Prediction of Corona Virus Disease Outcome and Future Forecast the Trend of COVID-19 in India: Observational Study

This work is motivated by the disease caused by the novel corona virus Covid-19, rapid spread in India. An encyclopaedic search from India and worldwide social networking sites was performed between 1 March 2020 and 20 Jun 2020. Nowadays social network platform plays a vital role to track spreading behaviour of many diseases earlier then government agencies. Here we introduced the approach to predict and future forecast the disease outcome spread through corona virus in society to give earlier warning to save from life threats. We compiled daily data of Covid-19 incidence from all state regions in India. Five states (Maharashtra, Delhi, Gujarat, Rajasthan and Madhya-Pradesh) with higher incidence and other states considered for time series analysis to construct a predictive model based on daily incidence training data. In this study we have applied the predictive model building approaches like k-nearest neighbour technique, Random-Forest technique and stochastic gradient boosting technique in COVID-19 dataset and the simulated outcome compared with the observed outcome to validate model and measure the performance of model by accuracy (ACC) and Kappa measures. Further forecast the future trends in number of cases of corona virus deceased patients using the Holt Winters Method. Time series analysis is effective tool for predict the outcome of corona virus disease. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

Vaccine Supply Forecasting and Optimization using Deterministic and Probabilistic Approaches

When the pandemic was at its peak, it was a quite difficult task for the government to schedule vaccine supply in various districts of a state. This task became further difficult when vaccines were required to be supplied to various Covid Vaccination Centers (CVCs) at a granular level. This is because there was no data regarding the trend being acquired at each CVC and the population distribution is non-uniform across the district. This led to the arousal of an ambiguous situation for a certain period and hence mismanagement. Now that we have sufficient data across each CVC, we can work on a time series analysis of vaccine requirements in which we can essentially forecast the number of administered doses and optimize the wastage at all atomic CVC levels. © 2023 IEEE.

Machine Learning Based Predictive Analytics for Agriculture Inventory Management System

When the globe was hit by the vicious Covid 19 pandemic, multiple industries faced the virus's wrath and that included the agricultural warehouse industry. Consequently, many warehouses which had received large shipment stocks of agricultural products were never to be used again as it had reached its expiration date. This led to major losses for the agricultural warehouses as well as losses in crops for farmers and large scale agriculturists. The main objective of this paper is to build a model which utilises 3 heavy-weight algorithms (Seasonal Autoregressive Integrated Moving Average-SARIMA, Long short term memory-LSTM and Holt Winters) and predicts the agricultural needs of retailers and consumers based on previous data from different warehouses. Deploying this system will not help in the regulation of goods in warehouses but will also aid in maximizing the profits and minimizing the losses for warehouses. The algorithm with the least MAE(Mean Absolute Error) value will be considered for forecasting the sales of the aforementioned product. © 2022 IEEE.

Model selection with decision support model for US natural gas consumption forecasting

Reliable prediction of natural gas consumption helps make the right decisions ensuring sustainable economic growth. This problem is addressed here by introducing a hybrid mathematical model defined as the Choquet integral-based model. Model selection is based on decision support model to consider the model performance more comprehensively. Different from the previous literature, we focus on the interaction between models when combine models. This paper adds grey accumulation generating operator to Holt-Winters model to capture more information in time series, and the grey wolf optimizer obtains the associated parameters. The proposed model can deal with seasonal (short-term) variability using season auto-regression moving average computation. Besides, it uses the long short term memory neural network to deal with long-term variability. The effectiveness of the developed model is validated on natural gas consumption due to the COVID-19 pandemic in the USA. For this, the model is customized using the publicly available datasets relevant to the USA energy sector. The model shows better robustness and outperforms other similar models since it consider the interaction between models. This means that it ensures reliable perdition, taking the highly uncertain factor (e.g., the COVID-19) into account. © 2023 Elsevier Ltd

Model selection with decision support model for US natural gas consumption forecasting

