Geographical and temporal patterns
Seroprevalence of Rift Valley Fever using ELISA for cattle was 0.7%(n = 137052), 0.1% (n = 128113), 0.2%(n = 1625), 0.01%(n = 126414),0.01%(n = 8316), and 0.2%(n = 447655) in River Nile, Khartoum, Blue Nile, White Nile, Sennar and Gezira states respectively, while in goats was 0.07%(n = 153151), 0.05%(n = 64380), 0.2%(n = 99168), 0.1% (n = 21159), 0.6% (n = 1005284) in River Nile, Khartoum, White Nile, Blue Nile, Gezira states respectively and in sheep was 0.1%(n = 42134),0.03%(n = 267089), 0.25%(n = 307449), 0.09% (n = 40530), 0.08%(n = 28348), 0.31%(n = 257333) in River Nile, Khartoum, White Nile, Blue Nile, Sennar and Gezira states respectively(Fig. 2,3, and 4).
Risk factor analysis in selected states
In univariate analysis, locality and species were significantly associated with seroprevalence of RVF (P-value < 0.05), Where animal population was not in Gezira state. In Sennar and Blue Nile, species were found to be statistically associated with seroprevalence of RVF (P-value > 0.05), ( χ2=3.879) and (χ2= 22.959) respectively. While locality and Animal population were not associated. In White Nile state, locality and animal population were highly statistically associated with RVF seroprevalence (P-value > 0.05),(χ2=76.034) and (χ2=29.507) respectively, while species was not. Whereas, in Khartoum state, locality, species and animal population were not statistically associated with RVF seroprevalence (P-value < 0.05) (Table 3).
Environmental risk factors
The study was shown significant difference between annual temperature and rain fall and RVF occurrence in Kosti, White Nile state (P < 0.05), with (t-test = -81.213), (t-test = 2.444) respectively. Also, annual temperature had significant difference with RVF occurrence (P > 0.05) (t-test= -89.352), while rain fall has no significant difference (P > 0.05),( t-test = 0.314) with mean value of (28.869), (286.083) in Madani, Gezira state. In Sennar state, annual temperature and annual rain fall were significantly different with (P < 0.05), (t-test = -79.746) and (t-test = 5.725) respectively (Tables 1,2 and 4).
Table 1
temperature, relative humidity and rainfall and their association to RVF in White Nile
Selected state
|
Year
|
Temp(C°)
|
Relative humidity (%)
|
Rainfall(mm)
|
White Nile
|
1973**
|
29.5
|
13.6
|
382.0
|
|
1977**
|
28.5
|
12.8
|
358.5
|
|
2007**
|
29.2
|
21.0
|
602.1
|
|
1984*
|
30.2
|
3.9
|
96.0
|
|
1988*
|
29.0
|
13.7
|
387.2
|
|
1996*
|
28.6
|
15.0
|
423.6
|
Subscription, **confirmed cases of RVF, * suspected cases of RVF in White Nile state
Table 2
temperature, relative humidity and rainfall and their association to RVF in Sennar
Selected state
|
Year
|
Temp(C°)
|
Relative humidity (%)
|
Rainfall(mm)
|
Sennar
|
1984**
|
29.1
|
1.72
|
174.7
|
|
1988**
|
28.1
|
1.68
|
580.7
|
|
2007**
|
28.3
|
1.69
|
773.9
|
|
1996*
|
27.8
|
1.67
|
562.6
|
|
2000*
|
28.6
|
1.70
|
550.9
|
|
2003*
|
28.9
|
1.71
|
419.1
|
Subscription, **confirmed cases of RVF, * suspected cases of RVF in Sennar state
Riskanalysis for Rift Valley Fever
In some scenarios, Sudan is exporting livestock and livestock products to the neighboring countries; however, until 2007, there was a directive from Sudan central bank that 20% of loan issued by the commercial bank to be directed to export sector. Therefore, improvement and preparation of veterinary services status and facilities, health situation of animals, availability of holding grounds, fed lots and quarantine stations to control RVF occurrence were estimated to be necessary. The impact or consequences of RVF occurrence was based on how far the impact could harm the livestock industry or public health. After year 2000 outbreak of RVF in Saudi Arabia, exports had generally decreased significantly in east of Africa including Sudan(1).
