Crimean‐Congo haemorrhagic fever virus (CCHFV) continues to cause new human cases in Iberia while its spatial distribution and ecological determinants remain unknown. The virus remains active in a silent tick‐animal cycle to which animals contribute maintaining the tick populations and the virus itself. Wild ungulates, in particular red deer, are essential hosts for Hyalomma ticks in Iberia, which are the principal competent vector of CCHFV. Red deer could be an excellent model to understand the ecological determinants of CCHFV as well as to predict infection risks for humans because it is large, gregarious, abundant and the principal host for Hyalomma lusitanicum. We designed a cross‐sectional study, analysed the presence of CCHFV antibodies in 1444 deer from 82 populations, and statistically modelled exposure risk with host and environmental predictors. The best‐fitted statistical model was projected for peninsular Spain to map infection risks. Fifty out of 82 deer populations were seropositive, with individual population prevalence as high as 88%. The highest prevalence of exposure to CCHFV occurred in the southwest of the Iberian Peninsula. Climate and ungulate abundance were the most influential predictors of the risk of exposure to the virus. The highest risk regions were those where H. lusitanicum is most abundant. Eight of the nine primary human cases occurred in or bordering these regions, demonstrating that the model predicts human infection risk accurately. A recent human case of CCHF occurred in northwestern Spain, a region that the model predicted as low risk, pointing out that it needs improvement to capture all determinants of the CCHFV infection risk. In this study, we have been able to identify the main ecological determinants of CCHFV, and we have also managed to create an accurate model to assess the risk of CCHFV infection.
Aujeszky's disease (AD) virus is enzootic in Iberian wild boar, thus posing a threat to the official eradication of AD on extensive domestic pig farms in Spain. Understanding the dynamics and drivers of ADV infection in wild boar will help prevent viral transmission at the wild boar–pig interface. This study analyses the dynamics of ADV infection in wild boar and tests relevant hypotheses in order to identify drivers of ADV infection dynamics. Wild boar sera (N = 971) and oropharyngeal tonsils (TN, N = 549) collected over 11 consecutive years in south‐western Spain were tested for ADV antibodies and DNA, respectively. We tested the hypotheses that population immunity modulates the risk of ADV infection (H1) and that detecting ADV DNA in TN is a good proxy of the annual ADV infection pressure (H2). This was done by building logistic regression models that were subsequently employed to test the influence of a series of host population and host individual factors—including predictors of ADV immunity in the population—on the annual risk of new ADV infections and on the presence of ADV DNA in TN. The premise of H1 was that there would be a negative association between the proportion of ADV antibody‐positive wild boar in a given year and the risk of ADV infection of naïve individuals. There was, however, a positive association, and H1 was, therefore, rejected. If detecting ADV in TN had been a good indicator of ADV infection pressure, a positive association with the proportion of ADV antibody‐positive wild boar would have been found. However, this was not the case and H2 was also rejected. We confirmed that ADV infection is a dynamic phenomenon. The risk of infection with ADV can change considerably between consecutive years, and these changes are positively associated with the proportion of infected wild boar in the population.
The objective of this study was to evaluate the spatial risk of exposure to Crimean-Congo haemorrhagic fever virus (CCHFV) infection of healthy blood donors in an enzootic region with a predicted risk gradient based on a virus-animal interaction risk model. We designed a cross-sectional study to test if the exposure pattern of the human population to CCHFV spatially matches the predicted risk. We randomly selected 1384 donors from different risk gradients and analyzed their sera searching for CCHFV antibodies. None of the selected blood donors showed exposure to CCHFV.This study shows that exposure risk spatial patterns, as predicted from animal-tickvirus models, does not necessarily match the pattern of human-infected tick interactions leading to CCHFV infection and CCHF cases, at least in a region of predicted moderate infection risk. The findings suggest that future studies should bear the potential drivers of tick-human encounter rates into account to more accurately predict risks.
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