Online Fuzzy Logic Prediction of Electrical Load Based on Real-Time Measurements During the Covid-19 Pandemic

As technologies advance and the population grows, electrical energy became one of the necessities for many peoples. Because the availability of electrical energy is limited, it requires various ways to be used efficiently. Electrical load monitoring usage in Indonesia still require an electrical officer to come to an electric panel location to record electrical usage. During the COVID-19 pandemic, it is not feasible to locally visit an electric panel because of the many restrictions. Remote monitoring using Internet of Things (IoT) can be used to address the problem. Going further, by knowing the electrical load usage, prediction can be done using fuzzy logic as a way to understand how to use electricity efficiently. Thus, a fuzzy logic load forecasting system IoT is developed in this research. Fuzzy variables used in this system are time of day, days of the week, measured loads, and forecasted loads. The research produced a system that predicts electrical load with one hour of accuracy based on the previous week's data. The average prediction error rate of the system is 9.48%. The implemented system is available on a web server and can be accessed via a web browser, either via a computer or cellphone. The system allows users to monitor and predict electrical load usage regardless of time and place. © 2023, The authors.

Machine Learning Approaches for Temporal and Spatio-Temporal Covid-19 Forecasting: A Brief Review and a Contribution

In December 2019 an outbreak of a new disease happened, in Wuhan city, China, in which the symptoms were very similar to pneumonia. The disease was attributed to SARS-CoV-2 as the infectious agent and it was called the new coronavirus or Covid-19. In March 2020, the World Health Organization declared a worldwide pandemic of the new coronavirus. We have already counted more than 110 million cases and almost 2.5 million deaths worldwide. In order to assist in decision-making to contain the disease, several scientists around the world have engaged in various efforts, and they have proposed a lot of systems and solutions for tracking, monitoring, and predicting confirmed cases and deaths from Covid-19. Mathematical models help to analyze and understand the evolution of the disease, but understanding the disease was not enough, it was necessary to understand the problem in a quantitative way to lead the decision-making during the pandemic. Several initiatives have made use of Artificial Intelligence, and models were designed using machine learning algorithms with features for temporal and spatio-temporal investigation and prediction of cases of Covid-19. Among the algorithms used are Support Vector Machine (SVM), Random Forest, Multilayer Perceptron (MLP), Graph Neural Networks (GNNs), Ecological Niche Models (ENMs), Long-Short Term Memory Networks (LSTM), linear regression, and others. And these had good results, and to analyze them, the Root Mean Squared Error (RMSE), Log Root Mean Squared Error (RMSLE), correlation coefficient, and others were used as metrics. Covid-19 presents a huge problem to public health worldwide, so it is of utmost importance to investigate it, and with these two approaches it is possible to track not only how the disease evolves but also to know which areas are at risk. And these solutions can help in supporting decision-making by health managers to make the best decisions for the disease that is in the outbreak. This chapter aims to present a literature review and a brief contribution to the use of machine learning methods for temporal and spatio-temporal prediction of Covid-19, using Brazil and its federative units as a case study. From canonical methods to deep networks and hybrid committee-based, approaches will be investigated. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

Towards improved thermal comfort predictions and higher energy savings: building energy model of an open-plan office based on indoor CO2 and temperature controls

In recent work, a Hierarchical Bayesian model was developed to predict occupants' thermal comfort as a function of thermal indoor environmental conditions and indoor CO2 concentrations. The model was trained on two large IEQ field datasets consisting of physical and subjective measurements of IEQ collected from over 900 workstations in 14 buildings across Canada and the US. Posterior results revealed that including measurements of CO2 in thermal comfort modelling credibly increases the prediction accuracy of thermal comfort and in a manner that can support future thermal comfort prediction. In this paper, the predictive model of thermal comfort is integrated into a building energy model (BEM) that simulates an open-concept mechanically-ventilated office space located in Vancouver. The model predicts occupants' thermal satisfaction and heating energy consumption as a function of setpoint thermal conditions and indoor CO2 concentrations such that, for the same thermal comfort level, higher air changes per hour can be achieved by pumping a higher amount of less-conditioned fresh air. The results show that it is possible to reduce the energy demand of increasing fresh air ventilation rates in winter by decreasing indoor air temperature setpoints in a way that does not affect perceived thermal satisfaction. This paper presents a solution for building managers that have been under pressure to increase current ventilation rates during the COVID-19 pandemic. © 2022 17th International Conference on Indoor Air Quality and Climate, INDOOR AIR 2022. All rights reserved.

