Potential for using climate forecasts in spatio-temporal prediction of dengue fever incidence in Malaysia submitted
Dengue fever is a viral infection transmitted by the bite of female Aedes aegypti mosquitoes. It is estimated that nearly 40% of the world’s population is now at risk from Dengue in over 100 endemic countries including Malaysia. Several studies in various countries in recent years have identified...
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格式: | Thesis |
語言: | English |
出版: |
2015
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主題: | |
在線閱讀: | http://eprints.uthm.edu.my/1744/1/24p%20NORZIHA%20CHE%20HIM.pdf |
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總結: | Dengue fever is a viral infection transmitted by the bite of female Aedes aegypti
mosquitoes. It is estimated that nearly 40% of the world’s population is now
at risk from Dengue in over 100 endemic countries including Malaysia. Several
studies in various countries in recent years have identified statistically significant
links between Dengue incidence and climatic factors. There has been relatively little
work on this issue in Malaysia, particularly on a national scale. This study attempts
to fill that gap. The primary research question is ‘to what extent can climate
variables be used to assist predictions of dengue fever incidence in Malaysia?’. The
study proposes a potential framework of modelling spatio-temporal variation in
dengue risk on a national scale in Malaysia using both climate and non-climate
information.
Early chapters set the scene by discussing Malaysia and Climate in Malaysia and
reviewing previous work on dengue fever and dengue fever in Malaysia. Subsequent
chapters focus on the analysis and modelling of annual dengue incidence rate (DIR)
for the twelve states of Peninsular Malaysia for the period 1991 to 2009 and monthly
DIR for the same states in the period 2001 to 2009.
Exploratory analyses are presented which suggest possible relationships between
annual and monthly DIR and climate and other factors. The variables that were
considered included annual trend, in year seasonal effects, population, population
density and lagged dengue incidence rate as well as climate factors such as average
rainfall and temperature, number of rainy days, ENSO and lagged values of these climate variables. Findings include evidence of an increasing annual trend in DIR
in all states of Malaysia and a strong in-year seasonal cycle in DIR with possible
differences in this cycle in different geographical regions of Malaysia. High population density is found to be positively related to monthly DIR as is the DIR in the
immediately preceding months. Relationships between monthly DIR and climate
variables are generally quite weak, nevertheless some relationships may be able to
be usefully incorporated into predictive models. These include average temperature and rainfall, number of rainy days and ENSO. However lagged values of these
variables need to be considered for up to 6 months in the case of ENSO and from
1-3 months in the case of other variables.
These exploratory findings are then more formally investigated using a framework
where dengue counts are modelled using a negative binomial generalised linear
model (GLM) with a population offset. This is subsequently extended to a negative binomial generalised additive model (GAM) which is able to deal more flexibly
with non-linear relationships between the response and certain of the explanatory
variables. The model successfully accounts for the large amount of overdispersion
found in the observed dengue counts. Results indicated that there are statisti�cally significant relationships with both climate and non-climate covariates using
this modelling framework. More specifically, smooth functions of year and month
differentiated by geographical areas of the country are significant in the model to
allow for seasonality and annual trend. Other significant covariates included were
mean rainfall at lag zero month and lag 3 months, mean temperature at lag zero
month and lag 1 month, number of rainy days at lag zero month and lag 3 months,
sea surface temperature at lag 6 months, interaction between mean temperature at
lag 1 month and sea surface temperature at lag 6 months, dengue incidence rate
at lag 3 months and population density.
Three final competing models were selected as potential candidates upon which
an early warning system for dengue in Malaysia might be able to be developed.
The model fits for the whole data set were compared using simulation experiments
to allow for both parameter and negative binomial model uncertainty and a single model preferred from the three models was identified. The ‘out of sample’ predictive
performance of this model was then compared and contrasted for different lead
times by fitting the model to the first 7 years of the 9 years monthly data set
covering 2001-2009 and then analysing predictions for the subsequent 2 years for
lead time of 3, 6 12 and 24 months. Again simulation experiments were conducted
to allow for both parameter and model uncertainty. Results were mixed. There
does seem to be predictive potential for lead times of up to six months from the
model in areas outside of the highly urbanised South Western states of Kuala
Lumpur and Selangor and such a model may therefore possibly be useful as a basis
for developing early warning systems for those areas. However, none of the models
developed work well for Kuala Lumpur and Selangor where there are clearly more
complex localised influences involved which need further study.
This study is one of the first to look at potential climatic influences on dengue
incidence on a nationwide scale in Malaysia. It is also one of the few studies
worldwide to explore the use of generalised additive models in the spatio-temporal
modelling of dengue incidence. Although, the results of the study show a mixed
picture, hopefully the framework developed will be able to be used as a starting
point to investigate further if climate information can valuably be incorporated in
an early warning system for dengue in Malaysia |
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