One hundred years of zoonoses research in the Horn of Africa: A scoping review.

BACKGROUND: One Health is particularly relevant to the Horn of Africa where many people's livelihoods are highly dependent on livestock and their shared environment. In this context, zoonoses may have a dramatic impact on both human and animal health, but also on country economies. This scoping review aimed to characterise and evaluate the nature of zoonotic disease research in the Horn region. Specifically, it addressed the following questions: (i) what specific zoonotic diseases have been prioritised for research, (ii) what data have been reported (human, animal or environment), (iii) what methods have been applied, and (iv) who has been doing the research? METHODOLOGY/PRINCIPAL FINDINGS: We used keyword combinations to search online databases for peer-reviewed papers and theses. Screening and data extraction (disease, country, domain and method) was performed using DistillerSR. A total of 2055 studies focusing on seven countries and over 60 zoonoses were included. Brucellosis attracted the highest attention in terms of research while anthrax, Q fever and leptospirosis have been comparatively under-studied. Research efforts did not always align with zoonoses priorities identified at national levels. Despite zoonoses being a clear target for 'One Health' research, a very limited proportion of studies report data on the three domains of human, animal and environment. Descriptive and observational epidemiological studies were dominant and only a low proportion of publications were multidisciplinary. Finally, we found that a minority of international collaborations were between Global South countries with a high proportion of authors having affiliations from outside the Horn of Africa. CONCLUSIONS/SIGNIFICANCE: There is a growing interest in zoonoses research in the Horn of Africa. Recommendations arising from this scoping review include: (i) ensuring zoonoses research aligns with national and global research agendas; (ii) encouraging researchers to adopt a holistic, transdisciplinary One Health approach following high quality reporting standards (COHERE, PRISMA, etc.); and (iii) empowering local researchers supported by regional and international partnerships to engage in zoonoses research.

Divide-and-conquer: machine-learning integrates mammalian and viral traits with network features to predict virus-mammal associations.

Our knowledge of viral host ranges remains limited. Completing this picture by identifying unknown hosts of known viruses is an important research aim that can help identify and mitigate zoonotic and animal-disease risks, such as spill-over from animal reservoirs into human populations. To address this knowledge-gap we apply a divide-and-conquer approach which separates viral, mammalian and network features into three unique perspectives, each predicting associations independently to enhance predictive power. Our approach predicts over 20,000 unknown associations between known viruses and susceptible mammalian species, suggesting that current knowledge underestimates the number of associations in wild and semi-domesticated mammals by a factor of 4.3, and the average potential mammalian host-range of viruses by a factor of 3.2. In particular, our results highlight a significant knowledge gap in the wild reservoirs of important zoonotic and domesticated mammals' viruses: specifically, lyssaviruses, bornaviruses and rotaviruses.

Predicting mammalian hosts in which novel coronaviruses can be generated.

Novel pathogenic coronaviruses - such as SARS-CoV and probably SARS-CoV-2 - arise by homologous recombination between co-infecting viruses in a single cell. Identifying possible sources of novel coronaviruses therefore requires identifying hosts of multiple coronaviruses; however, most coronavirus-host interactions remain unknown. Here, by deploying a meta-ensemble of similarity learners from three complementary perspectives (viral, mammalian and network), we predict which mammals are hosts of multiple coronaviruses. We predict that there are 11.5-fold more coronavirus-host associations, over 30-fold more potential SARS-CoV-2 recombination hosts, and over 40-fold more host species with four or more different subgenera of coronaviruses than have been observed to date at >0.5 mean probability cut-off (2.4-, 4.25- and 9-fold, respectively, at >0.9821). Our results demonstrate the large underappreciation of the potential scale of novel coronavirus generation in wild and domesticated animals. We identify high-risk species for coronavirus surveillance.

Distinct spread of DNA and RNA viruses among mammals amid prominent role of domestic species.

