ReviewCurrent opinion in virology2024
Modeling zoonotic and vector-borne viruses.
Review in Current opinion in virology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- A Scoping Review of Machine Learning Applications Across Epidemiological Stages of Zoonotic Disease.Transboundary and emerging diseases · 2026Article
- Vector-borne disease surveillance and control resource needs in Colorado public health organizations.PloS one · 2026Article
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
The 2013-2016 Ebola virus disease epidemic and the coronavirus disease 2019 pandemic galvanized tremendous growth in models for emerging zoonotic and vector-borne viruses. Therefore, we have reviewed the main goals and methods of models to guide scientists and decision-makers. The elements of models for emerging viruses vary across spectrums: from understanding the past to forecasting the future, using data across space and time, and using statistical versus mechanistic methods. Hybrid/ensemble models and artificial intelligence offer new opportunities for modeling. Despite this progress, challenges remain in translating models into actionable decisions, particularly in areas at highest risk for viral disease outbreaks. To address this issue, we must identify gaps in models for specific viruses, strengthen validation, and involve policymakers in model development.
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.