Time Series Analysis Using Different Forecast Methods and Case Fatality Rate for Covid‐19 Pandemic

This study presents forecasting methods using time series analysis for confirmed cases, the number of deaths and recovery cases, and individual vaccination status in different states of India. It aims to forecast the confirmed cases and mortality rate and develop an artificial intelligence method and different statistical methodologies that can help predict the future of Covid‐19 cases. Various forecasting methods in time series analysis such as ARIMA, Holt's trend, naive, simple exponential smoothing, TBATS, and MAPE are extended for the study. It also involved the case fatality rate for the number of deaths and confirmed cases for respective states in India. This study includes the forecast values for the number of positive cases, cured patients, mortality rate, and case fatality rate for Covid‐19 cases. Among all forecast methods involved in this study, the naive and simple exponential smoothing method shows an increased number of positive instances and cured patients.Alternate :Este estudio presenta métodos de pronóstico que utilizan el análisis de series temporales para los casos confirmados, el número de muertes y casos recuperados, y el estado de vacunación individual en diferentes estados de la India. Su objetivo es pronosticar los casos confirmados y la tasa de mortalidad y desarrollar un método de inteligencia artificial y diferentes metodologías estadísticas que puedan ayudar a predecir el futuro de los casos de Covid‐19. Para el estudio se adaptaron varios métodos de pronóstico para el análisis de series temporales como ARIMA, la tendencia de Holt, el ingenuo, el suavizado exponencial simple, TBATS y MAPE. También se incluyó la tasa de fatalidades para el número de muertes y casos confirmados para los respectivos estados de la India. Este estudio incluye los valores de pronóstico para el número de casos positivos, los pacientes curados, la tasa de mortalidad y la tasa de fatalidades para los casos de Covid‐19. Entre todos los métodos de pronóstico utilizados en este estudio, el método ingenuo y el de suavización exponencial simple muestran un mayor número de casos positivos y de pacientes curados.Alternate :抄録本研究は、インドの州における確定症例、死亡数及び回復例、および個人のワクチン接種状況に関する時系列分析を用いた予測方法を提示する。確定症例と死亡率を予測し、人工知能を用いた方法とCOVID‐19の症例の将来を予測するのに役立ついくつかの統計学的方法論を開発することを目指す。ARIMA、Holtのトレンド、単純法、単純指数平滑化法、TBATS、MAPEなどの時系列解析における各種予測法を拡張した。また、インドの各州の死亡者数と確定症例数の致死率も含んだ。本研究は、COVID‐19症例に対する、陽性症例数、治癒患者数、死亡率、および致死率に対する予測値を含む。この研究に含まれるすべての予測法の中で、単純法と単純指数平滑法は、陽性者数と治癒患者数の増加を予測した。

Observed versus estimated actual trend of COVID-19 case numbers in Cameroon: A data-driven modelling.

Controlling the COVID-19 outbreak remains a challenge for Cameroon, as it is for many other countries worldwide. The number of confirmed cases reported by health authorities in Cameroon is based on observational data, which is not nationally representative. The actual extent of the outbreak from the time when the first case was reported in the country to now remains unclear. This study aimed to estimate and model the actual trend in the number of COVID -19 new infections in Cameroon from March 05, 2020 to May 31, 2021 based on an observed disaggregated dataset. We used a large disaggregated dataset, and multilevel regression and poststratification model was applied prospectively for COVID-19 cases trend estimation in Cameroon from March 05, 2020 to May 31, 2021. Subsequently, seasonal autoregressive integrated moving average (SARIMA) modeling was used for forecasting purposes. Based on the prospective MRP modeling findings, a total of about 7450935 (30%) of COVID-19 cases was estimated from March 05, 2020 to May 31, 2021 in Cameroon. Generally, the reported number of COVID-19 infection cases in Cameroon during this period underestimated the estimated actual number by about 94 times. The forecasting indicated a succession of two waves of the outbreak in the next two years following May 31, 2021. If no action is taken, there could be many waves of the outbreak in the future. To avoid such situations which could be a threat to global health, public health authorities should effectively monitor compliance with preventive measures in the population and implement strategies to increase vaccination coverage in the population.

Coronavirus disease 2019 epidemic prediction in Shanghai under the "dynamic zero-COVID policy" using time-dependent SEAIQR model.

