Big Data approaches to host-pathogen mapping: EID2 - an open-access, taxonomically- and spatially-referenced database of pathogens and their hosts

Lead Research Organisation: University of Liverpool
Department Name: Institute of Infection and Global Health

Abstract

What are all of the known pathogens of humans, and those of the animals that associate closely with us, and hence might spread to us? What are all of the known pathogens of the plants that we eat? And where in the world are these pathogens found? The general public may be surprised that answers to these questions are hard to obtain. A recent (2013) estimate is that only about one in fifty of human diseases have been comprehensively mapped, and the situation for animal and plant diseases is probably worse.

In recent years the University of Liverpool has created an open-access database of human and animal pathogens, called the ENHanCED Infectious Diseases database or EID2. It stores information on pathogens, their hosts, and where both are found in the world, at national and sub-national (i.e. state or region) levels. All data are linked to evidence. The data entered into the database is obtained largely from three publicly available online resources: a taxonomy database (which describes which sort of organism a pathogen or host is - virus, insect, mammal etc.); a nucleotide sequence database (which provides information on the hosts and locations of pathogens); and a publication database called PubMed. Importantly, the data is obtained from these data sources by automated procedures, such that they can be regularly updated for relatively little effort. This is important as, for example, the numbers of nucleotide sequences entered into Genbank, one source of our information, is approaching 10 million per year; and there are over 1 million new papers indexed annually by PubMed (NCBI statistics).

We expect EID2 to become a major resource for people involved in health-related research, and other health professionals. The volume of data in EID2 is already large and, over time, as more data become available and are automatically entered into the database, we hope it will become more comprehensive, and the definitive source of pathogen/disease information.

EID2 offers numerous functions: identifying the pathogens of hosts, the hosts of pathogens, the known pathogens of a specific country or region, maps of the distribution of pathogens, and more besides.

The aim of this proposal is to expand the database to include the pathogens of crop plants, add a new data stream for notifiable animal diseases which will make it more comprehensive and timely, update it regularly as new data become available, increase its functionality and speed, allow users to request changes and download bespoke data outputs, continually assess its accuracy, and promote its use to research and other communities.

Technical Summary

The ENHanCEd Infectious Diseases database (EID2) is a novel, open-access database of pathogens and their hosts. Key aspects are that:

1 It is populated using automated procedures, such that it is regularly updated as new data become available.
2 It is built on the NCBI taxonomy tree. All pathogens and their hosts or vectors are taxonomically labelled; such that queries can be run at the species level, or at genus/family/order etc.
3 It is populated from two main sources: a. the NCBI sequence database. We have extracted information from the metadata of >20 million nucleotide sequences; b. PubMed. We have extracted information from the titles and abstracts of >6 million papers.
4 EID2 stores spatial locations at the national and sub-national level. It produces maps, based on either nucleotide metadata, PubMed or both.
5 Currently, EID2 is most comprehensive for human and domestic animal pathogens, but the automated procedures mean it also stores information on wildlife, fish and plant pathogens.
6 All information is linked to evidence - i.e., a paper or nucleotide upload
7 The scale is huge and increasing. EID2 currently stores information on >100,000 species of helminth, fungi, protozoa, bacteria and virus (i.e., the major groups that contain pathogens); it has records for >170,000 species of organism, obtained from (and linked to) > 20 million nucleotide sequences; and 7500 species of organism obtained from (and linked to) >6 million papers.

EID2 has the potential to become a major resource for health researchers and professionals worldwide - for disease mapping, risk assessment and more. We seek funding to extend EID2 to crop plant pathogens, add a new data stream for notifiable animal diseases, update it as new data become available, improve its comprehensiveness, functionality and speed, monitor the accuracy of uploaded data, allow users to request new functionality and download bespoke outputs, and promote its use to research and other communities

