Evidence map›Paper›PMID 36719726›Full record

ArticleJMIR public health and surveillance2023

Gastroenteritis Forecasting Assessing the Use of Web and Electronic Health Record Data With a Linear and a Nonlinear Approach: Comparison Study.

Canelle Poirier, Guillaume Bouzillé, Valérie Bertaud, Marc Cuggia, Mauricio Santillana, Audrey Lavenu

Abstract read
In one paragraph

Article in JMIR public health and surveillance, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

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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.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Canelle PoirierDepartment of Pediatrics, Harvard Medical School, Boston, MA, United States.ORCID 0000-0002-6972-2621
Guillaume BouzilléInstitut national de la santé et de la recherche médicale U1099, Rennes, France.ORCID 0000-0002-3637-6558
Valérie BertaudInstitut national de la santé et de la recherche médicale U1099, Rennes, France.ORCID 0000-0003-0293-5531
Marc CuggiaInstitut national de la santé et de la recherche médicale U1099, Rennes, France.ORCID 0000-0001-6943-3937
Mauricio Santillana *Department of Pediatrics, Harvard Medical School, Boston, MA, United States.ORCID 0000-0002-4206-418X
Audrey Lavenu *Faculté de médecine, Université de Rennes 1, Rennes, France.ORCID 0000-0002-0049-2397

Funding

Development of an Open-Source and Data-Driven Modeling Platform to Monitor and Forecast Disease ActivityR01GM130668 · NIGMS · NORTHEASTERN UNIVERSITY · PI SANTILLANA, MAURICIO · 2018 to 2022
$1.9M
NIGMS NIH HHS R01 GM130668
6 · The paper itself

Abstract

backgroundDisease surveillance systems capable of producing accurate real-time and short-term forecasts can help public health officials design timely public health interventions to mitigate the effects of disease outbreaks in affected populations. In France, existing clinic-based disease surveillance systems produce gastroenteritis activity information that lags real time by 1 to 3 weeks. This temporal data gap prevents public health officials from having a timely epidemiological characterization of this disease at any point in time and thus leads to the design of interventions that do not take into consideration the most recent changes in dynamics.

objectiveThe goal of this study was to evaluate the feasibility of using internet search query trends and electronic health records to predict acute gastroenteritis (AG) incidence rates in near real time, at the national and regional scales, and for long-term forecasts (up to 10 weeks).

methodsWe present 2 different approaches (linear and nonlinear) that produce real-time estimates, short-term forecasts, and long-term forecasts of AG activity at 2 different spatial scales in France (national and regional). Both approaches leverage disparate data sources that include disease-related internet search activity, electronic health record data, and historical disease activity.

resultsOur results suggest that all data sources contribute to improving gastroenteritis surveillance for long-term forecasts with the prominent predictive power of historical data owing to the strong seasonal dynamics of this disease.

conclusionsThe methods we developed could help reduce the impact of the AG peak by making it possible to anticipate increased activity by up to 10 weeks.

Indexed as

Disease OutbreaksElectronic Health RecordsFranceHumansInternetPublic Healthacute gastroenteritisdigital dataforecastinginfectious diseasemachine learningmachine learning in public healthmodelingmodeling disease outbreakspublic health

Identifiers

PMID36719726
PMCPMC9929730

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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.