dc.contributor.author |
Dalpadado, R. |
|
dc.contributor.author |
Amarasinghe, D. |
|
dc.contributor.author |
Gunathilaka, N. |
|
dc.contributor.author |
Wijayanayake, A. |
|
dc.date.accessioned |
2022-11-02T08:08:53Z |
|
dc.date.available |
2022-11-02T08:08:53Z |
|
dc.date.issued |
2022 |
|
dc.identifier.citation |
Dalpadado, R.,Amarasinghe, D.,Gunathilaka, N. and Wijayanayake, A.(2022),Dengue prediction modelling and development of area-specific thresholds for epidemic management in urban settings of Gampaha district, Sri Lanka,International Research and Innovation Symposium on Dengue amidst the Pandemic. 62-63p |
en_US |
dc.identifier.uri |
http://repository.kln.ac.lk/handle/123456789/25472 |
|
dc.description.abstract |
Introduction and objectives
The growing global threat of dengue in both endemic and non-endemic countries have
shifted the attention to establishing an early warning system to assist in dengue control and
effectively allocating scarce public health resources to manage outbreaks. Thus, the current
study was designed to develop localized thresholds to aid in sustainable dengue vector
control measures in three Medical Officer of Health (MOH) areas (Negombo, Wattala,
Kelaniya) in the Gampaha District.
Method
The cross-correlation function analysis (CCF) was performed to check the effects of
climatic variables (average rainfall, rainy days, average temperature, humidity) and Breteau
Index (BI) with dengue case incidence from 2014 to 2019. The dengue incidence at time t,
BIs with a one-month lag; Aedes aegypti; BIA(t-1), Aedes albopictus; BIB (t-1) and monthly
average rainfall; RFavg (t-2), rainy days; RD (t-2), Average relative humidity; RHavg (t-2) at twomonth
lag and monthly average temperature; Tavg at three-month lag were checked. Areaspecific
thresholds were derived from multiple linear regression. The model was validated
for the Jaela MOH area for the same period.
Results
Stepwise regression has excluded temperature, rainfall and BIB in urban areas and a
statistically significant strong association (r= 0.775) was displayed with BIA(t-1) and
RHavg(t-2). When the incidence of the cases exceeded 25, it reached an alarming situation
while exceeding 44 was classified as an epidemic in urban areas. BIA>1, RHavg >85%,
BIA>2; RHavg>81%, the model implies an early outbreak scenario and when BIA >3;
RHavg > 88%, BI>4; RHavg>84%, BIA>5; RHavg>81%, and whenever BIA > 6; RHavg>77%
it reached up to severe epidemics. The model accurately predicted all outbreaks in the Jaela
MOH area.
International Research and Innovation Symposium on Dengue amidst the Pandemic
63
Conclusions and recommendations
The common thresholds utilized for vector control entities remain ineffective and cannot
be applied throughout the country. Therefore, early warning indications can plan a prior
month source reduction in a low-risk zone. In contrast, government-led source reduction
programs should be maximized and an intense integrated vector control method must be
implemented before it reaches an epidemic. |
en_US |
dc.publisher |
International Research and Innovation Symposium on Dengue amidst the Pandemic |
en_US |
dc.subject |
Aedes, Dengue, Breteau Index, Climate, Entomological Threshold |
en_US |
dc.title |
Dengue prediction modelling and development of area-specific thresholds for epidemic management in urban settings of Gampaha district, Sri Lanka |
en_US |