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Quaife M, Medley GF, Jit M, Drake T, Asaria M, van Baal P, Baltussen R, Bollinger L, Bozzani F, Brady O, Broekhuizen H, Chalkidou K, Chi YL, Dowdy DW, Griffin S, Haghparast-Bidgoli H, Hallett T, Hauck K, Hollingsworth TD, McQuaid CF, Menzies NA, Merritt MW, Mirelman A, Morton A, Ruiz FJ, Siapka M, Skordis J, Tediosi F, Walker P, White RG, Winskill P, Vassall A, Gomez GB. Considering equity in priority setting using transmission models: Recommendations and data needs. Epidemics 2022; 41:100648. [PMID: 36343495 PMCID: PMC9623400 DOI: 10.1016/j.epidem.2022.100648] [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] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Revised: 09/20/2022] [Accepted: 10/21/2022] [Indexed: 11/06/2022] Open
Abstract
OBJECTIVES Disease transmission models are used in impact assessment and economic evaluations of infectious disease prevention and treatment strategies, prominently so in the COVID-19 response. These models rarely consider dimensions of equity relating to the differential health burden between individuals and groups. We describe concepts and approaches which are useful when considering equity in the priority setting process, and outline the technical choices concerning model structure, outputs, and data requirements needed to use transmission models in analyses of health equity. METHODS We reviewed the literature on equity concepts and approaches to their application in economic evaluation and undertook a technical consultation on how equity can be incorporated in priority setting for infectious disease control. The technical consultation brought together health economists with an interest in equity-informative economic evaluation, ethicists specialising in public health, mathematical modellers from various disease backgrounds, and representatives of global health funding and technical assistance organisations, to formulate key areas of consensus and recommendations. RESULTS We provide a series of recommendations for applying the Reference Case for Economic Evaluation in Global Health to infectious disease interventions, comprising guidance on 1) the specification of equity concepts; 2) choice of evaluation framework; 3) model structure; and 4) data needs. We present available conceptual and analytical choices, for example how correlation between different equity- and disease-relevant strata should be considered dependent on available data, and outline how assumptions and data limitations can be reported transparently by noting key factors for consideration. CONCLUSIONS Current developments in economic evaluations in global health provide a wide range of methodologies to incorporate equity into economic evaluations. Those employing infectious disease models need to use these frameworks more in priority setting to accurately represent health inequities. We provide guidance on the technical approaches to support this goal and ultimately, to achieve more equitable health policies.
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Affiliation(s)
- M. Quaife
- Department of Global Health and Development, London School of Hygiene and Tropical Medicine, UK,Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, UK
| | - GF Medley
- Department of Global Health and Development, London School of Hygiene and Tropical Medicine, UK
| | - M. Jit
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, UK
| | - T. Drake
- Center for Global Development in Europe (CGD Europe), UK
| | - M. Asaria
- LSE Health, London School of Economics, UK
| | - P. van Baal
- Erasmus School of Health Policy & Management, Erasmus University Rotterdam, the Netherlands
| | - R. Baltussen
- Nijmegen International Center for Health Systems Research and Education, Radboudmc, the Netherlands
| | | | - F. Bozzani
- Department of Global Health and Development, London School of Hygiene and Tropical Medicine, UK
| | - O. Brady
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, UK
| | - H. Broekhuizen
- Centre for Space, Place, and Society, Wageningen University and Research, Netherlands
| | - K. Chalkidou
- International Decision Support Initiative, Imperial College London, UK
| | - Y.-L. Chi
- International Decision Support Initiative, Imperial College London, UK
| | - DW Dowdy
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, USA
| | - S. Griffin
- Centre for Health Economics, University of York, UK
| | - H. Haghparast-Bidgoli
- Institute for Global Health, Centre for Global Health Economics, University College London, UK
| | - T. Hallett
- Department of Infectious Disease Epidemiology, Imperial College London, UK
| | - K. Hauck
- Department of Infectious Disease Epidemiology, Imperial College London, UK
| | - TD Hollingsworth
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, UK
| | - CF McQuaid
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, UK
| | - NA Menzies
- Department of Global Health and Population, Harvard T.H. Chan School of Public Health, USA
| | - MW Merritt
- Johns Hopkins Berman Institute of Bioethics and Department of International Health, Johns Hopkins Bloomberg School of Public Health, United States
| | - A. Mirelman
- Centre for Health Economics, University of York, UK
| | - A. Morton
- Department of Management Science, University of Strathclyde, UK