It's urgently needed to assess the COVID-19 epidemic under the "dynamic zero-COVID policy" in China, which provides a scientific basis for evaluating the effectiveness of this strategy in COVID-19 control. Here, we developed a time-dependent susceptible-exposed-asymptomatic-infected-quarantined-removed (SEAIQR) model with stage-specific interventions based on recent Shanghai epidemic data, considering a large number of asymptomatic infectious, the changing parameters, and control procedures. The data collected from March 1st, 2022 to April 15th, 2022 were used to fit the model, and the data of subsequent 7 days and 14 days were used to evaluate the model performance of forecasting. We then calculated the effective regeneration number (R t) and analyzed the sensitivity of different measures scenarios. Asymptomatic infectious accounts for the vast majority of the outbreaks in Shanghai, and Pudong is the district with the most positive cases. The peak of newly confirmed cases and newly asymptomatic infectious predicted by the SEAIQR model would appear on April 13th, 2022, with 1963 and 28,502 cases, respectively, and zero community transmission may be achieved in early to mid-May. The prediction errors for newly confirmed cases were considered to be reasonable, and newly asymptomatic infectious were considered to be good between April 16th to 22nd and reasonable between April 16th to 29th. The final ranges of cumulative confirmed cases and cumulative asymptomatic infectious predicted in this round of the epidemic were 26,477 âˆ¼ 47,749 and 402,254 âˆ¼ 730,176, respectively. At the beginning of the outbreak, R t was 6.69. Since the implementation of comprehensive control, R t showed a gradual downward trend, dropping to below 1.0 on April 15th, 2022. With the early implementation of control measures and the improvement of quarantine rate, recovery rate, and immunity threshold, the peak number of infections will continue to decrease, whereas the earlier the control is implemented, the earlier the turning point of the epidemic will arrive. The proposed time-dependent SEAIQR dynamic model fits and forecasts the epidemic well, which can provide a reference for decision making of the "dynamic zero-COVID policy".

Time series analysis using different forecast methods and case fatality rate for COVID-19 pandemic

This study presented forecasting methods using Time Series Analysis for confirmed cases, the number of deaths and recovery cases, and individual vaccination status in different states of India. It aims to forecast the confirmed cases and mortality rate and develop an Artificial Intelligence method and different statistical methodologies that can help predict the future of Covid-19 cases. Various Forecasting methods in Time Series Analysis like ARIMA, Holt?s Trend, Naive, Simple Exponential Smoothing, TBATS, and MAPE are extended for the study. It also involved the Case Fatality Rate for the number of deaths and confirmed cases for respective states in India. This study includes the forecast values for the number of positive cases, cured patients, mortality rate, and case fatality rate for Covid-19 cases. Among all forecast methods involved in this study, the naive and simple exponential smoothing method shows an increased number of positive instances and cured patients.

Determining the Learning Profile of Engineering Projects Students from Their Characteristic Motivational Profile

Engineering courses usually have a low success rate, and students that take them often consider them difficult and show little motivation towards them. In this context, it is essential to obtain information about the profile of the students so that the teaching can be adapted to their perceived needs and motivations as well to provide support to them. This descriptive-exploratory research study was carried out to determine the learning profile of engineering project students through their motivational profile based on five grouping variables (gender, type of high school of origin, access studies, specialty, repeater). The instrument used was a consolidated motivational assessment questionnaire consisting of items in a series of seven basic scales aligned and grouped together into three motivational dimensions (MAPE-3). As a result, a student profile was observed that was dominated by the dimension of motivation towards the task and characterized by a mixed reflective-practical learning profile based on analytical and predominantly practical individuals.

Predicting the Total number of COVID-19 Deaths by County for the State of Florida: Ridge Regression Approach

Coronavirus disease 2019 (COVID-19) is a novel infectious disease that was detected in Wuhan, China at the end of 2019. The virus quickly spread worldwide and caused a global pandemic. This paper investigates if there are any regressors that could help impact the number of deaths due to COVID-19. The variables that were used in the models were total deaths, hospitalizations, total cases, population, minimum temperature, average temperature, maximum temperature, precipitation, mobility index, median age, adults age 65 or older, PM2.5 average, ozone average, and positive non-residents. After fitting six different regression models, we found that the most significant regressors were hospitalizations per county, total cases per county, population per county, median age per county, positive adults 65 or older per county, and positive non-residents per county. The COVID-19 data of this paper will be an excellent source for illustrating the multicollinear linear regression models. © 2021, World Scientific and Engineering Academy and Society. All rights reserved.

Explorative predictive analysis of Covid-19 in US and its impact on US Presidential Election

Coronavirus is a large family of viruses, and it is declared as a global pandemic by World Health Organization. Millions of people have been affected and lost their lives due to COVID-19. In our work, we are showing the analysis and forecasting studies of corona cases in the United States from 22nd Jan 2020 to 27th Nov 2020, with the top 10 affected provinces of confirmed and deaths cases. A detailed comparison study was carried out to observe the confirmed and death cases with presidential election results in the US. This work is showing how the result become favorable to Mr. Joe Biden. The model building is done using the Time Series Algorithm of Autoregressive Integrated Moving Average (ARIMA) and evaluation is based on mean absolute percentage error (MAPE) © 2021 IEEE.

