Last data update: May 13, 2024. (Total: 46773 publications since 2009)
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Query Trace: George Dylan[original query] |
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Innovations in public health surveillance: updates from a forum convened by the WHO Hub for Pandemic and Epidemic Intelligence, 2 and 3 February 2022.
Morgan Oliver , Redies Isabel , Beatriz Leiva Rioja Zoila , Brownstein John , George Dylan , Golding Josie , Hanefeld Johanna , Horby Peter , Lee Christopher , Mikhailov Danil , Philip Wolfgang , Scarpino Samuel , Kifle Tessema Sofonias , Ihekweazu Chikwe . Euro Surveill 2022 27 (15) In the 2 years since the emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) there has been an unprecedented collective effort from the academic, public, and private sectors to advance surveillance for pandemic preparedness and response. The coronavirus disease (COVID-19) pandemic has created momentum that will define the future of public health intelligence. On 2 and 3 February 2022, the World Health Organization (WHO) Hub for Pandemic and Epidemic Intelligence convened a meeting of a small group of surveillance innovators to share insights and approaches about their initiatives and future directions. The meeting served as an opportunity for participants to share updates about their work, to explore potential for collaboration, exchange ideas, cross-fertilise our work and discuss challenges in the field of surveillance. Although the group of attendees was not geographically representative of the global surveillance community, the meeting was the first in a planned series of exchanges convened by the WHO Pandemic Hub that will generate dialogue among global thought leaders and new voices in the surveillance community. In this first convening we discussed several themes, including what is meaningful collaboration for success; how to bring the public back into public health; what are individual-centred approaches; how new kinds of data have new privacy concerns; how government structures affect the functioning of surveillance systems; how to inform the decisionmaking process; cross-scaling and down-scaling tools and technologies; investing in human talent and future practitioners; and achieving sustainability into surveillance. In this meeting report, we summarise the discussions on innovations in public health surveillance and provide a list with references and links to the organisations and initiatives represented at the meeting. |
An open challenge to advance probabilistic forecasting for dengue epidemics.
Johansson MA , Apfeldorf KM , Dobson S , Devita J , Buczak AL , Baugher B , Moniz LJ , Bagley T , Babin SM , Guven E , Yamana TK , Shaman J , Moschou T , Lothian N , Lane A , Osborne G , Jiang G , Brooks LC , Farrow DC , Hyun S , Tibshirani RJ , Rosenfeld R , Lessler J , Reich NG , Cummings DAT , Lauer SA , Moore SM , Clapham HE , Lowe R , Bailey TC , Garcia-Diez M , Carvalho MS , Rodo X , Sardar T , Paul R , Ray EL , Sakrejda K , Brown AC , Meng X , Osoba O , Vardavas R , Manheim D , Moore M , Rao DM , Porco TC , Ackley S , Liu F , Worden L , Convertino M , Liu Y , Reddy A , Ortiz E , Rivero J , Brito H , Juarrero A , Johnson LR , Gramacy RB , Cohen JM , Mordecai EA , Murdock CC , Rohr JR , Ryan SJ , Stewart-Ibarra AM , Weikel DP , Jutla A , Khan R , Poultney M , Colwell RR , Rivera-Garcia B , Barker CM , Bell JE , Biggerstaff M , Swerdlow D , Mier YTeran-Romero L , Forshey BM , Trtanj J , Asher J , Clay M , Margolis HS , Hebbeler AM , George D , Chretien JP . Proc Natl Acad Sci U S A 2019 116 (48) 24268-24274 A wide range of research has promised new tools for forecasting infectious disease dynamics, but little of that research is currently being applied in practice, because tools do not address key public health needs, do not produce probabilistic forecasts, have not been evaluated on external data, or do not provide sufficient forecast skill to be useful. We developed an open collaborative forecasting challenge to assess probabilistic forecasts for seasonal epidemics of dengue, a major global public health problem. Sixteen teams used a variety of methods and data to generate forecasts for 3 epidemiological targets (peak incidence, the week of the peak, and total incidence) over 8 dengue seasons in Iquitos, Peru and San Juan, Puerto Rico. Forecast skill was highly variable across teams and targets. While numerous forecasts showed high skill for midseason situational awareness, early season skill was low, and skill was generally lowest for high incidence seasons, those for which forecasts would be most valuable. A comparison of modeling approaches revealed that average forecast skill was lower for models including biologically meaningful data and mechanisms and that both multimodel and multiteam ensemble forecasts consistently outperformed individual model forecasts. Leveraging these insights, data, and the forecasting framework will be critical to improve forecast skill and the application of forecasts in real time for epidemic preparedness and response. Moreover, key components of this project-integration with public health needs, a common forecasting framework, shared and standardized data, and open participation-can help advance infectious disease forecasting beyond dengue. |
Technology to advance infectious disease forecasting for outbreak management.
George DB , Taylor W , Shaman J , Rivers C , Paul B , O'Toole T , Johansson MA , Hirschman L , Biggerstaff M , Asher J , Reich NG . Nat Commun 2019 10 (1) 3932 Forecasting is beginning to be integrated into decision-making processes for infectious disease outbreak response. We discuss how technologies could accelerate the adoption of forecasting among public health practitioners, improve epidemic management, save lives, and reduce the economic impact of outbreaks. |
Improving pandemic influenza risk assessment.
Russell CA , Kasson PM , Donis RO , Riley S , Dunbar J , Rambaut A , Asher J , Burke S , Davis CT , Garten RJ , Gnanakaran S , Hay SI , Herfst S , Lewis NS , Lloyd-Smith JO , Macken CA , Maurer-Stroh S , Neuhaus E , Parrish CR , Pepin KM , Shepard SS , Smith DL , Suarez DL , Trock SC , Widdowson MA , George DB , Lipsitch M , Bloom JD . Elife 2014 3 e03883 Assessing the pandemic risk posed by specific non-human influenza A viruses is an important goal in public health research. As influenza virus genome sequencing becomes cheaper, faster, and more readily available, the ability to predict pandemic potential from sequence data could transform pandemic influenza risk assessment capabilities. However, the complexities of the relationships between virus genotype and phenotype make such predictions extremely difficult. The integration of experimental work, computational tool development, and analysis of evolutionary pathways, together with refinements to influenza surveillance, has the potential to transform our ability to assess the risks posed to humans by non-human influenza viruses and lead to improved pandemic preparedness and response. |
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