Planned Impact

EID2 stores information on the pathogens of humans, animals and plants. Human infectious disease, and its causes, is the concern of a very large number of non-academic organisations, from W.H.O., the NHS, and government ministries and agencies, pharmaceutical companies, to NGOs and charities. The same is true of animal and plant disease. Infectious diseases of livestock and crops are, to varying extents, the business of the government ministries and international organisations concerned with animal health (e.g. O.I.E.) and food security (F.A.O.), pharmaceutical companies, NGOs concerned with development, and charities concerned with natural disasters. Considering government, the greatest relevance is (in the UK) Defra and its agency, APHA, but livestock and plant diseases also touch on the Department of Health (Zoonoses), Department for Business, Innovation and Skills (commercial opportunities, economic costs), and the Ministry of Defence (bioterrorism). This broad relevance is demonstrated to some extent by the range of organisations which have commissioned livestock-centred reports from Baylis in recent years: the UK government's Foresight programme (2005), the Health Protection Agency (2010), the World Bank (2011), the US Department of Defense (2011) and the Smith School of Enterprise and the Environment (2011).
EID2 stores equivalent information for wildlife and plant pathogens, indicating its relevance to organisations concerned with conservation and agriculture.
We believe government and other organisations can already or will shortly be able to use EID2 for the purposes of horizon scanning (for pathogens near to them, or present in specific trading countries, or most sensitive to climate), for information gathering in order to prepare briefings for government (on specific pathogens during an emergency or potential emergence event), or as a research tool for policy development for disease control. Longer term, it may serve a function in terms of disease surveillance (EID stores both time and space information), although its dependence on publications and sequence uploading means it cannot, and is not intended to be, as responsive as, for example, ProMed or HealthMap.
Many members of the public are interested in the pathogens that affect them and where they come from. Our aforementioned paper (ref [7] above), in addition to page views, has generated a very high level of online interest. With an altmetric score of 273, at the time of writing it is described as being in the 99th percentile (ranked 483rd) of > 160,000 tracked papers of a similar age, and ranked 1st for those in Scientific Data. Recently, an output of the paper (Fig 1 in the Case for Support) appeared in a popular science, online forum on Facebook (I F...ing Love Science), with a readership of > 22.5 million people; the feature received > 10,000 Likes in 24 hours and hundreds of Shares, and led to a late surge in the number of page views of the paper itself.
 
Description The Enhanced Infectious Diseases Database system (EID2) (Wardeh et al, 2015) utilises data and text mining tools, with minimal expert input, in order to extract information about pathogens and their hosts from multiple sources. To date EID2 has extracted information from 90M+ genetic sequence metadata (and processed 100Ms genetic sequence records), and 8.7M+ publication titles and abstracts to provide evidence for 455,000+ interactions between organisms, and 1,330,000+ locations of organisms. After processing, EID2 contains over 125,000 interactions between species of hosts/arthropod vectors and possible pathogens, including over 48,000 interactions between plants and their pathogens, and over 6,500 interactions between arthropod vectors and hosts. EID2 is the most comprehensive data source on the known pathogens of humans, animals, and plants and their geographical ranges.
Exploitation Route The EID2 database is open access and it being used by researchers in many parts of the world. EID2 has been instrumental in awarding of NPIF fellowship: Big Data approaches to identifying potential sources of emerging pathogens in humans, domesticated animals and crops (MR/R024898/1). The three-year project further explores the data collated within EID2, and utilises combination of complex networks, machine learning, and big data to explain sharing of pathogens between various categories of host species (including zoonoses, vector-borne and plant diseases), and to predict emergence of future pathogens. EID2 is being exploited currently in collaboration of Swansea University (Department of Biosciences) in order to develop new suite of models to explain pathogen sharing between arthropod vectors and vertebrates hosts. EID2 is also being utilised to predict the ability of vectors (biting insects and ticks) to acquire and transmit known, and potentially novel vector-borne viruses and bacteria (Future Leaders Fellowship application).

EID2 was used to identify important hosts in networks of shared pathogens, predict reservoirs of zoonotic pathogens, and identify drivers of zoonoses spill-over across all pathogen taxa (Wardeh et al 2020, Proc B) . Furthermore, EID2 data underpin important developments in predicting hosts in which novel coronaviruses might be generated (Wardeh et al 2021, two funding awards: BBSRC IAA COVID - 168478 and EP/V0334634/1). Novel pathogenic coronaviruses 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. EID2 data were used to construct a network linking all known coronaviruses with their known mammalian (and subsequently avian) hosts. This network was integrated with two complementary perspectives (hosts and viruses) to predict which mammalian species are hosts of multiple coronaviruses. The results indicated potentially 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. These results demonstrate the large under-appreciation of the potential scale of novel coronavirus generation in animals and are suitable identify high-risk species for coronavirus surveillance.

