Short-Term Forecasting of Monkeypox Cases Using a Novel Filtering and Combining Technique.

In the modern world, new technologies such as artificial intelligence, machine learning, and big data are essential to support healthcare surveillance systems, especially for monitoring confirmed cases of monkeypox. The statistics of infected and uninfected people worldwide contribute to the growing number of publicly available datasets that can be used to predict early-stage confirmed cases of monkeypox through machine-learning models. Thus, this paper proposes a novel filtering and combination technique for accurate short-term forecasts of infected monkeypox cases. To this end, we first filter the original time series of the cumulative confirmed cases into two new subseries: the long-term trend series and residual series, using the two proposed and one benchmark filter. Then, we predict the filtered subseries using five standard machine learning models and all their possible combination models. Hence, we combine individual forecasting models directly to obtain a final forecast for newly infected cases one day ahead. Four mean errors and a statistical test are performed to verify the proposed methodology's performance. The experimental results show the efficiency and accuracy of the proposed forecasting methodology. To prove the superiority of the proposed approach, four different time series and five different machine learning models were included as benchmarks. The results of this comparison confirmed the dominance of the proposed method. Finally, based on the best combination model, we achieved a forecast of fourteen days (two weeks). This can help to understand the spread and lead to an understanding of the risk, which can be utilized to prevent further spread and enable timely and effective treatment.

Arima Models to Forecast Covid-19 in Kingdom of Saudi Arabia during the Interval (1-2021 to 1-2022) Weekly Using E-Views Program

The main objective of this research is to analyze the fundamental differences in the basic indicators of the emerging corona virus, COVID-19, especially the number of total cases and the number of deaths resulting from it in Kingdom of Saudi Arabia, in order to evaluate the precautionary measures taken by KSA. In this research, time series models were studied to predict the number of cases infected with COVID-19 that can be expected weekly in KSA during a period spanning a whole year using the numbers of weekly infections (WC) in KSA during the period from January 2021 to January 2022. The future values of injuries and deaths resulting from them were predicted using the time series method according to the current and previous values, and the E-Views statistical software package was used, which was specifically designed to process time series data. The study proved that there were statistically significant differences in the number of weekly infections with the corona virus, in addition to the presence of statistically significant differences in the number of weekly deaths resulting from the corona virus in Kingdom of Saudi Arabia. The study also demonstrated the existence of a statistically significant correlation between the number of weekly infections with the corona virus and the deaths resulting from it in Kingdom of Saudi Arabia. The automatic regression integrated moving average (ARIMA) model was used as one of the time series prediction methods and the prediction procedures were determined using the ARIMA model. The results of the analysis showed that the ARIMA(1, 2, 0) model gave the best results for prediction and data analysis. It is highly advised to maintain the social distancing with all safety measures.

Robust autoregressive modeling and its diagnostic analytics with a COVID-19 related application

Autoregressive models in time series are useful in various areas. In this article, we propose a skew-t autoregressive model. We estimate its parameters using the expectation-maximization (EM) method and develop the influence methodology based on local perturbations for its validation. We obtain the normal curvatures for four perturbation strategies to identify influential observations, and then to assess their performance through Monte Carlo simulations. An example of financial data analysis is presented to study daily log-returns for Brent crude futures and investigate possible impact by the COVID-19 pandemic. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

On the Implementation of the Artificial Neural Network Approach for Forecasting Different Healthcare Events.

The rising number of confirmed cases and deaths in Pakistan caused by the coronavirus have caused problems in all areas of the country, not just healthcare. For accurate policy making, it is very important to have accurate and efficient predictions of confirmed cases and death counts. In this article, we use a coronavirus dataset that includes the number of deaths, confirmed cases, and recovered cases to test an artificial neural network model and compare it to different univariate time series models. In contrast to the artificial neural network model, we consider five univariate time series models to predict confirmed cases, deaths count, and recovered cases. The considered models are applied to Pakistan's daily records of confirmed cases, deaths, and recovered cases from 10 March 2020 to 3 July 2020. Two statistical measures are considered to assess the performances of the models. In addition, a statistical test, namely, the Diebold and Mariano test, is implemented to check the accuracy of the mean errors. The results (mean error and statistical test) show that the artificial neural network model is better suited to predict death and recovered coronavirus cases. In addition, the moving average model outperforms all other confirmed case models, while the autoregressive moving average is the second-best model.

