scholarly journals A comparative study of SIR Model, Linear Regression, Logistic Function and ARIMA Model for forecasting COVID-19 cases

2021 ◽  
Author(s):  
Saina Abolmaali
Keyword(s):  
Regression Models ◽  
Exploratory Data Analysis ◽  
Arima Model ◽  
Sir Model ◽  
Logistic Function ◽  
Infected People ◽  
History Of ◽  
Exploratory Data ◽  
Data Analytic ◽  
Epidemic Diseases

Starting February 2020, COVID-19 was confirmed in 11,946 people worldwide, with a mortality rate of almost 2%. A significant number of epidemic diseases including human Coronavirus display patterns. In this study with the benefit of data analytic, we develop regression models and a Susceptible-Infected-Recovered (SIR) model for the contagion to compare the performance of models to predict number of cases. first, we implement a good understanding of data and perform Exploratory Data Analysis (EDA). Then, we derive the parameters of the model from the available data corresponding to the top 4 regions based on the history of infections and the most infected people as of the end of August 2020. Then models are compared and further research are introduced.

scholarly journals A comparative study of SIR Model, Linear Regression, Logistic Function and ARIMA Model for forecasting COVID-19 cases

2021 ◽  
Vol 8 (4) ◽  
pp. 598-613
Author(s):  
Saina Abolmaali ◽  
◽  
Samira Shirzaei ◽  
Keyword(s):  
Regression Models ◽  
Exploratory Data Analysis ◽  
Arima Model ◽  
Sir Model ◽  
Logistic Function ◽  
Infected People ◽  
History Of ◽  
Exploratory Data ◽  
Data Analytic ◽  
Epidemic Diseases

<abstract> <p>Starting February 2020, COVID-19 was confirmed in 11,946 people worldwide, with a mortality rate of almost 2%. A significant number of epidemic diseases consisting of human Coronavirus display patterns. In this study, with the benefit of data analytic, we develop regression models and a Susceptible-Infected-Recovered (SIR) model for the contagion to compare the performance of models to predict the number of cases. First, we implement a good understanding of data and perform Exploratory Data Analysis (EDA). Then, we derive parameters of the model from the available data corresponding to the top 4 regions based on the history of infections and the most infected people as of the end of August 2020. Then models are compared, and we recommend further research.</p> </abstract>

scholarly journals Citizen Science for Traffic Monitoring: Investigating the Potentials for Complementing Traffic Counters with Crowdsourced Data

2022 ◽  
Vol 14 (2) ◽  
pp. 622
Author(s):  
Miha Janež ◽  
Špela Verovšek ◽  
Tadeja Zupančič ◽  
Miha Moškon
Keyword(s):  
Performance Indicators ◽  
Regression Models ◽  
Exploratory Data Analysis ◽  
Low Cost ◽  
Traffic Monitoring ◽  
Sustainable Cities ◽  
Traffic Patterns ◽  
Regression Procedure ◽  
Crowdsourced Data ◽  
Exploratory Data

Traffic counts are among the most frequently employed data to assess the traffic patterns and key performance indicators of next generation sustainable cities. Automatised counting is often based on conventional traffic monitoring systems such as inductive loop counters (ILCs). These are costly to install, maintain, and support. In this paper, we investigate the possibilities to complement and potentially replace the existing traffic monitoring infrastructure with crowdsourcing solutions. More precisely, we investigate the capabilities to predict the ILC-obtained data using Telraam counters, low-cost camera counters voluntarily employed by citizens and freely accessible by the general public. In this context, we apply different exploratory data analysis approaches and demonstrate a regression procedure with a selected set of regression models. The presented analysis is demonstrated on different urban and highway road segments in Slovenia. Our results show that the data obtained from low-cost and easily accessible counters can be used to replace the existing traffic monitoring infrastructure in different scenarios. These results confirm the prospective to directly apply the citizen engagement in the process of planning and maintaining sustainable future cities.

