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Kardos P, Correia de Sousa J, Heininger U, Konstantopoulos A, MacIntyre CR, Middleton D, Nolan T, Papi A, Rendon A, Rizzo A, Sampson K, Sette A, Sobczyk E, Tan T, Weil-Olivier C, Weinberger B, Wilkinson T, Wirsing von König CH. Understanding the impact of adult pertussis and current approaches to vaccination: A narrative review and expert panel recommendations. Hum Vaccin Immunother 2024; 20:2324547. [PMID: 38564339 PMCID: PMC10989709 DOI: 10.1080/21645515.2024.2324547] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Accepted: 02/25/2024] [Indexed: 04/04/2024] Open
Abstract
Pertussis has several notable consequences, causing economic burden, increased strain on healthcare facilities, and reductions in quality of life. Recent years have seen a trend toward an increase in pertussis cases affecting older children and adults. To boost immunity, and protect vulnerable populations, an enduring approach to vaccination has been proposed, but gaps remain in the evidence surrounding adult vaccination that are needed to inform such a policy. Gaps include: the true incidence of pertussis and its complications in adults; regional variations in disease recognition and reporting; and incidence of severe disease, hospitalizations, and deaths in older adults. Better data on the efficacy/effectiveness of pertussis vaccination in adults, duration of protection, and factors leading to poor vaccine uptake are needed. Addressing the critical evidence gaps will help highlight important areas of unmet need and justify the importance of adult pertussis vaccination to healthcare professionals, policymakers, and payers.
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Affiliation(s)
- Peter Kardos
- Group Practice & Center, Allergy, Respiratory and Sleep Medicine, Maingau Hospital of the Red Cross, Frankfurt am Main, Germany
| | - Jaime Correia de Sousa
- Life and Health Sciences Research Institute, School of Medicine, University of Minho School of Medicine, Braga, Portugal
| | - Ulrich Heininger
- Pediatric Infectious Diseases and Vaccinology, University of Basel Children’s Hospital, BaselSwitzerland
| | | | - C. Raina MacIntyre
- Kirby Institute, UNSW Medicine, University of New South Wales, Sydney, Australia
| | - Donald Middleton
- Department of Pediatrics, University of Pittsburgh Medical Center, Pittsburgh, USA
| | - Terry Nolan
- Department of Infectious Diseases, University of Melbourne, Melbourne, Australia
| | - Alberto Papi
- Department of Translational Medicine, University of Ferrara, Ferrara, Italy
| | - Adrian Rendon
- Pulmonary/Critical Care Division, Autonomous University of Nuevo León, San Nicolás de los Garza, Mexico
| | | | - Kim Sampson
- Immunisation Coalition, Melbourne, Australia
| | - Alessandro Sette
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, San Diego, USA
| | - Elizabeth Sobczyk
- AMDA – The Society for Post-Acute and Long-Term Care Medicine, Denver, USA
| | - Tina Tan
- Feinberg School of Medicine, Northwestern University, Chicago, USA
| | | | - Birgit Weinberger
- Institute for Biomedical Aging Research, Universität Innsbruck, Innsbruck, Austria
| | - Tom Wilkinson
- Faculty of Medicine, University of Southampton, Southampton, UK
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Freitas LP, Codeço CT, Bastos LS, Villela DAM, Cruz OG, Pacheco AG, Coelho FC, Lana RM, Carvalho LMFD, Niquini RP, Almeida WAFD, Silva DAD, Carvalho FCD, Gomes MFDC. Evaluation of the design of the influenza-like illness sentinel surveillance system in Brazil. CAD SAUDE PUBLICA 2024; 40:e00028823. [PMID: 39082558 PMCID: PMC11321611 DOI: 10.1590/0102-311xen028823] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Revised: 01/17/2024] [Accepted: 01/29/2024] [Indexed: 08/15/2024] Open
Abstract
The influenza-like illness (ILI) sentinel surveillance operates in Brazil to identify respiratory viruses of public health relevance circulating in the country and was first implemented in 2000. Recently, the COVID-19 pandemic reinforced the importance of early detection of the circulation of new viruses in Brazil. Therefore, an analysis of the design of the ILI sentinel surveillance is timely. To this end, we simulated a sentinel surveillance network, identifying the municipalities that would be part of the network according to the criteria defined in the design of the ILI sentinel surveillance and, based on data from tested cases of severe acute respiratory illness (SARI) from 2014 to 2019, we drew samples for each sentinel municipality per epidemiological week. The draw was performed 1,000 times, obtaining the median and 95% quantile interval (95%QI) of virus positivity by Federative Unit and epidemiological week. According to the ILI sentinel surveillance design criteria, sentinel units would be in 64 municipalities, distributed mainly in capitals and their metropolitan areas, recommending 690 weekly samples. The design showed good sensitivity (91.65% considering the 95%QI) for qualitatively detecting respiratory viruses, even those with low circulation. However, there was important uncertainty in the quantitative estimate of positivity, reaching at least 20% in 11.34% of estimates. The results presented here aim to assist in evaluating and updating the ILI sentinel surveillance design. Strategies to reduce uncertainty in positivity estimates need to be evaluated, as does the need for greater spatial coverage.
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Affiliation(s)
| | | | | | | | | | | | | | - Raquel Martins Lana
- Programa de Computação Científica, Fundação Oswaldo Cruz, Rio de Janeiro, Brasil
- Barcelona Supercomputing Center, Barcelona, España
| | | | - Roberta Pereira Niquini
- Instituto Federal de Educação, Ciência e Tecnologia do Rio de Janeiro, Rio de Janeiro, Brasil
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Hamid S, Grajeda L, de Leon O, Lopez M, Maldonado H, Gomez A, Lopman B, Clasen T, McCracken J. Variability in the Timing of Respiratory Syncytial Virus Epidemics in Guatemala, 2008-2018. Influenza Other Respir Viruses 2024; 18:e13334. [PMID: 38980961 PMCID: PMC11232890 DOI: 10.1111/irv.13334] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2024] [Revised: 05/12/2024] [Accepted: 05/19/2024] [Indexed: 07/11/2024] Open
Abstract
BACKGROUND The description of local seasonality patterns in respiratory syncytial virus (RSV) incidence is important to guide the timing of administration of RSV immunization products. METHODS We characterized RSV seasonality in Guatemala using the moving epidemic method (MEM) with absolute counts of RSV-associated acute respiratory infections (ARI) from hospital surveillance in Santa Rosa and Quetzaltenango departments of Guatemala. RESULTS From Week 17 of 2008 through Week 16 of 2018, 8487 ARI cases tested positive for RSV by rRT-PCR. Season onsets varied up to 5 months; early seasons starting in late May to early August and finishing in September to November were most common, but late seasons starting in October to November and finishing in March to April were also observed. Both epidemic patterns had similar durations ranging from 4 to 6 months. Epidemic thresholds (the levels of virus activity that signal the onset and end of a seasonal epidemic) calculated prospectively using previous seasons' data captured between 70% and 99% of annual RSV detections. Onset weeks differed by 2-10 weeks, and offset weeks differed by 2-16 weeks between the two surveillance sites. CONCLUSIONS Variability in the timing of seasonal RSV epidemics in Guatemala demonstrates the difficulty in precisely predicting the timing of seasonal RSV epidemics based on onset weeks from past seasons and suggests that maximal reduction in RSV disease burden would be achieved through year-round vaccination and immunoprophylaxis administration to at-risk infants.
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Affiliation(s)
- Sarah Hamid
- Department of Epidemiology, Rollins School of Public HealthEmory UniversityAtlantaGeorgiaUSA
| | - Laura M. Grajeda
- Centro de Estudios en SaludUniversidad del Valle de GuatemalaGuatemala CityGuatemala
- Global Health Institute, College of Public HealthUniversity of GeorgiaAthensGeorgiaUSA
| | - Oscar de Leon
- Department of Epidemiology, Rollins School of Public HealthEmory UniversityAtlantaGeorgiaUSA
- Centro de Estudios en SaludUniversidad del Valle de GuatemalaGuatemala CityGuatemala
- Gangarosa Department of Environmental Health, Rollins School of Public HealthEmory UniversityAtlantaGeorgiaUSA
| | - Maria Renee Lopez
- Centro de Estudios en SaludUniversidad del Valle de GuatemalaGuatemala CityGuatemala
| | - Herberth Maldonado
- Centro de Estudios en SaludUniversidad del Valle de GuatemalaGuatemala CityGuatemala
| | - Ana Beatriz Gomez
- Centro de Estudios en SaludUniversidad del Valle de GuatemalaGuatemala CityGuatemala
| | - Benjamin Lopman
- Department of Epidemiology, Rollins School of Public HealthEmory UniversityAtlantaGeorgiaUSA
| | - Thomas F. Clasen
- Department of Epidemiology, Rollins School of Public HealthEmory UniversityAtlantaGeorgiaUSA
- Gangarosa Department of Environmental Health, Rollins School of Public HealthEmory UniversityAtlantaGeorgiaUSA
| | - John P. McCracken
- Centro de Estudios en SaludUniversidad del Valle de GuatemalaGuatemala CityGuatemala
- Global Health Institute, College of Public HealthUniversity of GeorgiaAthensGeorgiaUSA
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Evans JMM, Young JJ, Mutch H, Blunsum A, Quinn J, Lowe DJ, Ho A, Marsh K, Mokogwu D. Implementation and evaluation of a SARI surveillance system in a tertiary hospital in Scotland in 2021/2022. Public Health 2024; 232:114-120. [PMID: 38772199 DOI: 10.1016/j.puhe.2024.04.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2024] [Revised: 04/09/2024] [Accepted: 04/10/2024] [Indexed: 05/23/2024]
Abstract
OBJECTIVE To set up and evaluate a new surveillance system for severe acute respiratory infection (SARI) in Scotland. STUDY DESIGN Cross-sectional study and evaluation of surveillance system. METHODS The SARI case definition comprised patients aged 16 years or over with an acute respiratory illness presentation requiring testing for influenza and SARS-CoV-2 and hospital admission. Data were collected from SARI cases by research nurses in one tertiary teaching hospital using a bespoke data collection tool from November 2021 to May 2022. Descriptive analyses of SARI cases were carried out. The following attributes of the surveillance system were evaluated according to Centers for Disease Control and Prevention (CDC) guidelines: stability, data quality, timeliness, positive predictive value, representativeness, simplicity, acceptability and flexibility. RESULTS The final surveillance dataset comprised 1163 records, with cases peaking in ISO week 50 (week ending 19/12/2021). The system produced a stable stream of surveillance data, with the proportion of SARI records with sufficient information for effective surveillance increasing from 65.4% during the first month to 87.0% over time. Similarly, the proportion where data collection was completed promptly was low initially, but increased to 50%-65% during later periods. CONCLUSION SARI surveillance was successfully established in one hospital, but for a national system, additional sentinel hospital sites across Scotland, with flexibility to ensure consistently high data completeness and timeliness are needed. Data collection should be automated where possible, and demands on clinicians minimised. SARI surveillance should be embedded and resourced as part of a national respiratory surveillance strategy.
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Affiliation(s)
- J M M Evans
- Public Health Scotland, Glasgow, United Kingdom.
| | - J J Young
- Public Health Scotland, Glasgow, United Kingdom
| | - H Mutch
- Public Health Scotland, Glasgow, United Kingdom
| | - A Blunsum
- Department of Infectious Diseases, Queen Elizabeth University Hospital, Glasgow, United Kingdom
| | - J Quinn
- Emergency Department, Queen Elizabeth University Hospital, Glasgow, United Kingdom
| | - D J Lowe
- Emergency Department, Queen Elizabeth University Hospital, Glasgow, United Kingdom; School of Health and Wellbeing, University of Glasgow, Glasgow, United Kingdom
| | - A Ho
- Department of Infectious Diseases, Queen Elizabeth University Hospital, Glasgow, United Kingdom; Medical Research Council-University of Glasgow Centre for Virus Research, Glasgow, United Kingdom
| | - K Marsh
- Public Health Scotland, Glasgow, United Kingdom
| | - D Mokogwu
- Public Health Scotland, Glasgow, United Kingdom
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5
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Kim M, Kim Y, Nah K. Predicting seasonal influenza outbreaks with regime shift-informed dynamics for improved public health preparedness. Sci Rep 2024; 14:12698. [PMID: 38830955 PMCID: PMC11148101 DOI: 10.1038/s41598-024-63573-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Accepted: 05/30/2024] [Indexed: 06/05/2024] Open
Abstract
In this study, we propose a novel approach that integrates regime-shift detection with a mechanistic model to forecast the peak times of seasonal influenza. The key benefit of this approach is its ability to detect regime shifts from non-epidemic to epidemic states, which is particularly beneficial with the year-round presence of non-zero Influenza-Like Illness (ILI) data. This integration allows for the incorporation of external factors that trigger the onset of the influenza season-factors that mechanistic models alone might not adequately capture. Applied to ILI data collected in Korea from 2005 to 2020, our method demonstrated stable peak time predictions for seasonal influenza outbreaks, particularly in years characterized by unusual onset times or epidemic magnitudes.
