Spatial Modelling of Malaria in South of Iran in Line with the Implementation of the Malaria Elimination Program: A Bayesian Poisson-Gamma Random Field Model
Background: Malaria is the third most important infectious disease in the world. WHO propose programs for controlling and elimination of the disease. Malaria elimination program has begun in first phase in Iran from 2010. Climate factors play an important role in transmission and occurrence of mala...
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Tehran University of Medical Sciences
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doaj-330df8ed79124b5eb79fed66ee92b8992021-07-02T21:12:24ZengTehran University of Medical SciencesJournal of Arthropod-Borne Diseases 1735-71792322-22712021-03-011511081251444Spatial Modelling of Malaria in South of Iran in Line with the Implementation of the Malaria Elimination Program: A Bayesian Poisson-Gamma Random Field ModelAmin Ghanbarnejad0Habibollah Turki1Mehdi Yaseri2Ahmad Raeisi3Abbas Rahimi-Foroushani4Department of Epidemiology and biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, IranInfectious and Tropical Diseases Research Center, Hormozgan Health Institute, Hormozgan University of Medical Sciences, Bandar Abbas, IranDepartment of Epidemiology and biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, IranDepartments of Medical Parasitology and Mycology, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran, Center for Communicable Diseases Control, Ministry of Health and Medical Education, Tehran, IranDepartment of Epidemiology and biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, IranBackground: Malaria is the third most important infectious disease in the world. WHO propose programs for controlling and elimination of the disease. Malaria elimination program has begun in first phase in Iran from 2010. Climate factors play an important role in transmission and occurrence of malaria infection. The main goal is to investigate the spatial distribution of incidence of malaria during April 2011 to March 2018 in Hormozgan Province and its association with climate covariates. Methods: The data included 882 confirmed cases gathered from CDC in Hormozgan University of Medical Sciences. A Poisson-Gamma Random field model with Bayesian approach was used for modeling the data and produces the smoothed standardized incidence rate (SIR). Results: The SIR for malaria ranged from 0 (Abu Musa and Haji Abad districts) to 280.57 (Bandar–e-Jask). Based on model, temperature (RR= 2.29; 95% credible interval: (1.92–2.78)) and humidity (RR= 1.04; 95% credible interval: (1.03–1.06)) had positive effect on malaria incidence, but rainfall (RR= 0.92; 95% credible interval: (0.90–0.95)) had negative impact. Also, smoothed map represent hot spots in the east of the province and in Qeshm Island. Conclusion: Based on the analysis of the study results, it was found that the ecological conditions of the region (temperature, humidity and rainfall) and population displacement play an important role in the incidence of malaria. Therefore, the malaria surveillance system should continue to be active in the region, focusing on high-risk areas of malaria.https://jad.tums.ac.ir/index.php/jad/article/view/1444bayesian; spatial; poisson-gamma; hormozgan; malaria elimination |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Amin Ghanbarnejad Habibollah Turki Mehdi Yaseri Ahmad Raeisi Abbas Rahimi-Foroushani |
spellingShingle |
Amin Ghanbarnejad Habibollah Turki Mehdi Yaseri Ahmad Raeisi Abbas Rahimi-Foroushani Spatial Modelling of Malaria in South of Iran in Line with the Implementation of the Malaria Elimination Program: A Bayesian Poisson-Gamma Random Field Model Journal of Arthropod-Borne Diseases bayesian; spatial; poisson-gamma; hormozgan; malaria elimination |
author_facet |
Amin Ghanbarnejad Habibollah Turki Mehdi Yaseri Ahmad Raeisi Abbas Rahimi-Foroushani |
author_sort |
Amin Ghanbarnejad |
title |
Spatial Modelling of Malaria in South of Iran in Line with the Implementation of the Malaria Elimination Program: A Bayesian Poisson-Gamma Random Field Model |
title_short |
Spatial Modelling of Malaria in South of Iran in Line with the Implementation of the Malaria Elimination Program: A Bayesian Poisson-Gamma Random Field Model |
title_full |
Spatial Modelling of Malaria in South of Iran in Line with the Implementation of the Malaria Elimination Program: A Bayesian Poisson-Gamma Random Field Model |
title_fullStr |
Spatial Modelling of Malaria in South of Iran in Line with the Implementation of the Malaria Elimination Program: A Bayesian Poisson-Gamma Random Field Model |
title_full_unstemmed |
Spatial Modelling of Malaria in South of Iran in Line with the Implementation of the Malaria Elimination Program: A Bayesian Poisson-Gamma Random Field Model |
title_sort |
spatial modelling of malaria in south of iran in line with the implementation of the malaria elimination program: a bayesian poisson-gamma random field model |
publisher |
Tehran University of Medical Sciences |
series |
Journal of Arthropod-Borne Diseases |
issn |
1735-7179 2322-2271 |
publishDate |
2021-03-01 |
description |
Background: Malaria is the third most important infectious disease in the world. WHO propose programs for controlling and elimination of the disease. Malaria elimination program has begun in first phase in Iran from 2010. Climate factors play an important role in transmission and occurrence of malaria infection. The main goal is to investigate the spatial distribution of incidence of malaria during April 2011 to March 2018 in Hormozgan Province and its association with climate covariates.
Methods: The data included 882 confirmed cases gathered from CDC in Hormozgan University of Medical Sciences. A Poisson-Gamma Random field model with Bayesian approach was used for modeling the data and produces the smoothed standardized incidence rate (SIR).
Results: The SIR for malaria ranged from 0 (Abu Musa and Haji Abad districts) to 280.57 (Bandar–e-Jask). Based on model, temperature (RR= 2.29; 95% credible interval: (1.92–2.78)) and humidity (RR= 1.04; 95% credible interval: (1.03–1.06)) had positive effect on malaria incidence, but rainfall (RR= 0.92; 95% credible interval: (0.90–0.95)) had negative impact. Also, smoothed map represent hot spots in the east of the province and in Qeshm Island.
Conclusion: Based on the analysis of the study results, it was found that the ecological conditions of the region (temperature, humidity and rainfall) and population displacement play an important role in the incidence of malaria. Therefore, the malaria surveillance system should continue to be active in the region, focusing on high-risk areas of malaria. |
topic |
bayesian; spatial; poisson-gamma; hormozgan; malaria elimination |
url |
https://jad.tums.ac.ir/index.php/jad/article/view/1444 |
work_keys_str_mv |
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