Evidence map›Paper›PMID 41921899›Full record

ArticleInternational journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases2026

Machine learning and probabilistic approaches for forecasting infectious disease transmission and cases.

Md Sakhawat Hossain, Ravi Goyal, Natasha K Martin, Victor DeGruttola, Tanvir Ahammed, Christopher McMahan, Lior Rennert

Abstract read
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In one paragraph

Article in International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases, 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

5 · Who and what money

Authors and funding

7 authors.

Md Sakhawat HossainDepartment of Public Health Sciences, Clemson University, Clemson, SC, USA; Center for Public Health Modeling and Response, Clemson University, Clemson, SC, USA.
Ravi GoyalDivision of Infectious Diseases & Global Public Health, University of California San Diego, La Jolla, CA, USA.
Natasha K MartinDivision of Infectious Diseases & Global Public Health, University of California San Diego, La Jolla, CA, USA.
Victor DeGruttolaDivision of Biostatistics, Herbert Wertheim School of Public Health and Longevity Science, University of California San Diego, San Diego, CA, USA.
Tanvir AhammedDepartment of Public Health Sciences, Clemson University, Clemson, SC, USA; Center for Public Health Modeling and Response, Clemson University, Clemson, SC, USA.
Christopher McMahanCenter for Public Health Modeling and Response, Clemson University, Clemson, SC, USA; School of Mathematical and Statistical Sciences, Clemson University, Clemson, SC, USA.
Lior RennertDepartment of Public Health Sciences, Clemson University, Clemson, SC, USA; Center for Public Health Modeling and Response, Clemson University, Clemson, SC, USA. Electronic address: liorr@clemson.edu.

Funding

Developing a dynamic modeling framework for surveillance, prediction, and real-time resource allocation to reduce health disparities during Covid-19 and future pandemicsR01LM014193 · NLM · CLEMSON UNIVERSITY · PI Lior Rennert · 2023 to 2026
$2.5M
NLM NIH HHS R01 LM014193
6 · The paper itself

Abstract

objectivesForecasting the effective reproductive number (R

methodsWe first estimated R

resultsThis ensemble-based approach outperformed EpiNow2 across different forecast horizons (7-day, 14-day, and 21-day). In the first forecast period (November 11, 2020-February 02, 2021), the ensemble achieved a median pepercentage agreement (PA) of 96.5% (IQR: 95.4-97.1%) for 7-day horizon R

conclusionThis study presents a flexible forecasting framework that integrates Bayesian estimation, spatial smoothing, and ensemble machine learning to improve the accuracy of COVID-19 transmission and case forecasts. The approach enhances epidemic forecasting performance and offers scalable tools to support data-driven public health preparedness and response.

Indexed as

Communicable DiseasesCOVID-19Machine LearningBasic Reproduction NumberBayes TheoremBoosting Machine Learning AlgorithmsForecastingHumansModels, StatisticalPandemicsPrediction AlgorithmsPredictive Learning ModelsRandom ForestSARS-CoV-2South CarolinaCOVID-19Effective reproductive numberForecastingInfectious disease modelingMachine learning

Identifiers

PMID41921899
PMCPMC13195639

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-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.