ABSTRACT
Piecewise linear regression (PLR) method is applied to study cumulative cases of COVID-19 evolving everyday in England up to 6th February 2022 just before travel restrictions are removed and people started not to get tested anymore in the UK and factors e.g. the lockdowns behind the spread COVID-19 are also investigated. It is clear that different periods exhibit distinct patterns depending on variants and government-imposed restriction. Therefore, the effectiveness of lockdown measures is evaluated by comparing the rate of increase after a certain period (delay effect of measures) and that of time before as well as how new variants take over as a dominant variant. In addition, autoregression function is studied to show strong effect of cases in the past on today's cases since the disease is highly infectious. Most of work is carried out thorough python built-in libraries such as pandas for preprocessing data and matplotlib which allows us to gain more insight and better visualization into the real scenario. Visualization is conducted by Geoda showing the regional level of infections. © 2023 SPIE.
ABSTRACT
The coronavirus outbreak in 2020 has made it difficult to implement macroeconomic initiatives and has affected the economy in all countries in Africa. There has been a lot of concern regarding how to stabilize the economy at least to where it was before the coronavirus outbreak. There was increased governmental allocation to combat the spread and reduce COVID-19's impacts. This study evaluates the economic impacts of the COVID-19 pandemic on some African countries and examines the cognitive analysis as it affects the economy considering layoffs and other revenue losses, as well as a consistent recession and deterioration in the banking and economic sectors. A linear regression method was used in the analysis of this work. Although the pandemic affects every aspect of life and society at large, this study examines how it affects the nation's economy. It was recognized that numerous policy instruments, including those connected to health and social protection, fiscal policy, and financial, industrial, and trade policies, needed to be implemented for the economy to recover properly from the financial loss. The analysis of the data, shows that there was a reduction in the GDP of each country during the Covid-19 pandemic. It is predicted that adopting these technologies may minimize suffering among people and aid in the economy's recovery from recession and bankruptcy. © 2022 IEEE.
ABSTRACT
This paper presents an approach to determine the Semaphore Covid in Mexico from the news to participate in the Rest-Mex 2022 evaluation forum. The purpose of the task is to determine the covid semaphore color (red, orange, yellow, and green) in different time spaces. The proposed approach consists of two main steps. First, to generate a list of topics of the news, and second, to implement several linear regressions methods in order to these results serve to feed a deep neural network. For the first step, the LDA algorithm was implemented, and for the second, well-known methods such as Lasso, Ridge, Lars, among others, were utilized. With this approach, a weighted average of 0.48 was obtained, which is considerably higher than the baseline proposed by the organizers, which is 0.12. The best result to classify the semaphore was two weeks in the future with 0.56 of F-measure. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
ABSTRACT
Owing to the global lockdown caused by the pandemic of COVID-19, the electricity demand is greatly affected, and the electricity market is also constantly fluctuating. During the pandemic period, the prediction of electricity demand is crucial to the economy and power dispatching. In this study, we combine the pandemic data and government anti-pandemic policies data to predict the electricity demand of the Contiguous United States by using the artificial neural network and recurrent neural network. In addition, the linear regression method is used to forecast the thermal generation with total generation data. Some experiments have developed to verify the effectiveness of the model. Then the model is used to forecast electricity demand and thermal generation under different policies and pandemic development, and the result were analyzed. © 2021 IEEE
ABSTRACT
Coronavirus disease (Covid-19) is a serious health problem for the world. Most of the countries are affected by this infectious disease. Many countries have started vaccination against Covid-19. The number of confirmed cases every day changes rapidly. Public health planners want to know these numbers in advance to arrange health facilities accordingly. Many machine learning models have been developed for the prediction of the number of Covid-infected people. The accuracy of these models depends upon the training data. Data collected during the period when there is no vaccination and data collected during the vaccination period have different properties. The models trained on different datasets perform differently. In this paper, we study the effect of the data collected during the vaccination period. The study will be helpful in generating more accurate prediction models for the vaccination period.
ABSTRACT
The COVID-19 pandemic is far from over. The government has carried out several policies to suppress the development of COVID-19 is no exception in Bogor Regency. However, the public still has to be vigilant especially now we will face a year-end holiday that can certainly be a trigger for the third wave of COVID-19. Therefore, researchers aim to make predictions of the increase in positive cases, especially in the Bogor Regency area to help the government in making policies related to COVID-19. The algorithms used are Gaussian Process, Linear Regression, and Random Forest. Each Algorithm is used to predict the total number of COVID-19 cases for the next 21 days. Researchers approached the Time Series Forecasting model using datasets taken from the COVID-19 Information Center Coordinationn Center website. The results obtained in this study, the method that has the highest probability of accurate and appropriate data contained in the Gaussian Process method. Prediction data on the Linear Regression method has accurate results with actual data that occur with Root Mean Square Error 1202.6262. © 2021 IEEE.
ABSTRACT
To ensure traffic safety, many related works have been done to avoid traveler injury during trips. However, new public health issues threaten traffic safety because travelers might get ill during trips. The more people infected by COVID-19, the more unsafe urban traffic becomes. This paper aims to verify whether COVID-19 has negative impacts on urban traffic recovery. Based on thirty Chinese cities' data, robust fixed-effects (within) regression was adopted to analyze impacts with a linear regression method. The regression results suggest that Urban Traffic Activity Index (UTAI) was positively associated with UTAI itself with short-term effect, meaning that UTAI could recover by itself, and new confirmed cases (NC) were negatively associated with UTAI with long-term effect, meaning that NC would prevent UTAI recovery. The findings also suggest that it is better for city governments to eliminate outbreaks before restarting economies. Future directions include improving models, grouping cities, and expanding data. © 2021 CICTP 2021: Advanced Transportation, Enhanced Connection - Proceedings of the 21st COTA International Conference of Transportation Professionals. All rights reserved.
ABSTRACT
A scientific understanding of the impact of COVID-19 on the psychological status of residents is important for improving medical services and responding to public health emergencies. With the help of some of the most popular network communication tools (including Wechat and Weiboand QQ), online questionnaires were completed by South China citizens during the early stage of the COVID-19 pandemic based on psychological stress theory and using a comprehensive sampling method. Through cooperation with experts from other institutions, the content of the questionnaire was designed to include interviewees' spatial locations and individual information, identify whether negative emotions were generated, and determine the level of psychological stress and the degree of perception change, etc. According to the data type, mathematical statistics and multiple logistic regression methods were used to examine regional differentiation and influencing factors regarding the psychological stress of residents using 1668 valid questionnaires from 53 municipal administrative units in South China. The results firstly showed that over the whole area there was typical regional differentiation in South China, especially in relation to negative expression and psychological stress, with this feature reflecting the dual urban-rural structure. Secondly, regional differences were obvious. Residents of Hainan showed stronger change of psychological stress than those of the other two provinces. In contrast, Guangdong residents were the least psychological stress, and the concept of a harmonious relationship between human beings and nature was not accepted as well as in the other two provinces. Thirdly, in each province the capital city acted as the regional pole, with greater psychological status. This polarization effect decreased with greater distance, reflecting the theory of growth poles in human geography. Fourthly, gender, education level, occupation, informational correction, and the possibility of infection were notable factors that affected the psychological status of interviewees facing COVID-19. However, the functions were different and were decided by the dependent variable. Lastly, based on conclusions summarized from three perspectives, it was found that regional differentiation, public information, and social structure need to focused upon in order to handle sudden major health issues.