Emerging data inputs for infectious diseases surveillance and decision making

被引:8
作者
Shausan, Aminath [1 ,2 ]
Nazarathy, Yoni [2 ]
Dyda, Amalie [1 ]
机构
[1] Univ Queensland, Sch Publ Hlth, Brisbane, Qld, Australia
[2] Univ Queensland, Sch Math & Phys, Brisbane, Qld, Australia
来源
FRONTIERS IN DIGITAL HEALTH | 2023年 / 5卷
关键词
infectious diseases; digital surveillance; emerging data inputs; field experiments; artificial intelligence; crowd sourcing; physiological measures; COVID-19;
D O I
10.3389/fdgth.2023.1131731
中图分类号
R19 [保健组织与事业(卫生事业管理)];
学科分类号
摘要
Infectious diseases create a significant health and social burden globally and can lead to outbreaks and epidemics. Timely surveillance for infectious diseases is required to inform both short and long term public responses and health policies. Novel data inputs for infectious disease surveillance and public health decision making are emerging, accelerated by the COVID-19 pandemic. These include the use of technology-enabled physiological measurements, crowd sourcing, field experiments, and artificial intelligence (AI). These technologies may provide benefits in relation to improved timeliness and reduced resource requirements in comparison to traditional methods. In this review paper, we describe current and emerging data inputs being used for infectious disease surveillance and summarize key benefits and limitations.
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页数:7
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