ReviewClinical infectious diseases : an official publication of the Infectious Diseases Society of America2015
Infectious disease modeling methods as tools for informing response to novel influenza viruses of unknown pandemic potential.
Review in Clinical infectious diseases : an official publication of the Infectious Diseases Society of America, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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
8 citing papers in PubMed.
- Review
- Research gaps and priorities for quantitative microbial risk assessment (QMRA).Risk analysis : an official publication of the Society for Risk Analysis · 2024Article
- Modeling zoonotic and vector-borne viruses.Current opinion in virology · 2024Review
- Data-rich modeling helps answer increasingly complex questions on variant and disease interactions: Comment on "Mathematical models for dengue fever epidemiology: A 10-year systematic review" by Aguiar et al.Physics of life reviews · 2023Article
- Modeling the coronavirus disease 2019 pandemic: A comprehensive guide of infectious disease and decision-analytic models.Journal of clinical epidemiology · 2021Review
- Modeling Infectious Behaviors: The Need to Account for Behavioral Adaptation in COVID-19 Models.Policy and complex systems · 2021Article
- Utility of Artificial Intelligence Amidst the COVID 19 Pandemic: A Review.Journal of medical systems · 2020Review
- Applying infectious disease forecasting to public health: a path forward using influenza forecasting examples.BMC public health · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
The rising importance of infectious disease modeling makes this an appropriate time for a guide for public health practitioners tasked with preparing for, and responding to, an influenza pandemic. We list several questions that public health practitioners commonly ask about pandemic influenza and match these with analytical methods, giving details on when during a pandemic the methods can be used, how long it might take to implement them, and what data are required. Although software to perform these tasks is available, care needs to be taken to understand: (1) the type of data needed, (2) the implementation of the methods, and (3) the interpretation of results in terms of model uncertainty and sensitivity. Public health leaders can use this article to evaluate the modeling literature, determine which methods can provide appropriate evidence for decision-making, and to help them request modeling work from in-house teams or academic groups.
Indexed as
Identifiers
What OpenQuestion holds
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.