Geospatial multivariate analysis of COVID-19: a global perspective.

This manuscript presents a geospatial and temporal analysis of the COVID'19 along with its mortality rate worldwide and an empirical evaluation of social distance policies on economic activities. Stock Market Indices, Purchasing Manager Index (PMI), and Stringency Index values are evaluated with respect to rising COVID-19 cases based on the collected data from Jan 2020 to June 2021. The findings for the stock market index reveal the highest negative correlation coefficient value, i.e., -0.2, for the Shanghai index, representing a negative relation on stock markets, whereas the value of the correlation coefficient is minimum for Indian markets, i.e., 0.3, indicating the most impact by COVID-19 spread. Further, the results concerning PMI show that the highest value of the correlation coefficient is for the China i.e., -0.52, points to the sharpest pace of contraction. This reflects the lower value of the correlation indicating that the economy is on the way of growth, which can be seen from the PMI value of the various countries. The manuscript presents a novel geospatial model by empirically evaluating the correlation coefficient of COVID-19 with stock market index, PMI, and stringency index to understand the effect of COVID-19 on the global economy.

A Heterogeneous Ensemble Forecasting Model for Disease Prediction.

The manuscript presents a bragging-based ensemble forecasting model for predicting the number of incidences of a disease based on past occurrences. The objectives of this research work are to enhance accuracy, reduce overfitting, and handle overdrift; the proposed model has shown promising results in terms of error metrics. The collated dataset of the diseases is collected from the official government site of Hong Kong from the year 2010 to 2019. The preprocessing is done using log transformation and z score transformation. The proposed ensemble model is applied, and its applicability to a specific disease dataset is presented. The proposed ensemble model is compared against the ensemble models, namely dynamic ensemble for time series, arbitrated dynamic ensemble, and random forest using different error metrics. The proposed model shows the reduced value of MAE (mean average error) by 27.18%, 3.07%, 11.58%, 13.46% for tuberculosis, dengue, food poisoning, and chickenpox, respectively. The comparison drawn between the proposed model and the existing models shows that the proposed ensemble model gives better accuracy in the case of all the four-disease datasets.