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Affiliation(s)
- Minhye Kim
- Department of Mathematics, Kyungpook National University, Daegu, 41566, Republic of Korea
| | - Yongkuk Kim
- Department of Mathematics, Kyungpook National University, Daegu, 41566, Republic of Korea
| | - Kyeongah Nah
- Busan Center for Medical Mathematics, National Institute for Mathematical Sciences, Busan, 49241, Republic of Korea.
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Gu X, Watson C, Agrawal U, Whitaker H, Elson WH, Anand S, Borrow R, Buckingham A, Button E, Curtis L, Dunn D, Elliot AJ, Ferreira F, Goudie R, Hoang U, Hoschler K, Jamie G, Kar D, Kele B, Leston M, Linley E, Macartney J, Marsden GL, Okusi C, Parvizi O, Quinot C, Sebastianpillai P, Sexton V, Smith G, Suli T, Thomas NPB, Thompson C, Todkill D, Wimalaratna R, Inada-Kim M, Andrews N, Tzortziou-Brown V, Byford R, Zambon M, Lopez-Bernal J, de Lusignan S. Postpandemic Sentinel Surveillance of Respiratory Diseases in the Context of the World Health Organization Mosaic Framework: Protocol for a Development and Evaluation Study Involving the English Primary Care Network 2023-2024. JMIR Public Health Surveill 2024; 10:e52047. [PMID: 38569175 PMCID: PMC11024753 DOI: 10.2196/52047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Revised: 01/02/2024] [Accepted: 01/17/2024] [Indexed: 04/05/2024] Open
Abstract
BACKGROUND Prepandemic sentinel surveillance focused on improved management of winter pressures, with influenza-like illness (ILI) being the key clinical indicator. The World Health Organization (WHO) global standards for influenza surveillance include monitoring acute respiratory infection (ARI) and ILI. The WHO's mosaic framework recommends that the surveillance strategies of countries include the virological monitoring of respiratory viruses with pandemic potential such as influenza. The Oxford-Royal College of General Practitioner Research and Surveillance Centre (RSC) in collaboration with the UK Health Security Agency (UKHSA) has provided sentinel surveillance since 1967, including virology since 1993. OBJECTIVE We aim to describe the RSC's plans for sentinel surveillance in the 2023-2024 season and evaluate these plans against the WHO mosaic framework. METHODS Our approach, which includes patient and public involvement, contributes to surveillance objectives across all 3 domains of the mosaic framework. We will generate an ARI phenotype to enable reporting of this indicator in addition to ILI. These data will support UKHSA's sentinel surveillance, including vaccine effectiveness and burden of disease studies. The panel of virology tests analyzed in UKHSA's reference laboratory will remain unchanged, with additional plans for point-of-care testing, pneumococcus testing, and asymptomatic screening. Our sampling framework for serological surveillance will provide greater representativeness and more samples from younger people. We will create a biomedical resource that enables linkage between clinical data held in the RSC and virology data, including sequencing data, held by the UKHSA. We describe the governance framework for the RSC. RESULTS We are co-designing our communication about data sharing and sampling, contextualized by the mosaic framework, with national and general practice patient and public involvement groups. We present our ARI digital phenotype and the key data RSC network members are requested to include in computerized medical records. We will share data with the UKHSA to report vaccine effectiveness for COVID-19 and influenza, assess the disease burden of respiratory syncytial virus, and perform syndromic surveillance. Virological surveillance will include COVID-19, influenza, respiratory syncytial virus, and other common respiratory viruses. We plan to pilot point-of-care testing for group A streptococcus, urine tests for pneumococcus, and asymptomatic testing. We will integrate test requests and results with the laboratory-computerized medical record system. A biomedical resource will enable research linking clinical data to virology data. The legal basis for the RSC's pseudonymized data extract is The Health Service (Control of Patient Information) Regulations 2002, and all nonsurveillance uses require research ethics approval. CONCLUSIONS The RSC extended its surveillance activities to meet more but not all of the mosaic framework's objectives. We have introduced an ARI indicator. We seek to expand our surveillance scope and could do more around transmissibility and the benefits and risks of nonvaccine therapies.
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Affiliation(s)
- Xinchun Gu
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Conall Watson
- Immunisation and Vaccine-Preventable Diseases Division, UK Health Security Agency, London, United Kingdom
| | - Utkarsh Agrawal
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Heather Whitaker
- Statistics, Modelling and Economics Department, UK Health Security Agency, London, United Kingdom
| | - William H Elson
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Sneha Anand
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Ray Borrow
- Vaccine Evaluation Unit, UK Health Security Agency, Manchester, United Kingdom
| | | | - Elizabeth Button
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Lottie Curtis
- Royal College of General Practitioners, London, United Kingdom
| | - Dominic Dunn
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Alex J Elliot
- Real-time Syndromic Surveillance Team, UK Health Security Agency, Birmingham, United Kingdom
| | - Filipa Ferreira
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Rosalind Goudie
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Uy Hoang
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Katja Hoschler
- Respiratory Virus Unit, UK Health Security Agency, London, United Kingdom
| | - Gavin Jamie
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Debasish Kar
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Beatrix Kele
- Respiratory Virus Unit, UK Health Security Agency, London, United Kingdom
| | - Meredith Leston
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Ezra Linley
- Vaccine Evaluation Unit, UK Health Security Agency, Manchester, United Kingdom
| | - Jack Macartney
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Gemma L Marsden
- Royal College of General Practitioners, London, United Kingdom
| | - Cecilia Okusi
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Omid Parvizi
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
- Respiratory Virus Unit, UK Health Security Agency, London, United Kingdom
| | - Catherine Quinot
- Immunisation and Vaccine-Preventable Diseases Division, UK Health Security Agency, London, United Kingdom
| | | | - Vanashree Sexton
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Gillian Smith
- Real-time Syndromic Surveillance Team, UK Health Security Agency, Birmingham, United Kingdom
| | - Timea Suli
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | | | - Catherine Thompson
- Respiratory Virus Unit, UK Health Security Agency, London, United Kingdom
| | - Daniel Todkill
- Real-time Syndromic Surveillance Team, UK Health Security Agency, Birmingham, United Kingdom
| | - Rashmi Wimalaratna
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | | | - Nick Andrews
- Immunisation and Vaccine-Preventable Diseases Division, UK Health Security Agency, London, United Kingdom
| | | | - Rachel Byford
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Maria Zambon
- Virus Reference Department, UK Health Security Agency, London, United Kingdom
| | - Jamie Lopez-Bernal
- Immunisation and Vaccine-Preventable Diseases Division, UK Health Security Agency, London, United Kingdom
| | - Simon de Lusignan
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
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Zhang L, Duan W, Ma C, Zhang J, Sun Y, Ma J, Wang Y, Zhang D, Wang Q, Liu J, Liu M. An Intense Out-of-Season Rebound of Influenza Activity After the Relaxation of Coronavirus Disease 2019 Restrictions in Beijing, China. Open Forum Infect Dis 2024; 11:ofae163. [PMID: 38585185 PMCID: PMC10995958 DOI: 10.1093/ofid/ofae163] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Accepted: 03/19/2024] [Indexed: 04/09/2024] Open
Abstract
Background The aim of this study was to investigate the changes of epidemic characteristics of influenza activity pre- and post-coronavirus disease 2019 (COVID-19) in Beijing, China. Methods Epidemiologic data were collected from the influenza surveillance system in Beijing. We compared epidemic intensity, epidemic onset and duration, and influenza transmissibility during the 2022-2023 season with pre-COVID-19 seasons from 2014 to 2020. Results The overall incidence rate of influenza in the 2022-2023 season was significantly higher than that of the pre-COVID-19 period, with the record-high level of epidemic intensity in Beijing. The onset and duration of the influenza epidemic period in 2022-2023 season was notably later and shorter than that of the 2014-2020 seasons. Maximum daily instantaneous reproduction number (Rt) of the 2022-2023 season (Rt = 2.31) was much higher than that of the pre-COVID-19 period (Rt = 1.49). The incidence of influenza A(H1N1) and A(H3N2) were the highest among children aged 0-4 years and 5-14 years, respectively, in the 2022-2023 season. Conclusions A late, intense, and short-term peak influenza activity was observed in the 2022-2023 season in Beijing. Children <15 years old were impacted the most by the interruption of influenza circulation during the COVID-19 pandemic. Maintaining continuous surveillance and developing targeted public health strategies of influenza is necessary.
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Affiliation(s)
- Li Zhang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Institute for Infectious Disease and Endemic Disease Control, Beijing Center for Disease Prevention and Control, Beijing, China
| | - Wei Duan
- Institute for Infectious Disease and Endemic Disease Control, Beijing Center for Disease Prevention and Control, Beijing, China
| | - Chunna Ma
- Institute for Infectious Disease and Endemic Disease Control, Beijing Center for Disease Prevention and Control, Beijing, China
| | - Jiaojiao Zhang
- Institute for Infectious Disease and Endemic Disease Control, Beijing Center for Disease Prevention and Control, Beijing, China
| | - Ying Sun
- Institute for Infectious Disease and Endemic Disease Control, Beijing Center for Disease Prevention and Control, Beijing, China
| | - Jiaxin Ma
- Institute for Infectious Disease and Endemic Disease Control, Beijing Center for Disease Prevention and Control, Beijing, China
| | - Yingying Wang
- Institute for Infectious Disease and Endemic Disease Control, Beijing Center for Disease Prevention and Control, Beijing, China
| | - Daitao Zhang
- Institute for Infectious Disease and Endemic Disease Control, Beijing Center for Disease Prevention and Control, Beijing, China
| | - Quanyi Wang
- Center Office, Beijing Center for Disease Prevention and Control, Beijing, China
- Beijing Research Center for Respiratory Infectious Diseases, Beijing, China
| | - Jue Liu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Min Liu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
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8
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Motlogeloa O, Fitchett JM. Assessing the impact of climatic variability on acute respiratory diseases across diverse climatic zones in South Africa. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 918:170661. [PMID: 38320698 DOI: 10.1016/j.scitotenv.2024.170661] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Revised: 02/01/2024] [Accepted: 02/01/2024] [Indexed: 02/13/2024]
Abstract
Acute respiratory diseases are a significant public health concern in South Africa, with climatic variables such as temperature and rainfall being key influencers. This study investigates the associations between these variables and the prevalence of acute respiratory diseases in Johannesburg, Cape Town, and Gqeberha (Port Elizabeth), representing distinct climatic zones. Spearman's correlation analyses showed negative correlations in Johannesburg for respiratory disease claims with maximum temperature (r = -0.12, p < 0.0001) and mean temperature (r = -0.13, p < 0.0001), and a negative correlation with daily rainfall (r = -0.12, p < 0.0001). Cape Town demonstrated a negative correlation with maximum temperature (r = -0.18, p < 0.0001) and a positive correlation with rainfall (r = 0.08, p < 0.0001). Utilizing Distributed Lag Non-linear Models (DLNM), the study revealed that in Johannesburg, the relative risk (RR) of respiratory claims increases notably at temperatures below 12 °C, and again at a Tmax between 16 and 23 °C. The risk escalates further at >30 °C, although with a considerable error margin. For Cape Town, a stable level of moderate RR is seen from Tmax 15-24 °C, with a significant increase in RR and error margin above 30 °C. In Gqeberha, the DLNM results are less definitive, reflecting the city's moderate climate and year-round rainfall. The RR of acute respiratory diseases did not show clear patterns with temperature changes, with increasing error margins outside the 22 °C threshold. These findings emphasize the imperative for region-specific public health strategies that account for the complex, non-linear influences of climate on respiratory health. This detailed understanding of the climate-health nexus provides a robust basis for enhancing public health interventions and future research directed at reducing the impacts of climate factors.
