Disease surveillance
Disease surveillance
Epi/Biostats
Epi/Biostats
Sensitivity and specificity
Positive and negative predictive value
Test precision and accuracy
Incidence and prevalence
Relative and absolute risk
Odds ratio
Attributable risk (AR)
Mortality rates and case-fatality
DALY and QALY
Direct standardization
Indirect standardization
Study designs
Ecologic study
Cross sectional study
Case-control study
Cohort study
Randomized control trial
Clinical trials
Sample size
Placebo effect and masking
Disease causality
Selection bias
Information bias
Confounding
Interaction
Modes of infectious disease transmission
Outbreak investigations
Disease surveillance
Vaccination and herd immunity
Prevention
Introduction to biostatistics
Mean, median, and mode
Probability
Range, variance, and standard deviation
Types of data
Normal distribution and z-scores
Standard error of the mean (Central limit theorem)
Paired t-test
Two-sample t-test
Hypothesis testing: One-tailed and two-tailed tests
One-way ANOVA
Two-way ANOVA
Repeated measures ANOVA
Correlation
Linear regression
Logistic regression
Type I and type II errors
Chi-squared test
Fisher's exact test
Kaplan-Meier survival analysis
Kappa coefficient
Mann-Whitney U test
Spearman's rank correlation coefficient
Flashcards
Disease surveillance
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Key Takeaways
Disease surveillance is the process of collecting, analyzing, and interpreting epidemiological data to monitor the spread of disease. This helps to predict and minimize the damage that harmful outbreaks can cause.