The Impact of Socioeconomic and Demographic Factors on COVID-19 Forecasting Model

Background: COVID-19 has become a primary public health issue in various countries across the world. The main difficulty in managing outbreaks of infectious diseases is due to the difference in geographical, demographic, economic inequalities and people's behavior in each region. The spread of disease acts like a series of diverse regional outbreaks;each part has its disease transmission pattern. Objective: This study aims to assess the association of socioeconomic and demographic factors to COVID-19 cases through cluster analysis and forecast the daily cases of COVID-19 in each cluster using a predictive modeling technique. Methods: This study applies a hierarchical clustering approach to group regencies and cities based on their socioeconomic and demographic similarities. After that, a time-series forecasting model, Facebook Prophet, is developed in each cluster to assess the transmissibility risk of COVID-19 over a short period of time. Results: A high incidence of COVID-19 was found in clusters with better socioeconomic conditions and densely populated. The Prophet model forecasted the daily cases of COVID-19 in each cluster, with Mean Absolute Percentage Error (MAPE) of 0.0869;0.1513;and 0.1040, respectively, for cluster 1, cluster 2, and cluster 3. Conclusion: Socioeconomic and demographic factors were associated with different COVID-19 waves in a region. From the study, we found that considering socioeconomic and demographic factors to forecast COVID-19 cases played a crucial role in determining the risk in that area. © 2023 The Authors. Published by Universitas Airlangga.

Machine learning and automatic ARIMA/Prophet models-based forecasting of COVID-19: methodology, evaluation, and case study in SAARC countries.

Machine learning (ML) has proved to be a prominent study field while solving complex real-world problems. The whole globe has suffered and continues suffering from Coronavirus disease 2019 (COVID-19), and its projections need to be forecasted. In this article, we propose and derive an autoregressive modeling framework based on ML and statistical methods to predict confirmed cases of COVID-19 in the South Asian Association for Regional Cooperation (SAARC) countries. Automatic forecasting models based on autoregressive integrated moving average (ARIMA) and Prophet time series structures, as well as extreme gradient boosting, generalized linear model elastic net (GLMNet), and random forest ML techniques, are introduced and applied to COVID-19 data from the SAARC countries. Different forecasting models are compared by means of selection criteria. By using evaluation metrics, the best and suitable models are selected. Results prove that the ARIMA model is found to be suitable and ideal for forecasting confirmed infected cases of COVID-19 in these countries. For the confirmed cases in Afghanistan, Bangladesh, India, Maldives, and Sri Lanka, the ARIMA model is superior to the other models. In Bhutan, the Prophet time series model is appropriate for predicting such cases. The GLMNet model is more accurate than other time-series models for Nepal and Pakistan. The random forest model is excluded from forecasting because of its poor fit.

Time Series Forecasting of COVID-19 Mortality in SADC region with Facebook Prophet Model

This study proposes Facebook-Prophet model for understanding and forecasting of corona virus (COVID-19) mortality in Southern African Development Community (SADC) region over a 90-day time period. Findings showed that COVID-19 mortality in SADC region is expected to degrade in the near future. Model performance metrics were used to compute prediction performance. These results implied that the selected model was satisfactory and reliable. Findings of the study are expected to raise situational awareness into better understanding of the pandemic and to provide support for strategic health decisions for better management of COVID-19 disease. Moreover, the study provides a series of recommendations to be followed in order to decline COVID-19 related deaths in SADC countries. © 2022 IEEE.

Can financial stress be anticipated and explained? Uncovering the hidden pattern using EEMD-LSTM, EEMD-prophet, and XAI methodologies.

Global financial stress is a critical variable that reflects the ongoing state of several key macroeconomic indicators and financial markets. Predictive analytics of financial stress, nevertheless, has seen very little focus in literature as of now. Futuristic movements of stress in markets can be anticipated if the same can be predicted with a satisfactory level of precision. The current research resorts to two granular hybrid predictive frameworks to discover the inherent pattern of financial stress across several critical variables and geography. The predictive structure utilizes the Ensemble Empirical Mode Decomposition (EEMD) for granular time series decomposition. The Long Short-Term Memory Network (LSTM) and Facebook's Prophet algorithms are invoked on top of the decomposed components to scrupulously investigate the predictability of final stress variables regulated by the Office of Financial Research (OFR). A rigorous feature screening using the Boruta methodology has been utilized too. The findings of predictive exercises reveal that financial stress across assets and continents can be predicted accurately in short and long-run horizons even at the time of steep financial distress during the COVID-19 pandemic. The frameworks appear to be statistically significant at the expense of model interpretation. To resolve the issue, dedicated Explainable Artificial Intelligence (XAI) methods have been used to interpret the same. The immediate past information of financial stress indicators largely explains patterns in the long run, while short-run fluctuations can be tracked by closely monitoring several technical indicators.

Machine Learning and Deep Learning Based Time Series Prediction and Forecasting of Ten Nations' COVID-19 Pandemic.

