A Novel Framework to Forecast COVID-19 Incidence Based on Google Trends Search Data

The global outbreak of coronavirus disease 2019 (COVID-19) has spread to more than 200 countries worldwide, leading to severe health and socioeconomic consequences. As such, the topic of monitoring and predicting epidemics has been attracting a lot of interest. Previous work reported search volumes from Google Trends are beneficial in decoding influenza dynamics, implying its potential for COVID-19 prediction. Therefore, a predictive model using the Wiener methods was built based on epidemic-related search queries from Google Trends, along with climate variables, aiming to forecast the dynamics of the weekly COVID-19 incidence in Washington, DC, USA. The Wiener model, which shares the merits of interpretability, low computation costs, and adaptation to nonlinear fluctuations, was used in this study. Models with multiple sets of features were constructed and further optimized by the highest weight selecting strategy. Furthermore, comparisons to the other two commonly used prediction models based on the autoregressive integrated moving average (ARIMA) and long short-term memory (LSTM) were also performed. Our results showed the predicted COVID-19 trends significantly correlated with the actual (rho <inline-formula> <tex-math notation="LaTeX">$=$</tex-math> </inline-formula> 0.88, <inline-formula> <tex-math notation="LaTeX">$p $</tex-math> </inline-formula> <inline-formula> <tex-math notation="LaTeX">$<$</tex-math> </inline-formula> 0.0001), outperforming those with ARIMA and LSTM approaches, indicating Google Trends data as a useful tool in terms of COVID-19 prediction. Also, the model using 20 search queries with the highest weighting outperformed all other models, supporting the highest weight feature selection as a feasible criterion. Google Trends search query data can be used to forecast the outbreak of COVID-19, which might assist health policymakers to allocate health care resources and taking preventive strategies. IEEE

Implementing Bisection Method on Forex Trading Database for Early Diagnosis of Inflection Point

Many people are trading in the forex market during the COVID-19 pandemic with the hope of earning money, but they are experiencing shortages due to the lack of information and technology-based tools for existing daily data. Sometimes traders only use moving averages in trading data, even though this information needs to be processed again to get the right inflection point. The objective of this research is to find inflection points based on Forex trading database. Another algorithm can also be used to determine the inflection point between two points on a moving average. This can be supported by the Bisection method used because it can guarantee that convergence will occur. The results show that the points resulting from the bisection calculation on the moving average provide a fairly accurate decision support for the location where the inflection point is located. From 10,000 data there is a standard deviation of 0.71 points which is very small compared to an average of 20 pips (points used as the difference in price values in forex). The use of the bisection method provides an accuracy of the results in seeing the inflection point of 87%. © 2023,International Journal of Advanced Computer Science and Applications. All Rights Reserved.

An Analysis of Dynamic Correlations among Oil, Natural Gas and Ethanol Markets: New Evidence from the Pre- and Post-COVID-19 Crisis

After the economic shock caused by COVID-19, with relevant effects on both the supply and demand for energy assets, there was greater interest in understanding the relationships between key energy prices. In order to contribute to a deeper understanding of energy price relationships, this paper analyzes the dynamics between the weekly spot prices of oil, natural gas and benchmark ethanol in the US markets. The analysis period started on 23 June 2006 and ended on 10 June 2022. This study used the DMCA cross-correlation coefficient in a dynamic way, using sliding windows. Among the main results, it was found that: (i) in the post-pandemic period, oil and natural gas were not correlated, in both short- and long-term timescales;and (ii) ethanol was negatively associated with natural gas in the most recent post-pandemic period, especially in short-term scales. The results of the present study are potentially relevant for both market and public agents regarding investment diversification strategies and can aid public policies due to the understanding of the interrelationship between energy prices. © 2023 by the authors.

A New Approach for Forecasting Crude Oil Prices Based on Stochastic and Deterministic Influences of LMD Using ARIMA and LSTM Models

