Toward optimal disease surveillance with graph-based active learning.

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第一作者： Joseph L-H,Tsui

第一单位： Department of Biology, University of Oxford, Oxford OX1 3SZ, United Kingdom.;Pandemic Sciences Institute, University of Oxford, Oxford OX3 7DQ, United Kingdom.

作者： Joseph L-H,Tsui ^[1] ; Mengyan,Zhang ^[2] ; Prathyush,Sambaturu ^[1] ; Simon,Busch-Moreno ^[3] ; Marc A,Suchard ^[4] ; Oliver G,Pybus ^[5] ; Seth,Flaxman ^[2] ; Elizaveta,Semenova ^[6] ; Moritz U G,Kraemer ^[1]

作者单位： Department of Biology, University of Oxford, Oxford OX1 3SZ, United Kingdom.;Pandemic Sciences Institute, University of Oxford, Oxford OX3 7DQ, United Kingdom. ^[1] Department of Computer Science, University of Oxford, Oxford OX1 3QG, United Kingdom. ^[2] Department of Biology, University of Oxford, Oxford OX1 3SZ, United Kingdom. ^[3] Department of Biostatistics, University of California, Los Angeles, CA 90095. ^[4] Department of Biology, University of Oxford, Oxford OX1 3SZ, United Kingdom.;Pandemic Sciences Institute, University of Oxford, Oxford OX3 7DQ, United Kingdom.;Department of Pathobiology and Population Sciences, Royal Veterinary College, University of London, Hatfield AL9 7TA, United Kingdom. ^[5] Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London SW7 2AZ, United Kingdom. ^[6]

关键词 active learning disease surveillance epidemiology network dynamics public health

主题词人类(Humans);贝叶斯定理(Bayes Theorem);疾病暴发流行(Disease Outbreaks);人群监测(Population Surveillance)

DOI 10.1073/pnas.2412424121

PMID 39700149

发布时间 2025-01-04

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