An Improved Granulated Convolutional Neural Network Data Analysis Model for COVID-19 Prediction

As COVID-19 poses a major threat to people's health and economy, there is an urgent need for forecasting methodologies that can anticipate its trajectory efficiently. In non-stationary time series forecasting jobs, there is frequently a hysteresis in the anticipated values relative to the real values. The multilayer deep-time convolutional network and a feature fusion network are combined in this paper's proposal of an enhanced Multilayer Deep Time Convolutional Neural Network (MDTCNet) for COVID-19 prediction to address this problem. In particular, it is possible to record the deep features and temporal dependencies in uncertain time series, and the features may then be combined using a feature fusion network and a multilayer perceptron. Last but not least, the experimental verification is conducted on the prediction task of COVID-19 real daily confirmed cases in the world and the United States with uncertainty, realizing the short-term and long-term prediction of COVID-19 daily confirmed cases, and verifying the effectiveness and accuracy of the suggested prediction method, as well as reducing the hysteresis of the prediction results.

Forecasting of COVID-19 Outbreak in India: A Time Series Analysis

The World Health Organization (WHO) declared the status of coronavirus disease 2019 (COVID-19) to a global pandemic on March 11, 2020. Since then, numerous statistical, epidemiological and mathematical models have been used and investigated by researchers across the world to predict the spread of this pandemic in different geographical locations. The data for COVID-19 outbreak in India has been collated on daily new confirmed cases from March 12, 2020 to April 10, 2021. A time series analysis using Auto Regressive Integrated Moving Average (ARIMA) model was used to investigate the dataset and then forecast for the next 30-day time-period from April 11, 2021, to May 10, 2021. The selected model predicts a surge in the number of daily new cases and number of deaths. An investigation into the daily infection rate for India has also been done. © 2023 The authors and IOS Press.

Prewhitening and normalization help detect a strong cross-correlation between daily wastewater SARS-CoV-2 RNA abundance and COVID-19 cases in a community

Wastewater surveillance is a promising technology for real-time tracking and even early detection of COVID-19 infections in a community. Although correlation analysis between wastewater surveillance data and the daily clinical COVID-19 case numbers has been frequently conducted, the importance of stationarity of the time series data has not been well addressed. In this study, we demonstrated that strong yet spurious correlation could arise from non-stationary time series data in wastewater surveillance. Data prewhitening to remove trends by the first differences of values between two consecutive times helped to reveal distinct cross-correlation patterns between daily clinical case numbers and daily wastewater SARS-CoV-2 RNA abundance during a lockdown period in 2020 in Honolulu, Hawaii. Normalization of wastewater SARS-CoV-2 RNA concentration by the endogenous fecal viral markers in the same samples significantly improved the cross-correlation, and the best correlation was detected at a two-day lag of the daily clinical case numbers. The detection of a significant correlation between the daily wastewater SARS-CoV-2 RNA abundance and the clinical case numbers also suggests that disease burden fluctuation in the community should not be excluded as a contributor to the often observed weekly cyclic patterns of clinical cases. © 2023 The Royal Society of Chemistry.

Transfer Function Model for COVID-19 Deaths in USA Using Case Counts as Input Series.

This paper presents a transfer function time series forecast model for COVID-19 deaths using reported COVID-19 case positivity counts as the input series. We have used deaths and case counts data reported by the Center for Disease Control for the USA from July 24 to December 31, 2021. To demonstrate the effectiveness of the proposed transfer function methodology, we have compared some summary results of forecast errors of the fitted transfer function model to those of an adequate autoregressive integrated moving average model and observed that the transfer function model achieved better forecast results than the autoregressive integrated moving average model. Additionally, separate autoregressive integrated moving average models for COVID-19 cases and deaths are also reported.

