Post on 23-Feb-2022
transcript
11/05/2016
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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