| | - FJ Ruiz
- International Decision Support Initiative, Imperial College London, UK
| | - M. Siapka
- Department of Global Health and Development, London School of Hygiene and Tropical Medicine, UK,Impact Elipsis, Greece
| | - J. Skordis
- Institute for Global Health, Centre for Global Health Economics, University College London, UK
| | - F. Tediosi
- Swiss Tropical and Public Health Institute and Universität Basel, Switzerland
| | - P. Walker
- Department of Infectious Disease Epidemiology, Imperial College London, UK
| | - RG White
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, UK
| | - P. Winskill
- Department of Infectious Disease Epidemiology, Imperial College London, UK
| | - A. Vassall
- Department of Global Health and Development, London School of Hygiene and Tropical Medicine, UK,Correspondence to: London School of Hygiene and Tropical Medicine, 15 – 17 Tavistock Place, London WC1H 9SH, UK
| | - GB Gomez
- Department of Global Health and Development, London School of Hygiene and Tropical Medicine, UK
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Koltai M, Krauer F, Hodgson D, van Leeuwen E, Treskova-Schwarzbach M, Jit M, Flasche S. Determinants of RSV epidemiology following suppression through pandemic contact restrictions. Epidemics 2022; 40:100614. [PMID: 35901639 DOI: 10.1016/j.epidem.2022.100614] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Revised: 06/26/2022] [Accepted: 07/20/2022] [Indexed: 01/21/2023] Open
Abstract
INTRODUCTION COVID-19 related non-pharmaceutical interventions (NPIs) led to a suppression of RSV circulation in winter 2020/21 in the UK and an off-season resurgence in Summer 2021. We explore how the parameters of RSV epidemiology shape the size and dynamics of post-suppression resurgence and what we can learn about them from the resurgence patterns observed so far. METHODS We developed an age-structured dynamic transmission model of RSV and sampled the parameters governing RSV seasonality, infection susceptibility and post-infection immunity, retaining simulations fitting the UK's pre-pandemic epidemiology by a set of global criteria consistent with likelihood calculations. From Spring 2020 to Summer 2021 we assumed a reduced contact frequency, returning to pre-pandemic levels from Spring 2021. We simulated transmission forwards until 2023 and evaluated the impact of the sampled parameters on the projected trajectories of RSV hospitalisations and compared these to the observed resurgence. RESULTS Simulations replicated an out-of-season resurgence of RSV in 2021. If unmitigated, paediatric RSV hospitalisation incidence in the 2021/22 season was projected to increase by 30-60% compared to pre-pandemic levels. The increase was larger if infection risk was primarily determined by immunity acquired from previous exposure rather than age-dependent factors, exceeding 90 % and 130 % in 1-2 and 2-5 year old children, respectively. Analysing the simulations replicating the observed early outbreak in 2021 in addition to pre-pandemic RSV data, we found they were characterised by weaker seasonal forcing, stronger age-dependence of infection susceptibility and higher baseline transmissibility. CONCLUSION COVID-19 mitigation measures in the UK stopped RSV circulation in the 2020/21 season and generated immunity debt leading to an early off-season RSV epidemic in 2021. A stronger dependence of infection susceptibility on immunity from previous exposure increases the size of the resurgent season. The early onset of the RSV resurgence in 2021, its marginally increased size relative to previous seasons and its decline by January 2022 suggest a stronger dependence of infection susceptibility on age-related factors, as well as a weaker effect of seasonality and a higher baseline transmissibility. The pattern of resurgence has been complicated by contact levels still not back to pre-pandemic levels. Further fitting of RSV resurgence in multiple countries incorporating data on contact patterns will be needed to further narrow down these parameters and to better predict the pathogen's future trajectory, planning for a potential expansion of new immunisation products against RSV in the coming years.
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McCabe R, Kont MD, Schmit N, Whittaker C, Løchen A, Walker PGT, Ghani AC, Ferguson NM, White PJ, Donnelly CA, Watson OJ. Communicating uncertainty in epidemic models. Epidemics 2021; 37:100520. [PMID: 34749076 PMCID: PMC8562068 DOI: 10.1016/j.epidem.2021.100520] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Revised: 10/26/2021] [Accepted: 11/01/2021] [Indexed: 12/29/2022] Open
Abstract
While mathematical models of disease transmission are widely used to inform public health decision-makers globally, the uncertainty inherent in results are often poorly communicated. We outline some potential sources of uncertainty in epidemic models, present traditional methods used to illustrate uncertainty and discuss alternative presentation formats used by modelling groups throughout the COVID-19 pandemic. Then, by drawing on the experience of our own recent modelling, we seek to contribute to the ongoing discussion of how to improve upon traditional methods used to visualise uncertainty by providing a suggestion of how this can be presented in a clear and simple manner.
