ArticleRoyal Society open science2024
Evaluating the forecasting performance of ensemble sub-epidemic frameworks and other time series models for the 2022-2023 mpox epidemic.
Article in Royal Society open science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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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.
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.
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Who cites it
6 citing papers in PubMed.
- The future of HIV: challenges in meeting the 2030 Ending the HIV Epidemic in the US (EHE) reduction goal.AIDS (London, England) · 2025Article
- The Future of HIV: Challenges in meeting the 2030medRxiv : the preprint server for health sciences · 2025Article
- StatModPredict: A user-friendly R-Shiny interface for fitting and forecasting with statistical models.PloS one · 2025Article
- The resurgence of monkeypox: Epidemiology, clinical features, and public health implications in the post-smallpox eradication era.New microbes and new infections · 2024Review
- Assessing the reliability of medical resource demand models in the context of COVID-19.BMC medical informatics and decision making · 2024Article
- Evaluating the forecasting performance of ensemble sub-epidemic frameworks and other time series models for the 2022-2023 mpox epidemic.Royal Society open science · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
During the 2022-2023 unprecedented mpox epidemic, near real-time short-term forecasts of the epidemic's trajectory were essential in intervention implementation and guiding policy. However, as case levels have significantly decreased, evaluating model performance is vital to advancing the field of epidemic forecasting. Using laboratory-confirmed mpox case data from the Centers for Disease Control and Prevention and Our World in Data teams, we generated retrospective sequential weekly forecasts for Brazil, Canada, France, Germany, Spain, the United Kingdom, the United States and at the global scale using an auto-regressive integrated moving average (ARIMA) model, generalized additive model, simple linear regression, Facebook's Prophet model, as well as the sub-epidemic wave and
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Registered trials
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.