1 Modelling malaria in Africa driven by DEMETER forecasts Anne Jones Department of Geography...

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Modelling malaria in Africa driven by Modelling malaria in Africa driven by DEMETER forecastsDEMETER forecasts

Anne Jones

Department of Geography

University of Liverpool

Liverpool

UK

[email protected]

NERC e-science studentship

Supervisor: Dr Andy Morse

ECMWF Forecast User Group Meeting, June 2006 [email protected]

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OverviewOverview

• Climate and malaria

• Liverpool malaria model

• Predicting malaria in Botswana using DEMETER forecasts

• Discussion of results

• Model spin up

• Bias correction

• Timing issues

• Conclusions


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Image from The Wellcome Trust

Anopheles Life Cycle and ClimateAnopheles Life Cycle and Climate

Anopheles breeding sites include cattle footprints, water tanks and rice fields


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Image from The Wellcome Trust

Anopheles Life Cycle and ClimateAnopheles Life Cycle and Climate

The adult biting/laying cycle and survivorship depend on temperature

Gonotrophic cycle length according to Detinova (1962)

Mosquito survival according to Craig et al (1999)

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Parasite Life Cycle and ClimateParasite Life Cycle and Climate

Temperature

18 °C


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Structure of the Liverpool Malaria Model (LMM) Structure of the Liverpool Malaria Model (LMM)

Mosquito population

Malaria transmission -

mosquito

Malaria transmission -

human

Daily Malaria incidence (number of new cases) and prevalence (proportion of population infected)

10 day rainfall

daily temperature

daily temperature

humidity (10 day rainfall)

daily temperature

Input data: station or gridded datasets (ERA-40, DEMETER)


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Botswana MalariaBotswana Malaria

MARA map

(Craig et al, 1999)

Tmin

Rain

Tmean

MARA run with ERA-40 1 deg (1958-2001)

MARA limiting variable (Tmin is min of 4 daily values)


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Malaria Anomalies Malaria Anomalies

-2.0

-1.0

0.0

1.0

2.0

1982 1984 1986 1988 1990 1992 1994 1996 1998 2000

Year

Mal

aria

An

om

aly

-0.6

-0.4

-0.2

0.0

0.2

0.4

0.6

Inci

den

ce A

no

mal

y

Malaria Index LMM incidence

Malaria index of Thomson et al. (2005) and ERA-40-driven LMM yearly total incidence.

Thomson et al. found a quadratic relationship between malaria and rainfall

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0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

1 31 61 91 121 151

Forecast Day

Ma

lari

a P

rev

ale

nc

eSeasonal ForecastsSeasonal Forecasts

• Assessed performance of forecasts made using LMM driven by DEMETER forecasts.

• Compared to ERA-40 driven forecasts (tier-2 validation) and yearly malaria anomalies (tier-3 validation)

• DEMETER daily rainfall and temperature series were corrected for model biases then used as input to the malaria model.

Botswana malaria forecast for February 1989, LMM driven by DEMETER multi-model

(ERA-driven model shown in red)

95

85

65

35

15

5ERA


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Tier-3 ROC AreasTier-3 ROC Areasfor November malaria forecastfor November malaria forecast

ROC Area (<0.5 = no skill), Upper Tercile event, forecast 6 month totals for Botswana grid average, () 95% confidence intervals calculated from 1000 bootstrap samples

Validated against Thomson et al (2005) Malaria Index

LMM Input Data ROC Area

ERA-40 0.78 (0.56-0.97)

Raw DEMETER 0.31(0.11-0.56)

Bias-corrected DEMETER

T correction only

0.67 (0.29-0.80)

0.70 (0.35-0.90)

ERA-40 "persistence"

(wrong year ensemble)0.32

(0.07-0.63)

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15

17

19

21

23

25

27

29

1 31 61 91 121 151

Forecast Day

Me

an

T (

de

gre

es

C)

Effect of temperature bias correctionEffect of temperature bias correction

• Temperature variability not a strong driver of malaria variability in this region