Crude oil is one of the non-renewable power sources and is the lifeblood of the contemporary industry. Every significant change in the price of crude oil (CO) will have an effect on how the global economy, including COVID-19, develops. This study developed a novel hybrid prediction technique that depends on local mean decomposition, Autoregressive Integrated Moving Average (ARIMA), and Long Short-term Memory (LSTM) models to increase crude oil price prediction accuracy. The original data is decomposed by local mean decomposition (LMD), and the decomposed components are reconstructed into stochastic and deterministic (SD) components by average mutual information to reduce the computation cost and enhance forecasting accuracy, predict each individual reconstructed component by ARIMA, and integrate the residuals with LSTM to capture the nonlinearity in residuals and help to find the final prediction result. The new hybrid model LMD-SD-ARIMA-LSTM has reduced the volatility and solved the issue of the overfitting problem of neural networks. The proposed hybrid technique is validated using publicly accessible data from the West Texas Intermediate (WTI), and forecast accuracy are compared using accuracy measures. The value of Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) for ARIMA, LSTM, LMD-ARIMA, LMD-SD-ARIMA, LMD-ARIMA-LSTM, LMD-SD-ARIMA-LSTM, and Naïve are 1.00, 1.539, 5.289, 0.873, 0.359, 0.106, 4.014 and 2.165, 1.832, 9.165, 1.359, 1.139, 1.124 and 3.821 respectively. From these results, it is concluded that the proposed model LMD-SD-ARIMA-LSTM has minimum values for MAE and MAPE which assured the superiority of the proposed model in One-step ahead forecasting. Moreover, forecasting performance is also compared up to five steps ahead. The findings demonstrate that the suggested approach is a helpful tool for predicting CO prices both in the short and long term. Furthermore, the current study reduces labor costs by combing the stationary and non-stationary Product Functions (PFs) into stochastic and deterministic components with improved accuracy. Meanwhile, the traditional econometric model can strengthen the prediction behavior of CO prices after decomposition and reconstruction, and the new hybrid forecasting method has better performance in medium and long-term forecasting of the CO price. Moreover, accurate predictions can provide reasonable advice for relevant departments to make correct decisions. © 2013 IEEE.

Raspberry-based Low-resolution Thermal image system using a Smoothing Filter-based Kalman

Since the emergence of global epidemics such as SARS-CoV-2, H1N1, SARS and MERS, a wide range of systems for measuring temperature have been developed based on computer vision to reduce and prevent the virus contagious. By implementing a Raspberry-based Low-resolution embedded system based and a FLIR Lepton® sensor human body temperature is measured and improved by four different algorithms implemented. Firstly, three traditional time-series processes solving such as, Simple Mean (SM), Simple Moving Average (SMA), and Multi Lineal Regression (MLR), and secondly, and online filter-based Kalman predictor were implemented to increase the signal to noise ratio of the acquired temperature magnitude. Results of average prediction for different benchmarks demonstrate the best performance of Kalman Filter upon traditional processes. In addition, this algorithm achieves to smooth output temperature with fewer samples (∼10% of total samples) in comparison MLR and SMA. Finally, Raspberry-based Low-resolution Thermal image system is a feasible tool as a high-speed temperature estimator, by implementation of algorithms codified in Python language. © 2022 IEEE.

Application of Time Series Analysis Methods to Assess the impact of COVID-19 on Carbon Dioxide Emission Reduction in Connecticut

The primary goals of this study are to determine if the datasets of positive COVID-19 test cases and CO2 emissions from Connecticut over the span of March 24th, 2020-October 31, 2021 are in any ways correlated. With climate change a prominent issue facing the entire world today, it is important to explore methods of providing records of past patterns of greenhouse gas emissions in order to inform decision making that could reduce future ones. Autoregressive integrated moving average (ARIMA) modeling is also implemented in this paper to provide forecasting based on CO2 emissions in CT starting from 2019. The most significant results from this paper are as follows: the CO2 emission data of transportation sectors including ground transportation, domestics aviation, and international aviation and weekly COVID-19 positive test cases data has a strong relationship during the first 28 weeks of the pandemic with a correlation of -86.34%. The CO2 emissions experienced on average a -22.96% change of pre-pandemic vs during initial quarantine conditions and at most a - 44.48% change when comparing the pre-pandemic mean to the during initial quarantine minimum value. Lastly, the ARIMA model found to have the lowest Akaike information criterion (AIC) was ARIMA (4,0,4). In conclusion, in the event of a collective global pandemic and lockdown conditions, less traveling resulting in a correlated decrease of CO2 emissions. This means that perhaps concentrated efforts on reducing unnecessary travel could help mitigate the levels of carbon dioxide emissions as a more long-term solution to climate change opposed to the pandemic's short-term example. © 2022 IEEE.

