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Fairbanks EL, Tambwe MM, Moore J, Mpelepele A, Lobo NF, Mashauri R, Chitnis N, Moore SJ. Evaluating human landing catches as a measure of mosquito biting and the importance of considering additional modes of action. Sci Rep 2024; 14:11476. [PMID: 38769342 DOI: 10.1038/s41598-024-61116-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2024] [Accepted: 05/02/2024] [Indexed: 05/22/2024] Open
Abstract
Entomological evaluations of vector control tools often use human landing catches (HLCs) as a standard measure of a direct human-vector contact. However, some tools have additional characteristics, such as mortality, and HLCS are not sensitive for measuring other effects beyond landing inhibition. Therefore, additional measures may need to be considered when evaluating these tools for public health use. This study has two main aims (1) the evaluate the accuracy of HLCs as a proxy for feeding and (2) to compare the predicted reduction in vectorial capacity when we do and do not consider these additional characteristics. To achieve this, we analyse previously published semi-field data from an experiment which used HLCs and another where mosquitoes were allowed to feed in the presence of different dosages of the volatile pyrethroid spatial repellent, transfluthrin. We compare results for two mathematical models: one which only considers the reduction in feeding effect and one which also considers mortality before and after feeding (using data gathered by the aspiration of mosquitoes after the semi-field feeding/landing period and 24 h survival monitoring). These Bayesian hierarchical models are parameterised using Bayesian inference. We observe that, for susceptible mosquitoes, reduction in landing is underestimated by HLCs. For knockdown resistant mosquitoes the relationship is less clear; with HLCs sometimes appearing to overestimate this characteristic. We find HLCs tend to under-predict the relative reduction in vectorial capacity in susceptible mosquitoes while over-predicting this impact in knockdown-resistant mosquitoes. Models without secondary effects have lower predicted relative reductions in vectorial capacities. Overall, this study highlights the importance of considering additional characteristics to reduction in biting of volatile pyrethroid spatial repellents. We recommend that these are considered when evaluating novel vector control tools.
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Affiliation(s)
- Emma L Fairbanks
- Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Kreuzstrasse 2, Allschwill, Basel, 4123, Switzerland.
- University of Basel, Petersplatz 1, 4001, Basel, Switzerland.
- The Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, Mathematics Institute, University of Warwick, Coventry, CV4 7AL, UK.
| | - Mgeni M Tambwe
- Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Kreuzstrasse 2, Allschwill, Basel, 4123, Switzerland
- University of Basel, Petersplatz 1, 4001, Basel, Switzerland
- Vector Control Product Testing Unit, Ifakara Health Institute, P.O. Box 74, Bagamoyo, Tanzania
| | - Jason Moore
- Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Kreuzstrasse 2, Allschwill, Basel, 4123, Switzerland
- University of Basel, Petersplatz 1, 4001, Basel, Switzerland
- Vector Control Product Testing Unit, Ifakara Health Institute, P.O. Box 74, Bagamoyo, Tanzania
| | - Ahmed Mpelepele
- Vector Control Product Testing Unit, Ifakara Health Institute, P.O. Box 74, Bagamoyo, Tanzania
| | - Neil F Lobo
- Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN, USA
| | - Rajabu Mashauri
- Vector Control Product Testing Unit, Ifakara Health Institute, P.O. Box 74, Bagamoyo, Tanzania
| | - Nakul Chitnis
- Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Kreuzstrasse 2, Allschwill, Basel, 4123, Switzerland
- University of Basel, Petersplatz 1, 4001, Basel, Switzerland
| | - Sarah J Moore
- Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Kreuzstrasse 2, Allschwill, Basel, 4123, Switzerland
- University of Basel, Petersplatz 1, 4001, Basel, Switzerland
- Vector Control Product Testing Unit, Ifakara Health Institute, P.O. Box 74, Bagamoyo, Tanzania
- The Nelson Mandela, African Institution of Science and Technology, School of Life Sciences and Bio Engineering, Tengeru, Arusha, United Republic of Tanzania
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Wang Y, Chitnis N, Fairbanks EL. Optimizing malaria vector control in the Greater Mekong Subregion: a systematic review and mathematical modelling study to identify desirable intervention characteristics. Parasit Vectors 2024; 17:162. [PMID: 38553759 PMCID: PMC10981350 DOI: 10.1186/s13071-024-06234-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Accepted: 03/04/2024] [Indexed: 04/01/2024] Open
