Current IssueEditorial BoardOnline First ArticlesArchive
Current IssueEditorial BoardOnline First ArticlesArchive

Review Article

Epidemiology and Detection of Peste des Petits Ruminants (PPR) in Africa: A Systematic Review

C
Columbas Chaitezvi1,2
S
Silvester Chikerema2,*
R
Rachel Jennifer Stewart3
D
Davies Pfukenyi4
1Department of Veterinary Medicine and Animal Production, University of Zimbabwe, P.O. Box MP 167, Mt Pleasant, Harare, Zimbabwe.
2Veterinary and Capture Unit, Zimbabwe Parks and Wildlife Management Authority, P.O. Box CY 140, Causeway, Harare, Zimbabwe.
3Department of Clinical Veterinary Sciences, University of Zimbabwe, P.O. Box MP 167, Mt Pleasant, Harare, Zimbabwe.
4Botswana University of Agriculture and Natural Resources, P. Bag 0027, Gaborone, Botswana.

ABSTRACT

Peste des petits ruminants (PPR) causes severe morbidity and mortality in small ruminants. Because of its socioeconomic importance and the availability of an effective vaccine, the Food and Agriculture Organisation (FAO) and the World Organisation for Animal Health (WOAH) have targeted PPR for eradication by 2030. Although the disease is widely spread in most parts of the African continent, it has not been reported in southern Africa. To achieve disease-free status certification, these countries need to have good surveillance programmes in place to confirm freedom from disease. A systematic literature search was done on the viral, spatial and animal-level risk factors for PPR. Literature on comparative performance of the common diagnostic tests used in PPR surveillance was also examined. The review brought out the major risk factors associated with PPR in small ruminants in Africa. The review also proposed how to employ the two common tests for PPR detection in disease-free settings.

INTRODUCTION

Peste des petits ruminants (PPR) is one of the major diseases that affect small ruminant production in Africa, the Middle East and Asia (Fentie et al., 2018). The disease has not been reported in most SADC countries and animal health professionals in these countries may have little to no experience with the disease. However, a good understanding of the disease is necessary for effective planning of surveillance and prevention programs. Although there exists several articles on PPR, few have reviewed the performance of the common diagnostic tests used in PPR detection and surveillance programs. The objective of this study was to review the epidemiology and diagnostic performance of the tests used in freedom-from-disease surveillance PPR programs. The knowledge gained would be essential in developing PPR awareness, surveillance and prevention programs.
 
Search strategy
 
A systematic search of the literature was done for the period 2005-2024 (20 years). The literature search was done on PubMed and Google Scholar databases using the search terms; peste des petits ruminants, PPR, epidemiology, risk factors, seroprevalence, surveillance, detection, diagnosis and Africa. The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 checklist (www.prisma-statement.org) was used to maintain study standards and ensure relevant literature was included in the study.
 
Eligibility criteria
 
Articles written in English with an abstract and full text and published in the period 2005-2024 were selected. Reference lists of the selected articles were also screened as potential leads for inclusion. Additional articles were obtained through manual searching. Articles on epidemiology of PPR were restricted to those with a focus on Africa, except those by Abubakar et al., (2011); Bao et al., (2011); Gur and Albayrak (2010) and Hoffman et al., (2012), who were selected as examples of PPR in non-African wildlife.
       
A total of 357 articles were identified through electronic searches. Out of these, 169 were removed at initial screening to remain with 188 articles. A further 157 articles were removed for a number of reasons (Fig 1) to remain with 31 articles. An additional 32 articles were identified through manual searching to give a total of 63 articles used for the study.

Fig 1: Flow diagram of the selection of studies for the review.


 
Epidemiology of PPR
 
Geographical distribution of PPR in Africa
 
PPR was first reported in 1942 in the Ivory Coast (Lefevre and Diallo, 1990). Since the beginning of the century, the disease has shown exponential dissemination from West to North Africa with several outbreaks reported in Tunisia (Sghaier et al., 2014). Its southwards spread resulted in reports of the disease in Tanzania in 2008 (Kivaria et al., 2013). Thus, the disease is now widely distributed in most parts of Africa, the Middle East, Asia and Europe (Georgia) (WOAH, 2022). Within the African continent, the disease is considered endemic in most parts of West, Central, North and East Africa, apart from a few southern African countries (Mdetele et al., 2021; Mulumba-Mfumu et al., 2021). In the southern African region, the disease has been reported in Angola, the Democratic Republic of Congo (DRC) and Tanzania. These countries share parts of their borders with disease-endemic countries outside the southern African region. In Tanzania, the first outbreak of the disease was detected in the Ngorongoro district in 2008 (Kivaria et al., 2013; Mdetele et al., 2021). The disease later spread to the southern regions of the country and the movement of goats was largely responsible for the spread (Kivaria et al., 2013; Torsson et al., 2016). In the DRC, the first reports of severe clinical disease were in the Bas State in 2008 (Torsson et al., 2016) and the disease is thought to have spread from Tanzania (Britton et al., 2019). Between 2008 and 2013 the disease spread to half the territories of the country, with the disease becoming endemic in these territories (Mulumba-Mfumu et al., 2021). In Angola, the disease was probably introduced from the DRC (Britton et al., 2019), with the first outbreaks reported in Cabinda Province in 2012 (Mantip et al., 2021). Mozambique reported serological evidence of exposure in the northern part of the country close to its border with Tanzania, but no cases of clinical disease have been identified (Mapaco et al., 2019). Similarly, Zambia reported seropositive goats, but no clinical cases were detected (Britton et al., 2019). Since outbreaks of PPR clinical cases are unlikely to go undetected especially in naïve populations, false positives due to cross-reactions cannot be ruled out for the reported seropositive cases, an issue that requires further investigations. Though the rest of the SADC countries (Botswana, Eswatini, Lesotho, Malawi, Namibia, South Africa and Zimbabwe) are historically free from the disease, the virus remains a potential threat to their small ruminant populations because of the often-porous borders between the infected and disease-free countries.
 
