Comparative performance and agreement of Machine learning algorithm (Boosted Regression Trees [BRT]) and a Bayesian geostatistical model (INLA) in modelling of RVF

1. Spatial Predictive RiskModelling of Rift
Valley Feverin Garissa, Kenya
Presented at 5th MVVR Conference at Boma Hotel Nairobi, 7-8th December 2017
Dr. Nanyingi Mark
GachieT, Muchemi GM, Thumbi SM , KiamaSG & Bett B
Theme:
“Onehealth- AcceleratingVirus ResearchinEastAfrica”

2. History, Etiology and Epidemiology
(Montgomery , 1912, Daubney 1931, Jost et al., 2010, Nanyingi et al., 2015)
 Rift Valley Fever isvector borneviral zoonosisoccurring cyclically (5-10 yrs),
described first in Kenyain 1912, isolated in 1931 in ruminants
 Historical Epidemics : Africa and Arabian Peninsula; in Egypt (1977), Kenya
(1997–1998, 2006-2007), Saudi Arabia (2000–2001) and Yemen (2000–2001),
Sudan (2007) and Mauritania(2010)
 Etiology: Phlebo virus, Bunyaviridae (F), S-(ve)-RNA Linear genome(L,M,S)
 Epidemiology: Economic burden and public health impacts(Livestock-
Abortions, morbidity & mortality $US470M, CFR=0.5-2% upto 50% in
haemorrhagic phase, 3.4 DALYs/1000)
 Risk factors:
 High Precipitation: > 600mm, Elevation: <1100 masl
 Mosquito Vectors: Aedes, culicines,manso nia
 Vegetation (NDVI: 0.1 units> 3 months)
 Soil types: Solonetz, Solanchaks, planosols

3. Objectives
To determinetherelationship between (environmental &
climatic) driversand seroprevalenceof Rift Valley Fever
in Garissa
To develop risk maps for spatial prediction of Rift
Valley Fever outbreaksin Garissa, Kenya
To comparemodel performance/agreement between
machinelearning SDM and bayesian geo-statisticsin
prediction of RVF.

4. Methodology : Study Area
Nanyingi et al., 2016

5. Disease data and Covariates
Nanyingi et al.,
2016
Input covariate Time
Period
Spatial
Resolution
Source (All data used is open source)
Rainfall 2013-2014 6 km2
http://chg.geog.ucsb.edu/data/chirps
Temperature 2013-2014 1km2
https://webfiles.york.ac.uk/KITE/AfriClim/
EVI Fixed-time 250 m2
https://earthexplorer.usgs.gov
Soil types Fixed-time 1km2
http://data.ilri.org/geoportal
Distance to rivers and
waterbodies
Fixed-time 1km2
http://data.ilri.org/geoportal
Digital Elevation Model
(DEM)
Fixed-time 90 m2
https://earthexplorer.usgs.gov
Human population 2014 100 m2
http://www.worldpop.org.uk
Goat and sheep
densities
2014 1km2
http://www.livestock.geo-wiki.org
RVF sero-positivity (Presencepoint datan= 16)
Explanatory variables: Demographic and environmental covariates
Hijmans & van Etten, 2012
(Geodetic processing and resampling to spatial resolution of 1km2 WGS 84)

6. Spatial regression and Bayesian geostastical modelling
Boosted Regression Trees(BRT)
�ሺ�ሺ= ��ሺ�ሺ =
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���ሺ�;�� ሺ
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Integrated Nested Laplace Approximation(INLA)
…….(1)
…….(2)
iη =Linear predictor linked to serostatus ,yi=seropositivity(+/-),
β0 = scalar intercept, y=β coefficients
xy, and f(zi)= spatial random effect functions.
 Stochastic Partial Differential Equationsapproach (SPDE) to fit aGaussian
random effect (GRE) to account for spatial autocorrelation, diseaseclumping,
clustering tendency, sampling bias.
(Elith et al., 1998; Rue et al., 2009; Lindgren, Rue, & Lindström, 2011; Bett et al.,

7. 7
RESULTS: Relative influence of covariates (BRT)

8. RESULTS : Spatial predictions of RVFoccurrence
AUC of ROC =
(0.7 ±0.001 s.d).
AUC of ROC =
(0.9 ±0.001 s.d).
INLA/BRT
global correlation,
r =0.44).
Models agreement
BRT INLA
Probability risk
Probability risk

9. 9
Discussion
 Risk factors: Significant transmission risk driversfor RVF
occurrencewerehigh precipitation, high human and/or livestock
densities
 Models performance: BRT overfitting “bias” in NW partsdue
to noisy classification and clustering whileINLA by spatial
random-effect accounted for spatial autocorrelation predicting
high risk in low risk areas.
 Real time risk mapping: INLA enablesanalysesof
surveillancedatain near realtime and risk mapscan be
automatically updated reducing thetimefrom field data
collection to reporting.
 Confounders : Consideration of temporal effectsassociated
with climatic variation, host population migration dynamicsmay
improveprediction.

10. 10
Conclusions
 Clumping /Clustering: High risk of RVF occurrence was in
NW Garissa with multiple foci of medium to low risk around
perennial water bodies (endemic foci due to host aggregation,
viral amplification).
 Model agreement: Comparisons of models performance leads
to greater confidence and specificity in predictions (Machine
learning vs Bayesian geostatistical models predicted risk in
similar areaswith AUC (0.7- 0.9).
 Targeted surveillance: Thespatially explicit, high-resolution
maps(1×1km) identify areaswheresurveillanceand intervention
measuresshould betargeted to reduceviral spillage/dispersion
and for herd vaccination.

11. 11
Recommendations
 Sentinel surveillance: The risk maps can be used for
establishment of human/animal sentinels and targeted sampling
to increasethechancesof detecting thevirus.
 Early warning systems : Integrating of processbased and
host(human/animal)-vector stochastic modelling to explain the
expanding RVF geographic range.
 Risk analysis and disease control :Sentinel surveillanceto
guidepolicy decision-makersto prioritizeintervention areasby
cost-effectiveresourcesallocation.

12. Acknowledgements
Contact : Dr. Mark Nanyingi (mnanyingi@gmail.com)