Exploratory Data Analysis

2018 ◽  
Author(s):  
Brian D. Haig
Keyword(s):  
Data Mining ◽  
Data Analysis ◽  
Statistical Modeling ◽  
Scientific Method ◽  
Exploratory Data Analysis ◽  
Resampling Methods ◽  
Data Mining Techniques ◽  
Exploratory Data ◽  
Data Analytic

Chapter 2 is concerned with modern data analysis. It focuses primarily on the nature, role, and importance of exploratory data analysis, although it gives some attention to computer-intensive resampling methods. Exploratory data analysis is a process in which data are examined to reveal potential patterns of interest. However, the use of traditional confirmatory methods in data analysis remains the dominant practice. Different perspectives on data analysis, as they are shaped by four different accounts of scientific method, are provided. A brief discussion of John Tukey’s philosophy of teaching data analysis is presented. The chapter does not consider the more recent exploratory data analytic developments, such as the practice of statistical modeling, the employment of data-mining techniques, and more flexible resampling methods.

Severity Model Based Prediction of Early Trend and Pattern Recognition of the COVID-19 Infection in India: Exploratory Data Analysis and Machine Learning Study (Preprint)

2020 ◽  
Author(s):  
Afreen Khan ◽  
Swaleha Zubair
Keyword(s):  
Machine Learning ◽  
Growth Rate ◽  
Data Analysis ◽  
Inflection Point ◽  
Exploratory Data Analysis ◽  
Logistic Function ◽  
Acute Stage ◽  
The Future ◽  
Exploratory Data ◽  
The Impact

UNSTRUCTURED Objective: Recent Coronavirus Disease 2019 (COVID-19) pandemic has inflicted the whole world critically. Despite the fact that India has not been listed amongst the top ten highly affected countries, one cannot rule out COVID-19 associated complications in the near future. The accumulative testing facilities has resulted in exponential increase in COVID-19 infection cases. In figures, the number of positive cases have risen up to 33,614 as of 30 April, 2020. Keeping into consideration the serious consequences of pandemic, we aim to establish correlations between the numerous features which was acquired from the various Indian-based COVID datasets, and the impact of the containment of the pandemic on the current state of Indian population using machine learning approach. We aim to build the COVID-19 severity model employing logistic function which determines the inflection point and help in prediction of the future number of confirmed cases. Methods: An empirical study was performed on the COVID-19 patient status in India. We performed the study commencing from 30 January, 2020 to 30 April, 2020 for the analysis. We applied the machine learning (ML) approach to gain the insights about COVID-19 incidences in India. Several diverse exploratory data analysis ML tools and techniques were applied to establish a correlation amongst the various features. Also, the acute stage of the disease was mapped in order to build a robust model. Results: We collected five different datasets to execute the study. The data sets were integrated extract the essential details. We found that men were more prone to get infected of the coronavirus disease as compared to women. Also, the age group was the middle-young age of patients. On 92-days based analysis, we found a trending pattern of number of confirmed, recovered, deceased and active cases of COVID-19 in India. The as-developed growth model provided an inflection point of 85.0 days. It also predicted the number of confirmed cases as 48,958.0 in the future i.e. after 30th April. Growth rate of 13.06 percent was obtained. We achieved statistically significant correlations amongst growth rate and predicted COVID-19 confirmed cases. Conclusion: This study demonstrated the effective application of exploratory data analysis and machine learning in building a mathematical severity model for COVID-19 in India.

Graphical Exploratory Data Analysis for Categorical Longitudinal and Time Series Data

2013 ◽  
Author(s):  
Stephen J. Tueller ◽  
Richard A. Van Dorn ◽  
Georgiy Bobashev ◽  
Barry Eggleston
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Time Series Data ◽  
Series Data ◽  
Exploratory Data

Covid-19 Cases in India: A Visual Exploratory Data Analysis Model (Preprint)

2020 ◽  
Author(s):  
Jayesh S
Keyword(s):  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Case Fatality ◽  
Public Health Emergency ◽  
Virus Spread ◽  
Analysis Model ◽  
Case Fatality Ratio ◽  
First Case ◽  
Exploratory Data ◽  
The Government

UNSTRUCTURED Covid-19 outbreak was first reported in Wuhan, China. The deadly virus spread not just the disease, but fear around the globe. On January 2020, WHO declared COVID-19 as a Public Health Emergency of International Concern (PHEIC). First case of Covid-19 in India was reported on January 30, 2020. By the time, India was prepared in fighting against the virus. India has taken various measures to tackle the situation. In this paper, an exploratory data analysis of Covid-19 cases in India is carried out. Data namely number of cases, testing done, Case Fatality ratio, Number of deaths, change in visits stringency index and measures taken by the government is used for modelling and visual exploratory data analysis.

Sign in / Sign up

Export Citation Format

Share Document