Viral factors
 
The Peste des Petits Ruminants virus, now called the Morbillivirus caprinae virus, belongs to the family Paramyxoviridae, order Mononegavirales, genus Morbillivirus. The genus consists of other viruses that cause important diseases of humans and animals. The International Committee on Taxonomy of Viruses (ICTV) changed the name Peste des Petits Ruminants virus to Small ruminant morbillivirus in 2017 (Eloiflin et al., 2019) and recently to Morbillivirus caprinae (ICTV, 2022). The virion consists of a genome with a single stranded negative-sense RNA that encodes for six structural proteins; nucleoproteins (N), phosphoproteins (P), matrix proteins (M), fusion proteins (F), hemagglutinin proteins (H) and large proteins (L) (Chauhan et al., 2009; Eloiflin et al., 2019). Genetically, the virus has been classified into four distinct lineages based on the N and F partial sequences (Dundon et al., 2020), with the lineages varying in geographical distribution. Lineages I and II have traditionally been found in West Africa, lineage III in East Africa and the Middle East and lineage IV in Asia (Kardjadj et al., 2015).  Although there are four lineages, there is only one serotype (Chauhan et al., 2009).
 
Host range of the virus
 
The virus is known to affect several domestic and wild ruminants. However, there are differing levels of susceptibility across species. Among the susceptible species, goats are the most susceptible, followed by sheep, which develop a mild form of the disease, while cattle develop a sub-clinical infection (Abraham et al., 2005). Although goats are more susceptible compared to sheep, goats and sheep seroprevalence studies in some settings and locality have shown that seropositivity can be higher in sheep than in goats (Saeed et al., 2018). This has been attributed partly to the lower number of sheep than goats in many instances and because goats are more susceptible to the virus than sheep there is a higher mortality in goats, leaving fewer numbers of goats than those of sheep after an outbreak (Abraham et al., 2005).
       
Seroconversion has also been reported in cattle by several researchers (Lembo et al., 2013; Ali et al., 2018). Even though cattle can seroconvert and develop subclinical infection (WOAH, 2022), the species is regarded as a dead-end host and may have no role to play in the transmission of the virus (Munir, 2013). The seroconversion of cattle suggests that they can be useful for surveillance of the virus circulation in a mixed system where sheep and goats are vaccinated (Lembo et al., 2013).
       
Seroconversion in wildlife has also been reported in Africa. For example, Mahapatra et al., (2015) reported seropositive samples from the African buffalo (Syncerus caffer), Grant’s gazelle (Nanger granti), wildebeest (Connochaetes taurinus) and impala (Aepyceros melampus) in wildlife-livestock interface areas in Tanzania. Seropositive samples from buffalo, the Kob (Kobus kob), waterbuck (Kobus ellipsiprymnus) and the hartebeest (Alcelaphus buselaphus) have been reported in the Ivory Coast by Couacy-Hymann et al. (2005). Similarly, Jones et al., (2021) reported seropositive samples from the buffalo, wildebeest, gazelles, impala and other wildlife species in the Serengeti Ecosystem. Despite the fact that PPR is historically considered a disease of domestic small ruminants in Africa, the disease has shown a high degree of host expansion recently with several reports of clinical disease in wildlife outside Africa, mainly in the Middle East (Fine et al., 2020), where clinical infection has been identified in gazelles in Saudi Arabia, in black-tailed gazelles (Gazella subgutturosa) in Turkey (Gur and Albayrak, 2010), in the Sindh Ibex (Capra aegagrus blythi) in Pakistan (Abubakar et al., 2011), in bharals (Pseudois nayaur) in China (Bao et al., 2011) and in wild goats in Iraq (Hoffman et al., 2012).
       
Notwithstanding the several reports of clinical disease in wildlife in the Middle East and Asia, there has been no substantive reports of clinical disease in free-range wildlife in Africa (Aguilar et al., 2020). This suggests that management systems play an important role in determining susceptibility and development of clinical disease and clinical disease is rare in wildlife under natural conditions. The several documented cases of seroconversion in wildlife species in Africa at wildlife-livestock interface areas imply that exposure of wildlife to the virus from livestock is relatively common in these settings. The seroconversion is due to virus spill over from livestock (Mahapatra et al., 2015) and in these settings wildlife are dead-end hosts for the virus in Africa. However, in a multi-host environment wildlife may be important in virus maintenance and therefore may probably play some role in the epidemiology of the disease in domestic livestock (Aguilar et al., 2020), though this warrants further investigation.
 
Risk factors to PPR
 
Animal-level risk factors
 
Species
 
The disease affects primarily sheep and goats. Cattle, buffalo and pigs develop subclinical infection but do not excrete the virus and thus are not considered important in the epidemiology of the virus (Parida et al., 2015). Between sheep and goats, goats are known to be the most susceptible with several reports showing that goats have a more severe disease than sheep in the same environmental setting (Abraham et al., 2005; Munir and Abubakar, 2014). Munir et al., (2009) showed that sheep develop higher levels of antibodies to the virus than goats, suggesting higher resistance to the virus than goats. Goats are also known to have a longer recovery period compared to sheep (Abraham et al., 2005).
       
Kihu et al., (2015), showed that goats had significantly higher prevalence (40%) than sheep (32%). Similar results were found in other studies (Rume et al., 2020; Mantip et al., 2021; Dubie et al., 2022; Abesha et al., 2023). Although many reports show a statistical association between species and seropositivity in sheep and goats, other researchers have found no such association between seropositivity and species (Woma et al., 2016). Conflicting results were also found by Abraham et al., (2005), who found a higher seropositivity in sheep (13%) than in goats (9%) in a pastoral management system in Ethiopia. In addition, Abdalla et al., (2012) also found a higher seropositivity in sheep (63%) compared to goats (60%) in Sudan. This was partly attributed to the better recovery rate in sheep than in goats and the fact that sheep can be kept for wool and thus are kept for a longer time influencing length of exposure (Rume et al., 2020). The heterogeneous seroprevalence between sheep and goats suggests that species is not the only factor that determines seroprevalence; other factors like production system, age and sex distribution, breed, endemicity and herd size may also influence seroprevalence in a herd. 
 
