Pandemic Analytics by Advanced Machine Learning for Improved Decision Making of COVID-19 Crisis

Konstantinos Demertzis,Panayotis Kikiras,Dimitrios Tsiotas,Lykourgos Magafas,Lazaros Iliadis,Dimitrios Taketzis

doi:10.3390/pr9081267

Abstract

With the advent of the first pandemic wave of Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2), the question arises as to whether the spread of the virus will be controlled by the application of preventive measures or will follow a different course, regardless of the pattern of spread already recorded. These conditions caused by the unprecedented pandemic have highlighted the importance of reliable data from official sources, their complete recording and analysis, and accurate investigation of epidemiological indicators in almost real time. There is an ongoing research demand for reliable and effective modeling of the disease but also the formulation of substantiated views to make optimal decisions for the design of preventive or repressive measures by those responsible for the implementation of policy in favor of the protection of public health. The main objective of the study is to present an innovative data-analysis system of COVID-19 disease progression in Greece and her border countries by real-time statistics about the epidemiological indicators. This system utilizes visualized data produced by an automated information system developed during the study, which is based on the analysis of large pandemic-related datasets, making extensive use of advanced machine learning methods. Finally, the aim is to support with up-to-date technological means optimal decisions in almost real time as well as the development of medium-term forecast of disease progression, thus assisting the competent bodies in taking appropriate measures for the effective management of the available health resources.

Highlights

The health crisis caused by the SARS-CoV-2 pandemic, combined with the economic consequences and the shock to health systems, has created serious concerns on how to make timely and valid decisions about prevention and social distancing measures to be taken [1]
This paper proposes a novel model for the near-real-time analysis of COVID-19 disease data, as well as an intelligent machine learning system for predicting disease progression, in order to assist in deciding on predictive or suppressive measures of social distancing or taking appropriate measures related to the management of the health system
The data used to mathematically model and predict disease spread are freely available for use at the COVID-19 data repository by the Center for Systems Science and Engineering at Johns Hopkins University [29], and they include the daily measurements during the period from 26 February 2020 to 31 May 2021 of the total recorded cases

Summary

Introduction

The health crisis caused by the SARS-CoV-2 pandemic, combined with the economic consequences and the shock to health systems, has created serious concerns on how to make timely and valid decisions about prevention and social distancing measures to be taken [1]. The COVID-19 pandemic has created a rapidly changing environment where a huge amount of data related to virus spread updates is daily presented. The analysis of the spread rate of COVID-19 disease is directly related to the general concerns and challenges of large-scale near real-time data analysis procedures. It is directly related to the high velocity with which the relevant information arrives, how this information is collected and stored (its volume), the variety of unstructured or semi-structured data forms that can be collected, their variability as epidemiological data change in importance over time, their visualization and the diagnosis of whether the information is accurate or incomplete and inaccurate (its veracity), and determining their final operational value [3]. Understanding how the parameters of these data are linked can help civil protection organizations identify in a clear and fully understandable way what capabilities they need to develop or acquire to make full use of the data they have to strengthen public safety, health, and safeguarding the state’s health system [4]

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Conclusion