Intelligent Data Analysis for Infection Spread Prediction

Abstract

Intelligent data analysis based on artificial intelligence and Big Data tools is widely used by the scientific community to overcome global challenges. One of these challenges is the worldwide coronavirus pandemic, which began in early 2020. Data science not only provides an opportunity to assess the impact caused by a pandemic, but also to predict the infection spread. In addition, the model expansion by economic, social, and infrastructural factors makes it possible to predict changes in all spheres of human activity in competitive epidemiological conditions. This article is devoted to the use of anonymized and personal data in predicting the coronavirus infection spread. The basic “Susceptible–Exposed–Infected–Recovered” model was extended by including a set of demographic, administrative, and social factors. The developed model is more predictive and applicable in assessing future pandemic impact. After a series of simulation experiment results, we concluded that personal data use in high-level modeling of the infection spread is excessive.

Automatic Calibration of Sociotechnical Systems Simulation Models on The Example of The Infection Spread Model

“…Furthermore, this method is successfully applied for finding solutions to nonlinear simultaneous equations and data approximation (Benzerrouk et al, 2013). In the present case, the SIR model variation was used, which constitutes simultaneous ordinary differential equations (SODE) (Borovkov et al, 2022). Therefore, the least squares method is appropriate for solving the problem of minimizing the sum of squared errors between the infected agents in the evaluated model and the current values (Ji et al, 2021;Parhusip et al, 2022;Qian, 2022;Smit et al, 2022).…”

“…The paper deals with the analysis and implementation of tools for automated calibration carried out based on a SIR model variant, considering additional factors (vaccination, including revaccination; recurrent morbidity; individual isolation) -Kermak-McKendrick SEIR model (Borovkov et al, 2022). The mathematical interpretation of the model is simultaneous homogeneous differential equations (a time series model).…”

“…Such propositions are based on data analysis, the exogenous and endogenous connections of an object, and their measurement (Antohonova, 2022). Therefore, predicting the consequences of a pandemic allows researchers to determine how various measures of influence on the system will affect the morbidity dynamics (Borovkov et al, 2022). This result is important for making managerial decisions to counteract the COVID-19 spread in various regions at the governmental level (Borovkov et al, 2022;Berawi, 2021).…”

“…The present research aims to select and implement an automated calibration method for predicting morbidity dynamics. In this paper, a regression model of time series based on the Kermack-McKendrick SEIR model is used (Borovkov et al, 2022). Besides factors accepted in the SEIR model, the considered model considers new conditions, with the help of which it was possible to track the dynamics of the COVID-19 outbreak more reliably and obtain a more plausible prediction.…”

Pengelolaan Aset Pemerintah Desa Berdasarkan Peraturan Menteri Dalam Negeri (Permendagri) Nomor 1 Tahun 2016 (Studi Kasus: Perencanaan, Pengadaan, Penatausahaan, Pelaporan, Pertanggungjawaban, Dan Pembinaan. Aset Desa Ulak Makam Kecamatan Tabir Ilir Kabupaten Merangin)

“…Dalam penelitian ini peneliti memilih insrumen dengan menggunakan salah satu sampling yang dianggap lebih cocok. Dalam peneliti ini yang dijadikan informan adalah 9 (sembilan) orang, yang terdiri: ANALISIS DATA Analisis data dalam penelitian secara teknis dilaksanakan secara induktif yaitu analisa yang dimulai dari pengumpulan data, reduksi data, penyajian data, dan verifikasi data (Badara & Saidin, 2014;Borovkov et al, 2022).…”