In the paper, the authors investigated and predicted the future environmental circumstances of a COVID-19 to minimize its effects using artificial intelligence techniques. The experimental investigation of COVID-19 instances has been performed in ten countries, including India, the United States, Russia, Argentina, Brazil, Colombia, Italy, Turkey, Germany, and France using machine learning, deep learning, and time series models. The confirmed, deceased, and recovered datasets from January 22, 2020, to May 29, 2021, of Novel COVID-19 cases were considered from the Kaggle COVID dataset repository. The country-wise Exploratory Data Analysis visually represents the active, recovered, closed, and death cases from March 2020 to May 2021. The data are pre-processed and scaled using a MinMax scaler to extract and normalize the features to obtain an accurate prediction rate. The proposed methodology employs Random Forest Regressor, Decision Tree Regressor, K Nearest Regressor, Lasso Regression, Linear Regression, Bayesian Regression, Theilsen Regression, Kernel Ridge Regressor, RANSAC Regressor, XG Boost, Elastic Net Regressor, Facebook Prophet Model, Holt Model, Stacked Long Short-Term Memory, and Stacked Gated Recurrent Units to predict active COVID-19 confirmed, death, and recovered cases. Out of different machine learning, deep learning, and time series models, Random Forest Regressor, Facebook Prophet, and Stacked LSTM outperformed to predict the best results for COVID-19 instances with the lowest root-mean-square and highest R 2 score values.

Evaluation of Time Series Models for Forecasting Daily Rise in Confirmed COVID-19 Cases During the Second Wave in India

The spread of Coronavirus began in Wuhan, China and very steadily went on to be a global pandemic. This virus, later identified as COVID-19 virus, was found to be extremely contagious in nature. The people contracting the virus displayed several symptoms, and in some cases, the contraction of the virus also proved to be fatal. The earliest COVID-19 case in India was found on 30th January 2020, and thereafter, the country witnessed a steady growth in the number of infections. During the following year, in the latter half of March 2021 onwards, the cases began rising exponentially indicating the start of the second wave. The intention behind this research is to predict the future of the daily COVID-19 confirmed cases in India during the second wave. This is done by utilizing three time series models, ‘Autoregressive Integrated Moving Average’ (ARIMA), ‘Long Short-Term Memory (LSTM) Neural Network’ and ‘Facebook Prophet’. The primary focus of this work is to compare and evaluate the three models to determine which model shows the least error. The results show that the performance of ARIMA is better than LSTM and Prophet. The error metrics show least amount of average error for ARIMA, followed by LSTM and Prophet. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

The prediction of coronavirus disease 2019 outbreak on Bangladesh perspective using machine learning: a comparative study

Coronavirus disease 2019 (COVID-19) has made a huge pandemic situation in many countries of the world including Bangladesh. If the increase rate of this threat can be forecasted, immediate measures can be taken. This study is an effort to forecast the threat of present pandemic situation using machine learning (ML) forecasting models. Forecasting was done in three categories in the next 30 days range. In our study, multiple linear regression performed best among the other algorithms in all categories with R2 score of 99% for first two categories and 94% for the third category. Ridge regression performed great for the first two categories with R2 scores of 99% each but performed poorly for the third category with R2 score of 43%. Lasso regression performed reasonably well with R2 scores of 97%, 99% and 75% for the three categories. We also used Facebook Prophet to predict 30 days beyond our train data which gave us healthy R2 scores of 92% and 83% for the first two categories but performed poorly for the third category with R2 score of 34%. Also, all the models’ performances were evaluated with a 40-day prediction interval in which multiple linear regression outperformed other algorithms. © 2022 Institute of Advanced Engineering and Science. All rights reserved.

Ensemble Model to Forecast the End of the Covid-19 Pandemic

The coronavirus disease 2019 (Covid-19) epidemic has caused a worldwide health catastrophe that has had a profound influence on how we see our planet and our daily lives. In this pandemic circumstance, machine learning (ML) based prediction models demonstrate their value in predicting perioperative outcomes to enhance decision-making on future course of action. Ensemble learning is used in the majority of ML based forecasting approaches. The ML models anticipate the number of patients who will be affected by Covid-19, and use this information to forecast the end of the pandemic is to be leveraged. Three types of predictions are made: the number of newly infected cases, the number of deaths, and the number of recoveries in the next ‘x’ number of days. By combining one of the forecasting models with classifiers, we can predict the end of the pandemic. The proposed idea combines the SIRF model from epidemiology and a forecasting machine learning model named Prophet and a Naïve Bayes Classifier to predict the end of the pandemic. Using the theoretical equations of the SIRF model, we developed a formula for infectious growth rate. The classifier uses this infectious growth rate to check if the infection is fading. With confirmed, recovered and fatalities data, the infectious growth rate is calculated. Naïve Bayes classifier is used to check if the pandemic is about to end or not. If not then forecast the data for ‘x’ number of days and do the calculations again. The process continues until we get a time frame where the pandemic may reach its end. The results are discussed for 2 countries India and Israel. The forecasts done for Israel were very accurate to the actual data, whilst for India it was less comparatively as India was hit by 2 waves of Covid-19 pandemic. By leveraging the forecasting and classification capabilities of machine learning models like FBProphet, Naïve Bayes Classifier, and the mathematical equations of the SIRF model from epidemiology, the life span of the pandemic is determined. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Prediction of Bontang City COVID-19 Data Time Series Using the Facebook Prophet Method

