ReviewCurrent epidemiology reports2016
Trends in the Mechanistic and Dynamic Modeling of Infectious Diseases.
Review in Current epidemiology reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 2 of them syntheses that pooled 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.
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
15 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Mechanistic models of Rift Valley fever virus transmission: A systematic review.PLoS neglected tropical diseases · 2022Pooled it
- Spatial connectivity in mosquito-borne disease models: a systematic review of methods and assumptions.Journal of the Royal Society, Interface · 2021Pooled it
- A real-time forecasting framework for emerging infectious diseases affecting animal populations.PLoS computational biology · 2026Article
- Advancing adoptability and sustainability of digital prediction tools for climate-sensitive infectious disease prevention and control.Nature communications · 2025Review
- Modeling zoonotic and vector-borne viruses.Current opinion in virology · 2024Review
- Mathematical models and analysis tools for risk assessment of unnatural epidemics: a scoping review.Frontiers in public health · 2024Article
- Impact of vaccination and non-pharmacological interventions on COVID-19: a review of simulation modeling studies in Asia.Frontiers in public health · 2023Review
- Article
- Planning as Inference in Epidemiological Dynamics Models.Frontiers in artificial intelligence · 2021Article
- Article
- A review of mechanistic models of viral dynamics in bat reservoirs for zoonotic disease.Pathogens and global health · 2020Review
- Evidence Synthesis for Stochastic Epidemic Models.Statistical science : a review journal of the Institute of Mathematical Statistics · 2018Article
- Article
- An Integrated Framework for Process-Driven Model Construction in Disease Ecology and Animal Health.Frontiers in veterinary science · 2017Review
- [Epidemiologic evidence on the role of hospitality venues in the transmission of COVID-19: A rapid review of the literature].Gaceta sanitariaReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The dynamics of infectious disease epidemics are driven by interactions between individuals with differing disease status (e.g., susceptible, infected, immune). Mechanistic models that capture the dynamics of such "dependent happenings" are a fundamental tool of infectious disease epidemiology. Recent methodological advances combined with access to new data sources and computational power have resulted in an explosion in the use of dynamic models in the analysis of emerging and established infectious diseases. Increasing use of models to inform practical public health decision making has challenged the field to develop new methods to exploit available data and appropriately characterize the uncertainty in the results. Here, we discuss recent advances and areas of active research in the mechanistic and dynamic modeling of infectious disease. We highlight how a growing emphasis on data and inference, novel forecasting methods, and increasing access to "big data" are changing the field of infectious disease dynamics. We showcase the application of these methods in phylodynamic research, which combines mechanistic models with rich sources of molecular data to tie genetic data to population-level disease dynamics. As dynamics and mechanistic modeling methods mature and are increasingly tied to principled statistical approaches, the historic separation between the infectious disease dynamics and "traditional" epidemiologic methods is beginning to erode; this presents new opportunities for cross pollination between fields and novel applications.
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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.