Am. J. Trop. Med. Hyg., 108(5), 2023, pp. 995–1002
doi:10.4269/ajtmh.22-0410
Copyright © 2023 The author(s)
Detection of Sporadic Outbreaks of Rift Valley Fever in Uganda through the National Viral
Hemorrhagic Fever Surveillance System, 2017–2020
Luke Nyakarahuka,1,2* Shannon Whitmer,3 John Klena,3 Stephen Balinandi,1 Emir Talundzic,3 Alex Tumusiime,1
Jackson Kyondo,1 Sophia Mulei,1 Ketan Patel,3 Jimmy Baluku,1 Gloria Akurut,4 Diana Namanya,4 Kilama Kamugisha,4
Caitlin Cossaboom,3 Amy Whitesell,3 Carson Telford,3 James Graziano,3 Joel Montgomery,3 Stuart Nichol,3 Julius Lutwama,1
and Trevor Shoemaker3
1
Department of Arbovirology, Emerging and Reemerging Infectious Diseases, Uganda Virus Research Institute, Entebbe, Uganda;
Department of Biosecurity, Ecosystems and Veterinary Public Health, Makerere University, Kampala, Uganda; 3Viral Special Pathogens Branch,
Division of High-Consequence Pathogens and Pathology, U.S. Centers for Disease Control and Prevention, Atlanta, Georgia;
4
Uganda Wildlife Authority, Kampala, Uganda
2
Abstract. Rift Valley fever (RVF) is a zoonotic disease of public health and economic importance. Uganda has
reported sporadic outbreaks of RVF in both humans and animals across the country, especially in the southwestern part
of the “cattle corridor” through an established viral hemorrhagic fever surveillance system. We report 52 human cases of
laboratory-confirmed RVF from 2017 to 2020. The case fatality rate was 42%. Among those infected, 92% were males
and 90% were adults ($ 18 years). Clinical symptoms were characterized by fever (69%), unexplained bleeding (69%),
headache (51%), abdominal pain (49%), and nausea and vomiting (46%). Most of the cases (95%) originated from central and western districts that are part of the cattle corridor of Uganda, where the main risk factor was direct contact with
livestock (P 5 0.009). Other predictors of RVF positivity were determined to be male gender (P 5 0.001) and being a
butcher (P 5 0.04). Next-generation sequencing identified the predominant Ugandan clade as Kenya-2, observed previously across East Africa. There is need for further investigation and research into the effect and spread of this neglected
tropical disease in Uganda and the rest of Africa. Control measures such as promoting vaccination and limiting
animal–human transmission could be explored to reduce the impact of RVF in Uganda and globally.
INTRODUCTION
Mosquitoes, which have been described as competent vectors. In Uganda, RVFV RNA has been detected in both mosquito genera.3 Other mosquito species may also be involved in
the transmission of RVFV, and mechanical transmission of
RVFV has been documented for hematophagous dipterans
and other insect vectors.6 Humans are infected most often
through contact with infected livestock or their by-products.7 A
study conducted in Uganda8 found that abattoir workers who
are regularly in contact with livestock body fluids were at significant risk of seropositivity to RVFV. That study also reported an
RVFV seroprevalence of 27% in cattle and 13% in humans.8
Similar studies in other countries also show that risk for human
infections is mainly from contact with livestock, but transmission
can occur by mosquito bite.9,10 Risk groups include occupations associated with farming, such as veterinarians, herdsmen, and farmers who cultivate the bush, where they interact
with mosquito vectors with zoophilic potential.11 Person-toperson transmission of RVFV has not been documented.12
Since 2010, Uganda has had the capacity to detect RVF
infections, together with other VHFs, at the Uganda Virus
Research Institute (UVRI) through the National Viral Hemorrhagic Fever Surveillance Program, where samples from
humans with suspect VHFs are submitted and tested routinely.13 After the detection of three human cases reported in
Kabale in 2016,3 Uganda has continued to report an increase
in sporadic outbreaks of human RVF with associated mortality. We describe the emerging epizootic of RVF human cases
reported from 2017 to 2020 as documented through the
national VHF surveillance system, focusing on the epidemiological and laboratory characteristics and findings.
