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Data driven estimation of novel COVID-19 transmission risks through hybrid soft-computing techniques

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  • Bhardwaj, Rashmi
  • Bangia, Aashima

Abstract

Coronavirus genomic infection-2019 (COVID-19) has been announced as a serious health emergency arising international awareness due to its spread to 201 countries at present. In the month of April of the year 2020, it has certainly taken the pandemic outbreak of approximately 11,16,643 infections confirmed leading to around 59,170 deaths have been recorded world-over. This article studies multiple countries-based pandemic spread for the development of the COVID-19 originated in the China. This paper focuses on forecasting via real-time responses data to inherit an idea about the increase and maximum number of virus-infected cases for the various regions. In addition, it will help to understand the panic that surrounds this nCoV-19 for some intensely affecting states possessing different important demographic characteristics that would be affecting the disease characteristics. This study aims at developing soft-computing hybrid models for calculating the transmissibility of this genome viral. The analysis aids the study of the outbreak of this virus towards the other parts of the continent and the world. A hybrid of wavelet decomposed data into approximations and details then trained & tested through neuronal-fuzzification approach. Wavelet-based forecasting model predicts for shorter time span such as five to ten days advanced number of confirmed, death and recovered cases of China, India and USA. While data-based prediction through interpolation applied through moving average predicts for longer time spans such as 50–60 days ahead with lesser accuracy as compared to that of wavelet-based hybrids. Based on the simulations, the significance level (alpha) ranges from 0.10 to 0.67, MASE varying from 0.06 to 5.76, sMAPE ranges from 0.15 to 1.97, MAE varies from 22.59 to 6024.76, RMSE shows a variation from 3.18 to 8360.29 & R2 varying through 0.0018 to 0.7149. MASE and sMAPE are relatively lesser applied and novel measures that aimed to achieve increase in accuracy. They eliminated skewness and made the model outlier-free. Estimates of the awaited outburst for regions in this study are India, China and the USA that will help in the improvement of apportionment of healthcare facilities as it can act as an early-warning system for government policy-makers. Thus, data-driven analysis will provide deep insights into the study of transmission of this viral genome estimation towards immensely affected countries. Also, the study with the help of transmission concern aims to eradicate the panic and stigma that has spread like wildfire and has become a significant part of this pandemic in these times.

Suggested Citation

  • Bhardwaj, Rashmi & Bangia, Aashima, 2020. "Data driven estimation of novel COVID-19 transmission risks through hybrid soft-computing techniques," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    • Handle: RePEc:eee:chsofr:v:140:y:2020:i:c:s0960077920305488
    DOI: 10.1016/j.chaos.2020.110152
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    References listed on IDEAS

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    1. Cleo Anastassopoulou & Lucia Russo & Athanasios Tsakris & Constantinos Siettos, 2020. "Data-based analysis, modelling and forecasting of the COVID-19 outbreak," PLOS ONE, Public Library of Science, vol. 15(3), pages 1-21, March.
    2. Chakraborty, Tanujit & Ghosh, Indrajit, 2020. "Real-time forecasts and risk assessment of novel coronavirus (COVID-19) cases: A data-driven analysis," Chaos, Solitons & Fractals, Elsevier, vol. 135(C).
    3. Melin, Patricia & Monica, Julio Cesar & Sanchez, Daniela & Castillo, Oscar, 2020. "Analysis of Spatial Spread Relationships of Coronavirus (COVID-19) Pandemic in the World using Self Organizing Maps," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).
    4. Salgotra, Rohit & Gandomi, Mostafa & Gandomi, Amir H, 2020. "Time Series Analysis and Forecast of the COVID-19 Pandemic in India using Genetic Programming," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).
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    Cited by:

    1. Chang, Yiming & Tao, YinYing & Shan, Wei & Yu, Xiangyuan, 2023. "Forecasting COVID-19 new cases through the Mixed Generalized Inverse Weibull Distribution and time series model," Chaos, Solitons & Fractals, Elsevier, vol. 175(P2).
    2. Tim K. Tsang & Qiurui Du & Benjamin J. Cowling & Cécile Viboud, 2024. "An adaptive weight ensemble approach to forecast influenza activity in an irregular seasonality context," Nature Communications, Nature, vol. 15(1), pages 1-12, December.

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