%0 Journal Article
%T Operationalisation of Model for Dynamics of COVID-19 in Kenya: Trajectory of Omicron Wave in Kenya
%A Shem Otoi O. Sam
%A Khama Rogo
%J Open Journal of Modelling and Simulation
%P 314-326
%@ 2327-4026
%D 2022
%I Scientific Research Publishing
%R 10.4236/ojmsi.2022.103018
%X <span><span style=\"font-family:&quot;\"><span style=\"font-family:Verdana;\">Kenya has experienced five COVID-19 surges driven by Alpha, Beta, Delta </span><span style=\"font-family:Verdana;\">(2x), and Omicron. These waves are accurately
predicted by the OTOI-NARIMA</span><span style=\"font-family:Verdana;\"> model. Consequently, in Kenyan Lake Region
Economic Bloc (LREB), private sector and NGO partnerships have been forged to
strengthen regional health systems and prepare effectively for epidemic
resurgence. The co-development and implementation of the so-called </span></span></span><span style=\"font-family:Verdana;\"><span style=\"font-family:Verdana;\"><span style=\"font-family:Verdana;\">¡°</span></span></span><span style=\"font-family:Verdana;\"><span style=\"font-family:Verdana;\"><span style=\"font-family:Verdana;\">LREB COVID-Dx</span></span></span><span style=\"font-family:Verdana;\"><span style=\"font-family:Verdana;\"><span style=\"font-family:Verdana;\">¡±</span></span></span><span><span><span style=\"font-family:&quot;\"><span style=\"font-family:Verdana;\"> digital platform enable efficient epidemic monitoring in semi-real time,
referral of patients, optimal use of limited resources, and community of
practice among regional health practitioners. In this paper, we describe the
practical implementation of the OTOI-NARIMA model and COVID-Dx digitized
platform in Kenyan COVID-19 reality, with emphasis on the latest Omicron wave. In
estimating the trajectory of Omicron wave, 612 data points of daily case
infections are </span><span style=\"font-family:Verdana;\">used. The order of moving
average is calculated and corresponds to reproduction</span><span style=\"font-family:Verdana;\"> number, </span></span></span></span><span style=\"font-family:Verdana;\"><span style=\"font-family:Verdana;\"><i><span style=\"font-family:Verdana;\">R</span></i></span></span><span style=\"font-family:Verdana;\"><span style=\"font-family:Verdana;\"><sub><span style=\"font-family:Verdana;\">0</span></sub></span></span><span><span><span style=\"font-family:&quot;\"><span style=\"font-family:Verdana;\">. The series are normalized, superimposed, and used to derive OTOI-NARIMA
model. The model is estimated and interpreted. Test </span><span style=\"font-family:Verdana;\">statistics including Ljung-Box test, ACF, and PACF are conducted. </span><span style=\"font-family:Verdana;\">The
COVID-Dx </span><span style=\"font-family:Verdana;\">data digitization is used to
inform epidemic preparedness. The OTOI-NARIMA</span><span style=\"font-family:Verdana;\"> model in
%K Omicron Trajectory
%K OTOI-NARIMA
%K COVID-Dx
%U http://www.scirp.org/journal/PaperInformation.aspx?PaperID=118874