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1Swadhin Behera, 2Masahiro Hashizume, 2Ataru Tsuzuki, 2Chisato Imai, 1Takeshi Doi, 1Yushi Morioka, 1Takayoshi Ikeda, 1Jayanthi Ratnam, 6Adrian Tompkins, 3Iwami Shingo, 4Philip Kruger, 5Raj Maharaj, and 2Noboru Minakawa 1Application Laboratory, Japan Agency for Marine-Earth Science and Technology, Yokohama, Japan. 2Institute of Tropical Medicine, Nagasaki University, Japan. 3Department of Biology, Kyushu University, Japan 4Department of Health, Limpopo, South Africa 5Medical Research Council, Durban, South Africa 6ICTP, Trieste, Italy

Disease transmission model

Regional climate model

(southern Africa)

Early Warning systems

District level climate model

Weather data

Refinement

Disease surveillance

systems

Downscaling Refinement

Disease prediction model

Weather and climate

information

ACCESS: Applied Center for

Climate & Earth System Science

CSIR, SAWS, UP, UV, UL,UWC

JAMSTEC: Japan Agency for Marine-

Earth Science and Technology

MRC: South African Medical

Research Council

NEKKEN: Institute of Tropical

Medicine, Nagasaki University

ACCESS & JAMSTEC

NEKKEN-MRC & UWC

ALL

MRC

JAMSTEC-CSIR-SAWS

A SATREPS project to establish a climate-based early warning system for the infectious diseases in southern Africa (iDEWS)

JAMSTEC-

CSIR-SAWS

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JICA/JST SATREPS (Science and Technology Research Partnership for Sustainable Development) Framework

http://www.jst.go.jp/global/english/index.html

Vhembe, Limpopo

Monthly incidence rate

IR

SON

DJF

J F M A M J J A S O N D

Temporal Analysis: Climate Links

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The strongest association with rainfall from 2 weeks before.

Time-series DLNM

Imai, Hasizume et al. 2016

Malaria

ISOD

Temp

Rain

Nino12

Nino3

Simple correlation

Nino12a

Nino3a

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Modes of Climate Variations

El Nino Modoki

Spatial Analysis : SOM & Regional Links

• Malaria case data (1998 Jan – 2014 Nov) -> incidence rate anomaly

• Malaria patterns were found for seasons SON, DJF, MAM

Ikeda et al. 2016

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Precipitation (SON)

Lag 0

Lag 1

Lag 2

Lag 3

Lag 4

Wet

Dry

Maximum Temperature (SON) Lag 0

Lag 1

Lag 2

Lag 3

Lag 4

Warm

Cool

Zonal Wind (SON)

Lag 0

Lag 1

Lag 2

Lag 3

Lag 4

Positive

Negative

Lag 0

Lag 1

Lag 2

Lag 3

Lag 4

Warm

Cool

SST (SON)

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The SINTEX-F1 seasonal prediction system (developed at JAMSTEC under the EU-Japan collaboration)

Initialization: SST-nudging system

9 ensemble members (3 nudging strength × 3 coupling physics)

Hindcast experiments (every month initialization in 1982—present, 2yr-lead time)

Real-time seasonal forecast & outlook

AGCM OGCM Coupling Sea Ice

SINTEX-F1 ECHAM4.6

T106L19

OPA8.2

2×(0.5-2) L31

Every 2 hour

No flux correction

No

SST-nudging run

(initialization)

OGCM restart files

AGCM restart files

~1982

2015.4.1 2015.4.30 2015.5.1

Forecast run (free run)

Current system (e.g. Prediction from May. 1st 2015 )

2017.4.30

(Luo et al. 2003)

Model Developments: Climate Model

9-member mean (1982-2004)

based on a semi-

multimodel ensemble

prediction system

Luo et al., J. Climate, 2005b.

>0.9

SINTEX-F Performance

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Model Developments: Malaria Model VECToR borne disease community model of ICTP, TRIeste

(VECTRI)

Fraction of grid box covered by pond breeding sites

Vector mosquito density

Larvae density

Larvae biomass

Entomological inoculation rate (number of

infectious bites) number person/day

Ratio of mosquitoes to people

Human bite rate

The parasite ratio (proportion of population with

parasite in blood)

The proportion of population with detectable

malaria

Some of the interesting outputs of the VECTRI

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In the experiment model is initialized with climate

reanalysis data on 31 December 2000.

Since the spatial data for vectors, parasites and

infected population is not accurately known, the

model starts with an assumption that there is a

prevalence of 5% of the population with parasite in

blood.

Population density is considered as realistic as

possible.

The VECTRI Experiment for southern Africa

VECTRI preliminary results: 2001-11 mean population

proportion with detectable malaria

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VECTRI preliminary results:

Proportion of population with detectable malaria

Summary Time series analysis shows interesting association of large-

scale climate variability and malaria incidences in southern

Africa

SOM and composite analysis show high/low malaria

incidence patterns linking with lagged effects of climate

patterns (at least 2 months):

Max temperature in Limpopo

Precipitation neighboring Limpopo (e.g. Mozambique)

Winds from east, over warmer SST

An early warning system is under development using

SINTEX-F climate model and VECTRI malaria model under

iDEWS

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Projected change in

annual-average

temperature and

rainfall over Africa,

for the time period

2071–2100 relative

to 1961–1990. The

90th percentile (left),

median (right)

Ensemble of CCAM

downscalings of six

GCM projections.

Engelbrecht et al

2015