Who should take this infectious disease modelling course?

The course is suitable for public-health students, epidemiologists, biostatisticians, clinicians, biomedical researchers, data analysts, microbiologists, infectious disease researchers, doctoral scholars, academicians, and public-health professionals.

How is R used in infectious disease modelling?

R can be used to clean outbreak data, calculate epidemiological indicators, visualize epidemic trends, build transmission models, run simulations, estimate model parameters, compare intervention scenarios, and generate reproducible reports.

What are SIR and SEIR models?

SIR and SEIR are compartmental disease-transmission models. SIR divides a population into susceptible, infectious, and recovered groups, while SEIR adds an exposed group for individuals who are infected but not yet infectious.

Does the course cover outbreak forecasting?

Yes. Learners examine how historical case counts, transmission parameters, intervention data, seasonality, and time-series methods can be used to forecast possible outbreak trends.

What is the reproduction number in epidemiology?

The reproduction number estimates how many secondary infections are generated by an infectious person. The basic reproduction number describes transmission in a fully susceptible population, while the effective reproduction number reflects current conditions.

Does the course include vaccination and intervention modelling?

Yes. The course introduces scenario analysis for vaccination, isolation, treatment, social distancing, contact reduction, testing, and other public-health interventions.

What epidemiological data can be analysed in R?

Relevant datasets may include daily case counts, hospitalizations, recoveries, deaths, vaccination records, demographic variables, contact patterns, laboratory results, mobility information, and geographic outbreak data.

How is model uncertainty assessed?

Model uncertainty can be assessed through sensitivity analysis, parameter ranges, confidence or credible intervals, scenario comparison, simulation, residual analysis, and validation against observed outbreak data.

What skills can learners gain from this course?

Learners can develop skills in R-based epidemiological data analysis, outbreak visualization, transmission modelling, parameter estimation, simulation, forecasting, intervention assessment, uncertainty analysis, and reproducible public-health reporting.

R For Infectious Disease Modelling And Analysis

Q: What does the R for Infectious Disease Modelling and Analysis course cover?

The course covers epidemiological data preparation, descriptive analysis, epidemic curves, SIR and SEIR models, reproduction-number estimation, outbreak simulation, time-series forecasting, intervention scenarios, uncertainty analysis, visualization, and reproducible reporting in R.

Attribute

Detail

Format

Online (e-LMS)

Level

Intermediate

Duration

3 Weeks

Certification

e-Certification + e-Marksheet

Tools

R, RStudio, deSolve, ggplot2, tidyverse, R Markdown

About the R Programming Course

This workshop focuses on utilizing R programming for mathematical modeling and analysis of infectious diseases.

Participants will gain practical skills in applying R to model disease spread, analyze epidemiological data, and understand the dynamics of infectious disease outbreaks. Hands‑on sessions cover simulations, result interpretation, and real‑world case studies, ideal for researchers, public health professionals, and anyone eager to combat infectious diseases with data‑driven approaches.

Program Highlights

• Comprehensive coverage of R for Mathematical Modelling and Analysis of Infectious Disease from fundamentals to advanced applications

• Hands-on projects and real-world case studies in biotechnology

• Expert-curated curriculum aligned with current industry standards

• Access to recorded lectures and e-LMS platform for flexible, self-paced learning

• e-Certification and e-Marksheet upon successful completion

• Dedicated mentor support and interactive doubt-clearing sessions

• Practical experience with tools: R, RStudio, deSolve, ggplot2

• Career-oriented training for academic and professional growth in biotechnology

Course Curriculum

Module 1: Week 1 – Introduction to Infectious Disease Modeling

Explore core epidemiology terms and disease dynamics
Build and simulate the classic SIR model in R
Interpret model outputs and basic reproduction numbers

Module 2: Week 2 – Extending Models & Implementing in R

Develop SEIR and asymptomatic extensions with time‑varying transmission
Solve ordinary differential equations using deSolve package
Estimate parameters, calibrate models, and compute R₀ / Rt

Module 3: Week 3 – Intervention Strategies & Final Project

Model vaccination, isolation, and non‑pharmaceutical interventions
Conduct sensitivity analysis and discuss model limitations
Create reproducible visualizations and reports using ggplot2 and R Markdown

Module 4: Final Project

Build, calibrate, and validate an outbreak model on real epidemiological data
Run multiple intervention scenarios and compare outcomes
Submit a comprehensive report with code, visualizations, and policy recommendations

Tools, Techniques, or Platforms Covered

R
RStudio
deSolve
ggplot2
tidyverse
R Markdown

Real-World Applications

Apply R for Mathematical Modelling and Analysis of Infectious Disease skills directly to academic research, thesis work, and publications
Build a professional portfolio showcasing practical biotechnology competencies
Solve industry-relevant problems using R for Mathematical Modelling and Analysis of Infectious Disease methodologies and tools
Contribute to open-source projects and collaborative research in biotechnology
Prepare for competitive examinations, interviews, and professional certifications in biotechnology

Who Should Attend & Prerequisites

Industry‑recognised e‑Certification + e‑Marksheet from NSTC
Hands‑on training with practical projects and real epidemiological datasets
Dedicated expert mentorship and doubt‑resolution sessions

Prerequisites:

Frequently Asked Questions

1. What is the format of this R for Mathematical Modelling and Analysis of Infectious Disease course?

This is an Online (e-LMS) course delivered via our e-LMS platform. You will have access to pre-recorded video lectures, reading materials, assignments, quizzes, and hands-on projects that you can complete at your own pace.

2. Will I receive a certificate after completing this course?

Yes! Upon successful completion of all modules, assignments, and assessments, you will receive an e-Certification along with an e-Marksheet from NanoSchool (NSTC) that you can showcase on your CV and LinkedIn profile.

3. What are the prerequisites for this course?

Learners should have a foundational understanding of biotechnology concepts. Familiarity with basic tools and programming is recommended.

4. How long will I have access to the course materials?

You will have access to all course materials for the duration of 3 Weeks. The self-paced format allows you to learn according to your own schedule through our online learning management system.

5. Is mentor support available during the course?

Yes, dedicated mentor support is available throughout the course. You can reach out for doubt-clearing sessions, project guidance, and career advice related to biotechnology. Our mentors are industry experts and experienced professionals.

Enroll in R for Mathematical Modelling and Analysis of Infectious Disease today and take the next step in your professional journey. With expert-curated content, practical projects, and industry-recognized certification, this course is your gateway to mastering biotechnology skills that matter.

Brand	NSTC
Format	Online (e-LMS)
Duration	3 Weeks
Level	Intermediate
Domain	biotechnology
Hands-On	Yes – Practical projects with industrial datasets
Tools Used	R, RStudio, deSolve, ggplot2, tidyverse, R Markdown

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