Ivelina Yonova


Centre Manager (Centre for Translation Studies)
BA; MA; PRINCE2 Practitioner
+44 (0)1483 684369
8A LC 03

My publications

Highlights

  1. de Lusignan S, Hoang U, Liyanage H, Yonova I, Ferreira F, Diez-Domingo J, Clark T. Feasibility of Point-of-Care Testing for Influenza Within a National Primary Care Sentinel Surveillance Network in England: Protocol for a Mixed Methods Study. JMIR Res Protoc. 2019 Nov 11;8(11):e14186. doi: 10.2196/14186. PubMed PMID: 31710303.
  2. Pebody RG, Whitaker H, Ellis J, Andrews N, Marques DFP, Cottrell S, Reynolds AJ, Gunson R, Thompson C, Galiano M, Lackenby A, Robertson C, O'Doherty MG, Owens K, Yonova I, Shepherd SJ, Moore C, Johnston J, Donati M, McMenamin J, Lusignan S, Zambon M. End of season influenza vaccine effectiveness in primary care in adults and children in the United Kingdom in 2018/19. Vaccine. 2019 Nov 1. pii: S0264-410X(19)31460-4. doi: 10.1016/j.vaccine.2019.10.071. PubMed PMID: 31685296.
  3. Hill EM, Petrou S, de Lusignan S, Yonova I, Keeling MJ. Seasonal influenza: Modelling approaches to capture immunity propagation. PLoS Comput Biol. 2019 Oct 28;15(10):e1007096. doi: 10.1371/journal.pcbi.1007096. eCollection 2019 Oct. PubMed PMID: 31658250
  4. Pebody R, Djennad A, Ellis J, Andrews N, Marques DFP, Cottrell S, Reynolds AJ, Gunson R, Galiano M, Hoschler K, Lackenby A, Robertson C, O'Doherty M, Sinnathamby M, Panagiotopoulos N, Yonova I, Webb R, Moore C, Donati M, Sartaj M, Shepherd SJ, McMenamin J, de Lusignan S, Zambon M. End of season influenza vaccine effectiveness in adults and children in the United Kingdom in 2017/18. Euro Surveill. 2019 Aug;24(31). doi: 10.2807/1560-7917.ES.2019.24.31.1800488. PubMed PMID: 31387673.
  5. de Lusignan S, Smith N, Livina V, Yonova I, Webb R, Thomas SA. Analysis of Primary Care Computerised Medical Records with Deep Learning. Stud Health Technol Inform. 2019; 258:249-250. PubMed PMID: 30942761.
  6. de Lusignan S, Borrow R, Tripathy M, Linley E, Zambon M, Hoschler K, Ferreira F, Andrews N, Yonova I, Hriskova M, Rafi I, Pebody R. Serological surveillance of influenza in an English sentinel network: pilot study protocol. BMJ Open. 2019 Mar 8;9(3):e024285. doi: 10.1136/bmjopen-2018-024285. PubMed PMID: 30852535; PubMed Central PMCID: PMC6429844.
  7. Kissling E, Rose A, Emborg HD, Gherasim A, Pebody R, Pozo F, Trebbien R, Mazagatos C, Whitaker H, Valenciano M; European Ive Group. Interim 2018/19 influenza vaccine effectiveness: six European studies, October 2018 to January 2019. Euro Surveill. 2019 Feb;24(8). doi: 10.2807/1560-7917.ES.2019.24.1900121. PubMed PMID: 30808440; PubMed Central PMCID: PMC6446950.
  8. de Lusignan S, Ferreira F, Damaso S, Byford R, Pathirannehelage S, Yeakey A, Yonova I, Schuind A, Dos Santos G. Enhanced passive surveillance of influenza vaccination in England, 2016-2017- an observational study using an adverse event reporting card. Hum Vaccin Immunother. 2019 Mar 20:1-12. doi: 10.1080/21645515.2019.1565258. [Epub ahead of print] PubMed PMID: 30648923.
  9. de Lusignan S, McGee C, Webb R, Joy M, Byford R, Yonova I, Hriskova M, Matos Ferreira F, Elliot AJ, Smith G, Rafi I. Conurbation, Urban, and Rural Living as Determinants of Allergies and Infectious Diseases: Royal College of General Practitioners Research and Surveillance Centre Annual Report 2016-2017. JMIR Public Health Surveill. 2018 Nov 26;4(4):e11354. doi: 10.2196/11354. PubMed PMID: 30478022; PubMed Central PMCID: PMC6288591.