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Affiliation(s)
- Ogone Motlogeloa
- School of Geography, Archaeology and Environmental Studies, University of the Witwatersrand, Johannesburg 2050, South Africa
| | - Jennifer M Fitchett
- School of Geography, Archaeology and Environmental Studies, University of the Witwatersrand, Johannesburg 2050, South Africa.
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Khaleel HA, Alhilfi RA, Rawaf S, Tabche C. Identify future epidemic threshold and intensity for influenza-like illness in Iraq by using the moving epidemic method. IJID REGIONS 2024; 10:126-131. [PMID: 38260712 PMCID: PMC10801321 DOI: 10.1016/j.ijregi.2023.12.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 12/16/2023] [Accepted: 12/18/2023] [Indexed: 01/24/2024]
Abstract
Objectives Influenza-like illness (ILI) entered the Iraq surveillance system in 2021. The alert threshold was determined using the cumulative sum 2 method, which did not provide other characteristics. This study uses the moving epidemic method (MEM) to describe duration and estimate alert thresholds for ILI in Iraq for 2023-2024. Methods MEM default package was used to estimate influenza 2023-2024 epidemic thresholds. Analysis was repeated using optimum parameter of epidemic timing for fixed criteria method, which is 3.3. Arithmetic means and 95% confidence interval upper limit were used to estimate threshold. Geometric mean and 40%, 90%, and 97.3% confidence interval upper limits were used to estimate intensity levels. Aggregated Centers for Disease Control and Prevention surveillance data were used to detect epidemic thresholds, length, sensitivity, and predictive values. Results ILI activity starts at week 30 and lasts 7 weeks. Optimized epidemic threshold is 4513 cases, lower than default (4540 cases). Optimized medium-intensity level was higher than default, and high and very high-intensity levels were lower. Conclusions MEM is essential to determine an influenza epidemic's threshold and intensity levels. Despite requiring 3-5 years of data, using it on data for 2.5 years has resulted in an epidemic threshold slightly higher than the threshold calculated using the cumulative sum 2 method.
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Affiliation(s)
| | | | - Salman Rawaf
- WHO Collaborating Centre, Department of Primary Care and Public Health, Imperial College London, UK
| | - Celine Tabche
- WHO Collaborating Centre, Department of Primary Care and Public Health, Imperial College London, UK
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Huang QS, Turner N, Wood T, Anglemyer A, McIntyre P, Aminisani N, Dowell T, Trenholme A, Byrnes C, Balm M, McIntosh C, Jefferies S, Grant CC, Nesdale A, Dobinson HC, Campbell‐Stokes P, Daniells K, Geoghegan J, de Ligt J, Jelley L, Seeds R, Jennings T, Rensburg M, Cueto J, Caballero E, John J, Penghulan E, Tan CE, Ren X, Berquist K, O'Neill M, Marull M, Yu C, McNeill A, Kiedrzynski T, Roberts S, McArthur C, Stanley A, Taylor S, Wong C, Lawrence S, Baker MG, Kvalsvig A, Van Der Werff K, McAuliffe G, Antoszewska H, Dilcher M, Fahey J, Werno A, Elvy J, Grant J, Addidle M, Zacchi N, Mansell C, Widdowson M, Thomas PG, Webby RJ. Impact of the COVID-19 related border restrictions on influenza and other common respiratory viral infections in New Zealand. Influenza Other Respir Viruses 2024; 18:e13247. [PMID: 38350715 PMCID: PMC10864123 DOI: 10.1111/irv.13247] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2023] [Revised: 12/12/2023] [Accepted: 12/13/2023] [Indexed: 02/15/2024] Open
Abstract
BACKGROUND New Zealand's (NZ) complete absence of community transmission of influenza and respiratory syncytial virus (RSV) after May 2020, likely due to COVID-19 elimination measures, provided a rare opportunity to assess the impact of border restrictions on common respiratory viral infections over the ensuing 2 years. METHODS We collected the data from multiple surveillance systems, including hospital-based severe acute respiratory infection surveillance, SHIVERS-II, -III and -IV community cohorts for acute respiratory infection (ARI) surveillance, HealthStat sentinel general practice (GP) based influenza-like illness surveillance and SHIVERS-V sentinel GP-based ARI surveillance, SHIVERS-V traveller ARI surveillance and laboratory-based surveillance. We described the data on influenza, RSV and other respiratory viral infections in NZ before, during and after various stages of the COVID related border restrictions. RESULTS We observed that border closure to most people, and mandatory government-managed isolation and quarantine on arrival for those allowed to enter, appeared to be effective in keeping influenza and RSV infections out of the NZ community. Border restrictions did not affect community transmission of other respiratory viruses such as rhinovirus and parainfluenza virus type-1. Partial border relaxations through quarantine-free travel with Australia and other countries were quickly followed by importation of RSV in 2021 and influenza in 2022. CONCLUSION Our findings inform future pandemic preparedness and strategies to model and manage the impact of influenza and other respiratory viral threats.
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Affiliation(s)
- Q. Sue Huang
- Institute of Environmental Science and ResearchWellingtonNew Zealand
| | | | - Tim Wood
- Institute of Environmental Science and ResearchWellingtonNew Zealand
| | - Andrew Anglemyer
- Institute of Environmental Science and ResearchWellingtonNew Zealand
- University of OtagoDunedinNew Zealand
| | | | | | | | - Adrian Trenholme
- Te Whatu Ora, Health New Zealand Counties ManukauAucklandNew Zealand
| | - Cass Byrnes
- Te Whatu Ora, Health New Zealand Te Toka Tumai AucklandAucklandNew Zealand
| | - Michelle Balm
- Te Whatu Ora, Health New Zealand Capital, Coast and Hutt ValleyWellingtonNew Zealand
| | | | - Sarah Jefferies
- Institute of Environmental Science and ResearchWellingtonNew Zealand
| | - Cameron C. Grant
- University of AucklandAucklandNew Zealand
- Te Whatu Ora, Health New Zealand Te Toka Tumai AucklandAucklandNew Zealand
| | - Annette Nesdale
- Regional Public HealthTe Whatu Ora, Health New Zealand Capital, Coast and Hutt ValleyWellingtonNew Zealand
| | - Hazel C. Dobinson
- Te Whatu Ora, Health New Zealand Capital, Coast and Hutt ValleyWellingtonNew Zealand
| | - Priscilla Campbell‐Stokes
- Regional Public HealthTe Whatu Ora, Health New Zealand Capital, Coast and Hutt ValleyWellingtonNew Zealand
| | - Karen Daniells
- Institute of Environmental Science and ResearchWellingtonNew Zealand
| | - Jemma Geoghegan
- Institute of Environmental Science and ResearchWellingtonNew Zealand
- University of OtagoDunedinNew Zealand
| | - Joep de Ligt
- Institute of Environmental Science and ResearchWellingtonNew Zealand
| | - Lauren Jelley
- Institute of Environmental Science and ResearchWellingtonNew Zealand
- University of OtagoDunedinNew Zealand
| | - Ruth Seeds
- Institute of Environmental Science and ResearchWellingtonNew Zealand
| | - Tineke Jennings
- Regional Public HealthTe Whatu Ora, Health New Zealand Capital, Coast and Hutt ValleyWellingtonNew Zealand
| | - Megan Rensburg
- Regional Public HealthTe Whatu Ora, Health New Zealand Capital, Coast and Hutt ValleyWellingtonNew Zealand
| | - Jort Cueto
- Regional Public HealthTe Whatu Ora, Health New Zealand Capital, Coast and Hutt ValleyWellingtonNew Zealand
| | - Ernest Caballero
- Regional Public HealthTe Whatu Ora, Health New Zealand Capital, Coast and Hutt ValleyWellingtonNew Zealand
| | - Joshma John
- Regional Public HealthTe Whatu Ora, Health New Zealand Capital, Coast and Hutt ValleyWellingtonNew Zealand
| | - Emmanuel Penghulan
- Regional Public HealthTe Whatu Ora, Health New Zealand Capital, Coast and Hutt ValleyWellingtonNew Zealand
| | - Chor Ee Tan
- Institute of Environmental Science and ResearchWellingtonNew Zealand
| | - Xiaoyun Ren
- Institute of Environmental Science and ResearchWellingtonNew Zealand
| | - Klarysse Berquist
- Institute of Environmental Science and ResearchWellingtonNew Zealand
| | - Meaghan O'Neill
- Institute of Environmental Science and ResearchWellingtonNew Zealand
| | - Maritza Marull
- Institute of Environmental Science and ResearchWellingtonNew Zealand
| | - Chang Yu
- Institute of Environmental Science and ResearchWellingtonNew Zealand
| | - Andrea McNeill
- Institute of Environmental Science and ResearchWellingtonNew Zealand
| | - Tomasz Kiedrzynski
- Te Pou Hauora Tūmatanui, the Public Health AgencyManatū Hauora, Ministry of HealthWellingtonNew Zealand
| | - Sally Roberts
- Te Whatu Ora, Health New Zealand Te Toka Tumai AucklandAucklandNew Zealand
| | - Colin McArthur
- Te Whatu Ora, Health New Zealand Te Toka Tumai AucklandAucklandNew Zealand
| | - Alicia Stanley
- Te Whatu Ora, Health New Zealand Te Toka Tumai AucklandAucklandNew Zealand
| | - Susan Taylor
- Te Whatu Ora, Health New Zealand Counties ManukauAucklandNew Zealand
| | - Conroy Wong
- Te Whatu Ora, Health New Zealand Counties ManukauAucklandNew Zealand
| | - Shirley Lawrence
- Te Whatu Ora, Health New Zealand Counties ManukauAucklandNew Zealand
| | | | | | - Koen Van Der Werff
- Te Whatu Ora, Health New Zealand Capital, Coast and Hutt ValleyWellingtonNew Zealand
| | - Gary McAuliffe
- Te Whatu Ora, Health New Zealand Te Toka Tumai AucklandAucklandNew Zealand
| | - Hanna Antoszewska
- Te Whatu Ora, Health New Zealand Te Toka Tumai AucklandAucklandNew Zealand
| | - Meik Dilcher
- Te Whatu Ora, Health New Zealand Waitaha CanterburyChristchurchNew Zealand
| | - Jennifer Fahey
- Te Whatu Ora, Health New Zealand Waitaha CanterburyChristchurchNew Zealand
| | - Anja Werno
- Te Whatu Ora, Health New Zealand Waitaha CanterburyChristchurchNew Zealand
| | - Juliet Elvy
- Southern Community LaboratoriesDunedinNew Zealand
| | - Jenny Grant
- Southern Community LaboratoriesDunedinNew Zealand
| | - Michael Addidle
- Te Whatu Ora, Health New Zealand Hauora a Toi Bay of PlentyTaurangaNew Zealand
| | - Nicolas Zacchi
- Te Whatu Ora, Health New Zealand Hauora a Toi Bay of PlentyTaurangaNew Zealand
| | - Chris Mansell
- Te Whatu Ora, Health New Zealand WaikatoHamiltonNew Zealand
| | | | - Paul G. Thomas
- WHO Collaborating CentreSt Jude Children's Research HospitalMemphisTennesseeUSA
| | - BorderRestrictionImpactOnFluRSV Consortium
- Institute of Environmental Science and ResearchWellingtonNew Zealand
- Te Whatu Ora, Health New Zealand Counties ManukauAucklandNew Zealand
- Te Whatu Ora, Health New Zealand Te Toka Tumai AucklandAucklandNew Zealand
- Regional Public HealthTe Whatu Ora, Health New Zealand Capital, Coast and Hutt ValleyWellingtonNew Zealand
- Te Whatu Ora, Health New Zealand Waitaha CanterburyChristchurchNew Zealand
| | - Richard J. Webby
- WHO Collaborating CentreSt Jude Children's Research HospitalMemphisTennesseeUSA
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11
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Ramos PIP, Marcilio I, Bento AI, Penna GO, de Oliveira JF, Khouri R, Andrade RFS, Carreiro RP, Oliveira VDA, Galvão LAC, Landau L, Barreto ML, van der Horst K, Barral-Netto M. Combining Digital and Molecular Approaches Using Health and Alternate Data Sources in a Next-Generation Surveillance System for Anticipating Outbreaks of Pandemic Potential. JMIR Public Health Surveill 2024; 10:e47673. [PMID: 38194263 PMCID: PMC10806444 DOI: 10.2196/47673] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Revised: 09/18/2023] [Accepted: 11/22/2023] [Indexed: 01/10/2024] Open
Abstract
Globally, millions of lives are impacted every year by infectious diseases outbreaks. Comprehensive and innovative surveillance strategies aiming at early alert and timely containment of emerging and reemerging pathogens are a pressing priority. Shortcomings and delays in current pathogen surveillance practices further disturbed informing responses, interventions, and mitigation of recent pandemics, including H1N1 influenza and SARS-CoV-2. We present the design principles of the architecture for an early-alert surveillance system that leverages the vast available data landscape, including syndromic data from primary health care, drug sales, and rumors from the lay media and social media to identify areas with an increased number of cases of respiratory disease. In these potentially affected areas, an intensive and fast sample collection and advanced high-throughput genome sequencing analyses would inform on circulating known or novel pathogens by metagenomics-enabled pathogen characterization. Concurrently, the integration of bioclimatic and socioeconomic data, as well as transportation and mobility network data, into a data analytics platform, coupled with advanced mathematical modeling using artificial intelligence or machine learning, will enable more accurate estimation of outbreak spread risk. Such an approach aims to readily identify and characterize regions in the early stages of an outbreak development, as well as model risk and patterns of spread, informing targeted mitigation and control measures. A fully operational system must integrate diverse and robust data streams to translate data into actionable intelligence and actions, ultimately paving the way toward constructing next-generation surveillance systems.
