Modelling of COVID-19 Transmission in Kenya Using Compound Poisson Regression Model

Abstract

Since the inception of the novel Corona Virus Disease-19 in December in China, the spread has been massive leading World Health Organization to declare it a world pandemic. While epicenter of COVID-19 was Wuhan city in China mainland, Italy has been affected most due to the high number of recorded deaths as at 21st April, 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. The Kenyan government need to make necessary plans for those who have tested positive through self-quarantine beds at Mbagathi Hospital as a way of containing the spread of the virus. In addition, 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 regression 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 would 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 capital city but also in the other 47 Kenyan counties.

Modelling the Impact of Key Pests of Watermelon on its Performance Using Linear Regression Models

“…Relationships between yield losses and damages caused by pests can be well expressed using regression statistics [8,9]. Generalized linear regression models have been shown to be effective and reliable for detecting relationships between parameters [11] and are widely acceptable. The linear regression models generated by this study are highly reliable, as their coefficients of determination (R 2 ) were mostly significant (P ≤ 0.05) and largely close or > 50 %.…”

“…Since crop pests are inherently part of every agroecosystem, estimating their contributions to crop performance is very important [10], and this area of investigation is currently receiving high attention in the field of pest management. The use of generalized linear regression has been shown to be proper and is widely acceptable in detecting the relationships between factors [11].…”

Regression Models for Predictions of COVID-19 New Cases and New Deaths in Ethiopia

“…In the multivariate analysis, the contact persons and flights to Kenya had 0.278 and 3,309 effects on COVID-19 cases in Kenya at the 5% and 10% levels of significance, respectively. Researchers in Kenya also used the compound Poisson regression model, which showed that as the COVID-19 day increased by 235, the COVID-19 new cases were projected to 83,418 new cases [17].…”

Regression Models for Predictions of COVID-19 New Cases and New Deaths Based on May/June Data in Ethiopia

“…However, the week 4 death counts significantly predicted the week 5 death count. And, there was a correlation between COVID-19 new cases and contact persons with confirmed status as well as the number of flights from foreign countries to Kenya (Anwar et al 2020;Ghosal et al 2020;Odhiambo et al 2020;Montalban et al 2020;Tadle et al 2020;Yonar 2020).…”