The other two courses in this specialisation require you to perform deterministic modelling - in other words, the epidemic outcome is predictable as all parameters are fully known. However, this course delves into the many cases – especially in the early stages of an epidemic – where chance events can be influential in the future of an epidemic. So, you'll be introduced to some examples of such ‘stochasticity’, as well as simple approaches to modelling these epidemics using R. You will examine how to model infections for which such ‘population structure’ plays an important role in the transmission dynamics, and will learn some of the basic approaches to modelling vector-borne diseases, including the Ross-McDonald Model. Even if you are not designing and simulating mathematical models in future, it is important to be able to critically assess a model so as to appreciate its strengths and weaknesses, and identify how it could be improved. One way of gaining this skill is to conduct a critical peer review of a modelling study as a reviewer, which is an opportunity you'll get by taking this course.
GC
I thought it was clear, the syntax problems were about the same topic and needed the solution talked about in the videos. The subject was completely covered. I already recommended it to like 5 people.
KM
The peer review grade is not working properly. It is not showing result.
DG
a great follow up to the R coding course and a good entree into the world of public health
AT
I have found it useful for increasing my insights into infectious disease modelling.
AL
Excellent course, good information. Excellent instructors.
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A great learning opportunity. Advanced content presented in a convincing and understandable manner. Simply amazing.
The importance of the SIR model is needs of an hour, due to COVID-19, HIV, Dengue, Hay-fewer and many more infectious diseases, in analysis and building of the Compartment model in which S-I-R; it stands for the Symptomatic-Infected-Recovered, When we need to build this model for our own country, we need to observed the PEAK prevalence (i.e. maximum number of symptomatic people during the epidemic), need to be considered, studied and analyzed. This course found to be helpful in order develop and build combination of paper-based and computer-based modelling, with the parameters their estimated values like R0 which is basic reproduction number, and so on.....thanks
I have learnt a lot about infectious diesease modelling using SIR model, different modelling approach, its parameters/factors involved in the entire course/specialisation. All three courses in the specialization are really thoughtful for learners to progress further. Thanks to all mentors, discussion prompts, and co-learners on the discussion forum for the support. Special thanks to Imeprial College of London - Dr. Nim, Dr, Halder, Dr. Bowman and Ms. Nora Schmit for coming up with such a good course on Coursera platform.
This course was both educational and entertaining. The quality of the peer grading assignments was impressive (both in terms of getting tested and the quality of the work I got to review). Thoroughly recommend this course (and the entire specialization, really) to anyone with interest in infectious disease epidemiology in general. Kudos to the Imperial College team for this excellent set of courses.
I thought it was clear, the syntax problems were about the same topic and needed the solution talked about in the videos. The subject was completely covered. I already recommended it to like 5 people.
This course is really amazing .Excellent insight learning and gives room for nurturing the knowledge gained.
I have found it useful for increasing my insights into infectious disease modelling.
The peer review grade is not working properly. It is not showing result.
Excellent course, good information. Excellent instructors.
Overall one was given more variations on the SIR Model. While several models were talked about, I would have liked more info on the stochastic site. This course should be split up in two to go deeper into the modelling and maybe some other approaches than in R. Rating a paper in the end is good to give you basically the tools to model, but it should be also a bit deeper. But to get a quick overlook, it is definitely worthwhile.
a great follow up to the R coding course and a good entree into the world of public health
Comprehensive. Notebooks were great
the whole grading system is unfair
… you have to wait for others to review
your assignment which can take ages and may not happen at all
सबसे अच्छी प्रस्तुतियाँ वे होंगी जो संतुलित आलोचना करती हैं; वे जो मॉडल की ताकत और कमजोरियों दोनों की पहचान करती हैं, और शोध प्रश्न के संदर्भ में अध्ययन की मजबूती को कैसे बेहतर बनाया जाए, इसकी स्पष्ट समझ दिखाती हैं।
V good
good content ,awerness to public
bad