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Affiliation(s)
- Ruth McCabe
- Department of Statistics, University of Oxford, 24-29 St Giles', Oxford OX1 3LB, UK; NIHR Health Protection Research Unit in Emerging and Zoonotic Diseases, The Ronald Ross Building, University of Liverpool, 8 West Derby Street, Liverpool L69 7BE, UK; MRC Centre for Global Infectious Disease Analysis & WHO Collaborating Centre for Infectious Disease Modelling, Abdul Latif Jameel Institute for Disease and Emergency Analytics, Imperial College London, St Mary's Campus, Norfolk Place, W2 1PG London, UK.
| | - Mara D Kont
- MRC Centre for Global Infectious Disease Analysis & WHO Collaborating Centre for Infectious Disease Modelling, Abdul Latif Jameel Institute for Disease and Emergency Analytics, Imperial College London, St Mary's Campus, Norfolk Place, W2 1PG London, UK
| | - Nora Schmit
- MRC Centre for Global Infectious Disease Analysis & WHO Collaborating Centre for Infectious Disease Modelling, Abdul Latif Jameel Institute for Disease and Emergency Analytics, Imperial College London, St Mary's Campus, Norfolk Place, W2 1PG London, UK
| | - Charles Whittaker
- MRC Centre for Global Infectious Disease Analysis & WHO Collaborating Centre for Infectious Disease Modelling, Abdul Latif Jameel Institute for Disease and Emergency Analytics, Imperial College London, St Mary's Campus, Norfolk Place, W2 1PG London, UK
| | - Alessandra Løchen
- MRC Centre for Global Infectious Disease Analysis & WHO Collaborating Centre for Infectious Disease Modelling, Abdul Latif Jameel Institute for Disease and Emergency Analytics, Imperial College London, St Mary's Campus, Norfolk Place, W2 1PG London, UK
| | - Patrick G T Walker
- MRC Centre for Global Infectious Disease Analysis & WHO Collaborating Centre for Infectious Disease Modelling, Abdul Latif Jameel Institute for Disease and Emergency Analytics, Imperial College London, St Mary's Campus, Norfolk Place, W2 1PG London, UK
| | - Azra C Ghani
- MRC Centre for Global Infectious Disease Analysis & WHO Collaborating Centre for Infectious Disease Modelling, Abdul Latif Jameel Institute for Disease and Emergency Analytics, Imperial College London, St Mary's Campus, Norfolk Place, W2 1PG London, UK
| | - Neil M Ferguson
- MRC Centre for Global Infectious Disease Analysis & WHO Collaborating Centre for Infectious Disease Modelling, Abdul Latif Jameel Institute for Disease and Emergency Analytics, Imperial College London, St Mary's Campus, Norfolk Place, W2 1PG London, UK; NIHR Health Research Protection Unit in Modelling and Health Economics, Imperial College London, St Mary's Campus, Norfolk Place, London W2 1PG, UK
| | - Peter J White
- MRC Centre for Global Infectious Disease Analysis & WHO Collaborating Centre for Infectious Disease Modelling, Abdul Latif Jameel Institute for Disease and Emergency Analytics, Imperial College London, St Mary's Campus, Norfolk Place, W2 1PG London, UK; NIHR Health Research Protection Unit in Modelling and Health Economics, Imperial College London, St Mary's Campus, Norfolk Place, London W2 1PG, UK; Modelling and Economics Unit, National Infection Service, Public Health England, London, UK
| | - Christl A Donnelly
- Department of Statistics, University of Oxford, 24-29 St Giles', Oxford OX1 3LB, UK; NIHR Health Protection Research Unit in Emerging and Zoonotic Diseases, The Ronald Ross Building, University of Liverpool, 8 West Derby Street, Liverpool L69 7BE, UK; MRC Centre for Global Infectious Disease Analysis & WHO Collaborating Centre for Infectious Disease Modelling, Abdul Latif Jameel Institute for Disease and Emergency Analytics, Imperial College London, St Mary's Campus, Norfolk Place, W2 1PG London, UK; NIHR Health Research Protection Unit in Modelling and Health Economics, Imperial College London, St Mary's Campus, Norfolk Place, London W2 1PG, UK
| | - Oliver J Watson
- MRC Centre for Global Infectious Disease Analysis & WHO Collaborating Centre for Infectious Disease Modelling, Abdul Latif Jameel Institute for Disease and Emergency Analytics, Imperial College London, St Mary's Campus, Norfolk Place, W2 1PG London, UK
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Chan J, Wu Y, Wood J, Muhit M, Mahmood MK, Karim T, Moushumi F, Jones CA, Snelling T, Khandaker G. Burden of Congenital Rubella Syndrome (CRS) in Bangladesh: Systematic Review of Existing Literature and Transmission Modelling of Seroprevalence Studies. Infect Disord Drug Targets 2021; 20:284-290. [PMID: 30289078 DOI: 10.2174/1871526518666181004092758] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [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: 08/18/2018] [Revised: 09/14/2018] [Accepted: 09/25/2018] [Indexed: 11/22/2022]
