Omicron Case Prediction using Machine Learning

It's been over two years that the world has been dealing with the novel Coronavirus Disease 2019 (COVID-19). It has rocked the world in the face of another major outbreak. Countries have undergone various lockdowns curfews in their own ways, which certainly has impacted our daily lives. COVID-19 has undergone various mutations till now. It is responsible for the spikes in COVID-19 cases across the world. The latest variant 'Omicron'., labeled as B.1.1.529, has been marked as a Variant of Concern by the World Health Organization (WHO). It has been proven to be the most infectious, but less deadly as of now. This paper attempts to propose an analysis and prediction of Omicron daily cases in India using SARIMA Exponential Smoothing Machine Learning models. Both of these machine learning models are based on the time series forecasting concept and rely on previous data to predict future outcomes. © 2022 IEEE.

COVID-19 Impact on Ethanol Sales in Fuel Stations: An ITS Econometric Analysis

This paper provides estimates of the impact of the COVID-19 outbreak on Brazilian Ethanol sales. To this end, weekly data on Ethanol sales volumes are analyzed through an ITS SARIMA model and a counterfactual analysis covering the 2019–2020. We find that the real effect of COVID-19 was a reduction above 77.97% in Brazil after the first COVID-19 death, in March 2020, and still a decrease of about 50.15% at the end of 2020. The empirical evidence confirms that the impact of the pandemic crisis, the counterfactual analysis allows estimating the real effect of COVID-19 is on average 3.76% greater than the observed against an index date reference. These results suggest that ethanol sales in Brazil were more affected than only when comparing previous results to the effects of the pandemic. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Health condition prediction and covid risk detection using healthcare 4.0 techniques

Healthcare 4.0 is one of the emerging concepts that has grabbed the interest among researchers as well as the medical sector. Using the Internet of Things (IoT) and sophisticated communication technologies, it is now possible to monitor the patient from a remote area. In this paper, we design a remote health monitoring system using IoT and Machine Learning (ML) to determine the health condition of a patient. Supervised ML algorithms along with a time-series model such as Seasonal Autoregressive Integrated Moving Average (SARIMA) model are applied on the gathered data from IoT medical sensors to predict the health status of a patient. We consider a use-case of covid and compared it with our sensor data by applying the unsupervised ML algorithm, Long Short Term Memory (LSTM) along with a stochastic model, namely Markov Model to detect the risk of getting covid for a particular patient. LSTM with Markov model provides better results for detection with root mean squared error (RMSE) of 0.18 as against the RMSE of 0.45 obtained with only LSTM. We further design an optimization algorithm using “fuzzy logic” that attains optimum results in detecting the risk of getting covid.

Hybrid approaches for container traffic forecasting in the context of anomalous events: The case of the Yangtze River Delta region in the COVID-19 pandemic.

The COVID-19 pandemic had a significant impact on container transportation. Accurate forecasting of container throughput is critical for policymakers and port authorities, especially in the context of the anomalous events of the COVID-19 pandemic. In this paper, we firstly proposed hybrid models for univariate time series forecasting to enhance prediction accuracy while eliminating the nonlinearity and multivariate limitations. Next, we compared the forecasting accuracy of different models with various training dataset extensions and forecasting horizons. Finally, we analysed the impact of the COVID-19 pandemic on container throughput forecasting and container transportation. An empirical analysis of container throughputs in the Yangtze River Delta region was performed for illustration and verification purposes. Error metrics analysis suggests that SARIMA-LSTM2 and SARIMA-SVR2 (configuration 2) have the best performance compared to other models and they can better predict the container traffic in the context of anomalous events such as the COVID-19 pandemic. The results also reveal that, with an increase in the training dataset extensions, the accuracy of the models is improved, particularly in comparison with standard statistical models (i.e. SARIMA model). An accurate prediction can help strategic management and policymakers to better respond to the negative impact of the COVID-19 pandemic.

SARIMA model-based forecasting required number of COVID-19 vaccines globally and empirical analysis of peoples' view towards the vaccines

Recent studies regarding COVID-19 show a growing tendency to talk about the COVID-19 Pandemic on online channels. With the recent release of the Pfizer vaccine of COVID-19, people keep posting many rumors regarding the safety concerns of the Vaccine, especially among older people. Due to the rapid spread of the COVID-19 virus and the worldwide Pandemic developed, the rush to develop the COVID-19 Vaccine has become an alarming priority in health care services worldwide. In this research work, we have systematically evaluated people's views towards the COVID-19 Vaccine, and shreds of evidence are supported empirically. The study mainly focuses on the empirical evidence and intensive discussions on what is currently known about the mechanism of action, efficacy, and toxicity of the most promising vaccines (Moderna), (Pfizer/BioNtech), (Astrazenac/Oxford), and (Sputnik V) against COVID-19. Our study's primary objective is to provide an analysis of the questionnaire regarding people's opinions, preferences, and acceptance of the COVID-19 vaccines. We have created an online questionnaire using a google form to collect data from various countries supposed to employ COVID-19 vaccines. The questionnaires were distributed to people in many Arab and foreign countries such as Egypt, Saudi Arabia, India, England, China, and Japan. A total of 516 responses were returned and analyzed using statistical, and Seasonal Autoregressive Integrated Moving Average (SARIMA) approaches. The SARIMA model is used to predict the total number of vaccines in the next few days. To attain the most accurate forecast and prediction, the SARIMA model parameters are investigated with a grid search method. Finally, the combination of the parameters (1, 0, 1) x (1, 0, 0, 1) is considered to be the best SAR-IMA model because it has the lowest AIC values of - 4100.11 and the best Correlation coefficients of 0.984. (C) 2022 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Alexandria University This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Seasonal Forecasting Model to Determine the Loss of Passengers Traveling Through Heathrow Airport Due to COVID-19