Rift Valley fever (RVF) is a zoonotic viral hemorrhagic fever
(VHF) caused by infection with Rift Valley fever virus (RVFV),
a negative-sense enveloped RNA virus in genus Phlebovirus
and the recently reclassified family Phenuiviridae.1 Rift Valley
fever virus was first described in the early 1900s in the Rift
Valley region of Kenya when it caused outbreaks in livestock.
The virus has caused outbreaks both in animals and humans
mainly in East Africa, but it has also been reported in Sudan,
Saudi Arabia, and Yemen.2 In 2016, Uganda reported the
first outbreak of RVF since 1963, when RVFV was confirmed
in samples from three humans and one goat from the southwestern district of Kabale.3 Rift Valley fever is a disease of
public health and economic importance, affecting humans
and livestock as well as international trade. It affects livestock production by reducing milk yield and causing abortions; however, most animals do not have obvious clinical
symptoms. Also, most human infections do not exhibit
severe clinical signs or symptoms. Documented clinical
symptoms in humans include fever, headache, nausea, musculoskeletal pain, diarrhea, vomiting, cough, and bleeding
from body orifices, characteristic of severe forms of VHFs.4
Rift Valley fever virus is diagnosed by the detection of viral
RNA in the blood of infected persons, animals, or mosquitoes by reverse transcription–quantitative polymerase chain
reaction (RT-qPCR), but anti-RVFV antibodies indicating
recent or past infection can also be detected by serological
approaches such as ELISA.5
Rift Valley fever virus is transmitted predominantly between
animals and humans by Aedes spp. and Culex spp.
METHODS
*Address correspondence to Luke Nyakarahuka, Collage of Veterinary
Medicine, P.O. Box 7062, Pool Road, Makerere University, Kampala,
Uganda. E-mail: nyakarahuka@gmail.com
Case Reporting. Rift Valley fever suspect cases are
reported through the national VHF surveillance system
995
996
NYAKARAHUKA AND OTHERS
coordinated through UVRI and the Ministry of Health using a
VHF suspect case definition as described previously.13 This is
achieved by both passive and active surveillance approaches
coupled with a sentinel VHF surveillance system. Viral hemorrhagic fever case definitions are distributed throughout health
facilities in Uganda according to the Integrated Disease Surveillance and Response guidelines, through the VHF surveillance system, and via VHF sentinel surveillance sites. The
suspect case definition is a patient with acute illness, fever
. 38 C, no clear alternative diagnosis, and at least four of the
following signs or symptoms: vomiting/nausea, diarrhea, muscle or joint pain, chills, rigors, intense fatigue, abdominal pain,
skin rash, difficulty in swallowing, headache, or unexplained
bleeding from any site.
Physicians assess patients presenting at health facilities
for suspected VHF. If the patients meet the VHF case definition, a blood sample is collected according to internationally
recognized biosafety and biosecurity measures.14 During
outbreaks, individuals are also included if they have high-risk
contact with a confirmed case or a high-risk exposure,
regardless of whether they meet the case definition. When
identified, individuals with suspect infection are isolated. To
collect blood samples, physicians and laboratory technicians don appropriate personal protective equipment (PPE).
The official Uganda VHF Case Investigation Form is completed for every suspect case and it accompanies the sample. Samples are triple-packaged and transported to the
VHF referral laboratory at UVRI Entebbe via the national
sample transport system.15 Upon arrival at UVRI, samples
are assessed for quality and appropriateness. RNA is extracted and tested for the presence of VHFs, including
RVFV, by RT-qPCR.16 For some samples, IgM and IgG antibodies targeting RVFV are detected by ELISA using methods
described previously.2,3,5
Confirmed Case Investigations. The UVRI VHF program
staff investigates all confirmed human cases to understand
the epidemiology of the diseases more fully. For RVF, investigations also focus on identifying the possible source of
infection (e.g., from a mosquito vector, direct contact with
infected livestock or their body fluids). Case investigation
questionnaires are administered to patients or their attendants. Also, geographic coordinates are collected to map
the location of cases and, later, to study the environment
where the patients are living. For homesteads with livestock
residing near patients, animal samples are collected according to zoonotic disease outbreak investigations, as available,
to assess potential zoonotic transmission. During field investigations, health education and outreach are conducted,
because communities want and need to understand aspects
of the epidemiology of RVF, and control and prevention
strategies are discussed as well. Health education posters
are supplied that are designed to provide information based
on data and observations from previous investigations and
knowledge assessments.17 These activities were reviewed
by the CDC and were conducted consistent with applicable
federal law and CDC policy.1
Data Management. Epidemiological data was managed
using the EpiInfo database18 and exported to R-studio
(R Foundation for Statistical Computing, Vienna, Austria) for
additional analysis. Data were analyzed for bivariate associations using the x2 test for normally distributed variables or
Fisher’s exact test for nonparametric variables. The logistic
regression model was used to assess for risk factors. We
compared RVF confirmed cases with suspect individuals
who tested negative for RVF. Those who tested negative
were either family members of the confirmed cases or individuals who lived in the same area as confirmed cases with
the same exposures.