  10. Pebody RG, Warburton F, Andrews N, Sinnathamby M, Yonova I, Reynolds A, Robertson C, Cottrell S, Sartaj M, Gunson R, Donati M, Moore C, Ellis J, de Lusignan S, McMenamin J, Zambon M. Uptake and effectiveness of influenza vaccine in those aged 65 years and older in the United Kingdom, influenza seasons 2010/11 to 2016/17. Euro Surveill. 2018 Sep;23(39). doi:10.2807/1560-7917.ES.2018.23.39.1800092. PubMed PMID: 30280688; PubMed Central PMCID: PMC6169201
  11. de Lusignan S, Dos Santos G, Byford R, Schuind A, Damaso S, Shende V, McGee C, Yonova I, Ferreira F. Enhanced Safety Surveillance of Seasonal Quadrivalent Influenza Vaccines in English Primary Care: Interim Analysis. Adv Ther. 2018 Jul 11. doi: 10.1007/s12325-018-0747-4. [Epub ahead of print] PubMed PMID: 29995300; PubMed Central PMCID: PMC6096981.
  12. Pebody RG, Sinnathamby MA, Warburton F, Andrews N, Boddington NL, Zhao H, Yonova I, Ellis J, Tessier E, Donati M, Elliot AJ, Hughes HE, Pathirannehelage S, Byford R, Smith GE, de Lusignan S, Zambon M. Uptake and impact of vaccinating primary school-age children against influenza: experiences of a live attenuated influenza vaccine programme, England, 2015/16. Euro Surveill. 2018 Jun;23(25). doi: 10.2807/1560-7917.ES.2018.23.25.1700496. PubMed PMID: 29945698.
  13. de Lusignan S, Correa A, Pebody R, Yonova I, Smith G, Byford R, Pathirannehelage SR, McGee C, Elliot AJ, Hriskova M, Ferreira FI, Rafi I, Jones S. Incidence of Lower Respiratory Tract Infections and Atopic Conditions in Boys and Young Male Adults: Royal College of General Practitioners Research and Surveillance Centre Annual Report 2015-2016. JMIR Public Health Surveill. 2018 Apr 30;4(2):e49. doi: 10.2196/publichealth.9307. PubMed PMID: 29712621.
  14. Pathirannehelage S, Kumarapeli P, Byford R, Yonova I, Ferreira F, de Lusignan S.  Uptake of a dashboard designed to give realtime feedback to a sentinel network about key data required for influenza vaccine effectiveness studies. Accepted for Publication:  Studies in Health Technology and Informatics, proceedings of MIE2018. 2018.
  15. Smith N, Livina V, Byford R, Ferreira F, Yonova I, de Lusignan S. Automated Differentiation of Incident and Prevalent Cases in Primary Care Computerised Medical Records (CMR). Stud Health Technol Inform. 2018;247:151-155. PubMed PMID: 29677941.
  16. Rondy M, Kissling E, Emborg HD, Gherasim A, Pebody R, Trebbien R, Pozo F, Larrauri A, McMenamin J, Valenciano M; I-MOVE/I-MOVE+ group. Interim 2017/18 influenza seasonal vaccine effectiveness: combined results from five European studies. Euro Surveill. 2018 Mar;23(9). doi: 10.2807/1560-7917.ES.2018.23.9.18-00086. PubMed PMID: 29510782; PubMed Central PMCID: PMC5840921
  17. Pebody R, Warburton F, Ellis J, Andrews N, Potts A, Cottrell S, Reynolds A, Gunson R, Thompson C, Galiano M, Robertson C, Gallagher N, Sinnathamby M, Yonova I, Correa A, Moore C, Sartaj M, de Lusignan S, McMenamin J, Zambon M. End-of-season influenza vaccine effectiveness in adults and children, United Kingdom, 2016/17. Euro Surveill. 2017 Nov;22(44). doi:10.2807/1560-7917.ES.2017.22.44.17-00306. PubMed PMID: 29113630.
  18. de Lusignan S, Correa A, Smith GE, Yonova I, Pebody R, Ferreira F, Elliot AJ, Fleming D. RCGP Research and Surveillance Centre: 50 years' surveillance of influenza, infections, and respiratory conditions. Br J Gen Pract. 2017 Oct;67(663):440-441. doi: 10.3399/bjgp17X692645. PubMed PMID: 28963401.
