General Framework for Evaluating Outbreak Prediction, Detection, and Annotation Algorithms

The COVID-19 pandemic has highlighted and accelerated the use of algorithmic-decision support for public health. The latters potential impact and risk of bias and harm urgently call for scrutiny and evaluation standards. One example is the early detection of local infectious disease outbreaks. Whereas many statistical models have been proposed and disparate systems are routinely used, each tai-lored to specific data streams and use, no systematic evaluation strategy of their performance in a real-world context exists. One difficulty in evaluating outbreak prediction, detection, or annotation lies in the scales of different approaches: How to compare slow but fine-grained genetic clustering of individual samples with rapid but coarse anomaly detection based on aggregated syndromic reports? Or alarms generated for different, overlapping geographical regions or demographics? We propose a general, data-driven, user-centric framework for evaluating hetero-geneous outbreak algorithms. Discrete outbreak labels and case counts are defined on a custom data grid, associated target probabilities are then computed and compared with algorithm output. The latter is defined as discrete "signals" are generated for a number of grid cells (the finest available in the benchmarking data set) with different weights and prior outbreak information from which then estimated outbreak label probabilities are derived. The prediction performance is quantified through a series of metrics, including confusion matrix, regression scores, and mutual information. The dimensions of the data grid can be weighted by the user to reflect epidemiological criteria.