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Kang H, Auzenbergs M, Clapham H, Maure C, Kim JH, Salje H, Taylor CG, Lim A, Clark A, Edmunds WJ, Sahastrabuddhe S, Brady OJ, Abbas K. Chikungunya seroprevalence, force of infection, and prevalence of chronic disability after infection in endemic and epidemic settings: a systematic review, meta-analysis, and modelling study. Lancet Infect Dis 2024; 24:488-503. [PMID: 38342105 DOI: 10.1016/s1473-3099(23)00810-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Revised: 12/09/2023] [Accepted: 12/14/2023] [Indexed: 02/13/2024]
Abstract
BACKGROUND Chikungunya is an arboviral disease transmitted by Aedes aegypti and Aedes albopictus mosquitoes with a growing global burden linked to climate change and globalisation. We aimed to estimate chikungunya seroprevalence, force of infection (FOI), and prevalence of related chronic disability and hospital admissions in endemic and epidemic settings. METHODS In this systematic review, meta-analysis, and modelling study, we searched PubMed, Ovid, and Web of Science for articles published from database inception until Sept 26, 2022, for prospective and retrospective cross-sectional studies that addressed serological chikungunya virus infection in any geographical region, age group, and population subgroup and for longitudinal prospective and retrospective cohort studies with data on chronic chikungunya or hospital admissions in people with chikungunya. We did a systematic review of studies on chikungunya seroprevalence and fitted catalytic models to each survey to estimate location-specific FOI (ie, the rate at which susceptible individuals acquire chikungunya infection). We performed a meta-analysis to estimate the proportion of symptomatic patients with laboratory-confirmed chikungunya who had chronic chikungunya or were admitted to hospital following infection. We used a random-effects model to assess the relationship between chronic sequelae and follow-up length using linear regression. The systematic review protocol is registered online on PROSPERO, CRD42022363102. FINDINGS We identified 60 studies with data on seroprevalence and chronic chikungunya symptoms done across 76 locations in 38 countries, and classified 17 (22%) of 76 locations as endemic settings and 59 (78%) as epidemic settings. The global long-term median annual FOI was 0·007 (95% uncertainty interval [UI] 0·003-0·010) and varied from 0·0001 (0·00004-0·0002) to 0·113 (0·07-0·20). The highest estimated median seroprevalence at age 10 years was in south Asia (8·0% [95% UI 6·5-9·6]), followed by Latin America and the Caribbean (7·8% [4·9-14·6]), whereas median seroprevalence was lowest in the Middle East (1·0% [0·5-1·9]). We estimated that 51% (95% CI 45-58) of people with laboratory-confirmed symptomatic chikungunya had chronic disability after infection and 4% (3-5) were admitted to hospital following infection. INTERPRETATION We inferred subnational heterogeneity in long-term average annual FOI and transmission dynamics and identified both endemic and epidemic settings across different countries. Brazil, Ethiopia, Malaysia, and India included both endemic and epidemic settings. Long-term average annual FOI was higher in epidemic settings than endemic settings. However, long-term cumulative incidence of chikungunya can be similar between large outbreaks in epidemic settings with a high FOI and endemic settings with a relatively low FOI. FUNDING International Vaccine Institute.
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Affiliation(s)
- Hyolim Kang
- London School of Hygiene and Tropical Medicine, London, UK; Seoul National University College of Medicine School, Seoul, South Korea.
