Volume 4, Issue 2, December 2020, Pages: 31-35
Received: Apr. 11, 2020;
Accepted: Aug. 24, 2020;
Published: Sep. 3, 2020
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Joab Onyango Odhiambo, School of Pure and Applied Sciences, Meru University of Science and Technology, Meru, Kenya
Jacob Oketch Okungu, School of Pure and Applied Sciences, Meru University of Science and Technology, Meru, Kenya
Christine Gacheri Mutuura, School of Pure and Applied Sciences, Meru University of Science and Technology, Meru, Kenya
Since the discovery of the novel Covid-19 in December in China, the spread has been massively felt across the world leading World Health Organization declaring it a global pandemic. Italy has been affected most due to the high number of recorded deaths as at 1st August, 2020 at the same time USA recording the highest number of virus reported cases. In addition, the spread has been experienced in many developing African countries including Kenya. While the Kenyan government have had plans for those who have tested positive through self-quarantine beds at Mbagathi Hospital, lack of a proper mathematical model that can be used to model and predict the spread of Covid-19 for adequate response security has been one of the main concerns for the government. Many mathematical models have been proposed for proper modeling and forecasting, but this paper will focus on using a generalized linear regression that can detect linear relationship between the risk factors. The paper intents to model and forecast the confirmed Covid-19 cases in Kenya as a Compound Poisson process where the parameter follows a generalized linear regression that is influenced by the number of daily contact persons and daily flights with the already confirmed cases of the virus. Ultimately, this paper should assist the government in proper resource allocation to deal with pandemic in terms of available of bed capacities, public awareness campaigns and virus testing kits not only in the virus hotbed within Nairobi county but also in the other remaining 46 Kenyan counties.
Joab Onyango Odhiambo,
Jacob Oketch Okungu,
Christine Gacheri Mutuura,
Stochastic Modeling and Prediction of the COVID-19 Spread in Kenya, Engineering Mathematics.
Vol. 4, No. 2,
2020, pp. 31-35.
Copyright © 2020 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/
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Yunlu Wang, Menghan Hu, Qingli Li, Xiao-Ping Zhang, Guangtao Zhai, and Nan Yao. “Abnormal respiratory patterns classier may contribute to large-scale screening of people infected with covid-19 in an accurate and unobtrusive manner”. arXiv preprint arXiv: 2002.05534, 2020.
Leon Danon, Ellen Brooks-Pollock, Mick Bailey, and Matt J Keeling. “A spatial model of covid-19 transmission in England and wales: early spread and peak timing”. medRxiv, 2020.
Gülden Kaya Uyanık and Nese Güler. “A study on multiple linear regression analysis”. Procedia-Social and Behavioral Sciences, 106 (1): 234–240, 2013.
Zixin Hu, Qiyang Ge, Li Jin, and Momiao Xiong. “Artificial intelligence forecasting of covid-19 in China”. arXiv preprint arXiv: 2002.07112, 2020.
Diego Giuliani, Maria Michela Dickson, Giuseppe Espa, and Flavio Santi. “Modelling and predicting the spread of coronavirus (covid-19) infection in nuts-3 Italian regions”. arXiv preprint arXiv: 2003.06664, 2020.
Dimitris Karlis and Evdokia Xekalaki. “Mixed Poisson distributions”. International Statistical Review, 73 (1): 35–58, 2005
Adam J Kucharski, Timothy W Russell, Charlie Diamond, Yang Liu, John Edmunds, Sebastian Funk, Rosalind M Eggo, Fiona Sun, Mark Jit, James D Munday, et al. “Early dynamics of transmission and control of covid-19: a mathematical modelling study”. The Lancet Infectious Diseases, 2020.
World Health Organization et al. Global surveillance for covid-19 disease caused by human infection with the 2019 novel coronavirus, interim guidance, 27 February 2020. 2020.
Jomar F Rabajante. “Insights from early mathematical models of 2019-ncov acute respiratory disease (covid-19) dynamics”. arXiv preprint arXiv: 2002.05296, 2020.
FanWu, Su Zhao, Bin Yu, Yan-Mei Chen, Wen Wang, Zhi-Gang Song, Yi Hu, Zhao-Wu Tao, Jun-Hua Tian, Yuan-Yuan Pei, et al. “A new coronavirus associated with human respiratory disease in china”. Nature, 579 (7798): 265–269, 2020.
Y. Zhou, Z. Ma, F. Brauer, A Discrete Epidemic Model for SARS Transmission and Control in China, Math. Comput. Model., 40 (2004), 1491–1506.7.
G. Chowell, C. Castillo-Chavez, P. Fenimore, M. Christopher, C. Kribs-Zaleta, L. Arriola, et al., Model Parameters and Outbreak Control for SARS, Emerg. Infect. Dis., 10 (2004), 1258–1263.8.
P. Lekone, B. Finkenst adt, Statistical Inference in a Stochastic Epidemic SEIR Model with Control Intervention: Ebola as a Case Study, Biometrics, 62 (2006), 1170–1177.
A. Morton, B. Finkenst adt, Discrete time modelling of disease incidence time series by using Markov chain Monte Carlo methods, J. R. Stat. Soc., 54 (2005), 575–594.
Huiming Zhang and Bo Li. “Characterizations of discrete compound Poisson distributions”. Communications in Statistics-Theory and Methods, 45 (22): 6789–6802, 2016.