Time Series based Models for Corona Data Analytics

There is a great challenge to deal with prediction of an epidemic or pandemic in the future through artificial intelligence or state-of-art technology. This is evident in the case of pandemic happened from January 2020 which is a result of corona virus. In early stages of covid-19 caused by corona virus, the symptoms are not severe and mostly cured through self-medication. In this situation, estimating the real spread based on the reports from various hospitals might be misleading. There might be lot of variation in the reports based on different types of measurements performed, and the tests conducted on only the symptomatic patients. In spite of all these constraints, a huge amount of covid-19 related data is published since 3 years and also updated on a daily basis. This serves as a motivation to consider various mathematical models to predict the course of change in an epidemic and result in effective control strategies. The challenge is to predict the peak and end of the epidemic together with its evolution through available incomplete data and intrinsic complexity. In this paper, time series models are proposed to analyze corona spread data and analyzing its impact based on gender, age and geographical location. The proposed algorithm leverages machine learning models to predict number of corona cases in the future. An early detection of spread of corona would help in stopping community transmission and this serves a major motivation for this research. ARIMA model and Recurrent Neural Networks (RNN) based LSTM model perform way better than the machine learning models based on regression and decision trees. © 2022 IEEE.

Direct and indirect mortality impacts of the COVID-19 pandemic in the United States, March 1, 2020 to January 1, 2022.

Excess mortality studies provide crucial information regarding the health burden of pandemics and other large-scale events. Here, we use time series approaches to separate the direct contribution of SARS-CoV-2 infection on mortality from the indirect consequences of the pandemic in the United States. We estimate excess deaths occurring above a seasonal baseline from March 1, 2020 to January 1, 2022, stratified by week, state, age, and underlying mortality condition (including COVID-19 and respiratory diseases; Alzheimer's disease; cancer; cerebrovascular diseases; diabetes; heart diseases; and external causes, which include suicides, opioid overdoses, and accidents). Over the study period, we estimate an excess of 1,065,200 (95% Confidence Interval (CI) 909,800-1,218,000) all-cause deaths, of which 80% are reflected in official COVID-19 statistics. State-specific excess death estimates are highly correlated with SARS-CoV-2 serology, lending support to our approach. Mortality from 7 of the 8 studied conditions rose during the pandemic, with the exception of cancer. To separate the direct mortality consequences of SARS-CoV-2 infection from the indirect effects of the pandemic, we fit generalized additive models (GAM) to age- state- and cause-specific weekly excess mortality, using covariates representing direct (COVID-19 intensity) and indirect pandemic effects (hospital intensive care unit (ICU) occupancy and measures of interventions stringency). We find that 84% (95% CI 65-94%) of all-cause excess mortality can be statistically attributed to the direct impact of SARS-CoV-2 infection. We also estimate a large direct contribution of SARS-CoV-2 infection (≥67%) on mortality from diabetes, Alzheimer's, heart diseases, and in all-cause mortality among individuals over 65 years. In contrast, indirect effects predominate in mortality from external causes and all-cause mortality among individuals under 44 years, with periods of stricter interventions associated with greater rises in mortality. Overall, on a national scale, the largest consequences of the COVID-19 pandemic are attributable to the direct impact of SARS-CoV-2 infections; yet, the secondary impacts dominate among younger age groups and in mortality from external causes. Further research on the drivers of indirect mortality is warranted as more detailed mortality data from this pandemic becomes available.

Evolution of global music trends: An exploratory and predictive approach based on Spotify data

Music listening choices are considered to be a factor capable of measuring people's emotions. Thanks to the explosion of streaming music applications in recent years, it is possible to describe listening trends of the global population based on emotional features. In this paper we have analysed the most popular songs from 52 countries on Spotify through their features of danceability, positivity and intensity. This analysis allows exploring how these song features reflect mood trends along with other contextual factors that may affect the population's listening behaviour, such as the weather or the influence of the COVID-19 pandemic. Finally, we have proposed a multivariate time series model to predict the preferred type of music in those countries based on their previous music listening patterns and the contextual factors. The results show some relevant behavioural changes in these patterns due to the effect of the pandemic. Furthermore, the resulting prediction model enables forecasting the type of music listened to in three different groups of countries in the next 4 months with an error around 1%. These results may help to better understand streaming music consumption in businesses related to the music and marketing industry. © 2022 Elsevier B.V.

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.