The increasing trend of COVID-19 cases in Bontang makes it the first order of the highest incident rate in East Kalimantan, with a value of 1161.78 cases per 100 thousand inhabitants. The purpose of this study was to predict the increase in COVID-19 cases in Bontang City with a data set of positive confirmed cases, recovered and died of COVID-19 in Bontang city. The data set used starts from March 24, 2020 - March 1, 2021, using the Facebook Prophet method, the Jupyter Notebook application, and the Python programming language. The research process consists of the data collection stage, prediction implementation stage (data preprocessing, processing, performance evaluation, dashboard creation), and analysis of the result. The prediction was performed for up to 92 days until May 5, 2021. The result shows a trend of increasing cases of covid reaching the highest positive value, the highest recovery, and highest death, respectively, of 8695, 6099, and 156 people. According to the model, the average positive predictive error (MAE) and the average positive predictive accuracy value (MAPE) are 0.17 and 17.4%, indicating the positive prediction of contracting covid has good accuracy criteria. The next evaluation for the death prediction is accounted as reasonable accuracy criteria in which MAE and MAPE are 0.27 and 27%, respectively. Lastly, the recovery prediction has MAE of 0.17 and MAPE of 17.4%, implying good accuracy criteria. The study also provides recommendations to the COVID-19 Task Force to prepare the minimum number of PCR Tests by 870 tests and increase the hospitalization occupancy by 294 to control the spreading of the Coronavirus. © 2021 IEEE.

Predicting the Spread of COVID-19 in Africa Using Facebook Prophet and Polynomial Regression

The COVID-19 pandemic is a noisy disease and a deadly one that has got the whole world’s attention. This deadly disease led to the whole world’s total lockdown for months before necessary measures were put in place for those who could not go out. Measures like regular hand washing, sanitizer, nose or face covering, social distances, and the like. This pandemic was first discovered in China and later in other parts of the world too. This study looked into the spread of COVID-19 in Africa using the US COVID-19 dataset, where data was extracted for analysis and prediction using Polynomial Regression. The results were further compared using a Facebook prophet. But at the end of the prediction, polynomial regression has the lowest Relative Mean Absolute Error (RMSE), which is now the model used for predicting the spread of COVID-19 in Africa. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Application of Machine Learning Time Series Analysis for Prediction of Human Future in Covid-19

The rate of spread and effects of the novel COVID-19 coronavirus on the human body in different parts of the world is highly variable in nature. In this paper, we have carried out a case study on the current COVID situation across the globe and implemented three machine learning models: Polynomial Regression, Holt's Linear Model, and Facebook Prophet to predict future cases. We have discussed the accuracy of each model in forecasting the future trends of the virus. We have visualized the effect of the pandemic across countries and calculated the rate of growth of the infection, as well as the mortality rates. It is found out that the number of confirmed, deceased and recovered cases all are increasing exponentially until now. However, on a positive note, the recovery rate of the virus across the world is almost 7 times more than the mortality rate. This early prediction can help doctors, hospitals, and govt. to be prepared with emergency services for confirmed cases to avoid mortality rate. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Intelligent computing on time-series data analysis and prediction of COVID-19 pandemics.

Covid-19 disease caused by novel coronavirus (SARS-CoV-2) is a highly contagious epidemic that originated in Wuhan, Hubei Province of China in late December 2019. World Health Organization (WHO) declared Covid-19 as a pandemic on 12th March 2020. Researchers and policy makers are designing strategies to control the pandemic in order to minimize its impact on human health and economy round the clock. The SARS-CoV-2 virus transmits mostly through respiratory droplets and through contaminated surfacesin human body.Securing an appropriate level of safety during the pandemic situation is a highly problematic issue which resulted from the transportation sector which has been hit hard by COVID-19. This paper focuses on developing an intelligent computing model for forecasting the outbreak of COVID-19. The Facebook Prophet model predicts 90 days future values including the peak date of the confirmed cases of COVID-19 for six worst hit countries of the world including India and six high incidence states of India. The model also identifies five significant changepoints in the growth curve of confirmed cases of India which indicate the impact of the interventions imposed by Government of India on the growth rate of the infection. The goodness-of-fit of the model measures 85% MAPE for all six countries and all six states of India. The above computational analysis may be able to throw some light on planning and management of healthcare system and infrastructure.