Abstract
BACKGROUND In the Greater Mekong Subregion (GMS), new vector-control tools are needed to target mosquitoes that bite outside during the daytime and night-time to advance malaria elimination. METHODS We conducted systematic literature searches to generate a bionomic dataset of the main malaria vectors in the GMS, including human blood index (HBI), parity proportion, sac proportion (proportion with uncontracted ovary sacs, indicating the amount of time until they returned to host seeking after oviposition) and the resting period duration. We then performed global sensitivity analyses to assess the influence of bionomics and intervention characteristics on vectorial capacity. RESULTS Our review showed that Anopheles minimus, An. sinensis, An. maculatus and An. sundaicus display opportunistic blood-feeding behaviour, while An. dirus is more anthropophilic. Multivariate regression analysis indicated that environmental, climatic and sampling factors influence the proportion of parous mosquitoes, and resting duration varies seasonally. Sensitivity analysis highlighted HBI and parity proportion as the most influential bionomic parameters, followed by resting duration. Killing before feeding is always a desirable characteristic across all settings in the GMS. Disarming is also a desirable characteristic in settings with a low HBI. Repelling is only an effective strategy in settings with a low HBI and low parity proportion. Killing after feeding is only a desirable characteristic if the HBI and parity proportions in the setting are high. CONCLUSIONS Although in general adopting tools that kill before feeding would have the largest community-level effect on reducing outdoor transmission, other modes of action can be effective. Current tools in development which target outdoor biting mosquitoes should be implemented in different settings dependent on their characteristics.
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Affiliation(s)
- Yuqian Wang
- Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Kreuzstrasse 2, Allschwill, 4123, Basel, Switzerland
- University of Basel, Petersplatz 1, 4001, Basel, Switzerland
| | - Nakul Chitnis
- Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Kreuzstrasse 2, Allschwill, 4123, Basel, Switzerland
- University of Basel, Petersplatz 1, 4001, Basel, Switzerland
| | - Emma L Fairbanks
- Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Kreuzstrasse 2, Allschwill, 4123, Basel, Switzerland.
- University of Basel, Petersplatz 1, 4001, Basel, Switzerland.
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Fairbanks EL, Saeung M, Pongsiri A, Vajda E, Wang Y, McIver DJ, Richardson JH, Tatarsky A, Lobo NF, Moore SJ, Ponlawat A, Chareonviriyaphap T, Ross A, Chitnis N. Inference for entomological semi-field experiments: Fitting a mathematical model assessing personal and community protection of vector-control interventions. Comput Biol Med 2024; 168:107716. [PMID: 38039890 DOI: 10.1016/j.compbiomed.2023.107716] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Revised: 09/19/2023] [Accepted: 11/15/2023] [Indexed: 12/03/2023]
Abstract
The effectiveness of vector-control tools is often assessed by experiments as a reduction in mosquito landings using human landing catches (HLCs). However, HLCs alone only quantify a single characteristic and therefore do not provide information on the overall impacts of the intervention product. Using data from a recent semi-field study which used time-stratified HLCs, aspiration of non-landing mosquitoes, and blood feeding, we suggest a Bayesian inference approach for fitting such data to a stochastic model. This model considers both personal protection, through a reduction in biting, and community protection, from mosquito mortality and disarming (prolonged inhibition of blood feeding). Parameter estimates are then used to predict the reduction of vectorial capacity induced by etofenpox-treated clothing, picaridin topical repellents, transfluthrin spatial repellents and metofluthrin spatial repellents, as well as combined interventions for Plasmodium falciparum malaria in Anopleles minimus. Overall, all interventions had both personal and community effects, preventing biting and killing or disarming mosquitoes. This led to large estimated reductions in the vectorial capacity, with substantial impact even at low coverage. As the interventions aged, fewer mosquitoes were killed; however the impact of some interventions changed from killing to disarming mosquitoes. Overall, this inference method allows for additional modes of action, rather than just reduction in biting, to be parameterised and highlights the tools assessed as promising malaria interventions.