Age
 
The age of an animal influences seropositivity as the chance of exposure to the virus increases with age. In support of this assertion, Waret-Szkuta et al. (2008) found that sheep and goats older than three years were more than two times more likely to be seropositive than animals under two years of age. Gari et al., (2017) also found that sheep and goats older than 18 months were more likely to be seropositive than younger ones. Similarly, Akwongo et al., (2022) found that goats aged three years or more were more than one and a half times likely to be seropositive than those younger than three years. In addition, Abesha et al., (2023) found higher seroprevalence in goats older than three years than in younger goats. Nkamwesiga et al., (2023), found that the odds of being seropositive were higher in adult sheep and goats than in young animals. Older animals would have had longer periods of exposure and more likely to have been vaccinated compared to younger animals.
       
There are, however, contradictory findings by Moumin et al., (2018) and Dubie et al., (2022), who found out that goats younger than two years were more likely to be seropositive than older goats. Kihu et al., (2015) found higher seroprevalence in animals less than six months and those older than two years, with lower prevalence in the middle- aged animals. The same authors suggested that the higher seroprevalence in young animals was due to maternal immunity while in adult animals it was due to a higher risk of exposure, with the middle-aged animals having the lowest seroprevalence due to waning maternal antibodies. Age may also influence vaccination status and level of maternal antibodies. Contradictory findings between age and seroprevalence in some studies may be due to different classification of age groups, different exposure patterns between flocks (the young from exposed parents have maternal antibodies, therefore more likely to be seropositive than young adults as maternal immunity wanes if no vaccination is practiced) and lack of differentiation between infection and vaccinal antibodies  e.g. where vaccination is done, older animals are more likely to be vaccinated than young animals, therefore more likely to be seropositive leading to an overestimation of prevalence in adult animals (Nkamwesiga et al., 2023). Other factors by the same author included road size, with areas having bigger roads being associated with higher seroprevalence compared to smaller roads (Nkamwesiga et al., 2023). This might be due to higher animal traffic in bigger roads, which may pose a higher risk of disease transmission compared to smaller roads.
 
Sex
 
Several studies have shown a significant association between seroprevalence of the virus and sex with the odds of being seropositive higher in females than in males. For example, Abraham et al., (2005), found a significantly higher seroprevalence in female goats than males. This was corroborated by Gari et al., (2017), Moumin et al. (2018), Dubie et al., (2022) and Nkamwesiga et al., (2023). The higher seroprevalence in females could be because they are key to productivity and herd maintenance, hence they are kept for a much longer time and have longer periods of exposure compared to males. Males are also sent to the market at a younger age. Megersa et al., (2012), further suggested that females may be more prone to infection because of the physiological stress they undergo during production and reproduction. However, not all studies agreed with this finding. For example, Saeed et al., (2018), found no significant association between seroprevalence and sex, while Waret-Szkuta et al. (2008); Gari et al., (2017) and Woma et al., (2016) also found no evidence of association between sex and seroprevalence.
 
Other risk factors
 
Herd size
 
Herd size is significantly associated with seroprevalence. This is supported by a few reports (Abesha et al., 2023; Akwongo et al., 2022; Ejigu et al., 2023), which showed that animals from larger herds were more likely to be seropositive than those from smaller herds. Akwongo et al., (2022), found higher prevalence in mixed species herds compared to single species herds. This could be because they are more likely to be overcrowded, thus increasing the chances of effective contacts between infected and susceptible animals, raising chances of transmission.
 
Husbandry practices
 
Production systems which allow movement of livestock from different areas were associated with increased seropositivity. For example, in Cameroon, Loul et al., (2020) found higher seroprevalence in urban/peri-urban areas where goats from different areas were brought for marketing, compared to flocks in rural areas. This was corroborated by Nkamwesiga et al., (2023), who found a higher seroprevalence in flocks that mixed with other flocks compared to flocks that did not. Besides mixing of flocks from different localities as an important risk factor, Saeed et al., (2018) also found higher seroprevalence in flocks that moved between states. Other factors include sharing common drinking points, which was associated with increased contacts of animals from different herds (Loul et al., 2020). Nkamwesiga et al., (2023) also found a correlation between likelihood of being seropositive and having a common water source. Pastoral systems (transhumance) in East and West Africa were also associated with a higher seroprevalence compared to non-pastoral systems (Loul et al., 2020). In addition, in regions where there is distinct seasonality in terms of feed and water availability, stock owners may be forced to move their animals to areas where there are better feed resources during seasons when resources are limited (Waret-Szkuta et al., 2008). This can lead to congregation of animal populations in areas with good feed and water resources during some periods, contributing to seasonal and spatial patterns in prevalence levels.
 
Seasonal factors
 
Seasonal patterns of disease can be influenced directly by climatic factors and/or indirectly by management practices that follow seasonal trends. For example, Nkamwesiga et al., (2022) found a negative association between rainfall and the number of PPR outbreaks, with the number of outbreaks highest in low rainfall periods. The same authors asserted that low rainfall periods are associated with increased livestock movement looking for water and better pastures and increased contact when animals congregate at watering points. Megersa et al., (2012) added that deficient feed and water resources in the dry season results in nutritional stress to the animals, which increases susceptibility to the disease. Not all studies detected seasonal patterns, as for example El-Yuguda et al. (2013), found no significant differences between seasons. Lack of seasonality in number of outbreaks could occur in regions where there are no considerable variations in feed and water resources between seasons and consequently no distinct changes in management practices that could influence PPR outbreaks.
 
