Superspreaders

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From the course by The Pennsylvania State University

Epidemics - the Dynamics of Infectious Diseases

571 оценки

The Pennsylvania State University

Epidemics - the Dynamics of Infectious Diseases

571 оценки

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!

From the lesson

Epidemiology

Where humans live and how they are connected to each other, have strong effects on how infectious diseases can spread. In this module, you will learn about some of the related key concepts of epidemiology. After looking at some fascinating history, we’ll learn about social networks as the ultimate “road map” on which diseases can travel and we’ll talk about how the structure of the network can influence disease dynamics. After that, we’ll cover three topics that all affect infectious disease epidemiology in dramatic ways: human transport systems, population density and so-called superspreaders - individuals who can spread a disease to dozens or even hundreds of other individuals. We’ll close by identifying what information is required for disease surveillance.

Roots of Epidemiology6:25

Social Networks5:45

Structure of Networks5:49

Superspreaders6:09

Meet the Instructors

Dr. Ottar N. Bjornstad
Professor of Entomology and Biology
Department of Biology
Dr. Rachel A. Smith
Associate Professor
Department of Communication Arts & Sciences and Human Development & Family Studies
Dr. Mary L. Poss
Professor of Biology
Department of Biology
Dr. David P. Hughes
Assistant Professor of Entomology and Biology
Department of Biology
Dr. Peter Hudson
Willaman Professor of Biology
Department of Biology
Dr. Matthew Ferrari
Assistant Professor of Biology
Department of Biology
Dr. Andrew Read
Alumni Professor in the Biological Sciences, and Professor of Entomology
Center for Infectious Disease Dynamics
Dr. Marcel Salathé
Assistant Professor of Biology
Department of Biology

My mother always told me that once infected, I

must just let the infection run its natural course.

She assumed that the whole process from infection

to disease symptoms through to recovery was predetermined.

And some intervention may help release the severity of symptoms,

but basically I must just lie there and suck it up.

Her logic was she'd experienced the process, so

had my older siblings, and now it's just

my turn to suffer. To some extent there's some truth in this.

If you look, incubation periods, infectious periods

and time to recovery, are fairly consistent for

an infection but vary between different causative

agents than an agent varies between individual hosts.

In epidemiological many, models, we often use many of

these parameters and just simply say they're the same,

quite similar, and we have set periods.

The underlying assumption is that there is a

homogeneity across the population of susceptibles, such that

any host-to-host variation is really trivial and of

little importance to the nature of the larger epidemic.

In reality, this may not be the case.

There can be a great deal of variation in the number of secondary cases

generated by an infectious host.

This can be because some individuals have a particularly long infectious period.

Because they shed a large number of pathogens.

Or quite simply because they have many, many contacts.

In some cases, all three may act together, so the infected person does not

show symptoms, keeps shedding large numbers of

pathogens, and has a high contact rate with

the other susceptible hosts. That is an asymptomatic carrier.

An example of this is Mary Malone, or what's become known as Typhoid Mary.

She was one of the best known examples of an asymptomatic carrier.

We did not know whether she suffered a normal infection and

then just harbored the salmonella type infection that causes Typhoid fever.

Or she never showed any symptoms

at all and was always an asymptomatic carrier.

What is apparent, is that she became

an asymptomatic carrier for at least 38 years.

And right up to her death at the age of 69.

She was a cook and what was truly unfortunate for her

victims was that she did not believe in washing her hands.

So, she moved from job to job, preparing food for other people.

She infected them, they became ill and then she would just move onto another job.

She infected at least 50 people, three of whom later died.

There's no doubt that she was an exception to the average infectious case.

She infected a large number of secondary cases.

She was in fact, a super spreader.

Now this term,

super spreader, was first coined in relation to

the SARS outbreak in 2002 and 2003 to

describe the large proportion of secondary cases that

arose from a very few number of infected individuals.

The first case in Singapore, was an airline steward, who brought the infection

from a hotel in Hong Kong, and then infected a large number of individuals.

Most of whom later died, including her family, her friends, and her priest.

Another individual once she infected, infected 143 individuals

including all the doctors and nurses that treated

her, while by far and away the majority

of infected people never infected anyone at all.

So if we were to describe this statistically,

what would be the expectation for the frequency

distribution of secondary cases per affected host.

Our first expectation is that this should be a random distribution.

Some individuals will affect none, some a

few, but nobody infects a very high number.

Indeed with the SARS situation, this was not the case.

In this instance, where we have the data, we can show there's unfair distribution.

A small

number of individuals caused the majority of secondary cases.

And there are a large number of

in, individuals that don't infect anyone at all.

From the data available on SARS, seems to have

the smallest proportion of responsible for the most infections.

But we also see similar patterns in measles,

monkeypox, smallpox, pneumonic plague, and many other diseases.

So this might

be much more common than we previously thought.

Now there're two very important consequences

for the discovery of super sperry events.

First, when the highly infectious individuals are also the most

susceptible, that is, the more likely to get the infection.

Then an infection which invades a population will spread faster.

It will effectively have a higher R-naught.

And would be more likely to

spread than one where the hosts are all the same.

Second, the same time, if we can identify the

characteristics that I could identify what determines a spreader.

Then we could just target these few individuals

for treatment or intervention of some kind to

reduce the likely hood of an infection getting

established and spreading through the population as a whole.

What a great money saving approach that would be.

If we could identify the targets, say just 20% of the population

to stop the disease, we could have a very big impact indeed.

Now, one of the infections I've been working on is a disease called

[UNKNOWN],

which kills children in Asia and eastern Europe.

And it's a disease that is passed from wild mice to humans by ticks.

We have found that in the mice, it is the larger mice.

The middle-aged males that pass this infections

to the ticks and then to the humans.

So if we could target them, then we

could stop the infection ever getting to the children.

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