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Affiliation(s)
- Emma L Fairbanks
- Department of Epidemiology and Public Health, Swiss Tropical and Public Health, Institute, Allschwill, Switzerland; University of Basel, Basel, Switzerland.
| | - Manop Saeung
- Department of Entomology, Faculty of Agriculture, Kasetsart University, Bangkok, Thailand
| | - Arissara Pongsiri
- Department of Entomology, Armed Forces Research Institute of Medical Sciences (AFRIMS), Bangkok, Thailand
| | - Elodie Vajda
- Department of Epidemiology and Public Health, Swiss Tropical and Public Health, Institute, Allschwill, Switzerland; University of Basel, Basel, Switzerland; Malaria Elimination Initiative, Institute for Global Health Sciences, University of California, San Francisco, USA
| | - Yuqian Wang
- Department of Epidemiology and Public Health, Swiss Tropical and Public Health, Institute, Allschwill, Switzerland; University of Basel, Basel, Switzerland
| | - David J McIver
- Malaria Elimination Initiative, Institute for Global Health Sciences, University of California, San Francisco, USA
| | | | - Allison Tatarsky
- Malaria Elimination Initiative, Institute for Global Health Sciences, University of California, San Francisco, USA
| | | | - Sarah J Moore
- Department of Epidemiology and Public Health, Swiss Tropical and Public Health, Institute, Allschwill, Switzerland; University of Basel, Basel, Switzerland; Vector Control Product Testing Unit, Ifakara Health Institute, Bagamoyo, United Republic of Tanzania; The Nelson Mandela, African Institution of Science and Technology, School of Life Sciences and Bio Engineering, Tengeru, Arusha, United Republic of Tanzania
| | - Alongkot Ponlawat
- Department of Entomology, Armed Forces Research Institute of Medical Sciences (AFRIMS), Bangkok, Thailand
| | | | - Amanda Ross
- Department of Epidemiology and Public Health, Swiss Tropical and Public Health, Institute, Allschwill, Switzerland; University of Basel, Basel, Switzerland
| | - Nakul Chitnis
- Department of Epidemiology and Public Health, Swiss Tropical and Public Health, Institute, Allschwill, Switzerland; University of Basel, Basel, Switzerland
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Fairbanks EL, Bolton KJ, Jia R, Figueredo GP, Knight H, Vedhara K. Influence of setting-dependent contacts and protective behaviours on asymptomatic SARS-CoV-2 infection amongst members of a UK university. Epidemics 2023; 43:100688. [PMID: 37270967 DOI: 10.1016/j.epidem.2023.100688] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2021] [Revised: 03/26/2023] [Accepted: 05/12/2023] [Indexed: 06/06/2023] Open
Abstract
We survey 62 users of a university asymptomatic SARS-CoV-2 testing service on details of their activities, protective behaviours and contacts in the 7 days prior to receiving a positive or negative SARS-CoV-2 PCR test result in the period October 2020-March 2021. The resulting data set is novel in capturing very detailed social contact history linked to asymptomatic disease status during a period of significant restriction on social activities. We use this data to explore 3 questions: (i) Did participation in university activities enhance infection risk? (ii) How do contact definitions rank in their ability to explain test outcome during periods of social restrictions? (iii) Do patterns in the protective behaviours help explain discrepancies between the explanatory performance of different contact measures? We classify activities into settings and use Bayesian logistic regression to model test outcome, computing posterior model probabilities to compare the performance of models adopting different contact definitions. Associations between protective behaviours, participant characteristics and setting are explored at the level of individual activities using multiple correspondence analysis (MCA). We find that participation in air travel or non-university work activities was associated with a positive asymptomatic SARS-CoV-2 PCR test, in contrast to participation in research and teaching settings. Intriguingly, logistic regression models with binary measures of contact in a setting performed better than more traditional contact numbers or person contact hours (PCH). The MCA indicates that patterns of protective behaviours vary between setting, in a manner which may help explain the preference for any participation as a contact measure. We conclude that linked PCR testing and social contact data can in principle be used to test the utility of contact definitions, and the investigation of contact definitions in larger linked studies is warranted to ensure contact data can capture environmental and social factors influencing transmission risk.