| | | | - Hannah Clapham
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore
| | - Clara Maure
- International Vaccine Institute, Seoul, South Korea
| | | | - Henrik Salje
- Department of Genetics, Cambridge University, Cambridge, UK
| | | | - Ahyoung Lim
- London School of Hygiene and Tropical Medicine, London, UK
| | - Andrew Clark
- London School of Hygiene and Tropical Medicine, London, UK
| | - W John Edmunds
- London School of Hygiene and Tropical Medicine, London, UK; School of Tropical Medicine and Global Health, Nagasaki University, Nagasaki, Japan
| | - Sushant Sahastrabuddhe
- International Vaccine Institute, Seoul, South Korea; Centre International de Recherche en Infectiologie, Université Jean Monnet, Université Claude Bernard Lyon, INSERM, Saint-Etienne, France
| | - Oliver J Brady
- London School of Hygiene and Tropical Medicine, London, UK
| | - Kaja Abbas
- London School of Hygiene and Tropical Medicine, London, UK; School of Tropical Medicine and Global Health, Nagasaki University, Nagasaki, Japan
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Auzenbergs M, Maure C, Kang H, Clark A, Brady O, Sahastrabuddhe S, Abbas K. Programmatic considerations and evidence gaps for chikungunya vaccine introduction in countries at risk of chikungunya outbreaks: Stakeholder analysis. PLoS Negl Trop Dis 2024; 18:e0012075. [PMID: 38574163 PMCID: PMC11020901 DOI: 10.1371/journal.pntd.0012075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2023] [Revised: 04/16/2024] [Accepted: 03/15/2024] [Indexed: 04/06/2024] Open
Abstract
Chikungunya can have longstanding effects on health and quality of life. Alongside the recent approval of the world's first chikungunya vaccine by the US Food and Drug Administration in November 2023 and with new chikungunya vaccines in the pipeline, it is important to understand the perspectives of stakeholders before vaccine rollout. Our study aim is to identify key programmatic considerations and gaps in Evidence-to-Recommendation criteria for chikungunya vaccine introduction. We used purposive and snowball sampling to identify global, national, and subnational stakeholders from outbreak prone areas, including Latin America, Asia, and Africa. Semi-structured in-depth interviews were conducted and analysed using qualitative descriptive methods. We found that perspectives varied between tiers of stakeholders and geographies. Unknown disease burden, diagnostics, non-specific disease surveillance, undefined target populations for vaccination, and low disease prioritisation were critical challenges identified by stakeholders that need to be addressed to facilitate rolling out a chikungunya vaccine. Future investments should address these challenges to generate useful evidence for decision-making on new chikungunya vaccine introduction.
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Affiliation(s)
- Megan Auzenbergs
- London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Clara Maure
- International Vaccine Institute, Seoul, South Korea
| | - Hyolim Kang
- London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Andrew Clark
- London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Oliver Brady
- London School of Hygiene and Tropical Medicine, London, United Kingdom
| | | | - Kaja Abbas
- London School of Hygiene and Tropical Medicine, London, United Kingdom
- School of Tropical Medicine and Global Health, Nagasaki University, Nagasaki, Japan
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Hartner AM, Li X, Echeverria-Londono S, Roth J, Abbas K, Auzenbergs M, de Villiers MJ, Ferrari MJ, Fraser K, Fu H, Hallett T, Hinsley W, Jit M, Karachaliou A, Moore SM, Nayagam S, Papadopoulos T, Perkins TA, Portnoy A, Minh QT, Vynnycky E, Winter AK, Burrows H, Chen C, Clapham HE, Deshpande A, Hauryski S, Huber J, Jean K, Kim C, Kim JH, Koh J, Lopman BA, Pitzer VE, Tam Y, Lambach P, Sim SY, Woodruff K, Ferguson NM, Trotter CL, Gaythorpe KAM. Estimating the health effects of COVID-19-related immunisation disruptions in 112 countries during 2020-30: a modelling study. Lancet Glob Health 2024; 12:e563-e571. [PMID: 38485425 PMCID: PMC10951961 DOI: 10.1016/s2214-109x(23)00603-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Revised: 12/14/2023] [Accepted: 12/16/2023] [Indexed: 03/19/2024]
Abstract