Finally, EID2 was utilised to predict mammalian hosts of known viruses (preprint: Wardeh et al 2020). EID2 data were supplemented with relevant ecological, geographical and viral predictors, and integrated into novel machine learning approaches to predict unobserved associations and levels of confidence in them. The results suggested over 20,000 unknown associations between known viruses and mammalian hosts, indicating that current knowledge underestimates the number of associations in wild and semi-domesticated mammals by a factor of 4.3, and the average mammalian host-range of viruses by a factor of 3.2. In particular, the results highlighted a significant knowledge gap in the wild reservoirs of important zoonotic and domesticated mammals' viruses: specifically, lyssaviruses, bornaviruses and rotaviruses.
Sectors Agriculture, Food and Drink,Environment,Healthcare

 
Description Global trade of coronavirus hosts: bringing geographically isolated hosts and viruses together risks novel recombination and spillover to humans
Amount £117,406 (GBP)
Funding ID BB/W00402X/1 
Organisation Biotechnology and Biological Sciences Research Council (BBSRC) 
Sector Public
Country United Kingdom
Start 06/2021 
End 05/2022
 
Description National Productivity Investment Fund (NPIF) Fellowships
Amount £259,054 (GBP)
Funding ID MR/R024898/1 
Organisation Medical Research Council (MRC) 
Sector Public
Country United Kingdom
Start 11/2017 
End 11/2020
 
Description Predicting mammalian and avian reservoirs of coronaviruses: identifying current reservoirs and co-infection hosts in which future novel coronavirus could be generated
Amount £11,008 (GBP)
Funding ID BBSRC IAA COVID - 168478 
Organisation Biotechnology and Biological Sciences Research Council (BBSRC) 
Sector Public
Country United Kingdom
Start 03/2021 
End 06/2021
 
Description Where coronaviruses hide, where novel strains are generated, and how they get to us.
Amount £76,000 (GBP)
Funding ID EP/V0334634/1 
Organisation Natural Environment Research Council 
Sector Public
Country United Kingdom
Start 06/2021 
End 05/2022
 
Description Where coronaviruses hide, where novel strains are generated, and how they get to us: Predicting reservoirs, recombination, and geographical hotspots
Amount £79,286 (GBP)
Funding ID NE/W002302/1 
Organisation Natural Environment Research Council 
Sector Public
Country United Kingdom
Start 03/2021 
End 03/2022
 
Title Data and code for: "Monkeypox virus shows potential to infect a diverse range of native animal species across Europe, indicating high risk of becoming endemic in the region." 
Description Background: Monkeypox is a zoonotic virus which persists in animal reservoirs and periodically spills over into humans, causing outbreaks. During the current 2022 outbreak, monkeypox virus has persisted via human-human transmission, across all major continents and for longer than any previous record. This unprecedented spread creates the potential for the virus to 'spillback' into local susceptible animal populations. Persistent transmission amongst such animals raises the prospect of monkeypox virus becoming enzootic in new regions. However, the full and specific range of potential animal hosts and reservoirs of monkeypox remains unknown, especially in newly at-risk non-endemic areas. Methods: Here, our pipeline utilises ensembles of classifiers comprising different class balancing techniques and incorporating instance weights, to identify which animal species are potentially susceptible to monkeypox virus. Subsequently, we generate spatial distribution maps to highlight high-risk geographic areas at high resolution. Findings: We show that the number of potentially susceptible species is currently underestimated by 2.4 to 4.3-fold. We show a high density of susceptible wild hosts in Europe. We provide lists of these species, and highlight high-risk hosts for spillback and potential long-term reservoirs, which may enable monkeypox virus to become endemic. 
Type Of Material Computer model/algorithm 
Year Produced 2022 
Provided To Others? Yes  
Impact N/A 
URL https://figshare.com/articles/software/Blagrove_et_al_2022_poxvriuses_data_and_code/20485332
 
Title Divide-and-conquer: data and codes 
Description Data and codes associated with: Divide-and-conquer: Wardeh, M., Blagrove, M.S.C., Sharkey, K.J. et al. Divide-and-conquer: machine-learning integrates mammalian and viral traits with network features to predict virus-mammal associations. Nat Commun 12, 3954 (2021). https://doi.org/10.1038/s41467-021-24085-w. Abstract: 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. 
Type Of Material Computer model/algorithm 
Year Produced 2021 
Provided To Others? Yes  
Impact The multi-perspective host-pathogen predictive framework undperins the following research awards: Where coronaviruses hide, where novel strains are generated, and how they get to us: Predicting reservoirs, recombination, and geographical hotspots (NE/W002302/1); Global trade of coronavirus hosts: bringing geographically isolated hosts and viruses together risks novel recombination and spillover to humans (BB/W00402X/1); and Predicting mammalian and avian reservoirs of coronaviruses: identifying current reservoirs and co-infection hosts in which future novel coronavirus could be generated (BBSRC IAA COVID - 168478) 
URL https://doi.org/10.6084/m9.figshare.13270304
 