Spatial factors
 
PPR prevalence has been shown to vary from region to region. For example, a study by Gebre et al., (2018) showed variation in prevalence between regions. This variation was due to agro-climatic factors which influence stocking density and husbandry practices like agro-pastoralism, which have been shown to increase risk to PPR. Woma et al., (2016) and Loul et al., (2020), found similar patterns, with regions of higher rainfall having lower prevalence compared to drier regions. This was supported by Ejigu et al., (2023), who found higher prevalence in lowland areas with lower rainfall. Prevalence was also higher in areas close to national borders, especially where the borders are porous with no movement restriction (Abdalla et al., 2012). Shyaka et al., (2021) also found similar results, with prevalence higher in border areas with cross-border trade compared to areas far from borders.
 
Detection of PPR
 
The laboratory detection of PPR is necessary for the differentiation of the disease from other diseases with similar clinical signs and for confirmation of the disease. Of greater importance is that the launch of the progressive eradication programme by the WOAH requires the use of accurate and validated methods (Baron et al., 2016). A few tests have been developed that are suitable for different epidemiological settings. For example, antigen detection methods like the reverse transcriptase polymerase chain reaction (RT-PCR), real time -PCR and immuno-capture ELISA are considered the best for confirmation of clinical disease, while antibody detection methods like the competitive ELISA (c-ELISA) are the best for determination of seroprevalence and post vaccination immune response (WOAH, 2022). In general, for surveillance purposes serological tests are preferable to molecular methods because of the short period of viraemia during infection (Tully et al., 2023). This restricted period during which samples with antigen can be obtained limits the general use of molecular techniques for surveillance, in addition to the considerable costs involved. Unlike the molecular methods, serological tests can be done up to three years after seroconversion (Zahur et al., 2015; Baron et al., 2016).
       
The review is limited to serological tests that can be used for the detection and confirmation of freedom from disease. The tests considered the best for demonstration of freedom from disease are the c-ELISA, the virus neutralisation test (VNT) and the recently developed H-based blocking ELISA (HPPR-b-ELISA®) (b-ELISA), with the VNT generally considered the gold standard. The need for facilities to handle live virus and the biosecurity measures involved require considerable resources, which limits the use of VNT in resource-limited settings (Tully et al., 2023). The c-ELISA test has been used for the detection of the virus in Africa, with the most common one being the ID Screen®PPR (IDvet Innovative Diagnostics, Grabels, France). The test is also recommended by the WOAH for the determination of population freedom from infection (WOAH, 2022). The test has been used for sero-surveillance of PPR in endemic countries  and in outbreak situations  in several studies (Lembo et al., 2013; Kihu et al., 2015; Woma et al., 2016; Almeshay et al., 2017; Torsson et al., 2017; Gebre et al., 2018; Fentie et al., 2018; Moumin et al., 2018; Burns et al., 2019; Mapaco et al., 2019; Rume et al., 2020; Akwongo et al., 2022; Abesha et al., 2023; Dubie et al., 2022; Ejigu et al., 2023; Nkamwesiga et al., 2023; Tully et al., 2023). The ID Screen® detects nucleoprotein antibodies with a sensitivity (Se) of 94.5% and a specificity (Sp) of 99.5% (Shyaka et al., 2021). A few studies have shown the relative performance of the two tests, with the VNT as the gold standard (Table 1).

Table 1: Comparative performance of ID Screen®PPR and HPPR-b-ELISA®) in sheep and goats.


       
A similar study by Tully et al., (2023), also demonstrated that the sensitivity of the c-ELISA test was superior to that of b-ELISA and the VNT across several species. Further, in the same study, the b-ELISA also showed the best specificity among the three tests. Although the two tests show very good performance and close to perfect agreement with the gold standard and thus may be recommended for testing in endemic situations, there is need for validation of the two tests in situations where there is need to prove freedom from disease in populations that have not been exposed to PPRV, in order to deal with the possibility of false positive cases, which can have costly implications for countries trying to prove absence of disease.
               
False positive reactions can be a result of cross-reactions with similar antigens in the same genus in environments where there is circulation of closely related antigens like Canine Distemper and Measles viruses, which are abundant in southern African countries. This is supported by findings by Mapaco et al., (2019), who, in a sero-survey in sheep and goats in a region of Mozambique historically free from the disease, found a seroprevalence of 0.46% using the c-ELISA. When however, the seropositive samples were re-tested using the b-ELISA, the results were negative. Similarly, in a study by Burns et al., (2019), c-ELISA results showed a seroprevalence of 2.2 % in goats from populations where the disease had not been previously reported. The same authors suggested the possibility of cross-reactors with antigenically similar viruses. When the objective of surveillance is to prove freedom from disease, false positives can have serious implications on the country’s disease status; and in disease-free settings results in higher surveillance costs because of larger sample sizes required for statistical confidence. Confirmatory testing may also be necessary, further raising surveillance costs. Because the c-ELISA has higher sensitivity than the b-ELISA, while the latter has superior specificity, we suggest the use of both tests to take advantage of their complementary performance. The tests could be used serially to increase the specificity and positive predictive value of test results in sero-surveillance programmes.

CONCLUSION

PPR has shown great geographical spread from West Africa in the last twenty years. SADC countries that are free from the disease are at risk due to the ease of spread of the disease due to livestock movement. Porous borders between African countries are not helpful. Understanding the epidemiology of the disease and performance of serological tests is important for implementation of effective surveillance programs. 
 
Disclaimers
 
The views and conclusions expressed in this article are solely those of the authors and do not necessarily represent the views of their affiliated institutions. The authors are responsible for the accuracy and completeness of the information provided, but do not accept any liability for any direct or indirect losses resulting from this content.

CONFLICT OF INTEREST

The authors declare no conflict of interest regarding the publication of this article.

