This course proposes an overview of current global health challenges drawing on the insights of several academic disciplines including medicine, public health, law, economics, social sciences and humanities. This interdisciplinary approach will guide the student into seven critical topics in global health.

From the lesson

Emerging Trends in Global Health: Infectious Diseases

There are 7 parts to module 3. Lessons 3 & 4 are both introductory sections that describe key concepts necessary to understand infectious diseases and how we deal with them. Lesson 5 constitutes the core lesson of module 3. This lesson focuses on the current global situation and future challenges for the most prominent infectious diseases of the world. In lessons 6-9 we discuss neglected tropical diseases, re-emerging infectious diseases and epidemics/pandemics. Finally, lesson 10 and 10b summarize module 3 in a set of key conclusions.

Institute of Global Health - Faculty of Medicine University of Geneva

Antoine Flahault

Professor of Public Health and Director of the Institute of Global Health (Faculty of Medicine, University of Geneva) and co-Director of Centre Virchow-Villermé (Université Paris Descartes) University of Geneva and Université Paris Descartes – Sorbonne Paris Cité

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In this session on mathematical modeling of communicable disease,

I will not pretend to give you many details on mathematics here.

Don't worry too much.

It will not be something where I can assume or

foresee that you will become mathematicians.

You will not develop mathematical modeling just after this session for sure.

I just wanna give you some clues to really realize that the mathematical modeling

of communicable disease is very important today when controlling infectious disease,

in particular outbreak of emerging infectious disease.

How does it work?

In fact it has been developed after the pandemic flu,

the Spanish Pandemic Flu of 1918 in the last century.

It has been developed because people wanted to know why

all the humanity was not attacked by the influenza virus.

At the time it was not known that it was a virus, at this period.

In fact, it was known that it was a communicable disease.

And the line of this model was to say that peoples were divided in three sections.

You were the susceptible when you did not have any contact with the virus.

You were an infected people when you had a contact with the virus or

you were immunized or removed from the chain of transmissions when

you were either recovered or died because of the virus infection.

So in the three compartments there are some interactions,

interaction between susceptible and infected people.

Of course when you have sufficient contact between a susceptible and

an infected people, the susceptible may get the infection and catch the disease.

And also the interaction which immunized people because when you are immunized,

you protect the other people from infection.

You are acting as a bacteria for an infection.

You can be immunized today, either because you have already catch the disease and

you have recovered, or maybe immunized because you have been vaccinated.

This kinda mathematical modeling is applied now for

many other disease, even for mosquito disease.

And how does it work and how can it help?

It can help in better understanding this communicable disease.

For instance, there is a parameter which is named the reproductive rate are not.

The reproductive rate can be defined

as the number of secondary cases due to one index case.

For instance,

if I say that the reproductive rate is two which is approximately two for

influenza, that means that when I have influenza, I am the index case.

I can transmit the disease to two other people.

So reproductive rate is two.

If I take the measles, so measles is a child disease.

If a child has the measles,

he can transmit the disease to 20 other children.

Of course, non-immunized children.

That means that is 20.

So hopefully to avoid is 20.

So this reproductive ratio is very important, because it acts as a threshold.

Below 1, there is no risk of epidemics.

Above 1, there is a high risk of an epidemic, of an outbreak.

Meaning that this tool, the mathematical modelling of communicable disease may be

used as an early warning system.

If for instance, we can define, we can estimate for

epidemiological records that there are not the reproductive right is above one,

you can trigger the epidemic layout of the country and that is used as that.

When there was an outbreak of any disease, of influenza in Mexico,

of Ebola in West Africa, in the recent years it was used for

assessing what was not [INAUDIBLE] above one and

there was a risk of epidemic at an international level or at a local level.

And it can be also used as a tool for

assessing the control measures against this disease,

until the reproductive rate is both one.

You have to control the disease, you have not succeeded to control it effectively.

But when you can really be sure that is below one,

after you have implemented all the control measures you may wanna implement,

control of the borders, screenings of patients, treating the patients,

insulating by quarantines and so on or maybe vaccinating the people.

You are below one, you have succeeded, it is the end of the process.

The end of the outbreak.

But when it is still above 1, you can say it's not enough.

We need to continue the effort.

So it is a tool which is very useful for driving,

steering the management of communicable disease and

particularly of emerging infectious disease.

But this tool can also be used for

simulating various scenarios on a computer.

It's not easy to propose to the policy makers the appropriate

measures to take for controlling an outbreak of infectious disease.

But thanks to the mathematical modeling tools we can simulate

what will be the actions of quarantine,

what would be actions of controlling the borders by stopping any flights.

It is very costly, economically to stop all the planes in an airport.

But if it is effective.

If it proven as effective under computer simulations,

you may give these results to the policy makers,

and they may take the appropriate actions they wanna take for that.

But if you see that there is not an effective action by doing that,

you may say to the policymakers that it is not effective.

You can also simulate values, policies, regarding vaccinations.

If you have certain amount, constrained amount of vaccines available for

your population.

You may say that if you vaccinate this class of ages or

this part of the population that will be effective or not.

So you can give some insight to the policy makers with a rational tool.

And last, but not least,

is the use of these mathematical tools for predictions, for forecasting.

Okay, you can use these tools for assessing scenarios on the computer.

So it is easy to understand that you can also use that

tool to forecast the future of the epidemic.

But be cautious regarding this forecasting, these predictions.

Of course, in imaginary fictitious disease, one never knows what will happen.

So the forecasting maybe prone to wrong predictions.

I would suggest, I would recommend not to use any predictions after one month.

Within one month of an epidemic particularly the start of the epidemic is

of exponential in nature you have a very high risk of providing wrong predictions.

After one month.

Because it goes very, very fast.

And sometimes it ends very, very quickly.

So you cannot really forecast the future today, even with these tools.

So we can forecast within the delay, the period of one month, not after.

So the predictions are prone to be wrong, as I said.

But you can view that as something which may be useful for

the policy maker to forecast a choice,

very useful even with some caution, but maybe also the people and

the journalists may be very interested by some predictions of what can happen.

And you can give some wrongs of what could happen with these epidemics.

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