Reliable prediction of natural gas consumption helps make the right decisions ensuring sustainable economic growth. This problem is addressed here by introducing a hybrid mathematical model defined as the Choquet integral-based model. Model selection is based on decision support model to consider the model performance more comprehensively. Different from the previous literature, we focus on the interaction between models when combine models. This paper adds grey accumulation generating operator to Holt-Winters model to capture more information in time series, and the grey wolf optimizer obtains the associated parameters. The proposed model can deal with seasonal (short-term) variability using season auto-regression moving average computation. Besides, it uses the long short term memory neural network to deal with long-term variability. The effectiveness of the developed model is validated on natural gas consumption due to the COVID-19 pandemic in the USA. For this, the model is customized using the publicly available datasets relevant to the USA energy sector. The model shows better robustness and outperforms other similar models since it consider the interaction between models. This means that it ensures reliable perdition, taking the highly uncertain factor (e.g., the COVID-19) into account.

Assessing the Impact of SARS-CoV-2 on Water Consumption in São Paulo State, Brazil

The pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in 2020 led to a significant change in human behaviors, mainly because of the quarantine to avoid the spread of the virus. Measures affected both economic activities and citizens' behaviors as they developed more intense hygiene habits to avoid contamination and switched to home offices. These exceptional behaviors also affected the way that water is consumed and need to be fully understood to manage supply systems. Therefore, this study aims to investigate changes in residential and commercial water consumption in 31 municipalities in the state of São Paulo, Brazil, during SARS-CoV-2. To do this, the expected consumption for the first half of 2020 was forecasted using the Holt-Winters multiplicative method and compared with the data observed for the same period. In addition, we compared monthly records of new contaminations and the social distancing index to establish a correlation with changes in water consumption. The results show an average difference between forecasted and observed consumption equal to +6.23% and -18.59% for residential and commercial activities, respectively. For the first one, the consumption per capita increased at the rate of 8.44 L.person-1.day-1. The observed changes in consumption seem to be a consequence of hygiene habits, social distancing and the closing of nonessential services in commerce. © 2022 American Society of Civil Engineers.

A Comparative Analysis of Forecasting Models on COVID-19

The COVID-19 spread all around the world, causing more than a million deaths and reaching over 50 million confirmed cases. A forecast of these numbers is vital for the adequate preparations of health care capacities and for the governments to take the necessary decisions. In this study, it is aimed to predict the evolution of COVID-19 figures, employing alternative statistical models such as the Holt-Winters, ARIMA, and ARIMAX while using the time series corresponding to different parameters of this disease such as daily cases, daily deaths, and the stringency index. Considered are the John Hopkins University epidemiological world data and the top ten countries with the highest cases, along with China. The fitting of the time series and the upcoming 10 days projections resulted in a high level of accuracy, presented with alternative error metrics and comparisons between the situations of countries. Holt-Winters is the best performing model, while ARIMAX gives the worst accuracy results. Moreover, it was found that the use of coefficient determination and Bayesian information criterion alone are not suitable, and scale independent metrics should be employed when the data ranges differ. The results of this study would be useful to set up benchmark results for other studies and the projections may be used for medical, economic, and social precaution and preparation. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Spatiotemporal Mapping of Online Interest in Cannabis and Popular Psychedelics before and during the COVID-19 Pandemic in Poland.

