key: cord-301829-6yrgkx96
authors: Bhardwaj, Rashmi; Bangia, Aashima
title: Data Driven Estimation of Novel COVID-19 Transmission Risks Through Hybrid Soft-Computing Techniques
date: 2020-07-25
journal: Chaos Solitons Fractals
DOI: 10.1016/j.chaos.2020.110152
sha: 
doc_id: 301829
cord_uid: 6yrgkx96

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.

The World Health Organization (WHO) as on January 30, 2020 has announced 2019-2020 corona-genomic-virus a public health-emergency of international concern that can be abbreviated as PHEIC. Situation further worsened worldwide which was declared pandemic on March 11, 2020 . Till now, local transmission of this epidemic is being recording and increasing the count in countries including the six WHO regions.

Basically, their structure observed so far can be described as enveloped non-segmented positive-sense RNAgenomic viruses having place in the clan of Corona viridae majorly circulated in humans with other mammals. However, in most cases studied, individual related coronavirus infections are mild having identified two Beta corona viruses:

severe-acute-respiratory-syndrome-coronavirus (SARS-CoV) & Middle-East-respiratory-syndrome-coronavirus (MERS-CoV).

The outburst of nCOVID-19 studied in detail through data-based modeling & forecast analysis [1] . Detailed explanation of mathematical perspective to understand spread of infectious diseases is provided [2] . Estimation of atmosphere pollutants through dynamic indicators, discussion of the meditating body complexity, statistical simulations towards dynamics of HIV, IoT-based wireless transmissions having malware spread were modelled and studied in detail [3] [4] [5] [6] . Coronavirus data analyzed for risk assessment and forecasts [7] . Transmission data of the virus outbreak to atudy gov interventions [8] . Towards tracking the rate of transmission of epidemic based on the data driven study of the situation was carried out [9] . Study of a mathematical model towards dynamics of transmission and its control provided [10] . Spatial spread relationships during Coronavirus pandemic spread into the world via self-organizing maps analyzed [11] . WHO report on novel coronavirus in Japan and MERS-CoV update has been surveyed [12, 13] . WHO report on Coronavirus updated on January 19, 2020 [14] . The rate of spread of the epidemic in the scale-free networks [15] . As per the outcomes of this pandemic, efficiency of control strategies towards reduction of social mixing in China is modelled [16] . The complexity in the forecast accuracy of nCOVID-19 pandemic is dealt with [17] . Futuristic estimations computed via supervised learning of COVID [18] . Time series forecasting of the genomic virus spread in India applying genetic programming [19] . This pandemic outbreak is studied on the basis of training testing of Multimodal data [20] .

The molecules that may perhaps enter into host cell and cause acute respiratory syndrome targeting towards coronavirus studied [21] . Study forecasted impending COVID-19 spread cases for China plus some other regions using mathematical & traditional time-series prediction models [22] . Mathematical model-based prediction at an early stage achieved for the outburst of this particular virus in China [23] . Extensive exploration of pneumonia outbreak via corona-genome originating from bat species [24] .

None of the authors have studied the wavelet based neuronal fuzzification hybrid model for the data of countrywise spread of COVID-19 genome. In this article, forecasts of the country-based day to day basis data of confirmed, deaths and recovered cases. Analysis has been carried out through the machine-learned WNF hybridization predicting for shorter time span and forecasts through interpolation alongwith moving averages method for longer time spans and performance measures through MASE and sMAPE which have not been applied in any of the studies yet. During exploration, daily data sets of China from December 31, 2019 to May 17, 2020 (a total of 139 days); for India from January 31, 2020 to May 17, 2020 (a total of 108 days) and for USA from is taken from January 20, 2020 to May 17, 2020 (a total of 119 days) trusted data sources provided by designated authorities. These three datatypes have been further divided into three data sets: Confirmed cases, Deaths cases and Recovered cases respectively as mentioned in Table- 

Conversion function is a function that converts a waveform into various rate of recurrence constituents. If conversion function is used in agreement with the scale then it is called wavelet transform, which converts the function alongwith the interval realm into the rate of recurrence realm. Wavelet decomposition is carried out for records handling as with the help of wavelet demonstration, the non-stationarity of the economic and financial time series can be explained.

possessing following characteristics: 6. Finding mean.