  19. de Lusignan S, Shinneman S, Yonova I, van Vlymen J, Elliot AJ, Bolton F, Smith GE, O'Brien S. An Ontology to Improve Transparency in Case Definition and Increase Case Finding of Infectious Intestinal Disease: Database Study in English General Practice. JMIR Med Inform. 2017 Sep 28;5(3):e34. doi: 10.2196/medinform.7641. PubMed PMID: 28958989.
  20. de Lusignan S, Dos Santos G, Correa A, Haguinet F, Yonova I, Lair F, Byford R, Ferreira F, Stuttard K, Chan T. Post-authorisation passive enhanced safety surveillance of seasonal influenza vaccines: protocol of a pilot study in England. BMJ Open. 2017 May 17;7(5):e015469. doi: 10.1136/bmjopen-2016-015469. PMID: 28515198
  21. de Lusignan S, Correa A, Pathirannehelage S, Byford R, Yonova I, Elliot AJ, Lamagni T, Amirthalingam G, Pebody R, Smith G, Jones S, Rafi I. RCGP Research and Surveillance Centre Annual Report 2014-2015: disparities in presentations to primary care. Br J Gen Pract. 2017 Jan;67(654):e29-e40. doi:10.3399/bjgp16X688573. Epub 2016 Dec 19. PubMed PMID: 27993900; PMCID: PMC5198624.
  22. R Pebody, F Warburton, J Ellis, N Andrews, A Potts, S Cottrell, J Johnston, A Reynolds, R Gunson, C Thompson, M Galiano, C Robertson, R Byford, N Gallagher, M Sinnathamby, I Yonova, S Pathirannehelage, M Donati, C Moore, S de Lusignan, J McMenamin, M Zambon. Effectiveness of seasonal influenza vaccine for adults and children in preventing laboratory-confirmed influenza in primary care in the United Kingdom: 2015/16 end-of-season results. Euro Surveill. 2016 Sep 22;21(38). doi: 10.2807/1560-7917.ES.2016.21.38.30348. PubMed PMID: 27684603; PubMed Central PMCID: PMC5073201.
  23. Correa A, Hinton W, McGovern A, van Vlymen J, Yonova I, Jones S, de Lusignan S. Royal College of General Practitioners Research and Surveillance Centre (RCGP RSC) sentinel network: a cohort profile. BMJ Open. 2016 Apr 20;6(4):e011092. doi: 10.1136/bmjopen-2016-011092.
  24. Pebody RG, Green HK, Andrews N, Boddington NL, Zhao H, Yonova I, Ellis J, Steinberger S, Donati M, Elliot AJ, Hughes HE, Pathirannehelage S, Mullett D, Smith GE, de Lusignan S, Zambon M. Uptake and impact of vaccinating school age children against influenza during a season with circulation of drifted influenza A and B strains, England, 2014/15. Euro Surveill. 2015 Oct 1;20(39). doi: 10.2807/1560-7917.ES.2015.20.39.30029.
  25. Pebody R1, Warburton F, Ellis J, Andrews N, Potts A, Cottrell S, Johnston J, Reynolds A, Gunson R, Thompson C, Galiano M, Robertson C, Mullett D, Gallagher N, Sinnathamby M, Yonova I, Moore C, McMenamin J, de Lusignan S, Zambon M. Effectiveness of seasonal influenza vaccine in preventing laboratory-confirmed influenza in primary care in the United Kingdom: 2015/16 mid-season results. Euro Surveill. 2016 Mar 31;21(13). doi: 10.2807/1560-7917.ES.2016.21.13.30179.
  26. Pebody R, Warburton F, Andrews N, Ellis J, von Wissmann B, Robertson C, Yonova I, Cottrell S, Gallagher N, Green H, Thompson C, Galiano M, Marques D, Gunson R, Reynolds A, Moore C, Mullett D, Pathirannehelage S, Donati M, Johnston J, de Lusignan S, McMenamin J, Zambon M. Effectiveness of seasonal influenza vaccine in preventing laboratory-confirmed influenza in primary care in the United Kingdom: 2014/15 end of season results. Euro Surveill. 2015 Sep 10;20(36). doi: 10.2807/1560-7917.ES.2015.20.36.30013.

