Data Analysis and Forecasting of COVID-19 Outbreak in India Using ARIMA Model

2021 ◽  
pp. 29-38
Author(s):  
Binal Kaka ◽  
Dweepna Garg ◽  
Parth Goel ◽  
Amit Ganatra
Keyword(s):  
Data Analysis ◽  
Arima Model

Modelling the Paucity of COVID-19 Pandemic Spread Incidence across the India Nation (Preprint)

2020 ◽  
Author(s):  
Alfonso J. Rodriguez-Morales ◽  
Ram Kumar Singh ◽  
S.S. Singh ◽  
A. K. Pandey ◽  
Vinod Kumar ◽  
...  
Keyword(s):  
Data Analysis ◽  
Arima Model ◽  
Time Estimation ◽  
The United States ◽  
World Health ◽  
Disease Spread ◽  
Analysis Model ◽  
Indian States ◽  
The World ◽  
Confirmatory Data Analysis

BACKGROUND The highly contagious Coronavirus disease (COVID-19) pandemic affected nearly all nations across the world. It was emerged as most swiftly affected disease across the world and more than 2934 lakhs population suffered in four months of the time period as on date April 26, 2020. Its first epicenter was at Wuhan city of China during the month of December 2019. Currently, the most affected people and new epicenter of Coronavirus is at the United States of America (USA). It is identified as the most severe pandemic disease in human history during the past 100 years. Due to non-availability of specific medication, the World Health Organization (WHO) suggested various measures of precautions and social distance in between the people for the restricting the spread of the COVID-19 disease. Various nation’s administration including the India government called for the regional and local lockdown. OBJECTIVE We predicted the confirmed COVID-19 cases for next May-2020 month, map the magnitude of COVID-19 disease for Indian states and model the paucity of COVID-19 disease with statistical confirmatory data analysis model for declining rate for the cases represented for the Indian proportion of population. METHODS The ARIMA model used to predict for next short-term cases, based moving average of past confirmed cases. The restriction of COVID-19 pandemic disease analyzed with predicted cases for month May 2020 data at 95 percent confidence is more than 2.5 lakh cases. RESULTS The confirmatory data analysis model for the time estimation for the paucity of cases it takes in between six to eighteen months of time frame. The Confirmatory model which considers recovery rate, social, economic and government policy. To complete recovery from the COVID-19 cases it takes on an average more than next ten months. CONCLUSIONS The disease impacts also depend upon administrative and local people support for self-quarantine and other measures. The India nation Gross Domestic Product (GDP) based on more than 17% of its agriculture production, due to longer affect of the disease and extended lockdown period it will be severely affected. However, all the economic activities with full of its intensity takes-up after complete paucity of COVID-19 disease spread. CLINICALTRIAL wqew ere re

scholarly journals Forecasting COVID-19 Confirmed Cases Using Empirical Data Analysis in Korea

Healthcare ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 254
Author(s):  
Da Hye Lee ◽  
Youn Su Kim ◽  
Young Youp Koh ◽  
Kwang Yoon Song ◽  
In Hong Chang
Keyword(s):  
Data Analysis ◽  
Empirical Data ◽  
Moving Average ◽  
Arima Model ◽  
Third Wave ◽  
Cumulative Number ◽  
Control And Prevention ◽  
Empirical Data Analysis ◽  
The Government ◽  
Increasing Trends

From November to December 2020, the third wave of COVID-19 cases in Korea is ongoing. The government increased Seoul’s social distancing to the 2.5 level, and the number of confirmed cases is increasing daily. Due to a shortage of hospital beds, treatment is difficult. Furthermore, gatherings at the end of the year and the beginning of next year are expected to worsen the effects. The purpose of this paper is to emphasize the importance of prediction timing rather than prediction of the number of confirmed cases. Thus, in this study, five groups were set according to minimum, maximum, and high variability. Through empirical data analysis, the groups were subdivided into a total of 19 cases. The cumulative number of COVID-19 confirmed cases is predicted using the auto regressive integrated moving average (ARIMA) model and compared with the actual number of confirmed cases. Through group and case-by-case prediction, forecasts can accurately determine decreasing and increasing trends. To prevent further spread of COVID-19, urgent and strong government restrictions are needed. This study will help the government and the Korea Disease Control and Prevention Agency (KDCA) to respond systematically to a future surge in confirmed cases.

scholarly journals Modelling the Paucity of COVID-19 Pandemic Spread Incidence across the India Nation

2020 ◽  
Author(s):  
Alfonso J. Rodriguez-Morales ◽  
Ram Kumar Singh ◽  
S. S. Singh ◽  
A. K. Pandey ◽  
Vinod Kumar ◽  
...  
Keyword(s):  
Data Analysis ◽  
Arima Model ◽  
Time Estimation ◽  
Complete Recovery ◽  
The United States ◽  
Disease Spread ◽  
Analysis Model ◽  
Indian States ◽  
The World ◽  
Confirmatory Data Analysis

