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.

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.

The effect of temperature, farm density and foot-and-mouth disease restrictions on the 2007 UK bluetongue outbreak

In 2006, bluetongue (BT), a disease of ruminants, was introduced into northern Europe for the first time and more than two thousand farms across five countries were affected. In 2007, BT affected more than 35,000 farms in France and Germany alone. By contrast, the UK outbreak beginning in 2007 was relatively small, with only 135 farms in southeast England affected. We use a model to investigate the effects of three factors on the scale of BT outbreaks in the UK: (1) place of introduction; (2) temperature; and (3) animal movement restrictions. Our results suggest that the UK outbreak could have been much larger had the infection been introduced into the west of England either directly or as a result of the movement of infected animals from southeast England before the first case was detected. The fact that air temperatures in the UK in 2007 were marginally lower than average probably contributed to the UK outbreak being relatively small. Finally, our results indicate that BT movement restrictions are effective at controlling the spread of infection. However, foot-and-mouth disease restrictions in place before the detection and control of BT in 2007 almost certainly helped to limit BT spread prior to its detection.

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).

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.

Canine babesiosis and tick activity monitored using companion animal electronic health records in the UK

Recent publications highlighting autochthonous Babesia canis infection in dogs from Essex that have not travelled outside the UK are a powerful reminder of the potential for pathogen emergence in new populations. Here the authors use electronic health data collected from two diagnostic laboratories and a network of 392 veterinary premises to describe canine Babesia cases and levels of Babesia concern from January 2015 to March 2016, and the activity of ticks during December 2015–March 2016. In most areas of the UK, Babesia diagnosis in this population was rare and sporadic. In addition, there was a clear focus of Babesia cases in the affected area in Essex. Until February 2016, analysis of health records indicated only sporadic interest in Babesia largely in animals coming from overseas. Following media coverage in March 2016, there was a spike in owner concern that was geographically dispersed beyond the at-risk area. Tick activity (identified as ticks being removed from animals in veterinary consultations) was consistent but low during the period preceding the infections (<5 ticks/10,000 consultations), but increased in March. This highlights the use of electronic health data to describe rapidly evolving risk and concern that follows the emergence of a pathogen.

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.

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.