Intelligent Data Analysis for Infection Spread Prediction
Abstract
:1. Introduction
2. Materials and Methods
2.1. Simulation Tools
2.2. Input Data
3. Results and Discussion
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Model Based on Data Source 1 | Model Based on Data Source 2 | |
---|---|---|
Individuals’ contact rate | 3.64 × 10−2 | 3.58 × 10−2 |
Isolation efficiency | −3.45 × 10−3 | −3.43 × 10−3 |
Determination coefficient (on calibration data) | 99.21% | 98.39% |
Forecast of Model 1 | Forecast of Model 2 | Observed Data | |
---|---|---|---|
The peak date of the current number of infections | 26 January 2021 | 26 January 2021 | 20 January 2021 |
The current number of infections on the peak date | 91,538 | 85,963 | 104,932 |
The peak date forecast error (days) | 6 | 6 | - |
The current number of infections forecast error | 12.76% | 18.08% | - |
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Borovkov, A.I.; Bolsunovskaya, M.V.; Gintciak, A.M. Intelligent Data Analysis for Infection Spread Prediction. Sustainability 2022, 14, 1995. https://doi.org/10.3390/su14041995
Borovkov AI, Bolsunovskaya MV, Gintciak AM. Intelligent Data Analysis for Infection Spread Prediction. Sustainability. 2022; 14(4):1995. https://doi.org/10.3390/su14041995
Chicago/Turabian StyleBorovkov, Alexey I., Marina V. Bolsunovskaya, and Aleksei M. Gintciak. 2022. "Intelligent Data Analysis for Infection Spread Prediction" Sustainability 14, no. 4: 1995. https://doi.org/10.3390/su14041995