Relationship between El Nino-Southern Oscillation and the incidence - - PowerPoint PPT Presentation

Relationship between El Nino-Southern Oscillation and the incidence of malaria in the Solomon Islands

Dr Yahya Abawi1,2 , Dr Sunil Dutta2, Lloyd Tahani3, Ms Jennifer Mitini4 , Ms Janita Pahalad1

1 National Climate Centre, Bureau of Meteorology 2University of Southern Queensland 3Solomon Islands Meteorological Services 4Solomon 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

avg.r=0.15

Ratings

May-Oct Nov-Apr

Concurrent relationship between rainfall and SOI (May – October)

avg.r=0.57

Ratings

May-Oct Nov-Apr

Concurrent relationship between rainfall and SOI (November - April)

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 5 10 15 20 25 30 35 Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Month PIR

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)

2 4 6 8 10 12 14 16 18 20 22 Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun PIR 40 80 120 160 200 240 280 320 360 400 440 Rainfall, mm

Average PIR (FMAM) vs November rainfall

y = -0.0837x + 35.604 R² = 0.3703 5 10 15 20 25 30 35 40 45 50 100 150 200 250 300 350 400 PIR November rainfall, mm

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)

Median rainfall Median PIR

Average monthly PIR (FMAM) related to average monthly rainfall from September through to February

Average PIR (FMAM) vs December rainfall

y = -0.0166x + 23.643 R² = 0.0318 5 10 15 20 25 30 35 40 45 50 100 150 200 250 300 350 400 450 500 550 600 PIR December rainfall, mm

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)

Median rainfall Median PIR

Average PIR(JFMAM) vs rainfall in SONDJ

Average PIR(JFMAM) vs rainfall in JFM

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) y = -0.0811x + 34.683 R2 = 0.3651 5 10 15 20 25 30 35 40 45 40 80 120 160 200 240 280 320 360 400 November average rainfall, mm PIR Median rain (N) Median PIR

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 Jan MaxT in Guadalcanal

y = ‐0.1411x2 + 8.9331x ‐ 139.99 R2 = 0.2554 0.5 1 1.5 2 29.5 30.0 30.5 31.0 31.5 32.0 32.5 33.0 Jan MaxT in Celcius Log PIR

LogPIR (DJFM) distribution against Feb MaxT in Guadalcanal

y = ‐0.1293x2 + 8.022x ‐ 123.01 R2 = 0.2227 0.5 1 1.5 2 28.0 29.0 30.0 31.0 32.0 33.0 Feb MaxT in Celcius Log PIR

LogPIR (DJFM) distribution against Mar MaxT in Guadalcanal

y = ‐0.1063x2 + 6.6931x ‐ 103.95 R2 = 0.1197 0.5 1 1.5 2 29.5 30.0 30.5 31.0 31.5 32.0 32.5 33.0 Mar MaxT in Celcius Log PIR

LogPIR (DJFM) distribution against Apr MaxT in Guadalcanal

y = ‐0.2252x2 + 14.057x ‐ 217.95 R2 = 0.0658 0.5 1 1.5 2 30.0 30.5 31.0 31.5 32.0 32.5 Apr MaxT in Celcius Log PIR

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)

y = ‐0.446x2 + 28.026x ‐ 438.88 R² = 0.752 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 29.5 30 30.5 31 31.5 32 32.5 Log PIR Jan MaxT in Celsius

LogPIR (FMAM) distribution against Jan MaxT in Solomon Islands

Median PIR Median MaxT

Confirmed to unconfirmed malaria cases in the Solomon islands (1975-2006)

Independence Control program Ethnic Tension

El Nino La Nina

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

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