Background: Rift Valley fever (RVF) is a vector-borne zoonotic disease that is caused by phlebovirus and transmitted primarily by aedes mosquitoes. RVF outbreaks have led to significant effects to human and animal health in the Horn of Africa and Arabian Peninsula. The economic impact of 1997-98, 2000 and 2006-2007 outbreaks due to massive livestock abortions, deaths, acute human illness and deaths was estimated at over $ 500 million. We hypothesize there is consistent virus circulation in RVF endemic areas of Northern Kenya and RVF epidemics have potential associations with environmental and climatic parameters. The objective of this study was to detect circulation of RVFV in goats, sheep and cattle in Garissa County, Kenya during the inter-epidemic period (IEP). Methodology: We performed a cross-sectional surveillance of ruminants in RVF high risk areas of Garissa County, Kenya. Periodic blood sampling of sheep, goats and cattle was done in March 2012 and July 2013. Serological analysis for total antiRVF antibodies for 370 ruminants was investigated using a multispecies competitive Enzyme-Linked Immunosorbent Assay (ELISA) kit. Host risk factors for RVFV seropositivity were examined by both univariable analysis and mixed effects logistic regression model. Unadjusted odds ratios (OR) for seropositivity were estimated using log linear regression model. Results: The overall seroprevalence for the 370 ruminants was 27.6%. Sheep (n= 87) and cattle (n= 12) had higher prevalence 32.2% (CI [20.6 -31]) and 33.3% (CI [6.7 -60]) respectively than goats (n = 271), 25.8% (CI [22.4 – 42]). Seropostivity in males was 31.8% (CI [22.2-31.8]) higher than 27% (CI [18.1-45.6]) in females. There was an increased likelihood of higher seropositivity in old (OR 18.24, CI [5.26 -116.4]), p < 0.0001) than young animals. Conclusions: This study demonstrates the widespread serological evidence and potential RVFV circulation among domestic ruminants in Garissa district thus indicative of an endemic reservoir of infection. There is need for increased preparedness and response in RVF endemic areas by conducting animal-human syndromic sero-surveillance as part of one health early warning system.

1. Presented at Speke Resort ,Munyonyo, Kampala, Monday 16th November 2015
†

2. Montgomery , 1912, Daubney 1931, Davies 1975, Jost et al., 2010, Nanyingi et al., 2015
 RVF is a viral zoonosis that occurs
in a (5-10yrs) cycle, it was 1st
described in Kenya(1912) & isolated
in 1931.
 Caused by a Phlebovirus in
Bunyaviridae(Family), transmitted by
mosquitoes: Aedes, culicine spp.
 RVFV is high impact
transboundary pathogen (OIE) and
category A select agent(CDC ).
 The RVFV genome has tripartite
RNA segments : large (L), medium
(M), and small (S) contained in a
spherical (80–120 nm in diameter)
lipid bilayer.
 Major epidemics have occurred
throughout Africa, Arabian Peninsula

3. 3
 Precipitation: ENSO/Elnino above
average rainfall leading flooding
( dambos”).
 Hydrological drivers of vector emergency:
(IEP transovarial maintenance by aedes 1º
and culicine 2º mosquitoes.(  vectorial
capacity/ competency)
 Dense green vegetation cover = Persistent
NDVI.(0.1 units > 3 months)
 Soil types: Solonetz, Solanchaks,
planosols (drainage/moisture).
 Elevation : altitude <1,100m asl (flooding)
Linthicum et al., 1999; Anyamba et al., 2009; Hightower et al., 2012

4. r
h
Culex
eggs
Aedes
eggs
t0Jan Dec
t20
h
Aedes
eggs
r
Culex
eggs
t0
Jan Dec
AdultDensityAdultDensity

5.  Humans
 Mild : IP(4-6 days)
Flu-like fever, myaglia,
joint pains, headache,
Neck stiffness,
photosensitivity,
inappetance & vomiting
 Severe:
Ocular form(Retinal)
blurred /Loss of vision
Meningoencephalitis
Memory loss,
Hallucinations,
Vertigo, convulsions,
lethargy and coma.
Neurological deficit
Haemorrhagic icterus
Jaundice, hematemesis
Hematochezia,
Ecchymoses. CFR≥50%
OCFR≤1%
 Animals :
Cattle, sheep,camels
Goats & Buffaloes
 Hyperacute form
Sporadic abortions
indicative of epidemic
Pyrexia (40-42 C)
Sudden death.
 Acute form
Death (24-48hrs)
Jaundice
Mortality rates up 60%
 Subacute and
inapparent forms
Detectable by serology
Burden to older animals
Decreased production
 1997/98 & 2006/7 EA outbreaks
led to livestock mortality, trade
losses $500 Million
 .
 Estimated 158 human deaths, 3.4
DALYs per 1000 people.
 Trade disruptions and High
intervention costs (vaccinations)
WHO, FAO factsheets, Bird et al., 2009

