Video 4.3: Incubation period

Loading...

来自 The University of Hong Kong 的课程

Epidemics

25 个评分

The University of Hong Kong

Epidemics

25 个评分

“If history is our guide, we can assume that the battle between the intellect and will of the human species and the extraordinary adaptability of microbes will be never-ending.” (1) Despite all the remarkable technological breakthroughs that we have made over the past few decades, the threat from infectious diseases has significantly accelerated. In this course, we will learn why this is the case by looking at the fundamental scientific principles underlying epidemics and the public health actions behind their prevention and control in the 21st century. This course covers the following four topics: 1. Origins of novel pathogens; 2. Analysis of the spread of infectious diseases; 3. Medical and public health countermeasures to prevent and control epidemics; 4. Panel discussions involving leading public health experts with deep frontline experiences to share their views on risk communication, crisis management, ethics and public trust in the context of infectious disease control. In addition to the original introductory sessions on epidemics, we revamped the course by adding: - new panel discussions with world-leading experts; and - supplementary modules on next generation informatics for combating epidemics. -------------------------------------------------------- (1) Fauci AS, Touchette NA, Folkers GK. Emerging Infectious Diseases: a 10-Year Perspective from the National Institute of Allergy and Infectious Diseases. Emerg Infect Dis 2005 Apr; 11(4):519-25. What you'll learn - Demonstrate knowledge of the origins, spread and control of infectious disease epidemics - Demonstrate understanding of the importance of effective communication about epidemics - Demonstrate understanding of key contemporary issues relating to epidemics from a global perspective

从本节课中

Theme Two: Spread (Infectious disease epidemiology)

Week 4 Introduction1:10

Video 4.1: The basics8:00

Video 4.2: Epidemic curve7:11

Video 4.3: Incubation period7:29

Video 4.4: Transmissibility of communicable diseases7:58

Video 4.5: Timescale of disease transmission5:51

Video 4.6: Severity of infectious disease7:17

与讲师见面

Gabriel M. Leung (HKU)
Dean
Li Ka Shing Faculty of Medicine
Joseph T. Wu (HKU)
Associate Professor
Li Ka Shing Faculty of Medicine
Kwok-Yung Yuen (HKU)
Professor
Li Ka Shing Faculty of Medicine
Tommy Lam (HKU)
Assistant Professor
Li Ka Shing Faculty of Medicine
Maria Huachen Zhu (HKU)
Associate Professor
Li Ka Shing Faculty of Medicine
Guan Yi (HKU)
Chair Professor
Li Ka Shing Faculty of Medicine
Benjamin Cowling (HKU)
Professor
Li Ka Shing Faculty of Medicine
Thomas Abraham (HKU)
Associate Professor
Journalism and Media Studies Centre
Mark Jit (LSHTM)
Professor
Department of Infectious Disease Epidemiology
Malik Peiris (HKU)
Chair Professor
Li Ka Shing Faculty of Medicine
Marc Lipsitch (Harvard)
Professor
Department of Epidemiology

In this session, we will discuss the definition

and some uses of incubation period.

The incubation period of an infectious disease

is defined as the time from infection

to symptoms onset.

The incubation period may be substantially different

among people infected with the same pathogen

due to several factors.

One of the most important factors

is the dose and route of infection.

For example, higher infection dose of salmonella

is associated with faster symptoms onset

and, therefore, a shorter incubation period.

Biological factors of the infected host

are also important.

Some people may be more susceptible

to faster disease progression

and severe complications because of their age,

genetics or competence of their immune systems.

For example, the incubation period of AIDS

tends to be longer in HIV-infected individuals

who are younger.

Pharmacologic factors are also important.

For example, between 1975 and 1985,

the incubation period of AIDS

among HIV-infected individuals

was substantially lengthened

by the advent and timely use

of effective antiretroviral therapies.

The frequency distributions of incubation period

for many diseases are typically right-skewed,

which means that they have long right tails,

which, in turn, means that the incubation period

is much longer than average

for a significant proportion of infected people.