Business, industry, and science all require data-driven decision-making. Decision-making and operation management require not only historic data but predictive data as well. Understanding the results delivered by applications of predictive analytics will help us prepare our business and operations not only qualitatively but quantitatively as well. To determine how passenger traffic at Heathrow Airport, London, was affected due to COVID-19, we have used the SARIMA model (using Python) to predict passenger traffic that should have been passing through the airport in between the months of January 2020 and August 2021 had the pandemic not struck the world. We aim to compare how the observed data vary from the predicted data and come up with possible routes to utilize the information gained from such a study. This predictive model will help in making operational decisions with much more ease and certainty, help to understand lost revenue, and cater to a case study that will help build better predictive models. © 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.

A Combined Model of SARIMA and Prophet Models in Forecasting AIDS Incidence in Henan Province, China.

Acquired immune deficiency syndrome (AIDS) is a serious public health problem. This study aims to establish a combined model of seasonal autoregressive integrated moving average (SARIMA) and Prophet models based on an L1-norm to predict the incidence of AIDS in Henan province, China. The monthly incidences of AIDS in Henan province from 2012 to 2020 were obtained from the Health Commission of Henan Province. A SARIMA model, a Prophet model, and two combined models were adopted to fit the monthly incidence of AIDS using the data from January 2012 to December 2019. The data from January 2020 to December 2020 was used to verify. The mean square error (MSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) were used to compare the prediction effect among the models. The results showed that the monthly incidence fluctuated from 0.05 to 0.50 per 100,000 individuals, and the monthly incidence of AIDS had a certain periodicity in Henan province. In addition, the prediction effect of the Prophet model was better than SARIMA model, the combined model was better than the single models, and the combined model based on the L1-norm had the best effect values (MSE = 0.0056, MAE = 0.0553, MAPE = 43.5337). This indicated that, compared with the L2-norm, the L1-norm improved the prediction accuracy of the combined model. The combined model of SARIMA and Prophet based on the L1-norm is a suitable method to predict the incidence of AIDS in Henan. Our findings can provide theoretical evidence for the government to formulate policies regarding AIDS prevention.

Empirical Quantitative Analysis of COVID-19 Forecasting Models

COVID-19 has been a public health emergency of international concern since early 2020. Reliable forecasting is critical to diminish the impact of this disease. To date, a large number of different forecasting models have been proposed, mainly including statistical models, compartmental models, and deep learning models. However, due to various uncertain factors across different regions such as economics and government policy, no forecasting model appears to be the best for all scenarios. In this paper, we perform quantitative analysis of COVID-19 forecasting of confirmed cases and deaths across different regions in the United States with different forecasting horizons, and evaluate the relative impacts of the following three dimensions on the predictive performance (improvement and variation) through different evaluation metrics: model selection, hyperparameter tuning, and the length of time series required for training. We find that if a dimension brings about higher performance gains, if not well-tuned, it may also lead to harsher performance penalties. Furthermore, model selection is the dominant factor in determining the predictive performance. It is responsible for both the largest improvement and the largest variation in performance in all prediction tasks across different regions. While practitioners may perform more complicated time series analysis in practice, they should be able to achieve reasonable results if they have adequate insight into key decisions like model selection. © 2021 IEEE.

Effects of Coronavirus Disease (COVID-19) Related Contact Restrictions in Germany, March to May 2020, on the Mobility and Relation to Infection Patterns.

In an effort to contain the spread of COVID-19, Germany has gradually implemented mobility restrictions culminating in a partial lockdown and contact restrictions on 22 March. The easing of the restrictions began 1 month later, on 20 April. Analysis of the consequences of these measures for mobility and infection incidence is of public health interest. A dynamic cohort of about 2,000 individuals in Germany aged 16-89 years provided individual information on demographic variables, and their continuous geolocation via a smartphone app. Using interrupted time series analysis, we investigated mobility by age, sex, and previous mobility habits from 13 January until 17 May 2020, measured as median daily distance traveled before and after restrictions were introduced. Furthermore, we have investigated the association of mobility with the number of new cases and the reproduction number. Median daily distance traveled decreased substantially in total and homogeneously across all subgroups considered. The decrease was strongest in the last week of March followed by a slight increase. Relative reduction of mobility developed parallel with number of new cases and the daily estimated reproduction number in the weeks after contact restrictions were implemented. The increase in mobility from mid-April onwards, however, did not result in increased case numbers but in further decrease. Other behavioral changes, e.g., wearing masks, individual distancing, or general awareness of the COVID-19 hazards may have contributed to the observed further reduction in case numbers and constant reproduction numbers below one until mid-July.