Recommendations for the analysis of leading indicators in epidemic surveillance

Leading indicator analyses, a type of time series analysis, are frequently used to assess how epidemic surveillance signals relate to one another. Strong leading indicators can drive early, and better decisions in public health systems. We conducted a narrative review of leading indicator studies and evaluated methodological limitations in their reporting and analysis, which we used to generate recommendations in retrospective surveillance analyses. Achievable recommendations are provided based on the reporting and analysis components of real-time epidemic time series analysis. We provide contextual examples from the literature of previous studies demonstrating good practice and highlighting areas for improvement. We present a checklist and workflow that emphases real-time constraints. These recommendations include causal mechanism reporting, sample coverage and bias, reporting delays and data revisions, multiple event measurement, spatio-temporal granularly, time varying relationships, smoothing, transformations, uncertainty, and predictive utility. By following these good practices, analysts can improve research on leading indicators to make analyses more directly actionable for public health decision makers. This work builds on the existing methodology literature by giving a broader framework for overall good practices.