Disease Surveillance - Classical

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Do curso por The Pennsylvania State University

Epidemias - A Dinâmica das Doenças Infecciosas

568 ratings

The Pennsylvania State University

Epidemias - A Dinâmica das Doenças Infecciosas

568 ratings

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!

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Global Health

In this final module we explore the global context of epidemics. The world is rapidly changing with the global population increasing and the speed of travel and extent of globalization piling on the pressure of infectious diseases. In this week we set out first to explore a textbook example of disease shaped by the conditions presented by modern living: the disease SARS. We will see how high density living in cities coupled with links to wildlife diseases through markets can create a pandemic. We then explore traditional methods of disease surveillance and then more recent ones afforded to us by the web and networks made possible through google, twitter and other social media. Important to this global view is human behavior, our evolving culture and health. We will also consider emerging disease and the global pattern of diseases across our recent history and how from our earliest beginnings of global travellers we have affected the spread of diseases. Finally, we discuss disease of our food plants and how models of disease spread are developed to promote better global health. As a postscript to the course we ask the questions, so beloved of modern media, could we become zombies?

SARS - A Modern Epidemic5:31

Disease Surveillance - Classical7:18

Disease Surveillance - Digital5:21

Culture and Health5:32

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

The current definition of public health

surveillance is the ongoing, systematic collection,

analysis, and interpretation of health data,

essential to the planning, implementation and

evaluation of public health practice, closely

integrated with the dissemination of these

data to those who need to know and linked to prevention and control.

Now that's a pretty broad definition.

For the purposes of

understanding infectious disease epidemiology, the

health data that we're most

often concerned with are the prevalence or incidence of an infection.

The prevalence of infection is the number of individuals in a population

that are currently infected with a pathogen at any point in time.

The incidence of infection is the number of individuals

newly infected with a pathogen within a given period

of time.

Thus, for understanding the spread of

infection within a population, prevalence tells us

about how the total amount of pathogen out there that could be spread.

While incidence tells us about the rate, or

risk, a successful infection that occurs per unit time.

In practice, both incidence and prevalence are often reported as a proportion

of the total population.

That is, the number of infections per 1,000 people,

which allows direct comparison across populations of different sizes.

In order to understand the dynamic process of epidemic spread,

we really want to know the incidence and prevalence of infection.

However, we are often only able to observe the incidence and prevalence of disease.

That is, people that are currently expressing the outcomes of infection.

This highlights an inherent challenge to the

study of the dynamics of infectious diseases.

We want to know how infection spreads, but more often than

not, we are limited to our ability to see where disease is.

For many infections the best measures available are

the numbers of individuals seeking care in health clinics.

This provides a measure of the incidence of infection, that

is, the number of new infections in a period of time.

Since these are necessarily only those

individuals that develop symptoms of infection, this

is very likely to be an under estimate of the true numbers infected.

As an example, a 2009 analysis of

the incidents of pandemic H1N1 influenza concluded that

the 43,677 cases of H1N1 that

were confirmed through laboratory tests in the US between April and July, likely

represented 1.8 to 5.7 million cases of influenza.

Largely because the vast majority of those infected

did not seek care or have specimens collected.

Though under reporting

may seem to be a major challenge in the study of

infectious diseases, it's relevance depends on

the goals of a surveillance program.

If the goal of the surveillance program is to estimate the total

burden of infection, which is often the case when public health agencies need

to set funding allocations for programs targeted at one disease over another, then

failing to account for under reporting can have a significant impact on outcomes.

However, if the goal of a surveillance program is

to detect changes in the incidence of infection; say

to evaluate whether new vaccines or other preventative measures

are reducing infection rates, then under-reporting may not be limiting.

In this case, the measure of program success is a reduction in the

rate of new cases relative to past rates, rather than an absolute number.

Measuring the numbers of infections that are

reported as a consequence of individuals seeking

care, that is, patients visiting doctors, is

an example of a passive surveillance system.

By passive here, I mean that the person or a group

doing a surveillance is simply recording the information as it arrives.

This contrasts with active surveillance, whereby the

surveyor is actively going into the population

of interest and recruiting individuals to assess whether or not they are infected.

Passive surveillance is commonplace because

it is comparatively easy and inexpensive.

Countries around the world have lists of reportable diseases that get

recorded into passive surveillance systems every time a patient is diagnosed.

Thus, in principle, if all cases of

infections seek care, then a passive system

will identify every case.

However, it is more likely the case that only some unknown fraction

of individuals will seek care, and thus be recorded by a passive system.

As a consequence, passive surveillance may be useful for monitoring relative

changes in incidences of infection, but

cannot directly inform the absolute numbers.

Active surveillance, by comparison,

requires that the surveyor select a subset,

called a sample, of the population to

evaluate and then quantify the numbers of

infected, or newly infected individuals in that sample.

Active surveillance, while more complicated to

implement, has a number of important advantages.

First, while passive surveillance most commonly records new

cases of disease, that is a measure of incidents,

active surveillance can be targeted to record

either new infections or all current infections.

That is either incidence or prevalence within a sample.

Second, because the surveyor defines the sample to be measured, it is

straight forward to scale these observations

up to level of the full population.

For example, if a simple random sample of five percent of the population

is measured, and ten infected individuals are detected, then we would

expect there to be about 200 infected individuals in the whole population.

Third, active surveillance can be used

to quantify infection directly, by enrolling individuals

in the sample even if they are not expressing the symptoms of disease.

This may be particularly important for infections that

cause disease only rarely.

For example, polio virus causes paralytic poliomyelitis, the paralytic disease

of the central nervous system in less than one percent of infected individuals.

Thus there may be quite a bit of infection

in a population before a passive system would detect it.

An active surveillance system that tested for infection in asymptomatic

individuals however, might have a much better chance

of detecting infection before it spreads too far.

Again, the types of surveillance used to monitor infection and

disease should be dictated by the objectives of a surveillance program.

In practice, though each has strengths and weaknesses, there are roles

to be played by both passive and active surveillance in the monitoring

of the spread of infection and evaluating the impact of public health controls.

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