Abstract Background: The highly contagious Co rona vi rus d isease (COVID-19) pandemic affected nearly all nations across the world. It was emerged as most swiftly affected disease across the world and more than 2934 lakhs population suffered in four months of the time period as on date April 26, 2020. Its first epicenter was at Wuhan city of China during the month of December 2019. Currently, the most affected people and new epicenter of Coronavirus is at the United States of America (USA). Various nation’s administration including the India government called for the regional and local lockdown. We predicted the confirmed COVID-19 cases for next May-2020 month, map the magnitude of COVID-19 disease for Indian states and model the paucity of COVID-19 disease with statistical confirmatory data analysis model for declining rate for the cases represented for the Indian proportion of population. Method: The ARIMA model used to predict for next short-term cases, based moving average of past confirmed cases. The restriction of COVID-19 pandemic disease analyzed with predicted cases for month May 2020 data at 95 percent confidence is more than 2.5 lakh cases. Results: The confirmatory data analysis model for the time estimation for the paucity of cases it takes in between six to eighteen months of time frame. The Confirmatory model which considers recovery rate, social, economic and government policy. To complete recovery from the COVID-19 cases it takes on an average more than next ten months. Conclusion: The disease impacts also depend upon administrative and local people support for self-quarantine and other measures. The India nation Gross Domestic Product (GDP) based on more than 17% of its agriculture production, due to longer affect of the disease and extended lockdown period it will be severely affected. However, all the economic activities with full of its intensity takes-up after complete paucity of COVID-19 disease spread. Keywords: SARS-CoV-2; Lockdown; GDP; Nobel-Corona; Confirmatory data model

scholarly journals Data Analysis Time Series For Forecasting The Greenhouse Effect

2019 ◽  
Vol 3 (2) ◽  
pp. 86
Author(s):  
Fauzah Umami ◽  
Hendra Cipta ◽  
Ismail Husein
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Time Series Analysis ◽  
Greenhouse Effect ◽  
Reference Data ◽  
Moving Average ◽  
Arima Model ◽  
Mean Square ◽  
Autoregressive Integrated Moving Average ◽  
The Earth

<span lang="EN-US">The greenhouse effect is a term used to describe the earth having a greenhouse effect where the sun's heat is trapped by the earth's atmosphere. This study aims to model the greenhouse effect and then predict the greenhouse effect in the coming period using the Autoregressive Integrated Moving Average (ARIMA) method. In this case, time series analysis and reference data for 31 months are used, from the period January 2017 - July 2019, the results of the ARIMA model that are suitable for forecasting the greenhouse effect are ARIMA (4.2.0) with Mean Square Error (MSE) of 161885</span>

Microchemical imaging in biomedical research

1992 ◽  
Vol 50 (2) ◽  
pp. 1118-1119
Author(s):  
P. Ingram
Keyword(s):  
Data Analysis ◽  
Electron Probe ◽  
The Other ◽  
X Ray ◽  
Cell Element ◽  
Physiological Information ◽  
Functional States ◽  
Elemental Maps ◽  
Electron Energy Loss ◽  
Secondary Ion

It is well established that unique physiological information can be obtained by rapidly freezing cells in various functional states and analyzing the cell element content and distribution by electron probe x-ray microanalysis. (The other techniques of microanalysis that are amenable to imaging, such as electron energy loss spectroscopy, secondary ion mass spectroscopy, particle induced x-ray emission etc., are not addressed in this tutorial.) However, the usual processes of data acquisition are labor intensive and lengthy, requiring that x-ray counts be collected from individually selected regions of each cell in question and that data analysis be performed subsequent to data collection. A judicious combination of quantitative elemental maps and static raster probes adds not only an additional overall perception of what is occurring during a particular biological manipulation or event, but substantially increases data productivity. Recent advances in microcomputer instrumentation and software have made readily feasible the acquisition and processing of digital quantitative x-ray maps of one to several cells.

Perceptions of a Collaborative Professional Learning Program: Seeing the “Bigger Picture”

2020 ◽  
Vol 5 (1) ◽  
pp. 290-303
Author(s):  
P. Charlie Buckley ◽  
Kimberly A. Murza ◽  
Tami Cassel
Keyword(s):  
Data Analysis ◽  
Professional Learning ◽  
Social Communication ◽  
Special Educators ◽  
Service Providers ◽  
Integrative Approach ◽  
Theory Approach ◽  
Learning Program ◽  
Speech Language Pathologists ◽  
Related Service

Purpose The purpose of this study was to explore the perceptions of special education practitioners (i.e., speech-language pathologists, special educators, para-educators, and other related service providers) on their role as communication partners after participation in the Social Communication and Engagement Triad (Buckley et al., 2015 ) yearlong professional learning program. Method A qualitative approach using interviews and purposeful sampling was used. A total of 22 participants who completed participation in either Year 1 or Year 2 of the program were interviewed. Participants were speech-language pathologists, special educators, para-educators, and other related service providers. Using a grounded theory approach (Glaser & Strauss, 1967 ) to data analysis, open, axial, and selective coding procedures were followed. Results Three themes emerged from the data analysis and included engagement as the goal, role as a communication partner, and importance of collaboration. Conclusions Findings supported the notion that educators see the value of an integrative approach to service delivery, supporting students' social communication and engagement across the school day but also recognizing the challenges they face in making this a reality.

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