RNA Extraction, NGS Library Preparation, bioinformatics
and phylogenetics. RNA was extracted from blood or
serum specimens with Tripure and Zymo RNA Clean and
Concentrator-25 kits at the BSL-4 laboratory at the U.S.
CDC or with MagMax in the High Containment Laboratory at
UVRI, Uganda. RNA was DNase-treated and NGS libraries
were made using the NEBNext Ultra II Directional RNA
library prep kit. Libraries were pooled to low plexy (three- to
six-plex) or were run individually on a MiSeq v2 300 cycle,
MiniSeq High output 300 cycle, or iSeq 300 cycle cartridge.
Initial RVFV genomes (20190877, 20190879, and 20190880)
were assembled using a guided de novo approach with viralngs (version 1.22.1; Broad Institute) and a custom-made
RVFV-specific lastal database. The 201902760 genome was
built by mapping reads to the 201900879 reference genome
using in-house scripts [Illumina index removal with cutadapt,
quality trim reads with prinseq-lite (-min_qual_mean 25 -trim_
qual_right 20 -min_len 50), mapped to reference with minimap2, removal of PCR duplicates with picard MarkDuplicates
and consensus genomes called with Geneious (threshold 0%,
majority; assign quality 5 Total; call N if coverage , 2)]. All
other consensus genomes were constructed using 1) de novo
assembly and blasting of contigs to identify the closest reference sequence in GenBank, followed by iterative mapping of
reads and contigs to the closest reference sequence; and
2) iterative mapping of reads and contigs to 201900879 and
201902760 genomes, which are representative members of
two separate RVFV clades in Uganda. Consensus genomes
were called from bam files with the best read coverage using
iVar (version 1.3, -m 2 -n N).19 For two genomes (201902733
and 201902754), mapping of reads and contigs introduced
large indels or frameshifts. In these cases, reads were remapped to the closest matching reference. To build phylogenetic trees, all available full-length S, M, and L RVFV segments
were downloaded from GenBank and aligned using MAFFT
(version 7.450), and trees were constructed with RAxML (version 7.3.0, -m GTRGAMMA -p $RANDOM -f a -x $RANDOM
-N 1000). Trees were visualized with ggtree and clades were
labeled according to Grobbelaar et al.20 and Samy et al.21
GenBank accession no. are ON060771 to 838.
RESULTS
Epidemiological Description of the Cases. From January 2017 to December 2020, RVFV was confirmed in 52
cases using either RT-qPCR (n 5 37) or ELISA (n 5 15).
Thirty-eight cases (73%) were detected through passive surveillance, whereas 14 cases (27%) were detected through
outbreak investigations (Table 1). The mean age of the cases
was 31.6 years (SD 5 15.5 years), with an age range of 10 to
69 years, although adults $ 18 years represented 90% of
cases. Ninety-two percent of cases (48 of 52) were male.