THE FACTORS INFLUENCING GROSS DOMESTIC PRODUCT GROWTH IN THE POST-PANDEMIC PERIOD: THE CASE OF KOSOVO

This paper examined the factors that influenced the Gross Domestic Product growth (GDP) in the post-Covid-19 period in Kosovo. This paper explored the impact of consumption, remittances, exports, imports, and inflation on Kosovo's GDP growth using fixed effects regression analysis with data from various secondary sources to analyze their impact from Kosovo's perspective. The results demonstrated that consumption, remittances, and exports had a statistically significant influence on GDP growth during the post-pandemic economic lockdown stage, whereby imports and inflation had a little inverse relation. Further, the Hausman test statistics on the adequacy of the fixed-effect model selection represent a superior performance compared to the random effect model. The paper is the first that extensively explores the impact of these factors that drove GDP growth in the post-pandemic period in Kosovo's economy. The novelty of this paper is that it recognizes the response of governments to the pandemic and accurately identifies the macroeconomic factors that influenced GDP growth.

Time Series Models for Forecasting the Number of Cases of SARS-COV-2 Dominant Variants During the Epidemic Waves in Chile

COVID-19 and its variants have created a global pandemic. In Chile, as of February 28 2022, more than 3 million people have been infected and more than 42 thousand people have died. In this article, a comparative study of different mathematical models used to model and predict the number of daily confirmed cases of COVID-19 in Chile is carried out. This research considers the daily records of confirmed cases since the beginning of the pandemic and therefore, includes those infected by the different variants of the virus (Delta, Gamma and Omicron), these variants have dominated the evolution of daily infections in Chile, being the Omicron variant the one that has shown to have a higher rate of infection at national level. The objective of this study is to provide relevant information on the evolution of the COVID-19 pandemic in Chile through time series models that have been validated in different investigations and to assess their validity with the appearance of the Omicron variant of the SARS-CoV-2 virus. © 2022, Escuela Politecnica Nacional. All rights reserved.

Anticipating extreme losses using score-driven shape filters

We suggest a new value-at-risk (VaR) framework using EGARCH (exponential generalized autoregressive conditional heteroskedasticity) models with score-driven expected return, scale, and shape filters. We use the EGB2 (exponential generalized beta of the second kind), NIG (normal-inverse Gaussian), and Skew-Gen-t (skewed generalized-t) distributions, for which the score-driven shape parameters drive the skewness, tail shape, and peakedness of the distribution. We use daily data on the Standard & Poor's 500 (S&P 500) index for the period of February 1990 to October 2021. For all distributions, likelihood-ratio (LR) tests indicate that several EGARCH models with dynamic shape are superior to the EGARCH models with constant shape. We compare the realized volatility with the conditional volatility estimates, and we find two Skew-Gen-t specifications with dynamic shape, which are superior to the Skew-Gen-t specification with constant shape. The shape parameter dynamics are associated with important events that affected the stock market in the United States (US). VaR backtesting is performed for the dot.com boom (January 1997 to October 2020), the 2008 US Financial Crisis (October 2007 to March 2009), and the coronavirus disease (COVID-19) pandemic (January 2020 to October 2021). We show that the use of the dynamic shape parameters improves the VaR measurements.

Assessing the effectiveness of quarantine measures during the COVID-19 pandemic in Chile using Bayesian structural time series models.

Background: With the emergence of the COVID-19 pandemic, all existing health protocols were tested under the worst health crisis humanity has experienced since the Black Death in the 14th century. Countries in Latin America have been the epicenter of the COVID-19 pandemic, with more than 1.5 million people killed. Worldwide health measures have included quarantines, border closures, social distancing, and mask use, among others. In particular, Chile implemented total or partial quarantine measures depending on the number of infections in each region of the country. Therefore, it is necessary to study the effectiveness of these quarantines in relation to the public health measures implemented by government entities at the national level. Objective: The main objective of this study is to analyze the effectiveness of national- and region-level quarantines in Chile during the pandemic based on information published by the Chilean Ministry of Health, and answers to the following question are sought: Were quarantine measures in Chile effective during the COVID-19 pandemic? Methods: The causal effect between the rates of COVID-19 infections and the population rates in Phase 1 and Phase 2 quarantines in the period from March 2020 to March 2021 in different regions of Chile were evaluated using intervention analyses obtained through Bayesian structural time series models. In addition, the Kendall correlation coefficient obtained through the copula approach was used to evaluate the comovement between these rates. Results: In 75% of the Chilean regions under study (12 regions out of a total of 16), an effective Phase 1 quarantine, which was implemented to control and reduce the number of cases of COVID-19 infection, was observed. The main regions that experienced a decrease in cases were those located in the north and center of Chile. Regarding Phase 2, the COVID-19 pandemic was effectively managed in 31% (5 out of 16) of the regions. In the south-central and extreme southern regions of Chile, the effectiveness of these phases was null. Conclusion: The findings indicate that in the northern and central regions of Chile, the Phase 1 quarantine application period was an effective strategy to prevent an increase in COVID-19 infections. The same observation was made with respect to Phase 2, which was effective in five regions of northern Chile; in the rest of the regions, the effectiveness of these phases was weak or null.