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Affiliation(s)
- Pablo Ivan P Ramos
- Center for Data and Knowledge Integration for Health (CIDACS), Instituto Gonçalo Moniz, Fundação Oswaldo Cruz (Fiocruz), Salvador, Brazil
| | - Izabel Marcilio
- Center for Data and Knowledge Integration for Health (CIDACS), Instituto Gonçalo Moniz, Fundação Oswaldo Cruz (Fiocruz), Salvador, Brazil
| | - Ana I Bento
- The Rockefeller Foundation, New York, NY, United States
| | - Gerson O Penna
- Núcleo de Medicina Tropical, Universidade de Brasília, Brasília, Brazil
- Escola Fiocruz de Governo, Fundação Oswaldo Cruz (Fiocruz), Brasília, Brazil
| | - Juliane F de Oliveira
- Center for Data and Knowledge Integration for Health (CIDACS), Instituto Gonçalo Moniz, Fundação Oswaldo Cruz (Fiocruz), Salvador, Brazil
| | - Ricardo Khouri
- Medicine and Precision Public Health Laboratory (MeSP2), Instituto Gonçalo Moniz, Fundação Oswaldo Cruz (Fiocruz), Salvador, Brazil
| | - Roberto F S Andrade
- Center for Data and Knowledge Integration for Health (CIDACS), Instituto Gonçalo Moniz, Fundação Oswaldo Cruz (Fiocruz), Salvador, Brazil
- Physics Institute, Federal University of Bahia, Salvador, Brazil
| | - Roberto P Carreiro
- Center for Data and Knowledge Integration for Health (CIDACS), Instituto Gonçalo Moniz, Fundação Oswaldo Cruz (Fiocruz), Salvador, Brazil
| | - Vinicius de A Oliveira
- Center for Data and Knowledge Integration for Health (CIDACS), Instituto Gonçalo Moniz, Fundação Oswaldo Cruz (Fiocruz), Salvador, Brazil
| | - Luiz Augusto C Galvão
- Centro de Relações Internacionais em Saúde (CRIS), Fundação Oswaldo Cruz (Fiocruz), Rio de Janeiro, Brazil
| | - Luiz Landau
- Department of Civil Engineering (COPPE), Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
| | - Mauricio L Barreto
- Center for Data and Knowledge Integration for Health (CIDACS), Instituto Gonçalo Moniz, Fundação Oswaldo Cruz (Fiocruz), Salvador, Brazil
| | | | - Manoel Barral-Netto
- Center for Data and Knowledge Integration for Health (CIDACS), Instituto Gonçalo Moniz, Fundação Oswaldo Cruz (Fiocruz), Salvador, Brazil
- Medicine and Precision Public Health Laboratory (MeSP2), Instituto Gonçalo Moniz, Fundação Oswaldo Cruz (Fiocruz), Salvador, Brazil
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12
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Perramon-Malavez A, Bravo M, de Rioja VL, Català M, Alonso S, Álvarez-Lacalle E, López D, Soriano-Arandes A, Prats C. A semi-empirical risk panel to monitor epidemics: multi-faceted tool to assist healthcare and public health professionals. Front Public Health 2024; 11:1307425. [PMID: 38259774 PMCID: PMC10801172 DOI: 10.3389/fpubh.2023.1307425] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Accepted: 12/19/2023] [Indexed: 01/24/2024] Open
Abstract
Introduction Bronchiolitis, mostly caused by Respiratory Syncytial Virus (RSV), and influenza among other respiratory infections, lead to seasonal saturation at healthcare centers in temperate areas. There is no gold standard to characterize the stages of epidemics, nor the risk of respiratory infections growing. We aimed to define a set of indicators to assess the risk level of respiratory viral epidemics, based on both incidence and their short-term dynamics, and considering epidemical thresholds. Methods We used publicly available data on daily cases of influenza for the whole population and bronchiolitis in children <2 years from the Information System for Infection Surveillance in Catalonia (SIVIC). We included a Moving Epidemic Method (MEM) variation to define epidemic threshold and levels. We pre-processed the data with two different nowcasting approaches and performed a 7-day moving average. Weekly incidences (cases per 105 population) were computed and the 5-day growth rate was defined to create the effective potential growth (EPG) indicator. We performed a correlation analysis to define the forecasting ability of this index. Results Our adaptation of the MEM method allowed us to define epidemic weekly incidence levels and epidemic thresholds for bronchiolitis and influenza. EPG was able to anticipate daily 7-day cumulative incidence by 4-5 (bronchiolitis) or 6-7 (influenza) days. Discussion We developed a semi-empirical risk panel incorporating the EPG index, which effectively anticipates surpassing epidemic thresholds for bronchiolitis and influenza. This panel could serve as a robust surveillance tool, applicable to respiratory infectious diseases characterized by seasonal epidemics, easy to handle for individuals lacking a mathematical background.
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Affiliation(s)
- Aida Perramon-Malavez
- Department of Physics, Computational Biology and Complex Systems (BIOCOM-SC) group, Barcelona School of Agri-Food and Biosystems Engineering, Universitat Politècnica de Catalunya, Castelldefels, Spain
| | - Mario Bravo
- Department of Physics, Computational Biology and Complex Systems (BIOCOM-SC) group, Barcelona School of Agri-Food and Biosystems Engineering, Universitat Politècnica de Catalunya, Castelldefels, Spain
| | - Víctor López de Rioja
- Department of Physics, Computational Biology and Complex Systems (BIOCOM-SC) group, Barcelona School of Agri-Food and Biosystems Engineering, Universitat Politècnica de Catalunya, Castelldefels, Spain
| | - Martí Català
- Health Data Sciences, NDORMS, University of Oxford, Oxford, United Kingdom
| | - Sergio Alonso
- Department of Physics, Computational Biology and Complex Systems (BIOCOM-SC) group, Barcelona School of Agri-Food and Biosystems Engineering, Universitat Politècnica de Catalunya, Castelldefels, Spain
| | - Enrique Álvarez-Lacalle
- Department of Physics, Computational Biology and Complex Systems (BIOCOM-SC) group, Barcelona School of Agri-Food and Biosystems Engineering, Universitat Politècnica de Catalunya, Castelldefels, Spain
| | - Daniel López
- Department of Physics, Computational Biology and Complex Systems (BIOCOM-SC) group, Barcelona School of Agri-Food and Biosystems Engineering, Universitat Politècnica de Catalunya, Castelldefels, Spain
| | - Antoni Soriano-Arandes
- Paediatric Infectious Diseases and Immunodeficiencies Unit, Children’s Hospital, Vall d’Hebron Barcelona Hospital Campus, Barcelona, Catalonia, Spain
- Infection and Immunity in Paediatric Patients, Vall d’Hebron Research Institute, Barcelona, Catalonia, Spain
| | - Clara Prats
- Department of Physics, Computational Biology and Complex Systems (BIOCOM-SC) group, Barcelona School of Agri-Food and Biosystems Engineering, Universitat Politècnica de Catalunya, Castelldefels, Spain
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13
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Morbey RA, Todkill D, Watson C, Elliot AJ. Machine learning forecasts for seasonal epidemic peaks: Lessons learnt from an atypical respiratory syncytial virus season. PLoS One 2023; 18:e0291932. [PMID: 37738241 PMCID: PMC10516409 DOI: 10.1371/journal.pone.0291932] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Accepted: 09/10/2023] [Indexed: 09/24/2023] Open
Abstract
Seasonal peaks in infectious disease incidence put pressures on health services. Therefore, early warning of the timing and magnitude of peak activity during seasonal epidemics can provide information for public health practitioners to take appropriate action. Whilst many infectious diseases have predictable seasonality, newly emerging diseases and the impact of public health interventions can result in unprecedented seasonal activity. We propose a Machine Learning process for generating short-term forecasts, where models are selected based on their ability to correctly forecast peaks in activity, and can be useful during atypical seasons. We have validated our forecasts using typical and atypical seasonal activity, using respiratory syncytial virus (RSV) activity during 2019-2021 as an example. During the winter of 2020/21 the usual winter peak in RSV activity in England did not occur but was 'deferred' until the Spring of 2021. We compare a range of Machine Learning regression models, with alternate models including different independent variables, e.g. with or without seasonality or trend variables. We show that the best-fitting model which minimises daily forecast errors is not the best model for forecasting peaks when the selection criterion is based on peak timing and magnitude. Furthermore, we show that best-fitting models for typical seasons contain different variables to those for atypical seasons. Specifically, including seasonality in models improves performance during typical seasons but worsens it for the atypical seasons.
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Affiliation(s)
- Roger A. Morbey
- Real-Time Syndromic Surveillance Team, Field Services, Health Protection Operations, UK Health Security Agency, Birmingham, United Kingdom
| | - Daniel Todkill
- Real-Time Syndromic Surveillance Team, Field Services, Health Protection Operations, UK Health Security Agency, Birmingham, United Kingdom
| | - Conall Watson
- Immunisation and Vaccine Preventable Diseases Division, UK Health Security Agency, London, United Kingdom
| | - Alex J. Elliot
- Real-Time Syndromic Surveillance Team, Field Services, Health Protection Operations, UK Health Security Agency, Birmingham, United Kingdom
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14
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Lampros A, Talla C, Diarra M, Tall B, Sagne S, Diallo MK, Diop B, Oumar I, Dia N, Sall AA, Barry MA, Loucoubar C. Shifting Patterns of Influenza Circulation during the COVID-19 Pandemic, Senegal. Emerg Infect Dis 2023; 29:1808-1817. [PMID: 37610149 PMCID: PMC10461650 DOI: 10.3201/eid2909.230307] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/24/2023] Open
Abstract
Historically low levels of seasonal influenza circulation were reported during the first years of the COVID-19 pandemic and were mainly attributed to implementation of nonpharmaceutical interventions. In tropical regions, influenza's seasonality differs largely, and data on this topic are scarce. We analyzed data from Senegal's sentinel syndromic surveillance network before and after the start of the COVID-19 pandemic to assess changes in influenza circulation. We found that influenza shows year-round circulation in Senegal and has 2 distinct epidemic peaks: during January-March and during the rainy season in August-October. During 2021-2022, the expected January-March influenza peak completely disappeared, corresponding to periods of active SARS-CoV-2 circulation. We noted an unexpected influenza epidemic peak during May-July 2022. The observed reciprocal circulation of SARS-CoV-2 and influenza suggests that factors such as viral interference might be at play and should be further investigated in tropical settings.