A Contact Matrix-Based Approach for Predicting COVID-19 Using Influenza Data

The global pandemic Corona Virus Disease 2019 (COVID-19) has become one of the deadliest epidemics in human history, bringing enormous harm to human society. To help health policymakers respond to the threat of COVID-19, prediction of outbreaks is needed. Research on COVID-19 prediction usually uses data-driven models and mechanism models. However, in the early stages of the epidemic, there were not enough data to establish a data-driven model. The inadequate understanding of the virus that causes COVID-19, SARS-COV-2, has also led to the inaccuracies of the mechanism model. This has left the government with the toughest Non-pharmaceutical interventions (NPIs) to curb the spread of the virus, such as the lockdown of Wuhan in 2020. Yet man is a social animal, and social relations and interactions are necessary for his existence. The novel coronavirus and containment measures have challenged human and community interactions, affecting the lives of individuals and collective societies. To help governments take appropriate and necessary actions in the early stages of an epidemic, and to mitigate its impact on people's psychology and lives, we used the COVID-19 pandemic as an example to develop a model that uses surveillance data from one epidemic to predict the development trend of another. Based on the fact that both influenza and COVID-19 are transmitted through infectious respiratory droplets, we hypothesized that they may have the same underlying contact structure, and we proposed the influenza data-based COVID-19 prediction (ICP) model. In this model, the underlying contact pattern is firstly inferred by using a singular value decomposition method from influenza surveillance data. Then the contact matrix was used to simulate the influenza virus transmission through close contact of people, and the influenza virus transmission model was established. In order to be able to simulate the spread of COVID-19 virus using influenza transmission models, we used influenza contact matrix and COVID-19 infection data to estimate the risk of a population contracting COVID-19, i.e. force of infection of COVID-19. Finally, we used force of infection and influenza virus transmission model to simulate and predict the spread of COVID-19 in the population. We obtained age-disaggregated influenza and COVID-19 infection data for the United States in 2020, as well as data for Europe, which was not disaggregated by age. We use correlation coefficients as an evaluation indicator, and the final results prove that the predicted value and the actual value are positively correlated. So, the development trend of COVID-19 can be predicted using influenza surveillance data. © 2022 IEEE.

Outbreak trends of fatality rate into coronavirus disease-2019 using deep learning

The World Health Organization (WHO) has declared the novel coronavirus as global pandemic on 11 March 2020. It was known to originate from Wuhan, China and its spread is unstoppable due to no proper medication and vaccine. The developed forecasting models predict the number of cases and its fatality rate for coronavirus disease 2019 (COVID-19), which is highly impulsive. This paper provides intrinsic algorithms namely - linear regression and long short-term memory (LSTM) using deep learning for time series-based prediction. It also uses the ReLU activation function and Adam optimiser. This paper also reports a comparative study on existing models for COVID-19 cases from different continents in the world. It also provides an extensive model that shows a brief prediction about the number of cases and time for recovered, active and deaths rate till January 2021.Copyright © 2023 Inderscience Enterprises Ltd.

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.

CX-R Classification Using DCNN Method

The virus SARS-CoV2 was identified in late 2019. Coronavirus Disease 2019 (COVID-19) is still a threat to global health and safety. Deep Learning (DL) is anticipated to be the most excellent strategy for reliably predicting COVID-19. Convolutional Neural Networks(CNNs) have achieved successful outcomes particularly in categorization and analyzing of medical image data. This work proposes a Deep CNN(DCNN) method for the classification of CX-R(Chest X-Ray) images in prediction of COVID-19. The dataset is preprocessed under many phases with different techniques for creating effective training dataset for the DCNN model to achieve best performance. This is done to deal various complexities like availability of very small sized imbalanced dataset with quality issues. In the first instance, model is trained using the train dataset. Then the model is tested for a separate validate X-ray image dataset and Confusion matrix is displayed. Up to 98.3% Accuracy is obtained, when proposed model was tested using the validate dataset. The Accuracy and Loss graph is plotted for the same. Later, random image prediction is made from prediction dataset which include both COVID and Normal X-rays. Other important performance metrics like F1 score, Recall, Precision for the model is displayed. © 2023 IEEE.

