The spatial ecology of free-ranging domestic pigs (Sus scrofa) in western Kenya

Background In many parts of the developing world, pigs are kept under low-input systems where they roam freely to scavenge food. These systems allow poor farmers the opportunity to enter into livestock keeping without large capital investments. This, combined with a growing demand for pork, especially in urban areas, has led to an increase in the number of small-holder farmers keeping free range pigs as a commercial enterprise. Despite the benefits which pig production can bring to a household, keeping pigs under a free range system increases the risk of the pig acquiring diseases, either production-limiting or zoonotic in nature. This study used Global Positioning System (GPS) technology to track free range domestic pigs in rural western Kenya, in order to understand their movement patterns and interactions with elements of the peri-domestic environment. Results We found that these pigs travel an average of 4,340 m in a 12 hr period and had a mean home range of 10,343 m2 (range 2,937–32,759 m2) within which the core utilisation distribution was found to be 964 m2 (range 246–3,289 m2) with pigs spending on average 47% of their time outside their homestead of origin. Conclusion These are the first data available on the home range of domestic pigs kept under a free range system: the data show that pigs in these systems spend much of their time scavenging outside their homesteads, suggesting that these pigs may be exposed to infectious agents over a wide area. Control policies for diseases such as Taenia solium, Trypanosomiasis, Trichinellosis, Toxoplasmosis or African Swine Fever therefore require a community-wide focus and pig farmers require education on the inherent risks of keeping pigs under a free range system. The work presented here will enable future research to incorporate movement data into studies of disease transmission, for example for the understanding of transmission of African Swine Fever between individuals, or in relation to the life-cycle of parasites including Taenia solium

Shrinking a large dataset to identify variables associated with increased risk of Plasmodium falciparum infection in Western Kenya

Large datasets are often not amenable to analysis using traditional single-step approaches. Here, our general objective was to apply imputation techniques, principal component analysis (PCA), elastic net and generalized linear models to a large dataset in a systematic approach to extract the most meaningful predictors for a health outcome. We extracted predictors for Plasmodium falciparum infection, from a large covariate dataset while facing limited numbers of observations, using data from the People, Animals, and their Zoonoses (PAZ) project to demonstrate these techniques: data collected from 415 homesteads in western Kenya, contained over 1500 variables that describe the health, environment, and social factors of the humans, livestock, and the homesteads in which they reside. The wide, sparse dataset was simplified to 42 predictors of P. falciparum malaria infection and wealth rankings were produced for all homesteads. The 42 predictors make biological sense and are supported by previous studies. This systematic data-mining approach we used would make many large datasets more manageable and informative for decision-making processes and health policy prioritization

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

Exploring vulnerability to infectious disease in a small-holder farming community in rural western Kenya

More than 2 billion people live on less than 2 US dollars per day. People in these conditions often have inadequate access to basic sanitation, safe water, and medical services. These individuals, households and communities may be at high risk for a wide range of preventable and treatable infectious diseases. The aims of this study were to: 1) describe the prevalence of endemic helminth, protozoal, bacterial and viral infections of people in a small-holder farming community in western Kenya; 2) explore the spatial distribution of infection risk; 3) quantify associations between social and environmental conditions and individual- and household-level infection; 4) identify shared risk factors operating on multiple pathogens. All data were collected between July 2010 and July 2012 as part of a cross-sectional survey of 416 households and 2113 people. This sample was considered representative of a population of 1.4 million people living in an area of western Kenya characterised by high levels of poverty. Sampled individuals were tested for exposure to, or infection with, 21 infectious agents using a range of faecal, blood and serological tests. Extensive questionnaire-based data were also collected. Individual- and household-level risk factors for infection with prevalent pathogens were explored using multilevel logistic regression, with a particular focus on examining the impact of socioeconomic position (SEP). Hierarchical zero-inflated binomial (ZIB) regression was used to derive an estimate of household pathogen ‘species richness’ with correction for imperfect detection. This modelling framework allowed assessment of the relationship between household-level infection with each parasite and a range of social and environmental conditions and, uniquely for a single study setting, the average response of the ‘group’ of parasites to these conditions. This study found very high levels of parasitism in the community, particularly with hookworm (36.3% (95% CI 32.8 – 39.9)), Entamoeba histolytica/dispar (30.1% (27.5 – 32.8)), Plasmodium falciparum (29.4% (26.8 – 32.0)), and Taenia spp. (19.7% (16.7 – 22.7)). Some degree of within-household clustering was found for all pathogens, and this was particularly large for the helminth species and HIV. Most pathogens also showed spatial heterogeneity in infection risk, with evidence of spatial clustering in household-level infection, most notably for HIV, Schistosoma mansoni, P. falciparum and the soiltransmitted helminths. A socioeconomic gradient was identified, even in this predominantly poor community. Increasing socioeconomic position (SEP) resulted in significantly reduced risk of individual infection for E. histolytica/dispar, P. falciparum, and hookworm. By contrast, individuals living in the richest households were at significantly elevated risk of infection with Mycobacterium spp.. Individuals living in the poorest households were least likely to report the recent use of medical treatments. The average pathogen species richness (out of 21 species) per household was 4.7 (range: 0 to 13). Following correction for detection error, the predicted average helminth species count (out of 6 species) was 3 (range: 0.94 to 5.96). While socioeconomic position had little effect on the probability that a household was infected with any of the helminth species of interest, domestic (within-household) transmission appeared to be greatest in the poorest households for hookworm, S. mansoni, Ascaris lumbricoides and Strongyloides stercoralis. Household size had a consistent effect on probably of household infection with each helminth species, so that the largest households were also the most pathogen diverse. Household-level helminth species richness was identified as a significant positive predictor of individual risk of HIV infection, raising potentially important questions about helminth-HIV interactions in the study area. This study integrates approaches from epidemiology and ecology to explore infectious disease risk and its determinants at a range of social and geographic scales in a small-holder farming community in western Kenya. Considering risk at both the individual and household level within the same community can contribute to better understanding of the factors that influence disease transmission in both domestic and public domains