BACKGROUND There have been declines in global immunisation coverage due to the COVID-19 pandemic. Recovery has begun but is geographically variable. This disruption has led to under-immunised cohorts and interrupted progress in reducing vaccine-preventable disease burden. There have, so far, been few studies of the effects of coverage disruption on vaccine effects. We aimed to quantify the effects of vaccine-coverage disruption on routine and campaign immunisation services, identify cohorts and regions that could particularly benefit from catch-up activities, and establish if losses in effect could be recovered. METHODS For this modelling study, we used modelling groups from the Vaccine Impact Modelling Consortium from 112 low-income and middle-income countries to estimate vaccine effect for 14 pathogens. One set of modelling estimates used vaccine-coverage data from 1937 to 2021 for a subset of vaccine-preventable, outbreak-prone or priority diseases (ie, measles, rubella, hepatitis B, human papillomavirus [HPV], meningitis A, and yellow fever) to examine mitigation measures, hereafter referred to as recovery runs. The second set of estimates were conducted with vaccine-coverage data from 1937 to 2020, used to calculate effect ratios (ie, the burden averted per dose) for all 14 included vaccines and diseases, hereafter referred to as full runs. Both runs were modelled from Jan 1, 2000, to Dec 31, 2100. Countries were included if they were in the Gavi, the Vaccine Alliance portfolio; had notable burden; or had notable strategic vaccination activities. These countries represented the majority of global vaccine-preventable disease burden. Vaccine coverage was informed by historical estimates from WHO-UNICEF Estimates of National Immunization Coverage and the immunisation repository of WHO for data up to and including 2021. From 2022 onwards, we estimated coverage on the basis of guidance about campaign frequency, non-linear assumptions about the recovery of routine immunisation to pre-disruption magnitude, and 2030 endpoints informed by the WHO Immunization Agenda 2030 aims and expert consultation. We examined three main scenarios: no disruption, baseline recovery, and baseline recovery and catch-up. FINDINGS We estimated that disruption to measles, rubella, HPV, hepatitis B, meningitis A, and yellow fever vaccination could lead to 49 119 additional deaths (95% credible interval [CrI] 17 248-134 941) during calendar years 2020-30, largely due to measles. For years of vaccination 2020-30 for all 14 pathogens, disruption could lead to a 2·66% (95% CrI 2·52-2·81) reduction in long-term effect from 37 378 194 deaths averted (34 450 249-40 241 202) to 36 410 559 deaths averted (33 515 397-39 241 799). We estimated that catch-up activities could avert 78·9% (40·4-151·4) of excess deaths between calendar years 2023 and 2030 (ie, 18 900 [7037-60 223] of 25 356 [9859-75 073]). INTERPRETATION Our results highlight the importance of the timing of catch-up activities, considering estimated burden to improve vaccine coverage in affected cohorts. We estimated that mitigation measures for measles and yellow fever were particularly effective at reducing excess burden in the short term. Additionally, the high long-term effect of HPV vaccine as an important cervical-cancer prevention tool warrants continued immunisation efforts after disruption. FUNDING The Vaccine Impact Modelling Consortium, funded by Gavi, the Vaccine Alliance and the Bill & Melinda Gates Foundation. TRANSLATIONS For the Arabic, Chinese, French, Portguese and Spanish translations of the abstract see Supplementary Materials section.
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Affiliation(s)
- Anna-Maria Hartner
- Medical Research Council Centre for Global Infectious Disease Analysis, Jameel Institute School of Public Health, Imperial College London, London, UK; Centre for Artificial Intelligence in Public Health Research, Robert Koch Institute, Wildau, Germany
| | - Xiang Li
- Medical Research Council Centre for Global Infectious Disease Analysis, Jameel Institute School of Public Health, Imperial College London, London, UK
| | - Susy Echeverria-Londono
- Medical Research Council Centre for Global Infectious Disease Analysis, Jameel Institute School of Public Health, Imperial College London, London, UK
| | - Jeremy Roth
