Comparison of prospective Hawkes and recursive point process models for Ebola in DRC. (2nd August 2021)

• Record Type: Journal Article
• Authors: Lee, Sarita D.; Shen, Andy A.; Park, Junhyung; Harrigan, Ryan J.; Hoff, Nicole A.; Rimoin, Anne W.; Paik Schoenberg, Frederic
• Is Part Of: Journal of forecasting. Volume 41:Number 1(2022)
• Page Start: 201
• Page End: 210
• Publication Date: 2021-08-02
• Subjects: disease epidemics -- forecasting -- Hawkes model -- point processes -- recursive -- self‐exciting; Forecasting -- Periodicals; Forecasting -- Mathematical models -- Periodicals; 003.2
• DOI: 10.1002/for.2803
• Languages: English
• ISSNs: 0277-6693

Abstract

Abstract: Point process models, such as Hawkes and recursive models, have recently been shown to offer improved accuracy over more traditional compartmental models for the purposes of modeling and forecasting the spread of disease epidemics. To explicitly test the performance of these two models in a real‐world and ongoing epidemic, we compared the fit of Hawkes and recursive models to outbreak data on Ebola virus disease (EVD) in the Democratic Republic of the Congo in 2018–2020. The models were estimated, and the forecasts were produced, time‐stamped, and stored in real time, so that their prospective value can be assessed and to guard against potential overfitting. The fit of the two models was similar, with both models resulting in much smaller errors in the beginning and waning phases of the epidemic and with slightly smaller error sizes on average for the Hawkes model compared with the recursive model. Our results suggest that both Hawkes and recursive point process models can be used in near real time during the course of an epidemic to help predict future cases and inform management and mitigation strategies.