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dc.contributor.authorNgari, Cyrus G.
dc.contributor.authorKitavi, Dominic M.
dc.date.accessioned2020-09-23T11:40:50Z
dc.date.available2020-09-23T11:40:50Z
dc.date.issued2020-09
dc.identifier.citationAnnual Research & Review in Biology 35(8): 102-114, 2020; Article no.ARRB.58158en_US
dc.identifier.issn2347-565X
dc.identifier.urihttp://repository.embuni.ac.ke/handle/embuni/3628
dc.description.abstractespite a study by [1] proposing a simple model of under five years pneumonia, doubt lingers regarding its reliability, sufficiency and validity. The research question is whether the model is valid for use or not? The objectives of this study were to: incorporate exit rate from under five-year age bracket in the model, use Kenya data to parameterize the model, taking into account the uncertainties and finally to predict the dynamics of pneumonia. The model was rescaled through nondimensionalization. Data was fitted using theory of general solutions of nonlinear Ordinary differential equations, numerical differentiation using Lagrange polynomials and least square approximation method. Uncertainties due to disparities and round off errors were simulated using Monte Carlo simulation. Predictions of dynamics of pneumonia were carried out using MATLAB inbuilt ode solvers. Excel software was used to predict dynamics of discrete ordinary differential equations and to fit data. The basic reproduction number () and effective reproduction number () were obtained as 61 and 7 respectively. Iteration of uncertainties on R was carried out 1000 times by Monte Carlo simulation. The maximum and minimum R were obtained as 90 and 55, respectively. Using MATLAB software and effective reproduction number, the ratio of infective class to the total population and the ratio of class under treatment to the total population will remain constant at 0.095 and 0.2297 respectively for the years 2021, 2022 and 2023. Research result indicted that it is more effective and efficient to use effective reproduction number () than basic reproduction number () in mathematical modelling of Infectious diseases whenever study focuses on proportion of population. On basis of large absolute errors in fitting data to model, findings cast doubt on model formulation and/or observed data.en_US
dc.language.isoenen_US
dc.subjectChildhood pneumoniaen_US
dc.subjectlagrange polynomialen_US
dc.subjectparameterizationen_US
dc.subjectmonte carloen_US
dc.subjectvalidationen_US
dc.subjectforecastingen_US
dc.titleParameterization and Forecasting of Childhood Pneumonia Model Using Least Square Approximation, Lagrange Polynomial and Monte Carlo Simulationen_US
dc.typeArticleen_US


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