- Medical Research Council Centre for Global Infectious Disease Analysis, Jameel Institute School of Public Health, Imperial College London, London, UK
| | - Kaja Abbas
- London School of Hygiene & Tropical Medicine, London, UK; School of Tropical Medicine and Global Health, Nagasaki University, Nagasaki, Japan
| | | | - Margaret J de Villiers
- Medical Research Council Centre for Global Infectious Disease Analysis, Jameel Institute School of Public Health, Imperial College London, London, UK
| | - Matthew J Ferrari
- Center for Infectious Disease Dynamics, Pennsylvania State University, Pennsylvania, PA, USA
| | - Keith Fraser
- Medical Research Council Centre for Global Infectious Disease Analysis, Jameel Institute School of Public Health, Imperial College London, London, UK
| | - Han Fu
- London School of Hygiene & Tropical Medicine, London, UK
| | - Timothy Hallett
- Medical Research Council Centre for Global Infectious Disease Analysis, Jameel Institute School of Public Health, Imperial College London, London, UK
| | - Wes Hinsley
- Medical Research Council Centre for Global Infectious Disease Analysis, Jameel Institute School of Public Health, Imperial College London, London, UK
| | - Mark Jit
- London School of Hygiene & Tropical Medicine, London, UK; School of Public Health, University of Hong Kong, Hong Kong Special Administrative Region, China
| | | | - Sean M Moore
- Department of Biological Sciences, University of Notre Dame, Notre Dame, IN, USA
| | - Shevanthi Nayagam
- Medical Research Council Centre for Global Infectious Disease Analysis, Jameel Institute School of Public Health, Imperial College London, London, UK; Section of Hepatology and Gastroenterology, Department of Metabolism, Digestion, and Reproduction, Imperial College London, London, UK
| | | | - T Alex Perkins
- Department of Biological Sciences, University of Notre Dame, Notre Dame, IN, USA
| | - Allison Portnoy
- Center for Health Decision Science, T H Chan School of Public Health, Harvard University, Boston, MA, USA
| | - Quan Tran Minh
- Department of Biological Sciences, University of Notre Dame, Notre Dame, IN, USA
| | | | - Amy K Winter
- Department of Epidemiology and Biostatistics and Center for the Ecology of Infectious Diseases, University of Georgia, Athens, GA, USA
| | - Holly Burrows
- School of Public Health, Yale University, New Haven, CT, USA
| | - Cynthia Chen
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore
| | - Hannah E Clapham
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore; Oxford University Clinical Research Unit, Ho Chi Minh City, Viet Nam; Nuffield Department of Medicine, Oxford University, Oxford, UK
| | | | - Sarah Hauryski
- Center for Infectious Disease Dynamics, Pennsylvania State University, Pennsylvania, PA, USA
| | - John Huber
- Department of Biological Sciences, University of Notre Dame, Notre Dame, IN, USA; School of Medicine, Washington University, St Louis, MO, USA
| | - Kevin Jean
- Laboratoire Modélisation, épidémiologie, et surveillance des risques sanitaires and Unit Cnam risques infectieux et émergents, Institut Pasteur, Conservatoire National des Arts et Metiers, Paris, France
| | - Chaelin Kim
- International Vaccine Institute, Seoul, South Korea
| | | | - Jemima Koh
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore
| | | | | | - Yvonne Tam
- Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
| | - Philipp Lambach
- Department of Immunization, Vaccines, and Biologicals, WHO, Geneva, Switzerland
| | - So Yoon Sim
- Department of Immunization, Vaccines, and Biologicals, WHO, Geneva, Switzerland
| | - Kim Woodruff
- Medical Research Council Centre for Global Infectious Disease Analysis, Jameel Institute School of Public Health, Imperial College London, London, UK
| | - Neil M Ferguson
- Medical Research Council Centre for Global Infectious Disease Analysis, Jameel Institute School of Public Health, Imperial College London, London, UK
| | - Caroline L Trotter
- Medical Research Council Centre for Global Infectious Disease Analysis, Jameel Institute School of Public Health, Imperial College London, London, UK; Veterinary Medicine, University of Cambridge, Cambridge, UK
| | - Katy A M Gaythorpe
- Medical Research Council Centre for Global Infectious Disease Analysis, Jameel Institute School of Public Health, Imperial College London, London, UK.
