DYNAMICAL MODEL OF AE. AEGYPTI WITH CLIMATE AND SPATIAL FACTOR
Until the early of June 2020, Indonesia has still known as the endemic country for Dengue Fever. According to the data retrieved from various sources, the number of Dengue patients in Indonesia is fluctuating each year. Specifically, the fluctuation is also portrayed by the regional data. Automat...
Saved in:
| Main Author: | |
|---|---|
| Format: | Final Project |
| Language: | Indonesia |
| Online Access: | https://digilib.itb.ac.id/gdl/view/47775 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |
| Summary: | Until the early of June 2020, Indonesia has still known as the endemic country for
Dengue Fever. According to the data retrieved from various sources, the number
of Dengue patients in Indonesia is fluctuating each year. Specifically, the fluctuation
is also portrayed by the regional data. Automatically, the regional outbreak
status of Dengue becomes uncertain. In order to minimize the impact of the dengue
outbreak, dengue prevention is carried out by the government, such as the Early
Warning System (EWS) for Dengue. In this report, the EWS is constructed by a
deterministic and statistic approach. A deterministic model of ae. Aegypti population
is constructed by involving the climate and spatial factors. The climate factors
involved are relative humidity, temperature, and rainfall which affect the entomological
parameters of ae. Aegypti. whereas spatial factor, the area of the observed
region, affects the carrying capacity of ae. Aegypty larvae. The alternative Early
warning system models are constructed for 5 regions in Jakarta and 4 regions in
Bali by associating the output of the deterministic model and the data of outbreak
with logistic regression. This results will be visualized in choropleth maps for 5
regions in Jakarta and 4 regions in Bali. Further, this approach also can be used for
projecting the outbreak status in those regions. The projections are conducted by
applying the predicted climate data to the constructed models. |
|---|