A Hybrid Method for Analyzing the Situation Based on Cumulative Fully Vaccinated and Confirmed Cases of Covid-19 in Malaysia

SARS-CoV-2 is an infection that affects several organs and has a wide range of symptoms in addition to producing severe acute respiratory syndrome. Millions of individuals were infected when it first started because of how quickly it travelled from its starting location to nearby countries. Anticipating positive Covid-19 incidences is required in order to better understand future risk and take the proper preventative and precautionary measures. As a result, it is critical to create mathematical models that are durable and have as few prediction errors as possible. This study suggests a unique hybrid strategy for examining the status of Covid-19 confirmed patients in conjunction with complete vaccination. First, the selective opposition technique is initially included into the Grey Wolf Optimizer (GWO) in this study to improve the exploration and exploitation capacity for the given challenge. Second, to execute the prediction task with the optimized hyper-parameter values, the Least Squares Support Vector Machines (LSSVM) method is integrated with Selective Opposition based GWO as an objective function. The data source includes daily occurrences of confirmed cases in Malaysia from February 24, 2021 to July 27, 2022. Based on the experimental results, this paper shows that SOGWO-LSSVM outperforms a few other hybrid techniques with ideally adjusted parameters. © 2022 IEEE.

The value of probabilistic forecasting in emergency medical resource planning under uncertainty

PurposeMost epidemic transmission forecasting methods can only provide deterministic outputs. This study aims to show that probabilistic forecasting, in contrast, is suitable for stochastic demand modeling and emergency medical resource planning under uncertainty.Design/methodology/approachTwo probabilistic forecasting methods, i.e. quantile regression convolutional neural network and kernel density estimation, are combined to provide the conditional quantiles and conditional densities of infected populations. The value of probabilistic forecasting in improving decision performances and controlling decision risks is investigated by an empirical study on the emergency medical resource planning for the COVID-19 pandemic.FindingsThe managerial implications obtained from the empirical results include (1) the optimization models using the conditional quantile or the point forecasting result obtain better results than those using the conditional density;(2) for sufficient resources, decision-makers' risk preferences can be incorporated to make tradeoffs between the possible surpluses and shortages of resources in the emergency medical resource planning at different quantile levels;and (3) for scarce resources, the differences in emergency medical resource planning at different quantile levels greatly decrease or disappear because of the existing of forecasting errors and supply quantity constraints.Originality/valueVery few studies concern probabilistic epidemic transmission forecasting methods, and this is the first attempt to incorporate deep learning methods into a two-phase framework for data-driven emergency medical resource planning under uncertainty. Moreover, the findings from the empirical results are valuable to select a suitable forecasting method and design an efficient emergency medical resource plan.

Digitisation & Health - Reflections from the Covid Pandemic

When COVID-19 reached Dr. Helmi Zakariah's home country of Malaysia in January 2020, he was consulting in Brazil as CEO of Artificial Intelligence in Medical Epidemiology (AIME) on Machine Learning application for dengue outbreak forecasting. A trained physician, public health professional, and digital health entrepreneur, Dr. Zakariah found himself in high demand as the Malaysian government began to mount it's COVID-19 response. He was asked to return home to his state of Selangor to lead the Digital Epidemiology portfolio for the Selangor State Task Force for COVID-19, and upon arrival immediately began to address the many challenges COVID-19 presented. This session will bring the audience along the sobering journey of health digitisation & adoption in the heat of the pandemic and beyond-not only what works, but more importantly-what doesn't-and to reflect on the case that the cost of underinvestment and inaction for digital innovation in health is simply too high in the face of another pandemic.Copyright © 2023