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Affiliation(s)
- Emma L Fairbanks
- School of Veterinary Medicine and Science, University of Nottingham, United Kingdom; School of Mathematical Sciences, University of Nottingham, United Kingdom
| | - Kirsty J Bolton
- School of Mathematical Sciences, University of Nottingham, United Kingdom.
| | - Ru Jia
- School of Medicine, University of Nottingham, United Kingdom
| | | | - Holly Knight
- School of Medicine, University of Nottingham, United Kingdom
| | - Kavita Vedhara
- School of Medicine, University of Nottingham, United Kingdom
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Fairbanks EL, Baylis M, Daly JM, Tildesley MJ. Inference for a spatio-temporal model with partial spatial data: African horse sickness virus in Morocco. Epidemics 2022; 39:100566. [DOI: 10.1016/j.epidem.2022.100566] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2021] [Revised: 04/04/2022] [Accepted: 04/10/2022] [Indexed: 11/03/2022] Open
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Fairbanks EL, Brennan ML, Mertens PPC, Tildesley MJ, Daly JM. Re-parameterisation of a mathematical model of African horse sickness virus using data from a systematic literature search. Transbound Emerg Dis 2021; 69:e671-e681. [PMID: 34921513 PMCID: PMC9543668 DOI: 10.1111/tbed.14420] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Revised: 09/29/2021] [Accepted: 09/29/2021] [Indexed: 11/26/2022]
Abstract
African horse sickness (AHS) is a vector‐borne disease transmitted by Culicoides spp., endemic to sub‐Saharan Africa. There have been many examples of historic and recent outbreaks in the Middle East, Asia and Europe. However, not much is known about infection dynamics and outbreak potential in these naive populations. In order to better inform a previously published ordinary differential equation model, we performed a systematic literature search to identify studies documenting experimental infection of naive (control) equids in vaccination trials. Data on the time until the onset of viraemia, clinical signs and death after experimental infection of a naive equid and duration of viraemia were extracted. The time to viraemia was 4.6 days and the time to clinical signs was 4.9 days, longer than the previously estimated latent period of 3.7 days. The infectious periods of animals that died/were euthanized or survived were found to be 3.9 and 8.7 days, whereas previous estimations were 4.4 and 6 days, respectively. The case fatality was also found to be higher than previous estimations. The updated parameter values (along with other more recently published estimates from literature) resulted in an increase in the number of host deaths, decrease in the duration of the outbreak and greater prevalence in vectors.