Title Machine learning ensemble models to predict and quantify mammalian reservoirs of zoonoses 
Description State of the art machine learning models to predict, quantify and explain sharing of zoonoses between humans and mammalian hosts. 
Type Of Material Computer model/algorithm 
Year Produced 2020 
Provided To Others? Yes  
Impact N.A 
URL https://figshare.com/articles/R-codes_and_datasets/11536470
 
Title Models to explain Centrality in networks of shared pathogens 
Description State of the art machine learning model to explain driver of centrality (host importance) and influence in networks of shared pathogens between hosts (e.g. non-human mammals). Uniquely the model integrates a new metric of centrality, and systematic tool to select centrality measures in complex networks. 
Type Of Material Computer model/algorithm 
Year Produced 2020 
Provided To Others? Yes  
Impact N/A 
URL https://figshare.com/articles/R-codes_and_datasets/11536470
 
Title Predicting mammalian hosts in which novel coronaviruses can be generated - codes 
Description 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. This novel method, deploys a meta-ensemble of similarity learners from three complementary perspectives (viral, mammalian and network), topredict which mammals are hosts of multiple coronaviruses. The results 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). 
Type Of Material Computer model/algorithm 
Year Produced 2021 
Provided To Others? Yes  
Impact These models undperin the following research awards: Where coronaviruses hide, where novel strains are generated, and how they get to us: Predicting reservoirs, recombination, and geographical hotspots (NE/W002302/1); Global trade of coronavirus hosts: bringing geographically isolated hosts and viruses together risks novel recombination and spillover to humans (BB/W00402X/1); and Predicting mammalian and avian reservoirs of coronaviruses: identifying current reservoirs and co-infection hosts in which future novel coronavirus could be generated (BBSRC IAA COVID - 168478) 
URL https://figshare.com/articles/software/covs-recombination-hosts/13110896
 
Title Species Interaction Dataset 
Description Species interactions contained in this dataset indicate the possibility of one species (Cargo) having an interaction with another species (Carrier). This dataset comprises the following fields: 1 Cargo: name of cargo species. 2 Cargo classification: the taxonomic classification of the cargo (e.g. bacteria, virus, etc.) 3 Carrier: name of carrier species. 4 Carrier classification: the taxonomic classification of the carrier (e.g. human, domestic, primates, mammals, etc.) 5 Sequences count: Total number of nucleotide sequences supporting the interaction. 6 Publication count: Total number of PubMed publications supporting the interaction. 7 Sequences: semi colon separated list of the nucleotide sequence identifiers (GIs) that can be used to retrieve these sequences from the NCBI Nucleotide database. For readability purposes this list was restricted to a maximum of 100 identifiers. 8 Publications: semi colon separated list of the PubMed citation identifiers (PMIDs) that can be used to retrieve these publications from the NCBI PubMed database. For readability purposes this list was restricted to a maximum of 100 identifiers. 
Type Of Material Database/Collection of data 
Year Produced 2015 
Provided To Others? Yes  
Impact publication. citations 
URL https://figshare.com/articles/LocationInteractions_EID2/1381853
 
Title Species Location Dataset 
Description Location interactions indicate the possibility of a species being found in a location. Locations were interpreted at two levels: country and region. Regions correspond to first administrative divisions (e.g., states (USA), departments (France,) home nations of the United Kingdom, etc.). Evidence for these interactions was extracted from NCBI sequences database and NCBI PubMed database. This dataset comprises the following fields: 1 Species: name of species. 2 Species classification: the taxonomic classification of the Species. 3 Country: official name of the country 4 Region: where available the official name of the region (sub-country) part of the location is listed under this header. 5 Sequences count: Total number of nucleotide sequences supporting the interaction. 6 Publication count: Total number of PubMed publications supporting the interaction. 7 Sequences: semi colon separated list of the nucleotide sequence identifiers (GIs) that can be used to retrieve these sequences from the NCBI Nucleotide database. For readability purposes this list was restricted to a maximum of 100 identifiers. 8 Publications: semi colon separated list of the PubMed citation identifiers (PMIDs) that can be used to retrieve these publications from the NCBI PubMed database. For readability purposes this list was restricted to a maximum of 100 identifiers. 
Type Of Material Database/Collection of data 
Year Produced 2015 
Provided To Others? Yes  
Impact publication. citations. 
URL https://figshare.com/articles/LocationInteractions_EID2/1381854
 
