Force of Infection

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Do curso por The Pennsylvania State University

Epidemias - A Dinâmica das Doenças Infecciosas

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The Pennsylvania State University

Epidemias - A Dinâmica das Doenças Infecciosas

619 classificações

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!

Na lição

Basics of Ecology

The third module of the course covers ecological factors that influence the dynamic patterns of infectious disease. We will discuss the conditions under which a pathogen can drive its host to extinction. We will look at how the demography of the host may allow the pathogen to persist in some populations but not others. We will talk about how host and pathogen characteristics interact to shape different temporal patterns of disease incidence at the population level and discuss the ecological consequences of infecting more than one host species.

Pathogens and Host Extinction5:32

Force of Infection5:04

Seasonality3:52

Host Diversity, Host Range3:52

Conheça os instrutores

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

In an epidemic, there is often great concern about the degree to which

an infected individual can spread infection

to the susceptible individuals in a population.

We already know that one way of measuring this is with R-naught,

but we can consider using another concept known as the force of infection.

We can formally define this as the per

capita rate at which susceptible individuals contract infection.

Intuitively, the force of infection should increase with the transmissibility

of the pathogen and the prevalence of infection in the population.

The transmissibility of the pathogen will depend on

both characteristics of the pathogen, what is the

mechanism of transmission, how prolific is the pathogen

within each host, and the characteristics of the population.

How many potentially infectious contacts does each individual make in a day?

The prevalence of infection, defined as the proportion

of the population that is infectious at any given

time, gives a measure of the likelihood that

any potentially infectious contact is with an infectious individual.

Thinking about the force of an infection

from the perspective of each susceptible individual offers

some new insights into the patterns we expect

to see at the scale of the population.

In particular,

if the force of infection is high then

the rate at which individuals come in contact with

infection is high and on average, we would

expect the interval between infectious contacts to be short.

For an immunizing infection, one where getting infected

once protects you forever, this would mean that

if the force of infection is high we

would expect to see infection in younger individuals.

On the other hand,

if the force of infection is low, either because we're talking

about a pathogen with lower inherent transmissibility or a population with a

lower rate of contact, then we would expect individuals to be older,

on average, by the time they are first exposed to an infection.

Given the intuitive relationship between the

force of infection and the basic reproductive

number from earlier, we would then expect that the higher the basic reproductive

number, the earlier on average that individuals would become infected.

In fact, one can derive an approximate relationship between the mean

age of infection in the population and the basic reproductive number.

R-naught equals 1 plus L divided by A where A is

the mean age of infection and L is the average life expectancy.

This provides a way of estimating the transmissibility

of a pathogen, a hard thing to observe directly, from a

pattern, the age of first infection that is relatively easy to observe.

Here is a survey of the mean age of infection for a variety

of pathogens in different places as presented by Andersen and May in 1991.

And from this relationship, we can make an approximate estimate of

R-naught for each, which indicates differences in R-naught for each pathogen,

but also differences for the same pathogen in different places.

Of course this simple view has presumed that the force

of infection is a constant value for everyone in the population.

And intuitively, we may think that not

everyone will encounter infection at the same rate.

The force of infection is often presumed

to be different for individuals of different ages.

For example, school children

often have very high contact rates within schools compared to adults.

Thus we might expect the force of infection to be higher at younger ages.

From the patterns we've discussed, we would still expect that the older you are

the more likely that it is that you've been infected sometime in the past.

But rather than being a function of age alone, this pattern is

now expected to be a function of the cumulative force of infection

up to your current age.

And we can directly measure whether someone has ever been infected

by looking for the presence of antibodies to that specific pathogen.

Again, while this age specific force of infection

might be very difficult to observe directly, the resulting

pattern of the proportion of individuals at each

age that have antibodies indicating prior exposure is something

that can be measured relatively simply by conducting a serological survey.

Using calculus, we can back calculate the age specific force of infection from this

observed pattern of the proportion of individuals

at each age with antibodies indicating prior exposure.

These types of observations have been used in the past

to highlight which classes of individuals are at the highest risk

for infection based on the existing

pattern of infection across all individuals.

Ultimately, the force of infection provides a way of translating

the individual base process of

transmission into a population scale pattern

of infection that can allow us to quantify properties of pathogens

spread in real populations that are otherwise very difficult to see.

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