A multimodal hybrid stochastic-based deterministic ARFIMA model for the sustainable analysis of COVID-19 pandemic

The revise is given as follows: The rapid emergence of the super-spreader COVID-19 with severe economic calamities with devastating social impact worldwide created the demand for effective research on the spread dynamics of the disease to combat and create surveillance systems on a global scale. In this study, a novel hybrid Deterministic Autoregressive Fractional Integral Moving Average (ARFIMA) model is presented to forecast the bimodal COVID-19 transmission dynamics. The heterogeneity of multimodal behavior of the COVID-19 pandemic in Pakistan is modeled by a hybrid paradigm, in which a deterministic pattern is combined with the ARFIMA model to absorb the inherent chaotic pattern of the pandemic spread. The fractional fluctuation of the real epidemic system is effectively taken as a paradigm by stochastic type improved the deterministic model and ARFIMA process. Special transformations are also introduced to enhance the convergent rate of the bimodal paradigm in deterministic modeling. The outcome of the improved deterministic model is combined with the ARFIMA model is evaluated on the spread pattern of pandemic data in Pakistan for the next 30 days. The performance-indices of the hybrid-model based on Relative-Errors and RMSE statistics confirmed the effectiveness of the proposed paradigm for long-term epidemic modeling compared to other classical and machine learning algorithms. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

Predicting the upshot of covid-19 on crude-oil prices in nigeria using mlparima model

Researchers and investors have been paying close attention to the application of Artificial Intelligence models to the economics, agriculture and other fields in recent years. This study uses a Multilayer Perceptron Artificial Neural Network to anticipate the effect of covid-19 on crude-oil prices, continuing the deep learning trend and also applied the use of time series model known as Autoregressive Integrated Moving Average (ARIMA) to validate the result gotten from MLP-ANN. The results produced accurately predicted crude oil prices, and covid-19 data was also analyzed, as well as the association between crude-oil prices and covid-19. Because of the substantial causative association between the coronavirus (number of confirmed cases), crude oil prices, this study is intriguing. Ten years forecast was done using both MLP-ANN and ARIMA and from result gotten, MLP-ANN has accuracy of 96% while ARIMA has 39% accuracy. © 2022 IEEE.

Lightweight IoT-Based System for COVID-19 Patient Health Monitoring and Prediction

With the present Coronavirus disease (COVID-19) pandemic, Internet of Things (IoT)-based health monitoring devices are precious to COVID-19 patients. We present a real-time IoT-based health monitoring system that monitors patients' heart rate and oxygen saturation, the most significant measures necessary for critical care. Specifically, the proposed IoT-based system is built with Arduino Uno-based hardware and a web application for retrieving the patients' health information. In addition, we implement the Autoregressive Integrated Moving Average (ARIMA) method in the back-end server to predict future patient measurements based on current and past measurements. Compared to commercially available devices, the system's results are adequately accurate, with an acceptable RMSE for predicted value. © 2022 IEEE.

The first 2 years of COVID-19 in Italy: Incidence, lethality, and health policies.

Background: The novel coronavirus disease 2019 (COVID-19) is an ongoing pandemic that was first recognized in China in December 2019. This paper aims to provide a detailed overview of the first 2 years of the pandemic in Italy. Design and methods: Using the negative binomial distribution, the daily incidence of infections was estimated through the virus's lethality and the moving-averaged deaths. The lethality of the original strain (estimated through national sero-surveys) was adjusted daily for age of infections, hazard ratios of virus variants, and the cumulative distribution of vaccinated individuals. Results: From February 24, 2020, to February 28, 2022, there were 20,833,018 (20,728,924-20,937,375) cases distributed over five waves. The overall lethality rate was 0.73%, but daily it ranged from 2.78% (in the first wave) to 0.15% (in the last wave). The first two waves had the highest number of daily deaths (about 710) and the last wave showed the highest peak of daily infections (220,487). Restriction measures of population mobility strongly slowed the viral spread. During the 2nd year of the pandemic, vaccines prevented 10,000,000 infections and 115,000 deaths. Conclusion: Almost 40% of COVID-19 infections have gone undetected and they were mostly concentrated in the first year of the pandemic. From the second year, a massive test campaign made it possible to detect more asymptomatic cases, especially among the youngest. Mobility restriction measures were an effective suppression strategy while distance learning and smart working were effective mitigation strategies. Despite the variants of concern, vaccines strongly reduced the pandemic impact on the healthcare system avoiding strong restriction measures.