Predicting the infection probability distribution due to airborne and droplet transmission

In this study, a method was proposed to predict the infection probability distribution rather than the room-averaged value. The infection probability by airborne transmission was predicted based on the CO2 concentration. The infection probability by droplet transmission was predicted based on occupant position information. Applying the proposed method to an actual office confirmed that it could be used for quantitatively predicting the infection probability by integrating the ventilation efficiency and distance between occupants. The infection probability by airborne transmission was relatively high in a zone where the amount of outdoor air supply was relatively small. The infection probability by droplet transmission varied with the position of the occupants. The ability of the proposed method to analyze the relative effectiveness of countermeasures for airborne transmission and droplet transmission was verified in this study. © 2022 17th International Conference on Indoor Air Quality and Climate, INDOOR AIR 2022. All rights reserved.

VOLATILE FINANCIAL CONDITIONS, ASSETS PRICES AND INVESTMENT DECISIONS: ANALYSIS OF DAILY DATA OF DJIA AND S&P500, FROM JANUARY TO APRIL OF 2022

This paper is a logical outgrowth of empirical work in a Securities Analysis and Portfolio Management course at Houghton University between the start of January 2022 and end of April 2022. Remarkably, the exposure of global financial markets to two unmistakable events (shocks), the COVID-19 pandemic and the Russo-Ukrainian war (February 24, 2022)—sources of systematic risk—coincided with our empirical inquiry. The dual shocks, which exacerbated negative investment prospects for multiple product and financial sectors, heightened the uncertainty of profitable returns from investments in financial markets. The performance of eight publicly traded companies in the US and composite indices—the Dow Jones Industrial Average (DJIA) and the S& P 500—were tracked on a daily basis from January 10, 2022 to April 29, 2022, generating a total of 77 observations. Using the superior performance of a moving average model and the Holt-Winters algorithm, we found that profitable investment prospects existed during the period of systematic risk. We conclude that technical analysis provided time sensitive information for leveraged financial investments during turbulent periods of systematic risk. © 2023 Asociacion Euro-Americana de Estudios del Desarrollo. All rights reserved.

Validation of the russian version of the 4C Mortality Score and prediction of outcomes of severe COVID-19

The aim of the study is to validate the Russian version of the 4C Mortality Score scale and evaluate its accuracy in predicting the outcomes of severe COVID-19. Material and methods. The staff of the Center for Validation of International Scales and Questionnaires of the Research Center of Neurology received official permission from the authors to conduct a validation study of the 4C Mortality Score scale in Russia. In the course of the work, the linguistic and cultural ratification of the scale was carried out and its Russian-language version was prepared. Psychometric properties (reliability and validity) The Russian-language version was evaluated on a group of 78 patients (37 of whom were men, aged 34 to 88 years) with a confirmed diagnosis of COVID-19, hospitalized in the City Clinical Hospital No. 15 named after O.M. Filatov (Moscow) in the period from June to August 2021. Results. The linguocultural adaptation of the 4C Mortality Score scale was successfully carried out. High levels of reliability were obtained (Spearman correlation coefficient rho=0.91, p<0.0001;Cronbach's alpha alpha=0.73, p=0.0002;Cohen's kappa kappa=0.85, p<0.0001). It is shown that the 4C Mortality Score scores have a significant correlation with the COVID-GRAM scores (r=0.72, p=0.002) and NEWS2 (r=0.54, p=0.004). Conclusion. As a result of the validation study, the official Russian version of the 4C Mortality Score scale was developed. It is recommended for use by medical professionals of various specialties at all stages of providing medical care to patients with COVID-19. The scale is available for download on the website of the Center for Validation of International Scales and Questionnaires of the Research Center of Neurology (https://www.neurology.ru/reabilitaciya/centr-validacii-mezhdunarodnyh-shkal-i-oprosnikov).Copyright © 2022 by the authors.