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    Review Article

    Epidemiology and Detection of Peste des Petits Ruminants (PPR) in Africa: A Systematic Review

    C
    Columbas Chaitezvi1,2
    S
    Silvester Chikerema2,*
    R
    Rachel Jennifer Stewart3
    D
    Davies Pfukenyi4
    1Department of Veterinary Medicine and Animal Production, University of Zimbabwe, P.O. Box MP 167, Mt Pleasant, Harare, Zimbabwe.
    2Veterinary and Capture Unit, Zimbabwe Parks and Wildlife Management Authority, P.O. Box CY 140, Causeway, Harare, Zimbabwe.
    3Department of Clinical Veterinary Sciences, University of Zimbabwe, P.O. Box MP 167, Mt Pleasant, Harare, Zimbabwe.
    4Botswana University of Agriculture and Natural Resources, P. Bag 0027, Gaborone, Botswana.

    ABSTRACT

    Peste des petits ruminants (PPR) causes severe morbidity and mortality in small ruminants. Because of its socioeconomic importance and the availability of an effective vaccine, the Food and Agriculture Organisation (FAO) and the World Organisation for Animal Health (WOAH) have targeted PPR for eradication by 2030. Although the disease is widely spread in most parts of the African continent, it has not been reported in southern Africa. To achieve disease-free status certification, these countries need to have good surveillance programmes in place to confirm freedom from disease. A systematic literature search was done on the viral, spatial and animal-level risk factors for PPR. Literature on comparative performance of the common diagnostic tests used in PPR surveillance was also examined. The review brought out the major risk factors associated with PPR in small ruminants in Africa. The review also proposed how to employ the two common tests for PPR detection in disease-free settings.

    INTRODUCTION

    Peste des petits ruminants (PPR) is one of the major diseases that affect small ruminant production in Africa, the Middle East and Asia (Fentie et al., 2018). The disease has not been reported in most SADC countries and animal health professionals in these countries may have little to no experience with the disease. However, a good understanding of the disease is necessary for effective planning of surveillance and prevention programs. Although there exists several articles on PPR, few have reviewed the performance of the common diagnostic tests used in PPR detection and surveillance programs. The objective of this study was to review the epidemiology and diagnostic performance of the tests used in freedom-from-disease surveillance PPR programs. The knowledge gained would be essential in developing PPR awareness, surveillance and prevention programs.
     
    Search strategy
     
    A systematic search of the literature was done for the period 2005-2024 (20 years). The literature search was done on PubMed and Google Scholar databases using the search terms; peste des petits ruminants, PPR, epidemiology, risk factors, seroprevalence, surveillance, detection, diagnosis and Africa. The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 checklist (www.prisma-statement.org) was used to maintain study standards and ensure relevant literature was included in the study.
     
    Eligibility criteria
     
    Articles written in English with an abstract and full text and published in the period 2005-2024 were selected. Reference lists of the selected articles were also screened as potential leads for inclusion. Additional articles were obtained through manual searching. Articles on epidemiology of PPR were restricted to those with a focus on Africa, except those by Abubakar et al., (2011); Bao et al., (2011); Gur and Albayrak (2010) and Hoffman et al., (2012), who were selected as examples of PPR in non-African wildlife.
           
    A total of 357 articles were identified through electronic searches. Out of these, 169 were removed at initial screening to remain with 188 articles. A further 157 articles were removed for a number of reasons (Fig 1) to remain with 31 articles. An additional 32 articles were identified through manual searching to give a total of 63 articles used for the study.

    Fig 1: Flow diagram of the selection of studies for the review.


     
    Epidemiology of PPR
     
    Geographical distribution of PPR in Africa
     
    PPR was first reported in 1942 in the Ivory Coast (Lefevre and Diallo, 1990). Since the beginning of the century, the disease has shown exponential dissemination from West to North Africa with several outbreaks reported in Tunisia (Sghaier et al., 2014). Its southwards spread resulted in reports of the disease in Tanzania in 2008 (Kivaria et al., 2013). Thus, the disease is now widely distributed in most parts of Africa, the Middle East, Asia and Europe (Georgia) (WOAH, 2022). Within the African continent, the disease is considered endemic in most parts of West, Central, North and East Africa, apart from a few southern African countries (Mdetele et al., 2021; Mulumba-Mfumu et al., 2021). In the southern African region, the disease has been reported in Angola, the Democratic Republic of Congo (DRC) and Tanzania. These countries share parts of their borders with disease-endemic countries outside the southern African region. In Tanzania, the first outbreak of the disease was detected in the Ngorongoro district in 2008 (Kivaria et al., 2013; Mdetele et al., 2021). The disease later spread to the southern regions of the country and the movement of goats was largely responsible for the spread (Kivaria et al., 2013; Torsson et al., 2016). In the DRC, the first reports of severe clinical disease were in the Bas State in 2008 (Torsson et al., 2016) and the disease is thought to have spread from Tanzania (Britton et al., 2019). Between 2008 and 2013 the disease spread to half the territories of the country, with the disease becoming endemic in these territories (Mulumba-Mfumu et al., 2021). In Angola, the disease was probably introduced from the DRC (Britton et al., 2019), with the first outbreaks reported in Cabinda Province in 2012 (Mantip et al., 2021). Mozambique reported serological evidence of exposure in the northern part of the country close to its border with Tanzania, but no cases of clinical disease have been identified (Mapaco et al., 2019). Similarly, Zambia reported seropositive goats, but no clinical cases were detected (Britton et al., 2019). Since outbreaks of PPR clinical cases are unlikely to go undetected especially in naïve populations, false positives due to cross-reactions cannot be ruled out for the reported seropositive cases, an issue that requires further investigations. Though the rest of the SADC countries (Botswana, Eswatini, Lesotho, Malawi, Namibia, South Africa and Zimbabwe) are historically free from the disease, the virus remains a potential threat to their small ruminant populations because of the often-porous borders between the infected and disease-free countries.
     