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Affiliation(s)
- Alexandre Lampros
- Hôpital Européen Georges Pompidou, Paris, France (A. Lampros)
- Institut Pasteur de Dakar, Dakar, Senegal (A. Lampros, C. Talla, M. Diarra, B. Tall, S. Sagne, M. Korka Diallo, N. Dia, A.A. Sall, M.A. Barry, C. Loucoubar)
- Government of Senegal Ministry of Health and Social Action, Dakar (A. Lampros, B. Diop)
- World Health Organization, Dakar (A. Lampros, I. Oumar)
| | - Cheikh Talla
- Hôpital Européen Georges Pompidou, Paris, France (A. Lampros)
- Institut Pasteur de Dakar, Dakar, Senegal (A. Lampros, C. Talla, M. Diarra, B. Tall, S. Sagne, M. Korka Diallo, N. Dia, A.A. Sall, M.A. Barry, C. Loucoubar)
- Government of Senegal Ministry of Health and Social Action, Dakar (A. Lampros, B. Diop)
- World Health Organization, Dakar (A. Lampros, I. Oumar)
| | - Maryam Diarra
- Hôpital Européen Georges Pompidou, Paris, France (A. Lampros)
- Institut Pasteur de Dakar, Dakar, Senegal (A. Lampros, C. Talla, M. Diarra, B. Tall, S. Sagne, M. Korka Diallo, N. Dia, A.A. Sall, M.A. Barry, C. Loucoubar)
- Government of Senegal Ministry of Health and Social Action, Dakar (A. Lampros, B. Diop)
- World Health Organization, Dakar (A. Lampros, I. Oumar)
| | - Billo Tall
- Hôpital Européen Georges Pompidou, Paris, France (A. Lampros)
- Institut Pasteur de Dakar, Dakar, Senegal (A. Lampros, C. Talla, M. Diarra, B. Tall, S. Sagne, M. Korka Diallo, N. Dia, A.A. Sall, M.A. Barry, C. Loucoubar)
- Government of Senegal Ministry of Health and Social Action, Dakar (A. Lampros, B. Diop)
- World Health Organization, Dakar (A. Lampros, I. Oumar)
| | - Samba Sagne
- Hôpital Européen Georges Pompidou, Paris, France (A. Lampros)
- Institut Pasteur de Dakar, Dakar, Senegal (A. Lampros, C. Talla, M. Diarra, B. Tall, S. Sagne, M. Korka Diallo, N. Dia, A.A. Sall, M.A. Barry, C. Loucoubar)
- Government of Senegal Ministry of Health and Social Action, Dakar (A. Lampros, B. Diop)
- World Health Organization, Dakar (A. Lampros, I. Oumar)
| | - Mamadou Korka Diallo
- Hôpital Européen Georges Pompidou, Paris, France (A. Lampros)
- Institut Pasteur de Dakar, Dakar, Senegal (A. Lampros, C. Talla, M. Diarra, B. Tall, S. Sagne, M. Korka Diallo, N. Dia, A.A. Sall, M.A. Barry, C. Loucoubar)
- Government of Senegal Ministry of Health and Social Action, Dakar (A. Lampros, B. Diop)
- World Health Organization, Dakar (A. Lampros, I. Oumar)
| | - Boly Diop
- Hôpital Européen Georges Pompidou, Paris, France (A. Lampros)
- Institut Pasteur de Dakar, Dakar, Senegal (A. Lampros, C. Talla, M. Diarra, B. Tall, S. Sagne, M. Korka Diallo, N. Dia, A.A. Sall, M.A. Barry, C. Loucoubar)
- Government of Senegal Ministry of Health and Social Action, Dakar (A. Lampros, B. Diop)
- World Health Organization, Dakar (A. Lampros, I. Oumar)
| | - Ibrahim Oumar
- Hôpital Européen Georges Pompidou, Paris, France (A. Lampros)
- Institut Pasteur de Dakar, Dakar, Senegal (A. Lampros, C. Talla, M. Diarra, B. Tall, S. Sagne, M. Korka Diallo, N. Dia, A.A. Sall, M.A. Barry, C. Loucoubar)
- Government of Senegal Ministry of Health and Social Action, Dakar (A. Lampros, B. Diop)
- World Health Organization, Dakar (A. Lampros, I. Oumar)
| | - Ndongo Dia
- Hôpital Européen Georges Pompidou, Paris, France (A. Lampros)
- Institut Pasteur de Dakar, Dakar, Senegal (A. Lampros, C. Talla, M. Diarra, B. Tall, S. Sagne, M. Korka Diallo, N. Dia, A.A. Sall, M.A. Barry, C. Loucoubar)
- Government of Senegal Ministry of Health and Social Action, Dakar (A. Lampros, B. Diop)
- World Health Organization, Dakar (A. Lampros, I. Oumar)
| | - Amadou Alpha Sall
- Hôpital Européen Georges Pompidou, Paris, France (A. Lampros)
- Institut Pasteur de Dakar, Dakar, Senegal (A. Lampros, C. Talla, M. Diarra, B. Tall, S. Sagne, M. Korka Diallo, N. Dia, A.A. Sall, M.A. Barry, C. Loucoubar)
- Government of Senegal Ministry of Health and Social Action, Dakar (A. Lampros, B. Diop)
- World Health Organization, Dakar (A. Lampros, I. Oumar)
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15
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Sominina A, Danilenko D, Komissarov AB, Pisareva M, Fadeev A, Konovalova N, Eropkin M, Petrova P, Zheltukhina A, Musaeva T, Eder V, Ivanova A, Komissarova K, Stolyarov K, Karpova L, Smorodintseva E, Dorosh A, Krivitskaya V, Kuznetzova E, Majorova V, Petrova E, Boyarintseva A, Ksenafontov A, Shtro A, Nikolaeva J, Bakaev M, Burtseva E, Lioznov D. Assessing the Intense Influenza A(H1N1)pdm09 Epidemic and Vaccine Effectiveness in the Post-COVID Season in the Russian Federation. Viruses 2023; 15:1780. [PMID: 37632122 PMCID: PMC10458445 DOI: 10.3390/v15081780] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Revised: 07/31/2023] [Accepted: 08/16/2023] [Indexed: 08/27/2023] Open
Abstract
The COVID-19 pandemic had a profound impact on influenza activity worldwide. However, as the pandemic progressed, influenza activity resumed. Here, we describe the influenza epidemic of high intensity of the 2022-2023 season. The epidemic had an early start and peaked in week 51.2022. The extremely high intensity of the epidemic may have been due to a significant decrease in herd immunity. The results of PCR-testing of 220,067 clinical samples revealed that the influenza A(H1N1)pdm09 virus dominated, causing 56.4% of positive cases, while A(H3N2) influenza subtype accounted for only 0.6%, and influenza B of Victoria lineage-for 34.3%. The influenza vaccine was found to be highly effective, with an estimated effectiveness of 92.7% in preventing admission with laboratory-confirmed influenza severe acute respiratory illness (SARI) cases and 54.7% in preventing influenza-like illness/acute respiratory illness (ILI/ARI) cases due to antigenic matching of circulated viruses with influenza vaccine strains for the season. Full genome next-generation sequencing of 1723 influenza A(H1N1)pdm09 viruses showed that all of them fell within clade 6B.1A.5.a2; nine of them possessed H275Y substitution in the NA gene, a genetic marker of oseltamivir resistance. Influenza A(H3N2) viruses belonged to subclade 3C.2a1b.2a.2 with the genetic group 2b being dominant. All 433 influenza B viruses belonged to subclade V1A.3a.2 encoding HA1 substitutions A127T, P144L, and K203R, which could be further divided into two subgroups. None of the influenza A(H3N2) and B viruses sequenced had markers of resistance to NA inhibitors. Thus, despite the continuing circulation of Omicron descendant lineages, influenza activity has resumed in full force, raising concerns about the intensity of fore coming seasonal epidemics.
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Affiliation(s)
- Anna Sominina
- Smorodintsev Research Institute of Influenza, 197376 Saint Petersburg, Russia; (D.D.); (E.K.)
| | - Daria Danilenko
- Smorodintsev Research Institute of Influenza, 197376 Saint Petersburg, Russia; (D.D.); (E.K.)
| | - Andrey B. Komissarov
- Smorodintsev Research Institute of Influenza, 197376 Saint Petersburg, Russia; (D.D.); (E.K.)
| | - Maria Pisareva
- Smorodintsev Research Institute of Influenza, 197376 Saint Petersburg, Russia; (D.D.); (E.K.)
| | - Artem Fadeev
- Smorodintsev Research Institute of Influenza, 197376 Saint Petersburg, Russia; (D.D.); (E.K.)
| | - Nadezhda Konovalova
- Smorodintsev Research Institute of Influenza, 197376 Saint Petersburg, Russia; (D.D.); (E.K.)
| | - Mikhail Eropkin
- Smorodintsev Research Institute of Influenza, 197376 Saint Petersburg, Russia; (D.D.); (E.K.)
| | - Polina Petrova
- Smorodintsev Research Institute of Influenza, 197376 Saint Petersburg, Russia; (D.D.); (E.K.)
| | - Alyona Zheltukhina
- Smorodintsev Research Institute of Influenza, 197376 Saint Petersburg, Russia; (D.D.); (E.K.)
| | - Tamila Musaeva
- Smorodintsev Research Institute of Influenza, 197376 Saint Petersburg, Russia; (D.D.); (E.K.)
| | - Veronika Eder
- Smorodintsev Research Institute of Influenza, 197376 Saint Petersburg, Russia; (D.D.); (E.K.)
| | - Anna Ivanova
- Smorodintsev Research Institute of Influenza, 197376 Saint Petersburg, Russia; (D.D.); (E.K.)
| | - Kseniya Komissarova
- Smorodintsev Research Institute of Influenza, 197376 Saint Petersburg, Russia; (D.D.); (E.K.)
| | - Kirill Stolyarov
- Smorodintsev Research Institute of Influenza, 197376 Saint Petersburg, Russia; (D.D.); (E.K.)
| | - Ludmila Karpova
- Smorodintsev Research Institute of Influenza, 197376 Saint Petersburg, Russia; (D.D.); (E.K.)
| | - Elizaveta Smorodintseva
- Smorodintsev Research Institute of Influenza, 197376 Saint Petersburg, Russia; (D.D.); (E.K.)
| | - Anna Dorosh
- Smorodintsev Research Institute of Influenza, 197376 Saint Petersburg, Russia; (D.D.); (E.K.)
| | - Vera Krivitskaya
- Smorodintsev Research Institute of Influenza, 197376 Saint Petersburg, Russia; (D.D.); (E.K.)
| | - Elena Kuznetzova
- Smorodintsev Research Institute of Influenza, 197376 Saint Petersburg, Russia; (D.D.); (E.K.)
| | - Victoria Majorova
- Smorodintsev Research Institute of Influenza, 197376 Saint Petersburg, Russia; (D.D.); (E.K.)
| | - Ekaterina Petrova
- Smorodintsev Research Institute of Influenza, 197376 Saint Petersburg, Russia; (D.D.); (E.K.)
| | - Anastassia Boyarintseva
- Smorodintsev Research Institute of Influenza, 197376 Saint Petersburg, Russia; (D.D.); (E.K.)
| | - Andrey Ksenafontov
- Smorodintsev Research Institute of Influenza, 197376 Saint Petersburg, Russia; (D.D.); (E.K.)
| | - Anna Shtro
- Smorodintsev Research Institute of Influenza, 197376 Saint Petersburg, Russia; (D.D.); (E.K.)
| | - Julia Nikolaeva
- Smorodintsev Research Institute of Influenza, 197376 Saint Petersburg, Russia; (D.D.); (E.K.)
| | - Mikhail Bakaev
- Smorodintsev Research Institute of Influenza, 197376 Saint Petersburg, Russia; (D.D.); (E.K.)
| | - Elena Burtseva
- National Research Center for Epidemiology and Microbiology Named after N.F. Gamaleya, 123098 Moscow, Russia
| | - Dmitry Lioznov
- Smorodintsev Research Institute of Influenza, 197376 Saint Petersburg, Russia; (D.D.); (E.K.)
- Department of Infectious Diseases, First Pavlov State Medical University, 197022 Saint Petersburg, Russia
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Dieng S, Adebayo-Ojo TC, Kruger T, Riddin M, Trehard H, Tumelero S, Bendiane MK, de Jager C, Patrick S, Bornman R, Gaudart J. Geo-epidemiology of malaria incidence in the Vhembe District to guide targeted elimination strategies, South-Africa, 2015-2018: a local resurgence. Sci Rep 2023; 13:11049. [PMID: 37422504 PMCID: PMC10329648 DOI: 10.1038/s41598-023-38147-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2022] [Accepted: 07/04/2023] [Indexed: 07/10/2023] Open
Abstract
In South Africa, the population at risk of malaria is 10% (around six million inhabitants) and concern only three provinces of which Limpopo Province is the most affected, particularly in Vhembe District. As the elimination approaches, a finer scale analysis is needed to accelerate the results. Therefore, in the process of refining local malaria control and elimination strategies, the aim of this study was to identify and describe malaria incidence patterns at the locality scale in the Vhembe District, Limpopo Province, South Africa. The study area comprised 474 localities in Vhembe District for which smoothed malaria incidence curve were fitted with functional data method based on their weekly observed malaria incidence from July 2015 to June 2018. Then, hierarchical clustering algorithm was carried out considering different distances to classify the 474 smoothed malaria incidence curves. Thereafter, validity indices were used to determine the number of malaria incidence patterns. The cumulative malaria incidence of the study area was 4.1 cases/1000 person-years. Four distinct patterns of malaria incidence were identified: high, intermediate, low and very low with varying characteristics. Malaria incidence increased across transmission seasons and patterns. The localities in the two highest incidence patterns were mainly located around farms, and along the rivers. Some unusual malaria phenomena in Vhembe District were also highlighted as resurgence. Four distinct malaria incidence patterns were found in Vhembe District with varying characteristics. Findings show also unusual malaria phenomena in Vhembe District that hinder malaria elimination in South Africa. Assessing the factors associated with these unusual malaria phenome would be helpful on building innovative strategies that lead South Africa on malaria elimination.