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Affiliation(s)
- Emma L Fairbanks
- School of Veterinary Medicine and Science, University of Nottingham, Loughborough, LE12 5RD, UK
| | - Marnie L Brennan
- School of Veterinary Medicine and Science, University of Nottingham, Loughborough, LE12 5RD, UK
| | - Peter P C Mertens
- School of Veterinary Medicine and Science, University of Nottingham, Loughborough, LE12 5RD, UK
| | - Michael J Tildesley
- The Zeeman Institute for Systems Biology & Infectious Disease Epidemiology Research, School of Life Sciences and Mathematics Institute, University of Warwick, Coventry, CV4 7AL, UK
| | - Janet M Daly
- School of Veterinary Medicine and Science, University of Nottingham, Loughborough, LE12 5RD, UK
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Enright J, Hill EM, Stage HB, Bolton KJ, Nixon EJ, Fairbanks EL, Tang ML, Brooks-Pollock E, Dyson L, Budd CJ, Hoyle RB, Schewe L, Gog JR, Tildesley MJ. SARS-CoV-2 infection in UK university students: lessons from September-December 2020 and modelling insights for future student return. R Soc Open Sci 2021; 8:210310. [PMID: 34386249 PMCID: PMC8334840 DOI: 10.1098/rsos.210310] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Accepted: 07/16/2021] [Indexed: 06/06/2023]
Abstract
In this paper, we present work on SARS-CoV-2 transmission in UK higher education settings using multiple approaches to assess the extent of university outbreaks, how much those outbreaks may have led to spillover in the community, and the expected effects of control measures. Firstly, we found that the distribution of outbreaks in universities in late 2020 was consistent with the expected importation of infection from arriving students. Considering outbreaks at one university, larger halls of residence posed higher risks for transmission. The dynamics of transmission from university outbreaks to wider communities is complex, and while sometimes spillover does occur, occasionally even large outbreaks do not give any detectable signal of spillover to the local population. Secondly, we explored proposed control measures for reopening and keeping open universities. We found the proposal of staggering the return of students to university residence is of limited value in terms of reducing transmission. We show that student adherence to testing and self-isolation is likely to be much more important for reducing transmission during term time. Finally, we explored strategies for testing students in the context of a more transmissible variant and found that frequent testing would be necessary to prevent a major outbreak.
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Affiliation(s)
- Jessica Enright
- School of Computing Science, University of Glasgow, Glasgow G12 8QQ, UK
| | - Edward M. Hill
- The Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, School of Life Sciences and Mathematics Institute, University of Warwick, Coventry CV4 7AL, UK
- Joint UNIversities Pandemic and Epidemiological Research, UK https://maths.org/juniper/
| | - Helena B. Stage
- Joint UNIversities Pandemic and Epidemiological Research, UK https://maths.org/juniper/
- Department of Mathematics, The University of Manchester, Oxford Road, Manchester, UK
| | - Kirsty J. Bolton
- Centre for Mathematical Medicine and Biology, School of Mathematical Sciences, University of Nottingham, University Park, Nottingham, UK
| | - Emily J. Nixon
- Joint UNIversities Pandemic and Epidemiological Research, UK https://maths.org/juniper/
- Veterinary Public Health, Bristol Veterinary School, University of Bristol, Bristol, UK
| | - Emma L. Fairbanks
- Centre for Mathematical Medicine and Biology, School of Mathematical Sciences, University of Nottingham, University Park, Nottingham, UK
- School of Veterinary Medicine and Science, University of Nottingham, Loughborough, UK
| | - Maria L. Tang
- School of Veterinary Medicine and Science, University of Nottingham, Loughborough, UK
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK
| | - Ellen Brooks-Pollock
- Joint UNIversities Pandemic and Epidemiological Research, UK https://maths.org/juniper/
- Bristol Medical School, Population Health Sciences, University of Bristol, Bristol, UK
| | - Louise Dyson
- The Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, School of Life Sciences and Mathematics Institute, University of Warwick, Coventry CV4 7AL, UK
- Joint UNIversities Pandemic and Epidemiological Research, UK https://maths.org/juniper/
| | - Chris J. Budd
- School of Mathematical Sciences, University of Bath, Claverton Down, Bath, UK
| | - Rebecca B. Hoyle
- School of Mathematical Sciences, University of Southampton, Southampton, UK
| | - Lars Schewe
- University of Edinburgh, School of Mathematics, James Clerk Maxwell Building, Peter Guthrie Tait Road, Edinburgh, UK
| | - Julia R. Gog
- Joint UNIversities Pandemic and Epidemiological Research, UK https://maths.org/juniper/
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK
| | - Michael J. Tildesley
- The Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, School of Life Sciences and Mathematics Institute, University of Warwick, Coventry CV4 7AL, UK
- Joint UNIversities Pandemic and Epidemiological Research, UK https://maths.org/juniper/
