At the World Health International Conference in Geneva in January 2020, the World Health Organization (WHO) announced an outbreak of the new coronavirus. The novel coronavirus (severe acute respiratory syndrome [SARS] coronavirus 2) from Wuhan, China has continued to spread around the world since January 2020 and has turned into a pandemic of the coronavirus disease (COVID-19) [1,2]. Due to the rapid spreading potential and the absence of vaccines and drugs, the contagious COVID-19 devastated normal life around the world. Currently, COVID-19 has infected more than half a million of the population, has killed more than 25,000 people, and has forced more than 3 billion to stay in their homes [3]. Many people started getting pneumonia without any reason, and most of the cases were linked to Wuhan Seafood Market, where they sell fish and trade live animals. The new coronaviruses lurking around the world are threatening our rule, and the prevalence of fear and panic is increasing. This has also affected the cryptocurrency market [4,5]. The country in which the coronavirus has caused the most devastation after China is Italy. In Italy, hundreds of people are dying every day due to this deadly virus. The corona virus is 900 times smaller than a human hair. Despite its size, this small virus has scared the whole world. In December 2019, the first case of COVID-19 came from Wuhan City in China [6-8].

During the Chinese New Year migration, the virus spread to other Chinese provinces in early and mid-January 2020. The WHO [3] revealed that cases began to be detected in other countries by international travelers. Due to a lack of knowledge about this virus, the COVID-19 pandemic placed tremendous strain on everyone around the world. To prevent further transmission, strong preventive measures have intensified week-to-week; however, the numbers of infected cases are consistently increasing around the world, even after undergoing lockdown. Mathematical approaches have been widely used to infer critical epidemiological transitions and parameters of COVID-19. Epidemic curve fitting, surveillance data during the early transmission, and other epidemic models have been frequently applied to generate forecasts of the COVID-19 pandemic across the world [9-11]. This study aims to identify the top 15 countries with the most confirmed cases with spatial mapping. A comparison was done between the identified top 15 countries for confirmed cases, deaths, and recoveries, and an advanced autoregressive integrated moving average (ARIMA) model was used for predicting the spread of COVID-19 trajectories for the next 2 months (until July 7, 2020).

Various studies have been presented for forecasting many epidemic diseases. This research study analyzes dynamic models to generate 20-day forecasts of cumulative confirmed deaths and recoveries from COVID-19 cases by country, territory, or conveyance generated on April 24, 2020. The United States, Spain, Italy, France, Germany, the United Kingdom, Turkey, Iran, China, Russia, Brazil, Canada, Belgium, the Netherlands, and Switzerland were taken from the top 20 countries based on cumulative effect data. The ARIMA model assigns a weight to the considered past values and an error value to correct the modelling; other basic regression and exponential models use all past values to predict future values, so the ARIMA model is preferred. This study analyzed and extracted worldwide data based upon a time series data-based advanced prediction ARIMA model approach for the top 15 COVID-19-infected countries.

We used data from Worldometer, which reports the approximate data of cumulative cases for more than 170 countries worldwide including state- or province-level cases for some countries [12]. We have collected the case data for each day at given stipulated times, from January 21, 2020, to April 24, 2020. Furthermore, we preprocessed the top 15 countries’ data with their spatial locations to collect and create some spatial attributes for the overall available data sets to forecast the trajectory of COVID-19 cases. In addition, as whole worldwide data is not available for stipulated times, we did not create any worldwide pandemic forecasting. Some dates with cases of confirmed COVID-19 along with total cumulative results of recovered cases and death cases were analyzed using statistical analysis along with spatial extinct. We used the ARIMA model with R (R Foundation for Statistical Computing) and validated it using Akaike information criterion (AIC). The new projected data was used up to July 2, 2020, for the creation of a trajectory projected score for each category: case confirmed, recovered, and death.

Recent reported cumulative data of confirmed cases, deaths, and recoveries of COVID-19 from January 21 to April 26, 2020, were obtained from Worldometer. The reported data were used to predict more than 60 days and to understand the positive effects in the near future as well as the projected trends over trajectories. The different statistical phenomenological models in the R-language platform were used to analyze the disease-based trajectories model for prediction purposes. The four models were used to analyze the aggregate data set for time series analysis. This includes the ARIMA model, which is a mass model of two different models, including the autoregression (AR) model and the moving average (MA) model [13]. This model also used AIC statistics and coverage of regression analysis.

Another type of COVID-19, like SARS disease, was analyzed without breaking the current situation or predicting the future perspective [14]. The vector auto-average model was used to predict the spatial extinct while using remote sensing data for the purpose of the creation of a worldwide geographic information system (GIS) map for three different variables [15]. These three variables in the GIS environment created a map of cumulative confirmed cases by country as well as recovered and death maps [16]. The use of another statistical analysis was a generalized logistic growth model, which generally is depicted as a scaling parameter for integrating an additional result-oriented value put method [17]. Some epidemic models used in disease epidemic conditions measure oscillates, which are multiple peak parameters inferred in subepidemic and pandemic conditions to determine the projected outcomes [18].

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, which were analyzed up to July 2, 2020. We used the GIS and remote sensing to determine the pandemic mapping and analyze the upcoming effects of COVID-19.

MA is the present value of a series, which is defined as a linear combination of past errors. Assuming the errors to be independently distributed with the normal distribution [13,19], order q is defined as:

Where:

Order q of the MA process is obtained from the autocorrelation function (ACF) plot; this is the lag after which ACF crosses the upper confidence interval for the first time. We combined differencing with MA and AR models, and the combined model can be expressed as:

Here, y′_t is the differenced series. The “predictors” on the right-hand side include both lagged values of y_t and lagged errors. We call this an ARIMA (p, d, q) model, where:

The COVID-19 daily data was collected and cumulatively represented as a spatial map for more than 170 countries and territories. The spatial map is useful to identify the intensity of COVID-19 infections in the top 15 countries and the continents. The recent reported data for confirmed cases, deaths, and recoveries for the last 3 months from January to April 2020 was represented and compared between the top 15 infected countries. The ARIMA model was used to predict estimated confirmed cases, deaths, and recoveries for the top 15 countries from April 24 to July 7, 2020. Its value was represented with 95%, 80%, and 70% confidence intervals, and the 95% confidence intervals were shown as the median interval between the 80% and 70% wide values. The validation of the ARIMA model was carried out using the AIC for the available recent data; its values were about 20, 14, and 16 for cumulative confirmed cases, deaths, and recoveries from COVID-19, respectively, which represents acceptable results. The observed predicted values showed that the confirmed cases, deaths, and recoveries will double in all countries except China, Switzerland, and Germany. It was also observed that the death and recovery rates were faster when compared to confirmed cases during the next 2 months. The associated mortality rate will be much higher in the United States, Spain, and Italy followed by France, Germany, and the United Kingdom. The limitation of the ARIMA model is that it does not support any volatility or in-between changes in the prediction periods. The accuracy of the countries’ data accumulated from Worldometer was a matter of trust for the representation of the whole study.

The forecast analysis of COVID-19 dynamics showed a different angle for the whole world, and it looks scarier than imagined. Interestingly, the recovery numbers also look promising, with resistance starting by July 2020. Thus, a slowdown in the surge of the COVID-19 pandemic during the proceeding months depends upon various administrative interventions and public awareness about the spread of the COVID-19 pandemic.