BACKGROUND: Psychedelics represent a unique subset of psychoactive substances that can induce an aberrant state of consciousness principally via the neuronal 5-HT2A receptor. There is limited knowledge concerning the interest in these chemicals in Poland and how they changed during the pandemic. Nonetheless, these interests can be surveyed indirectly via the web. OBJECTIVES: We aim to conduct a spatial-temporal mapping of online information-seeking behavior concerning cannabis and the most popular psychedelics before and during the pandemic. METHODS: We retrieved online information search data via Google Trends concerning twenty of the most popular psychedelics from 1 January 2017 to 1 January 2022 in Poland. We conducted Holt-Winters exponential smoothing for time series analysis to infer potential seasonality. We utilized hierarchical clustering analysis based on Ward's method to find similarities of psychedelics' interest within Poland's voivodships before and during the pandemic. RESULTS: Twelve (60%) psychedelics had significant seasonality; we proved that psilocybin and ayahuasca had annual seasonality (p-value = 0.0120 and p = 0.0003, respectively), and four substances-LSD, AL-LAD, DXM, and DOB-exhibited a half-yearly seasonality, while six psychedelics had a quarterly seasonal pattern, including cannabis, dronabinol, ergine, NBOMe, phencyclidine, and salvinorin A. Further, the pandemic influenced a significant positive change in the trends for three substances, including psilocybin, ergine, and DXM. CONCLUSIONS: Different seasonal patterns exist for psychedelics, and some might correlate with school breaks or holidays in Poland. The pandemic induced some changes in the temporal and spatial trends. The spatial-temporal trends could be valuable information to health authorities and policymakers responsible for monitoring and preventing addictions.

An Evaluation of Time Series-Based Modeling and Forecasting of Infectious Diseases Progression using Statistical Versus Compartmental Methods

As a case study for our research, COVID-19, that was caused by a unique coronavirus, has substantially affected the globe, not only in terms of healthcare, but also in terms of economics, education, transportation, and politics. Predicting the pandemic's course is critical to combating and tracking its spread. The objective of our study is to evaluate, optimize and fine-Tune state of the art prediction models in order to enhance its performance and to automate its function as possible. Therefore, a comparison between statistical versus compartmental methods for time series-based modeling and forecasting of infectious disease progression was conducted. The comparison included several classical univariate time series statistical models, including Exponential Smoothing, Holt, Holt-Winters, and Seasonal Auto Regressive Integrated Moving Average (SARIMA), as opposed to an optimized version of the compartmental multivariate epidemiological model SEIRD, which is referred to in our study, as, Non-Linear L-BFGS-B Fitted SEIRD. The mentioned methods were implemented and fine-Tuned to model and forecast COVID-19 outbreak situation represented by confirmed cases, recoveries, and fatalities in (Australia, Canada, Egypt, India, United States of America and United Kingdom). Through the implementing and tuning of both types of models, we have observed that while univariate time series forecasting models such as SARIMA produce highly accurate predictions due to their ease of use and procedure, as well as their ability to deal with seasonality and cycles in time series, multivariate epidemiological models are more powerful and extendible. Despite their complexity, epidemiological models have aided extensively in understanding the spread and severity of infectious disease pandemics such as the COVID-19 global pandemic. Using our optimized SEIRD, we have obtained a Mean Squared Log Error of 10-3 order, demonstrating the forecasts' elevated accuracy and reliability. In addition to forecasting the course of the pandemic for a 3 months season in all countries under investigation, we were able to estimate the transmission potential of COVID-19 represented by its effective reproduction number Rt. With Rt=1 is considered as the pandemic control threshold, it is evident that all of the countries under investigation are hovering just above the control threshold. This study might be relieving since it can demonstrate that the world is on the right track in terms of putting an end to the pandemic as soon as possible. The whole study shows how powerful is compartmental methods compared to classical statistical methods when used to model and forecast an infectious disease outbreak which encourages our further related research concerning the study of implementing advanced compartmental models considering additional parameters and controls. © 2022 IEEE.

Predictive models to the COVID-19

Following the World Health Organization proclaims a pandemic due to a disease that originated in China and advances rapidly across the globe, studies to predict the behavior of epidemics have become increasingly popular, mainly related to COVID-19. The critical point of these studies is to discuss the disease’s behavior and the progression of the virus’s natural course. However, the prediction of the actual number of infected people has proved to be a difficult task, due to a wide range of factors, such as mass testing, social isolation, underreporting of cases, among others. Therefore, the objective of this work is to understand the behavior of COVID-19 in the state of Ceará to forecast the total number of infected people and to aid in government decisions to control the outbreak of the virus and minimize social impacts and economics caused by the pandemic. So, to understand the behavior of COVID-19, this work discusses some forecast techniques using machine learning, logistic regression, filters, and epidemiologic models. Also, this work brings a new approach to the problem, bringing together data from Ceará with those from China, generating a hybrid dataset, and providing promising results. Finally, this work still compares the different approaches and techniques presented, opening opportunities for future discussions on the topic. The study obtains predictions with score of 0.99 to short-term predictions and 0.93 to long-term predictions. © 2021 Elsevier Inc. All rights reserved.

