Genotyping of African swine fever virus (ASFV) isolates associated with disease outbreaks in Uganda in 2007

Samples from infected domestic pigs associated with an outbreak of African swine fever (ASF) in three districts of central Uganda in 2007 were confirmed as being infected with African swine fever virus (ASFV) using a P72 gene-based polymerase chain reaction amplification (PCR) assay combined with restriction analysis. None of the sera collected from pigs with clinical symptoms were positive using the OIE serological prescribed tests. However, seven haemadsorbing viruses were isolated in macrophage culture and genotyped by partial p72 and full length p54-gene sequencing. Four of these viruses were isolated directly from serum samples. All the viruses were classified within the domesticpig cycle-associated p72 and p54 genotype IX which also includes viruses responsible for ASF outbreaks in Kenya in 2006 and 2007 and Uganda in 2003. To define virus relationships at higher resolution, typing was performed by analysis of tetrameric amino acid repeat regions within the central variable region (CVR) of the B602L gene. Ugandan isolates sequences exhibited 100% identity to viruses isolated from outbreaks in Kenya in 2007. The identity was greater than the viruses obtained from an earlier outbreak in Kenya in 2006. This provides further evidence that genetically similar ASFV virus within p72 Genotype IX may be circulating between Kenya and Uganda.Key words: African swine fever virus (ASFV), restriction analysis, serological detection, genotyping, p72, p54, central variable region (CVR)

Genotyping of African swine fever virus (ASFV) isolates associated with disease outbreaks in Uganda in 2007

Samples from infected domestic pigs associated with an outbreak of African swine fever (ASF) in three districts of central Uganda in 2007 were confirmed as being infected with African swine fever virus (ASFV) using a P72 gene-based polymerase chain reaction amplification (PCR) assay combined with restriction analysis. None of the sera collected from pigs with clinical symptoms were positive using the OIE serological prescribed tests. However, seven haemadsorbing viruses were isolated in macrophage culture and genotyped by partial p72 and full length p54-gene sequencing. Four of these viruses were isolated directly from serum samples. All the viruses were classified within the domesticpig cycle-associated p72 and p54 genotype IX which also includes viruses responsible for ASF outbreaks in Kenya in 2006 and 2007 and Uganda in 2003. To define virus relationships at higher resolution, typing was performed by analysis of tetrameric amino acid repeat regions within the central variable region (CVR) of the B602L gene. Ugandan isolates sequences exhibited 100% identity to viruses isolated from outbreaks in Kenya in 2007. The identity was greater than the viruses obtained from an earlier outbreak in Kenya in 2006. This provides further evidence that genetically similar ASFV virus within p72 Genotype IX may be circulating between Kenya and Uganda. © 2011 Academic Journals

Mapping the risk of Rift Valley fever in Uganda using national seroprevalence data from cattle, sheep and goats

Uganda has had repeated outbreaks of Rift Valley fever (RVF) since March 2016 when human and livestock cases were reported in Kabale after a long interval. The disease has a complex and poorly described transmission patterns which involves several mosquito vectors and mammalian hosts (including humans). We conducted a national serosurvey in livestock to determine RVF virus (RVFV) seroprevalence, risk factors, and to develop a risk map that could be used to guide risk-based surveillance and control measures. A total of 3,253 animals from 175 herds were sampled. Serum samples collected were screened at the National Animal Disease Diagnostics and Epidemiology Centre (NADDEC) using a competition multispecies anti-RVF IgG ELISA kit. Data obtained were analyzed using a Bayesian model that utilizes integrated nested Laplace approximation (INLA) and stochastic partial differential equation (SPDE) approaches to estimate posterior distributions of model parameters, and account for spatial autocorrelation. Variables considered included animal level factors (age, sex, species) and multiple environmental data including meteorological factors, soil types, and altitude. A risk map was produced by projecting fitted (mean) values, from a final model that had environmental factors onto a spatial grid that covered the entire domain. The overall RVFV seroprevalence was 11.39% (95% confidence interval: 10.35–12.51%). Higher RVFV seroprevalences were observed in older animals compared to the young, and cattle compared to sheep and goats. RVFV seroprevalence was also higher in areas that had (i) lower precipitation seasonality, (ii) haplic planosols, and (iii) lower cattle density. The risk map generated demonstrated that RVF virus was endemic in several regions including those that have not reported clinical outbreaks in the northeastern part of the country. This work has improved our understanding on spatial distribution of RVFV risk in the country as well as RVF burden in livestock

Modeling the live-pig trade network in Georgia: Implications for disease prevention and control

Live pig trade patterns, drivers and characteristics, particularly in backyard predominant systems, remain largely unexplored despite their important contribution to the spread of infectious diseases in the swine industry. A better understanding of the pig trade dynamics can inform the implementation of risk-based and more cost-effective prevention and control programs for swine diseases. In this study, a semi-structured questionnaire elaborated by FAO and implemented to 487 farmers was used to collect data regarding basic characteristics about pig demographics and live-pig trade among villages in the country of Georgia, where very scarce information is available. Social network analysis and exponential random graph models were used to better understand the structure, contact patterns and main drivers for pig trade in the country. Results indicate relatively infrequent (a total of 599 shipments in one year) and geographically localized (median Euclidean distance between shipments = 6.08 km; IQR = 0-13.88 km) pig movements in the studied regions. The main factors contributing to live-pig trade movements among villages were being from the same region (i.e., local trade), usage of a middleman or a live animal market to trade live pigs by at least one farmer in the village, and having a large number of pig farmers in the village. The identified villages' characteristics and structural network properties could be used to inform the design of more cost-effective surveillance systems in a country which pig industry was recently devastated by African swine fever epidemics and where backyard production systems are predominant