Multivariate economic analysis of the government policies and COVID-19 on financial sector

The sudden pandemic outbreak of COVID-19 has led to disruption in trade, travel and commerce by halting manufacturing, industries, and all other sundry activities. Global markets plummeted, leading to erosion of around US $6 trillion within just one week during February 2020. During the same week, the S&P 500 index alone experienced a loss of more than US $5 trillion in the USA, while other top 10 companies in the S&P 500 suffered a loss of more than US $1.4 trillion. This manuscript performs multivariate analysis of the financial markets during the COVID-19 period and thus correlates its impact on the worldwide economy. An empirical evaluation of the effect of containment policies on financial activity, stock market indices, purchasing manager index and commodity prices are also carried out. The obtained results reveal that the number of lockdown days, fiscal stimulus and overseas travel bans significantly influences the level of economic activity.

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.