ReviewPhilosophical transactions of the Royal Society of London. Series B, Biological sciences2011
How to make predictions about future infectious disease risks.
Review in Philosophical transactions of the Royal Society of London. Series B, Biological sciences, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 65 papers, 4 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
65 citing papers in PubMed, 4 syntheses or guidelines pooled it, 165 citations in OpenAlex.
- Quantitative risk assessment for infectious disease introduction in animal populations: a comprehensive review.Frontiers in veterinary science · 2025Pooled it
- Incorporating Social Determinants of Health in Infectious Disease Models: A Systematic Review of Guidelines.Medical decision making : an international journal of the Society for Medical Decision Making · 2024Pooled it
- A rapid review of best practices in the development of risk registers for public health emergency management.Frontiers in public health · 2023Pooled it
- Global mapping of infectious disease.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2013Pooled it
- The Effect of Climate Change on Emergence and Evolution of Zoonotic Diseases in Asia.Zoonoses and public health · 2025Review
- One Health: enabler of effective prevention, control and elimination of emerging and re-emerging infectious diseases.Infectious diseases of poverty · 2025Review
- Meeting Global Health Needs via Infectious Disease Forecasting: Development of a Reliable Data-Driven Framework.JMIR public health and surveillance · 2025Article
- How does policy modelling work in practice? A global analysis on the use of epidemiological modelling in health crises.PLOS global public health · 2025Article
- Using real-time modelling to inform the 2017 Ebola outbreak response in DR Congo.Nature communications · 2024Article
- Understanding underlying physical mechanism reveals early warning indicators and key elements for adaptive infections disease networks.PNAS nexus · 2024Article
- A Comparative Study on Deep Learning Models for COVID-19 Forecast.Healthcare (Basel, Switzerland) · 2023Article
- Predicting Kyasanur forest disease in resource-limited settings using event-based surveillance and transfer learning.Scientific reports · 2023Article
- Network analysis of pig movement data as an epidemiological tool: an Austrian case study.Scientific reports · 2023Article
- Predicting infectious disease for biopreparedness and response: A systematic review of machine learning and deep learning approaches.One health (Amsterdam, Netherlands) · 2022Review
- There are no equal opportunity infectors: Epidemiological modelers must rethink our approach to inequality in infection risk.PLoS computational biology · 2022Article
- Influence of contract commitment system in reducing information asymmetry, and prevention and control of livestock epidemics: Evidence from pig farmers in China.One health (Amsterdam, Netherlands) · 2021Article
- Challenges in modelling the dynamics of infectious diseases at the wildlife-human interface.Epidemics · 2021Review
- Predictors of COVID-19 epidemics in countries of the World Health Organization African Region.Nature medicine · 2021Article
- Towards Building a Sustainable Future: Positioning Ecological Modelling for Impact in Ecosystems Management.Bulletin of mathematical biology · 2021Article
- Social structure defines spatial transmission of African swine fever in wild boar.Journal of the Royal Society, Interface · 2021Article
5 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author at 1 institution in 1 country.
Funding
Abstract
Formal, quantitative approaches are now widely used to make predictions about the likelihood of an infectious disease outbreak, how the disease will spread, and how to control it. Several well-established methodologies are available, including risk factor analysis, risk modelling and dynamic modelling. Even so, predictive modelling is very much the 'art of the possible', which tends to drive research effort towards some areas and away from others which may be at least as important. Building on the undoubted success of quantitative modelling of the epidemiology and control of human and animal diseases such as AIDS, influenza, foot-and-mouth disease and BSE, attention needs to be paid to developing a more holistic framework that captures the role of the underlying drivers of disease risks, from demography and behaviour to land use and climate change. At the same time, there is still considerable room for improvement in how quantitative analyses and their outputs are communicated to policy makers and other stakeholders. A starting point would be generally accepted guidelines for 'good practice' for the development and the use of predictive models.
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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.