Almost half the confirmed cases died, representing a case
fatality rate (CFR) of 42% (22 of 52). Most of the patients
(55%) reported being in contact with livestock either through
tending to them as herdsmen or through slaughter. The largest
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SPORADIC RIFT VALLEY FEVER OUTBREAKS IN UGANDA
TABLE 1
Sociodemographic variables for RVF cases (N 5 52) detected in
Uganda, 2017 to 2020
Characteristic
Status
Alive
Dead
Surveillance method
Active
Passive
Age, years
, 18
$ 18
Gender
Female
Male
Region of Uganda
East
West
Central
Farmer occupation
Butcher occupation
Travel history
Contact with animals
n (%)
30 (58)
22 (42)
14 (27)
38 (73)
5 (10)
45 (90)
4 (7.7)
48 (92)
3
36
13
23
9
4
22
TABLE 2
Clinical symptoms of the patients confirmed of having RVF
Characteristic
Fever present
Unexplained bleeding
Intense fatigue
Anorexia
Abdominal pain
Headache
Vomiting and nausea
Diarrhea
Muscle pain
Joint pain
Jaundice
Chest pain
Conjunctivitis
Sore throat
Difficulty breathing
Hiccups
Cough
Skin rash
% (n/N*)
69
69
61
61
49
51
46
38
36
32
31
28
26
24
19
15
11
3.0
(31/45)
(31/45)
(23/38)
(22/36)
(19/39)
(19/37)
(18/39)
(15/40)
(13/36)
(12/37)
(11/35)
(10/36)
(9/34)
(8/34)
(7/36)
(5/34)
(4/35)
(1/33)
(5.8)
(69)
(25)
(44)
(17)
(10)
(55)
RVF 5 Rift Valley fever.
* Denominator (N) indicates records with complete data in the respective variable. Missing
records were dropped during analysis.
proportion of cases occurred in districts inside the “cattle
corridor” of Uganda (67%, n 5 36), especially in southwestern
Uganda, followed by districts in Central Uganda (25%, n 5 13).
Individual cases were also identified in the northern districts
of Yumbe and Obongi, and the eastern district of Iganga
(Figure 1). Most of the patients had fever (69%) and unexplained bleeding (69%) (Table 2). Other symptoms included
diarrhea (38%), abdominal pain (49%), intense fatigue (61%),
headache (51%), and anorexia (61%). Although outbreaks
seemed to follow a seasonal pattern, following the heavy rains
of March, April, and May in year 2018, they did not follow a
similar pattern for the rest of the years (Figure 2). However, it
is important to note that 2018 had heightened surveillance
because of Ebola virus disease (EVD) outbreak in the neighboring Democratic Republic of the Congo (DRC).22
When comparing individuals who tested positive for RVFV
with those who tested negative from the same homesteads,
statistically significant relationships were found for most
sociodemographic factors (Table 3). Bivariate analysis using
a x2 test found that RVF positivity was associated with age,
gender, geographic region of Uganda, being a butcher, and
having contacts with livestock (Table 3). Multivariate logistic
regression found significant predictors of RVF positivity: male
RVF 5 Rift Valley fever.
FIGURE 1. Map of Uganda showing the location of Rift Valley fever (RVF)–confirmed human cases, cattle corridor of Uganda, and physical features.
K/E 5 K/E clade.
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NYAKARAHUKA AND OTHERS
FIGURE 2. Confirmed cases of Rift Valley fever in Uganda from 2017 to 2020.
gender (P 5 0.001), being a butcher (P 5 0.04), and having
direct animal contact (P 5 0.03) (Table 4).
We assessed clinical symptoms associated with RVF positivity (Table 5). In bivariate analyses, only cough (P 5 0.03) and
jaundice (P 5 0.02) were associated with RVF positivity. However, the multivariate logistic regression analysis revealed that
RVF positivity was best predicted by clinical symptoms of
fever, anorexia, intense fatigue, chest pain, cough, sore throat,
and conjunctivitis (Table 6).
In Figure 3, clades are highlighted in orange, yellow, or
green according to specimen collection locations in Figure 1.