Risk estimation of RVF in Sudan
The likelihood of RVF to occur in Sudan is likely to occur, however, there is free zone from the disease.
Export risk analysis
RVF is unlikely to happen in the Sudan at this scenario, in condition for exported livestock that are vaccinated against RVF, quarantined with veterinary certification before its consignment to importing country.
Import risk analysis
In this scenario, RVF is likely to occur through livestock and livestock products that are carrying RVFV from endemic country to Sudan without consignment regulation for heath and veterinary certifications.
In case of suspicion in RVF, surveillance for the disease by epidemiologists in relevant units in Animal Health Epidemiological Disease Control Directorate is carried out to investigate the suspected cases or outbreak. Also, field investigations are routinely done as a project designated to investigate and provide epidemiological data for priority disease e.g. RVF. There is a program for autumn diseases; whereas water associated diseases are investigated. The program is including health and veterinary situation in states. In addition, there are projects for entomological control, extension and preparation for field teams for rapid intervention.
In 2007, one outbreak of RVF in White Nile had reported to OIE by official of Federal Ministry of Animal Resources. Total animals affected were (110) cattle and (400) sheep in Zealot, White Nile. Epidemiological investigation has revealed that vectors were the main source of infections and vaccination was decided as control for the outbreak , beside other measure such as movement control inside the country , screening, Dipping or /and spraying of vectors or parasites, quarantine and treatment of affected animals. Sample was sent to Central Veterinary Research Laboratory (CVRL), (Soba) for diagnosis with Enzyme-Linked Immunosorbent Assay and the test was positive for RVF antibodies presence and for confirmation samples was sent to OIE Reference Laboratory, Onderstepoort Veterinary Institute (South Africa).
Table 3
Estimated prevalence and relative risk on livestock population at study site
State
|
Locality
|
Animal population
|
Estimate prevalence
|
χ2
|
Relative risk(proportion*)
|
P-value
|
Sheep
|
Goats
|
Cattle
|
|
|
|
|
Gazer
|
Giza East
|
6,632,760.79
|
7,402,322.80
|
34.4%
|
12.629
|
2.9
|
0.049*
|
Alkaline
|
1,482,313.20
|
428,324.40
|
1,290,230.40
|
51.9%
|
|
1.9
|
|
Alhisaiheesa
|
2,252,974.20
|
1,015,933.00
|
1,878,796.40
|
47.5%
|
|
2.1
|
|
Um_alquraa
|
3,017,025.00
|
685,687.50
|
2,605,612.50
|
37.0%
|
|
2.7
|
|
Wad MadaniAlkoobra
|
256,339.60
|
221,384.20
|
23.9%
|
|
4.2
|
|
South Algazeera
|
737,978.00
|
702,916.80
|
35.6%
|
|
2.8
|
|
Almanagil
|
1,493,054.20
|
1,430,689.70
|
29.4%
|