Activity based Traffic Indicator System for Monitoring the COVID-19 Pandemic

This study describes an activity based traffic indicator system to provide information for COVID-19 pandemic management. The activity based traffic indicator system does this by utilizing a social probability model based on the birthday paradox to determine the exposure risk, the probability of meeting someone infected (PoMSI). COVID-19 data, particularly the 7-day moving average of the daily growth rate of cases (7-DMA of DGR) and cumulative confirmed cases of next week covering a period from April to September 2020, were then used to test PoMSI using Pearson correlation to verify whether it can be used as a factor for the indicator. While there is no correlation for the 7-DMA of DGR, PoMSI is strongly correlated (0.671 to 0.996) with the cumulative confirmed cases and it can be said that as the cases continuously rise, the probability of meeting someone COVID positive will also be higher. This shows that indicator not only shows the current exposure risk of certain activities but it also has a predictive nature since it correlates to cumulative confirmed cases of next week and can be used to anticipate the values of confirmed cumulative cases. This information can then be used for pandemic management. Copyright © 2021 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved.

Stock Market Comparison and Analysis in Preceding and Following Pandemic in Bangladesh using Machine Learning Approaches

For a long time, stock price forecasting has been a significant research topic. However stock prices depend on various factors that cannot be predicted, and that's the reason it is almost impossible to predict stock prices accurately. Many researchers have already worked in this area. Recently, the COVID-19 pandemic had a great effect on the stock market. The main purpose of this paper is to predict the stock closing prices for two major stock exchanges in Bangladesh and compare the prediction accuracy based on before and after pandemic data. The implemented models are Autoregressive Integrated Moving Average(ARIMA) and Support Vector Machine(SVM) and Long Short-Term Memory (LSTM). Raw datasets were considered, which were collected from Dhaka Stock Exchange(DSE) and Chittagong Stock Exchange(CSE). Data preprocessing was done on both of the datasets. After analyzing the overall accuracy for each algorithm, it was found that LSTM provided better accuracy than ARIMA and SVM for both the DSE and CSE datasets. © 2022 ACM.

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.

Multiplicative Seasonal ARIMA Modeling and Forecasting of COVID_19 Daily Deaths in Hungary

The coronavirus disease (COVID-19) is a terrifying pandemic that is rapidly spreading over the world. Up to this point, Hungary has had a significant COVID-19 death rate. The main purpose of this article is to model and forecast basic seasonal time series for COVID-19 death rates. The COVID 19 data, which was collected between 2020-10-04 and 2021-05-12 by the Hungarian government and the World Health Organization (WHO), has been used. The data was analyzed and models were fitted using R software version 4.1.2. The statistical time series model is fitted with the Multiplicative Seasonal Autoregressive Integrated Moving Average (SARIMA) model. Forecasts are made using the fitted model. The data output is used to find seasonality, trend patterns, and unstable variance patterns in the time series plot. The trend is made stationary using the starting difference of the converted data approach, and the variance is made constant using the logarithmic transformation of the original data set. Based on the Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) plot data, the ARIMA (1, 1, 2) (1, 0, 1) (7) model is proposed. The standardized residuals, ACF of residuals, normal Q-Q plot, and p-value for Ljung-Box statistics of the fitted model were found to be within confidence limits and to have no distinct behavioral pattern. The ARIMA (1, 1, 2) (1, 0, 1) (7) model has the smallest estimated value, with a sigma square estimated value of 0.02764, log-likelihood = 80.41, and an Akaike Information Criterion (AIC) value of 148.82. As a consequence, the fitted model ARIMA (1,1,2) (1,0,1) (7) is identified as the best model for forecasting the COVID-19 daily death rate in the country. © 2022 IEEE.

Automating the Body Temperature Scanning System and Predictive Analysis Using Machine Learning Models

In December 2019, the COVID-19 broke out. From that point, the situation has become much dire. The number of cases kept spiking and a cure is still unknown for COVID-19. For this reason, we must be more cautious and take all possible precautions. We know a few things about this disease. Fever happens to be one of the early symptoms of COVID-19. Hence, we do thermal scanning in public places. Our paper proposes a way to make this process more efficient. We can scan body temperature using various sensors and store it in the cloud. Doing so, it gives us more flexibility to monitor the data and predict if someone might suffer from fever in the future. In our analysis, we have found that among the different machine learning algorithms, moving averages smoothing was able to predict the data better. Now, in order to run this machine learning model automatically, we used AWS. Also, due to GUI, it is much easier to use the system. Overall, the main purpose of our work is to collect daily thermal scan reports and use that data for our benefit. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Seasonal Autoregressive Integrated Moving Average Time Series Model for Tourism Demand: The Case of Sal Island, Cape Verde