Major clades are labeled according to Grobbalaar et al.20
and Samy et al.21 (in parentheses). Nodes with bootstrap
TABLE 3
Bivariate analysis assessing the association between RVF positivity
and sociodemographic factors
Characteristic
Overall
(N 5 146), n (%)
Status
Alive
113
Dead
33
Surveillance method
Active
14
Passive
132
Age, years
, 18
27
$ 18
117
Gender
Female
38
Male
108
Region
East
3
West
113
Central
30
Farmer
52
Butcher
11
Animal contact
48
RVF negative
(n 5 94), n (%)
RVF positive
(n 5 52), n (%)
P value
(77)
(23)
83 (88)
11 (12)
30 (58)
22 (42)
< 0.001
(10)
(90)
0 (0)
94 (100)
14 (27)
38 (73)
< 0.001
(19)
(81)
22 (23)
72 (77)
5 (10)
45 (90)
0.050
(26)
(74)
34 (36)
60 (64)
4 (7.7)
48 (92)
< 0.001
(2.1)
(77)
(21)
(36)
(7.5)
(38)
0
77
17
29
2
26
3
36
13
23
9
22
(0)
(82)
(18)
(31)
(2.1)
(31)
RVF 5 Rift Valley fever. P values in bold type are significant.
(5.8)
(69)
(25)
(44)
(17)
(55)
0.032
0.11
0.002
0.009
support . 70% are labeled in red, and the scale bar is in units
of substitutions/site.
Phylogenetic Relatedness of RVFV Strains Collected in
Uganda. Next-generation sequencing of RVFV RT-qPCR–
positive specimens identified two distinct clades circulating
within Uganda (Figure 3). We observed that the majority of
new RVFV sequences collected between 2017 and 2020
clustered in the Kenya-2 clade. This clade is distributed
broadly across East Africa and includes sequences collected
from a large epizootic outbreak in Kenya in 2006 to 2007, as
well as sequences collected in Sudan in 2007 and 2010, and
from Uganda in 2016 to 2020. The Kenya-2 clade has been
TABLE 4
Multivariate logistic regression model of sociodemographic factors
as predictors of RVF positivity in humans
Characteristic
Status
Alive
Dead
Age, years
, 18
$ 18
Gender
Female
Male
Farmer
No
Yes
Butcher
No
Yes
Travel history
No
Yes
Animal contact
No
Yes
OR
Ref
16.0
95% CI
P value
–
4.41–73.2
< 0.001
–
0.50–19.3
0.3
–
7.89–1,454
< 0.001
–
0.50–5.35
0.4
–
2.22–1,935
0.041
Ref
1.21
–
0.17–7.06
0.8
Ref
3.77
–
1.14–13.9
0.035
Ref
2.52
Ref
59.0
Ref
1.63
Ref
32.1
OR 5 odds ratio; Ref 5 reference; RVF 5 Rift Valley fever. P values in bold type are
significant.
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SPORADIC RIFT VALLEY FEVER OUTBREAKS IN UGANDA
TABLE 5
Bivariate analysis assessing RVF positivity and clinical symptoms
RVF test result
Characteristic
n
Fever
Unexplained bleeding
Vomiting and nausea
Diarrhea
Intense fatigue
Anorexia
Abdominal pain
Chest pain
Muscle pain
Joint pain
Headache
Cough
Difficulty breathing
Sore throat
Jaundice
Conjunctivitis
Skin rash
Hiccups
Overall (N 5 146), n (%)
109
118
106
102
98
95
104
90
100
91
101
89
92
93
85
88
89
88
81
75
52
41
64
49
58
27
42
33
57
21
13
18
17
18
6
7
(74)
(64)
(49)
(40)
(65)
(52)
(56)
(30)
(42)
(36)
(56)
(24)
(14)
(19)
(20)
(20)
(6.7)
(8.0)
Negative (N 5 94), % (n/N*)
78
60
51
42
68
46
60
31
45
39
59
31
11
17
12
17
9
3.7
(50/64)
(44/73)
(34/67)
(26/62)
(41/60)
(27/59)
(39/65)
(17/54
(29/64)
(21/54)
(38/64)
(17/54)
(6/56)
(10/59)
(6/50)
(9/54)
(5/56)
(2/54)
Positive (n 5 52), % (n/N*)
69
69
46
38
61
61
49
28
36
32
51
11
19
24
31
26
3
15
(31/45)
(31/45)
(18/39)
(15/40)
(23/38)
(22/36)
(19/39)
(10/36)
(13/36)
(12/37)
(19/37)
(4/35)
(7/36)
(8/34)
(11/35)
(9/34)
(1/33)
(5/34)
P value†
0.3
0.3
0.6
0.7
0.4
0.15
0.3
0.7
0.4
0.5
0.4
0.030
0.4
0.4
0.028
0.3
0.4
0.10
RVF 5 Rift Valley fever. P values in bold type are significant.
