Spatial multi-criteria decision analysis to predict suitability for African swine fever endemicity in Africa

Background African swine fever (ASF) is endemic in several countries of Africa and may pose a risk to all pig producing areas on the continent. Official ASF reporting is often rare and there remains limited awareness of the continent-wide distribution of the disease. In the absence of accurate ASF outbreak data and few quantitative studies on the epidemiology of the disease in Africa, we used spatial multi-criteria decision analysis (MCDA) to derive predictions of the continental distribution of suitability for ASF persistence in domestic pig populations as part of sylvatic or domestic transmission cycles. In order to incorporate the uncertainty in the relative importance of different criteria in defining suitability, we modelled decisions within the MCDA framework using a stochastic approach. The predictive performance of suitability estimates was assessed via a partial ROC analysis using ASF outbreak data reported to the OIE since 2005. Results Outputs from the spatial MCDA indicate that large areas of sub-Saharan Africa may be suitable for ASF persistence as part of either domestic or sylvatic transmission cycles. Areas with high suitability for pig to pig transmission (‘domestic cycles’) were estimated to occur throughout sub-Saharan Africa, whilst areas with high suitability for introduction from wildlife reservoirs (‘sylvatic cycles’) were found predominantly in East, Central and Southern Africa. Based on average AUC ratios from the partial ROC analysis, the predictive ability of suitability estimates for domestic cycles alone was considerably higher than suitability estimates for sylvatic cycles alone, or domestic and sylvatic cycles in combination. Conclusions This study provides the first standardised estimates of the distribution of suitability for ASF transmission associated with domestic and sylvatic cycles in Africa. We provide further evidence for the utility of knowledge-driven risk mapping in animal health, particularly in data-sparse environments.</p

Household socio-economic position and individual infectious disease risk in rural Kenya

The importance of household socio-economic position (SEP) in shaping individual infectious disease risk is increasingly recognised, particularly in low income settings. However, few studies have measured the extent to which this association is consistent for the range of pathogens that are typically endemic among the rural poor in the tropics. This cross-sectional study assessed the relationship between SEP and human infection within a single community in western Kenya using a set of pathogens with diverse transmission routes. The relationships between household SEP and individual infection with Plasmodium falciparum, hookworm (Ancylostoma duodenale and/or Necator americanus), Entamoeba histolytica/dispar, Mycobacterium tuberculosis, and HIV, and co-infections between hookworm, P. falciparum and E. histolytica/dispar, were assessed using multivariable logistic and multinomial regression. Individuals in households with the lowest SEP were at greatest risk of infection with P. falciparum, hookworm and E. histolytica/dispar, as well as co-infection with each pathogen. Infection with M. tuberculosis, by contrast, was most likely in individuals living in households with the highest SEP. There was no evidence of a relationship between individual HIV infection and household SEP. We demonstrate the existence of a household socio-economic gradient within a rural farming community in Kenya which impacts upon individual infectious disease risk. Structural adjustments that seek to reduce poverty, and therefore the socio-economic inequalities that exist in this community, would be expected to substantially reduce overall infectious disease burden. However, policy makers and researchers should be aware that heterogeneous relationships can exist between household SEP and infection risk for different pathogens in low income settings

Environmental predictors of bovine Eimeria infection in western Kenya

Eimeriosis is caused by a protozoan infection affecting most domestic animal species. Outbreaks in cattle are associated with various environmental factors in temperate climates but limited work has been done in tropical settings. The objective of this work was to determine the prevalence and environmental factors associated with bovine Eimeria spp. infection in a mixed farming area of western Kenya. A total of 983 cattle were sampled from 226 cattle-keeping households. Faecal samples were collected directly from the rectum via digital extraction and analysed for the presence of Eimeria spp. infection using the MacMaster technique. Individual and household level predictors of infection were explored using mixed effects logistic regression. The prevalence of individual animal Eimeria infection was 32.8% (95% CI 29.9–35.9). A positive linear relationship was found between risk of Eimeria infection and increasing temperature (OR = 1.4, 95% CI 1.06–1.86) and distance to areas at risk of flooding (OR = 1.49, 95% CI 1.17–1.91). There was weak evidence of non-linear relationship between Eimeria infection and the proportion of the area around a household that was classified as swamp (OR = 1.12, 95% CI 0.87–1.44; OR (quadratic term) = 0.85, 95% CI 0.73–1.00), and the sand content of the soil (OR = 1.18, 95% CI 0.91–1.53; OR (quadratic term) = 1.1, 95% CI 0.99–1.23). The risk of animal Eimeria spp. infection is influenced by a number of climatic and soil-associated conditions.</p

Assessing the impact of tailored biosecurity advice on farmer behaviour and pathogen presence in beef herds in England and Wales

