1 / 25

Remote Sensing Application for Epidemic Intensity Prediction in Indonesia

This research focuses on using remote sensing and GIS technology to predict the intensity of epidemics, such as malaria and avian influenza, in Indonesia. Data from Landsat and NOAA satellites are used to analyze environmental factors and develop predictive models. The research aims to support disaster management and spatial planning efforts.

davidberry
Download Presentation

Remote Sensing Application for Epidemic Intensity Prediction in Indonesia

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. REMOTE SENSING APPLICATION FOR PREDICTION OF EPIDEMIC INTENSITY IN INDONESIAIta Carolita*, Kustiyo*, Sigit Herumurti**, Dr. Totok Gunawan **Remote Sensing Application & Technology Development Center,Indonesian Remote Sensing Aeronautics and Space Institute (LAPAN)*Faculty of Geography, Gajah Mada University

  2. CONTENTS • LAPAN Remote Sensing Centre and it’s activities • REMOTE SENSING and GIS for Malaria in West Java • Recent Research : a. RS Application for Dengue Fever and Malaria b. RS Application for Avian Influenza

  3. 2. REMOTE SENSING & GIS for Malaria in West Java (LAPAN & Min of Health, 2005) • Malaria pandemic is the one of serious health problem in Indonesia, especially in transmigration area. • Hunter et al., (1960) : one of the supported epidemiology components for malaria pandemic is : Water body and suitable area for the habitat of the mosquitos as the vector of malaria.

  4. Data I. Predictors Data* : Landsat 7 : 2001 - 2002 NOAA Data : 2001 – 2004 Climate Data from Meteorology and Geophisics Agnecy. II. Intensity of Malaria Epidemic. * From Landsat : Water body, sedimentation in shorline, mix garden, paddy – water phase, paddy – vegetative phase, * From NOAA : Temperature and Land Moisture

  5. Methodology

  6. The formula that has been got to predict the intensity is: Y = -1.103 +2.399 * (Temperature Index) + 1.885 * (Moisture Index). Y : Intensity of Malaria Epidemic

  7. Related Agencie Data Numeric Operation SIG AI Link Code Numeric Data Spatial Data MAPS in various scales and themes Spatial Operation 3. Recent Research : a. RS Application for Dengue Fever and Malaria b. RS Application for Avian Influenza • These research are carrying out to support the ISDN that developed BAKOSURTANAL for AI Disaster Management in Indonesia and support Local Government in Spatial Planning. Source : BAKOSURTANAL

  8. RS Application for Dengue Fever and Malaria(LAPAN and Gajah Mada University) Objectives : 1. Investigate the capability of RS satellite data in supporting environment parameters measurement. 2. Investigate the analysis of spatial modeling for investigate the influences of ecology factors to Dengue Fever epidemic Study Area : Central Java

  9. Methodology Data : RS data (ALOS, Landsat, IKONOS, MODIS) Field Trip Data Secondary data (Climate, Infected Human) Method : • Extraction of Environment Parameters from RS data • Field Trip and Secondary data Gathering • Area Zoning based on characteristics of area • Selection the parameters : cluster analysis, factor analysis, principle component analysis. • Analysis site selection approach • GIS analysis to get the Potential area • Modeling • Mapping

  10. b. RS Application for Avian Influenza(LAPAN as the counterpart of Hampshire University USA)research on Quantifying Agro-Ecological Risk Factors of HPAI in Java Indonesia. The increasing number of people who have been infected by the avian flu virus has seen an increase in Indonesia. Since the discovered of human cases of avian flu in Indonesia, there has been no significant outcome of prevention programmes; although prevention is a central of the health maintenance programme (Dhillon and Philip, 1994).

  11. Avian flu in Indonesia • There have been new cases of avian flu among 33 provinces in Indonesia. Recent data from the WHO shows that since 2005, after the first case was founded in Indonesia, the number of cases has risen. In 2005, only 20 cases avian flu in human were identified. In 2006, there was significant increase to 55 cases. After 3 years, the number of people identified as positive with avian flu increased to 135 cases, and so far in 2008, the people who are infected by avian flu virus was 18 cases. Of the 135 cases confirmed to date in Indonesia, 110 have been fatal (http://www.who.int/csr/disease/avian_influenza/country/ • cases_table_2008_06_19/en/index.html).

  12. Objectives : • Investigate the Environment Factors that Influence to AI Outbreaks. • Getting the Map spatial distribution and temporal dynamics of paddy rice and natural wetland in Java. • Getting AI Outbreaks Prediction Model.

  13. STATUS of RESEARCH • Processing of RS data: • a. Investigate the capability of PALSAR data in Land Use (including wet land areas) mapping • b. Processing of MODIS data (EVI) • c. ALOS Data (PALSAR, PRISM, AVNIR) Collecting.

  14. EVI (weekly) data for 2006 Jan 08 (I). Human H5N1 outbreaks in : Jakarta, West Java Jan 08(IV) Human H5N1 outbreaks in: Jakarta, Depok, Tangerang, West Java, East Java

More Related