    Viral factors
     
    The Peste des Petits Ruminants virus, now called the Morbillivirus caprinae virus, belongs to the family Paramyxoviridae, order Mononegavirales, genus Morbillivirus. The genus consists of other viruses that cause important diseases of humans and animals. The International Committee on Taxonomy of Viruses (ICTV) changed the name Peste des Petits Ruminants virus to Small ruminant morbillivirus in 2017 (Eloiflin et al., 2019) and recently to Morbillivirus caprinae (ICTV, 2022). The virion consists of a genome with a single stranded negative-sense RNA that encodes for six structural proteins; nucleoproteins (N), phosphoproteins (P), matrix proteins (M), fusion proteins (F), hemagglutinin proteins (H) and large proteins (L) (Chauhan et al., 2009; Eloiflin et al., 2019). Genetically, the virus has been classified into four distinct lineages based on the N and F partial sequences (Dundon et al., 2020), with the lineages varying in geographical distribution. Lineages I and II have traditionally been found in West Africa, lineage III in East Africa and the Middle East and lineage IV in Asia (Kardjadj et al., 2015).  Although there are four lineages, there is only one serotype (Chauhan et al., 2009).
     
    Host range of the virus
     
    The virus is known to affect several domestic and wild ruminants. However, there are differing levels of susceptibility across species. Among the susceptible species, goats are the most susceptible, followed by sheep, which develop a mild form of the disease, while cattle develop a sub-clinical infection (Abraham et al., 2005). Although goats are more susceptible compared to sheep, goats and sheep seroprevalence studies in some settings and locality have shown that seropositivity can be higher in sheep than in goats (Saeed et al., 2018). This has been attributed partly to the lower number of sheep than goats in many instances and because goats are more susceptible to the virus than sheep there is a higher mortality in goats, leaving fewer numbers of goats than those of sheep after an outbreak (Abraham et al., 2005).
           
    Seroconversion has also been reported in cattle by several researchers (Lembo et al., 2013; Ali et al., 2018). Even though cattle can seroconvert and develop subclinical infection (WOAH, 2022), the species is regarded as a dead-end host and may have no role to play in the transmission of the virus (Munir, 2013). The seroconversion of cattle suggests that they can be useful for surveillance of the virus circulation in a mixed system where sheep and goats are vaccinated (Lembo et al., 2013).
           
    Seroconversion in wildlife has also been reported in Africa. For example, Mahapatra et al., (2015) reported seropositive samples from the African buffalo (Syncerus caffer), Grant’s gazelle (Nanger granti), wildebeest (Connochaetes taurinus) and impala (Aepyceros melampus) in wildlife-livestock interface areas in Tanzania. Seropositive samples from buffalo, the Kob (Kobus kob), waterbuck (Kobus ellipsiprymnus) and the hartebeest (Alcelaphus buselaphus) have been reported in the Ivory Coast by Couacy-Hymann et al. (2005). Similarly, Jones et al., (2021) reported seropositive samples from the buffalo, wildebeest, gazelles, impala and other wildlife species in the Serengeti Ecosystem. Despite the fact that PPR is historically considered a disease of domestic small ruminants in Africa, the disease has shown a high degree of host expansion recently with several reports of clinical disease in wildlife outside Africa, mainly in the Middle East (Fine et al., 2020), where clinical infection has been identified in gazelles in Saudi Arabia, in black-tailed gazelles (Gazella subgutturosa) in Turkey (Gur and Albayrak, 2010), in the Sindh Ibex (Capra aegagrus blythi) in Pakistan (Abubakar et al., 2011), in bharals (Pseudois nayaur) in China (Bao et al., 2011) and in wild goats in Iraq (Hoffman et al., 2012).
           
    Notwithstanding the several reports of clinical disease in wildlife in the Middle East and Asia, there has been no substantive reports of clinical disease in free-range wildlife in Africa (Aguilar et al., 2020). This suggests that management systems play an important role in determining susceptibility and development of clinical disease and clinical disease is rare in wildlife under natural conditions. The several documented cases of seroconversion in wildlife species in Africa at wildlife-livestock interface areas imply that exposure of wildlife to the virus from livestock is relatively common in these settings. The seroconversion is due to virus spill over from livestock (Mahapatra et al., 2015) and in these settings wildlife are dead-end hosts for the virus in Africa. However, in a multi-host environment wildlife may be important in virus maintenance and therefore may probably play some role in the epidemiology of the disease in domestic livestock (Aguilar et al., 2020), though this warrants further investigation.
     
    Risk factors to PPR
     
    Animal-level risk factors
     
    Species
     
    The disease affects primarily sheep and goats. Cattle, buffalo and pigs develop subclinical infection but do not excrete the virus and thus are not considered important in the epidemiology of the virus (Parida et al., 2015). Between sheep and goats, goats are known to be the most susceptible with several reports showing that goats have a more severe disease than sheep in the same environmental setting (Abraham et al., 2005; Munir and Abubakar, 2014). Munir et al., (2009) showed that sheep develop higher levels of antibodies to the virus than goats, suggesting higher resistance to the virus than goats. Goats are also known to have a longer recovery period compared to sheep (Abraham et al., 2005).
           
    Kihu et al., (2015), showed that goats had significantly higher prevalence (40%) than sheep (32%). Similar results were found in other studies (Rume et al., 2020; Mantip et al., 2021; Dubie et al., 2022; Abesha et al., 2023). Although many reports show a statistical association between species and seropositivity in sheep and goats, other researchers have found no such association between seropositivity and species (Woma et al., 2016). Conflicting results were also found by Abraham et al., (2005), who found a higher seropositivity in sheep (13%) than in goats (9%) in a pastoral management system in Ethiopia. In addition, Abdalla et al., (2012) also found a higher seropositivity in sheep (63%) compared to goats (60%) in Sudan. This was partly attributed to the better recovery rate in sheep than in goats and the fact that sheep can be kept for wool and thus are kept for a longer time influencing length of exposure (Rume et al., 2020). The heterogeneous seroprevalence between sheep and goats suggests that species is not the only factor that determines seroprevalence; other factors like production system, age and sex distribution, breed, endemicity and herd size may also influence seroprevalence in a herd. 
     
