Evidence map›Paper›PMID 31395373›Full record

ArticleEpidemics2019

Real-time predictions of the 2018-2019 Ebola virus disease outbreak in the Democratic Republic of the Congo using Hawkes point process models.

J Daniel Kelly, Junhyung Park, Ryan J Harrigan, Nicole A Hoff, Sarita D Lee, Rae Wannier, Bernice Selo, Mathias Mossoko, Bathe Njoloko, Emile Okitolonda-Wemakoy and 7 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Epidemics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

16 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Rates of SARS-CoV-2 transmission between and into California state prisons.medRxiv : the preprint server for health sciences · 2023
    Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Estimating COVID-19Machine learning with applications · 2021
    Article
  10. Article
  11. Review
  12. Article
  13. Article
  14. Article
  15. The challenges of modeling and forecasting the spread of COVID-19.Proceedings of the National Academy of Sciences of the United States of America · 2020
    Article
  16. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

17 authors.

J Daniel KellyDepartment of Epidemiology and Biostatistics, University of California, San Francisco, CA, USA; F.I. Proctor Foundation, University of California, San Francisco, CA USA. Electronic address: Dan.Kelly@ucsf.edu.
Junhyung ParkDepartment of Statistics, University of California, Los Angeles, CA, USA.
Ryan J HarriganCenter for Tropical Research, Institute of the Environment and Sustainability, University of California, Los Angeles, CA, USA.
Nicole A HoffDepartment of Epidemiology, University of California, Los Angeles, CA, USA.
Sarita D LeeDepartment of Statistics, University of California, Los Angeles, CA, USA.
Rae WannierDepartment of Epidemiology and Biostatistics, University of California, San Francisco, CA, USA.
Bernice SeloMinistry of Health, Kinshasa, Congo.
Mathias MossokoMinistry of Health, Kinshasa, Congo.
Bathe NjolokoMinistry of Health, Kinshasa, Congo.
Emile Okitolonda-WemakoySchool of Public Health, University of Kinshasa, Kinshasa, Congo.
Placide Mbala-KingebeniInstitut National de Recherche Biomedicale, Kinshasa, Congo.
George W RutherfordDepartment of Epidemiology and Biostatistics, University of California, San Francisco, CA, USA.
Thomas B SmithCenter for Tropical Research, Institute of the Environment and Sustainability, University of California, Los Angeles, CA, USA.
Steve Ahuka-MundekeInstitut National de Recherche Biomedicale, Kinshasa, Congo.
Jean Jacques Muyembe-TamfumInstitut National de Recherche Biomedicale, Kinshasa, Congo.
Anne W RimoinDepartment of Epidemiology, University of California, Los Angeles, CA, USA.
Frederic Paik SchoenbergDepartment of Statistics, University of California, Los Angeles, CA, USA.

Funding

Understanding the epidemiology and natural history of unrecognized Ebola virus infectionK23AI146268 · NIAID · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI KELLY, JOHN DANIEL · 2019 to 2022
$748k
NIAID NIH HHS K23 AI146268NIAID NIH HHS L30 AI126521
6 · The paper itself

Abstract

As of June 16, 2019, an Ebola virus disease (EVD) outbreak has led to 2136 reported cases in the northeastern region of the Democratic Republic of the Congo (DRC). As this outbreak continues to threaten the lives and livelihoods of people already suffering from civil strife and armed conflict, relatively simple mathematical models and their short-term predictions have the potential to inform Ebola response efforts in real time. We applied recently developed non-parametrically estimated Hawkes point processes to model the expected cumulative case count using daily case counts from May 3, 2018, to June 16, 2019, initially reported by the Ministry of Health of DRC and later confirmed in World Health Organization situation reports. We generated probabilistic estimates of the ongoing EVD outbreak in DRC extending both before and after June 16, 2019, and evaluated their accuracy by comparing forecasted vs. actual outbreak sizes, out-of-sample log-likelihood scores and the error per day in the median forecast. The median estimated outbreak sizes for the prospective thee-, six-, and nine-week projections made using data up to June 16, 2019, were, respectively, 2317 (95% PI: 2222, 2464); 2440 (95% PI: 2250, 2790); and 2544 (95% PI: 2273, 3205). The nine-week projection experienced some degradation with a daily error in the median forecast of 6.73 cases, while the six- and three-week projections were more reliable, with corresponding errors of 4.96 and 4.85 cases per day, respectively. Our findings suggest the Hawkes point process may serve as an easily-applied statistical model to predict EVD outbreak trajectories in near real-time to better inform decision-making and resource allocation during Ebola response efforts.

Indexed as

Disease OutbreaksData CollectionDecision MakingDemocratic Republic of the CongoEbolavirusHemorrhagic Fever, EbolaHumansModels, StatisticalModels, TheoreticalProspective StudiesTimeCompartmental modelsDemocratic Republic of CongoEbola virus diseaseHawkes point processMathematical modeling

Identifiers

PMID31395373
PMCPMC7358183

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.