COVID-19 what have we learned? The rise of social machines and connected devices in pandemic management following the concepts of predictive, preventive and personalized medicine

Abstract

Objectives Review, compare and critically assess digital technology responses to the COVID-19 pandemic around the world. The specific point of interest in this research is on predictive, preventive and personalized interoperable digital healthcare solutions. This point is supported by failures from the past, where the separate design of digital health solutions has led to lack of interoperability. Hence, this review paper investigates the integration of predictive, preventive and personalized interoperable digital healthcare systems. The second point of interest is the use of new mass surveillance technologies to feed personal data from health professionals to governments, without any comprehensive studies that determine if such new technologies and data policies would address the pandemic crisis. Method This is a review paper. Two approaches were used: A comprehensive bibliographic review with R statistical methods of the COVID-19 pandemic in PubMed literature and Web of Science Core Collection, supported with Google Scholar search. In addition, a case study review of emerging new approaches in different regions, using medical literature, academic literature, news articles and other reliable data sources. Results Most countries' digital responses involve big data analytics, integration of national health insurance databases, tracing travel history from individual's location databases, code scanning and individual's online reporting. Public responses of mistrust about privacy data misuse differ across countries, depending on the chosen public communication strategy. We propose predictive, preventive and personalized solutions for pandemic management, based on social machines and connected devices. Solutions The proposed predictive, preventive and personalized solutions are based on the integration of IoT data, wearable device data, mobile apps data and individual data inputs from registered users, operating as a social machine with strong security and privacy protocols. We present solutions that would enable much greater speed in future responses. These solutions are enabled by the social aspect of human-computer interactions (social machines) and the increased connectivity of humans and devices (Internet of Things). Conclusion Inadequate data for risk assessment on speed and urgency of COVID-19, combined with increased globalization of human society, led to the rapid spread of COVID-19. Despite an abundance of digital methods that could be used in slowing or stopping COVID-19 and future pandemics, the world remains unprepared, and lessons have not been learned from previous cases of pandemics. We present a summary of predictive, preventive and personalized digital methods that could be deployed fast to help with the COVID-19 and future pandemics.

Challenging management of severe chronic disorders in acute pandemic situation: Chronic liver disease under COVID-19 pandemic as the proof-of-principle model to orchestrate the measures in 3PM context

“…Importantly, we will have to communicate and pre-conceive this strategy with patients as well as with our referring counterparts from secondary care. At the same time, we are preparing communication lines for telemedicine (physician-physician; patient-physician, [ 11 ].…”

“…The circumstances may invite for a more widespread consideration for medicine that is based on predictive, preventive, and personalized tools. The approach may be generalized to the entire population encouraging government authorities for using individual behavior data from a myriad of devices and social networks [ 11 ] to trace contacts and to monitor outbreak dynamics in real time [ 12 ]. Also, several COVID-19 disease predictive models have been recently proposed, while newer models based on the unique character of the immune response are emerging [ 11 , 13 ].…”

“…The approach may be generalized to the entire population encouraging government authorities for using individual behavior data from a myriad of devices and social networks [ 11 ] to trace contacts and to monitor outbreak dynamics in real time [ 12 ]. Also, several COVID-19 disease predictive models have been recently proposed, while newer models based on the unique character of the immune response are emerging [ 11 , 13 ]. Finally, in the context of specialized care, disease-specific prognostic models play an integral part in the disease management and are ready for being evaluated as tools of predictive, preventive, and personalized approaches during pandemic times.…”

COVID-19: advance in laboratory diagnostic strategy and technology

“…The Internet of Things (LoT) provides a platform for public health agencies to access data to monitor the COVID-19 pandemic, such as daily reports issued by the World Health Organization [ 75 ]. The role of mobile health applications is mainly to collect data and distribute information [ 73 ]. The former includes the upload of diagnostic results, the recording of health data of wearing devices, the recording of moving tracks, etc., while the latter includes health pass codes, the location of confirmed or suspected cases in nearby areas, and relevant news on social media.…”

Infections of the lung: a predictive, preventive and personalized perspective through the lens of evolution, the emergence of SARS-CoV-2 and its pathogenesis

“…The real-time monitoring of individuals based on laboratory tests is the optimal evidence-based predictive and preventive strategy in the case of a pandemic [135]. Also, real-time predictive, preventive, and personalized (PPP) solutions may integrate data from the internet, wearable or mobile device, and individual inputs from registered users under strong security and privacy act, and thus can operate as human-computer interactions (social machines) to provide predictive and preventive inputs with the evolving situation [136]. In contrast, lack of PPP may lead to incorrect political decisions causing either chronic massquarantine which may lead to a long-term economic crisis or under-restriction of movement of the population that may lead to a post-containment pandemic rebound [135].…”