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46 Cards in this Set

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Nidus:
a localized reservoir that persists for a
long time. Animals become infected when they
intrude upon a nidus.
Incidental (aka dead-end or accidental)
host:
one that does not usually transmit
an infectious agent to other animals.
Airborne spread
Only small particles get past upper respiratory tract and into lungs
Pathogenicity
proportion of infections that result in clinical disease
Virulence
proportion of infections that result in severe or fatal illness;
virulence may change over time.
Case-fatality risk:
the proportion of clinical illnesses that are fatal.
As a measure of disease severity, it is closely related to virulence
A: Endemic level
B: Ascending slope
C: Plateau
D: Descending slope
E: Secondary peak
A: Endemic level – typical for population
B: Ascending slope – affected by transmission efficiency, incubation period
C: Plateau – length depends on availability of susceptibles
D: Descending slope – also dependent on availability of susceptibles
E: Secondary peak – introduction of new susceptibles
Availability of susceptibles
Epidemic disease: point-source
Epidemic disease: Propagated
Under what conditions can herd
immunity exist?
Infectiousness
Contagiousness
Intrinsic animal factors
factors that are relatively
unchangeable
How can we prove causation?
Association vs. causation
Factor X “causes” disease Y if
Criteria for causation:
Koch’s postulates
Criteria for causation:
Austin Bradford Hill’s postulates
Component, necessary, & sufficient
causes
Categorical data
Interval / measurement / continuous data
Dichotomous Characteristics
Normally-distributed data use mean, median, or mode
Mean is best
summary
measure
Skewed data use mean, median, or mode
Standard Deviation (SD)
Measures of dispersion (spread)
in interval data
Measures of dispersion (spread)
in interval data
Precision (repeatability)
Accuracy affected by
Selection bias
Information bias
Confounding
Cross-sectional study
Cohort study
Clinical / field trial
Case-control study
Confidence values (aka Z-scores)
Standard error of a proportion =
square root [p(1-p)/n]
Is the difference real? Interval data
Method:Z-test for difference bt 2 means
Estimate:Mean1-Mean2
Confidence
interval:z-score x square root[(SD₁squared/count₁)+ (SD₂squared/count₂)]
Decision
rule:Averages are not different if confidence
interval includes 0
Is the difference real? Categorical data
Method:Z-test for difference between 2 proportions
Estimate:Proportion1-Proportion2
Confidence
interval:z-score x square root[(p₁(1-p₁)/count₁)+(p₂(1-p₂)/count₂)]
Decision
rule:Proportions are not different if confidence
interval includes 0
Chi − square =
[(AxD)-(BxC)]sq[total]/[(A+C)(B+D)(A+B)(C+D)]