ArticleBiosecurity and bioterrorism : biodefense strategy, practice, and science2011
Improving the evidence base for decision making during a pandemic: the example of 2009 influenza A/H1N1.
Article in Biosecurity and bioterrorism : biodefense strategy, practice, and science, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 90 papers, 3 of them syntheses that pooled it.
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Who cites it
90 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Key questions for modelling COVID-19 exit strategies.Proceedings. Biological sciences · 2020Pooled it
- A systematic review of studies on forecasting the dynamics of influenza outbreaks.Influenza and other respiratory viruses · 2014Pooled it
- Estimating age-specific cumulative incidence for the 2009 influenza pandemic: a meta-analysis of A(H1N1)pdm09 serological studies from 19 countries.Influenza and other respiratory viruses · 2013Pooled it
- Inferring influenza infection attack rate from seroprevalence data.PLoS pathogens · 2014Trial
- A disease-agnostic approach to ensemble learning for infectious disease forecasting.Nature communications · 2026Article
- Nowcasting epidemic trends using hospital- and community-based virologic test data.Nature communications · 2025Article
- Mapping Incidence and Prevalence Peak Data for SIR Modeling Applications.Journal of mathematical biology · 2025Article
- The importance of playing the long game when it comes to pandemic surveillance.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
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- Characterising information gains and losses when collecting multiple epidemic model outputs.Epidemics · 2024Article
- Infodemiology of Influenza-like Illness: Utilizing Google Trends' Big Data for Epidemic Surveillance.Journal of clinical medicine · 2024Article
- Infectious disease surveillance needs for the United States: lessons from Covid-19.Frontiers in public health · 2024Review
- Incorporating connectivity among Internet search data for enhanced influenza-like illness tracking.PloS one · 2024Article
- Differences in COVID-19 cyclicity and predictability among U.S. counties and states reflect the effectiveness of protective measures.Scientific reports · 2023Article
- Retrospective evaluation of real-time estimates of global COVID-19 transmission trends and mortality forecasts.PloS one · 2023Article
- A spatiotemporal machine learning approach to forecasting COVID-19 incidence at the county level in the USA.International journal of data science and analytics · 2023Article
- Impact of COVID-19 on ports, multimodal logistics and transport sector in India: Responses and policy imperatives.Transport policy · 2023Article
- PAN-cODE: COVID-19 forecasting using conditional latent ODEs.Journal of the American Medical Informatics Association : JAMIA · 2022Article
- Uncomfortable science: How mathematical models, and consensus, come to be in public policy.Sociology of health & illness · 2022Article
30 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
6 authors.
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Abstract
This article synthesizes and extends discussions held during an international meeting on "Surveillance for Decision Making: The Example of 2009 Pandemic Influenza A/H1N1," held at the Center for Communicable Disease Dynamics (CCDD), Harvard School of Public Health, on June 14 and 15, 2010. The meeting involved local, national, and global health authorities and academics representing 7 countries on 4 continents. We define the needs for surveillance in terms of the key decisions that must be made in response to a pandemic: how large a response to mount and which control measures to implement, for whom, and when. In doing so, we specify the quantitative evidence required to make informed decisions. We then describe the sources of surveillance and other population-based data that can presently--or in the future--form the basis for such evidence, and the interpretive tools needed to process raw surveillance data. We describe other inputs to decision making besides epidemiologic and surveillance data, and we conclude with key lessons of the 2009 pandemic for designing and planning surveillance in the future.
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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.