This article appears as an essential contribution for decision-makers in the Cape Verdean tourism sector given the impact that the number of overnight stays has on the economy of the country and the Sal Island, which until 2018 had been increasing every year. Since seasonality is a strong feature of the island’s tourism, decision-makers are interested in knowing the seasonal variation in tourism demand. Thus, this study focussed on the application of the Box-Jenkins method to the time series of the monthly number of nights stays in tourist establishments on the Sal Island, Cape Verde, over the period from January 2000 to December 2018, to find a model that better describes the series and with good forecast results for the year 2019. Several SARIMA models were studied using the Box-Jenkins method, with the SARIMA (1, 1, 1 ) (0, 1, 1 ) 12 and the SARIMA (2, 1, 0 ) (0, 1, 1 ) 12 demonstrating the best predictive performance in the test phase. However, in forecasting the series for the year 2019, the SARIMA (2, 1, 0 ) (0, 1, 1 ) 12 achieved the best results with a MAPE = 6.77%. This model can be used to simulate and analyze the number of overnight stays that be expected on the Island, if the tourism sector was not affected by the pandemic caused by COVID-19. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Comparative Analysis of Time Series Models on COVID-19 Predictions

Many Research papers for Covid-19 prediction have been written, where researchers used different models to predict future cases. So, the objective of this paper is to perform a comparative study on all the major models and validate the results obtained before. The analysis will be performed on Indian and American Dataset. The evaluation of all the models will be performed using RMS and r2error. The forecast models used are ARIMA (Autoregressive integrated moving average), SARIMAX (Seasonal Auto-Regressive Integrated Moving Average with exogenous factors), and Recurrent Neural Network-based LSTM (Long Short-Term Memory) variants like Standard LSTM, Stacked LSTM, Bi-directional LSTM, Convolutional LSTM, GRU (Gated recurrent units) LSTM, and Attention LSTM. These predictive models can offer a crucial insight to policymakers and help normal citizens to prepare accordingly. Among all the mentioned models, GRU LSTM performed the best with a r2score of 0.986024 followed by Bi-LSTM, Attention LSTM and Stacked LSTM. Furthermore, this research study has also performed the analysis using a multivariate stacked LSTM model which outperformed all the univariate models. © 2022 IEEE.

VMD-based Multiscaled LSTM-ARIMA to Forecast Post-COVID-19 US Air Traffic

Accurate traffic flow forecasting is an essential component of the Intelligent Transportation System (ITS). However, existing traffic forecasting methods using deep learning pay little attention to the pandemic's repercussions. This paper proposes a multiscaled deep learning framework called VMD-LSTM-ARIMA, which couples the variational mode decomposition (VMD) algorithm, long short-term memory (LSTM) neural network, and autoregressive integrated moving average (ARIMA) to accurately predict traffic flow time series. Just like any hybrid model, the proposal takes advantages of each one of these approaches, which enhances the performance of the overall forecasting model. Experiments were conducted on a US public traffic datasets, and the results showed that VMD-LSTM-ARIMA effectively increased the prediction accuracy. © 2022 IEEE.

Comparative Analysis of Different Models for Covid-19 Trend Prediction

The critical situation of Covid-19 in recent years is gradually easing. However, it is still important to keep an eye on predicting future data about the pandemic. In this study, we compared the following three models: Long short-term memory (LSTM), Autoregressive Integrated Moving Average (ARIMA), and Logistic to identify and evaluate the cons and pros with the implementation of python code. The data we used is based on the COVID-19 pandemic data of China from 'Our World in Data'. The experimental results indicated that the Logistic achieved the best prediction performance, i.e, with the optimal RMSE. © 2022 IEEE.

Forecasting the COVID-19 Pandemic in Bangladesh Using ARIMA Model

The effects of the coronavirus disease in 2019 are visible in every corner of the globe. The public health system is mostly affected, and the economic and social crises are also increasing day by day. Due to the widespread nature and the unavailability of drugs or vaccines for this pandemic, it is urgent to predict the COVID-19 infected cases to handle the situation more efficiently. Time series prediction is a crucial technique of the machine learning domain to deal with the issue. This research aims to predict the number of daily confirmed COVID-19 cases for a successful time. To forecast COVID-19 instances in Bangladesh, we use the Autoregressive Integrated Moving Average (ARIMA) model. The experimental results show that the estimated best models are: ARIMA(3,1,0) with drift, ARIMA(3,1,2) with drift, ARIMA(5,1,0) perform significant predictions on three different kinds of COVID-19 datasets. © 2021 IEEE.