Lakhasly

We used the data of cumulative confirmed death and recovery of COVID-19 cases reported from January 21 until March 26, 2020, that were obtained from John Hopkins Coronavirus resource center (https://coronavirus.jhu.edu/).By using GIS and Remote sensing to determine the epidemic mapping and analyzing the upcoming effects of COVID-19.The use of another statistical analysis is a generalized logistic growth model (GLM), which generally depicted as a scaling parameter of integrating an additional result-oriented value put method [11].After standardizing all the models, the data of the top 20 countries were included to analyze the forecasting models of differential spatial adjacent, and projected trajectories are analyzed up to April 30, 2020.VAR model (Vector Auto Average) model is used to predict the spatial extinct while using remote sensing data and for the purpose of creation of GIS map of worldwide on three different variables [9].Some epidemic models used in disease epidemic conditions measure oscillates some multiple peak parameters inferred in sub-epidemic and pandemic conditions to determine the projected outcomes [12].medRxiv preprint doi: https://doi.org/10.1101/2020.03.30.20046227.

We used the data of cumulative confirmed death and recovery of COVID-19 cases reported from
January 21 until March 26, 2020, that were obtained from John Hopkins Coronavirus resource
center (https://coronavirus.jhu.edu/). We analyzed the data using dynamic models to generate 30
days forecasts and to understand the positive effect in the near future as well as projecting trends
over trajectories. We used different statistical phenomenological models in the R-language
platform to analyze the disease-based trajectories model for prediction purposes. We precisely
used four models to analyze the aggregate data set for time series analysis. This includes the
It is made available under a CC-BY-NC-ND 4.0 International license .
author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
medRxiv preprint doi: https://doi.org/10.1101/2020.03.30.20046227. The copyright holder for this preprint (which was not peer-reviewed) is the
ARIMA model, which is a mass model of two different models, including the AR model (Auto
Regression) and the MA (Moving Average) model [7]. This model results over Akaike
information criterion (AIC) statistics and coverage of regression analysis.
Another type of COVID-19, like SARS disease (Severe Acute Respiratory Syndrome), is
analyzed without breaking the current situation and predicting the future perspective [8]. VAR
model (Vector Auto Average) model is used to predict the spatial extinct while using remote
sensing data and for the purpose of creation of GIS map of worldwide on three different
variables [9]. These three variables in the GIS environment create a map of cumulative
confirmed cases country-wise as well as recovered and death map [10]. The use of another
statistical analysis is a generalized logistic growth model (GLM), which generally depicted as a
scaling parameter of integrating an additional result-oriented value put method [11]. Some
epidemic models used in disease epidemic conditions measure oscillates some multiple peak
parameters inferred in sub-epidemic and pandemic conditions to determine the projected
outcomes [12].
After standardizing all the models, the data of the top 20 countries were included to analyze the
forecasting models of differential spatial adjacent, and projected trajectories are analyzed up to
April 30, 2020. By using GIS and Remote sensing to determine the epidemic mapping and
analyzing the upcoming effects of COVID-19.