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Auzenbergs M, Fu H, Abbas K, Procter SR, Cutts FT, Jit M. Health effects of routine measles vaccination and supplementary immunisation activities in 14 high-burden countries: a Dynamic Measles Immunization Calculation Engine (DynaMICE) modelling study. Lancet Glob Health 2023; 11:e1194-e1204. [PMID: 37474227 PMCID: PMC10369016 DOI: 10.1016/s2214-109x(23)00220-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Revised: 04/17/2023] [Accepted: 05/02/2023] [Indexed: 07/22/2023]
Abstract
BACKGROUND WHO recommends at least 95% population coverage with two doses of measles-containing vaccine (MCV). Most countries worldwide use routine services to offer a first dose of measles-containing vaccine (MCV1) and later, a second dose of measles-containing vaccine (MCV2). Many countries worldwide conduct supplementary immunisation activities (SIAs), offering vaccination to all people in a specific age range irrespective of previous vaccination history. We aimed to estimate the relative effects of each dose and delivery route in 14 countries with high measles burden. METHODS We used an age-structured compartmental dynamic model, the Dynamic Measles Immunization Calculation Engine (DynaMICE), to assess the effects of different vaccination strategies on measles susceptibility and burden during 2000-20 in 14 countries with high measles incidence (containing 53% of the global birth cohort and 78% of the global measles burden). Country-specific routine MCV1 and MCV2 coverage data during 1980-2020 were obtained from the WHO and UNICEF Estimates of National Immunization Coverage database for all modelled countries and SIA data were obtained from the WHO summary of measles and rubella SIAs. We estimated the incremental health effects of different vaccination strategies using prevented cases of measles and deaths from measles and their efficiency using the incremental number needed to vaccinate (NNV) to prevent an additional measles case. FINDINGS Compared with no vaccination, MCV1 implementation was estimated to have prevented 824 million cases of measles and 9·6 million deaths from measles, with a median NNV of 1·41 (IQR 1·35-1·44). Adding routine MCV2 to MCV1 was estimated to have prevented 108 million cases and 404 270 deaths, whereas adding SIAs to MCV1 was estimated to have prevented 256 million cases and 4·4 million deaths. Despite larger incremental effects, adding SIAs to MCV1 (median incremental NNV 6·02, 5·30-7·68) showed reduced efficiency compared with adding routine MCV2 (5·41, 4·76-6·11). INTERPRETATION Vaccination strategies, including non-selective SIAs, reach a greater proportion of children who are unvaccinated and reduce measles burden more than MCV2 alone, but efficiency is lower because of the wide age range targeted by SIAs. This analysis provides information to help improve the health effects and efficiency of measles vaccination strategies. The interplay between MCV1, MCV2, and SIAs should be considered when planning future measles vaccination strategies. FUNDING Gavi, the Vaccine Alliance and the Bill & Melinda Gates Foundation.
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Affiliation(s)
- Megan Auzenbergs
- Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK.
| | - Han Fu
- Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - Kaja Abbas
- Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK; Public Health Foundation of India, New Delhi, India
| | - Simon R Procter
- Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - Felicity T Cutts
- Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - Mark Jit
- Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK; School of Public Health, University of Hong Kong, Hong Kong Special Administrative Region, China
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Auzenbergs M, Fountain H, Macklin G, Lyons H, O'Reilly KM. The impact of surveillance and other factors on detection of emergent and circulating vaccine derived polioviruses. Gates Open Res 2022; 5:94. [PMID: 35299831 PMCID: PMC8913522.2 DOI: 10.12688/gatesopenres.13272.2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [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] [Accepted: 04/26/2022] [Indexed: 11/20/2022] Open
Abstract
Background: Circulating vaccine derived poliovirus (cVDPV) outbreaks remain a threat to polio eradication. To reduce cases of polio from cVDPV of serotype 2, the serotype 2 component of the vaccine has been removed from the global vaccine supply, but outbreaks of cVDPV2 have continued. The objective of this work is to understand the factors associated with later detection in order to improve detection of these unwanted events. Methods: The number of nucleotide differences between each cVDPV outbreak and the oral polio vaccine (OPV) strain was used to approximate the time from emergence to detection. Only independent emergences were included in the analysis. Variables such as serotype, surveillance quality, and World Health Organization (WHO) region were tested in a negative binomial regression model to ascertain whether these variables were associated with higher nucleotide differences upon detection. Results: In total, 74 outbreaks were analysed from 24 countries between 2004-2019. For serotype 1 (n=10), the median time from seeding until outbreak detection was 284 (95% uncertainty interval (UI) 284-2008) days, for serotype 2 (n=59), 276 (95% UI 172-765) days, and for serotype 3 (n=5), 472 (95% UI 392-603) days. Significant improvement in the time to detection was found with increasing surveillance of non-polio acute flaccid paralysis (AFP) and adequate stool collection. Conclusions: cVDPVs remain a risk; all WHO regions have reported at least one VDPV outbreak since the first outbreak in 2000 and outbreak response campaigns using monovalent OPV type 2 risk seeding future outbreaks. Maintaining surveillance for poliomyelitis after local elimination is essential to quickly respond to both emergence of VDPVs and potential importations as low-quality AFP surveillance causes outbreaks to continue undetected. Considerable variation in the time between emergence and detection of VDPVs were apparent, and other than surveillance quality and inclusion of environmental surveillance, the reasons for this remain unclear.