Title The Enhanced Infectious Diseases Database, EID2 
Description In order to provide answers to these questions the EID2 system comprises the following components: 1) Data repositories: EID2 maintains a number of complex data repositories and mapping dictionaries to facilitate interaction discovery and named entity recognition, including: 1) Organisms and their taxonomic lineage relationships (over 1 million organisms to date). 2) Alternative names (e.g. common names, common misspelling, breeds and acronyms), inclusion (AND) and exclusion (NOT) terms for the organisms. 3) Geographical names and hierarchies, including countries, administrative divisions, major cities and natural features. 4) Climate (e.g., temperature and rainfall) and demographic (human and livestock) data for the whole world. 2) Data acquisition layer: EID2 continually retrieves and classifies evidence from two sources: NCBI Nucleotide Sequences database; and PubMed (and soon to include Scopus as a third). Each piece of evidence is then linked to the organisms and geographical location. Sequences are often linked to one "cargo" organism which is either microbe (pathogen) or arthropod vector, one host organism and one location. Publications however are often linked to multiple organisms and locations. One powerful utilisation of EID2 is our ability to quickly extract and filter evidence based on the number of hosts/pathogens/vectors species or locations it mentions. 3) Interactions discovery pipeline: EID2 extracts three types of interactions from its evidence bases: organism-organism interactions, organism-location interactions and organism-organism-location interactions. 4) EID2 Portal: publically accessible at: https://eid2.liverpool.ac.uk/. The portal enables users to browse through EID2 data, lookup interactions for one or more organisms, and produce tailored maps. Papers further describing the resource Wardeh, M., Risely C., McIntyre K.M., Setzkorn C. and Baylis M. 2015. Database of host-pathogen and related species interactions, and their global distribution. Sci. Data. 2:150049. DOI:10.1038/sdata.2015.49. 
Type Of Material Database/Collection of data 
Year Produced 2012 
Provided To Others? Yes  
Impact publications. 
URL https://eid2.liverpool.ac.uk/
 
Description Office International des Epizooties 
Organisation World Organisation for Animal Health, France
Country France 
Sector Public 
PI Contribution Discussions about our EID2 data contributing to the WAHIS database
Collaborator Contribution Discussions about our WAHIS data contributing to our EID2 database
Impact n/a
Start Year 2017
 
Description Sapienza University of Rome 
Organisation Sapienza University of Rome
Country Italy 
Sector Academic/University 
PI Contribution Exploring the mammalian virome to detect patterns of compatibility between mammal species and viruses at a global scale, identifying eco-biological profiles of viral carriers along the fast-slow continuum of mammalian life-history.
Collaborator Contribution Provided insight into virus-mammal interactions, and role virus traits have on the transmission/spill-over of viruses.
Impact Publication: Identifying patterns along the fast-slow continuum of mammalian viral carriers (in prep/under review)
Start Year 2022
 
Description Species360 - Impact of global trade in wildlife on virus spread. 
Organisation IDAs og Berg-Nielsens Studie-og støttefond
Country Denmark 
Sector Charity/Non Profit 
PI Contribution This partnership aims at quantifying the impact of global trade in wild animals on the potential spread of emerging infectious zoonses prioritized by the WHO Research and Development Blueprint Strategy. Our group provided data on animal species in which these zoonoses has been found to date; and the ecological role of these animals in the transmission of these pathogens (such as reservoirs; dead-end; and amplifying hosts), as well as information on how these pathogens manifest in the animal host (mortality, morbidity, minor symptoms, or no disease).
Collaborator Contribution Our partners are leading on the analyses to identify geographical patterns of trade in the animals identified above, and the key data gaps that need to be resolved to fully assess risks from international wildlife trade and put this information in the context of other drivers of zoonotic diseases.
Impact NA
Start Year 2020
 
