Integrating health indices towards the development of a Typhoid disease model using STEM

Integrating health indices towards the development of a Typhoid disease model using STEM

Typhoid Fever is a health concern that should not be overlooked, especially after natural disasters. Even with vaccinations and antibiotics available, the number of incidence remains high. Considered to be one of the communicable food- and water-borne diseases that can spread especially after a natu...

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Main Authors: Estuar, Ma. Regina Justina E, Co, Isabelle-Lyn T, Espina, Kennedy E, Lara, Raymond Josef Edward A, De los Reyes, Vikki Car D
格式: text
出版: Archīum Ateneo 2016
主題:
Surveillance
Infectious diseases
Sociology
Statistics
Data models
Software
Applied Statistics
Computer Sciences
Databases and Information Systems
Vital and Health Statistics
在線閱讀:https://archium.ateneo.edu/discs-faculty-pubs/28
https://ieeexplore.ieee.org/document/7857211
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總結:Typhoid Fever is a health concern that should not be overlooked, especially after natural disasters. Even with vaccinations and antibiotics available, the number of incidence remains high. Considered to be one of the communicable food- and water-borne diseases that can spread especially after a natural disaster, Typhoid Fever is a disease that imposes health threats to the community proven by its constant appearance in the top ten leading causes of morbidity in different Philippine regions. Modeling and analyzing the spatio-temporal epidemiological behavior of Typhoid Fever can aid in its early detection and possibly prevent outbreaks. This paper aims to identify disease parameters that lead to suspected and identified spread of Typhoid Fever in Region VI (Western Visayas) Philippines by mining pertinent data from a government-based project called PIDSR and an electronic medical record (EMR) system, SHINE OS+ Moreover, this paper aims to utilize the modeling tools of Spatio-Temporal Epidemiological Modeler (STEM) to provide a new and localized disease model for Typhoid Fever using appropriate local disease parameters to form differential equations. Sensitivity analysis showed three parameters that greatly contribute to the outcome of the simulation: transmissionRate, transmissionRateC, and recovery Rate. Further statistical analysis showed an average of 61% correlation coefficient and 1.34 % normalized mean squared error between the simulated result and the actual data from PIDSR.

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