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A Novel Generalized Form of Cure Rate Model for an Infectious Disease with Co-infection

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Part of the Advances in Intelligent Systems and Computing book series (AISC,volume 1351)

Abstract

Recently, researchers allow the analysis of the survival function of disease by the examination of the cure fraction. The extant literature discovers that the cure rate model is used for infectious but curable diseases and not infectious diseases with co-infection. Hence, a survival model that incorporates the cure rate of the management of infectious diseases with co-infection was adopted. This investigation aims to extend and develop a generalized model using the Bounded Cumulative Hazard (BCH) model, a non-mixture model for any infectious disease with a co-infection to estimate the performance of the management. The objective is to derive the appropriate probability density functions for sole infectious and co-infection disease and estimate the cure rate parameter for the two situations (sole and co-infection) using simulation data. The exponential distribution is commonly used in literature, but two-parameter Weibull distribution, a unique form of Weibull distribution, was employed in this study. The result compared using exponential distribution to accomplish the set objectives using the R package as the estimation procedure. The study concluded that the modified model, a generalized form of the cure rate model can accommodate an infectious disease with co-infection and derives the appropriate probability density function. It is also possible to extend the generalized model to other forms of distributions. This study explains the limitation of the study, the contributions, managerial implications and suggest future work.

Keywords

  • Cure rate model
  • Weibull
  • Loglikelihood
  • Infectious disease
  • Co-infection
  • BCH model

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Correspondence to Oluwafemi Samson Balogun .

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Balogun, O.S., Olaleye, S.A., Gao, XZ., Toivanen, P. (2021). A Novel Generalized Form of Cure Rate Model for an Infectious Disease with Co-infection. In: Abraham, A., Piuri, V., Gandhi, N., Siarry, P., Kaklauskas, A., Madureira, A. (eds) Intelligent Systems Design and Applications. ISDA 2020. Advances in Intelligent Systems and Computing, vol 1351. Springer, Cham. https://doi.org/10.1007/978-3-030-71187-0_7

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