Data Analytics and Visualization of Coronavirus COVID-19 Epidemic in Nigeria Based on Recovered and Death Cases

Ajinaja,, M. O.; Olarinde, Muftau Olaiya; Olaniyan, L; Adeola, C

doi:10.2139/ssrn.3632420

Cited by 1 publication

(2 citation statements)

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“…(1) Use a browser to open Tencent News Network, and use the browser's “censorship element” to view the source code and “network” feedback messages 29 . (2) Use Python to compile the code, send a request to the website and obtain the real‐time JSON data of Tencent's epidemic situation, 30,31 and the output results are sorted by the names of provinces and countries in China according to the number of confirmed cases 32 . (3) Analyze and clean the captured epidemic data and store it in a CSV file, named after the current date 33 …”

Section: Visual Analysismentioning

confidence: 99%

“…29 (2) Use Python to compile the code, send a request to the website and obtain the real-time JSON data of Tencent's epidemic situation, 30,31 China according to the number of confirmed cases. 32 (3) Analyze and clean the captured epidemic data and store it in a CSV file, named after the current date. 33 We use the Seaborn function in the Python library to visually analyze the crawled data.…”

Section: Visual Analysismentioning

confidence: 99%

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An intelligent forecast for COVID‐19 based on single and multiple features

Wang

Zhang

et al. 2022

Int J of Intelligent Sys

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It is urgent to identify the development of the Corona Virus Disease 2019 (COVID-19) in countries around the world. Therefore, visualization is particularly important for monitoring the COVID-19. In this paper, we visually analyze the real-time data of COVID-19, to monitor the trend of COVID-19 in the form of charts. At present, the COVID-19 is still spreading. However, in the existing works, the visualization of COVID-19 data has not established a certain connection between the forecast of the epidemic data and the forecast of the epidemic. To better predict the development trend of the COVID-19, we establish a logistic growth model to predict the development of the epidemic by using the same data source in the visualization. However, the logistic growth model only has a single feature. To predict the epidemic situation in an allround way, we also predict the development trend of the COVID-19 based on the Susceptible Exposed Infected Removed epidemic model with multiple features. We fit the data predicted by the model to the real COVID-19 epidemic data. The simulation results show that the predicted epidemic development trend is consistent with the actual epidemic development trend, and our model performs well in predicting the trend of COVID-19.

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