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Educational Resources

Ongoing Online Courses

These courses feature contributions by MIDAS members, including Matt Ferrari, Joseph Wu, and Ben Cowling. They are free, with the option to pay for a certificate upon completion of the course.

Epidemics

The Dynamics of Infectious Diseases

Penn State

“Not so long ago, it was almost guaranteed that you would die of an infectious disease. In fact, had you been born just 150 years ago, your chances of dying of an infectious disease before you’ve reached the tender age of 5 would have been extremely high.

 

Since then, science has come a long way in understanding infectious diseases – what they are, how they spread, and how they can be prevented. But diseases like HIV/AIDS, Malaria, Tuberculosis, or the flu are still major killers worldwide, and novel emerging diseases are a constant threat to public health. In addition, the bugs are evolving. Antibiotics, our most potent weapon against bacterial infections, are losing their power because the bacteria are becoming resistant. In this course, we’ll explore the major themes of infectious diseases dynamics.

 

After we’ve covered the basics, we’ll be looking at the dynamics of the flu, and why we’re worried about flu pandemics. We’ll be looking at the dynamics of childhood diseases such as measles and whooping cough, which were once considered almost eradicated, but are now making a comeback. We’ll explore Malaria, and use it as a case study of the evolution of drug resistance. We’ll even be looking at social networks – how diseases can spread from you to your friends to your friends’ friends, and so on. And of course we’ll be talking about vaccination too. We’ll also be talking about how mobile phones, social media and crowdsourcing are revolutionizing disease surveillance, giving rise to a new field of digital epidemiology. And yes, we will be talking about Zombies – not human zombies, but zombie ants whose brains are hijacked by an infectious fungus.”

 

We’re looking forward to having you join us for an exciting course!

Epidemics-Origins, Spread, Control and Communication (Professional Certificate)

Hong Kong University

Despite all the remarkable technological breakthroughs that we have made over the past few decades, the threat from infectious diseases has significantly accelerated. In this online course, we will learn why this is the case by looking at the fundamental scientific principles underlying epidemics/pandemics and the global/public health actions behind their prevention and control in the 21st century. Explore the science, prevention and control of epidemics in this program. In addition to lectures by leading scientists in this field from HKU, this program will feature panel discussions on Ebola and Zika Outbreak, Anti-vaccination and more, with world leading experts in epidemics.

 

“If history is our guide, we can assume that the battle between the intellect and will of the human species and the extraordinary adaptability of microbes will be never-ending.” (1)

 

This program covers the following topics:

Origins of novel pathogens.

Analysis of the spread of infectious diseases.

Medical and public health countermeasures to prevent and control epidemics.

Panel discussions involving leading public health experts with deep frontline experiences to share their views on risk communication, crisis management, ethics and public trust in the context of infectious disease control, including Ebola and Zika Outbreak, Anti-vaccination and more.

Supplementary modules on next generation informatics for combating epidemics.

Supplementary modules on SARS-COV-2 and COVID-19.

Epidemics

Gabriel M. Leung (+10 other instructors)

Hong Kong University

Despite all the remarkable technological breakthroughs that we have made over the past few decades, the threat from infectious diseases has significantly accelerated. In this course, we will learn why this is the case by looking at the fundamental scientific principles underlying epidemics and the public health actions behind their prevention and control in the 21st century.

 

This course covers the following four topics:

1. Origins of novel pathogens;

2. Analysis of the spread of infectious diseases;

3. Medical and public health countermeasures to prevent and control epidemics;

4. Panel discussions involving leading public health experts with deep frontline experiences to share their views on risk communication, crisis management, ethics and public trust in the context of infectious disease control.

 

In addition to the original introductory sessions on epidemics, we revamped the course by adding:

– new panel discussions with world-leading experts; and
– supplementary modules on next generation informatics for combating epidemics.


(1) Fauci AS, Touchette NA, Folkers GK. Emerging Infectious Diseases: a 10-Year Perspective from the National Institute of Allergy and Infectious Diseases. Emerg Infect Dis 2005 Apr; 11(4):519-25.


What you’ll learn
– Demonstrate knowledge of the origins, spread and control of infectious disease epidemics
– Demonstrate understanding of the importance of effective communication about epidemics – Demonstrate understanding of key contemporary issues relating to epidemics from a global perspective

Infectious Disease Modeling Specialization

Imperial College London

Mathematical modelling is increasingly being used to support public health decision-making in the control of infectious diseases. This specialisation aims to introduce some fundamental concepts of mathematical modelling with all modelling conducted in the programming language R – a widely used application today.

