Digital public health: Automation based on new datasets and the Internet of Things
Introduction
Over the past year, we saw the global community join forces to overcome a significant new challenge facing humanity – the COVID-19 pandemic and its wide-ranging consequences. As part of concerted efforts to curtail the spread of COVID-19, public health experienced an unknown challenge, to which modern societies were found to be at least vulnerable. The traditional public health model was oriented at stability and allowed for a quick provision of medical services in rare cases. Still, in most cases, medical services are typically provided in the planned order. As medical services for broad groups of the population are a public (non-commercial) benefit, national governments supply such services on “planning principles” even in market-based economies. The unprecedented challenges faced by public health in connection with the pandemic and the global crisis in 2020 are described in the works of [[1], [2], [3], [4], [5], [6], [7]].
The number of state establishments of healthcare is rationalized around physician's and hospital bed's numbers, given the population and past statistics. The considerable variety of drugs and vaccines is set based on average demand. And even the most recent attempt to progress all medical services to the digital form is merely hindered by unequal accessibility of the telecommunication infrastructure and incomplete coverage of the population with digital literacy. The ineffectiveness in the social sphere of public health spiked amid the COVID-19 pandemic. The demand for medical services became unpredictable, and we now base its novelty on unaware cases and unanticipated emergencies. The traditional public health model cannot be so effective in pandemic conditions. It does not ensure the required high flexibility and controllability of the standard practices of medical services'sspecific provision.
As the successful experience of the most influential countries’ health public service (e.g., China, countries of the EU, and the OECD) spurred demand for an alternative model of typically supplying practical solutions amid pandemic conditions. Digital public health might willingly allow for continuous monitoring and high-precision forecasting of public health characteristics to achieve more likely effectiveness.
But while there are no guarantees, there are some predictions about the next virus pandemics. And whatever future pandemics will look like in the years to come across countries and continents, our societies call for data-based, ground-breaking “smart” management solutions to identify and respond to challenges swiftly and ordered. And this paper's unique challenge is that “smart” management based on the IoT and artificial intelligence should be based on big data. However, we still notice a lack of such statistics availability. This paper offers an alternative solution to this problem: it involves datasets – interactive platforms for gathering, collection, and analyzing of big data.
The paper's originality and novelty showcase a successfully applied development through the dataset for intellectual monitoring of digital public health and its “smart” management based on IoT and artificial intelligence. This paper shows the capabilities, advantages, and perspectives of using datasets in digital public health in the conditions of a crisis virus threat, and the COVID-19 pandemic is our paper's example.
For the first time, the prospects for the development of public health are associated not with a partial transformation of individual medical services into electronic form but with a full-scale and systemic automation based on the advanced technologies of Industry 4.0 - datasets and the Internet of Things.
After this introduction, the literature review is in section 2. The materials and methods of the research are described in section 3. Among our findings, we present a case study based on the dataset “COVID-19 and the 2020 crisis: healthcare system capabilities and ramifications for the economy and businesses all over the world” and some perspectives of using datasets for intellectual monitoring and “smart” management of digital public health based on the IoT and artificial intelligence. The conclusion sums up the paper.
Section snippets
Literature review
The theoretical basis of this paper consists of multiple published works on digital public health, including [[8], [9], [10], [11], [12], [13], [14], [15], [16], [17], [18], [19], [20], [21], [22]]. [23,24].
[25] substantiate the necessity for equal access to free and cheap primary healthcare in Aotearoa and analyze the results of repeated waves of the New Zealand Overview of Healthcare in 1996–2016 [26] present the authors' view of health in cities from evolutionary complex systems [27] deem it
Materials and method
To create the dataset “COVID-19 and the 2020 Crisis: Healthcare System Capabilities and Ramifications for the Economy and Business all Over the World”, we collected the most recent statistics that allow a matrix of data by countries and indicators (table of Big data). The indicators are systematized and classified: three logical blocks are distinguished in the dataset for convenience.
The first block contains indicators that characterize the current state of the COVID -19 pandemic according to
The case study is based on the dataset “COVID-19 and the 2020 crisis: healthcare system capabilities and ramifications for the economy and business all over the world."
Using the dataset, a user requests and received the required data in four consecutive steps. In the first step, groups of countries are to be selected. The following groups of countries, formed by the criterion of the level of socio-economic development and integration (e.g., G7 and BRICS) and by the geographical measure with two levels of details (first level: e.g., Europe, America, and Africa; second level: e.g., America includes North America, Central America, and South America), are
Conclusion
This paper has introduced a new dataset and offered an innovative solution through interactive platforms to gather, process, and analyze big data applied to the health care system. This approach describes the ultimate advantages of developing and employing a solution to implement the digital public health concept connected to creating and expanding datasets. On “smart” management of digital public health system based on the IoT and the dataset “COVID-19 and the 2020 crisis: healthcare system
Author statement
Elena G. Popkova: Conceptualization, Methodology, Software, Data curation, Writing- Original draft preparation.
Bruno S. Sergi: Visualization, Investigation, Supervision, Writing- Reviewing and Editing.
Elena G. Popkova – Doctor of Science (Economics), the founder and president of the Institute of Scientific Communications (Russia) and Leading researcher of the Center for applied research of the chair “Economic policy and public-private partnership” of Moscow State Institute of International Relations (MGIMO) (Moscow, Russia). Her scientific interests include the theory of economic growth, sustainable development, globalization, humanization of economic growth, emerging markets, social
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Elena G. Popkova – Doctor of Science (Economics), the founder and president of the Institute of Scientific Communications (Russia) and Leading researcher of the Center for applied research of the chair “Economic policy and public-private partnership” of Moscow State Institute of International Relations (MGIMO) (Moscow, Russia). Her scientific interests include the theory of economic growth, sustainable development, globalization, humanization of economic growth, emerging markets, social entrepreneurship, and the digital economy and Industry 4.0. Elena G. Popkova organizes all-Russian and international scientific and practical conferences and is the editor and author of collective monographs, and serves as a guest editor of international scientific journals. She has published more than 300 works in Russian and foreign peer-reviewed scientific journals and books.
Bruno S. Sergi is an instructor at Harvard University with a particular interest focus on emerging markets' economics in Asia and Eastern Europe. At Harvard, he is a faculty affiliate at the Center for International Development and an Associate of the Davis Center for Russian and Eurasian Studies and the Harvard Ukrainian Research Institute. Sergi is the Series Editor of Cambridge Elements in the Economics of Emerging Markets (Cambridge University Press), the Editor of Entrepreneurship and Global Economic Growth (Emerald Publishing), and an Associate Editor of The American Economist. He also teaches Political Economy and International Finance at the University of Messina, Italy, and co-direct the Lab for Entrepreneurship and Development (LEAD), a research lab based in Cambridge, MA, that aims to generate and share knowledge about entrepreneurship development and sustainability. 1
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The authors would like to acknowledge the editor Prof. Sang-Bing Tsai and two reviewers for their valuable time and specific comments, whose inputs have helped enhance the manuscript's current version, and Piper DeLo for her research assistance.