Abstract
BACKGROUND AND OBJECTIVES Congenital Rubella Syndrome (CRS) is the leading cause of vaccine-preventable congenital anomalies. Comprehensive country-level data on the burden of CRS in low and middle-income countries, such as Bangladesh, are scarce. This information is essential for assessing the impact of rubella vaccination programs. We aim to systematically review the literature on the epidemiology of CRS and estimate the burden of CRS in Bangladesh. METHODS We conducted a systematic review of existing literature and transmission modelling of seroprevalence studies to estimate the pre-vaccine period burden of CRS in Bangladesh. OVID Medline (1948 - 23 November 2016) and OVID EMBASE (1974 - 23 November 2016) were searched using a combination of the database-specific controlled vocabulary and free text terms. We used an age-stratified deterministic model to estimate the pre-vaccination burden of CRS in Bangladesh. FINDINGS Ten articles were identified, published between 2000 and 2014, including seven crosssectional studies, two case series and one analytical case-control study. Rubella seropositivity ranged from 47.0% to 86.0% among all age population. Rubella sero-positivity increased with age. Rubella seropositivity among women of childbearing age was 81.0% overall. The estimated incidence of CRS was 0·99 per 1,000 live births, which corresponds to approximately 3,292 CRS cases annually in Bangladesh. CONCLUSION The estimated burden of CRS in Bangladesh during the pre-vaccination period was high. This will provide important baseline information to assess the impact and cost-effectiveness of routine rubella immunisation, introduced in 2012 in Bangladesh.
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Affiliation(s)
- Jocelyn Chan
- Murdoch Childrens Research Institute, Royal Children’s Hospital, Melbourne, Australia
| | - Yue Wu
- Curtin University, School of Public Health, Perth, Western Australia, Wesfarmers Centre of Vaccines & Infectious Diseases, Telethon Kids Institute, University of Western Australia, Perth, Australia
| | - James Wood
- School of Public Health and Community Medicine, University of New South Wales, Kensington, Australia
| | - Mohammad Muhit
- CSF Global, Dhaka, Bangladesh,Asian Institute of Disability and Development (AIDD), University of South Asia, Dhaka, Bangladesh
| | - Mohammed K Mahmood
- CSF Global, Dhaka, Bangladesh,Asian Institute of Disability and Development (AIDD), University of South Asia, Dhaka, Bangladesh
| | - Tasneem Karim
- CSF Global, Dhaka, Bangladesh,Asian Institute of Disability and Development (AIDD), University of South Asia, Dhaka, Bangladesh
| | - Farhana Moushumi
- Central Queensland Hospital and Health Services, Queensland, Australia
| | - Cheryl A Jones
- Murdoch Childrens Research Institute, Royal Children’s Hospital, Melbourne, Australia,Dept of Paediatrics, University of Melbourne, Parkville, Melbourne, Australia,Dept of Infectious Diseases, Royal Children’s Hospital Melbourne, Melbourne, Australia
| | - Tom Snelling
- Curtin University, School of Public Health, Perth, Western Australia, Wesfarmers Centre of Vaccines & Infectious Diseases, Telethon Kids Institute, University of Western Australia, Perth, Australia,Perth Children's Hospital, Perth, Western Australia, Australia,Menzies School of Health Research, Charles Darwin University, Darwin, Australia
| | - Gulam Khandaker
- CSF Global, Dhaka, Bangladesh,Asian Institute of Disability and Development (AIDD), University of South Asia, Dhaka, Bangladesh,Central Queensland Hospital and Health Services, Queensland, Australia,The Children's Hospital at Westmead (Clinical School), Sydney Medical School, University of Sydney, Sydney, NSW, Australia
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5
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Panovska-Griffiths J, Crowe S, Pagel C, Shiri T, Grove P, Utley M. A method for evaluating and comparing immunisation schedules that cover multiple diseases: Illustrative application to the UK routine childhood vaccine schedule. Vaccine 2018; 36:5340-5347. [PMID: 30055970 DOI: 10.1016/j.vaccine.2018.05.083] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [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: 11/23/2017] [Revised: 05/15/2018] [Accepted: 05/22/2018] [Indexed: 10/28/2022]
Abstract