|
3.4
|
|
Sub-total
|
15,872,444.99
|
2,129,944.90
|
15,531,952.80
|
|
|
|
|
White Nile
|
Algitaina
|
546,590.00
|
945, 139
|
642,528
|
36.0%
|
76.034
|
2.7
|
0.000**
|
Omramtaa
|
1,017,598.20
|
1,981,638.60
|
1,124,713.80
|
14.9%
|
|
6.7
|
|
Aldowaim
|
610,141.70
|
1,194, 792.5
|
689,085.30
|
35.4%
|
|
2.8
|
|
Rabak
|
654,752.50
|
1,256,646.40
|
721,629.60
|
17.7%
|
|
5.6
|
|
Aljabalain
|
466,486
|
862,999.10
|
489, 810.3
|
28.8%
|
|
3.4
|
|
Kosti
|
1,437,656.40
|
2,955,182.60
|
1,916,875.20
|
14.3%
|
|
6.9
|
|
Sub-total
|
4,733,224.80
|
7,056,466.70
|
5,094,831.90
|
|
|
|
|
Khartoum
|
Karari
|
1,346,960.50
|
1,616,352.60
|
808,176.30
|
14.3
|
5.263
|
6.9
|
0.511
|
Ombaddaa
|
1,864,897.50
|
2,237, 877.00
|
1,118,938.50
|
7.1%
|
|
14
|
|
Omdurman
|
819,910.90
|
858,626.60
|
429,113.30
|
7.1%
|
|
14
|
|
Bahri
|
339,273
|
407,127.60
|
203,563.80
|
0%
|
|
|
|
Shareq_Alneel
|
232,070
|
277,795.70
|
217,071.70
|
0.0%
|
|
|
|
Alkhartoum
|
681,869.50
|
954, 617.3
|
272,727.80
|
7.1%
|
|
14
|
|
Jabal_awliya
|
681,869.50
|
523,845.70
|
149,670.20
|
0.0%
|
|
|
|
Sub-total
|
5,966,850.90
|
3,683,748.20
|
3,199,261.60
|
|
|
|
|
River Nile
|
Ubu Hamad
|
240,688.50
|
391,082.30
|
26555.8
|
4.3%
|
9.243
|
23.2
|
1.00
|
Berbar
|
245,520.40
|
464,103. 2
|
14138.1
|
4.3%
|
|
23.2
|
|
Atbara
|
323,913.60
|
493,851.60
|
44521.1
|
26.1%
|
|
3.8
|
|
Eldamar
|
360,387
|
660,709.50
|
26841
|
8.7%
|
|
11.4
|
|
Shendi
|
723,883.60
|
1,408,501.60
|
64022.8
|
4.3%
|
|
23.2
|
|
Elmatamma
|
216,834.80
|
414,473.80
|
19632.8
|
13.6%
|
|
7.3
|
|
Sub-total
|
2,111,227.90
|
3,368,618.80
|
195,711.60
|
|
|
|
|
Table 4
Annual temperature and rain falls and RVF in selected areas
Factor
|
(Period of study/year*)
|
Mean(µ)
|
Stud deviation(Sd)
|
Predictive value
|
|
10
|
|
|
Annual temperature
|
|
10
|
0.252
|
0.004
|
Annual rain
|
|
10
|
0.319
|
0.126
|
KOSTI
|
Annual temperature
|
35
|
29.163
|
0.7509
|
|
Annual rain
|
35
|
344.826
|
108.5063
|
DUEIM
|
Annual temperature
|
35
|
29.660
|
1.2248
|
|
Annual rain
|
35
|
239.54
|
94.083
|
MADANI
|
Annual temperature
|
35
|
28.869
|
0.5384
|
|
Annual rain
|
35
|
286.083
|
80.4990
|
SENNAR
|
Annual temperature
|
35
|
28.391
|
0.6386
|
|
Annul rain
|
35
|
421.654
|
125.7245
|
UMBANIEN
|
Annual temperature
|
35
|
28.743
|
0.7261
|
|
Annual rain
|
35
|
512.431
|
120.4924
|
ABU NAAMA
|
Annual temperature
|
35
|
28.966
|
0.5477
|
|
Annual rain
|
35
|
570.091
|
111.1104
|
DMAZIEN
|
Annual temperature
|
35
|
28.294
|
0.4589
|
|
Annual rain
|
35
|
691.426
|
106.8644
|
KHARTOUM
|
Annual temperature
|
35
|
30.037
|
0.4923
|
|
Annual rain
|
35
|
124.174
|
74.0731
|
|
|
|
|
|
* Correlation between RVF epizootiology and environment was confirmed by scaling up annual temperature and rainfalls for period of 10 year before and after 1976