Evidence map›Paper›PMID 37905035›Full record

ArticlemedRxiv : the preprint server for health sciences2023

Retrospective evaluation of short-term forecast performance of ensemble sub-epidemic frameworks and other time-series models: The 2022-2023 mpox outbreak across multiple geographical scales, July 14

Amanda Bleichrodt, Ruiyan Luo, Alexander Kirpich, Gerardo Chowell

Abstract readPreprint
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

4 authors.

Amanda BleichrodtDepartment of Population Health Sciences, School of Public Health, Georgia State University, Atlanta, GA, USA.
Ruiyan LuoDepartment of Population Health Sciences, School of Public Health, Georgia State University, Atlanta, GA, USA.
Alexander KirpichDepartment of Population Health Sciences, School of Public Health, Georgia State University, Atlanta, GA, USA.
Gerardo ChowellDepartment of Population Health Sciences, School of Public Health, Georgia State University, Atlanta, GA, USA.

Funding

Ebola modeling: behavior, asymptomatic infection, and contacts (2019-nCoV Admin Supplement)R01GM130900 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI PORCO, TRAVIS CHRISTIAN · 2019 to 2022
$1.6M
NIGMS NIH HHS R01 GM130900
6 · The paper itself

Abstract

In May 2022, public health officials noted an unprecedented surge in mpox cases in non-endemic countries worldwide. As the epidemic accelerated, multi-model forecasts of the epidemic's trajectory were critical in guiding the implementation of public health interventions and determining policy. As the case levels have significantly decreased as of early September 2022, evaluating model performance is essential to advance the growing field of epidemic forecasting. Using laboratory-confirmed mpox case data from the Centers for Disease Control and Prevention (CDC) and Our World in Data (OWID) teams through the week of January 26th, 2023, we generated retrospective sequential weekly forecasts (e.g., 1-4-weeks) for Brazil, Canada, France, Germany, Spain, the United Kingdom, the USA, and at the global scale using models that require minimal input data including the auto-regressive integrated moving average (ARIMA), general additive model (GAM), simple linear regression (SLR), Facebook's Prophet model, as well as the sub-epidemic wave (spatial-wave) and

Identifiers

PMID37905035
PMCPMC10615009

What OpenQuestion holds

Textmetadata
LicenceCC BY-ND
Read underepoch 390

Registered trials

None linked

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.