BACKGROUND In the UK, the childhood immunisation programme is given in the first 5 years of life and protects against 12 vaccine-preventable diseases. Recently, this programme has undergone changes with addition of vaccination against Meningitis B from September 2015 and the removal of the primary dose of protection against Meningitis C from July 2016. These hanges have direct impact on the associated diseases but in addition may induce indirect effects on the vaccines that are given simultaneously or later in the programme. In this work, we developed a novel formal method to evaluate the impact of vaccination changes to one aspect of the programme across an entire vaccine programme. METHODS Firstly, we combined transmission modelling (for four diseases) and historic data synthesis (for eight diseases) to project, for each disease, the disease burden at different levels of effective coverage against the associated disease. Secondly, we used a simulation model to determine the vector of effective coverage against each disease under three variations of the current childhood schedule. Combining these, we calculated the vector of disease burden across the programme under different scenarios, and assessed the direct and indirect effects of the schedule changes. RESULTS Through illustrative application of our novel framework to three scenarios of the current childhood immunisation programme in the UK, we demonstrated the feasibility of this unifying approach. For each disease in the programme, we successfully quantified the residual disease burden due to the change. For some diseases, the change was indirectly beneficial and reduced the burden, whereas for others the effect was adverse and the change increased the disease burden. CONCLUSIONS Our results demonstrate the potential benefit of considering the programme-wide impact of changes to an immunisation schedule, and our framework is an important step in the development of a means for systematically doing so.
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Affiliation(s)
- Jasmina Panovska-Griffiths
- Clinical Operational Research Unit, Department of Mathematics, University College London, WC1E 6BT, UK; Department of Applied Health Research, University College London, WC1E 6BT, UK; Department of Global Health and Development, London School of Hygiene and Tropical Medicine, 15-17 Tavistock Place, WC1H 9SH, UK.
| | - Sonya Crowe
- Clinical Operational Research Unit, Department of Mathematics, University College London, WC1E 6BT, UK
| | - Christina Pagel
- Clinical Operational Research Unit, Department of Mathematics, University College London, WC1E 6BT, UK; Department of Applied Health Research, University College London, WC1E 6BT, UK
| | - Tinevimbo Shiri
- Warwick Medical School, Clinical Trials Unit, University of Warwick, Coventry, CV4 7AL, UK
| | - Peter Grove
- Department of Health, Area 330, Wellington House, 133 - 155 Waterloo Road, London, SE1 8UG, UK
| | - Martin Utley
- Clinical Operational Research Unit, Department of Mathematics, University College London, WC1E 6BT, UK
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Kwong JC, Lane CR, Romanes F, Gonçalves da Silva A, Easton M, Cronin K, Waters MJ, Tomita T, Stevens K, Schultz MB, Baines SL, Sherry NL, Carter GP, Mu A, Sait M, Ballard SA, Seemann T, Stinear TP, Howden BP. Translating genomics into practice for real-time surveillance and response to carbapenemase-producing Enterobacteriaceae: evidence from a complex multi-institutional KPC outbreak. PeerJ 2018; 6:e4210. [PMID: 29312831 PMCID: PMC5756455 DOI: 10.7717/peerj.4210] [Citation(s) in RCA: 50] [Impact Index Per Article: 8.3] [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: 10/12/2017] [Accepted: 12/09/2017] [Indexed: 12/21/2022] Open
Abstract
Background Until recently, Klebsiella pneumoniae carbapenemase (KPC)-producing Enterobacteriaceae were rarely identified in Australia. Following an increase in the number of incident cases across the state of Victoria, we undertook a real-time combined genomic and epidemiological investigation. The scope of this study included identifying risk factors and routes of transmission, and investigating the utility of genomics to enhance traditional field epidemiology for informing management of established widespread outbreaks. Methods All KPC-producing Enterobacteriaceae isolates referred to the state reference laboratory from 2012 onwards were included. Whole-genome sequencing was performed in parallel with a detailed descriptive epidemiological investigation of each case, using Illumina sequencing on each isolate. This was complemented with PacBio long-read sequencing on selected isolates to establish high-quality reference sequences and interrogate characteristics of KPC-encoding plasmids. Results Initial investigations indicated that the outbreak was widespread, with 86 KPC-producing Enterobacteriaceae isolates (K. pneumoniae 92%) identified from 