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Affiliation(s)
- Sokhna Dieng
- Aix Marseille Univ, IRD, INSERM, ISSPAM, SESSTIM, 13005, Marseille, France.
| | | | - Taneshka Kruger
- School of Health Systems and Public Health (SHSPH), University of Pretoria Institute for Sustainable Malaria Control (UP ISMC), University of Pretoria, Pretoria, South Africa
| | - Megan Riddin
- School of Health Systems and Public Health (SHSPH), University of Pretoria Institute for Sustainable Malaria Control (UP ISMC), University of Pretoria, Pretoria, South Africa
| | - Helene Trehard
- Aix Marseille Univ, IRD, INSERM, ISSPAM, SESSTIM, 13005, Marseille, France
| | - Serena Tumelero
- Aix Marseille Univ, IRD, INSERM, ISSPAM, SESSTIM, 13005, Marseille, France
| | | | - Christiaan de Jager
- School of Health Systems and Public Health (SHSPH), University of Pretoria Institute for Sustainable Malaria Control (UP ISMC), University of Pretoria, Pretoria, South Africa
| | - Sean Patrick
- School of Health Systems and Public Health (SHSPH), University of Pretoria Institute for Sustainable Malaria Control (UP ISMC), University of Pretoria, Pretoria, South Africa
| | - Riana Bornman
- School of Health Systems and Public Health (SHSPH), University of Pretoria Institute for Sustainable Malaria Control (UP ISMC), University of Pretoria, Pretoria, South Africa
| | - Jean Gaudart
- Aix Marseille Univ, IRD, INSERM, ISSPAM, SESSTIM, APHM, Hop. La Timone, BioSTIC, Biostatistic & ICT, 13005, Marseille, France
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Zhang R, Lai KY, Liu W, Liu Y, Cai W, Webster C, Luo L, Sarkar C. Association of climatic variables with risk of transmission of influenza in Guangzhou, China, 2005-2021. Int J Hyg Environ Health 2023; 252:114217. [PMID: 37418782 DOI: 10.1016/j.ijheh.2023.114217] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Revised: 06/16/2023] [Accepted: 06/29/2023] [Indexed: 07/09/2023]
Abstract
BACKGROUND Climatic variables constitute important extrinsic determinants of transmission and seasonality of influenza. Yet quantitative evidence of independent associations of viral transmissibility with climatic factors has thus far been scarce and little is known about the potential effects of interactions between climatic factors on transmission. OBJECTIVE This study aimed to examine the associations of key climatic factors with risk of influenza transmission in subtropical Guangzhou. METHODS Influenza epidemics were identified over a 17-year period using the moving epidemic method (MEM) from a dataset of N = 295,981 clinically- and laboratory-confirmed cases of influenza in Guangzhou. Data on eight key climatic variables were collected from China Meteorological Data Service Centre. Generalized additive model combined with the distributed lag non-linear model (DLNM) were developed to estimate the exposure-lag-response curve showing the trajectory of instantaneous reproduction number (Rt) across the distribution of each climatic variable after adjusting for depletion of susceptible, inter-epidemic effect and school holidays. The potential interaction effects of temperature, humidity and rainfall on influenza transmission were also examined. RESULTS Over the study period (2005-21), 21 distinct influenza epidemics with varying peak timings and durations were identified. Increasing air temperature, sunshine, absolute and relative humidity were significantly associated with lower Rt, while the associations were opposite in the case of ambient pressure, wind speed and rainfall. Rainfall, relative humidity, and ambient temperature were the top three climatic contributors to variance in transmissibility. Interaction models found that the detrimental association between high relative humidity and transmissibility was more pronounced at high temperature and rainfall. CONCLUSION Our findings are likely to help understand the complex role of climatic factors in influenza transmission, guiding informed climate-related mitigation and adaptation policies to reduce transmission in high density subtropical cities.
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Affiliation(s)
- Rong Zhang
- Healthy High Density Cities Lab, HKUrbanLab, The University of Hong Kong, Knowles Building, Pokfulam Road, Pokfulam, Hong Kong, China; Department of Urban Planning and Design, The University of Hong Kong, Knowles Building, Pokfulam Road, Pokfulam, Hong Kong, China
| | - Ka Yan Lai
- Healthy High Density Cities Lab, HKUrbanLab, The University of Hong Kong, Knowles Building, Pokfulam Road, Pokfulam, Hong Kong, China; Department of Urban Planning and Design, The University of Hong Kong, Knowles Building, Pokfulam Road, Pokfulam, Hong Kong, China
| | - Wenhui Liu
- Guangzhou Center for Disease Control and Prevention, Guangzhou, Guangdong, China
| | - Yanhui Liu
- Guangzhou Center for Disease Control and Prevention, Guangzhou, Guangdong, China
| | - Wenfeng Cai
- Guangzhou Center for Disease Control and Prevention, Guangzhou, Guangdong, China
| | - Chris Webster
- Healthy High Density Cities Lab, HKUrbanLab, The University of Hong Kong, Knowles Building, Pokfulam Road, Pokfulam, Hong Kong, China; Department of Urban Planning and Design, The University of Hong Kong, Knowles Building, Pokfulam Road, Pokfulam, Hong Kong, China; Urban Systems Institute, The University of Hong Kong, Hong Kong, China
| | - Lei Luo
- Guangzhou Center for Disease Control and Prevention, Guangzhou, Guangdong, China.
| | - Chinmoy Sarkar
- Healthy High Density Cities Lab, HKUrbanLab, The University of Hong Kong, Knowles Building, Pokfulam Road, Pokfulam, Hong Kong, China; Department of Urban Planning and Design, The University of Hong Kong, Knowles Building, Pokfulam Road, Pokfulam, Hong Kong, China; Department of Psychiatry, University of Oxford, Warneford Hospital, Oxford, OX3 7JX, UK; Urban Systems Institute, The University of Hong Kong, Hong Kong, China.
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18
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Wu H, Xue M, Wu C, Ding Z, Wang X, Fu T, Yang K, Lin J, Lu Q. Estimation of influenza incidence and analysis of epidemic characteristics from 2009 to 2022 in Zhejiang Province, China. Front Public Health 2023; 11:1154944. [PMID: 37427270 PMCID: PMC10328336 DOI: 10.3389/fpubh.2023.1154944] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Accepted: 04/28/2023] [Indexed: 07/11/2023] Open
Abstract
Background Influenza infection causes a huge burden every year, affecting approximately 8% of adults and approximately 25% of children and resulting in approximately 400,000 respiratory deaths worldwide. However, based on the number of reported influenza cases, the actual prevalence of influenza may be greatly underestimated. The purpose of this study was to estimate the incidence rate of influenza and determine the true epidemiological characteristics of this virus. Methods The number of influenza cases and the prevalence of ILIs among outpatients in Zhejiang Province were obtained from the China Disease Control and Prevention Information System. Specimens were sampled from some cases and sent to laboratories for influenza nucleic acid testing. Random forest was used to establish an influenza estimation model based on the influenza-positive rate and the percentage of ILIs among outpatients. Furthermore, the moving epidemic method (MEM) was applied to calculate the epidemic threshold for different intensity levels. Joinpoint regression analysis was used to identify the annual change in influenza incidence. The seasonal trends of influenza were detected by wavelet analysis. Results From 2009 to 2021, a total of 990,016 influenza cases and 8 deaths were reported in Zhejiang Province. The numbers of estimated influenza cases from 2009 to 2018 were 743,449, 47,635, 89,026, 132,647, 69,218, 190,099, 204,606, 190,763, 267,168 and 364,809, respectively. The total number of estimated influenza cases is 12.11 times the number of reported cases. The APC of the estimated annual incidence rate was 23.33 (95% CI: 13.2 to 34.4) from 2011 to 2019, indicating a constant increasing trend. The intensity levels of the estimated incidence from the epidemic threshold to the very high-intensity threshold were 18.94 cases per 100,000, 24.14 cases per 100,000, 141.55 cases per 100,000, and 309.34 cases per 100,000, respectively. From the first week of 2009 to the 39th week of 2022, there were a total of 81 weeks of epidemics: the epidemic period reached a high intensity in 2 weeks, the epidemic period was at a moderate intensity in 75 weeks, and the epidemic period was at a low intensity in 2 weeks. The average power was significant on the 1-year scale, semiannual scale, and 115-week scale, and the average power of the first two cycles was significantly higher than that of the other cycles. In the period from the 20th week to the 35th week, the Pearson correlation coefficients between the time series of influenza onset and the positive rate of pathogens, including A(H3N2), A (H1N1)pdm2009, B(Victoria) and B(Yamagata), were - 0.089 (p = 0.021), 0.497 (p < 0.001), -0.062 (p = 0.109) and - 0.084 (p = 0.029), respectively. In the period from the 36th week of the first year to the 19th week of the next year, the Pearson correlation coefficients between the time series of influenza onset and the positive rate of pathogens, including A(H3N2), A (H1N1)pdm2009, B(Victoria) and B(Yamagata), were 0.516 (p < 0.001), 0.148 (p < 0.001), 0.292 (p < 0.001) and 0.271 (p < 0.001), respectively. Conclusion The disease burden of influenza has been seriously underestimated in the past. An appropriate method for estimating the incidence rate of influenza may be to comprehensively consider the influenza-positive rate as well as the percentage of ILIs among outpatients. The intensity level of the estimated incidence from the epidemic threshold to the very high-intensity threshold was calculated, thus yielding a quantitative standard for judging the influenza prevalence level in the future. The incidence of influenza showed semi-annual peaks in Zhejiang Province, including a main peak from December to January of the next year followed by a peak in summer. Furthermore, the driving factors of the influenza peaks were preliminarily explored. While the peak in summer was mainly driven by pathogens of A(H3N2), the peak in winter was alternately driven by various pathogens. Our research suggests that the government urgently needs to address barriers to vaccination and actively promote vaccines through primary care providers.
