Generalized cure rate model for infectious diseases with possible co-infections
Balogun, Oluwafemi Samson; Gao, Xiao-Zhi; Jolayem, Emmanuel Teju; Olaleye, Sunday Adewale (2020-09-11)
Balogun OS, Gao X-Z, Jolayemi ET, Olaleye SA (2020) Generalized cure rate model for infectious diseases with possible co-infections. PLoS ONE 15(9): e0239003. https://doi.org/10.1371/journal.pone.0239003
© 2020 Balogun et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
https://creativecommons.org/licenses/by/4.0/
https://urn.fi/URN:NBN:fi-fe2020092170199
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Abstract
This research mainly aims to develop a generalized cure rate model, estimate the proportion of cured patients and their survival rate, and identify the risk factors associated with infectious diseases. The generalized cure rate model is based on bounded cumulative hazard function, which is a non-mixture model, and is developed using a two-parameter Weibull distribution as the baseline distribution, to estimate the cure rate using maximum likelihood method and real data with R and STATA software. The results showed that the cure rate of tuberculosis (TB) patients was 26.3%, which was higher than that of TB patients coinfected with human immunodeficiency virus (HIV; 23.1%). The non-parametric median survival time of TB patients was 51 months, while that of TB patients co-infected with HIV was 33 months. Moreover, no risk factors were associated with TB patients co-infected with HIV, while age was a significant risk factor for TB patients among the suspected risk factors considered. Furthermore, the bounded cumulative hazard function was extended to accommodate infectious diseases with co-infections by deriving an appropriate probability density function, determining the distribution, and using real data. Governments and related health authorities are also encouraged to take appropriate actions to combat infectious diseases with possible co-infections.
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