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Transcript
This presentation is made available through a Creative Commons AttributionNoncommercial license. Details of the license and permitted uses are available at
http://creativecommons.org/licenses/by-nc/3.0/
© 2010 Dr. Juliet Pulliam and the Clinic on the Meaningful Modeling of Epidemiological
Data
Title: Introduction to Infectious Disease Data
Attribution: Dr. Juliet Pulliam, Clinic on the Meaningful Modeling of Epidemiological Data
Source URL: http://lalashan.mcmaster.ca/theobio/mmed/index.php/IntroIDData
For further information please contact Dr. Juliet Pulliam ([email protected]).
Introduction to infectious
disease data
Dr. Juliet Pulliam
Clinic on the Meaningful Modeling of Biological Data
RAPIDD Program, DIEPS
and BSc Honours Course in Biomathematics
Fogarty International Center, NIH, USA and
African Institute for the Mathematical Sciences
Department of Ecology & Evolutionary Biology
Muizenberg, South Africa
University of California at Los Angeles (UCLA)
26 May 2010
What is an infectious disease?
Disease
A deviation from the normal physiological status of an
organism that negatively affects its survival or
reproduction
Disease
A deviation from the normal physiological status of an
organism that negatively affects its survival or
reproduction
Infectious Disease
A disease in one organism (the host) that is caused by
another organism (pathogen or parasite) which has
entered the host’s body
Case definition
“a set of standard criteria for deciding whether a person
has a particular disease [or infection]”
Case definition
“a set of standard criteria for deciding whether a person
has a particular disease [or infection]”
Person:
Place:
Time:
Clinical description:
A “case” study
Case definition
“a set of standard criteria for deciding whether a person
has a particular disease [or infection]”
Person: Residents of Mexico, recent visitors to Mexico
Place: North America (Mexico, US, Canada)
Time: On or after March 15, 2009
Clinical description: severe influenza-like illness (fever,
cough, sore throat, body aches, headache, chills,
fatigue, and/or severe respiratory distress)
Sunday, 26 April
Confirmed case: signs and symptoms plus
laboratory confirmation
Probable case: signs and symptoms in an
individual meeting person, place, and time
criteria plus contact with a known case or
more specific clinical signs
Possible case: signs and symptoms in an
individual meeting person, place, and time
criteria plus a physician diagnosis
Suspect case: signs and symptoms in an
individual meeting person, place, and time
criteria
Not a case: failure to fulfill the criteria for a
confirmed, probable, possible, or suspect
case
Sunday, 26 April
Monday, 27 April
Tuesday, 28 April
Wednesday, 29 April
Thursday, 30 April
Friday, 1 May
Saturday, 2 May
Sunday, 3 May
Monday, 4 May
1
1
X
14
Tuesday, 5 May (?)
?
?
Monday, 4 May
Case definition
“a set of standard criteria for deciding whether a person
has a particular disease [or infection]”
Person:
Place:
Time:
Clinical description:
Case definition
Person: Residents of and recent visitors to North
America, and their close contacts or others in their
community
Place: Worldwide
Time: On or after March 15, 2009
Clinical description: fever, cough, sore throat, body
aches, headache, chills, fatigue, and/or severe
respiratory distress; diarrhea and vomiting do not rule
out diagnosis
Incidence of disease
Date of onset
Incidence of infection
Mathematical expression?
Ways of collecting data on cases
Hampson et al. 2009
Surveillance
Surveillance + Contact
Tracing/Outbreak investigation
Ways of collecting data on cases
Epidemiological studies
Prevalence
Mathematical expression?
of infection
of disease
of antibodies (seroprevalence)
Source: UNAIDS HIV Surveillance Database
Seroprevalence
Can be related to:
Prevalence of infection
Past exposure
May or may not be:
Prevalence of resistance
Specific to infection of interest
Seroprevalence
Can be related to:
Prevalence of infection
Past exposure
May or may not be:
Prevalence of resistance
Specific to infection
Pulliam et al. (In review)
Seroconversion
Mathematical expression?
Levels of data aggregation
Aggregated data
De-identified data
Personally identifying data
Sources
Slides 8 and 10-22: Images are reproduced from The New York Times
(online edition), April 2009.
Slide 25: Data are available from The Centers for Disease Control and
Prevention, Atlanta, GA, USA. www.cdc.gov/flu/weekly - Image available
at: http://www.cdc.gov/flu/weekly/weeklyarchives2008-2009/weekly36.htm
Slide 27: Hampson et al. (2009) Transmission dynamics and prospects for
the elimination of canine rabies. PLoS Biology 7(3): e1000053.
doi:10.1371/journal.pbio.1000053
Slide 29: Data from UNAIDS HIV Surveillance Database, provided
courtesy of USAID.
Slide 31: Pulliam et al. (In review) Agricultural intensification, priming for
persistence, and the emergence of Nipah virus, a lethal bat-borne
zoonosis.