6. Hypothesis:
 There is persistent RVF virus circulation in disease endemic
areas of Northern Kenya and epidemics have potential
associations with environmental and climatic parameters
Objective:
 To detect circulation of RVF virus in ruminants in Garissa
County, Kenya during an inter-epidemic period (IEP).
Bird et al., 2009

7.  Garissa County a semi-arid zone in North eastern
part of Kenya, bordering Somalia to the East.
Located between latitude 0° 58’ N and 1° 30’ S and
longitudes 38° 34’ E and 41°05’ W
 It covers approximately 33,620 km2, with a
population of 623,060 persons and 1.5 million
livestock.
 Garissa County has low altitude ranging from 70-
400 m above sea level.(Flood plain)
 It experiences long rains (MAM) and short rains
(OND) with annual averages of 300-600 mm and
diurnal temperature ranges of 20-38°C.
 Sampling: Danyere, Kone and Sankuri, Korakora,
Bouralgi, Disso and Yumbis and Hulugho divisions.

8.  A cross-sectional study conducted in March 2013 and July 2014 .
 A multistage sampling : Two stage cluster sampling technique, with
divisions selected a priori , the herd was used as PSU.
 415 animals were sampled from the identified herds by jugular
venipuncture into vacutainer tubes.
 370 Serum samples collected from sheep, goats and cattle were
analyzed for total antiRVFV (IgG) antibodies using a competetive indirect
competitive ELISA (cELISA)-ID Vet®.
 Absorbance (Optical density-OD) was measured at 450 nm using a
microplate reader, Gen5 v1.05 software (BioTek) was used for data
analysis.
 Serological positivity was detectable as suspect or negative (S/N %)
value of ≤ 40%[21]. Samples with S/N ≥ 40 - ≤ 50%

9.  Descriptive analyis was done for host demographic characteristics:
 Host risk factors for RVFV seropositivity were examined by univariable
analysis. Unadjusted odds ratios (OR) for seropositivity were estimated
using log linear regression model.
 A mixed effects logistic regression model MELM (glmer) was used to
determine the association of (Age, Sex, Species) on RVF
seroconversion with location as the random effect.
 Using ArcGIS 10.2.2 (ESRI, 2014), GPS data were imported in
ArcMap. hydrological layers were overlaid on the county and
hydrological profile.
 All statistical analyses was performed using R version 3.1.3 software.

10. Study location Species Number sampled Age
Male Female ≤12months >12months
Bouralgi Cattle 0 0 0 0
Goats 0 14 2 12
Sheep 2 9 3 8
Danyere Cattle 0 0 0 0
Goats 1 27 0 28
Sheep 2 14 0 16
Disso Cattle 0 0 0 0
Goats 2 17 10 9
Sheep 1 15 4 12
Hulugho Cattle 0 0 0 0
Goats 1 12 0 13
Sheep 3 13 3 13
Kone Goats 9 100 25 84
Sheep 3 12 1 14
Korakora Cattle 2 10 6 6
Goats 0 19 0 19
Sheep 1 5 0 6
Sankuri Goats 12 26 11 27
Sheep 0 0 0 0
Yumbis Goats 3 28 7 24
Sheep 2 5 0 7
Total 44 326 72 298

11.  Seropositivity was
evenly distributed.
 Visual
examination
suggests high
correlation of
seropositivity with
waterbodies,
forests.
 Spatial
dependency was
not tested.
 R-INLA for
spatiotemporal
analysis??

12. Goats Sheep Cattle
Location N SP (%) 95% C.I N SP (%) 95% C.I N SP (%) 95% C.I
Bouralgi 14 50 23.8-76.2 11 45.5 16-74.9 _ _ _
Danyere 28 35.7 18-53.5 16 37.5 13.8-61.2 _ _ _
Disso 19 5.3 4.3-15.3 16 12.5 3.7-28.7 _ _ _
Hulugho 13 76.9 54-99.8 16 31.2 8.5-54 _ _ _
Kone 109 16.5 9.5-23.5 15 40 15.2-64.8 _ _ _
Korakora 19 36.8 15.2-58.5 6 16.7 13.2-46.5 12 33.3 6.7- 60
Sankuri 38 21.1 8.1-34 _ _ _ _ _ _
Yumbis 31 29 13.1-45 7 42.9 6.2-79.5 _ _ _
 The overall RVFV IgG antibody seroprevalence of the 370 analyzed
sera from all species in the 8 study locations was 27.6% (CI 23, 32.1).
 The overall seropositivity for cattle, sheep and goats was 33.3%
(4/12), 32.2 % (28/87) and 25.8% (70/271) respectively.

