Presented at Speke Resort ,Munyonyo, Kampala, Monday 16th November 2015
†
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
 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
r
h
Culex
eggs
Aedes
eggs
t0Jan Dec
t20
h
Aedes
eggs
r
Culex
eggs
t0
Jan Dec
AdultDensityAdultDensity
 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
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
 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.
 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%
 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.
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
 Seropositivity was
evenly distributed.
 Visual
examination
suggests high
correlation of
seropositivity with
waterbodies,
forests.
 Spatial
dependency was
not tested.
 R-INLA for
spatiotemporal
analysis??
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.
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%.
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
 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
 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.
 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.
 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/
 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

Interepidemic Seroepidemiological Survey of Rift Valley Fever in Garissa, Kenya

  • 1.
    Presented at SpekeResort ,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/Elninoabove 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.
  • 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 ispersistent 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 Countya 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-sectionalstudy 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 analyiswas 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 SpeciesNumber 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 evenlydistributed.  Visual examination suggests high correlation of seropositivity with waterbodies, forests.  Spatial dependency was not tested.  R-INLA for spatiotemporal analysis??
  • 12.
    Goats Sheep Cattle LocationN 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 LevelsOR 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 detectionof 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 lackof 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 resultspresented 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 etal. 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 participantsfrom 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