AIM: Emerging infectious diseases arising from pathogen spillover from mammals to humans constitute a substantial health threat. Tracing virus origin and predicting the most likely host species for future spillover events are major objectives in One Health disciplines.We assessed patterns of virus sharing among a large diversity of mammals, including humans and domestic species. LOCATION: Global. TIME PERIOD: Current. MAJOR TAXA STUDIED: Mammals and associated viruses. METHODS: We used network centrality analysis and trait-based Bayesian hierarchical models to explore patterns of virus sharing among mammals. We analysed a global database that compiled the associations between 1,785 virus species and 725 mammalian host species as sourced from automatic screening of meta-data accompanying published nucleotide sequences between 1950 and 2019. RESULTS: We show that based on current evidence, domesticated mammals hold the most central positions in networks of known mammal-virus associations. Among entire host-virus networks, Carnivora and Chiroptera hold central positions for mainly sharing RNA viruses, whereas ungulates hold central positions for sharing both RNA and DNA viruses with other host species. We revealed strong evidence that DNA viruses were phylogenetically more host specific than RNA viruses. RNA viruses exhibited low functional host specificity despite an overall tendency to infect phylogenetically related species, signifying high potential to shift across hosts with different ecological niches. The frequencies of sharing viruses among hosts and the proportion of zoonotic viruses in hosts were larger for RNA than for DNA viruses. MAIN CONCLUSIONS: Acknowledging the role of domestic species in addition to host and virus traits in patterns of virus sharing is necessary to improve our understanding of virus spread and spillover in times of global change. Understanding multi-host virus-sharing pathways adds focus to curtail disease spread.

Integration of shared-pathogen networks and machine learning reveals the key aspects of zoonoses and predicts mammalian reservoirs.

Diseases that spread to humans from animals, zoonoses, pose major threats to human health. Identifying animal reservoirs of zoonoses and predicting future outbreaks are increasingly important to human health and well-being and economic stability, particularly where research and resources are limited. Here, we integrate complex networks and machine learning approaches to develop a new approach to identifying reservoirs. An exhaustive dataset of mammal-pathogen interactions was transformed into networks where hosts are linked via their shared pathogens. We present a methodology for identifying important and influential hosts in these networks. Ensemble models linking network characteristics with phylogeny and life-history traits are then employed to predict those key hosts and quantify the roles they undertake in pathogen transmission. Our models reveal drivers explaining host importance and demonstrate how these drivers vary by pathogen taxa. Host importance is further integrated into ensemble models to predict reservoirs of zoonoses of various pathogen taxa and quantify the extent of pathogen sharing between humans and mammals. We establish predictors of reservoirs of zoonoses, showcasing host influence to be a key factor in determining these reservoirs. Finally, we provide new insight into the determinants of zoonosis-sharing, and contrast these determinants across major pathogen taxa.

Small animal disease surveillance: GI disease and salmonellosis.

Presentation for gastrointestinal (GI) disease comprised 2.2 per cent of cat, 3.2 per cent of dog and 2.2 per cent of rabbit consultations between April 1, 2016 and March 31, 2017Diarrhoea and vomiting without blood were the most frequently reported GI disease clinical signs (34.4 and 38.9 per cent in cats and 42.8 and 37.3 per cent in dogs, respectively)The mean percentage of samples testing positive for Salmonella in dogs was double that in cats (0.82 per cent and 0.41 per cent, respectively) from January 1, 2011 to December 31, 2016In dogs, autumn was associated with a greater proportion of Salmonella-positive sample submissions; no clear suggestion of seasonal variation in cats was observedIn both cats and dogs, isolates belonging to Salmonella enterica group B serotypes were the most common (68.9 per cent in cats and 55.0 per cent in dogs).

Co-occurrence of viruses and mosquitoes at the vectors' optimal climate range: An underestimated risk to temperate regions?

Mosquito-borne viruses have been estimated to cause over 100 million cases of human disease annually. Many methodologies have been developed to help identify areas most at risk from transmission of these viruses. However, generally, these methodologies focus predominantly on the effects of climate on either the vectors or the pathogens they spread, and do not consider the dynamic interaction between the optimal conditions for both vector and virus. Here, we use a new approach that considers the complex interplay between the optimal temperature for virus transmission, and the optimal climate for the mosquito vectors. Using published geolocated data we identified temperature and rainfall ranges in which a number of mosquito vectors have been observed to co-occur with West Nile virus, dengue virus or chikungunya virus. We then investigated whether the optimal climate for co-occurrence of vector and virus varies between "warmer" and "cooler" adapted vectors for the same virus. We found that different mosquito vectors co-occur with the same virus at different temperatures, despite significant overlap in vector temperature ranges. Specifically, we found that co-occurrence correlates with the optimal climatic conditions for the respective vector; cooler-adapted mosquitoes tend to co-occur with the same virus in cooler conditions than their warmer-adapted counterparts. We conclude that mosquitoes appear to be most able to transmit virus in the mosquitoes' optimal climate range, and hypothesise that this may be due to proportionally over-extended vector longevity, and other increased fitness attributes, within this optimal range. These results suggest that the threat posed by vector-competent mosquito species indigenous to temperate regions may have been underestimated, whilst the threat arising from invasive tropical vectors moving to cooler temperate regions may be overestimated.