Linear Regression and Holt's Winter Algorithm in Forecasting Daily Coronavirus Disease 2019 Cases in Malaysia: Preliminary Study

Coronavirus disease 2019 is a fatal viral disease presently sweeping the globe. COVID-19 is a novel coronavirus that produces an infectious illness. Thus, COVID-19 detection in the general population may be helpful. The involvement of machine learning in combating COVID-19 had rapidly increased because of its efficiency to scale up, faster processing capacity, and more dependable than humans in some healthcare activities. This paper will focus on two models which are Linear Regression (LR) model and Holt's Winter model. The COVID-19 dataset was taken from the Ministry of Health for Malaysia's website. Daily confirmed cases were recorded from 24th of January 2020 to 31st July 2021 and stored in Microsoft Excel. Waikato Environment for Knowledge Analysis (WEKA) software was utilized to perform the prediction of daily cases in the next 14-days and the quality of forecasting models is evaluated by two performance metrics, Mean Absolute Deviation (MAD) and Mean Absolute Percentage Error (MAPE). The best model is selected by the lowest value of performance metrics. The comparison shows that the forecasting result of Holt's Winter is more accurate than the LR model. The developed prediction model can help public health officials make better decisions and manage resources to decrease COVID-19 pandemic morbidity and mortality. Therefore, preparation and control procedures can be established. © 2021 IEEE.

Estimating the Optimal Parameters of SEIR model for Predicting the Trajectory of COVID-19 Pandemic in Japan

A system that uses the susceptible-exposed-infectious-removed (SEIR) model and some methods to predict COVID-19 (SARS-Cov-2) trajectory of the pandemic which is currently prevalent. This paper reports on how to estimate the optimal parameters of the SEIR model, which can predict the pandemics of infectious diseases. © 2021 IEEE.

Short-Term Statistical Forecasts of COVID-19 Infections in India.

COVID-19 cases in India have been steadily increasing since January 30, 2020 and have led to a government-imposed lockdown across the country to curtail community transmission with significant impacts on societal systems. Forecasts using mathematical-epidemiological models have played and continue to play an important role in assessing the probability of COVID-19 infection under specific conditions and are urgently needed to prepare health systems for coping with this pandemic. In many instances, however, access to dedicated and updated information, in particular at regional administrative levels, is surprisingly scarce considering its evident importance and provides a hindrance for the implementation of sustainable coping strategies. Here we demonstrate the performance of an easily transferable statistical model based on the classic Holt-Winters method as means of providing COVID-19 forecasts for India at different administrative levels. Based on daily time series of accumulated infections, active infections and deaths, we use our statistical model to provide 48-days forecasts (28 September to 15 November 2020) of these quantities in India, assuming little or no change in national coping strategies. Using these results alongside a complementary SIR model, we find that one-third of the Indian population could eventually be infected by COVID-19, and that a complete recovery from COVID-19 will happen only after an estimated 450 days from January 2020. Further, our SIR model suggests that the pandemic is likely to peak in India during the first week of November 2020.

Model-based ensembles: Lessons learned from retrospective analysis of COVID-19 infection forecasts across 10 countries.