I was also acknowledged in a paper published in the Lancet:

Williams R, Alexander G, Armstrong I, Baker A, Bhala N, Camps-Walsh G, CrampME, de Lusignan S, Day N, Dhawan A, Dillon J, Drummond C, Dyson J, Foster G, Gilmore I, Hudson M, Kelly D, Langford A, McDougall N, Meier P, Moriarty K,Newsome P, O'Grady J, Pryke R, Rolfe L, Rice P, Rutter H, Sheron N, Taylor A, Thompson J, Thorburn D, Verne J, Wass J, Yeoman A. Disease burden and costs from excess alcohol consumption, obesity, and viral hepatitis: fourth report of the Lancet Standing Commission on Liver Disease in the UK. Lancet. 2017 Nov 29. pii: S0140-6736(17)32866-0. doi: 10.1016/S0140-6736(17)32866-0. [Epub ahead of print] Review. Erratum in: Lancet. 2017 Dec 7: PubMed PMID: 29198562.

Publications

Pebody R, Warburton F, Ellis J, Andrews N, Potts A, Cottrell S, Johnston J, Reynolds A, Gunson R, Thompson C, Galiano M, Robertson C, Byford R, Gallagher N, Sinnathamby M, Yonova I, Pathirannehelage S, Donati M, Moore C, de Lusignan S, McMenamin J, Zambon M (2016) Effectiveness of seasonal influenza vaccine for adults and children in preventing laboratory-confirmed influenza in primary care in the United Kingdom: 2015/16 end-of-season results,Eurosurveillance21(38) European Centre for Disease Prevention and Control
The United Kingdom (UK) is in the third season of introducing universal paediatric influenza vaccination with a quadrivalent live attenuated influenza vaccine (LAIV). The 2015/16 season in the UK was initially dominated by influenza A(H1N1)pdm09 and then influenza of B/Victoria lineage, not contained in that season?s adult trivalent inactivated influenza vaccine (IIV). Overall adjusted end-of-season vaccine effectiveness (VE) was 52.4% (95% confidence interval (CI): 41.0?61.6) against influenza-confirmed primary care consultation, 54.5% (95% CI: 41.6?64.5) against influenza A(H1N1)pdm09 and 54.2% (95% CI: 33.1?68.6) against influenza B. In 2?17 year-olds, adjusted VE for LAIV was 57.6% (95% CI: 25.1 to 76.0) against any influenza, 81.4% (95% CI: 39.6?94.3) against influenza B and 41.5% (95% CI: ?8.5 to 68.5) against influenza A(H1N1)pdm09. These estimates demonstrate moderate to good levels of protection, particularly against influenza B in children, but relatively less against influenza A(H1N1)pdm09. Despite lineage mismatch in the trivalent IIV, adults younger than 65 years were still protected against influenza B. These results provide reassurance for the UK to continue its influenza immunisation programme planned for 2016/17.
de Lusignan S, Pebody R, Warburton F, Andrews N, Ellis J, von Wissman B, Robertson C, Yonova I, Cottrell S, Gallagher N, Green H, Thompson C, Galiano M, Marques D, Gunson R, Reynolds A, Moore C, Mullett D, Pathirannehelage S, Donati M, Johnston J, McMenamin J, Zambon M (2015) Effectiveness of trivalent seasonal influenza vaccine in preventing laboratory-confirmed influenza in primary care in the United Kingdom: 2014/15 end of season results,Eurosurveillance20(36)15-00338pp. 1-18 European Centre for Disease Prevention and Control
The 2014/15 influenza season in the United Kingdom (UK) was characterised by circulation of predominantly antigenically and genetically drifted influenza A(H3N2) and B viruses. A universal paediatric influenza vaccination programme using a quadrivalent live attenuated influenza vaccine (LAIV) has recently been introduced in the UK. This study aims to measure the end-of-season influenza vaccine effectiveness (VE), including for LAIV, using the test negative case?control design. The overall adjusted VE against all influenza was 34.3% (95% confidence interval (CI) 17.8 to 47.5); for A(H3N2) 29.3% (95% CI: 8.6 to 45.3) and for B 46.3% (95% CI: 13.9 to 66.5). For those aged under 18 years, influenza A(H3N2) LAIV VE was 35% (95% CI: ?29.9 to 67.5), whereas for influenza B the LAIV VE was 100% (95% CI:17.0 to 100.0). Although the VE against influenza A(H3N2) infection was low, there was still evidence of significant protection, together with moderate, significant protection against drifted circulating influenza B viruses. LAIV provided non-significant positive protection against influenza A, with significant protection against B. Further work to assess the population impact of the vaccine programme across the UK is underway.
Correa A, Hinton W, McGovern A, van Vlymen J, Yonova I, Jones S, de Lusignan S (2016) Royal College of General Practitioners Research and Surveillance Centre (RCGP RSC) sentinel network: a cohort profile,BMJ OPEN6(4)ARTN e011092 BMJ PUBLISHING GROUP
de Lusignan S, Dos Santos G, Correa A, Haguinet F, Yonova I, Lair F, Byford R, Ferreira F, Stuttard K, Chan T T (2017) Post-authorisation passive enhanced safety surveillance of seasonal influenza vaccines: protocol of a pilot study in England,BMJ Open78(5)pp. 1-11 BMJ Publishing Group
Aim

