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

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 & R 2 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.

Challenges Caused by Imported Cases Abroad for the Prevention and Control of COVID-19 in China

“…Incubation period, from the earliest exposure time to the time of symptoms onset, was necessary for preventing and controlling the epidemic, so numerous studies were focused on this issue. The median incubation period was ranged from 3.9 days to 8.6 days due to the different definition of exposure time ( 13 , 14 , 16 – 19 , 21 – 25 , 33 , 34 ). However, in a real world study, most patients could not provide the specific exposure time.…”

Machine learned hybrid Gaussian analysis of COVID-19 pandemic in India

“… Always stay updated about the virus with trusted sources Staying updated from trusted sources will help in maintaining your welfare and safety. [10] , [11] , [12] , [13] , [14] , [15] , [16] , [17] , [18] , [19] , [20] , [21] , [22] , [23] , [24] , [25] , [26] , [27] , [28] , [29] , [30] , [31] , [32] , [33] , [34] , [35] , [36] , [37] , [38] …”

“…For control of pandemic the value of Cumulative active cases to be minimum with cumulative death cases as zero and cumulative recovered cases as maximum [18] .…”

Pulmonary COVID-19: Learning Spatiotemporal Features Combining CNN and LSTM Networks for Lung Ultrasound Video Classification

“…COVID-19 transmission is difficult to control. It depends on many aspects, being the object of study by several researchers who have proposed different artificial intelligence (AI) techniques to forecast the spread [ 8 , 9 , 10 , 11 ] and aid in the medical diagnosis of COVID-19 [ 12 , 13 , 14 ]. Other studies have considered the impact of climatic and urban variables on the forecast models for the spread of the disease [ 15 , 16 ].…”