Theorem: The root mean squared error is square root for Mean Squared Errors calculated via actual outcomes and the expected quantities.

Remark: 

Neuronal setup gets trained and tested through fuzzification having hybrid method for simulation of training and testing. For the country of China, the data has been analyzed under three main distinctions that are: Confirmed cases, Deaths' cases and Recovery cases that are being recorded every day and provided through public bulletin from designated authorities as depicted through fig.7 to fig.21 . For the country of India, the data has been analyzed under three main distinctions that are: Confirmed cases, Deaths' cases and Recovery cases that are being recorded every day and provided through public bulletin from designated authorities as depicted through fig.22 to fig.36 . 

It is the need of the hour to model the factors of COVID-19 transmission to minimize its spread and the extent to which it can be harmful. Since, China is the first country to record and report such cases so it is in a way the breeding place of this epidemic. Thus, it is necessary to understand the scenario. Prevention measures should be followed at its best so that the virus does not communicate to more people and to stop its breeding further. The wavelet decomposition depicts the data filtered through high and low pass filters filtering the noise in the sense normalizing for further computations. The trained responses are plotted with the actual data values to compare the scenario of confirmed, deaths and recovered cases respectively. Simulations through time progression will aid in detailed study of virus structure dynamic evolution and perhaps indicate the emergence of randomness of the system. Then the regression fit for the predicted data depicts the goodness of fit of predicted data upon the actual data. 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. Clearly, in this study sMAPE and MASE have lower performance errors and therefore effective in forecast. Contribute towards better understanding of the scenario. Thus, the daily datasets pertaining to those of USA have a great variability as compared to China and India. Although, the spread has different timelines where India & America with the short time span have the greatest number of confirmed cases increasing uncontrollably at present. The forecast of 50-60 days ahead varying in every case helps to understand the clear picture of the pandemic spread and the manner in which the transmission rate may change in the following time periods in these three countries India, China and America.

The outcomes of this study can provide an efficient learning and understanding of the future spread estimation and to eradicate the panic and stigmas of the people worldwide towards COVID-19. Also, it may aid to improve clinical strategies against this pandemic. The best alternative left for the mankind at this moment is to follow preventive measures such as no direct human interaction, self-quarantine, keeping the living area hygienic and maintaining social distance.

Data-based analysis, modelling and forecasting of the COVID-19 outbreak

The Mathematical Theory of lnfectious Diseases. 2 nd edition

Statistical Time series Analysis of Dynamics of HIV

Dynamic Indicator for the prediction of Atmospheric Pollutants

Forensic Investigations and Risk Management in Mobile and Wireless Communications

Real-time forecasts and risk assessment of novel coronavirus (COVID-19) cases: A data-driven analysis

Transmission dynamics of the COVID-19 outbreak and effectiveness of government interventions: A data-driven analysis

A data driven time-dependent transmission rate for tracking an epidemic: a case study of 2019-nCoV

Early dynamics of Transmission and Control of COVID-19: A Mathematical Modelling Study. The Lancet Infectious Diseases

Analysis of Spatial Spread Relationships of Coronavirus (COVID-19) Pandemic in the World using Self Organizing Maps

Novel Coronavirus -Japan (ex-China). World Health Organization

Middle East respiratory syndrome coronavirus (MERS-CoV) -update:2

World Health Organization (WHO)

Epidemic spreading in scale-free networks

The effect of control strategies to reduce social mixing on outcomes of the COVID-19 epidemic in Wuhan, China: a modelling study

Why is it difficult to accurately predict the COVID-19 epidemic?

COVID-19 Future Forecasting Using Supervised Machine Learning Models

Time Series Analysis and Forecast of the COVID-19

Pandemic in India using Genetic Programming

AI-Driven Tools for Coronavirus Outbreak: Need of Active Learning and Cross-Population Train/Test Models on Multitudinal/Multimodal Data

Small molecules targeting severe acute respiratory syndrome human coronavirus

Nowcasting and forecasting the potential domestic and international spread of the 2019-nCoV outbreak originating in Wuhan, China: a modelling study

Early Prediction of the 2019 Novel Coronavirus Outbreak in the Mainland China Based on Simple Mathematical Model

A pneumonia outbreak associated with a new coronavirus of probable bat origin

Authors thankful to GGSIP University for providing research facilities. The author(s) declare that there is no conflict of interest.
