ReviewEpidemics2018
Modelling the global spread of diseases: A review of current practice and capability.
Review in Epidemics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 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
38 citing papers in PubMed.
- SIRI+Q model with a limited capacity of isolation.Theory in biosciences = Theorie in den Biowissenschaften · 2025Article
- Modeling severe uncontrolled asthma: Transitioning away from health states.Contemporary clinical trials communications · 2024Article
- Modeling the Regional Distribution of International Travelers in Spain to Estimate Imported Cases of Dengue and Malaria: Statistical Inference and Validation Study.JMIR public health and surveillance · 2024Article
- What Influence Could the Acceptance of Visitors Cause on the Epidemic Dynamics of a Reinfectious Disease?: A Mathematical Model.Acta biotheoretica · 2024Article
- How to avoid a local epidemic becoming a global pandemic.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- An implementation framework to improve the transparency and reproducibility of computational models of infectious diseases.PLoS computational biology · 2023Article
- Article
- Why COVID-19 modelling of progression and prevention fails to translate to the real-world.Advances in biological regulation · 2022Article
- Can infectious modelling be applicable globally - lessons from COVID 19.Current opinion in environmental science & health · 2022Review
- Unraveling the dynamic impacts of COVID-19 on metro ridership: An empirical analysis of Beijing and Shanghai, China.Transport policy · 2022Article
- Assessing the risk of pandemic outbreaks across municipalities with mathematical descriptors based on age and mobility restrictions.Chaos, solitons, and fractals · 2022Article
- Dynamically adjusting case reporting policy to maximize privacy and public health utility in the face of a pandemic.Journal of the American Medical Informatics Association : JAMIA · 2022Article
- Constructing and validating a transferable epidemic risk index in data scarce environments using open data: A case study for dengue in the Philippines.PLoS neglected tropical diseases · 2022Article
- Priority and age specific vaccination algorithm for the pandemic diseases: a comprehensive parametric prediction model.BMC medical informatics and decision making · 2022Article
- A meta-epidemiological assessment of transparency indicators of infectious disease models.PloS one · 2022Article
- Article
- Systems dynamics and the uncertainties of diagnostics, testing and contact tracing for COVID-19.Methods (San Diego, Calif.) · 2021Article
- PolSIRD: Modeling Epidemic Spread Under Intervention Policies: Analyzing the First Wave of COVID-19 in the USA.Journal of healthcare informatics research · 2021Article
- The city turned off: Urban dynamics during the COVID-19 pandemic based on mobile phone data.Applied geography (Sevenoaks, England) · 2021Article
- Monitoring COVID-19 contagion growth.Statistics in medicine · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Mathematical models can aid in the understanding of the risks associated with the global spread of infectious diseases. To assess the current state of mathematical models for the global spread of infectious diseases, we reviewed the literature highlighting common approaches and good practice, and identifying research gaps. We followed a scoping study method and extracted information from 78 records on: modelling approaches; input data (epidemiological, population, and travel) for model parameterization; model validation data. We found that most epidemiological data come from published journal articles, population data come from a wide range of sources, and travel data mainly come from statistics or surveys, or commercial datasets. The use of commercial datasets may benefit the modeller, however makes critical appraisal of their model by other researchers more difficult. We found a minority of records (26) validated their model. We posit that this may be a result of pandemics, or far-reaching epidemics, being relatively rare events compared with other modelled physical phenomena (e.g. climate change). The sparsity of such events, and changes in outbreak recording, may make identifying suitable validation data difficult. We appreciate the challenge of modelling emerging infections given the lack of data for both model parameterisation and validation, and inherent complexity of the approaches used. However, we believe that open access datasets should be used wherever possible to aid model reproducibility and transparency. Further, modellers should validate their models where possible, or explicitly state why validation was not possible.
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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.