Description Swansea University, Department of Biosciences 
Organisation Swansea University
Country United Kingdom 
Sector Academic/University 
PI Contribution Collaboration was formed with Swansea University, in order to develop new models for network analysis of shared pathogens between arthropod vectors and hosts. The collaboration resulted to date in 1 publication, and 1 grant application.
Collaborator Contribution Collaboration was formed with Dr K Wells (Swansea University, Department of Biosciences) in order to develop new models for network analysis of shared pathogens between arthropod vectors and hosts. The aim of this partnership is to seek further funding to develop set of tools to predict vector-borne disease emergence in UK/European livestock.
Impact N/A
Start Year 2018
 
Description University of Salford 
Organisation University of Salford
Department School of Environment and Life Sciences
Country United Kingdom 
Sector Academic/University 
PI Contribution Discussion about use of EID2 data to help in categorisation of plant pathogens and quantification of their risk.
Collaborator Contribution Discussion about use of EID2 data to help in categorisation of plant pathogens and quantification of their risk. Insight into plant pathogens, their transmission routes, vectors and their categorisation.
Impact n/a
Start Year 2017
 
Title Network and machine analysis tools reveal reservoirs of zoonoses - Network builder 
Description codes, data and additional figures associated with manuscript (Integration of shared-pathogen networks and machine learning reveal key aspects of zoonoses and predict mammalian reservoirs, doi:10.1098/rspb.2019.2882) 
Type Of Technology Software 
Year Produced 2020 
Open Source License? Yes  
Impact N/A 
URL https://figshare.com/articles/NetworkBuilder/11537742
 
Title Network and machine analysis tools reveal reservoirs of zoonoses - R solution and datasets (2020) 
Description Codes, data and additional figures associated with manuscript (Integration of shared-pathogen networks and machine learning reveal key aspects of zoonoses and predict mammalian reservoirs, doi:10.1098/rspb.2019.2882) 
Type Of Technology Software 
Year Produced 2020 
Open Source License? Yes  
Impact N/A 
URL https://figshare.com/articles/R-codes_and_datasets/11536470
 
Description BBC - Coronavirus: This is not the last pandemic 
Form Of Engagement Activity A broadcast e.g. TV/radio/film/podcast (other than news/press)
Part Of Official Scheme? No
Geographic Reach National
Primary Audience Media (as a channel to the public)
Results and Impact BBC coverage of big data and virology research in university of liverpool. Includes coverage of EID2.
Year(s) Of Engagement Activity 2020
 
Description BBC interview - AI used to 'predict the next coronavirus' 
Form Of Engagement Activity A press release, press conference or response to a media enquiry/interview
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Public/other audiences
Results and Impact NA
Year(s) Of Engagement Activity 2021
URL https://www.bbc.co.uk/news/science-environment-56076716?fbclid=IwAR0AIa3il2XTl6QbrIlP7aCovc79To-tjPI...
 
Description Big Data epidemiology: turning trends into useful preventive medicine. Workshop at Society for Veterinary Epidemiology and Preventive Medicine Annual Conference 2018 
Form Of Engagement Activity Participation in an activity, workshop or similar
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Professional Practitioners
Results and Impact Research workshop demonstrating the usefulness of Big Data techniques and discussing potential uses within veterihary epidemiology. We particularly hightlighted what we had been able to achieve in developing the EID2.
Year(s) Of Engagement Activity 2018
 
Description Climate change impacts on health presentation to Civil Service Environment Network 
Form Of Engagement Activity A talk or presentation
Part Of Official Scheme? No
Geographic Reach National
Primary Audience Policymakers/politicians
Results and Impact Short presentation to the CSEN on the origins of pandemics, and how climate change is a driver of their emergence. The key message was that climate change is an important driver, but there are others and it cannot be considered in isolation
Year(s) Of Engagement Activity 2022
URL https://www.civilserviceenvironmentnetwork.org/
 
Description Contribution to popular science article. With Big Data and Predictive Analytics, Scientists Are Getting Smarter About Outbreaks. Discover magazine 
Form Of Engagement Activity A magazine, newsletter or online publication
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Public/other audiences
Results and Impact Popular science article in the media
Year(s) Of Engagement Activity 2017,2018
URL http://discovermagazine.com/2018/dec/outsmarting-outbreaks
 
Description FRANC24 interview - Quand l'IA part à la chasse au prochain coronavirus chez les mammifères 
Form Of Engagement Activity A press release, press conference or response to a media enquiry/interview
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Media (as a channel to the public)
Results and Impact Interview and newspaper article in France24.
Year(s) Of Engagement Activity 2021
URL https://www.france24.com/fr/%C3%A9co-tech/20210218-quand-l-ia-part-%C3%A0-la-chasse-au-prochain-coro...
 