35 different locations across metropolitan and rural Victoria between 2012 and 2015. Initial combined analyses of the epidemiological and genomic data resolved the outbreak into distinct nosocomial transmission networks, and identified healthcare facilities at the epicentre of KPC transmission. New cases were assigned to transmission networks in real-time, allowing focussed infection control efforts. PacBio sequencing confirmed a secondary transmission network arising from inter-species plasmid transmission. Insights from Bayesian transmission inference and analyses of within-host diversity informed the development of state-wide public health and infection control guidelines, including interventions such as an intensive approach to screening contacts following new case detection to minimise unrecognised colonisation. Conclusion A real-time combined epidemiological and genomic investigation proved critical to identifying and defining multiple transmission networks of KPC Enterobacteriaceae, while data from either investigation alone were inconclusive. The investigation was fundamental to informing infection control measures in real-time and the development of state-wide public health guidelines on carbapenemase-producing Enterobacteriaceae surveillance and management.
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Affiliation(s)
- Jason C Kwong
- Doherty Applied Microbial Genomics, The University of Melbourne at The Peter Doherty Institute for Infection and Immunity, Melbourne, VIC, Australia.,Department of Microbiology and Immunology, The University of Melbourne at The Peter Doherty Institute for Infection and Immunity, Melbourne, VIC, Australia.,Department of Infectious Diseases, Austin Health, Heidelberg, VIC, Australia.,Microbiological Diagnostic Unit Public Health Laboratory, The University of Melbourne at The Peter Doherty Institute for Infection and Immunity, Melbourne, VIC, Australia
| | - Courtney R Lane
- Microbiological Diagnostic Unit Public Health Laboratory, The University of Melbourne at The Peter Doherty Institute for Infection and Immunity, Melbourne, VIC, Australia.,Health Protection Branch, Department of Health and Human Services, Victoria State Government, Melbourne, VIC, Australia
| | - Finn Romanes
- Health Protection Branch, Department of Health and Human Services, Victoria State Government, Melbourne, VIC, Australia
| | - Anders Gonçalves da Silva
- Doherty Applied Microbial Genomics, The University of Melbourne at The Peter Doherty Institute for Infection and Immunity, Melbourne, VIC, Australia.,Microbiological Diagnostic Unit Public Health Laboratory, The University of Melbourne at The Peter Doherty Institute for Infection and Immunity, Melbourne, VIC, Australia
| | - Marion Easton
- Health Protection Branch, Department of Health and Human Services, Victoria State Government, Melbourne, VIC, Australia
| | - Katie Cronin
- Department of Microbiology, St Vincent's Hospital Melbourne, Fitzroy, VIC, Australia
| | - Mary Jo Waters
- Department of Microbiology, St Vincent's Hospital Melbourne, Fitzroy, VIC, Australia
| | - Takehiro Tomita
- Microbiological Diagnostic Unit Public Health Laboratory, The University of Melbourne at The Peter Doherty Institute for Infection and Immunity, Melbourne, VIC, Australia
| | - Kerrie Stevens
- Microbiological Diagnostic Unit Public Health Laboratory, The University of Melbourne at The Peter Doherty Institute for Infection and Immunity, Melbourne, VIC, Australia
| | - Mark B Schultz
- Doherty Applied Microbial Genomics, The University of Melbourne at The Peter Doherty Institute for Infection and Immunity, Melbourne, VIC, Australia.,Department of Microbiology and Immunology, The University of Melbourne at The Peter Doherty Institute for Infection and Immunity, Melbourne, VIC, Australia.,Microbiological Diagnostic Unit Public Health Laboratory, The University of Melbourne at The Peter Doherty Institute for Infection and Immunity, Melbourne, VIC, Australia
| | - Sarah L Baines
- Doherty Applied Microbial Genomics, The University of Melbourne at The Peter Doherty Institute for Infection and Immunity, Melbourne, VIC, Australia.,Department of Microbiology and Immunology, The University of Melbourne at The Peter Doherty Institute for Infection and Immunity, Melbourne, VIC, Australia
| | - Norelle L Sherry
- Doherty Applied Microbial Genomics, The University of Melbourne at The Peter Doherty Institute for Infection and Immunity, Melbourne, VIC, Australia.,Department of Microbiology and Immunology, The University of Melbourne at The Peter Doherty Institute for Infection and Immunity, Melbourne, VIC, Australia.,Department of Infectious Diseases, Austin Health, Heidelberg, VIC, Australia
