A communicable disease prediction benchmarking platform

The paper presents a platform for benchmarking disease prediction algorithms and mathematical models. The platform is applied to compare Bayesian and compartmental disease prediction models using. We used weekly aggregated cases of various diseases collected from a microbiology laboratory that covers northern Norway. The platform enables integration and benchmarking of various disease prediction models. Our benchmark shows that the Bayesian model was better on predicting the number of cases on a weekly basis. Normalized root mean square error (NRMSE) for the Bayesian prediction was within the range 0.072-0.1498 for weekly predictions, 0.171-0.254 for monthly. The compartmental SIR(S) model achieved a NRMSE of 0.133 for the weekly prediction against Influenza A data. Disease prediction models benchmarking platforms can help to improve the status of disease prediction systems, investment and time of development. It can speeds up mathematical modeling through its integrated environment for testing and evaluation.