To summarize the statistical variations

of incubation period,

we often describe its empirical frequency distribution

with commonly-used probability distributions,

for example, lognormal distribution,

which means that the frequency distribution

looks like a normal distribution

if the incubation period is plotted on a log-scale

along the horizontal axis.

Let us now look at some real examples.

This is the frequency distribution

of the incubation period of 168 SARS patients

in Hong Kong in 2003.

The best-fit lognormal distribution for these data

has a median of 4.4 days

and a 90th percentile of around 10 days,

which means that 90% of SARS patients

had incubation period shorter than 10 days.

Obviously, the incubation periods

of different infectious diseases can be very different.

The incubation period of human cases

of avian influenza H5N1 and H7N9

has a median of around four days.

In comparison, the incubation period

of seasonal influenza is shorter

with a median of around one day,

while the incubation period of measles is longer

with a median of around 12 days.

Knowledge about the incubation period

of an infectious disease

is important for clinical management.

For example, shorter incubation period

is associated with high risk

of severe complications for tetanus.

Similarly, delaying the onset of symptoms

using antiretroviral therapies

can reduce the risk of opportunistic infections

and lengthen the survival time for AIDS patients.

Knowledge about the incubation period

is also important for public health control.

For example, to control the spread

of a communicable disease,

especially for emerging

infectious diseases such as SARS,

health policymakers may decide to quarantine

suspected cases and contacts of confirmed cases.

Quarantine means isolating

suspected cases and contacts

from the general community

to prevent disease transmission

in case they have been infected

but do not yet have the clinical

or virological evidence of infection.

The incubation period is often used as the basis

for determining the duration of quarantine.

For example, by setting the quarantine duration

to be the 90th percentile

of the incubation period distribution,

we can expect that at least 90%

of the infected people who are quarantined

will show symptoms during quarantine

and, therefore, be identified and followed up.

During the SARS epidemic in Hong Kong in 2003,

the quarantine duration for suspected cases

and contacts of confirmed cases was 10 days,

which corresponded to the 90th percentile

of the best estimate

of incubation period distribution at that time.

An accurate estimate

of the incubation period distribution

is also crucial for interpreting

disease surveillance data

and understanding epidemic dynamics.

During the early decades of the HIV epidemic,

disease surveillance of HIV

was mostly based on AIDS diagnoses

because there was no HIV screening.

During those years, the incubation period of AIDS

had a median of around nine years

and could range from three to more than 12 years,

so the observed AIDS cases

represented only a small proportion

of the total number of HIV infections

in the population.

As a hypothetical example,

suppose the incubation period distribution

of AIDS looks like this,

which ranges from 3 to 15 years

with a median of 9 years,

and suppose the annual number of new HIV infections

from Year 1 to 20 looks like this.

Those infected in Year 1

would develop AIDS in Year 4 to 16

according to the incubation period distribution.

Similarly, those infected in Year 2

would develop AIDS in Year 5 to 17, and so on.

In any given Year t,

the cumulative number of AIDS cases

is equal to the number of HIV infections

in Year 1 times the proportion

of infections whose incubation period

is shorter than t minus 1 years,

plus the number of HIV infections

in Year 2 times the probability

that the incubation period

is shorter than t minus 2 years, and so on.

For example, the cumulative number

of AIDS cases in Year 10,

which is six years after the first diagnosis of AIDS,

represents only 20% of the total number

of HIV infections in this example.

In short, the cumulative number of AIDS cases

in Year t is equal to the sum

of the number of HIV infections

in all years before t,

weighted by the probability that the incubation period

is shorter than the time elapsed

between the year of infection and Year t.

This method of inferring HIV incidence

from AIDS epi-curves and incubation period

is called backcalculation.

Robust backcalculation

of HIV incidence from AIDS epi-curves

obviously requires an accurate estimate

of the incubation period distribution.

To summarize, in this session,

we have defined incubation period

and discussed some practical uses

of knowledge about incubation period.

探索我们的目录

免费加入并获得个性化推荐、更新和优惠。

Coursera 致力于普及全世界最好的教育，它与全球一流大学和机构合作提供在线课程。

© 2018 Coursera Inc. 保留所有权利。

通过 App Store 下载

通过 Google Play 获取