To pilot enhanced safety surveillance of seasonal influenza vaccine meeting the European Medicines Agency (EMA) requirement to rapidly detect a significant increase in the frequency or severity of adverse events of interest (AEIs), which may indicate risk from the new season?s vaccine.

Study design

A prospective passive enhanced safety surveillance combining data collection from adverse drug reaction (ADR) cards with automated collection of pseudonymised routinely collected electronic health record (EHR) data. This study builds on a feasibility study carried out at the start of the 2015/2016 influenza season. We will report influenza vaccine exposure and any AEIs reported via ADR card or recorded directly into the EHR, from the commencement of influenza vaccination and ends as specified by EMA (30 November 2016).

Setting

Ten volunteer English general practices, primarily using the GSK influenza vaccines. They had selected this vaccine in advance of the study.

Participants

People who receive a seasonal influenza vaccine, in each age group defined in EMA interim guidance: 6 months to 5 years, 6?12 years, 13?17 years, 18?65 years and >65 years.

Outcome measures

The primary outcome measure is the rate of AEIs occurring within 7 days postvaccination, using passive surveillance of general practitioner (GP) EHR systems enhanced by a card-based ADR reporting system. Extracted data will be presented overall by brand (Fluarix Tetra vs others), by age strata and risk groups. The secondary outcome measure is the vaccine uptake among the subjects registered in the enrolled general practices.

