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Presenter outlines how to choose analytic approaches for public‑health questions
Summary
A presenter in a public‑health training described four analytic outputs—qualitative assessments, nowcasts, short‑term forecasts and scenario models—and advised choosing among them based on the decision to be made, time available, and data quality; the series will explore each in depth.
The presenter opened a public‑health training session by urging listeners to choose analytic approaches that match the question being asked and the timing of the decision. "To begin this training series, let’s discuss how to decide which type of analytic approach is best for your public health need or question," the presenter said.
The session framed model selection around the "5 W" of decision-making: what question is being answered, how quickly a decision is needed and how much data are available. The presenter listed typical questions that call for different approaches, including choosing among interventions, improving real‑time situational awareness, predicting future disease trends and evaluating past public‑health actions. "Each of these could require a different type of modeling approach and output," the presenter said.
The presenter described four classes of analytic outputs and when each is most useful. Qualitative assessments are rapid, judgment‑based appraisals of outbreak trajectory and population risk that can be applied very early or revisited as data improve. "The first are qualitative assessments. These are rapid analyses of potential outbreak trajectory and risk posed to a given population, and can be used at any time horizon," the presenter said.
Nowcasts were presented as tools for estimating real‑time disease burden from partially reported data. "As the word ‘now’ in the name implies, they are helpful in the present to estimate real time disease burden based on partially reported data," the presenter said. Short‑term forecasts were described as useful for projecting burden over coming days and weeks. Scenario models were framed as longer‑term, hypothetical comparisons that vary assumptions about measures or pathogen behavior and can be used months into the future or to explore past counterfactuals. "Last are scenario models in dark blue, which are used to compare different potential, hypothetical, versions of the longer‑term future – or scenarios – based on varied assumptions," the presenter said.
The presenter emphasized that these outputs are complementary rather than exclusive. For example, a short‑term forecasting model might be adapted for scenario modeling or include a nowcasting component. The series will cover each output type in more detail in later sessions, the presenter said.
The training closed with the presenter's roadmap for future sessions and an expectation that subsequent lessons would cover methods, data requirements and validation considerations for each output type.
