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Fighting Dengue with Data

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Fighting Dengue with Data

We may be nowhere near eradicating Dengue, but we may be able to prevent it more effectively. Through Time-Series Modeling and geospatial mapping, Team Flex was able to predict and forecast cases and deaths up to 4 months and identify potential dengue hotspots in selected cities of the CALABARZON region.

The global threat of disease outbreaks is real and it is felt more than ever now as the world struggles to contain the spread of a certain virus. But before it even created a pandemic, there is already another disease that threatens our existence, perhaps has been doing so for the longest time – Dengue.
Dengue affects thousands of lives each year and continues to be a major public health problem in the Philippines.
In the last year alone, the Philippines experienced the worst dengue outbreak since 2012 as reported cases reached beyond epidemic thresholds. On top of that, the delayed reporting of official case and death counts makes it even more difficult to pinpoint heavily dengue affected areas early on and initiate a targeted public health response. To address this problem, Team Flex members, Janine Padilla, Mox Ballo, and Rache Melendres, developed a publicly accessible web application
that can be used by concerned government agencies and public health officials to predict the spread of dengue and visualize potential breeding sites of mosquitoes.

We may be nowhere near eradicating Dengue, but we may be able to prevent it more effectively. Through Time-Series Modeling and geospatial mapping, Team Flex was able to predict and forecast cases and deaths up to 4 months and identify potential dengue hotspots in selected cities of the CALABARZON region.

The global threat of disease outbreaks is real and it is felt more than ever now as the world struggles to contain the spread of a certain virus. But before it even created a pandemic, there is already another disease that threatens our existence, perhaps has been doing so for the longest time – Dengue.
Dengue affects thousands of lives each year and continues to be a major public health problem in the Philippines.
In the last year alone, the Philippines experienced the worst dengue outbreak since 2012 as reported cases reached beyond epidemic thresholds. On top of that, the delayed reporting of official case and death counts makes it even more difficult to pinpoint heavily dengue affected areas early on and initiate a targeted public health response. To address this problem, Team Flex members, Janine Padilla, Mox Ballo, and Rache Melendres, developed a publicly accessible web application
that can be used by concerned government agencies and public health officials to predict the spread of dengue and visualize potential breeding sites of mosquitoes.

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Fighting Dengue with Data

  1. 1. FLEXTEAM Fighting Dengue with Data Capstone Presentation 8 February 2020
  2. 2. 1,607 429,409 as of 21-Dec-2019 Source: World Health Organization
  3. 3. Top 5 Regions with highest cases, Jan 2019 - Aug 2019 CALABARZON region is one the dengue-affected areas
  4. 4. Where & When?
  5. 5. Problem How can we address delayed reporting of dengue surveillance data through early prediction?
  6. 6. Solution A web application that: 1. Predicts the spread of dengue
  7. 7. Solution A web application that: 1. Predicts the spread of dengue 2. Visualizes potential dengue hotspot locations
  8. 8. FLEXTEAM Computers Mobile Browsers
  9. 9. Before anything else..
  10. 10. Cities with highest reported dengue cases in CALABARZON CALABARZON Region is one of the dengue affected areas Source: https://newsinfo.inquirer.net/1145624/dengue-outbreak-declared-in-cavite
  11. 11. Official Dengue Reports Google Searches Climate Data Data Sources Mosquitoes infect and spread Dengue Sickness and Death occurs
  12. 12. Our Insights
  13. 13. 2019 Highest peaks in August for Cases, Deaths, and Google Searches
  14. 14. Dengue Cases, JAN 2015- AUG 2019
  15. 15. Dengue Deaths, JAN 2015- AUG 2019
  16. 16. Google Searches Jan 2015- Aug 2019
  17. 17. Forecast Model
  18. 18. Dengue Cases in CALABARZON
  19. 19. Dengue Cases in CALABARZON
  20. 20. Satellite Model
  21. 21. Geospatial Map
  22. 22. Geospatial Map Vegetation
  23. 23. Geospatial Map Vegetation Water
  24. 24. Geospatial Mapping using + = Stagnant WaterVegetation Water
  25. 25. Tools that we used :
  26. 26. WebApp Demo
  27. 27. Challenges
  28. 28. Challenges Data Poverty
  29. 29. Challenges Data Poverty Time Series Complexity
  30. 30. Challenges Data Poverty Time Series Complexity Availability of weather data e
  31. 31. Challenges
  32. 32. Challenges Copernicus satellite imagesAccess
  33. 33. Impact
  34. 34. Impact Every week we can reduce the lag in public health response to CALABARZON and save: 4 1,151Lives Cases
  35. 35. Recommendations
  36. 36. FOR GOVE R N MENT • Make data more accessible • Validate hotspot locations • Initialize targeted preventive measures
  37. 37. • Make data more accessible • Validate hotspot locations • Initialize targeted preventive measures (saving resources:) FOR GOVE R N MENT
  38. 38. FOR THE PUB LIC • Use information to identify high-risk areas for dengue
  39. 39. FLEXTEAM The Folks Behind Our Capstone RACHELLE LEAH MELENDRES Connector AIKO JANINE PADILLA Scrummaster ROXANNE BALLO Maven
  40. 40. https://opendata.org.ph/flex/
  41. 41. Q & A

Hinweis der Redaktion

  • 18. 2. 2020
  • 18. 2. 2020
  • 18. 2. 2020
  • 18. 2. 2020
  • 18. 2. 2020
  • 18. 2. 2020

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