Modelling the relationship between covid-19 restrictive measures and mobility patterns across Europe using time-series analysis

Since early 2020, strict restrictions on non-essential movements were imposed globally as countermeasures to the rapid spread of COVID-19. The various containment and closures strategies, taken by the majority of countries, have directly affected travel behavior. This paper aims to investigate and model the relationship between covid-19 restrictive measures and mobility patterns across Europe using time-series analysis. Driving and walking data, as well as confinement policies were collected from February 2020 to February 2021 for twenty-five European countries and were implemented into Seasonal AutoRegressive Integrated Moving Average with eXogenous regressors (SARIMAX) time-series models. Results reveal a significant number of models in order to estimate mobility during pandemic almost in every country of the study. School closing was found to be the most important exogenous factor for describing driving or walking, while “Stay at home” orders had not a significant effect on the evolution of people movements. In addition, countries which suffered the most due to the pandemic indicated a strong correlation with the restrictive measures. No time-series models were found to describe the countries which implemented weak confinement policies. © 2022 Marianthi Kallidoni, Christos Katrakazas, George Yannis.

Data-driven tools for assessing and combating COVID-19 outbreaks in Brazil based on analytics and statistical methods

The COVID-19 pandemic is one of the worst public health crises in Brazil and the world that has ever been faced. One of the main challenges that the healthcare systems have when decision-making is that the protocols tested in other epidemics do not guarantee success in controlling the spread of COVID-19, given its complexity. In this context, an effective response to guide the competent authorities in adopting public policies to fight COVID-19 depends on thoughtful analysis and effective data visualization, ideally based on different data sources. In this paper, we discuss and provide tools that can be helpful using data analytics to respond to the COVID-19 outbreak in Recife, Brazil. We use exploratory data analysis and inferential study to determine the trend changes in COVID-19 cases and their effective or instantaneous reproduction numbers. According to the data obtained of confirmed COVID-19 cases disaggregated at a regional level in this zone, we note a heterogeneous spread in most megaregions in Recife, Brazil. When incorporating quarantines decreed, effectiveness is detected in the regions. Our results indicate that the measures have effectively curbed the spread of the disease in Recife, Brazil. However, other factors can cause the effective reproduction number to not be within the expected ranges, which must be further studied. [ FROM AUTHOR] Copyright of Signa Vitae is the property of Pharmamed Mado Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

A Comparative Study between Univariate and Multivariate Time Series Models for COVID-19 Forecasting

COVID-19, a disease produced by the SARS-CoV-2 virus, has had and continues to have a major influence on humankind. This pandemic has wreaked havoc on the global economy, pushing governments to take drastic steps to control its spread. Forecasting the growth of COVID-19 can assist healthcare providers, policymakers, manufacturers, and merchants predict the pandemic's recurrence and the general public to have faith in the decisions made by them. Various existing findings showed that time-series techniques could learn and scale to properly anticipate how many people would be harmed by Covid-19 in the future. In this research, we did a comparative analysis of univariate time series models and multivariate time series models for confirming a better model at the end. As a result, we aim to bring out a time series model that is more suitable for forecasting the progression of pandemics worldwide, thus being a more reliable model. The research results showed that multivariate time series forecasting produced much better results for long-range than univariate time series models, which showed better results when expecting shorter periods. © 2021 IEEE.

An Ensemble Machine Learning Approach for Time Series Forecasting of COVID-19 Cases

Forecasting assists governments, epidemiologists, and policymakers make calculated decisions to mitigate the spread of the COVID-19 pandemic, thus saving lives. This paper presents an ensemble machine learning model by combining the distinctive strengths of autoregressive integrated moving averages (ARIMA) and stacked long short-term memory networks (S-LSTM) using extensive training procedures and model integration algorithms. We validated the model's generalization capabilities by analyzing time series data of four countries, such as the Philippines, United States, India, and Brazil spanning 467 days. The quantitative results show that our ensemble model outperforms stand-alone models of ARIMA and S-LSTM for a 15-day forecast accuracy of 93.50% (infected cases) and 87.97% (death cases). © 2021 IEEE.

A time series-based statistical approach for outbreak spread forecasting: Application of COVID-19 in Greece.

