The first half introduces the core concepts and tools of epidemiology: risk, relative risk and attack rate; case-control studies; cohort studies; confidence intervals, significance tests and sample sizes; sensitivity, specificity, positive predictive value and misclassification; multivariate analysis, confounding, regression models, and interaction; and survival analysis.
The mathematics in this is kept rudimentary, with the underlying statistical theory hidden almost completely and concrete numerical examples preferred to general constructions. An equation for the error factor, for example, is provided without any theoretical explanation but with a worked example. I occasionally found this annoying — for those with a statistics background the detailed step-through calculations are arguably more of a distraction than the mathematics would have been — but at this level this is probably the best approach.
The second half of Modern Infectious Disease Epidemiology applies the methods of the first half to infectious diseases, covering models of epidemics, detection and handling of outbreaks, routine surveillance, measuring infectivity, the natural history of infectious diseases, seroepidemiology, contact patterns, deciding whether an illness is infectious, vaccination, and AIDS and variant CJD.
Much of this seems to be aimed at doctors or other medical professionals who will be facing clinical decisions, but it could be read by anyone with a basic biology background, probably even bright high school students. Infectious diseases are among the more striking and engaging epidemiological topics and its plentiful case studies make Modern Infectious Disease Epidemiology especially well motivated.
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