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Affiliation(s)
- Megan Auzenbergs
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, WC1E 7HT, UK
| | - Holly Fountain
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, WC1E 7HT, UK
| | - Grace Macklin
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, WC1E 7HT, UK
- Polio Eradication, World Health Organization, Geneva, Switzerland
| | - Hil Lyons
- Institute for Disease Modeling, Bellevue, Washington, USA
| | - Kathleen M O'Reilly
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, WC1E 7HT, UK
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Auzenbergs M, Fountain H, Macklin G, Lyons H, O'Reilly KM. The impact of surveillance and other factors on detection of emergent and circulating vaccine derived polioviruses. Gates Open Res 2021; 5:94. [PMID: 35299831 PMCID: PMC8913522 DOI: 10.12688/gatesopenres.13272.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/01/2021] [Indexed: 11/20/2022] Open
Abstract
Background: Circulating vaccine derived poliovirus (cVDPV) outbreaks remain a threat to polio eradication. To reduce cases of polio from cVDPV of serotype 2, the serotype 2 component of the vaccine has been removed from the global vaccine supply, but outbreaks of cVDPV2 have continued. The objective of this work is to understand the factors associated with later detection in order to improve detection of these unwanted events. Methods: The number of nucleotide differences between each cVDPV outbreak and the oral polio vaccine (OPV) strain was used to approximate the time from emergence to detection. Only independent emergences were included in the analysis. Variables such as serotype, surveillance quality, and World Health Organization (WHO) region were tested in a negative binomial regression model to ascertain whether these variables were associated with higher nucleotide differences upon detection. Results: In total, 74 outbreaks were analysed from 24 countries between 2004 and 2019. For serotype 1 (n=10), the median time from seeding until outbreak detection was 284 (95% uncertainty interval (UI) 284-2008) days, for serotype 2 (n=59), 276 (95% UI 172-765) days, and for serotype 3 (n=5), 472 (95% UI 392-603) days. Significant improvement in the time to detection was found with increasing surveillance of non-polio acute flaccid paralysis (AFP) and adequate stool collection. Conclusions: cVDPVs remain a risk globally; all WHO regions have reported at least one VDPV outbreak since the first outbreak in 2001. Maintaining surveillance for poliomyelitis after local elimination is essential to quickly respond to both emergence of VDPVs and potential importations. Considerable variation in the time between emergence and detection of VDPVs were apparent, and other than surveillance quality and inclusion of environmental surveillance, the reasons for this remain unclear.
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Affiliation(s)
- Megan Auzenbergs
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, WC1E 7HT, UK
| | - Holly Fountain
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, WC1E 7HT, UK
| | - Grace Macklin
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, WC1E 7HT, UK
- Polio Eradication, World Health Organization, Geneva, Switzerland
| | - Hil Lyons
- Institute for Disease Modeling, Bellevue, Washington, USA
| | - Kathleen M O'Reilly
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, WC1E 7HT, UK
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Auzenbergs M, Fountain H, Macklin G, Lyons H, O'Reilly KM. The impact of surveillance and other factors on detection of emergent and circulating vaccine derived polioviruses. Gates Open Res 2021; 5:94. [PMID: 35299831 PMCID: PMC8913522 DOI: 10.12688/gatesopenres.13272.3] [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] [Accepted: 04/03/2023] [Indexed: 04/14/2023] Open
Abstract
Background: Circulating vaccine derived poliovirus (cVDPV) outbreaks remain a threat to polio eradication. To reduce cases of polio from cVDPV of serotype 2, the serotype 2 component of the vaccine has been removed from the global vaccine supply, but outbreaks of cVDPV2 have continued. The objective of this work is to understand the factors associated with later detection in order to improve detection of these unwanted events. Methods: The number of nucleotide differences between each cVDPV outbreak and the oral polio vaccine (OPV) strain was used to approximate the time from emergence to detection. Only independent emergences were included in the analysis. Variables such as serotype, surveillance quality, and World Health Organization (WHO) region were tested in a negative binomial regression model to ascertain whether these variables were associated with higher nucleotide differences upon detection. Results: In total, 74 outbreaks were analysed from 24 countries between 2004-2019. For serotype 1 (n=10), the median time from seeding until outbreak detection was 572 (95% uncertainty interval (UI) 279-2016), for serotype 2 (n=59), 276 (95% UI 172-765) days, and for serotype 3 (n=5), 472 (95% UI 392-603) days. Significant improvement in the time to detection was found with increasing surveillance of non-polio acute flaccid paralysis (AFP) and adequate stool collection. Conclusions: cVDPVs remain a risk; all WHO regions have reported at least one VDPV outbreak since the first outbreak in 2000 and outbreak response campaigns using monovalent OPV type 2 risk seeding future outbreaks. Maintaining surveillance for poliomyelitis after local elimination is essential to quickly respond to both emergence of VDPVs and potential importations as low-quality AFP surveillance causes outbreaks to continue undetected. Considerable variation in the time between emergence and detection of VDPVs were apparent, and other than surveillance quality and inclusion of environmental surveillance, the reasons for this remain unclear.