| | - Glen P Carter
- Doherty Applied Microbial Genomics, The University of Melbourne at The Peter Doherty Institute for Infection and Immunity, Melbourne, VIC, Australia.,Department of Microbiology and Immunology, The University of Melbourne at The Peter Doherty Institute for Infection and Immunity, Melbourne, VIC, Australia
| | - Andre Mu
- Doherty Applied Microbial Genomics, The University of Melbourne at The Peter Doherty Institute for Infection and Immunity, Melbourne, VIC, Australia.,Department of Microbiology and Immunology, The University of Melbourne at The Peter Doherty Institute for Infection and Immunity, Melbourne, VIC, Australia
| | - Michelle Sait
- Microbiological Diagnostic Unit Public Health Laboratory, The University of Melbourne at The Peter Doherty Institute for Infection and Immunity, Melbourne, VIC, Australia
| | - Susan A Ballard
- Doherty Applied Microbial Genomics, The University of Melbourne at The Peter Doherty Institute for Infection and Immunity, Melbourne, VIC, Australia.,Microbiological Diagnostic Unit Public Health Laboratory, The University of Melbourne at The Peter Doherty Institute for Infection and Immunity, Melbourne, VIC, Australia
| | - Torsten Seemann
- Doherty Applied Microbial Genomics, The University of Melbourne at The Peter Doherty Institute for Infection and Immunity, Melbourne, VIC, Australia.,Melbourne Bioinformatics, The University of Melbourne, Carlton, VIC, Australia
| | - Timothy P Stinear
- Doherty Applied Microbial Genomics, The University of Melbourne at The Peter Doherty Institute for Infection and Immunity, Melbourne, VIC, Australia.,Department of Microbiology and Immunology, The University of Melbourne at The Peter Doherty Institute for Infection and Immunity, Melbourne, VIC, Australia
| | - Benjamin P Howden
- Doherty Applied Microbial Genomics, The University of Melbourne at The Peter Doherty Institute for Infection and Immunity, Melbourne, VIC, Australia.,Department of Microbiology and Immunology, The University of Melbourne at The Peter Doherty Institute for Infection and Immunity, Melbourne, VIC, Australia.,Department of Infectious Diseases, Austin Health, Heidelberg, VIC, Australia.,Microbiological Diagnostic Unit Public Health Laboratory, The University of Melbourne at The Peter Doherty Institute for Infection and Immunity, Melbourne, VIC, Australia
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7
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Camacho A, Eggo RM, Goeyvaerts N, Vandebosch A, Mogg R, Funk S, Kucharski AJ, Watson CH, Vangeneugden T, Edmunds WJ. Real-time dynamic modelling for the design of a cluster-randomized phase 3 Ebola vaccine trial in Sierra Leone. Vaccine 2016; 35:544-551. [PMID: 28024952 DOI: 10.1016/j.vaccine.2016.12.019] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [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: 07/14/2016] [Revised: 11/17/2016] [Accepted: 12/12/2016] [Indexed: 01/03/2023]
Abstract
BACKGROUND Declining incidence and spatial heterogeneity complicated the design of phase 3 Ebola vaccine trials during the tail of the 2013-16 Ebola virus disease (EVD) epidemic in West Africa. Mathematical models can provide forecasts of expected incidence through time and can account for both vaccine efficacy in participants and effectiveness in populations. Determining expected disease incidence was critical to calculating power and determining trial sample size. METHODS In real-time, we fitted, forecasted, and simulated a proposed phase 3 cluster-randomized vaccine trial for a prime-boost EVD vaccine in three candidate regions in Sierra Leone. The aim was to forecast trial feasibility in these areas through time and guide study design planning. RESULTS EVD incidence was highly variable during the epidemic, especially in the declining phase. Delays in trial start date were expected to greatly reduce the ability to discern an effect, particularly as a trial with an effective vaccine would cause the epidemic to go extinct more quickly in the vaccine arm. Real-time updates of the model allowed decision-makers to determine how trial feasibility changed with time. CONCLUSIONS This analysis was useful for vaccine trial planning because we simulated effectiveness as well as efficacy, which is possible with a dynamic transmission model. It contributed to decisions on choice of trial location and feasibility of the trial. Transmission models should be utilised as early as possible in the design process to provide mechanistic estimates of expected incidence, with which decisions about sample size, location, timing, and feasibility can be determined.