* Denominators were computed only for variables with complete data. Missing data records were dropped.
† Pearson’s x2 test for normally distributed variables; Fisher’s exact test for non-normally distributed variables.
detected in both West and Central Uganda (Figures 1 and 3,
green). We also noted that sequences collected from 2018 in
Uganda cluster in a second clade that is distinct from
Kenya-2 (Figure 3, yellow and orange). This K/E clade contains sequences collected in 2009 in South Africa, and the
Ugandan sequences are either ancestral to the South African
clade or a member of the clade. One Ugandan sequence
collected in January 2018 from a refugee camp in the Yumbe
district (northwestern Uganda) shares a most recent common
ancestor with a sequence imported into Beijing from a
forklift worker originally working in Luanda, Angola, in
201623 (Figures 1 and 3, orange). In contrast, the ancestral
Ugandan K/E clade contains four sequences collected in July
2018 from districts in southwestern Uganda: Ibanda, Mubende,
Isingiro, and Sheema (Figures 1 and 3, yellow).
TABLE 6
Multivariate logistic regression model of clinical symptoms as
predictors of RVF positivity in humans
Characteristic
Fever
No
Yes
Intense fatigue
No
Yes
Anorexia
No
Yes
Chest pain
No
Yes
Cough
No
Yes
Sore throat
No
Yes
Conjunctivitis
No
Yes
OR
95% CI
P value
Ref
0.26
–
0.07–0.86
0.033
Ref
0.35
–
0.06–1.62
0.2
Ref
4.56
–
1.02–26.4
0.061
Ref
4.38
–
1.02–21.8
0.055
Ref
0.34
–
0.06–1.50
0.2
Ref
3.27
–
0.69–17.5
0.14
Ref
0.37
–
0.08–1.53
0.2
OR 5 odds ratio; RVF 5 Rift Valley fever. P values in bold type are significant.
DISCUSSION
Uganda reported the first contemporary case of RVF in
2016, which was the first report since 1963, when cases of
RVF were detected in mosquitoes and humans in Lunyo,
Entebbe.24 Reemergence of the virus was reported in the
southwestern district of Kable in two males, both of whom
survived.3 Detection of these cases was made possible by
the establishment of a diagnostic and surveillance system
for VHFs at UVRI beginning in 2010. These cases were initially suspected to be EVD or Marburg virus disease, given
that the initial case presented with hemorrhagic symptoms
and there had been a past outbreak of Marburg virus disease
in the region.25 Follow-up studies showed that the prevalence of RVF in the Kabale region was 13% in both humans
and animals, indicating the virus could have been circulating
well before 2016.8 Because of the heightened interest in RVF
in Uganda, 52 additional cases of RVF have since been confirmed (2017–2020) (Table 1). The CFR was 42% (22 of 52),
which is considerably higher than that of other documented
outbreaks of RVF in East Africa. For example, an outbreak in
Kenya in 2006 and 2007 reported a fatality rate of 26% to
29%.26,27 The high CFR of RVF in Uganda reinforces the status of RVF as a severe disease that merits the attention of
both national and international health surveillance systems.
Because RVF typically presents as a mild febrile illness, we
suspect that the high CFR seen in Ugandan cases may result
from only the more severe cases presenting to health facilities and being detected by disease surveillance. Most RVF
cases may first be confused with malaria, with some patients
also having concurrent malaria, only for them to deteriorate
clinically with more severe symptoms. Rift Valley fever and
other hemorrhagic fevers are usually suspected when
patients do not improve with antimalarial treatment and start
showing bleeding symptoms, which are typically manifested
in the end stage of the disease, when clinical recovery is less
likely. The cases in these outbreaks were first detected using
passive surveillance; additional cases were identified
through active case finding during outbreak investigations.