 

The specialisation will suit you if you have a basic working knowledge of R, but would also like to learn the necessary basic coding skills to write simple mathematical models in this language. While no advanced mathematical skills are required, you should be familiar with ordinary differential equations, and how to interpret them. You’ll receive clear instruction in the basic theory of infectious disease modelling alongside practical, hands-on experience of coding models in the programming language R.

Online Resources

These are resources both for learning specific methods and for implementing methods through software packages. They may include free tutorials, R packages, and/or practice datasets and problems to help improve one’s knowledge of specific modeling techniques.

Mathematical Models of Infectious Diseases

Drake Lab site

The objectives of this course are (1) To introduce the student to several models that express the core theory for the propagation of epidemics; (2) To teach the numerical methods needed to study these models; and (3) To teach the statistical methods needed to parameterize these models for specific applications.

MCMC I for Infectious Diseases

Minin github site

This module is an introduction to Markov chain Monte Carlo (MCMC) methods with some simple applications in infectious disease studies. The course includes an introduction to Bayesian statistics, Monte Carlo, MCMC, some background theory, and convergence diagnostics. Algorithms include Gibbs sampling, Metropolis-Hastings and their combinations. Familiarity with the R statistical package or other computing language would be helpful.

Stochastic Epidemic Models with Inference

SISMID site

The course first studies some basic stochastic models for the spread of an infectious disease and presents large population results for them including threshold phenomenon (Ro), distribution of the final number infected, and the critical vaccination coverage (the fraction needed to vaccinate to avoid future epidemics). Several extensions towards realism are then discussed: different types of individuals and social structures in the community including households and networks.

epirecipes

This project aims to collate mathematical models of infectious disease transmission, with implementations in R, Python, and Julia. Categories of models include simple deterministic models using ordinary differential equations; simple stochastic models; models with time- varying parameters; spatial models; network models; and applications to specific disease systems.

IDEMA Course

developed by Andreas Handel

This course provides an introduction to infectious disease epidemiology using a model-based approach. We will use simulation models to understand the dynamics of transmission and spread of infectious diseases. You do not have to (but could) build models or write computer code as part of this course.

ICI3D Tutorials

R tutorials and labs from the MMED and DAIDD clinics

Simulation-Based Inference for Epidemiological Dynamics

SISMID course, Aaron King

This module introduces statistical inference techniques and computational methods for dynamic models of epidemiological systems. The course will explore deterministic and stochastic formulations of epidemiological dynamics and develop inference methods appropriate for a range of models. Special emphasis will be on exact and approximate likelihood as the key elements in parameter estimation, hypothesis testing, and model selection. Specifically, the course will cover sequential Monte Carlo and synthetic likelihood techniques. Students will learn to implement these in R to carry out maximum likelihood and Bayesian inference.

EpiModel

R program and tutorials for building ID models

For each of the three model classes in EpiModel (deterministic compartmental models, individual contact models, and network models) the tutorials are organized into basic “built-in” models to guide new users in the features of the model class, and advanced extension models to build out the models to address new research questions.

Short Courses

These courses are generally held in-person and are meant to provide hands-on training to those who are accepted into the course. They are only held during certain times of the year and may require an application, so please make sure to check each course requirement and date.

Summer Institute in Statistics and Modeling of Infectious Diseases

SISMID

The Summer Institute in Statistics and Modeling in Infectious Diseases (SISMID) is designed to introduce infectious disease researchers to modern methods of statistical analysis and mathematical modeling and to introduce statisticians and mathematical modelers to the statistical and dynamic problems posed by modern infectious disease data. M. Elizabeth Halloran serves as the Director of SISMID.


SISMID is partially supported by the National Institute of Allergy and Infectious Diseases of the National Institutes of Health under Award Number R25 AI147391.

Clinic on Dynamical Approaches to Infectious Disease Data

DAIDD is a week-long modeling clinic that provides an introduction to dynamical models used in the study of infectious disease dynamics. Instruction focuses on the conceptual foundations of modeling and model formulation for infectious disease research.

Clinic on Meaningful Modeling of Epidemiological Data

Mathematical modelling is increasingly being used to support public health decision-making in the control of infectious diseases. This specialisation aims to introduce some fundamental concepts of mathematical modelling with all modelling conducted in the programming language R – a widely used application today.

 

The specialisation will suit you if you have a basic working knowledge of R, but would also like to learn the necessary basic coding skills to write simple mathematical models in this language. While no advanced mathematical skills are required, you should be familiar with ordinary differential equations, and how to interpret them. You’ll receive clear instruction in the basic theory of infectious disease modelling alongside practical, hands-on experience of coding models in the programming language R.

If you know of a resource that you'd like to add to this page, please email questions@midasnetwork.us.