How do mathematicians model infectious disease outbreaks?

Models. They are dictating our Lockdown lives. But what is a mathematical model? We hear about the end result, but how is it put together? What are the assumptions? And how accurate can it be?
In our first online only Oxford Mathematics Public Lecture Robin Thompson, Research Fellow in Mathematical Epidemiology in Oxford and expert on the modelling of infectious diseases such as Covid-19, explains all.
Oxford Mathematics Public Lectures are generously supported by XTX Markets.

[@MindMathMoney]

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

[@Avicenna10]

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!

[@sergiopedro9368]

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

[@Satyagraha-ql3pf]

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?

[@rameshgautam1105]

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

[@chaedarindrapramana8903]

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

[@MahmoudAhmed-mi7rh]

Me too

[@davidsweeney111]

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

[@crustyoldfart]

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.

[@pedroromero1026]

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

[@noahhughes8931]

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

[@epiminutesbykazi]

Such an excellent talk. Very useful. Thank you!

[@swatikanani323]

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

[@nl7247]

Very clear explanation. Thank you very much 😊

[@agboolasamson1996]

Excellent explanation!

[@timtunnel1996]

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?

[@abdulwasia4623]

great job sir

[@marofe]

It was a really great lecture!

[@mukeshprajapat9074]

i like it. thanks you to show that.

[@girlthatcooks4079]

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?

[@SairaMoodley786]

There is no new compartment, the recovered enters the susceptible compartment again.

[@svpltech]

Useful Information and Amazing Video

[@evan.5967]

Vinay Kumar Kotakonda
Thanks

[@gwenyneitzel7695]

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

[@gwenyneitzel7695]

@Bob Trenwith Well... I (and many others) don't have any experience using such numerical software and the solutions would have helped to understand better how exactly the different aspects contribute to the resulting graph.
It would not have been difficult for the producers to add the functions for us, they might just have thought that those might not be of huge interst to the audience.

[@abdulwasia4623]

very nice sir

[@akhilvijaykumar7064]

Nice video

[@warnford]

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

[@mandirahaldar6529]

Information in Models &the graphs r not clearly visible

[@jesuschristbiblebiblestudy]

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

[@sarahmorgan7765]

Please let me tidy that shelf behind you!

[@abbdemsel400]

Yeah

[@jeyaramsathees6128]

Yeah

[@naufalramadhani9166]

Yeah

[@mdrahman2711]

2+2=4 how to proof

[@anowarhossen9905]

I am Bangladeshi ..

[@pinkumandal5797]

Beggar

[@pinkumandal5797]

You are a Bangladeshi beggar.
I am from Austria.

[@jeyaramsathees6128]

Yeah

[@user-wy8ki2ef1m]

Yeah