de Lusignan Simon, McGee Christopher, Webb Rebecca, Joy Mark, Byford Rachel, Yonova Ivelina, Hriskova Mariya, Ferreira Filipa, Elliot Alex J, Smith Gillian, Rafi Imran (2018) Conurbation, Urban, and Rural Living as Determinants of Allergies and Infectious Diseases: Royal College of General Practitioners Research and Surveillance Centre Annual Report 2016-2017,JMIR Public Health and Surveillance4(4)e11354 JMIR Publications
Background: Living in a conurbation, urban, or rural environment is an important determinant of health. For example, conurbation and rural living is associated with increased respiratory and allergic conditions, whereas a farm or rural upbringing has been shown to be a protective factor against this. Objective: The objective of the study was to assess differences in general practice presentations of allergic and infectious disease in those exposed to conurbation or urban living compared with rural environments. Methods: The population was a nationally representative sample of 175 English general practices covering a population of over 1.6 million patients registered with sentinel network general practices. General practice presentation rates per 100,000 population were reported for allergic rhinitis, asthma, and infectious conditions grouped into upper and lower respiratory tract infections, urinary tract infection, and acute gastroenteritis by the UK Office for National Statistics urban-rural category. We used multivariate logistic regression adjusting for age, sex, ethnicity, deprivation, comorbidities, and smoking status, reporting odds ratios (ORs) with 95% CIs. Results: For allergic rhinitis, the OR was 1.13 (95% CI 1.04-1.23; P=.003) for urban and 1.29 (95% CI 1.19-1.41; P<.001) for conurbation compared with rural dwellers. Conurbation living was associated with a lower OR for both asthma (OR 0.70, 95% CI 0.67-0.73; P<.001) and lower respiratory tract infections (OR 0.94, 95% CI 0.90-0.98; P=.005). Compared with rural dwellers, the OR for upper respiratory tract infection was greater in urban (OR 1.06, 95% CI 1.03-1.08; P<.001) but no different in conurbation dwellers (OR 1.00, 95% CI 0.97-1.03; P=.93). Acute gastroenteritis followed the same pattern: the OR was 1.13 (95% CI 1.01-1.25; P=.03) for urban dwellers and 1.04 (95% CI 0.93-1.17; P=.46) for conurbation dwellers. The OR for urinary tract infection was lower for urban dwellers (OR 0.94, 95% CI 0.89-0.99; P=.02) but higher in conurbation dwellers (OR 1.06, 95% CI 1.00-1.13; P=.04). Conclusions: Those living in conurbations or urban areas were more likely to consult a general practice for allergic rhinitis and upper respiratory tract infection. Both conurbation and rural living were associated with an increased risk of urinary tract infection. Living in rural areas was associated with an increased risk of asthma and lower respiratory tract infections. The data suggest that living environment may affect rates of consultations for certain conditions. Longitudinal analyses of these data would be useful in providing insights into important determinants.
Thomas Spencer A., Smith Nadia A., Livina Valerie, Yonova Ivelina, Webb Rebecca, de Lusignan Simon (2019) Analysis of Primary Care Computerized Medical Records (CMR) Data With Deep Autoencoders (DAE),Frontiers in Applied Mathematics and Statistics542 Frontiers
The use of deep learning is becoming increasingly important in the analysis of medical data such as pattern recognition for classification. The use of primary healthcare computational medical records (CMR) data is vital in prediction of infection prevalence across a population, and decision making at a national scale. To date, the application of machine learning algorithms to CMR data remains under-utilized despite the potential impact for use in diagnostics or prevention of epidemics such as outbreaks of influenza. A particular challenge in epidemiology is how to differentiate incident cases from those that are follow-ups for the same condition. Furthermore, the CMR data are typically heterogeneous, noisy, high dimensional and incomplete, making automated analysis difficult. We introduce a methodology for converting heterogeneous data such that it is compatible with a deep autoencoder for reduction of CMR data. This approach provides a tool for real time visualization of these high dimensional data, revealing previously unknown dependencies and clusters. Our unsupervised nonlinear reduction method can be used to identify the features driving the formation of these clusters that can aid decision making in healthcare applications. The results in this work demonstrate that our methods can cluster more than 97.84% of the data (clusters >5 points) each of which is uniquely described by three attributes in the data: Clinical System (CMR system), Read Code (as recorded) and Read Term (standardized coding). Further, we propose the use of Shannon Entropy as a means to analyse the dispersion of clusters and the contribution from the underlying attributes to gain further insight from the data. Our results demonstrate that Shannon Entropy is a useful metric for analysing both the low dimensional clusters of CMR data, and also the features in the original heterogeneous data. Finally, we find that the entropy of the low dimensional clusters are directly representative of the entropy of the input data (Pearson Correlation = 0.99, R2 = 0.98) and therefore the reduced data from the deep autoencoder is reflective of the original CMR data variability.

Additional publications