    Age
     
    The age of an animal influences seropositivity as the chance of exposure to the virus increases with age. In support of this assertion, Waret-Szkuta et al. (2008) found that sheep and goats older than three years were more than two times more likely to be seropositive than animals under two years of age. Gari et al., (2017) also found that sheep and goats older than 18 months were more likely to be seropositive than younger ones. Similarly, Akwongo et al., (2022) found that goats aged three years or more were more than one and a half times likely to be seropositive than those younger than three years. In addition, Abesha et al., (2023) found higher seroprevalence in goats older than three years than in younger goats. Nkamwesiga et al., (2023), found that the odds of being seropositive were higher in adult sheep and goats than in young animals. Older animals would have had longer periods of exposure and more likely to have been vaccinated compared to younger animals.
           
    There are, however, contradictory findings by Moumin et al., (2018) and Dubie et al., (2022), who found out that goats younger than two years were more likely to be seropositive than older goats. Kihu et al., (2015) found higher seroprevalence in animals less than six months and those older than two years, with lower prevalence in the middle- aged animals. The same authors suggested that the higher seroprevalence in young animals was due to maternal immunity while in adult animals it was due to a higher risk of exposure, with the middle-aged animals having the lowest seroprevalence due to waning maternal antibodies. Age may also influence vaccination status and level of maternal antibodies. Contradictory findings between age and seroprevalence in some studies may be due to different classification of age groups, different exposure patterns between flocks (the young from exposed parents have maternal antibodies, therefore more likely to be seropositive than young adults as maternal immunity wanes if no vaccination is practiced) and lack of differentiation between infection and vaccinal antibodies  e.g. where vaccination is done, older animals are more likely to be vaccinated than young animals, therefore more likely to be seropositive leading to an overestimation of prevalence in adult animals (Nkamwesiga et al., 2023). Other factors by the same author included road size, with areas having bigger roads being associated with higher seroprevalence compared to smaller roads (Nkamwesiga et al., 2023). This might be due to higher animal traffic in bigger roads, which may pose a higher risk of disease transmission compared to smaller roads.
     
    Sex
     
    Several studies have shown a significant association between seroprevalence of the virus and sex with the odds of being seropositive higher in females than in males. For example, Abraham et al., (2005), found a significantly higher seroprevalence in female goats than males. This was corroborated by Gari et al., (2017), Moumin et al. (2018), Dubie et al., (2022) and Nkamwesiga et al., (2023). The higher seroprevalence in females could be because they are key to productivity and herd maintenance, hence they are kept for a much longer time and have longer periods of exposure compared to males. Males are also sent to the market at a younger age. Megersa et al., (2012), further suggested that females may be more prone to infection because of the physiological stress they undergo during production and reproduction. However, not all studies agreed with this finding. For example, Saeed et al., (2018), found no significant association between seroprevalence and sex, while Waret-Szkuta et al. (2008); Gari et al., (2017) and Woma et al., (2016) also found no evidence of association between sex and seroprevalence.
     
    Other risk factors
     
    Herd size
     
    Herd size is significantly associated with seroprevalence. This is supported by a few reports (Abesha et al., 2023; Akwongo et al., 2022; Ejigu et al., 2023), which showed that animals from larger herds were more likely to be seropositive than those from smaller herds. Akwongo et al., (2022), found higher prevalence in mixed species herds compared to single species herds. This could be because they are more likely to be overcrowded, thus increasing the chances of effective contacts between infected and susceptible animals, raising chances of transmission.
     
    Husbandry practices
     
    Production systems which allow movement of livestock from different areas were associated with increased seropositivity. For example, in Cameroon, Loul et al., (2020) found higher seroprevalence in urban/peri-urban areas where goats from different areas were brought for marketing, compared to flocks in rural areas. This was corroborated by Nkamwesiga et al., (2023), who found a higher seroprevalence in flocks that mixed with other flocks compared to flocks that did not. Besides mixing of flocks from different localities as an important risk factor, Saeed et al., (2018) also found higher seroprevalence in flocks that moved between states. Other factors include sharing common drinking points, which was associated with increased contacts of animals from different herds (Loul et al., 2020). Nkamwesiga et al., (2023) also found a correlation between likelihood of being seropositive and having a common water source. Pastoral systems (transhumance) in East and West Africa were also associated with a higher seroprevalence compared to non-pastoral systems (Loul et al., 2020). In addition, in regions where there is distinct seasonality in terms of feed and water availability, stock owners may be forced to move their animals to areas where there are better feed resources during seasons when resources are limited (Waret-Szkuta et al., 2008). This can lead to congregation of animal populations in areas with good feed and water resources during some periods, contributing to seasonal and spatial patterns in prevalence levels.
     
    Seasonal factors
     
    Seasonal patterns of disease can be influenced directly by climatic factors and/or indirectly by management practices that follow seasonal trends. For example, Nkamwesiga et al., (2022) found a negative association between rainfall and the number of PPR outbreaks, with the number of outbreaks highest in low rainfall periods. The same authors asserted that low rainfall periods are associated with increased livestock movement looking for water and better pastures and increased contact when animals congregate at watering points. Megersa et al., (2012) added that deficient feed and water resources in the dry season results in nutritional stress to the animals, which increases susceptibility to the disease. Not all studies detected seasonal patterns, as for example El-Yuguda et al. (2013), found no significant differences between seasons. Lack of seasonality in number of outbreaks could occur in regions where there are no considerable variations in feed and water resources between seasons and consequently no distinct changes in management practices that could influence PPR outbreaks.
     