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NYAKARAHUKA AND OTHERS
FIGURE 3. Phylogenetic tree of Rift Valley fever S segments from 2017 to 2020 for human cases. Green, yellow, and orange shading highlights
new sequences according to specimen collection locations in Figure 1; purple shading highlights historical Ugandan sequences.
Although the majority of the cases (73%) were detected by
passive surveillance, it is important to note that active case
finding helped detect 14 additional cases. Active case finding was performed during each outbreak investigation following initial case detections found by passive surveillance,
and this should be maintained as common practice. Most
confirmed cases (90%) were in adults 18 years and older.
We believe this is likely related to specific occupational
groups, because RVF tends to be an occupational hazard
for certain professions. Adults are more likely to be exposed
to infected animals, or their infected body fluids or products
through occupations such as abattoir workers, animal health
workers, herdsmen, and cattle keeping. In a study conducted in Kabale, Uganda, in 2016, eight individuals younger
than 19 years had no detectable antibodies against RVFV. In
the reporting period 2017–2020, 92% of all confirmed RVF
cases were male who tended to have high-risk occupations
in Uganda. Studies in Uganda and other areas in East Africa
reinforce the fact that people involved in contact with livestock are at greater risk of contracting RVF.7–10,28 Most of
the patients we identified through surveillance resided within
the southwestern districts that make up the cattle corridor of
western and central Uganda, representing 93% of all cases.
The cattle corridor of Uganda is where a majority of the cattle
keeping and commercial livestock trade is carried out, and
hence more contact with livestock is likely to occur. Other
risk factors for acute RVF positivity in humans include being
a butcher or animal slaughterhouse worker and having direct
contact with livestock.
We observed that the majority of RVF cases were detected in
2017 and 2018 (Figure 2), following enhanced surveillance for
EVD, which was ongoing because of a large outbreak in the
neighboring DRC, leading to a high index of suspicion of a VHF.
Often, surveillance activities increase after an outbreak of diseases of public health importance, such as VHFs that have clinical characteristics similar to RVF in the early stages of illness
onset, as was the case with the EVD outbreak in 2018. However, we did not see this same increase in suspect VHF cases
in our surveillance with the COVID-19 outbreak that began in
late 2019 and throughout 2020, as the detection of other infectious diseases reduced drastically. This could be related to
many factors, including the lockdowns that were instituted to
control COVID-19, while at the same time limiting overall
health-care access to patients because of restricted movement.
We hypothesize that most cases of RVF, and even other more
severe diseases, could have gone undetected because of
COVID-19 lockdowns, but we cannot quantify the effect of
such measures using the large surveillance system. There is a
need to discuss surveillance of other equally severe and more
infectious diseases with respect to the COVID-19 pandemic
and other consequences arising out of these outbreaks.
Clinically, most of the confirmed cases presented with fever
(69%) and unexplained bleeding (69%) (Table 2), as well as
other nonspecific symptoms, making it difficult to differentiate
RVF from other more common infectious diseases such as
malaria, typhoid, brucellosis, and other emerging infectious diseases in the tropics. Some patients did not present with fever,
especially cases that were detected late in illness onset or had
1001
SPORADIC RIFT VALLEY FEVER OUTBREAKS IN UGANDA
mild, subclinical infections. Therefore, using fever alone as a
required element of the VHF case definition for surveillance of
RVF may miss true cases. The same applies to cases presenting with bleeding, which is sometimes used by clinicians as the
sole indicator of suspect hemorrhagic fever cases. Bleeding
does not occur in all cases, hence the need to develop case
definitions that are sensitive enough not to miss cases, but also
specific to save surveillance resources.
The first cases identified in 2016 were infected by RVFV of
the Kenya-2 clade; this clade had been found previously to circulate in Kenya in 2006 and 2007, and in Sudan in 2007 and
2010. With continued surveillance in Uganda, we identified
that this clade continues to prevail and circulate in-country. Of
particular interest is the identification of Ugandan RVFV K/E
strains from 2018 that are distinct from the Kenya-2 clade that
has predominated in eastern Africa since 2006. It is currently
unclear whether this K/E branch is a historic clade or a newly
developed clade that is arising because of a potential antigenic
escape in livestock or people (or a result of transmission from
a different mosquito vector). The majority of these sequences
(four of five sequences) originated in districts from southwestern Uganda in 2018 and could represent a RVFV clade
spreading from nearby Rwanda, Tanzania, or DRC. Increased
RVFV surveillance in these countries could help shed light on
the ancestry of this K/E clade. The relationship between the
January 2018 sequence from the Yumbe District (Northwest
Uganda) with a sequence originating from Luanda, Angola, in
2016 (a distance of 2,400 km) further highlights the need for
increased RVFV surveillance across western, central, and eastern Africa, with special emphasis in countries where RVFV is
currently presumed to be nonendemic (i.e., Angola and DRC).
