Abstract
Predictive computing tools are increasingly being used and have demonstrated successfulness in providing insights that can lead to better health policy and management. However, as these technologies are still in their infancy stages, slow progress is being made in their adoption for serious consideration at national and international policy levels. However, a recent case evidences that the precision of Artificial Intelligence (AI) driven algorithms are gaining in accuracy. AI modelling driven by companies such as BlueDot and Metabiota anticipated the Coronavirus (COVID-19) in China before it caught the world by surprise in late 2019 by both scouting its impact and its spread. From a survey of past viral outbreaks over the last 20 years, this paper explores how early viral detection will reduce in time as computing technology is enhanced and as more data communication and libraries are ensured between varying data information systems. For this enhanced data sharing activity to take place, it is noted that efficient data protocols have to be enforced to ensure that data is shared across networks and systems while ensuring privacy and preventing oversight, especially in the case of medical data. This will render enhanced AI predictive tools which will influence future urban health policy internationally.
Highlights
The technological progress in the health sector is well demonstrated in the detection speed involving the recent case of novel coronavirus (COVID-19) where its identification was made relatively earlier
“(“artificial intelligence”) OR (“machine learning”) OR (“deep learning”) AND (“disease surveillance”) AND (“1999/01/01”[Date-Publication]: “2019/12/31”[Date-Publication])”, further supports that there has been an increasing trend in research involving the development of Artificial Intelligence (AI)-based algorithms to better predict the outcomes of current healthcare data, and to predict disease outbreaks in advance
While this paper only aims at surveying the emergence of AI-based literature in healthcare, a further in-depth study is warranted to better understand the specific AI processes that are being favoured and the most popular health dimensions
Summary
The advent of advanced computer modelling technologies, and their adoption, in many sectors internationally have led to the improvement and quality risk assessments of local and global economies. The technological progress in the health sector is well demonstrated in the detection (here referred to as the process of identification of the disease) speed involving the recent case of novel coronavirus (COVID-19) where its identification was made relatively earlier Such occurred in just seven days for human identification [3] compared to past outbreaks, like the Severe Acute Respiratory Syndrome (SARS), which took four months to be identified [4]. Among the successful companies is BlueDot (https://bluedot.global/), that scoured data from news reports, airline ticketing and animal disease outbreaks, to predict areas that are would be prone to the outbreak, expanding from regions in China [10]. The virus could be used to develop tests to help identify people who might be infected and are not presently showing any symptoms of the virus, thereby aiding in ensuring that the spread of the disease is curtailed [11]
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