Small animal disease surveillance: pruritus, and coagulase-positive staphylococci.

Presentation for pruritus comprised 6.5 per cent, 3.6 per cent and 2.0 per cent of canine, feline and rabbit consultations, respectively, between January 2014 and June 2016Topical antimicrobials were the most commonly prescribed pruritus treatments for dogs (33.6 per cent of consultations); for cats, it was systemic glucocorticoids (53.5 per cent)In surveillance of coagulase-positive staphylococci, 16 per cent of 176 coagulase-positive staphylococci isolated from canine diagnostic samples were sensitive to all tested antibacterial classes; multidrug resistance (resistance to three or more antibacterial classes) was found in 6.8 per cent.

Small animal disease surveillance: respiratory disease.

Presentation for respiratory disease comprised 1.7 per cent, 2.3 per cent and 2.5 per cent of canine, feline and rabbit consultations, respectively, between January 2014 and December 2015. Coughing was the most frequent respiratory sign reported in dogs (71.1 per cent of consultations); in cats it was sneezing (42.6 per cent). Mean percentage of samples testing positive for feline calicivirus (FCV) was 30.1 per cent in 2014 and 27.9 per cent in 2015. January was the month with the highest percentage of FCV-positive samples in both 2014 and 2015.

Using the H-index to assess disease priorities for salmon aquaculture.

Atlantic salmon's (Salmo salar) annual aquaculture production exceeds 2M tonnes globally, and for the UK forms the largest single food export. However, aquaculture production is negatively affected by a range of different diseases and parasites. Effort to control pathogens should be focused on those which are most "important" to aquaculture. It is difficult to specify what makes a pathogen important; this is particularly true in the aquatic sector where data capture systems are less developed than for human or terrestrial animal diseases. Mortality levels might be one indicator, but these can cause a range of different problems such as persistent endemic losses, occasional large epidemics or control/treatment costs. Economic and multi-criteria decision methods can incorporate this range of impacts, however these have not been consistently applied to aquaculture and the quantity and quality of data required is large, so their potential for comparing aquatic pathogens is currently limited. A method that has been developed and applied to both human and terrestrial animal diseases is the analysis of published scientific literature using the H-index method. We applied this method to salmon pathogens using Web of Science searches for 23 pathogens. The top 3 H-indices were obtained for: sea lice, furunculosis, and infectious salmon anaemia; post 2000, Amoebic Gill Disease (AGD) replaced furunculosis. The number of publications per year describing bacterial disease declined significantly, while those for viruses and sea lice increased significantly. This reflects effective bacterial control by vaccination, while problems related to viruses and sea lice have increased. H-indices by country reflected different national concerns (e.g. AGD ranked top for Australia). Averaged national H-indices for salmon diseases tend to increase with log of salmon production; countries with H-Indices significantly below the trend line have suffered particularly large disease losses. The H-index method, supported by other literature analyses, is consistent with the nature and history of salmon diseases and so provides a useful quantitative measure for comparing different diseases in the absence of other measures.

Small animal disease surveillance.

This is the first UK small animal disease surveillance report from SAVSNET. Future reports will expand to other syndromes and diseases. As data are collected for longer, the estimates of changes in disease burden will become more refined, allowing more targeted local and perhaps national interventions. Anonymised data can be accessed for research purposes by contacting the authors. SAVSNET welcomes feedback on this report.

Database of host-pathogen and related species interactions, and their global distribution.

Interactions between species, particularly where one is likely to be a pathogen of the other, as well as the geographical distribution of species, have been systematically extracted from various web-based, free-access sources, and assembled with the accompanying evidence into a single database. The database attempts to answer questions such as what are all the pathogens of a host, and what are all the hosts of a pathogen, what are all the countries where a pathogen was found, and what are all the pathogens found in a country. Two datasets were extracted from the database, focussing on species interactions and species distribution, based on evidence published between 1950-2012. The quality of their evidence was checked and verified against well-known, alternative, datasets of pathogens infecting humans, domestic animals and wild mammals. The presented datasets provide a valuable resource for researchers of infectious diseases of humans and animals, including zoonoses.