Analysis and forecast of COVID-19 spreading in India using Nonlinear curve tting model with machine learning

In this paper, an analysis and forecasting of Indian COVID-19 data is discussed by using scipy optimize curve tting model of machine learning. We demonstrates the month wise analysis of coming cases, daily recovered cases, death cases and test cases conducted by the Government of India, of COVID-19 from 01 st March 2020 to 02 nd August 2020, and also forecast for the new cases, recover cases & death cases from 03 rd August 2020 to 01 st November 2020. Our study show that the total numbers of affected persons due to COVID-19 up to 01 st November 2020 will be total cases 13,690,491, recover cases 10,499,593 and death of 129,271.


Introduction
The World Health Organization (WHO) declared COVID-19 (scienti cally referred to as the severe acute respiratory syndrome-coronavirus 2 or SARS-CoV-2) a pandemic on 11 March 2020 [1]. This virus had already spread from China to other Asian countries, Europe and the United States. As of 5 th July 2020, cases have been identi ed in 188 countries or regions [2]. It is a respiratory disorder in human having cough, fever and breath problems. The global response to the COVID-19 pandemic has led to a sudden reduction of both greenhouse gas (GHG) emissions and air pollutants [3][4][5]. This has led to unprecedented enforced and voluntary restrictions on travel and work. On 31 st December 2019, China reported to WHO, some persons are infected from pneumonia (caused by a novel coronavirus, currently known as 2019-nCoV) in Wuhan City [6]. Analysis of mobility data from Google [7] and Apple [8] shows that mobility declined by 10% or more during April 2020 in all but one of the 125 nations tracked. The mobility declined by 80% in ve or more nations.
The rst case of COVID-19 in India is found on 30 th January 2020, a female student studying at Wuhan city, she belongs to Kerala [9]. After nding more cases, Indian Government has started the lockdown process with many phases from 25 th March 2020, so the preventive methods are used during this lockdown to protect the people from COVID-19, such as wearing the mask, hands sanitizing, frequent hand washing, restrictions in travelling, avoid social gathering, staying at home etc. [10]. Indian Government also ordered to close all the schools, colleges, markets and cinema halls during this period. People can only move out in emergency conditions by taking the permission from local authorities. India has very slow growth in the initial stage of COVID-19 pandemic but it increases exponentially later on [11,12].
There are many mathematical models and machine learning models given for analysis and prediction of COVID-19 pandemic situation. The spatial distribution and region wise spreading of COVID-19 prediction across the India is given by using Geospatial Approach with the help of GIS Software for distribution and trend analysis till 11 th April 2020 [13]. SIER and regression models are used for forecasting for next two weeks on the basis of analysis collected by Johns Hopkins University from 30 th January 2020 to 30 th March 2020 [14]. The RMSLE calculates the error rate of 1.52 for SEIR model and 1.75 for regression model for above analysis. The time series analysis based on ARIMA are also used for forecast. The time series based study indicates that the number of cases increase exponentially [15]. The linear regression model with machine learning is also used for forecasting and this study used the linear regression, multilayer perceptron and vector autoregression methods for analysis and prediction on the COVID-19 Kaggle dataset [16]. The containment model also used for COVID-19 in India, with prediction for reduction the number of upcoming cases [17].
There are many more research papers are available not only for Indian but also describes the covid 19 pandemic situations of China, Italy, France and United States, which can be helpful for planning and decision making [18,19,20].

In this paper, a comprehensive nonlinear curve tting model for analysis and forecast of COVID-19 in India is
proposed. This study is divided into two parts; (1) we analyzed the new cases; recover cases, death cases and test cases on daily basis (2) we forecasted the values of news cases, recover cases and death cases weekly by using the nonlinear curve tting model, The python, Pandas and Scipy optimize curve tting model are used in our computational work [21].

Methodology
The nonlinear regression is a powerful technique to x a broad range of values in nonlinear manner. The nonlinear regression determines the values of parameters that minimize the sum of squares of the distances of the data points (least square method) to the curve. Generally this method is used when experimental values are Gaussian in nature. The nonlinear regression procedure adjusts these values and produce new values to make curve tted . We use exponential function to t our data in this model [22]. The analysis for date wise new cases are shown in Fig.(1a), (2a), (3a), (4a), (5a) for the month of March, April, May, June and July. Similarly date wise recovered cases are shown in Fig.(1b), (2b), (3b), (4b), (5b) for these months. Fig.   (1c), (2c), (3c), (4c), (5c) has date wise death cases and in Fig.(1d)

Declarations
Compliance with Ethical Standards Funding: There are no nancial con icts of interest to disclose.