The aim of this paper is the generation of a time-series based statistical data-driven procedure in order to track an outbreak. At first are used univariate time series models in order to predict the evolution of the reported cases. Moreover, are considered combinations of the models in order to provide more accurate and robust results. Additionally, statistical probability distributions are considered in order to generate future scenarios. Final step is the build and use of an epidemiological model (tSIR) and the calculation of an epidemiological ratio (R0) for estimating the termination of the outbreak. The time series models include Exponential Smoothing and ARIMA approaches from the classical models, also Feed-Forward Artificial Neural Networks and Multivariate Adaptive Regression Splines from the machine learning toolbox. Combinations include simple mean, Newbolt-Granger and Bates-Granger approaches. Finally, the tSIR model and the R0 ratio are used for estimating the spread and the reversion of the pandemic. The suggested procedure is used to track the COVID-19 epidemic in Greece. This epidemic has appeared in China in December 2019 and has been widespread since then to all over the world. Greece is the center of this empirical study as is considered an early successful paradigm of resistance against the virus.

Assessing the future progression of COVID-19 in Iran and its neighbors using Bayesian models.

BACKGROUND: The short term forecasts regarding different parameters of the COVID-19 are very important to make informed decisions. However, majority of the earlier contributions have used classical time series models, such as auto regressive integrated moving average (ARIMA) models, to obtain the said forecasts for Iran and its neighbors. In addition, the impacts of lifting the lockdowns in the said countries have not been studied. The aim of this paper is to propose more flexible Bayesian structural time series (BSTS) models for forecasting the future trends of the COVID-19 in Iran and its neighbors, and to compare the predictive power of the BSTS models with frequently used ARIMA models. The paper also aims to investigate the casual impacts of lifting the lockdown in the targeted countries using proposed models. METHODS: We have proposed BSTS models to forecast the patterns of this pandemic in Iran and its neighbors. The predictive power of the proposed models has been compared with ARIMA models using different forecast accuracy criteria. We have also studied the causal impacts of resuming commercial/social activities in these countries using intervention analysis under BSTS models. The forecasts for next thirty days were obtained by using the data from March 16 to July 22, 2020. These data have been obtained from Our World in Data and Humanitarian Data Exchange (HDX). All the numerical results have been obtained using R software. RESULTS: Different measures of forecast accuracy advocated that forecasts under BSTS models were better than those under ARIMA models. Our forecasts suggested that the active numbers of cases are expected to decrease in Iran and its neighbors, except Afghanistan. However, the death toll is expected to increase at more pace in majority of these countries. The resuming of commercial/social activities in these countries has accelerated the surges in number of positive cases. CONCLUSIONS: The serious efforts would be needed to make sure that these expected figures regarding active number of cases come true. Iran and its neighbors need to improve their extensive healthcare infrastructure to cut down the higher expected death toll. Finally, these countries should develop and implement the strict SOPs for the commercial activities in order to prevent the expected second wave of the pandemic.

Tailoring time series models for forecasting coronavirus spread: Case studies of 187 countries.

When will the coronavirus end? Are the current precautionary measures effective? To answer these questions it is important to forecast regularly and accurately the spread of COVID-19 infections. Different time series forecasting models have been applied in the literature to tackle the pandemic situation. The current research efforts developed few of these models and validates its accuracy for selected countries. It becomes difficult to draw an objective comparison between the performance of these models at a global scale. This is because, the time series trend for the infection differs between the countries depending on the strategies adopted by the healthcare organizations to decrease the spread. Consequently, it is important to develop a tailored model for a country that allows healthcare organizations to better judge the effect of the undertaken precautionary measures, and provision more efficiently the needed resources to face this disease. This paper addresses this void. We develop and compare the performance of the time series models in the literature in terms of root mean squared error and mean absolute percentage error.

Forecasting the spread of the COVID-19 pandemic in Saudi Arabia using ARIMA prediction model under current public health interventions.

The substantial increase in the number of daily new cases infected with coronavirus around the world is alarming, and several researchers are currently using various mathematical and machine learning-based prediction models to estimate the future trend of this pandemic. In this work, we employed the Autoregressive Integrated Moving Average (ARIMA) model to forecast the expected daily number of COVID-19 cases in Saudi Arabia in the next four weeks. We first performed four different prediction models; Autoregressive Model, Moving Average, a combination of both (ARMA), and integrated ARMA (ARIMA), to determine the best model fit, and we found out that the ARIMA model outperformed the other models. The forecasting results showed that the trend in Saudi Arabia will continue growing and may reach up to 7668 new cases per day and over 127,129 cumulative daily cases in a matter of four weeks if stringent precautionary and control measures are not implemented to limit the spread of COVID-19. This indicates that the Umrah and Hajj Pilgrimages to the two holy cities of Mecca and Medina in Saudi Arabia that are supposedly scheduled to be performed by nearly 2 million Muslims in mid-July may be suspended. A set of extreme preventive and control measures are proposed in an effort to avoid such a situation.