Early detection of emerging infectious diseases - implications for vaccine development

被引:0
|
作者
Macintyre, C. Raina [1 ,2 ,3 ]
Lim, Samsung [1 ]
Gurdasani, Deepti [1 ]
Miranda, Miguel [1 ]
Metcalf, David [1 ]
Quigley, Ashley [1 ]
Hutchinson, Danielle [1 ,5 ]
Burr, Allan [1 ]
Heslop, David J. [4 ]
机构
[1] Univ New South Wales, Kirby Inst, Fac Med & Hlth Sci, Biosecur Program, Kensington, NSW, Australia
[2] Arizona State Univ, Coll Hlth Solut, Tempe, AZ USA
[3] Arizona State Univ, Watts Coll Publ Serv & Community Serv, Tempe, AZ USA
[4] Univ New South Wales, Fac Med & Hlth Sci, Sch Populat Hlth, Sydney, NSW, Australia
[5] UNSW, Kirby Inst, Biosecur Res Program, Gate 9,High St,Randwick, Sydney 2052, Australia
基金
英国医学研究理事会;
关键词
Artificial intelligence; Surveillance; Infectious diseases; Vaccines; Open-source; Data science; Epidemic; Pandemic; mpox; SURVEILLANCE;
D O I
10.1016/j.vaccine.2023.05.069
中图分类号
R392 [医学免疫学]; Q939.91 [免疫学];
学科分类号
100102 ;
摘要
Vast quantities of open-source data from news reports, social media and other sources can be harnessed using artificial intelligence and machine learning, and utilised to generate valid early warning signals of emerging epidemics. Early warning signals from open-source data are not a replacement for traditional, validated disease surveillance, but provide a trigger for earlier investigation and diagnostics. This may yield earlier pathogen characterisation and genomic data, which can enable earlier vaccine development or deployment of vaccines. Early warning also provides a more feasible prospect of stamping out epidemics before they spread. There are several of such systems currently, but they are not used widely in public health practice, and only some are publicly available. Routine and widespread use of opensource intelligence, as well as training and capacity building in digital surveillance, will improve pandemic preparedness and early response capability. (c) 2023 The Author(s). Published by Elsevier Ltd.
引用
收藏
页码:1826 / 1830
页数:5
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