• However malaria model requires realistic temperatures

• DEMETER temperatures need to be bias corrected to achieve this, because models is sensitive to biases in uncorrected data of ~ 2 degrees

DEMETER temperature forecasts for Botswana, November 1997

95

85

65

35

15

5ERA

Uncorrected Temperature Corrected Temperature

15

17

19

21

23

25

27

29

1 31 61 91 121 151

Forecast Day

Me

an

T (

de

gre

es

C)


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0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

180 30 60 90 120 150

Forecast Day

Ma

lari

a p

rev

ale

nc

e

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

180 30 60 90 120 150

Forecast Day

Ma

lari

a p

rev

ale

nc

e

• Improvement in skill due to temperature correction

• If temperatures too low, delay in model is increased

Uncorrected input

Corrected temperature

DEMETER-driven malaria forecasts for November 1997

95

85

65

35

15

5ERA

Effect of temperature bias correction contd.Effect of temperature bias correction contd.

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Tier-1 ROC Areas for November rainfall forecast

Event Uncorrected Corrected

Lower Tercile 0.88(0.67-1.0)

0.75(0.45-1.0)

Above the median

0.65(0.37-0.89)

0.68(0.39-0.92)

Upper Tercile 0.72(0.46-0.93)

0.63(0.37-0.86)

ROC Area (<0.5 = no skill),forecast 6 month totals for Botswana grid average, () 95% confidence intervals calculated from 1000 bootstrap samples

Validated against ERA-40 rainfall totals

Effect of rainfall bias correction Effect of rainfall bias correction

•Bias correction of rainfall causes decrease in skill


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• Correct for frequency and intensity separately

• If distributions are very different, large numbers of rainfall days are removed

• Correction to mean climate but reduction in skill - need alternative method

Effect of rainfall bias correction contd.Effect of rainfall bias correction contd.

model crfc, daily rainfall over 20 years for Feb at 25E, 22.5S

= ERA-40, = uncorr. DEM, = corr. DEM

ERA-40 Crfc model

November rainfall totals in mm for all grid points in Botswana over 20 years

Daily rainfall (mm)

Cum

ulat

ive

freq

uenc

y


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Timing IssuesTiming Issues

Botswana grid-averaged values for November 1988 forecast

Bias corrected data

Rainfall LMM Mosquitoes LMM Malaria

• Bad/lack of bias correction means rainfall can be too low

• Model peak can be outside the forecast window due to lag between rainfall and malaria cases (made worse when rainfall too low)

• Skill not improved by extending forecast window with previous year of ERA data

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Tier-3 ROC AreasTier-3 ROC Areas--alternative model outputsalternative model outputs

LMM Input Data LMM Malaria Anomalies

LMM Mosquito Anomalies

ERA-40 0.78 (0.56-0.97)

0.89 (0.71-1.0)

Raw DEMETER 0.31(0.11-0.56)

0.73 (0.42-0.97)

Bias corrected DEMETER

0.67 (0.29-0.80)

0.56

(0.42-0.91)

ROC Area (<0.5 = no skill), Upper Tercile event, November forecast 6 month totals for Botswana grid average, () 95% confidence intervals calculated from 1000 bootstrap samples

Validated against Thomson et al (2005) Malaria Index

• Skill improved by using model mosquito numbers

• Bias correction decreases skill due to strong rainfall driver

• Cannot use in other areas where temperature a stronger driver

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ConclusionsConclusions

• DEMETER-driven forecasts were skilful, better than climatology and persistence forecasts

• Bias correction of temperature is important even if variability in temperature not important - temperatures must be "realistic" for the application model

• Bias correction of rainfall is unsatisfactory - use of daily rainfall output is problematic and need to consider other methods using monthly anomalies instead (e.g. weather generator)

• Lag in model mean malaria cases may occur outside forecast window - can be solved for Botswana using mosquito model but not applicable to other areas


1 Modelling malaria in Africa driven by DEMETER forecasts Anne Jones Department of Geography...

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