Mathematical models of different types and data intensities are highly used by researchers, epidemiologists, and national authorities to explore the inherently unpredictable progression of COVID-19, including the effects of different non-pharmaceutical interventions. Regardless of model complexity, forecasts of future COVID-19 infections, deaths and hospitalization are associated with large uncertainties, and critically depend on the quality of the training data, and in particular how well the recorded national or regional numbers of infections, deaths and recoveries reflect the the actual situation. In turn, this depends on, e.g., local test and abatement strategies, treatment capacities and available technologies. Other influencing factors including temperature and humidity, which are suggested by several authors to affect the spread of COVID-19 in some countries, are generally only considered by the most complex models and further serve to inflate the uncertainty. Here we use comparative and retrospective analyses to illuminate the aggregated effect of these systematic biases on ensemble-based model forecasts. We compare the actual progression of active infections across ten of the most affected countries in the world until late November 2020 with "re-forecasts" produced by two of the most commonly used model types: (i) a compartment-type, susceptible-infected-removed (SIR) model; and (ii) a statistical (Holt-Winters) time series model. We specifically examine the sensitivity of the model parameters, estimated systematically from different subsets of the data and thereby different time windows, to illustrate the associated implications for short- to medium-term forecasting and for probabilistic projections based on (single) model ensembles as inspired by, e.g., weather forecasting and climate research. Our findings portray considerable variations in forecasting skill in between the ten countries and demonstrate that individual model predictions are highly sensitive to parameter assumptions. Significant skill is generally only confirmed for short-term forecasts (up to a few weeks) with some variation across locations and periods.

Multiplicative Holts Winter Model for Trend Analysis and Forecasting of COVID-19 Spread in India.

The surge of the novel COVID-19 caused a tremendous effect on the health and life of the people resulting in more than 4.4 million confirmed cases in 213 countries of the world as of May 14, 2020. In India, the number of cases is constantly increasing since the first case reported on January 30, 2020, resulting in a total of 81,997 cases including 2649 deaths as of May 14, 2020. To assist the government and healthcare sector in preventing the transmission of disease, it is necessary to predict the future confirmed cases. To predict the dynamics of COVID-19 cases, in this paper, we project the forecast of COVID-19 for five most affected states of India such as Maharashtra, Tamil Nadu, Delhi, Gujarat, and Andhra Pradesh using the real-time data. Using Holt-Winters method, a forecast of the number of confirmed cases in these states has been generated. Further, the performance of the method has been determined using RMSE, MSE, MAPE, MAE and compared with other standard algorithms. The analysis shows that the proposed Holt-Winters model generates RMSE value of 76.0, 338.4, 141.5, 425.9, 1991.5 for Andhra Pradesh, Maharashtra, Gujarat, Delhi and Tamil Nadu, which results in more accurate predictions over Holt's Linear, Auto-regression (AR), Moving Average (MA) and Autoregressive Integrated Moving Average (ARIMA) model. These estimations may further assist the government in employing strong policies and strategies for enhancing healthcare support all over India.

COVID-19's First Wave: Examination of Impact on Food Purchasing Behaviour in the Eurozone.

COVID-19 has had a negative impact on the living conditions of people in all countries worldwide. With a devastating economic crisis where many families are finding it difficult to pay bills and make ends meet, increases in prices of food basket staples can be very worrying. This study examines the relationship between the incidence of the pandemic during the first wave in 16 Eurozone countries with the variation experienced in food prices. We analysed the harmonised index of consumer food prices (included in HICP) and the classification of the degree of pandemic impact by country, the latter established with the index of deaths provided by the Johns Hopkins Center. The procedure used compared actual food prices during the first wave (March to June 2020) with those foreseeable in the absence of the pandemic. Time series analysis was used, dividing the research period into two phases. In both phases, the Holt-Winters model was applied for estimation and subsequent prediction. After a contrast using Kendall's tau correlation index, it was concluded that in the countries with the highest death rates during the first wave, there was a higher increase in food prices than in the least affected countries of the Eurozone.

Comparative Analysis of Different Univariate Forecasting Methods in Modelling and Predicting the Romanian Unemployment Rate for the Period 2021-2022.