Analysis of the Unemployment Rate in the Czech Republic by Age Groups with a Focus on the Impact of Covid-19

This paper analyzes the development of unemployment in the labor market in 2009-2020 in Czechia, however, the focus is drawn to the year 2020 and to the impact of Covid-19. The main aim is to describe how the Covid-19 and connected restrictions affected unemployment in national economy and in five age categories. The main indicators - the general unemployment rate, the NAIRU, and the unemployment gap were compared and analyzed for the national economy and five age groups (15-39). While the general unemployment rate and the unemployment gap have risen due to Covid-19 and the associated restrictions, the NAIRU was not significantly affected. The effect of the pandemic manifested itself most rapidly in the 2nd age category (20-24). For the rest of the age categories, we found a gradual increase in unemployment, or its acceleration a period later after the easing of the hard lockdown. The expectation is that in the following periods of 2020/21 the full impact of the Covid-19 pandemic will be more visible by an increasing tendency in the unemployment rate in the entire national economy.

The impact of climate change on budget balances and debt in the Middle East and North Africa (MENA) region.

Lower tax revenues and greater government spending result in higher deficits and public debt. As a result, determining the degree of budgetary effects is vital, but important to assess the persistence of these effects. We aim to investigate the impact of climate change on the fiscal balance and public debt in the countries of the Middle East and North Africa. The empirical analysis relies on panel data in the period 1990-2019 and employs various models. The findings show that temperature changes adversely affect the government budget and increase debt, but we find no significant impact of changes in rainfall. The average temperature decreases fiscal balance by 0.3 percent and increases debt by 1.87 percent. Using projections of temperature and rainfall over the years 2020 to 2099, we find a significant decrease in the fiscal balance at 7.3 percent and an increase in the public debt at 16 percent in 2060-2079 and 18 percent in 2080-2099 under the assumption of a high greenhouse gas (GHG) emission scenario. On the contrary, under the low GHG emission scenario, the fiscal balance deteriorates by 1.7 percent in 2020-2039 and 2.2 percent in 2080-2099, while public debt rises by 5 percent in 2020-2039 and 6.3 percent in 2080-2099. Supplementary Information: The online version contains supplementary material available at 10.1007/s10584-022-03388-x.

Stochastic forecasting of COVID-19 daily new cases across countries with a novel hybrid time series model.

An unprecedented outbreak of the novel coronavirus (COVID-19) in the form of peculiar pneumonia has spread globally since its first case in Wuhan province, China, in December 2019. Soon after, the infected cases and mortality increased rapidly. The future of the pandemic's progress was uncertain, and thus, predicting it became crucial for public health researchers. These predictions help the effective allocation of health-care resources, stockpiling, and help in strategic planning for clinicians, government authorities, and public health policymakers after understanding the extent of the effect. The main objective of this paper is to develop a hybrid forecasting model that can generate real-time out-of-sample forecasts of COVID-19 outbreaks for five profoundly affected countries, namely the USA, Brazil, India, the UK, and Canada. A novel hybrid approach based on the Theta method and autoregressive neural network (ARNN) model, named Theta-ARNN (TARNN) model, is developed. Daily new cases of COVID-19 are nonlinear, non-stationary, and volatile; thus, a single specific model cannot be ideal for future prediction of the pandemic. However, the newly introduced hybrid forecasting model with an acceptable prediction error rate can help healthcare and government for effective planning and resource allocation. The proposed method outperforms traditional univariate and hybrid forecasting models for the test datasets on an average.

Health Care Expenditure in the European Union Countries: New Insights about the Convergence Process.

This paper assesses the convergence process in the health care expenditure for selected European Union (EU) countries over the past 50 years. As a novel contribution, we use bound unit root tests and, for robustness purposes, a series of tests for strict stationarity to provide new insights about the convergence process. We make a comparison between public and private health expenditure per capita and as a percentage of the gross domestic product (GDP), with a focus on six EU countries with different health care systems in place. When we consider the health expenditure per capita, we report mixed findings. We show that the spread from the group average is stationary in the cases of Finland and Portugal when the overall and public expenditure is considered. In terms of private expenditure, the convergence process is noticed only for Austria. For all other countries included in our sample, we document a non-stationary process, indicating a lack of convergence. This result is robust to the different tests we use. However, when we assess the convergence in terms of the health-expenditure-to-GDP ratio, the convergence process is recorded for Austria only. The robustness check we performed using strict stationarity tests partially confirmed the mixed results we obtained. Therefore, our findings highlight the heterogeneity of the EU health care systems and the need for identification of common solutions to the EU health care systems' problems in order to enhance their convergence processes.

