Nowcast-It: A Practical Toolbox for Real-Time Adjustment of Reporting Delays in Epidemic Surveillance

Reporting delays caused by delays in case detection, symptom onset after infection, seeking medical care, or diagnostics distort the accurate forecasting of diseases during epidemics and pandemics. This inherent delay between the time of symptom onset and the time a case is reported is known as the reporting delay. To address this, we introduce a practical nowcasting approach grounded in survival analysis and actuarial science, explicitly allowing for non-stationarity in reporting delay patterns to better capture real-world variability. Despite its relevance, no flexible and accessible toolbox currently exists for non-stationary delay adjustment. Here, we present Nowcast-It, a tutorial-based toolbox that includes three components: (1) an R codebase for delay adjustment, (2) MATLAB algorithms for performance evaluation, and (3) a user-friendly R-Shiny application to enable interactive visualization and reporting delay correction without requiring coding expertise. The toolbox supports daily, weekly, or monthly resolution data and enables model performance assessment using metrics such as mean absolute error, mean squared error, and 95% prediction interval coverage. We demonstrate the utility of Nowcast-It using publicly available weekly Ebola case data from the Democratic Republic of Congo. We and others have adjusted for reporting delays in real-time analyses (e.g., Singapore) and produced early COVID-19 forecasts; here we package those delay-adjustment routines into an accessible toolbox. It is designed for researchers, students, and policymakers alike, offering a scalable and accessible solution for addressing reporting delays during outbreaks.