Abstract
The daily number of hospital admissions due to mosquito-borne diseases can vary greatly. This variability can be explained by different factors such as season of the year, temperature and pollution levels, among others. In this paper, we propose a new class of non-homogeneous Poisson processes which incorporates seasonality factors to more realistically fit data related to rare events, and in particular we show how the modifications applied to the special NHPP intensity function improve the analysis and fit of daily hospital admissions, due to dengue in Ribeirão Preto, São Paulo state, Brazil.
Highlights
Non-homogeneous Poisson processes (NHPPs) have become an important alternate tool to homogeneous processes
The paper is organized as follows: in Section 2 we introduce some special cases of NHPP intensity functions and their uses (MMOP, Goel-Okumoto process (GOP), etc); in Section 3 we propose the intensity and mean value functions of
Cifuentes-Amado and Cepeda-Cuervo the new non-homogeneous Poisson process for seasonal events; Section 4 contains some comments about the likelihood function and its usefulness in Bayesian parameter estimation; Section 5 contains the application of NHPP to data of Riberão Preto and shows the fit of non-homogeneous Poisson process superposition to these cases, the behavior of the new NHPP proposed in Section 4; and Section 6 summarizes our conclusions regarding the advantage of using a seasonal NHPP model to count epidemic cases
Summary
Non-homogeneous Poisson processes (NHPPs) have become an important alternate tool to homogeneous processes. This article deals with the daily number of admissions to public hospitals in Ribeirão Preto, São Paulo state, Brazil, and the large variations due to several factors: season of the year, climatic changes, variation in levels of different pollutants, among many others. Modeling these daily numbers is of great interest to public health administrators to avoid problems in the hospitals such as shortage of beds, equipment, drugs and health professionals. Our main goal is to propose seasonal count models of hospital admissions due to dengue fever using nonhomogeneous Poisson processes with different intensity functions. The new non-homogeneous Poisson process for seasonal events; Section 4 contains some comments about the likelihood function and its usefulness in Bayesian parameter estimation; Section 5 contains the application of NHPP to data of Riberão Preto and shows the fit of non-homogeneous Poisson process superposition to these cases, the behavior of the new NHPP proposed in Section 4 (with seasonality included); and Section 6 summarizes our conclusions regarding the advantage of using a seasonal NHPP model to count epidemic cases
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Statistics in Medical Research
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
