Egg storage has been a problem due to ineffective methods subjecting many farmers and egg retailers to losses. These techniques include various models involving statistical analysis of the storage conditions on the egg quality. Apparent deficiencies of the information from the randomized complete block design model prompted this study. The study evaluated the effect of storage temperature at three levels (5 ° C , 19.5 ° C , 30 ° C ) and duration at four levels (2 nd , 12 th , 22 nd , 32 nd ) on egg quality using a fixed and mixed-effect model. We used a total of 618 fresh and unfertilized eggs from ISA (Institut de Sélection Animale) brown layers. We determined egg quality by the changes of physical characterization under storage conditions. The study used Restricted maximum likelihood and analysis of variance methods to assess the efficiency of fixed and mixed effect models. Results showed that the physical components of the egg were significantly affected at 5 ° C , 19.5 ° C , and 30 ° C . The effect was more adverse on eggs stored at 30 ° C for 32 days. However, storage temperatures of 5 ° C and 19.5 ° C led to an extensive reduction in the Haugh unit, yolk index, and egg white height. On the other hand, it increased the weight loss and albumen diameter under storage for 2 nd , 12 th , 22 nd and 32 nd -time intervals. Based on these findings, the study recommends 5 ° C for egg quality preservation. The eggs should be refrigerated for 32 days, stored at 19.5 ° C for 14 days, and lastly kept at 30 ° C for a maximum of 7 days. The fixed-effect models exhibited more minor variances in diameter and height of albumen, yolk index, weight loss, and Haugh unit. This overlapped instances where the fixed-effect models were significantly the same as the mixed-effect models. This study proposes that the fixed effect model is the most appropriate for randomized completely block design experiments.

Since the inception of Covid-19 in China, the economies around the world have been on the turmoil. This is because China has a direct correlation with most economies in the world; they depend on it directly or indirectly. On 13th March, 2020 the first case of COVID-19 in Kenya a 27-year-old Kenyan woman who traveled from the US via London, was confirmed. The Kenyan government identified and isolated a number of people who had come into contact with the first case. On 15 March 2020, the president of Kenya directed that a number of measures be taken to curb COVID-19, some of those measures included; dusk to dawn curfew, secession of movement and mandatory quarantine of suspected cases. Based on the available literature, probabilistic predictions using steady state Markov chain allow to assess the uncertainty of the COVID-19 comprehensively. Therefore they are preferable to forecasts for the mean or median COVID-19 only. The probabilistic COVID-19 predictions allow to derive probabilistic forecasts for the number of patients who are still at the ICU at a certain day in future. This may be useful for planning purposes. From the probabilities for single patients, one may compute the probability that any given number of patients is still at the ICU after t days. However, in Kenya there is scanty information on analysis of COVID-19 using steady state Markov Chain. The aim of this study was therefore be to carry out probabilistic analysis of COVID-19 pandemic in Kenya using Markov chain. The study was a literature based, in which the researcher reviewed surveys books, scholarly journals, and other secondary sources relevant to the current study topic. The findings revealed that one of the most important uses of steady state Markov chain in analyzing COVID-19 pandemic situation in Kenya is that it compares performances for different states of affairs and courses of action within the health sector, by using system steady state performance measurements. The study concludes that steady state Markov chain is beneficial in simulating the corona infection in numerous stages. It is thus recommended that there is need for policy-makers to seek regional and global solutions to COVID-19 disease instead of limited solutions within the country. Keywords: Steady State, Markov chain, COVID-19 Pandemic, Transition Matrix