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Affiliation(s)
- Megan Auzenbergs
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, WC1E 7HT, UK
| | - Holly Fountain
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, WC1E 7HT, UK
| | - Grace Macklin
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, WC1E 7HT, UK
- Polio Eradication, World Health Organization, Geneva, Switzerland
| | - Hil Lyons
- Institute for Disease Modeling, Bellevue, Washington, USA
| | - Kathleen M O'Reilly
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, WC1E 7HT, UK
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O'Reilly KM, Auzenbergs M, Jafari Y, Liu Y, Flasche S, Lowe R. Effective transmission across the globe: the role of climate in COVID-19 mitigation strategies. Lancet Planet Health 2020; 4:e172. [PMID: 32389182 PMCID: PMC7202845 DOI: 10.1016/s2542-5196(20)30106-6] [Citation(s) in RCA: 57] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2020] [Revised: 04/21/2020] [Accepted: 04/22/2020] [Indexed: 05/19/2023]
Affiliation(s)
- Kathleen M O'Reilly
- Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London WC1E 7HT, UK.
| | - Megan Auzenbergs
- Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London WC1E 7HT, UK
| | - Yalda Jafari
- Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London WC1E 7HT, UK
| | - Yang Liu
- Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London WC1E 7HT, UK
| | - Stefan Flasche
- Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London WC1E 7HT, UK
| | - Rachel Lowe
- Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London WC1E 7HT, UK; Centre on Climate Change and Planetary Health, London School of Hygiene & Tropical Medicine, London WC1E 7HT, UK
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Auzenbergs M, Correia-Gomes C, Economou T, Lowe R, O'Reilly KM. Desirable BUGS in models of infectious diseases. Epidemics 2019; 29:100361. [PMID: 31668494 DOI: 10.1016/j.epidem.2019.100361] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [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: 02/26/2019] [Revised: 08/07/2019] [Accepted: 08/19/2019] [Indexed: 11/24/2022] Open
Abstract
Bayesian inference using Gibbs sampling (BUGS) is a set of statistical software that uses Markov chain Monte Carlo (MCMC) methods to estimate almost any specified model. Originally developed in the late 1980s, the software is an excellent introduction to applied Bayesian statistics without the need to write a MCMC sampler. The software is typically used for regression-based analyses, but any model that can be specified using graphical nodes are possible. Advanced topics such as missing data, spatial analysis, model comparison and dynamic infectious disease models can be tackled. Three examples are provided; a linear regression model to illustrate parameter estimation, the steps to ensure that the estimates have converged and a comparison of run-times across different computing platforms. The second example describes a model that estimates the probability of being vaccinated from cross-sectional and surveillance data, and illustrates the specification of different models, model comparison and data augmentation. The third example illustrates estimation of parameters within a dynamic Susceptible-Infected-Recovered model. These examples show that BUGS can be used to estimate parameters from models relevant for infectious diseases, and provide an overview of the relative merits of the approach taken.
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Affiliation(s)
- Megan Auzenbergs
- Faculty of Infectious and Tropical Disease, London School of Hygiene and Tropical Medicine, London, UK; Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, UK.
| | | | - Theo Economou
- College of Life and Environmental Sciences, University of Exeter, Exeter, UK.
| | - Rachel Lowe
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, UK; Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK; Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain.
| | - Kathleen M O'Reilly
- Faculty of Infectious and Tropical Disease, London School of Hygiene and Tropical Medicine, London, UK; Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, UK.
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