How do mathematicians model infectious disease outbreaks? 

Mathematics is so important during these times... Stay safe! 🙏

Very nice posting to amplify the knowledge on epidemics and it is advisable to be considered in all committee of crisis in the world

Really lucid and motivational speaker; I will be very happy, If you publish similar video for vector borne disease like malaria.

This is an extremely clear explanation of these epidemiological models we are all hearing about every day in the news. It was very enlightening. Thank you so much for posting!

Excellent explanation sir. Very structured and easy to follow especially for a non-native speaker like myself

One might well ask what is the predictive power of a mathematical model. During my carer as an engineer I recognized that models have no predictive power at all. They are dependent fully on the input parameters. That is a formal way of saying they depend on factors that you have not measured in the real world [ for various reasons ]. So that means that parameters are guesses. By extension by guessing you can " predict " any end result you want. SO what are they good for ? Well I found that they can often aid your thinking by testing what behaviour the model will predict according to arbitrary variation of each parameter - in other words they can be useful in their way by showing up parameters which have little effect and those which have an important effect. In essence they are a useful tool for a sensitivity analysis. Far too frequently various people from different disciplines or professions will jump in and claim they can predict things because they have a fully tested model. Politicians and CEO's like this approach because they in turn can make tactical decisions and claim that they are based on sound science. The recent political moves which have been made in response to the perceived problem of a COVID19 pandemic, have been made because a frightened public demanded that decisions be made. This resulted in decisions being made based on worst-case scenarios resulting from ad hoc models. Let's be quite clear that if an accurate model is finally constructed, it will occur after the fact when all the historic data has been collected, and in turn when the accurate data has been extracted from false data. In other words we will have been able to formulate an explanation of past history, when essentially it doesn't matter anymore because the exact conditions which pertained then are unlikely to pertain again in the future. That is why nobody learns anything from history, and why future conditions will remain unknown and unknowable. It would be my hope that the wise people in Oxford would be able to explain some such realities to their gung-ho colleagues who purport to have found the right answers with their particular " mathematical " approach, which because few understand the math are accepted like the emperor's new clothes.

First class video. A couple of questions: 1. For the very rapid spread versus the flattened curve spread, is the area under the curve the same? Put another way, do we all need to get the infection, it's just a matter of timing? Or how many of us do? Or another way, does flattening the curve do anything really beyond managing the ICU capacity, and possibly allow time for development of contra-symptomatic therapeutics 2. Is there any exit strategy other than vaccination: i.e. will we be in this yo-yo rebound scenario until then?

Thanks for your cristal clear lecture. Only hope some politians grab some ideas from you guys.

Is there a reason why the age contact graph isn’t symmetrical about the line y=x?

Can we use a simple SIR model to predict an epidemic? How do we choose the susceptible population of a country? Do we use the whole population?

For studying hospital acquired infections studies,which model is suitable???

Very clear explanation. Thank you very much 😊

Such an excellent talk. Very useful. Thank you!

this is of interest even more so because it is topical, have the models been very useful so far?

Excellent explanation!

It was a really great lecture!

great job sir

i like it. thanks you to show that.

Information in Models &the graphs r not clearly visible

that was very very good - thanks very much - to Gweny - why dont they give the functions that solve them? There arent any - the differential equations are NOT linear and have no analytical solutions

what happens when the person gets reinfected in the SEIR model after waning of immunity, do we add compartment between susceptible(S) and E(latent) or between R(recovered) and S?

Useful Information and Amazing Video

It would have been nice if you did not only show the differential equations but also the functions that solve them.

very nice sir

Nice video

"When you move, the virus moves." Is this statement axiomatic, or non-axiomatic? Please explain the rationale for your answer.

Please let me tidy that shelf behind you!

Yeah

2+2=4 how to proof

I am Bangladeshi ..

Yeah