Unemployment has risen as the economy has shrunk. The coronavirus crisis has affected many sectors in Romania, some companies diminishing or even ceasing their activity. Making forecasts of the unemployment rate has a fundamental impact and importance on future social policy strategies. The aim of the paper is to comparatively analyze the forecast performances of different univariate time series methods with the purpose of providing future predictions of unemployment rate. In order to do that, several forecasting models (seasonal model autoregressive integrated moving average (SARIMA), self-exciting threshold autoregressive (SETAR), Holt-Winters, ETS (error, trend, seasonal), and NNAR (neural network autoregression)) have been applied, and their forecast performances have been evaluated on both the in-sample data covering the period January 2000-December 2017 used for the model identification and estimation and the out-of-sample data covering the last three years, 2018-2020. The forecast of unemployment rate relies on the next two years, 2021-2022. Based on the in-sample forecast assessment of different methods, the forecast measures root mean squared error (RMSE), mean absolute error (MAE), and mean absolute percent error (MAPE) suggested that the multiplicative Holt-Winters model outperforms the other models. For the out-of-sample forecasting performance of models, RMSE and MAE values revealed that the NNAR model has better forecasting performance, while according to MAPE, the SARIMA model registers higher forecast accuracy. The empirical results of the Diebold-Mariano test at one forecast horizon for out-of-sample methods revealed differences in the forecasting performance between SARIMA and NNAR, of which the best model of modeling and forecasting unemployment rate was considered to be the NNAR model.

Application of one-, three-, and seven-day forecasts during early onset on the COVID-19 epidemic dataset using moving average, autoregressive, autoregressive moving average, autoregressive integrated moving average, and naïve forecasting methods.

The coronavirus disease 2019 (COVID-19) spread rapidly across the world since its appearance in December 2019. This data set creates one-, three-, and seven-day forecasts of the COVID-19 pandemic's cumulative case counts at the county, health district, and state geographic levels for the state of Virginia. Forecasts are created over the first 46 days of reported COVID-19 cases using the cumulative case count data provided by The New York Times as of April 22, 2020. From this historical data, one-, three-, seven, and all-days prior to the forecast start date are used to generate the forecasts. Forecasts are created using: (1) a Naïve approach; (2) Holt-Winters exponential smoothing (HW); (3) growth rate (Growth); (4) moving average (MA); (5) autoregressive (AR); (6) autoregressive moving average (ARMA); and (7) autoregressive integrated moving average (ARIMA). Median Absolute Error (MdAE) and Median Absolute Percentage Error (MdAPE) metrics are created with each forecast to evaluate the forecast with respect to existing historical data. These error metrics are aggregated to provide a means for assessing which combination of forecast method, forecast length, and lookback length are best fits, based on lowest aggregated error at each geographic level. The data set is comprised of an R-Project file, four R source code files, all 1,329,404 generated short-range forecasts, MdAE and MdAPE error metric data for each forecast, copies of the input files, and the generated comparison tables. All code and data files are provided to provide transparency and facilitate replicability and reproducibility. This package opens directly in RStudio through the R Project file. The R Project file removes the need to set path locations for the folders contained within the data set to simplify setup requirements. This data set provides two avenues for reproducing results: 1) Use the provided code to generate the forecasts from scratch and then run the analyses; or 2) Load the saved forecast data and run the analyses on the stored data. Code annotations provide the instructions needed to accomplish both routes. This data can be used to generate the same set of forecasts and error metrics for any US state by altering the state parameter within the source code. Users can also generate health district forecasts for any other state, by providing a file which maps each county within a state to its respective health-district. The source code can be connected to the most up-to-date version of The New York Times COVID-19 dataset allows for the generation of forecasts up to the most recently reported data to facilitate near real-time forecasting.

Using the kalman filter with Arima for the COVID-19 pandemic dataset of Pakistan.

The current pandemic of the Novel Corona virus (COVID-19) has resulted in multifold challenges related to health, economy, and society, etc. for the entire world. Many mathematical epidemiological models have been tried for the available data of the COVID-19 pandemic with the core objective to observe the trend and trajectories of infected cases, recoveries, and deaths, etc. However, these models have their own assumptions and parameters and vary with regional demography. This article suggests the use of a more pragmatic approach of the Kalman filter with the Autoregressive Integrated Moving Average (ARIMA) models in order to obtain more precise forecasts for the figures of prevalence, active cases, recoveries, and deaths related to the COVID-19 outbreak in Pakistan.