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Affiliation(s)
- A Camacho
- Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, Keppel Street, London WC1E 7HT, UK
| | - R M Eggo
- Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, Keppel Street, London WC1E 7HT, UK.
| | | | | | - R Mogg
- Janssen Research & Development, LLC, Spring House, PA, USA
| | - S Funk
- Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, Keppel Street, London WC1E 7HT, UK
| | - A J Kucharski
- Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, Keppel Street, London WC1E 7HT, UK
| | - C H Watson
- Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, Keppel Street, London WC1E 7HT, UK
| | | | - W J Edmunds
- Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, Keppel Street, London WC1E 7HT, UK
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Elbers ARW, Meiswinkel R. Culicoides (Diptera: Ceratopogonidae) host preferences and biting rates in the Netherlands: comparing cattle, sheep and the black-light suction trap. Vet Parasitol 2014; 205:330-7. [PMID: 24957001 DOI: 10.1016/j.vetpar.2014.06.004] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.0] [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: 04/14/2014] [Revised: 06/02/2014] [Accepted: 06/03/2014] [Indexed: 11/19/2022]
Abstract
Host preference is an important determinant of feeding behaviour in biting insects and a critical component in the transmission of vector-borne diseases. The aim of the study was to quantify Culicoides (Diptera: Ceratopogonidae) host preferences and biting rates using tethered livestock at pasture (a dairy cow and a sheep) and to compare the numbers of biting midges aspirated off them to those captured simultaneously in a black-light suction trap acting as a surrogate host. Culicoides collections were made hourly over seven hours (from five hours before official sunset to two hours after) between 27 May and 19 June, 2013 at a dairy farm (eastern Netherlands). The study involved 13 replicates of a site × host randomised design. Culicoides collected by black-light suction trap and by direct aspiration were identified to species morphologically and age-graded. The C. obsoletus complex, C. dewulfi and C. pulicaris predominated on the back and flanks of the animals, C. punctatus on the belly, and C. chiopterus on the legs. Using comparable collection periods, 9.3 times (95% confidence interval: 8.6-10.0) more Culicoides were caught on the cow than on the sheep and 25.4 times (95% confidence interval: 18.4-35.1) less in the black-light suction trap compared to the sheep. Mean Culicoides biting rates on the cow across the 7-h collection period were 4.6, 3.5, 1.0, 1.0 and 0.5 min(-1) for C. dewulfi, the C. obsoletus complex, C. chiopterus, C. punctatus and C. pulicaris, respectively; for the sheep they were 0.6, 0.4 and 0.1 min(-1) for the C. obsoletus complex, C. dewulfi and C. punctatus, respectively. Though midges were aspirated off livestock during each of the seven hours, they only began to appear in the black-light suction trap 5h later, from sunset onwards. After sunset, its efficacy improved markedly, but occurred when midge activity overall had begun to decline. Though it was quite accurate in ranking Culicoides species abundance, the black-light suction trap proved to be of limited value for determining hours of peak biting activity, levels of abundance, and host preference, in Culicoides.
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Affiliation(s)
- A R W Elbers
- Department of Epidemiology, Crisis Organisation and Diagnostics, Central Veterinary Institute (CVI), Part of Wageningen UR, P.O. Box 65, NL-8200AB Lelystad, The Netherlands.
| | - R Meiswinkel
- Santa Maria del Monte, Via Pratarone 14, Rocca di Cave, Roma 00030, Italy.
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