Evidence map›Paper›PMID 41505721›Full record

ArticleMSMR2026

Strategies for forecasting long COVID in the active component U.S. military.

Mark L Bova, Tara N Palmore, Guoqing Diao, Jamaal A Russell, Manya Magnus

Abstract read
In one paragraph

Article in MSMR, 2026. 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

5 authors.

Mark L BovaIntegrated Biosurveillance Branch, Armed Forces Health Surveillance Division, Silver Spring, MD.
Tara N PalmoreDepartment of Medicine, School of Medicine and Health Sciences, George Washington University, Washington, DC.
Guoqing DiaoDepartment of Biostatistics and Bioinformatics, Milken Institute School of Public Health, George Washington University, Washington, DC.
Jamaal A RussellIntegrated Biosurveillance Branch, Armed Forces Health Surveillance Division, Silver Spring, MD.
Manya MagnusDepartment of Epidemiology, Milken Institute School of Public Health, George Washington University, Washington, DC.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Long COVID, or post-acute coronavirus disease syndrome, represents a potentially serious threat to military readiness. Forecasts of future long COVID diagnoses could help prepare senior leaders for disruptions. Few studies predicting the incidence of long COVID have been published to date, however. Using existing COVID-19 and long COVID diagnoses, as well as demographic and outpatient encounter data, 1- to 6-month ahead and full 6-month forecasts were generated using time series and machine learning models trained on various covariate data. Forecasting models generated accurate predictions of long COVID diagnoses up to 6 months ahead of the forecasted date. Several model and covariate combinations were within 5% of the observed number of diagnoses over the full 6-month testing period, while monthly forecasts of long COVID diagnoses had median absolute percentage errors ranging from 3% to 10% for the best performing model combinations. Simple forecasting models and distribution-based forecasts that utilize existing clinical databases can provide accurate predictions of incident long COVID up to 6 months in advance and can be used to prepare for the burden of new long COVID diagnoses. Accurate predictions of long COVID cases over a 6-month period were achieved by utilizing existing COVID-19 case and outpatient encounter data from January 1, 2020, through December 31, 2022. Long COVID symptoms can cause disruptions to military readiness and prevent a healthy force, especially after surges in COVID-19 cases. The ability to use existing data sources to accurately predict future cases of long COVID allows senior leaders to anticipate and prepare for potential changes in the availability of service members.

Indexed as

COVID-19Military PersonnelForecastingHumansIncidencePost-Acute COVID-19 SyndromePrediction AlgorithmsPredictive Learning ModelsUnited Statesarmed forcescoronavirus disease 2019COVID-19effectsforecastinglearninglong COVIDmachinemilitarymodelingmodelspost-acute coronavirus disease syndromepredictingsequelaeUnited States

Identifiers

PMID41505721
PMCPMC12874400

What OpenQuestion holds

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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.