Analysis of COVID-19 Trends in Bangladesh: A Machine Learning Analysis

The globe has reached a critical juncture in the last recent years. According to data we collected from an official internet source, Bangladesh recorded 913,258 confirmed cases, 14,646 death cases with a 1.60% mortality rate, and 85% recovery rate as of June 30, 2021. Furthermore, the delta variant currently has a significant impact on improving the current COVID situation in Bangladesh. So, one of the efficient ways to prevent this outbreak is to building multiple Bangladesh outbreak prediction models to analyze historical data and predict with it for making decisions and implementing appropriate COVID-19 control measures. In this study, a machine learning model, an Auto-Regressive Integrated Moving Average (ARIMA) and Prophet, were developed using time series analysis to forecast new cases in Bangladesh in the coming days. This study examined the model outputs, compared their performance, and created predicted values from these models using the Python programming language. The ARIMA model is the best fit model among the algorithms used to predict the new COVID-19 situation in Bangladesh. The primary goals of this paper are to analyze COVID-19 trends and predict the new upcoming cases and assist decision-makers in controlling the Bangladesh outbreak. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Forecasting Brazilian Ethanol Spot Prices Using LSTM

Ethanol is one of the most used fuels in Brazil, which is the second-largest producer of this biofuel in the world. The uncertainty of price direction in the future increases the risk for agents operating in this market and can affect a dependent price chain, such as food and gasoline. This paper uses the architecture of recurrent neural networks—Long short-term memory (LSTM)—to predict Brazilian ethanol spot prices for three horizon-times (12, 6 and 3 months ahead). The proposed model is compared to three benchmark algorithms: Random Forest, SVM Linear and RBF. We evaluate statistical measures such as MSE (Mean Squared Error), MAPE (Mean Absolute Percentage Error), and accuracy to assess the algorithm robustness. Our findings suggest LSTM outperforms the other techniques in regression, considering both MSE and MAPE but SVM Linear is better to identify price trends. Concerning predictions per se, all errors increase during the pandemic period, reinforcing the challenge to identify patterns in crisis scenarios.

Time series forecasting of new cases and new deaths rate for COVID-19 using deep learning methods.

The first known case of Coronavirus disease 2019 (COVID-19) was identified in December 2019. It has spread worldwide, leading to an ongoing pandemic, imposed restrictions and costs to many countries. Predicting the number of new cases and deaths during this period can be a useful step in predicting the costs and facilities required in the future. The purpose of this study is to predict new cases and deaths rate one, three and seven-day ahead during the next 100 days. The motivation for predicting every n days (instead of just every day) is the investigation of the possibility of computational cost reduction and still achieving reasonable performance. Such a scenario may be encountered in real-time forecasting of time series. Six different deep learning methods are examined on the data adopted from the WHO website. Three methods are LSTM, Convolutional LSTM, and GRU. The bidirectional extension is then considered for each method to forecast the rate of new cases and new deaths in Australia and Iran countries. This study is novel as it carries out a comprehensive evaluation of the aforementioned three deep learning methods and their bidirectional extensions to perform prediction on COVID-19 new cases and new death rate time series. To the best of our knowledge, this is the first time that Bi-GRU and Bi-Conv-LSTM models are used for prediction on COVID-19 new cases and new deaths time series. The evaluation of the methods is presented in the form of graphs and Friedman statistical test. The results show that the bidirectional models have lower errors than other models. A several error evaluation metrics are presented to compare all models, and finally, the superiority of bidirectional methods is determined. This research could be useful for organisations working against COVID-19 and determining their long-term plans.

Grey forecasting models based on internal optimization for Novel Corona virus (COVID-19).

Pandemic forecasting has become an uphill task for the researchers on account of the paucity of sufficient data in the present times. The world is fighting with the Novel Coronavirus to save human life. In a bid to extend help to the concerned authorities, forecasting engines are invaluable assets. Considering this fact, the presented work is a proposal of two Internally Optimized Grey Prediction Models (IOGMs). These models are based on the modification of the conventional Grey Forecasting model (GM(1,1)). The IOGMs are formed by stacking infected case data with diverse overlap periods for forecasting pandemic spread at different locations in India. First, IOGM is tested using time series data. Its two models are then employed for forecasting the pandemic spread in three large Indian states namely, Rajasthan, Gujarat, Maharashtra and union territory Delhi. Several test runs are carried out to evaluate the performance of proposed grey models and conventional grey models GM(1,1) and NGM(1,1,k). It is observed that the prediction accuracies of the proposed models are satisfactory and the forecasted results align with the mean infected cases. Investigations based on the evaluation of error indices indicate that the model with a higher overlap period provides better results.