Relationship between El Nino-Southern Oscillation and the incidence of malaria in the Solomon Islands Dr Yahya Abawi 1,2 , Dr Sunil Dutta 2 , Lloyd Tahani 3 , Ms Jennifer Mitini 4 , Ms Janita Pahalad 1 1 National Climate Centre, Bureau of Meteorology 2 University of Southern Queensland 3 Solomon Islands Meteorological Services 4 Solomon Islands Medical Research Institute
Pacific Islands – Climate Prediction Project ( PI-CPP) www.bom.gov.au/climate/pi-cpp/ Develop a software called SCOPIC • (Seasonal Climate Outlook for Pacific Island Countries) to provide local NMS with the ability to issue seasonal climate forecasts specific to their country • Training in SCF and Risk Management • Conduct pilot project on the impact of climate on vulnerable sectors in each participating country
Pacific Islands – Climate Prediction Project Prediction of Vector-born diseases (Malaria) Aims • Determine whether malaria epidemics in the Solomon Islands are related to the ENSO, rainfall and other hydro-climatic variables; and • Determine if such relationship can be used as an early warning system for predicting heightened risk of a malarial epidemic and therefore in assisting targeted control strategies.
Climate of Solomon Islands
Concurrent relationship between SOI and Rainfall
Concurrent relationship between rainfall and SOI (May – October) Ratings May-Oct Nov-Apr avg.r=0.15
Concurrent relationship between rainfall and SOI (November - April) Ratings May-Oct Nov-Apr avg.r=0.57
Rainfall Prediction Skill
Malaria Snapshot • 100 countries, 40% of world population live in areas where malaria transmission occurs 300 – 500 million cases each year • world wide 750,000 – 2 million deaths each year • • Plasmodium falciparum accounts for 60-70% of all cases in SI. Transmitted by Anopheles Mosquitoes Ideal breeding condition (25-30 C, RH • 60%)
Average monthly malaria PIR distributions for different regions in Solomon Islands Average monthly PIR for different regions in Solomon Islands 35 30 25 20 PIR 15 10 5 0 Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Month Central Provinces (28) Western &Choiseul (28) Makira(28) Malaita(28) Temotu(28) Solomon Islands (28)
Positive Incidence Ratio (PIR) per 1000 population Average monthly PIR and Rainfall in Solomon Islands (1975-2007) 22 440 20 400 18 360 16 320 14 280 Rainfall, mm 12 240 PIR 10 200 8 160 6 120 4 80 2 40 0 0 Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun
Average PIR (FMAM) vs November rainfall Average PIR(FMAM) related to November rainfall at Solomon Islands (triangle is El Niño, and diamond is La Niña and filled circle is Non-ENSO year) 45 Median rainfall 40 y = -0.0837x + 35.604 R² = 0.3703 35 30 25 PIR 20 Median PIR 15 10 5 0 0 50 100 150 200 250 300 350 400 November rainfall, mm
Average monthly PIR (FMAM) related to average monthly rainfall from September through to February
Average PIR (FMAM) vs December rainfall Average PIR(FMAM) related to December rainfall at Solomon Islands (triangle is El Niño, and diamond is La Niña and filled circle is Non-ENSO year) 45 40 y = -0.0166x + 23.643 Median rainfall R² = 0.0318 35 30 25 PIR 20 Median PIR 15 10 5 0 0 50 100 150 200 250 300 350 400 450 500 550 600 December rainfall, mm
Average PIR(JFMAM) vs rainfall in SONDJ Average PIR(JFMAM) vs rainfall in September Average PIR(JFMAM) related to November rainfall at Solomon Islands (red indicates PIR before and including 1992 and blue indicates PIR 1993 and onwards, triangle is El Nino, and square is La Nina and filled circle is Non-ENSO year) 45 40 y = -0.0811x + 34.683 R 2 = 0.3651 Average PIR(JFMAM) vs rainfall in JFM 35 Median rain (N) 30 25 PIR 20 Median PIR 15 10 5 0 0 40 80 120 160 200 240 280 320 360 400 November average rainfall, mm
PIR(FMAM) distribution in Makira region based on ENSO years
Mosquito life cycle is affected by temperature
PIR and Maximum Temperature LogPIR (DJFM) distribution against LogPIR (DJFM) distribution against Jan MaxT in Guadalcanal Feb MaxT in Guadalcanal 2 2 1.5 Log PIR 1.5 Log PIR 1 1 y = ‐ 0.1411x 2 + 8.9331x ‐ 139.99 y = ‐ 0.1293x 2 + 8.022x ‐ 123.01 R 2 = 0.2554 0.5 0.5 R 2 = 0.2227 0 0 28.0 29.0 30.0 31.0 32.0 33.0 29.5 30.0 30.5 31.0 31.5 32.0 32.5 33.0 Feb MaxT in Celcius Jan MaxT in Celcius LogPIR (DJFM) distribution against LogPIR (DJFM) distribution against Mar MaxT in Guadalcanal Apr MaxT in Guadalcanal 2 2 Log PIR 1.5 Log PIR 1.5 1 1 y = ‐ 0.1063x 2 + 6.6931x ‐ 103.95 y = ‐ 0.2252x 2 + 14.057x ‐ 217.95 R 2 = 0.1197 0.5 0.5 R 2 = 0.0658 0 0 29.5 30.0 30.5 31.0 31.5 32.0 32.5 33.0 30.0 30.5 31.0 31.5 32.0 32.5 Mar MaxT in Celcius Apr MaxT in Celcius
PIR (FMAM) distribution of malaria as a function of maximum temperature in January in Solomon Islands (Triangle indicates El Niño, Diamond is La Niña and the rest are Non-ENSO years) LogPIR (FMAM) distribution against Jan MaxT in Solomon Islands 1.8 1.6 1.4 Median PIR 1.2 1 Log PIR Median 0.8 MaxT 0.6 y = ‐ 0.446x 2 + 28.026x ‐ 438.88 R² = 0.752 0.4 0.2 0 29.5 30 30.5 31 31.5 32 32.5 Jan MaxT in Celsius
Confirmed to unconfirmed malaria cases in the Solomon islands (1975-2006) Control program El Nino La Nina Independence Ethnic Tension
Non-climatic and climate related inter-annual variability in annual confirmed malarial incidence for Solomon Islands for 1975-2006 Model 1: JFM average monthly rainfall Model 2: Model 1 and JFM temperature Model 3: Model 2 and Policy Intervention
Rainfall, Maximum and Minimum Temperature (Honiara)
Predictability of rainfall in Solomon Rainfall prediction based on SST 1 and 9 have good skill during the wet season for most of the provinces except Western and Choiseul. It is therefore possible to forecast malaria epidemic well ahead of time and take preventative measure to reduce its impact on the population
Continued support is essential for successful adoption
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