    Spatial factors
     
    PPR prevalence has been shown to vary from region to region. For example, a study by Gebre et al., (2018) showed variation in prevalence between regions. This variation was due to agro-climatic factors which influence stocking density and husbandry practices like agro-pastoralism, which have been shown to increase risk to PPR. Woma et al., (2016) and Loul et al., (2020), found similar patterns, with regions of higher rainfall having lower prevalence compared to drier regions. This was supported by Ejigu et al., (2023), who found higher prevalence in lowland areas with lower rainfall. Prevalence was also higher in areas close to national borders, especially where the borders are porous with no movement restriction (Abdalla et al., 2012). Shyaka et al., (2021) also found similar results, with prevalence higher in border areas with cross-border trade compared to areas far from borders.
     
    Detection of PPR
     
    The laboratory detection of PPR is necessary for the differentiation of the disease from other diseases with similar clinical signs and for confirmation of the disease. Of greater importance is that the launch of the progressive eradication programme by the WOAH requires the use of accurate and validated methods (Baron et al., 2016). A few tests have been developed that are suitable for different epidemiological settings. For example, antigen detection methods like the reverse transcriptase polymerase chain reaction (RT-PCR), real time -PCR and immuno-capture ELISA are considered the best for confirmation of clinical disease, while antibody detection methods like the competitive ELISA (c-ELISA) are the best for determination of seroprevalence and post vaccination immune response (WOAH, 2022). In general, for surveillance purposes serological tests are preferable to molecular methods because of the short period of viraemia during infection (Tully et al., 2023). This restricted period during which samples with antigen can be obtained limits the general use of molecular techniques for surveillance, in addition to the considerable costs involved. Unlike the molecular methods, serological tests can be done up to three years after seroconversion (Zahur et al., 2015; Baron et al., 2016).
           
    The review is limited to serological tests that can be used for the detection and confirmation of freedom from disease. The tests considered the best for demonstration of freedom from disease are the c-ELISA, the virus neutralisation test (VNT) and the recently developed H-based blocking ELISA (HPPR-b-ELISA®) (b-ELISA), with the VNT generally considered the gold standard. The need for facilities to handle live virus and the biosecurity measures involved require considerable resources, which limits the use of VNT in resource-limited settings (Tully et al., 2023). The c-ELISA test has been used for the detection of the virus in Africa, with the most common one being the ID Screen®PPR (IDvet Innovative Diagnostics, Grabels, France). The test is also recommended by the WOAH for the determination of population freedom from infection (WOAH, 2022). The test has been used for sero-surveillance of PPR in endemic countries  and in outbreak situations  in several studies (Lembo et al., 2013; Kihu et al., 2015; Woma et al., 2016; Almeshay et al., 2017; Torsson et al., 2017; Gebre et al., 2018; Fentie et al., 2018; Moumin et al., 2018; Burns et al., 2019; Mapaco et al., 2019; Rume et al., 2020; Akwongo et al., 2022; Abesha et al., 2023; Dubie et al., 2022; Ejigu et al., 2023; Nkamwesiga et al., 2023; Tully et al., 2023). The ID Screen® detects nucleoprotein antibodies with a sensitivity (Se) of 94.5% and a specificity (Sp) of 99.5% (Shyaka et al., 2021). A few studies have shown the relative performance of the two tests, with the VNT as the gold standard (Table 1).

    Table 1: Comparative performance of ID Screen®PPR and HPPR-b-ELISA®) in sheep and goats.


           
    A similar study by Tully et al., (2023), also demonstrated that the sensitivity of the c-ELISA test was superior to that of b-ELISA and the VNT across several species. Further, in the same study, the b-ELISA also showed the best specificity among the three tests. Although the two tests show very good performance and close to perfect agreement with the gold standard and thus may be recommended for testing in endemic situations, there is need for validation of the two tests in situations where there is need to prove freedom from disease in populations that have not been exposed to PPRV, in order to deal with the possibility of false positive cases, which can have costly implications for countries trying to prove absence of disease.
                   
    False positive reactions can be a result of cross-reactions with similar antigens in the same genus in environments where there is circulation of closely related antigens like Canine Distemper and Measles viruses, which are abundant in southern African countries. This is supported by findings by Mapaco et al., (2019), who, in a sero-survey in sheep and goats in a region of Mozambique historically free from the disease, found a seroprevalence of 0.46% using the c-ELISA. When however, the seropositive samples were re-tested using the b-ELISA, the results were negative. Similarly, in a study by Burns et al., (2019), c-ELISA results showed a seroprevalence of 2.2 % in goats from populations where the disease had not been previously reported. The same authors suggested the possibility of cross-reactors with antigenically similar viruses. When the objective of surveillance is to prove freedom from disease, false positives can have serious implications on the country’s disease status; and in disease-free settings results in higher surveillance costs because of larger sample sizes required for statistical confidence. Confirmatory testing may also be necessary, further raising surveillance costs. Because the c-ELISA has higher sensitivity than the b-ELISA, while the latter has superior specificity, we suggest the use of both tests to take advantage of their complementary performance. The tests could be used serially to increase the specificity and positive predictive value of test results in sero-surveillance programmes.

    CONCLUSION

    PPR has shown great geographical spread from West Africa in the last twenty years. SADC countries that are free from the disease are at risk due to the ease of spread of the disease due to livestock movement. Porous borders between African countries are not helpful. Understanding the epidemiology of the disease and performance of serological tests is important for implementation of effective surveillance programs. 
     
    Disclaimers
     
    The views and conclusions expressed in this article are solely those of the authors and do not necessarily represent the views of their affiliated institutions. The authors are responsible for the accuracy and completeness of the information provided, but do not accept any liability for any direct or indirect losses resulting from this content.

    CONFLICT OF INTEREST

    The authors declare no conflict of interest regarding the publication of this article.

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