How does improved forecasting benefit detection? An application to biosurveillance [PDF]
While many methods have been proposed for detecting disease outbreaks from pre-diagnostic data, their performance is usually not well understood. We argue that most existing temporal detection methods for biosurveillance can be characterized as a ...
Lotze, Thomas H., Shmueli, Galit
core
Genome-resolved surveillance and predictive ecological risk modeling of urban microbiomes. [PDF]
Aminu S +5 more
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Artificial intelligence-enhanced biosurveillance for antimicrobial resistance in sub-Saharan Africa. [PDF]
Ayesiga I +9 more
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GRUMB: a genome-resolved metagenomic framework for monitoring urban microbiomes and diagnosing pathogen risk. [PDF]
Aminu S +3 more
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A framework for evaluating implementation, impact, and cost-effectiveness of wastewater and environmental surveillance. [PDF]
Willis HH +7 more
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A watershed-scale potential pathogenic bacteria dataset from the Yangtze River Basin. [PDF]
Wang J, Wang S, Li T, Hou W, Deng Y.
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CasCADE: Cas<i>-C</i>RISPR Automated Design and Evaluation for targeted gRNA detection assays. [PDF]
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Biodiversity science and biosurveillance are fellow travelers. [PDF]
Poisot T +5 more
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Nonfatal Overdose Biosurveillance: A Cross-Sectional Pilot Study. [PDF]
Bates MN +4 more
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Artificial intelligence in microbiology: implications for metagenomics, diagnostics, and AMR surveillance. [PDF]
Khangarot R +3 more
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