Corporate Restructuring Laws Under Stress

Bail-outs by way of loan have a similar effect (on the debtor: plainly, the cost of delivering relief is allocated differently as between a bail-out and a bail-in) in that they enable the debtor to meet current fixed costs through borrowing, in effect swapping shorter-term liabilities with a longer-term liability. The authors acknowledge the support of the Oxford Law Faculty in funding the Conference "Corporate Restructuring Laws Under Stress" (St Hugh's College, Oxford, 10 October 2022) at which the papers in this special issue were first presented, and the support of the Covid-19 Research Response Fund at Oxford University, which provided funding for the wider project of which the Conference formed one part. Most authors, however, express some concerns in relation to Covid-19 bail-out design, and in particular query whether some bail-outs may have been too generous. [Extracted from the article] Copyright of European Business Organization Law Review is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Machine Learning Algorithm for Trend Analysis in Short term Forecasting of COVID-19 using Lung X-ray Images

With the development of medical technology, the diagnosis of lung diseases relies more on the determination of medical images. With increasingly huge data, a powerful data processing model is urgently needed to provide favorable support for this field. The goal of this study is to develop a computer-assisted method to identify COVID-19 from X-ray pictures of the lungs at the very beginning of the disease. The architecture is implemented as a software system on a computer that can assist in the affordable and accurate early identification of cardiac illness. The performance of CNN architecture is best among all other classification algorithms to detect COVID-9 from Lung X-ray images. The datasets consist of COVID-19 established cases for 4 weeks which included the X-ray images of the chest. Then the distribution of the data was examined according to the statistical distribution. For this prediction, time series models are used for forecasting the pandemic situation. The performances of the methods were compared according to the MSE metric and it was seen that the Convolutional Neural Networks (CNN) achieved the optimal trend pattern.

Epidemiology: global spread, risk factors for disease incidence, severity and mortality

There are many unknowns surrounding COVID-19 and the ongoing pandemic. Standard epidemiological methods helped to determine the initial and ongoing distribution of COVID-19 in time and space, with unprecedented global coverage in almost real-time, and the forecasting methods used already had a reasonable predictive ability. Cumulative incidence and other complex epidemiological estimators have been widely disseminated via the media and are becoming lay terms thanks to persistent use, but their thresholds to determine public health interventions are yet to achieve consensus. The natural history of SARS-CoV-2, the interplay of risk factors and the effectiveness of mitigating factors in subpopulations remain unmet challenges. Establishing standard definitions of COVID-19 and its consequences is essential to the implementation of research. Pending widespread vaccine coverage, the world is experiencing unleashed community transmission in many countries, and the COVID-19 endgame is a distant goal. Several characteristics differentiate the transmissibility of SARS-CoV-2 from other viruses, making COVID-19 much more difficult to control with universal hygiene interventions. Epidemiology remains a necessary discipline to help end the COVID-19 pandemic;economic, social and health policy decision-making analysis are also needed.Copyright © ERS 2021.

Global Health: An Agile Open-Source Repository of Infectious Disease Outbreaks to Support Global Surveillance and Research Efforts

Intro: The COVID-19 pandemic highlighted a need for an open-source repository of line-list case data for infectious disease surveillance and research efforts. Global.health was launched in January 2020 as a global resource for public health data research. Here, we describe the data and systems underlying the Global.health datasets and summarize the project's 2.5 years of operations and the curation of the COVID-19 and monkeypox repositories. Method(s): The COVID-19 repository is curated daily through an automated system, verified by a team of researchers. The monkeypox dataset is curated manually by a team of researchers, Monday-Friday. Both repositories include metadata fields on demographics, symptomology, disease confirmation date, and others1,2. Data is de-identified and ingested from trusted sources, such as government public health agencies, trusted media outlets, and established openaccess repositories. Finding(s): The Global.health COVID-19 dataset is the largest repository of publicly available validated line-list data in the world, with over 100 million cases from more than 100 countries, including 60+ fields of metadata, comprising over 1 billion unique data points. The monkeypox dataset has over 35,000 data entries, from 100 different countries. 7,325 users accessed the COVID-19 repository and 3,005 accessed the monkeypox repository. Conclusion(s): The Global.health repositories provide verified, de-identified case data for two global outbreaks and are used by CDC, WHO, and other national public health organizations for surveillance and forecasting efforts. The repositories were utilized to share insights into the COVID-19 pandemic and track the monkeypox outbreak using real-time data3-6. We are collaborating with WHO Hub for Pandemic and Epidemic Intelligence to improve coordination, data schemas, and downstream use of data to inform and evaluate public health policy7. Future work will focus on creating a 'turnkey' data system to be used in future outbreaks for quicker infectious disease surveillance.Copyright © 2023