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Affiliation(s)
- Haocheng Wu
- Center for Disease Control and Prevention (Zhejiang CDC), Zhejiang, Hangzhou, China
| | - Ming Xue
- Hangzhou Center for Disease Control and Prevention (HZCDC), Hangzhou, China
| | - Chen Wu
- Center for Disease Control and Prevention (Zhejiang CDC), Zhejiang, Hangzhou, China
| | - Zheyuan Ding
- Center for Disease Control and Prevention (Zhejiang CDC), Zhejiang, Hangzhou, China
| | - Xinyi Wang
- Center for Disease Control and Prevention (Zhejiang CDC), Zhejiang, Hangzhou, China
| | - Tianyin Fu
- Center for Disease Control and Prevention (Zhejiang CDC), Zhejiang, Hangzhou, China
| | - Ke Yang
- Center for Disease Control and Prevention (Zhejiang CDC), Zhejiang, Hangzhou, China
| | - Junfen Lin
- Center for Disease Control and Prevention (Zhejiang CDC), Zhejiang, Hangzhou, China
| | - Qinbao Lu
- Center for Disease Control and Prevention (Zhejiang CDC), Zhejiang, Hangzhou, China
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19
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Igboh LS, Roguski K, Marcenac P, Emukule GO, Charles MD, Tempia S, Herring B, Vandemaele K, Moen A, Olsen SJ, Wentworth DE, Kondor R, Mott JA, Hirve S, Bresee JS, Mangtani P, Nguipdop-Djomo P, Azziz-Baumgartner E. Timing of seasonal influenza epidemics for 25 countries in Africa during 2010-19: a retrospective analysis. Lancet Glob Health 2023; 11:e729-e739. [PMID: 37061311 PMCID: PMC10126228 DOI: 10.1016/s2214-109x(23)00109-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2022] [Revised: 02/06/2023] [Accepted: 02/20/2023] [Indexed: 04/17/2023]
Abstract
BACKGROUND Using country-specific surveillance data to describe influenza epidemic activity could inform decisions on the timing of influenza vaccination. We analysed surveillance data from African countries to characterise the timing of seasonal influenza epidemics to inform national vaccination strategies. METHODS We used publicly available sentinel data from African countries reporting to the WHO Global Influenza Surveillance and Response FluNet platform that had 3-10 years of data collected during 2010-19. We calculated a 3-week moving proportion of samples positive for influenza virus and assessed epidemic timing using an aggregate average method. The start and end of each epidemic were defined as the first week when the proportion of positive samples exceeded or went below the annual mean, respectively, for at least 3 consecutive weeks. We categorised countries into five epidemic patterns: northern hemisphere-dominant, with epidemics occurring in October-March; southern hemisphere-dominant, with epidemics occurring in April-September; primarily northern hemisphere with some epidemic activity in southern hemisphere months; primarily southern hemisphere with some epidemic activity in northern hemisphere months; and year-round influenza transmission without a discernible northern hemisphere or southern hemisphere predominance (no clear pattern). FINDINGS Of the 34 countries reporting data to FluNet, 25 had at least 3 years of data, representing 46% of the countries in Africa and 89% of Africa's population. Study countries reported RT-PCR respiratory virus results for a total of 503 609 specimens (median 12 971 [IQR 9607-20 960] per country-year), of which 74 001 (15%; median 2078 [IQR 1087-3008] per country-year) were positive for influenza viruses. 248 epidemics occurred across 236 country-years of data (median 10 [range 7-10] per country). Six (24%) countries had a northern hemisphere pattern (Algeria, Burkina Faso, Egypt, Morocco, Niger, and Tunisia). Eight (32%) had a primarily northern hemisphere pattern with some southern hemisphere epidemics (Cameroon, Ethiopia, Mali, Mozambique, Nigeria, Senegal, Tanzania, and Togo). Three (12%) had a primarily southern hemisphere pattern with some northern hemisphere epidemics (Ghana, Kenya, and Uganda). Three (12%) had a southern hemisphere pattern (Central African Republic, South Africa, and Zambia). Five (20%) had no clear pattern (Côte d'Ivoire, DR Congo, Madagascar, Mauritius, and Rwanda). INTERPRETATION Most countries had identifiable influenza epidemic periods that could be used to inform authorities of non-seasonal and seasonal influenza activity, guide vaccine timing, and promote timely interventions. FUNDING None. TRANSLATIONS For the Berber, Luganda, Xhosa, Chewa, Yoruba, Igbo, Hausa and Afan Oromo translations of the abstract see Supplementary Materials section.
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Affiliation(s)
- Ledor S Igboh
- Influenza Division, National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, Atlanta, GA, USA; Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, UK; Immunization Systems Branch, Global Immunization Division, Center for Global Health, Centers for Disease Control and Prevention, Atlanta, GA, USA.
| | - Katherine Roguski
- National Center for Emerging and Zoonotic Infectious Diseases, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Perrine Marcenac
- Influenza Division, National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | | | - Myrna D Charles
- Influenza Division, National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Stefano Tempia
- School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa; Department of Infectious Hazard Management, World Health Organization, Geneva, Switzerland
| | - Belinda Herring
- World Health Organization-Regional Office for Africa, Brazzaville, Congo
| | - Katelijn Vandemaele
- Department of Infectious Hazard Management, World Health Organization, Geneva, Switzerland
| | - Ann Moen
- Influenza Division, National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Sonja J Olsen
- Influenza Division, National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - David E Wentworth
- Influenza Division, National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Rebecca Kondor
- Influenza Division, National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Josh A Mott
- Department of Infectious Hazard Management, World Health Organization, Geneva, Switzerland
| | - Siddhivinayak Hirve
- Department of Infectious Hazard Management, World Health Organization, Geneva, Switzerland
| | | | - Punam Mangtani
- Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, UK
| | - Patrick Nguipdop-Djomo
- Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, UK
| | - Eduardo Azziz-Baumgartner
- Influenza Division, National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, Atlanta, GA, USA
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20
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Vega-Alonso T, Lozano-Alonso JE, Ordax-Díez A. Comprehensive surveillance of acute respiratory infections during the COVID-19 pandemic: a methodological approach using sentinel networks, Castilla y León, Spain, January 2020 to May 2022. Euro Surveill 2023; 28:2200638. [PMID: 37227298 PMCID: PMC10283458 DOI: 10.2807/1560-7917.es.2023.28.21.2200638] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Accepted: 02/14/2023] [Indexed: 05/26/2023] Open
Abstract
BackgroundSince 1996, epidemiological surveillance of acute respiratory infections (ARI) in Spain has been limited to seasonal influenza, respiratory syncytial virus (RSV) and potential pandemic viruses. The COVID-19 pandemic provides opportunities to adapt existing systems for extended surveillance to capture a broader range of ARI.AimTo describe how the Influenza Sentinel Surveillance System of Castilla y León, Spain was rapidly adapted in 2020 to comprehensive sentinel surveillance for ARI, including influenza and COVID-19.MethodsUsing principles and methods of the health sentinel network, we integrated electronic medical record data from 68 basic surveillance units, covering 2.6% of the regional population between January 2020 to May 2022. We tested sentinel and non-sentinel samples sent weekly to the laboratory network for SARS-CoV-2, influenza viruses and other respiratory pathogens. The moving epidemic method (MEM) was used to calculate epidemic thresholds.ResultsARI incidence was estimated at 18,942 cases per 100,000 in 2020/21 and 45,223 in 2021/22, with similar seasonal fold increases by type of respiratory disease. Incidence of influenza-like illness was negligible in 2020/21 but a 5-week epidemic was detected by MEM in 2021/22. Epidemic thresholds for ARI and COVID-19 were estimated at 459.4 and 191.3 cases per 100,000 population, respectively. More than 5,000 samples were tested against a panel of respiratory viruses in 2021/22.ConclusionExtracting data from electronic medical records reported by trained professionals, combined with a standardised microbiological information system, is a feasible and useful method to adapt influenza sentinel reports to comprehensive ARI surveillance in the post-COVID-19 era.
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Affiliation(s)
- Tomás Vega-Alonso
- Regional Public Health Directorate, Regional Health Ministry, Valladolid, Spain
| | | | - Ana Ordax-Díez
- Regional Public Health Directorate, Regional Health Ministry, Valladolid, Spain
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21
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Wang D, Guerra A, Wittke F, Lang JC, Bakker K, Lee AW, Finelli L, Chen YH. Real-Time Monitoring of Infectious Disease Outbreaks with a Combination of Google Trends Search Results and the Moving Epidemic Method: A Respiratory Syncytial Virus Case Study. Trop Med Infect Dis 2023; 8:tropicalmed8020075. [PMID: 36828491 PMCID: PMC9962753 DOI: 10.3390/tropicalmed8020075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Revised: 01/07/2023] [Accepted: 01/16/2023] [Indexed: 01/22/2023] Open
Abstract
The COVID-19 pandemic has disrupted the seasonal patterns of several infectious diseases. Understanding when and where an outbreak may occur is vital for public health planning and response. We usually rely on well-functioning surveillance systems to monitor epidemic outbreaks. However, not all countries have a well-functioning surveillance system in place, or at least not for the pathogen in question. We utilized Google Trends search results for RSV-related keywords to identify outbreaks. We evaluated the strength of the Pearson correlation coefficient between clinical surveillance data and online search data and applied the Moving Epidemic Method (MEM) to identify country-specific epidemic thresholds. Additionally, we established pseudo-RSV surveillance systems, enabling internal stakeholders to obtain insights on the speed and risk of any emerging RSV outbreaks in countries with imprecise disease surveillance systems but with Google Trends data. Strong correlations between RSV clinical surveillance data and Google Trends search results from several countries were observed. In monitoring an upcoming RSV outbreak with MEM, data collected from both systems yielded similar estimates of country-specific epidemic thresholds, starting time, and duration. We demonstrate in this study the potential of monitoring disease outbreaks in real time and complement classical disease surveillance systems by leveraging online search data.
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Affiliation(s)
- Dawei Wang
- Health Economic and Decision Sciences, Merck & Co., Inc., Kenilworth, NJ 07065, USA
- Correspondence:
| | - Andrea Guerra
- Clinical Development, MSD, Kings Cross, London EC2M 6UR, UK
| | | | - John Cameron Lang
- Health Economic and Decision Sciences, Merck & Co., Inc., Kenilworth, NJ 07065, USA
| | - Kevin Bakker
- Health Economic and Decision Sciences, Merck & Co., Inc., Kenilworth, NJ 07065, USA
| | - Andrew W. Lee
- Clinical Development, Merck & Co., Inc., Kenilworth, NJ 07065, USA
| | - Lyn Finelli
- Clinical Development, Merck & Co., Inc., Kenilworth, NJ 07065, USA
| | - Yao-Hsuan Chen
- Health Economic and Decision Sciences, MSD, Kings Cross, London EC2M 6UR, UK
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22
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Motlogeloa O, Fitchett JM, Sweijd N. Defining the South African Acute Respiratory Infectious Disease Season. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:1074. [PMID: 36673827 PMCID: PMC9858855 DOI: 10.3390/ijerph20021074] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Revised: 01/04/2023] [Accepted: 01/05/2023] [Indexed: 06/17/2023]
Abstract
The acute respiratory infectious disease season, or colloquially the "flu season", is defined as the annually recurring period characterized by the prevalence of an outbreak of acute respiratory infectious diseases. It has been widely agreed that this season spans the winter period globally, but the precise timing or intensity of the season onset in South Africa is not well defined. This limits the efficacy of the public health sector to vaccinate for influenza timeously and for health facilities to synchronize efficiently for an increase in cases. This study explores the statistical intensity thresholds in defining this season to determine the start and finish date of the acute respiratory infectious disease season in South Africa. Two sets of data were utilized: public-sector hospitalization data that included laboratory-tested RSV and influenza cases and private-sector medical insurance claims under ICD 10 codes J111, J118, J110, and J00. Using the intensity threshold methodology proposed by the US CDC in 2017, various thresholds were tested for alignment with the nineteen-week flu season as proposed by the South African NICD. This resulted in varying thresholds for each province. The respiratory disease season commences in May and ends in September. These findings were seen in hospitalization cases and medical insurance claim cases, particularly with influenza-positive cases in Baragwanath hospital for the year 2019. These statistically determined intensity thresholds and timing of the acute respiratory infectious disease season allow for improved surveillance and preparedness among the public and private healthcare.
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Affiliation(s)
- Ogone Motlogeloa
- School of Geography, Archaeology and Environmental Studies, University of the Witwatersrand, Johannesburg 2050, South Africa
| | - Jennifer M. Fitchett
- School of Geography, Archaeology and Environmental Studies, University of the Witwatersrand, Johannesburg 2050, South Africa
| | - Neville Sweijd
- Alliance for Collaboration on Climate and Earth Systems Science (ACCESS), Council for Scientific and Industrial Research (CSIR), Pretoria 0001, South Africa
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Markers of Infection-Mediated Cardiac Damage in Influenza and COVID-19. Pathogens 2022; 11:pathogens11101191. [PMID: 36297248 PMCID: PMC9607279 DOI: 10.3390/pathogens11101191] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Revised: 09/18/2022] [Accepted: 10/13/2022] [Indexed: 11/07/2022] Open
Abstract
Introduction: Influenza and the coronavirus disease 2019 (COVID-19) are two potentially severe viral infections causing significant morbidity and mortality. The causative viruses, influenza A/B and the severe acute respiratory syndrome coronavirus 2 (SARS-CoV2) can cause both pulmonary and extra-pulmonary disease, including cardiovascular involvement. The objective of this study was to determine the levels of cardiac biomarkers in hospitalized patients infected with influenza or COVID-19 and their correlation with secondary outcomes. Methods: We performed a retrospective comparative analysis of cardiac biomarkers in patients hospitalized at our department with influenza or COVID-19 by measuring high-sensitivity troponin-T (hs-TnT) and creatinine kinase (CK) in plasma. Secondary outcomes were intensive care unit (ICU) admission and all-cause in-hospital mortality. Results: We analyzed the data of 250 influenza patients and 366 COVID-19 patients. 58.6% of patients with influenza and 46.2% of patients with COVID-19 presented with increased hs-TnT levels. Patients of both groups with increased hs-TnT levels were significantly more likely to require ICU treatment or to die during their hospital stay. Compared with COVID-19, cardiac biomarkers were significantly higher in patients affected by influenza of all age groups, regardless of pre-existing cardiovascular disease. In patients aged under 65 years, no significant difference in ICU admission and mortality was detected between influenza and COVID-19, whereas significantly more COVID-19 patients 65 years or older died or required intensive care treatment. Conclusions: Our study shows that increased cardiac biomarkers are associated with higher mortality and ICU admission in both, influenza and SARS-CoV-2-infected patients. Cardiac biomarkers are higher in the influenza cohort; however, this does not translate into worse outcomes when compared with the COVID-19 cohort.
