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Integración de la información climática para la previsión de riesgos

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Autor: Xavier Rodó, ICREA
Climate and Health Program

Published in: Environment
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Integración de la información climática para la previsión de riesgos

  1. 1. Xavier Rodó, ICREA Climate & Health Program Integración de la información climática para la previsión de riesgos ‘El día después...’
  2. 2. Climate Services
  3. 3. Global Seasonal Forecasts (ECMWF) summer (JAS) temperature summer (JAS) rainfall 1-3 meses Summer Temperature Summer Precipitation Regional
  4. 4. A QGIS project to interactively overlap skill contours for different variables, seasons and terciles. The forecast system is ECMWF System4 with 15 ens. members. The example above shows areas with medium skill (ROCSS > 0.2) for dry (blue contour) and warm (red contour) conditions for the boreal summer (JJA). The forecasts started 1 month before the season. Interesting hotspots: Central America-Caribbean, northern coast of Guinea gulf, Cambodia-Vietnam, Indonesia- Philippines, Horn of Africa… IC3 Climate Skill Finder Garcia et al., in prep Skill finder and the geography of ‘predictable’ diseases
  5. 5.  Warm El Niño events cause major disasters and economic burden in many parts of the world.  Operational forecasts at a few years lead time could save lives and resources, but are still a major challenge for the climate community.  Our model has successfully hind casted such events at leads of up to 29 months.  Application to predicting dengue (Brazil, Ecuador, Puerto Rico and Thailand) Prediction of El Niño-Southern Oscillation events
  6. 6. Modelling Infectious Diseases
  7. 7. Malaria (endemic vs epidemic areas) Senegal: The ‘perfect’ VBD case (for climate/immunity) 5Km
  8. 8. SDE using MIF (Including Population change, rainfall/T and drug treatment) Dielmo Ndiop Ethiopia Senegal Moçambiq Kenya Malaria (Senegal) CLIMATE
  9. 9. 0 4 8 12 1995 1998 2001 2004 2007 2010 2013 2016 Time Choleracasesper10000 A Mechanistic temporal model 0 2 4 1995 1998 2001 2004 2007 2010 2013 2016 Time Avg.choleracasesper10000 B Statistical spatio-temporal model Training Hindcast Forecast Observed data 1995-2015 2016 Cholera forecast for Dhaka... Large outbreak!
  10. 10. DENGUE BBC News: http://www.bbc.com/news/health-27441789 Dengue forecast for a major global event: The 2014 soccer World Cup Microregion Warning Probability p(low, medium, high) Skill score (RPSS) Belo Horizonte Medium p(65%, 24%, 11%) 0·14 Brasília Low p(73%, 20%, 7%) 0·14 Cuiabá Low p(71%, 22%, 7%) 0·01 Curitiba Low p(100%, 0%, 0%) 1 Fortaleza High p(34%, 20%, 46%) 0·5 Manaus Medium p(63%, 25%, 12%) 0·15 Natal High p(32%, 20%, 48%) 0·67 Porto Alegre Low p(100%, 0%, 0%) 1 Recife High p(57%, 24%, 19%) 0·23 Salvador Medium p(56%, 27%, 17%) 0·14 São Paulo Low p(99%, 1%, 0%) 0·99 Rio de Janeiro Medium p(62%, 25%, 13%) 0·21 1.Hit 2.False hit rate: 57% (33%) false alarm rate (type I error rate): 23% (13%) miss rate (type II error rate): 43% (67%)
  11. 11. Contact networks Chikungunya in the Caribbean 2014
  12. 12. COVID-19 Prediction Platform Tau Bangladesh Brazil Kenya Italy I I I RRR DDD
  13. 13. www.arbocat.org

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