Ability to implement and analyze epidemiological models in R
  • Hands-on experience with deterministic models and simulations
  • Knowledge of disease intervention strategies and public health impact
  • Proficiency in data visualization and interpretation for decision-making

FREEDOM TO LEARN 10% OFF All Courses & Workshops Use Code: NANOINDIA10 ⏳ Offer Ends In: Loading... Learn Today. Lead Tomorrow. Explore Programs →
FREEDOM TO LEARN 10% OFF All Courses & Workshops Use Code: NANOINDIA10 ⏳ Offer Ends In: Loading... Learn Today. Lead Tomorrow. Explore Programs →