13. Total
sampled
RFV
positive
Seroprevalence (%)
confidence interval (C.I)
Goats sex Female 243 64 26.3 (20.8-31.9)
Male 28 6 21.4 (6.2-36.6)
Age >12months 216 69 31.9 (25.7- 38.2)
≤12months 55 1 1.8 (-1.7-5.3)
Sheep sex Female 73 22 30.1(19.6-40.7)
Male 14 6 42.6 (16.9-68.8)
Age >12months 76 27 35.5 (24.8-46.3)
≤12months 11 1 9.1(-7.9-26.1)
 The overall seroprevalence for all male species was 31.8% and
females 27%.

14. RVF Seroprevalence
Variable Levels OR 95%CI p value
Sex Female 1* - -
Male
1.17 0.55-2.48 0.65
Species Caprine 1* _ -
Bovine
1.07 0.55-15.87 0.19
Ovine
1.05 0.58-1.88 0.86
Age <12 1* - -
>12
18.91 5.51-120.17
< 0.0001†††
 Seropositivity increased with advanced age, animals >12 months old
had an 18 fold likelihood to be seropositive than animals ≤12 months
OR= Odds Ratio, CI= Confidence Interval, *= Reference level, † = Significance level

15.  The detection of RVFV IgG antibodies in ruminants from Garissa
County during inter-epidemic (IEP) period, corroborates earlier studies
reporting high seropositivity .
 Increased likelihood for high RVF seropositivity with age has been
demonstrated in Mozambique, Madagascar and Tanzania in livestock
and humans, hence illustrating the one health dimension in disease
transmission.
 Human longitudinal studies in Garissa have indicated presence of
RVFV that was likely be related to livestock migration via
transboundary trade from Somalia.
 IgG antibodies suggest a previous exposure of animals to RVFV and
may indicate sub-clinical circulation.
High correlation of animal- human cases in Garissa. In 2006, 11
human deaths due to RVF was reported and in 2011 >1000 humans
had 15% seroprevalence for RVFV.
⃰ Lichoti et al., 2014 , Owange et al., 2015, Fafetine et al ., 2013, Heinrich et al., 2012

16.  The lack of financial resources greatly influenced the sampling ability
to obtain the optimal effective sample size (ESS) of study animals.
 There was skewed distribution in sampled species by age and sex,
difficulty in determining the exact age of animals and livestock density.
 Logistic challenges led to our inability to restrain and hence sample
more cattle in other locations. therefore no remarkable statistical
inferences can be concluded on this species.
 This is a one point estimate of disease in a highly mobile animal
population and may not account for host migration patterns and
prospective longitudinal cohort studies may be recommended.

17.  The results presented here suggest long-term animal exposure to
RVFV in the area and confirm that high proportions of animals in
Garissa County are still at risk of RVF infection.
 This is the first study to estimate ICC() for RVF in Garissa and forms
basis for estimating ESS in multistage cluster sampling for studies
investigating low contagious infectious vector-borne diseases
 There is need for increased preparedness and response in RVF
endemic areas by conducting animal-human syndromic sero-
surveillance as part of one health early warning system.
 Molecular and ecological investigations focusing on pathogen
discovery in vectors and soils should be fostered at a regional level as
part of one health EWS outbreak preparedness.

18.  Anyamba et al. Prediction, assessment of the Rift Valley fever activity
in East and Southern Africa 2006-2008 and possible vector control
strategies. Am J Trop Med Hyg. 2010;83(2 Suppl):43-51.
 Centers for Disease Control, Prevention. Rift Valley Fever--East
Africa, 1997-1998. MMWR Morb Mortal Wkly Rep. 1998;47(13):261-4.
 Hightower et al. Relationship of climate, geography, and geology to
the incidence of Rift Valley fever in Kenya during the 2006-2007
outbreak. Am J Trop Med Hyg. 2012;86(2):373-80.
 LaBeaud et al. Spectrum of Rift Valley fever virus transmission in
Kenya: insights from three distinct regions. Am J Trop Med Hyg
2007;76:795–800.
 Nanyingi et al. A systematic review of Rift Valley Fever epidemiology
1931-2014. Infect Ecol Epidemiol. 2015;5:28024
 WHO. Rift Valley Fever in Kenya, Somalia and the United Republic of
Tanzania 2007. http://www.who.int/csr/don/2007_05_09/en/

19.  Study participants from Garissa County(animal owners)
 Drs. Jackson Kinyua, Rashid Mohammed, Stephen Gathogo for
administrative, logistical support and expert guidance during field
sampling
 Ngatia Mathenge and Nahashon Thuo for their assistance in animal
sampling and sample preparation.
DVS(CVL),KEMRI-CDC,USAMRU
Contact : mnanyingi@kemricdc.org, mnanyingi@gmail.com