This report is limited only to incident cases of RVF and
does not reflect the true burden of RVF in Uganda. We may
be detecting only the more severe spectrum of RVF cases in
Uganda, which make up a small percentage of the true number of active RVF infections. There is a need to conduct a
wider study to describe the burden of RVF in Uganda, and to
examine morbidity and mortality by looking at the impact of
this disease and how it affects families. This can be combined with a countrywide animal study that also looks at the
burden of the disease in humans and animals, as livestock
acts as a major carrier and source of infection to humans.
We observed sporadic cases of RVF in Uganda from 2017
to 2020 (N 5 52), with a high CFR (42%) compared with other
East African countries. We also identified contact with animals
as the greatest risk factor for RVFV infection. We confirmed
the presence of two clades of RVFV circulating in Uganda
using next-generation sequencing. This information can be
used in designing control and prevention measures against
RVF, such as providing health education to groups at risk to
encourage the use PPE when handling suspected animals,
making sure animal products are well cooked before consumption, using mosquito nets and other insect repellent
chemicals to reduce exposure to mosquito vectors, and vaccinating livestock to reduce the potential for human exposure.
Received June 20, 2022. Accepted for publication November 22,
2022.
Published online March 13, 2023.
Acknowledgment: We thank members of the district outbreak task
force and national task force for their support during outbreak
investigations.
Financial support: This work was financed through a cooperative
agreement between the Uganda Virus Research Institute and the
U.S. CDC.
Disclaimers: The opinions expressed by authors contributing to this
journal do not necessarily reflect the opinions of the CDC or the institutions with which the authors are affiliated.
Author’s addresses: Luke Nyakarahuka, Department of Arbovirology,
Emerging and Reemerging Infectious Diseases, Uganda Virus
Research Institute, Entebbe, Uganda, and Department of Biosecurity,
Ecosystems and Veterinary Public Health, Makerere University, Kampala, Uganda, E-mail: nyakarahuka@gmail.com. Shannon Whitmer,
John Klena, Emir Talundzic, Ketan Patel, Caitlin Cossaboom, Amy
Whitesell, Carson Telford, James Graziano, Joel Montgomery, Stuart
Nichol, and Trevor Shoemaker, Viral Special Pathogens Branch,
Division of High-Consequence Pathogens and Pathology, U.S. Centers for Disease Control and Prevention, Atlanta, GA, E-mails: evk3@
cdc.gov, irc4@cdc.gov, nwx6@cdc.gov, kqn5@cdc.gov, nrm9@cdc.
gov, nmm9@cdc.gov, pwv0@cdc.gov, ikw6@cdc.gov, ztq9@cdc.
gov, stnichol23@gmail.com, and tis8@cdc.gov. Stephen Balinandi,
Alex Tumusiime, Jackson Kyondo, Sophia Mulei, Jimmy Baluku,
and Julius Lutwama, Department of Arbovirology, Emerging and
Reemerging Infectious Diseases, Uganda Virus Research Institute,
Entebbe, Uganda, E-mails: sbalinandi@uvri.go.ug, atumusiimeug@
gmail.com, jacksonkyondo@gmail.com, mbulamulei@gmail.com,
jimmybalmeso2@gmail.com, and jjlutwama03@yahoo.com. Gloria
Akurut, Diana Namanya, and Kilama Kamugisha, Uganda Wildlife
Authority, Kampala, Uganda, E-mails: akurutgloria@gmail.com,
dnamanya24@gmail.com, and kamugishakilama@gmail.com.
This is an open-access article distributed under the terms of the
Creative Commons Attribution (CC-BY) License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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