Surveillance, Outbreak Investigation, and Field Data Analysis

This chapter introduces the essential role of epidemiological surveillance in monitoring population health, detecting unusual increases in disease occurrence, and supporting timely, evidence-based public health decisions. The chapter defines surveillance as a systematic, ongoing process of collecting, analyzing, interpreting, and sharing health data. Students learn to distinguish key epidemiological indicators, including incidence, prevalence, attack rate, case fatality rate (CFR), mortality rate, and the basic reproduction number (R0). It also examines operational indicators for evaluating surveillance performance, such as sensitivity, positive predictive value (PPV), timeliness, and completeness. It compares different reporting systems—including passive, active, sentinel, and laboratory-based surveillance—by examining their purposes, advantages, and limitations. Through these concepts, Statistics students develop an understanding of how surveillance data can monitor disease trends, identify potential outbreaks, and evaluate public health interventions.

The second major focus is outbreak investigation and the statistical analysis of field data. Students learn the key steps in investigating an outbreak, including confirming an outbreak, establishing a consistent case definition, preparing a line list, and describing cases by person, place, and time. The chapter also emphasizes epidemic curves (epi curves), which plot cases by symptom-onset date and help identify transmission patterns such as common-source, propagated, and mixed outbreaks. The chapter illustrates these methods using an acute diarrheal disease outbreak in a school, demonstrating how attack rates can be compared between exposed and unexposed groups to investigate suspected sources of infection. Students also learn to select appropriate analytical study designs, particularly retrospective cohort and case-control studies, depending on the availability of population records and the circumstances of the outbreak. The course introduces measures of association—including the risk ratio (RR), odds ratio (OR), and risk difference (RD)—alongside confidence intervals and statistical tests to quantify exposure–disease relationships and assess the uncertainty of the findings.

The final component covers time-trend monitoring, risk communication, and data-driven public health decision-making. Students explore how to distinguish random fluctuations from systematic changes in disease occurrence and identify long-term trends, seasonal patterns, and irregular variations in epidemiological time-series data. The chapter introduces practical statistical techniques, including line plots, moving averages, historical thresholds, control charts, linear regression, Poisson regression, and Negative Binomial regression when overdispersion is present. It also emphasizes the importance of consistent population denominators and careful interpretation of reporting delays, missing data, and changes in case definitions. In addition, students learn to communicate epidemiological findings through clear visualizations, confidence intervals, absolute and relative risk measures, and policy briefs that explain both the magnitude of health risks and the uncertainty surrounding estimates. Overall, this chapter equips Statistics students with practical skills to analyze surveillance data, investigate outbreaks, quantify exposure–disease associations, monitor disease trends, and translate statistical evidence into timely, transparent, and responsible public health action.