Emerging data inputs for infectious diseases surveillance and decision making

Aminath Shausan; Yoni Nazarathy; Amalie Dyda

doi:10.3389/fdgth.2023.1131731

Emerging data inputs for infectious diseases surveillance and decision making

Front Digit Health. 2023 Apr 4:5:1131731. doi: 10.3389/fdgth.2023.1131731. eCollection 2023.

Authors

Aminath Shausan^{1

2}, Yoni Nazarathy², Amalie Dyda¹

Affiliations

¹ School of Public Health, The University of Queensland, Brisbane, QLD, Australia.
² School of Mathematics and Physics, The University of Queensland, Brisbane, QLD, Australia.

Abstract

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.

Keywords: COVID-19; artificial intelligence; crowd sourcing; digital surveillance; emerging data inputs; field experiments; infectious diseases; physiological measures.

Publication types

Review

Grants and funding

Funds for incidentals and research assistance were supplied by the University of Queensland’s AI for Pandemics initiative.