Description Interview and contribution to an article for the UKRI Partnership for Conflict, Crime and Security Research (PaCCS) initiative 
Form Of Engagement Activity A press release, press conference or response to a media enquiry/interview
Part Of Official Scheme? No
Geographic Reach National
Primary Audience Professional Practitioners
Results and Impact Interview and contribution to an article highlighting the impacts and outcomes of the grant on the UKRI Partnership for Conflict, Crime and Security Research initiative website.
Year(s) Of Engagement Activity 2020
 
Description Invited keynote discussing "Climate and Infectious Diseases" at the 30th National Immunisation Conference for Health Care Workers, 30th National Immunisation Conference for Health Care Workers, Manchester Conference Centre, Manchester, UK 
Form Of Engagement Activity A talk or presentation
Part Of Official Scheme? No
Geographic Reach National
Primary Audience Professional Practitioners
Results and Impact Introduced the main approaches to studying and important infectious diseases likely to be impacted by climate change and therefore be relevant for clinicains concerned with immunisation of patients
Year(s) Of Engagement Activity 2019
 
Description Invited participant & plenary speaker, ECDC Expert consultation in One Health Preparedness 
Form Of Engagement Activity A formal working group, expert panel or dialogue
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Policymakers/politicians
Results and Impact 2 day workshop in Stockholm for global experts in One Health. There were a series of presentations and interactive sessions. The objective was to help ECDC (European Centre of Disease Control) to develop its One Health strategy.
Year(s) Of Engagement Activity 2017
 
Description New York Times Interview - AI used to 'predict the next coronavirus' 
Form Of Engagement Activity A press release, press conference or response to a media enquiry/interview
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Media (as a channel to the public)
Results and Impact Media interview and newspaper article in NYT.
Year(s) Of Engagement Activity 2021
URL https://www.nytimes.com/2021/02/16/science/Covid-reemerging-viruses.html?fbclid=IwAR0AIa3il2XTl6QbrI...
 
Description Public Science event 
Form Of Engagement Activity A talk or presentation
Part Of Official Scheme? No
Geographic Reach Local
Primary Audience Public/other audiences
Results and Impact "Big data: snog, marry, avoid" - presentation by Maya Wardeh.
Location: The Vines pub, Liverpool
Year(s) Of Engagement Activity 2016
 
Description The Coronavirus Menagerie - New York Times coverage 
Form Of Engagement Activity A press release, press conference or response to a media enquiry/interview
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Media (as a channel to the public)
Results and Impact Newspaper report in NYT.
Year(s) Of Engagement Activity 2022
URL https://www.nytimes.com/2022/02/22/health/coronavirus-animals.html
 
Description The Search for Animals That Could Carry the Next Deadly Virus - Wall Street Journal Interview 
Form Of Engagement Activity A press release, press conference or response to a media enquiry/interview
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Media (as a channel to the public)
Results and Impact Media interview with Wall Street Journal.
Year(s) Of Engagement Activity 2021
URL https://www.wsj.com/articles/the-search-for-animals-that-could-carry-the-next-deadly-virus-116166736...
 
Description The big deal with big data - large scale assessment of the sensitivity of pathogens to climate. BBSRC UK-US International Partnering Award Meeting: Vector-borne diseases in the UK & US: common threats and shared solutions, University of California, Davis, USA 
Form Of Engagement Activity A talk or presentation
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Professional Practitioners
Results and Impact Presentation and small group work at an international meeting
Year(s) Of Engagement Activity 2017
 
Description Virologists use AI to work on next pandemic outbreak. 
Form Of Engagement Activity A broadcast e.g. TV/radio/film/podcast (other than news/press)
Part Of Official Scheme? No
Geographic Reach National
Primary Audience Media (as a channel to the public)
Results and Impact Radio interview in New Zealand
Year(s) Of Engagement Activity 2021
URL https://www.rnz.co.nz/national/programmes/first-up/audio/2018783912/virologists-use-ai-to-work-on-ne...