Activation and regulation of the innate immune system in response to Ureaplasma infection

The bacteria Ureaplasma has long been associated with a wide range of adverse health implications, including preterm birth, preterm premature rupture of the membrane and lung disorders, such as bronchopulmonary dysplasia in neonatal infants, but still, little is known about the pathogenic properties of Ureaplasma and possible direct association with adverse health complications. Estimated prevalence of Ureaplasma colonisation in sexually active adults is between 40 – 80%, therefore further understanding of its pathogenic properties and its ability to initiate an immune response is crucial. Specifically selected human cell-lines were examined in vitro to determine whether an innate immune response could be activated by Ureaplasma infection. If inflammatory immune responses were detected in human cell-lines, pathogenic properties of Ureaplasma would be confirmed, and its role in pregnancy and neonatal complications could be supported. Using a range of techniques, activation of immune response pathways were examined, as too were the production of detrimental pro-inflammatory cytokines that would strengthen the suggested associations of Ureaplasma infection with the above-mentioned complications. Myeloid-derived leukocytic monocytes, human bronchial epithelial cells and human amniotic epithelial cells were examined, as these would be the most relevant cell lines to determine if Ureaplasma could induce preterm birth, preterm premature rupture of the membrane and bronchopulmonary dysplasia. All cell lines studied showed immune response and inflammatory cytokine production after stimulation with Ureaplasma. This supports that Ureaplasma is capable of causing tissue damage in neonatal respiratory tracts that may lead to bronchopulmonary dysplasia and damage to the amniotic and chorion membranes that may lead to preterm premature rupture of the membrane. Ureaplasma was detected at the cell surface of human amniotic epithelial cells (HAECs) by TLR2 and TLR2/6 heterodimers. Results suggest that Ureaplamsma multiple banded antigen (MBA) is the strong ligand for TLR2 and TLR6 and stimulation of HAECs with MBA alone caused an immune response. TLR9 was responsible for the detection of internalised Ureaplasma, which is also able to initiate an immune response and inflammatory cytokine production. v Ureaplasma stimulation results in the production of the inflammatory cytokines TNF-α, IL-8 IL-6 via the NF-κB signaling pathway. Production of the potent inflammatory cytokine IL-1β was also observed, which would suggest the formation of inflammasome complexes. NLRs were investigated to find which NLR inflammasome were activated. It was shown that genetically knocking down NLRP7 significantly reduced the amount of IL-1β that was produced after Ureaplasma stimulation, suggesting that NLRP7 inflammsones are activated by Ureaplasma. Reduction in IL-1β was also observed, but to a lesser extent, when NLRP3 was knocked down. We decided to investigate the role of NLRP7 further and found a novel immune pathway, where NH3 causes activation and formation of the NRLP7 inflammasone. NH3 is produced as a bi-product of urease activity, which an essential process for Ureaplasma. The addition of a potent urease inhibitor to HAECs being stimulated with Ureaplasma significantly reduced the production of IL-1β, strongly supporting that NH3 plays a significant role in the detection of Ureaplasma infection and is responsible for causing the tissue damage that contributes to preterm premature rupture of the membrane leading to preterm birth. This investigation strongly supports that Ureaplasma is responsible for causing preterm birth and health complications in neonates, and that more robust treatment and monitoring of Ureaplasma is required, especially in pregnant women. These undertakings will hopefully reduce the rates of preterm birth and the associated health implications, in addition to reducing rates of bronchopulmonary dysplasia in neonates