ArticleJournal of applied statistics2022
A non-parametric Hawkes model of the spread of Ebola in west Africa.
Article in Journal of applied statistics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Global infectious disease early warning models: An updated review and lessons from the COVID-19 pandemic.Infectious Disease Modelling · 2025Review
- On the use and misuse of time-rescaling to assess the goodness-of-fit of self-exciting temporal point processes.Journal of applied statistics · 2025Article
- Rates of SARS-CoV-2 transmission between and into California state prisons.medRxiv : the preprint server for health sciences · 2023Article
- Current Landscape of Coccidioidomycosis.Journal of fungi (Basel, Switzerland) · 2022Review
- BAYESIAN MITIGATION OF SPATIAL COARSENING FOR A HAWKES MODEL APPLIED TO GUNFIRE, WILDFIRE AND VIRAL CONTAGION.The annals of applied statistics · 2022Article
- Point Process Models for the Spread of Coccidioidomycosis in California.Infectious disease reports · 2021Article
- Simple discrete-time self-exciting models can describe complex dynamic processes: A case study of COVID-19.PloS one · 2021Article
- A time-modulated Hawkes process to model the spread of COVID-19 and the impact of countermeasures.Annual reviews in control · 2021Review
- Real-time predictions of the 2018-2019 Ebola virus disease outbreak in the Democratic Republic of the Congo using Hawkes point process models.Epidemics · 2019Article
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4 authors.
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Abstract
Recently developed methods for the non-parametric estimation of Hawkes point process models facilitate their application for describing and forecasting the spread of epidemic diseases. We use data from the 2014 Ebola outbreak in West Africa to evaluate how well a simple Hawkes point process model can forecast the spread of Ebola virus in Guinea, Sierra Leone, and Liberia. For comparison, SEIR models that fit previously to the same data are evaluated using identical metrics. To test the predictive power of each of the models, we simulate the ability to make near real-time predictions during an actual outbreak by using the first 75% of the data for estimation and the subsequent 25% of the data for evaluation. Forecasts generated from Hawkes models more accurately describe the spread of Ebola in each of the three countries investigated and result in a 38% reduction in RMSE for weekly case estimation across all countries when compared to SEIR models (total RMSE of 59.8 cases/week using SEIR compared to 37.1 for Hawkes). We demonstrate that the improved fit from Hawkes modeling cannot be attributed to overfitting and evaluate the advantages and disadvantages of Hawkes models in general for forecasting the spread of epidemic diseases.
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