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46 Cards in this Set
- Front
- Back
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Nidus:
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a localized reservoir that persists for a
long time. Animals become infected when they intrude upon a nidus. |
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Incidental (aka dead-end or accidental)
host: |
one that does not usually transmit
an infectious agent to other animals. |
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Airborne spread
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Only small particles get past upper respiratory tract and into lungs
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Pathogenicity
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proportion of infections that result in clinical disease
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Virulence
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proportion of infections that result in severe or fatal illness;
virulence may change over time. |
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Case-fatality risk:
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the proportion of clinical illnesses that are fatal.
As a measure of disease severity, it is closely related to virulence |
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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 |
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Availability of susceptibles
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Epidemic disease: point-source
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Epidemic disease: Propagated
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Under what conditions can herd
immunity exist? |
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Infectiousness
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Contagiousness
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Intrinsic animal factors
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factors that are relatively
unchangeable |
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How can we prove causation?
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Association vs. causation
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Factor X “causes” disease Y if
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Criteria for causation:
Koch’s postulates |
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Criteria for causation:
Austin Bradford Hill’s postulates |
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Component, necessary, & sufficient
causes |
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Categorical data
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Interval / measurement / continuous data
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Dichotomous Characteristics
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Normally-distributed data use mean, median, or mode
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Mean is best
summary measure |
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Skewed data use mean, median, or mode
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Standard Deviation (SD)
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Measures of dispersion (spread)
in interval data |
Measures of dispersion (spread)
in interval data |
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Precision (repeatability)
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Accuracy affected by
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Selection bias
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Information bias
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Confounding
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Cross-sectional study
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Cohort study
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Clinical / field trial
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Case-control study
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Confidence values (aka Z-scores)
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Standard error of a proportion =
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square root [p(1-p)/n]
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Is the difference real? Interval data
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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 |
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Is the difference real? Categorical data
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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 |
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Chi − square =
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[(AxD)-(BxC)]sq[total]/[(A+C)(B+D)(A+B)(C+D)]
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