Seroprevalence of Coxiella burnetii in a dairy cattle herd from the Al Ain region, United Arab Emirates.

The present study assessed the serostatus of Coxiella burnetii in a dairy cattle herd from the Al Ain region, UAE. In addition, the study evaluated the relationship between C. burnetii seropositivity and history of abortion and cattle age. Blood/sera from 759 cattle were tested by ELISA, and the data analyzed by the Pearson Chi-square test (PCST), odds ratios, and logistic regression. Of the 759 study cattle, 36.5% (277/759) were seropositive while 63.5% (482/759) were seronegative for C. burnetii. Additionally, 19.5% (148/759) of the cattle had ever aborted while 80.5% (611/759) had not. Meanwhile, 34.3% (95/277) of the C. burnetii-seropositive cattle (CBSPos) had history of abortion while 65.7% (182/277) did not. By comparison, only 11.0% (53/482) of the C. burnetii-seronegative (CBSNeg) cattle had history of abortion while 89.0% (429/482) had never aborted. The difference in the conditional probabilities of CBSPos or CBSNeg cattle that had history of abortion was statistically significant (PCST; p value 0.001), as was the association between a CBSPos outcome and history of abortion (p value 0.0001). Moreover, the odds of prior abortion having occurred in CBSPos were 4.3 times higher than in CBSNeg cattle. Similarly, the log of the odds of past abortions was positively correlated with C. burnetii seropositivity and cattle age (p value < 0.001). These data demonstrate apparent C. burnetii infections in cattle from the UAE in which the risk of abortion appears to increase with cattle age. Molecular testing for C. burnetii and comprehensive investigations of other abortigenic pathogens in the study cattle herd are recommended.

A Mathematical Model that Simulates Control Options for African Swine Fever Virus (ASFV).

A stochastic model designed to simulate transmission dynamics of African swine fever virus (ASFV) in a free-ranging pig population under various intervention scenarios is presented. The model was used to assess the relative impact of the timing of the implementation of different control strategies on disease-related mortality. The implementation of biosecurity measures was simulated through incorporation of a decay function on the transmission rate. The model predicts that biosecurity measures implemented within 14 days of the onset of an epidemic can avert up to 74% of pig deaths due to ASF while hypothetical vaccines that confer 70% immunity when deployed prior to day 14 of the epidemic could avert 65% of pig deaths. When the two control measures are combined, the model predicts that 91% of the pigs that would have otherwise succumbed to the disease if no intervention was implemented would be saved. However, if the combined interventions are delayed (defined as implementation from > 60 days) only 30% of ASF-related deaths would be averted. In the absence of vaccines against ASF, we recommend early implementation of enhanced biosecurity measures. Active surveillance and use of pen-side diagnostic assays, preferably linked to rapid dissemination of this data to veterinary authorities through mobile phone technology platforms are essential for rapid detection and confirmation of ASF outbreaks. This prediction, although it may seem intuitive, rationally confirms the importance of early intervention in managing ASF epidemics. The modelling approach is particularly valuable in that it determines an optimal timing for implementation of interventions in controlling ASF outbreaks.

Estimating the Basic Reproductive Number (R0) for African Swine Fever Virus (ASFV) Transmission between Pig Herds in Uganda.

African swine fever (ASF) is a highly contagious, lethal and economically devastating haemorrhagic disease of domestic pigs. Insights into the dynamics and scale of virus transmission can be obtained from estimates of the basic reproduction number (R0). We estimate R0 for ASF virus in small holder, free-range pig production system in Gulu, Uganda. The estimation was based on data collected from outbreaks that affected 43 villages (out of the 289 villages with an overall pig population of 26,570) between April 2010 and November 2011. A total of 211 outbreaks met the criteria for inclusion in the study. Three methods were used, specifically; (i) GIS- based identification of the nearest infectious neighbour based on the Euclidean distance between outbreaks, (ii) epidemic doubling time, and (iii) a compartmental susceptible-infectious (SI) model. For implementation of the SI model, three approaches were used namely; curve fitting (CF), a linear regression model (LRM) and the SI/N proportion. The R0 estimates from the nearest infectious neighbour and epidemic doubling time methods were 3.24 and 1.63 respectively. Estimates from the SI-based method were 1.58 for the CF approach, 1.90 for the LRM, and 1.77 for the SI/N proportion. Since all these values were above one, they predict the observed persistence of the virus in the population. We hypothesize that the observed variation in the estimates is a consequence of the data used. Higher resolution and temporally better defined data would likely reduce this variation. This is the first estimate of R0 for ASFV in a free range smallholder pig keeping system in sub-Saharan Africa and highlights the requirement for more efficient application of available disease control measures.