Spatio-temporal modelling of dengue fever incidence in Malaysia

Previous studies reported significant relationship between dengue incidence rate (DIR) and both climatic and non-climatic factors. Therefore, this study proposes a generalised additive model (GAM) framework for dengue risk in Malaysia by using both climatic and non�climatic factors. The data use...

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Main Authors: Che-Him, Norziha, Kamardan, M. Ghazali, Rusiman, Mohd Saifullah, Sufahani, Suliadi Firdaus, Mohamad, Mahathir, Kamaruddin, Nafisah @ Kamariah
Format: Conference or Workshop Item
Language:English
Published: 2017
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Online Access:http://eprints.uthm.edu.my/7033/1/P10153_5e4142f853442708f7293738ac2c4b9a.pdf
http://eprints.uthm.edu.my/7033/
https://doi.org/10.1088/1742-6596/995/1/012003
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Institution: Universiti Tun Hussein Onn Malaysia
Language: English
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spelling my.uthm.eprints.70332022-05-24T01:33:08Z http://eprints.uthm.edu.my/7033/ Spatio-temporal modelling of dengue fever incidence in Malaysia Che-Him, Norziha Kamardan, M. Ghazali Rusiman, Mohd Saifullah Sufahani, Suliadi Firdaus Mohamad, Mahathir Kamaruddin, Nafisah @ Kamariah T Technology (General) Previous studies reported significant relationship between dengue incidence rate (DIR) and both climatic and non-climatic factors. Therefore, this study proposes a generalised additive model (GAM) framework for dengue risk in Malaysia by using both climatic and non�climatic factors. The data used is monthly DIR for 12 states of Malaysia from 2001 to 2009. In this study, we considered an annual trend, seasonal effects, population, population density and lagged DIR, rainfall, temperature, number of rainy days and El Niño-Southern Oscillation (ENSO). The population density is found to be positively related to monthly DIR. There are generally weak relationships between monthly DIR and climate variables. A negative binomial GAM shows that there are statistically significant relationships between DIR with climatic and non-climatic factors. These include mean rainfall and temperature, the number of rainy days, sea surface temperature and the interaction between mean temperature (lag 1 month) and sea surface temperature (lag 6 months). These also apply to DIR (lag 3 months) and population density. 2017 Conference or Workshop Item PeerReviewed text en http://eprints.uthm.edu.my/7033/1/P10153_5e4142f853442708f7293738ac2c4b9a.pdf Che-Him, Norziha and Kamardan, M. Ghazali and Rusiman, Mohd Saifullah and Sufahani, Suliadi Firdaus and Mohamad, Mahathir and Kamaruddin, Nafisah @ Kamariah (2017) Spatio-temporal modelling of dengue fever incidence in Malaysia. In: ISMAP 2017, October 28, 2017, Batu Pahat, Johor. https://doi.org/10.1088/1742-6596/995/1/012003
institution Universiti Tun Hussein Onn Malaysia
building UTHM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tun Hussein Onn Malaysia
content_source UTHM Institutional Repository
url_provider http://eprints.uthm.edu.my/
language English
topic T Technology (General)
spellingShingle T Technology (General)
Che-Him, Norziha
Kamardan, M. Ghazali
Rusiman, Mohd Saifullah
Sufahani, Suliadi Firdaus
Mohamad, Mahathir
Kamaruddin, Nafisah @ Kamariah
Spatio-temporal modelling of dengue fever incidence in Malaysia
description Previous studies reported significant relationship between dengue incidence rate (DIR) and both climatic and non-climatic factors. Therefore, this study proposes a generalised additive model (GAM) framework for dengue risk in Malaysia by using both climatic and non�climatic factors. The data used is monthly DIR for 12 states of Malaysia from 2001 to 2009. In this study, we considered an annual trend, seasonal effects, population, population density and lagged DIR, rainfall, temperature, number of rainy days and El Niño-Southern Oscillation (ENSO). The population density is found to be positively related to monthly DIR. There are generally weak relationships between monthly DIR and climate variables. A negative binomial GAM shows that there are statistically significant relationships between DIR with climatic and non-climatic factors. These include mean rainfall and temperature, the number of rainy days, sea surface temperature and the interaction between mean temperature (lag 1 month) and sea surface temperature (lag 6 months). These also apply to DIR (lag 3 months) and population density.
format Conference or Workshop Item
author Che-Him, Norziha
Kamardan, M. Ghazali
Rusiman, Mohd Saifullah
Sufahani, Suliadi Firdaus
Mohamad, Mahathir
Kamaruddin, Nafisah @ Kamariah
author_facet Che-Him, Norziha
Kamardan, M. Ghazali
Rusiman, Mohd Saifullah
Sufahani, Suliadi Firdaus
Mohamad, Mahathir
Kamaruddin, Nafisah @ Kamariah
author_sort Che-Him, Norziha
title Spatio-temporal modelling of dengue fever incidence in Malaysia
title_short Spatio-temporal modelling of dengue fever incidence in Malaysia
title_full Spatio-temporal modelling of dengue fever incidence in Malaysia
title_fullStr Spatio-temporal modelling of dengue fever incidence in Malaysia
title_full_unstemmed Spatio-temporal modelling of dengue fever incidence in Malaysia
title_sort spatio-temporal modelling of dengue fever incidence in malaysia
publishDate 2017
url http://eprints.uthm.edu.my/7033/1/P10153_5e4142f853442708f7293738ac2c4b9a.pdf
http://eprints.uthm.edu.my/7033/
https://doi.org/10.1088/1742-6596/995/1/012003
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