Theta Autoregressive Neural Network: A Hybrid Time Series Model for Pandemic Forecasting

Forecasting time series present a perpetual topic of research in statistical machine learning for the last five decades. Due to the unprecedented outbreak of the novel coronavirus (COVID-19), forecasting the COVID-19 pandemic became a key research interest for both epidemiologists and statisticians. These future predictions are useful for the effective allocation of health care resources, stockpiling, and help in strategic planning for clinicians, government authorities, and public-health policymakers. This paper develops an effective forecasting model that can generate real-time short-term (ten days) and long-term (fifty days) out-of-sample forecasts of COVID-19 outbreaks for eight profoundly affected countries, namely the United States of America, Brazil, India, Russia, South Africa, Mexico, Spain, and Iran. A novel hybrid approach based on the Theta method and Autoregressive neural network (ARNN) model, named Theta-ARNN (TARNN) model, is proposed. The proposed method outperforms previously available single and hybrid forecasting models for COVID-19 predictions in most data sets. The ergodicity and asymptotic stationarity of the TARNN model are also studied.

Fractional unit-root tests allowing for a fractional frequency flexible Fourier form trend: predictability of Covid-19.

In this study we propose a fractional frequency flexible Fourier form fractionally integrated ADF unit-root test, which combines the fractional integration and nonlinear trend as a form of the Fourier function. We provide the asymptotics of the newly proposed test and investigate its small-sample properties. Moreover, we show the best estimators for both fractional frequency and fractional difference operator for our newly proposed test. Finally, an empirical study demonstrates that not considering the structural break and fractional integration simultaneously in the testing process may lead to misleading results about the stochastic behavior of the Covid-19 pandemic.

ARIMA modelling and forecasting of irregularly patterned COVID-19 outbreaks using Japanese and South Korean data.

The World Health Organization (WHO) upgraded the status of the coronavirus disease 2019 (COVID-19) outbreak from epidemic to global pandemic on March 11, 2020. Various mathematical and statistical models have been proposed to predict the spread of COVID-2019 [1]. We collated data on daily new confirmed cases of the COVID-19 outbreaks in Japan and South Korea from January 20, 2020 to April 26, 2020. Auto Regressive Integrated Moving Average (ARIMA) model were introduced to analyze two data sets and predict the daily new confirmed cases for the 7-day period from April 27, 2020 to May 3, 2020. Also, the forecasting results and both data sets are provided.

GIS-based spatial modeling of COVID-19 incidence rate in the continental United States.

During the first 90 days of the COVID-19 outbreak in the United States, over 675,000 confirmed cases of the disease have been reported, posing unprecedented socioeconomic burden to the country. Due to inadequate research on geographic modeling of COVID-19, we investigated county-level variations of disease incidence across the continental United States. We compiled a geodatabase of 35 environmental, socioeconomic, topographic, and demographic variables that could explain the spatial variability of disease incidence. Further, we employed spatial lag and spatial error models to investigate spatial dependence and geographically weighted regression (GWR) and multiscale GWR (MGWR) models to locally examine spatial non-stationarity. The results suggested that even though incorporating spatial autocorrelation could significantly improve the performance of the global ordinary least square model, these models still represent a significantly poor performance compared to the local models. Moreover, MGWR could explain the highest variations (adj. R2: 68.1%) with the lowest AICc compared to the others. Mapping the effects of significant explanatory variables (i.e., income inequality, median household income, the proportion of black females, and the proportion of nurse practitioners) on spatial variability of COVID-19 incidence rates using MGWR could provide useful insights to policymakers for targeted interventions.