The term ‘biosecurity’ encompasses many measures farmers can take to reduce the risk of pathogen incursion or spread. As the best strategy will vary between settings, veterinarians play an important role in assessing risk and providing advice, but effectiveness requires farmer acceptance and implementation. The aim of this study was to assess the effectiveness of specifically-tailored biosecurity advice packages in reducing endemic pathogen presence on UK beef suckler farms. One hundred and sixteen farms recruited by 10 veterinary practices were followed for three years. Farms were randomly allocated to intervention (receiving specifically-tailored advice, with veterinarians and farmers collaborating to develop an improved biosecurity strategy) or control (receiving general advice) groups. A spreadsheet-based tool was used annually to attribute a score to each farm reflecting risk of entry or spread of bovine viral diarrhoea virus (BVDV), bovine herpesvirus-1 (BHV1), Mycobacterium avium subsp. paratuberculosis (MAP), Leptospira interrogans serovar hardjo (L. hardjo) and Mycobacterium bovis (M. bovis). Objectives of these analyses were to identify evidence of reduction in risk behaviours during the study, as well as evidence of reductions in pathogen presence, as indications of effectiveness. Risk behaviours and pathogen prevalences were examined across study years, and on intervention compared with control farms, using descriptive statistics and multilevel regression. There were significant reductions in risk scores for all five pathogens, regardless of intervention status, in every study year compared with the outset. Animals on intervention farms were significantly less likely than those on control farms to be seropositive for BVDV in years 2 and 3 and for L. hardjo in year 3 of the study. Variations by study year in animal-level odds of seropositivity to BHV1 or MAP were not associated with farm intervention status. All farms had significantly reduced odds of BHV1 seropositivity in year 2 than at the outset. Variations in farm-level MAP seropositivity were not associated with intervention status. There were increased odds of M. bovis on intervention farms compared with control farms at the end of the study. Results suggest a structured annual risk assessment process, conducted as a collaboration between veterinarian and farmer, is valuable in encouraging improved biosecurity practices. There were some indications, but not conclusive evidence, that tailored biosecurity advice packages have potential to reduce pathogen presence. These findings will inform development of a collaborative approach to biosecurity between veterinarians and farmers, including adoption of cost-effective strategies effective across pathogens

Analysing livestock network data for infectious diseases control:an argument for routine data collection in emerging economies

Livestock movements are an important mechanism of infectious disease transmission. Where these are well recorded, network analysis tools have been used to successfully identify system properties, highlight vulnerabilities to transmission, and inform targeted surveillance and control. Here we highlight the main uses of network properties in understanding livestock disease epidemiology and discuss statistical approaches to infer network characteristics from biased or fragmented datasets. We use a ‘hurdle model’ approach that predicts (i) the probability of movement and (ii) the number of livestock moved to generate synthetic ‘complete’ networks of movements between administrative wards, exploiting routinely collected government movement permit data from northern Tanzania. We demonstrate that this model captures a significant amount of the observed variation. Combining the cattle movement network with a spatial between-ward contact layer, we create a multiplex, over which we simulated the spread of ‘fast’ (R0 = 3) and ‘slow’ (R0 = 1.5) pathogens, and assess the effects of random versus targeted disease control interventions (vaccination and movement ban). The targeted interventions substantially outperform those randomly implemented for both fast and slow pathogens. Our findings provide motivation to encourage routine collection and centralization of movement data to construct representative networks. This article is part of the theme issue ‘Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control’. This theme issue is linked with the earlier issue ‘Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes’

An Anti-Human ICAM-1 Antibody Inhibits Rhinovirus-Induced Exacerbations of Lung Inflammation

Human rhinoviruses (HRV) cause the majority of common colds and acute exacerbations of asthma and chronic obstructive pulmonary disease (COPD). Effective therapies are urgently needed, but no licensed treatments or vaccines currently exist. Of the 100 identified serotypes, ∼90% bind domain 1 of human intercellular adhesion molecule-1 (ICAM-1) as their cellular receptor, making this an attractive target for development of therapies; however, ICAM-1 domain 1 is also required for host defence and regulation of cell trafficking, principally via its major ligand LFA-1. Using a mouse anti-human ICAM-1 antibody (14C11) that specifically binds domain 1 of human ICAM-1, we show that 14C11 administered topically or systemically prevented entry of two major groups of rhinoviruses, HRV16 and HRV14, and reduced cellular inflammation, pro-inflammatory cytokine induction and virus load in vivo. 14C11 also reduced cellular inflammation and Th2 cytokine/chemokine production in a model of major group HRV-induced asthma exacerbation. Interestingly, 14C11 did not prevent cell adhesion via human ICAM-1/LFA-1 interactions in vitro, suggesting the epitope targeted by 14C11 was specific for viral entry. Thus a human ICAM-1 domain-1-specific antibody can prevent major group HRV entry and induction of airway inflammation in vivo