Forecasting COVID-19 cases in Egypt using ARIMA-based time-series analysis

Objectives: The World Health Organization declared the novel coronavirus (COVID-19) outbreak a public health emer-gency of international concern on January 30, 2020. Since it was first identified, COVID-19 has infected more than one hundred million people worldwide, with more than two million fatalities. This study focuses on the interpretation of the distribution of COVID-19 in Egypt to develop an effective forecasting model that can be used as a decision-making mechanism to administer health interventions and mitigate the transmission of COVID-19. Method(s): A model was developed using the data collected by the Egyptian Ministry of Health and used it to predict possible COVID-19 cases in Egypt. Result(s): Statistics obtained based on time-series and kinetic model analyses suggest that the total number of CO-VID-19 cases in mainland Egypt could reach 11076 per week (March 1, 2020 through January 24, 2021) and the number of simple regenerations could reach 12. Analysis of the ARIMA (2, 1, 2) and (2, 1, 3) sequences shows a rise in the number of COVID-19 events. Conclusion(s): The developed forecasting model can help the government and medical personnel plan for the imminent conditions and ensure that healthcare systems are prepared to deal with them.Copyright © 2021 by Eurasian Journal of Medicine and Oncology.

How Australia's economy gained momentum because of Covid-19

The objective of the paper is to assess the resilience of the economy of Australia following the Covid-19 pandemic that hit the global economy in Q4 2019, in years 2020, 2021 and 2022. Quarterly growth rates (annualised) of the Real GDP of Australia and Canada are forecasted between Q2 2022 and Q4 2050. Two sets of forecasts are generated: forecasts using historical data including the pandemic (from Q1 1961 to Q1 2022) and excluding the pandemic (from Q1 1961 to Q3 2019). The computation of the difference of their averages is an indicator of the resilience of the economies during the pandemic, the greater the difference the greater the resilience. Used as a benchmark, Canada's economy shows a slightly lower resilience to the Covid-19 pandemic (+0.37%) than Australia's economy (+0.39%) based on Q2 2022-2050 forecasts. However, driven by stronger growth than Canada, the average estimate of the Q2 2022-Q4 2050 quarterly (annualised) growth rate forecasts of Australia is expected to be +2.09% with the Q1 1961-Q1 2022 historical data while it should be +1.61% for Canada. Supported by higher growth, Australia's Real GDP is expected to overtake Canada's in Q1 2040.

Determinants of Softwood Lumber Prices in the US Northwest

Lumber prices can be volatile and hard to predict from month to month yet are important for many sectors of the economy, ranging from forestry and construction. An economic model of lumber prices was developed and applied to data representing multiple supply and demand determinants of lumber. Using a suite of econometric models, monthly lumber prices were related back to variables including construction permits, US reserve bank credit, tariffs with Canada, exchange rates with Canada, and variables representing shocks associated with the COVID-19 pandemic. Preferred models use relatively small amounts of publicly available information, making them more accessible to industry participants who want to make their own price predictions. Such information can help guide decisions about whether to expand or scale back an operation in preparation for likely future price movements. Study Implications: This study shows that Douglas-fir lumber prices in the US Northwest can be predicted quite accurately with selected macro-economic variables that are commonly reported in the public domain. Using statistical techniques, monthly lumber prices in the United States were related back to variables including new home construction permits, US reserve bank credit, tariffs, and exchange rates. With suitable assumptions about future economic conditions, the models could be used by researchers as well as professionals at lumber mills, wholesales, and retailers to make near term predictions.