Project AEDES Working Repository - UNICEF funded 2022 Enhancement

What is Project AEDES?

Advanced Early Dengue Prediction and Exploration Service (AEDES) aims to improve public health response against dengue in the Philippines by predicting dengue cases from climate and digital data and pinpointing possible hotspots from satellite data.

The Problem

Dengue is now at epidemic levels in the Philippines with over 271,000 cases and more than 1,100 deaths as of August 2019. Data on dengue takes time to be manually gathered which hampers the health sector’s ability to deal with the threat. Dengue is spread between infected cases through the Aedes Aegypti mosquito and mosquitoes are known to breed in damp locations and stagnant water pools.

Our Solution

We propose an automated information portal that correlates dengue cases and deaths with real-time data from climate, google searches, and satellite maps, giving an advance indicator of when dengue will emerge and potential dengue hotspot locations. This portal is accessible publicly but is targeted towards public health and local government agencies to give them advanced notice of dengue outbreaks and help prioritize resources.

The service relies on 3 data sets:

• Satellite Data: Satellite imaging data from Sentinel Online Copernicus
• Local Weather Data: Climate data from DOST-PAGASA
• Google Data: Search trends for 'dengue' and related terms
• Disease Surveillance Data: Regional cases and deaths data from Department of Health

To populate the information portal, we propose 3 models:

• Predict dengue cases from climate and search data
• Predict dengue deaths from dengue cases
• Determine likely dengue hotspots by detecting stagnant water areas from satellite data

By doing this we are addressing 2 key challenges for public health and local government officials:

• Get ahead of the lagged delay of dengue reporting by using real-time information (e.g. climate, searches) to infer if dengue cases and deaths are about to spike.
• Precisely anticipate areas that may be affected by dengue to prioritize health aid, supplies, and proactive fumigation to prevent the outbreaks.

Local Dependency Set up

Required

1. Install dependencies from requirements.txt
2. Browse /datasets/Consolidated_regions.xlsx to read or update cases, deaths, google trends, weather, and region data.
3. Run denguemodel.py under /code to produce the forecast data.
4. Run selectdata.py under /code to read data from the SQLite Database.
5. To export data, run exporttocsv.py.

Deploy AEDES Portal

1. Download PHP7.
2. Copy /site to htdocs folder.
3. Browse localhost/site/index.php to locally deploy AEDES Portal.

Documentation and Enhancement References

• Technical Proposal
• Research Paper
• INFORM Epidemic Risk Model

Awards and Publication

• Global Award for Best Use of Data, 2019 NASA Space Apps Challenge
• 2020 Earth Observation for the Sustainable Development Goals (GEO SDG) Award, Group on Earth Observations
• 2021 Digital Public Good, DPGA
• 2021 SSRN, Academia Letters, https://dx.doi.org/10.2139/ssrn.3902598

Licenses

Project AEDES uses the following open licenses:

• MIT License
• Creative Commons Attribution Share Alike 4.0 International

About

Project AEDES is in active development and continously maintained by Cirrolytix Research Services.

Enhancement Team Members:

• Dominic Ligot, Enhancement Team Lead
• Mark Toledo, Solution Architect
• Emily Vizmonte, Project and Research Lead
• Mark Pascual, Application Engineering Lead
• Xavier Puspus, Data Science and Engineering Lead

Original Team Members:

• Dominic Ligot, Chief Technology Officer
• Mark Toledo, Solution Architect
• Claire Tayco, Head of Research and Analytics
• Rache Melendres, Analytics Consultant
• Cricket Soong, Emerging Technologies Consultant

We welcome collaborators. Contact us via email at support@aedesproject.org.

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