ReviewCurrent opinion in infectious diseases2025
Data science for pediatric infectious disease: utilizing COVID-19 as a model.
Review in Current opinion in infectious diseases, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
purpose of reviewDuring the COVID-19 pandemic, governments and public health agencies used data science tools and data sources in real time to evaluate pathogen transmissibility, disease burden, healthcare capacity, and evaluate treatment and preventive measures. The purpose of the review is to highlight the application of these data sources and methods during the COVID-19 response. RECENT
findingsAdvances in the development of common data models enabled multisite data networks to overcome healthcare data fragmentation, enabling national surveillance platforms, and offering unprecedented statistical power to conduct national surveillance and detect emerging clinical entities like MIS-C and long COVID in diverse pediatric populations. These integrated networks were also used in evaluating the effectiveness of vaccines and therapies. New surveillance approaches combining traditional clinical data with novel data sources including wastewater detection, web-based search engines, and mobility patterns yielded comprehensive ensemble approaches that informed public health policy. SUMMARY: The COVID-19 pandemic highlighted the importance of timely evidence for decision-making during outbreak responses and the benefits of using data science tools to help provide real time, actionable insights, which can help guide our public health response to infectious diseases threats in the future.
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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.