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Malik-Sheriff RS, Glont M, Nguyen TVN, Tiwari K, Roberts MG, Xavier A, Vu MT, Men J, Maire M, Kananathan S, Fairbanks EL, Meyer JP, Arankalle C, Varusai TM, Knight-Schrijver V, Li L, Dueñas-Roca C, Dass G, Keating SM, Park YM, Buso N, Rodriguez N, Hucka M, Hermjakob H. BioModels-15 years of sharing computational models in life science. Nucleic Acids Res 2020; 48:D407-D415. [PMID: 31701150 PMCID: PMC7145643 DOI: 10.1093/nar/gkz1055] [Citation(s) in RCA: 111] [Impact Index Per Article: 27.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2019] [Revised: 10/22/2019] [Accepted: 11/06/2019] [Indexed: 01/05/2023] Open
Abstract
Computational modelling has become increasingly common in life science research. To provide a platform to support universal sharing, easy accessibility and model reproducibility, BioModels (https://www.ebi.ac.uk/biomodels/), a repository for mathematical models, was established in 2005. The current BioModels platform allows submission of models encoded in diverse modelling formats, including SBML, CellML, PharmML, COMBINE archive, MATLAB, Mathematica, R, Python or C++. The models submitted to BioModels are curated to verify the computational representation of the biological process and the reproducibility of the simulation results in the reference publication. The curation also involves encoding models in standard formats and annotation with controlled vocabularies following MIRIAM (minimal information required in the annotation of biochemical models) guidelines. BioModels now accepts large-scale submission of auto-generated computational models. With gradual growth in content over 15 years, BioModels currently hosts about 2000 models from the published literature. With about 800 curated models, BioModels has become the world’s largest repository of curated models and emerged as the third most used data resource after PubMed and Google Scholar among the scientists who use modelling in their research. Thus, BioModels benefits modellers by providing access to reliable and semantically enriched curated models in standard formats that are easy to share, reproduce and reuse.
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Affiliation(s)
- Rahuman S Malik-Sheriff
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Mihai Glont
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Tung V N Nguyen
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Krishna Tiwari
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK.,Babraham Institute, Babraham Research Campus, Cambridge CB22 3AT, UK
| | - Matthew G Roberts
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Ashley Xavier
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Manh T Vu
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Jinghao Men
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Matthieu Maire
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Sarubini Kananathan
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Emma L Fairbanks
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Johannes P Meyer
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Chinmay Arankalle
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Thawfeek M Varusai
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | | | - Lu Li
- Babraham Institute, Babraham Research Campus, Cambridge CB22 3AT, UK
| | - Corina Dueñas-Roca
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Gaurhari Dass
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Sarah M Keating
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Young M Park
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Nicola Buso
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Nicolas Rodriguez
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK.,Babraham Institute, Babraham Research Campus, Cambridge CB22 3AT, UK
| | - Michael Hucka
- California Institute of Technology, Pasadena, 91125, CA, USA
| | - Henning Hermjakob
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK.,State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Lifeomics, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing 102206, China
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Barba M, Fairbanks EL, Daly JM. Equine viral encephalitis: prevalence, impact, and management strategies. Vet Med (Auckl) 2019; 10:99-110. [PMID: 31497528 PMCID: PMC6689664 DOI: 10.2147/vmrr.s168227] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/02/2019] [Accepted: 07/08/2019] [Indexed: 12/11/2022]
Abstract
Members of several different virus families cause equine viral encephalitis, the majority of which are arthropod-borne viruses (arboviruses) with zoonotic potential. The clinical signs caused are rarely pathognomonic; therefore, a clinical diagnosis is usually presumptive according to the geographical region. However, recent decades have seen expansion of the geographical range and emergence in new regions of numerous viral diseases. In this context, this review presents an overview of the prevalence and distribution of the main viral causes of equine encephalitis and discusses their impact and potential approaches to limit their spread.
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Affiliation(s)
- Marta Barba
- Veterinary Faculty, Universidad Cardenal Herrera-CEU, CEU Universities, Valencia, Spain
| | - Emma L Fairbanks
- School of Veterinary Medicine and Science, University of Nottingham, Sutton Bonington, Leicestershire, UK
| | - Janet M Daly
- School of Veterinary Medicine and Science, University of Nottingham, Sutton Bonington, Leicestershire, UK
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