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Analysis of Epidemic Diseases Using Big Data Analytics

Y. Deepthi1 , A. Radhika2 , Ch. Praneeth3

Section:Research Paper, Product Type: Journal Paper
Volume-6 , Issue-7 , Page no. 756-761, Jul-2018

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v6i7.756761

Online published on Jul 31, 2018

Copyright © Y. Deepthi, A. Radhika, Ch. Praneeth . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: Y. Deepthi, A. Radhika, Ch. Praneeth, “Analysis of Epidemic Diseases Using Big Data Analytics,” International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.756-761, 2018.

MLA Style Citation: Y. Deepthi, A. Radhika, Ch. Praneeth "Analysis of Epidemic Diseases Using Big Data Analytics." International Journal of Computer Sciences and Engineering 6.7 (2018): 756-761.

APA Style Citation: Y. Deepthi, A. Radhika, Ch. Praneeth, (2018). Analysis of Epidemic Diseases Using Big Data Analytics. International Journal of Computer Sciences and Engineering, 6(7), 756-761.

BibTex Style Citation:
@article{Deepthi_2018,
author = {Y. Deepthi, A. Radhika, Ch. Praneeth},
title = {Analysis of Epidemic Diseases Using Big Data Analytics},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {7 2018},
volume = {6},
Issue = {7},
month = {7},
year = {2018},
issn = {2347-2693},
pages = {756-761},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=2506},
doi = {https://doi.org/10.26438/ijcse/v6i7.756761}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v6i7.756761}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=2506
TI - Analysis of Epidemic Diseases Using Big Data Analytics
T2 - International Journal of Computer Sciences and Engineering
AU - Y. Deepthi, A. Radhika, Ch. Praneeth
PY - 2018
DA - 2018/07/31
PB - IJCSE, Indore, INDIA
SP - 756-761
IS - 7
VL - 6
SN - 2347-2693
ER -

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Abstract

There are a number of epidemic diseases such as Ebola Virus, Zika Virus, Dengue, Malaria etc that are spreading all over the World. It is necessary to provide awareness about these contiguous diseases to the people. To provide this, a thorough analysis is done on all these diseases and analysis is done on the type of people who effected mostly due to certain climatic conditions and country they are living in. For epidemic diseases analysis, R programming plays a vital role in data science and analysis. Data science is a multidisciplinary blend of data inference, algorithm development, and technology in order to solve complex problems analytically. The "Analysis of Epidemic Diseases" is an application which provides an opportunity for various Countries to estimate the severity of occurrence of various diseases, death counts etc in the coming years on the basis of previous Countries statistics, climatic conditions, death rates, confirmed or suspected cases and so on.

Key-Words / Index Term

Epidemic, Zika, Dengue, Malaria, Ebola Virus, Analysis, Statistics, Prediction

References

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[3] Richard Cotton, “Learning R: A Step-by-Step Function Guide to Data Analysis”, O Reilly Media, USA, pp.250-300, 2013.
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