Ensemble bootstrap methodology for forecasting dynamic growth processes using differential equations: application to epidemic outbreaks
نویسندگان
چکیده
Abstract Background Ensemble modeling aims to boost the forecasting performance by systematically integrating predictive accuracy across individual models. Here we introduce a simple-yet-powerful ensemble methodology for trajectory of dynamic growth processes that are defined system non-linear differential equations with applications infectious disease spread. Methods We propose and assess two schemes different parametric bootstrapping procedures uncertainty quantification. Specifically, conduct sequential probabilistic forecasts evaluate their using simple dynamical models good track records including Richards model, generalized-logistic Gompertz model. first test verify functionality method simulated data from phenomenological mechanistic transmission Next, is demonstrated diversity epidemic datasets scenario outbreak Ebola Forecasting Challenge real-world outbreaks influenza, plague, Zika, COVID-19. Results found randomly selects model set each time point frequently outcompeted as well an alternative based on weighted combination yields broader more realistic bounds envelope, achieving not only better coverage rate 95% prediction interval but also improved mean scores datasets. Conclusion Our new outcompete component differ in how variance evaluated generation intervals forecasts.
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ژورنال
عنوان ژورنال: BMC Medical Research Methodology
سال: 2021
ISSN: ['1471-2288']
DOI: https://doi.org/10.1186/s12874-021-01226-9