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Sominina A, Danilenko D, Komissarov A, Karpova L, Pisareva M, Fadeev A, Konovalova N, Eropkin M, Stolyarov K, Shtro A, Burtseva E, Lioznov D. Resurgence of Influenza Circulation in the Russian Federation during the Delta and Omicron COVID-19 Era. Viruses 2022; 14:1909. [PMID: 36146716 PMCID: PMC9506591 DOI: 10.3390/v14091909] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2022] [Revised: 08/24/2022] [Accepted: 08/26/2022] [Indexed: 11/25/2022] Open
Abstract
Influenza circulation was substantially reduced after March 2020 in the European region and globally due to the wide introduction of non-pharmaceutical interventions (NPIs) against COVID-19. The virus, however, has been actively circulating in natural reservoirs. In summer 2021, NPIs were loosened in Russia, and influenza activity resumed shortly thereafter. Here, we summarize the epidemiological and virological data on the influenza epidemic in Russia in 2021-2022 obtained by the two National Influenza Centers. We demonstrate that the commonly used baseline for acute respiratory infection (ARI) is no longer sufficiently sensitive and BL for ILI incidence was more specific for early recognition of the epidemic. We also present the results of PCR detection of influenza, SARS-CoV-2 and other respiratory viruses as well as antigenic and genetic analysis of influenza viruses. Influenza A(H3N2) prevailed this season with influenza B being detected at low levels at the end of the epidemic. The majority of A(H3N2) viruses were antigenically and genetically homogenous and belonged to the clade 3C.2a1b.2a.2 of the vaccine strain A/Darwin/9/2021 for the season 2022-2023. All influenza B viruses belonged to the Victoria lineage and were similar to the influenza B/Austria/1359417/2021 virus. No influenza A(H1N1)pdm09 and influenza B/Yamagata lineage was isolated last season.
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Affiliation(s)
- Anna Sominina
- Smorodintsev Research Institute of Influenza, 197376 Saint Petersburg, Russia
| | - Daria Danilenko
- Smorodintsev Research Institute of Influenza, 197376 Saint Petersburg, Russia
| | - Andrey Komissarov
- Smorodintsev Research Institute of Influenza, 197376 Saint Petersburg, Russia
| | - Ludmila Karpova
- Smorodintsev Research Institute of Influenza, 197376 Saint Petersburg, Russia
| | - Maria Pisareva
- Smorodintsev Research Institute of Influenza, 197376 Saint Petersburg, Russia
| | - Artem Fadeev
- Smorodintsev Research Institute of Influenza, 197376 Saint Petersburg, Russia
| | - Nadezhda Konovalova
- Smorodintsev Research Institute of Influenza, 197376 Saint Petersburg, Russia
| | - Mikhail Eropkin
- Smorodintsev Research Institute of Influenza, 197376 Saint Petersburg, Russia
| | - Kirill Stolyarov
- Smorodintsev Research Institute of Influenza, 197376 Saint Petersburg, Russia
| | - Anna Shtro
- Smorodintsev Research Institute of Influenza, 197376 Saint Petersburg, Russia
| | - Elena Burtseva
- National Research Center for Epidemiology and Microbiology Named after N.F. Gamaleya, 123098 Moscow, Russia
| | - Dmitry Lioznov
- Smorodintsev Research Institute of Influenza, 197376 Saint Petersburg, Russia
- Department of Infectious Diseases and Epidemiology, First Pavlov State Medical University, 197022 Saint Petersburg, Russia
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Epidemiological survey to establish thresholds for influenza among children in satellite cities of Tokyo, Japan, 2014-2018. Western Pac Surveill Response J 2022; 13:1-9. [PMID: 36452216 PMCID: PMC9671205 DOI: 10.5365/wpsar.2022.13.3.911] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/05/2022] Open
Abstract
OBJECTIVE We described the characteristics of children reported as having influenza across five consecutive influenza seasons and investigated the usefulness of setting influenza thresholds in two satellite cities of Tokyo, Japan. METHODS An annual survey was conducted among parents of children at preschools (kindergartens and nursery schools), elementary schools and junior high schools in Toda and Warabi cities, Saitama prefecture, at the end of the 2014-2018 influenza seasons. Using the World Health Organization method, we established seasonal, high and alert thresholds. RESULTS There were 64 586 children included in the analysis. Over the five seasons, between 19.1% and 22% of children annually were reported as having tested positive for influenza. Influenza type A was reported as the dominant type, although type B was also reported in more than 40% of cases in the 2015 and 2017 seasons. The median period of the seasonal peak was 3 weeks in mid-January, regardless of school level. Of the five surveyed seasons, the high threshold was reached in 2014 and 2018, with no season exceeding the alert threshold. DISCUSSION This study provides insights into the circulation of influenza in children in the study areas of Toda and Warabi, Japan, from 2014 to 2018. Although we were able to utilize these annual surveys to calculate influenza thresholds from five consecutive seasons, the prospective usefulness of these thresholds is limited as the survey is conducted at the end of the influenza season.
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Mukka M, Pesälä S, Juutinen A, Virtanen MJ, Mustonen P, Kaila M, Helve O. Online searches of children’s oseltamivir in public primary and specialized care: Detecting influenza outbreaks in Finland using dedicated databases for health care professionals. PLoS One 2022; 17:e0272040. [PMID: 35930527 PMCID: PMC9355218 DOI: 10.1371/journal.pone.0272040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Accepted: 07/12/2022] [Indexed: 11/18/2022] Open
Abstract
Introduction
Health care professionals working in primary and specialized care typically search for medical information from Internet sources. In Finland, Physician’s Databases are online portals aimed at professionals seeking medical information. As dosage errors may occur when prescribing medication to children, professionals’ need for reliable medical information has increased in public health care centers and hospitals. Influenza continues to be a public health threat, with young children at risk of developing severe illness and easily transmitting the virus. Oseltamivir is used to treat children with influenza. The objective of this study was to compare searches for children’s oseltamivir and influenza diagnoses in primary and specialized care, and to determine if the searches could aid detection of influenza outbreaks.
Methods
We compared searches in Physician’s Databases for children’s oral suspension of oseltamivir (6 mg/mL) for influenza diagnoses of children under 7 years and laboratory findings of influenza A and B from the National Infectious Disease Register. Searches and diagnoses were assessed in primary and specialized care across Finland by season from 2012–2016. The Moving Epidemic Method (MEM) calculated seasonal starts and ends, and paired differences in the mean compared two indicators. Correlation was tested to compare seasons.
Results
We found that searches and diagnoses in primary and specialized care showed visually similar patterns annually. The MEM-calculated starting weeks in searches appeared mainly in the same week. Oseltamivir searches in primary care preceded diagnoses by −1.0 weeks (95% CI: −3.0, −0.3; p = 0.132) with very high correlation (τ = 0.913). Specialized care oseltamivir searches and diagnoses correlated moderately (τ = 0.667).
Conclusion
Health care professionals’ searches for children’s oseltamivir in online databases linked with the registers of children’s influenza diagnoses in primary and specialized care. Therefore, database searches should be considered as supplementary information in disease surveillance when detecting influenza epidemics.
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Affiliation(s)
- Milla Mukka
- University of Helsinki, Helsinki, Finland
- * E-mail:
| | - Samuli Pesälä
- University of Helsinki, Helsinki, Finland
- Epidemiological Operations Unit, City of Helsinki, Helsinki, Finland
| | - Aapo Juutinen
- Department of Health Security, Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Mikko J. Virtanen
- Department of Health Security, Finnish Institute for Health and Welfare, Helsinki, Finland
| | | | - Minna Kaila
- Clinicum, University of Helsinki, Helsinki, Finland
| | - Otto Helve
- Department of Health Security, Finnish Institute for Health and Welfare, Helsinki, Finland
- Children’s Hospital, Pediatric Research Center, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
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Vega T, Hilario F, Pérez-Caro M, Núñez-Torres R, Pinto RM, González-Neira A. Genetic, environmental and life-style factors associated with longevity. Protocol and response of the LONGECYL Study. GACETA SANITARIA 2022; 36:260-264. [PMID: 35339311 DOI: 10.1016/j.gaceta.2022.01.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/26/2021] [Revised: 01/03/2022] [Accepted: 01/03/2022] [Indexed: 11/18/2022]
Abstract
OBJECTIVE To describe the objectives, the methodological approach, the response rate of the Genetic, Environmental and Life-style Factors Study in Castilla y León (Spain). METHOD The Health Sentinel Network studied a sample of long-lived individuals aged 95 or more (LLI). The study included biological samples processed with the Global Screening Array v3.0 that contains a total of 730,059 markers. Written consent was obtained before the examination. CONCLUSIONS The LLI contacted were 944, and 760 were completed studied. The 87.4% of LLI were born in Castile and Leon and only 1% were non-native of Spain. Severe cognitive impairment was declared in 8.1% of men and 19.2% of women. Genotyping was performed in 739 LLI, the 78.3% of the contacted sample. Family doctors and nurses achieve high participation in population-based studies. DNA samples were taken from 94% of fully studied LLI, and 100% of these samples where successfully genotyped.
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Affiliation(s)
- Tomás Vega
- Dirección General de Salud Pública, Consejería de Sanidad, Valladolid, Spain.
| | - Fernando Hilario
- Dirección General de Salud Pública, Consejería de Sanidad, Valladolid, Spain
| | - María Pérez-Caro
- Banco Nacional de ADN, Universidad de Salamanca, Salamanca, Spain
| | - Rocío Núñez-Torres
- Unidad de Genotipado Humano-CEGEN, Centro Nacional de Investigaciones Oncológicas, Madrid, Spain
| | - Rosa M Pinto
- Banco Nacional de ADN, Universidad de Salamanca, Salamanca, Spain
| | - Anna González-Neira
- Unidad de Genotipado Humano-CEGEN, Centro Nacional de Investigaciones Oncológicas, Madrid, Spain
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Jiang Y, Tong YQ, Fang B, Zhang WK, Yu XJ. Applying the Moving Epidemic Method to Establish the Influenza Epidemic Thresholds and Intensity Levels for Age-Specific Groups in Hubei Province, China. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19031677. [PMID: 35162701 PMCID: PMC8834852 DOI: 10.3390/ijerph19031677] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/11/2021] [Revised: 01/28/2022] [Accepted: 01/28/2022] [Indexed: 12/07/2022]
Abstract
BACKGROUND School-aged children were reported to act as the main transmitter during influenza epidemic seasons. It is vital to set up an early detection method to help with the vaccination program in such a high-risk population. However, most relative studies only focused on the general population. Our study aims to describe the influenza epidemiology characteristics in Hubei Province and to introduce the moving epidemic method to establish the epidemic thresholds for age-specific groups. METHODS We divided the whole population into pre-school, school-aged and adult groups. The virology data from 2010/2011 to 2017/2018 were applied to the moving epidemic method to establish the epidemic thresholds for the general population and age-specific groups for the detection of influenza in 2018/2019. The performances of the model were compared by the cross-validation process. RESULTS The epidemic threshold for school-aged children in the 2018/2019 season was 15.42%. The epidemic thresholds for influenza A virus subtypes H1N1 and H3N2 and influenza B were determined as 5.68%, 6.12% and 10.48%, respectively. The median start weeks of the school-aged children were similar to the general population. The cross-validation process showed that the sensitivity of the model established with school-aged children was higher than those established with the other age groups in total influenza, H1N1 and influenza B, while it was only lower than the general population group in H3N2. CONCLUSIONS This study proved the feasibility of applying the moving epidemic method in Hubei Province. Additional influenza surveillance and vaccination strategies should be well-organized for school-aged children to reduce the disease burden of influenza in China.
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Affiliation(s)
- Yuan Jiang
- State Key Laboratory of Virology, School of Public Health, Wuhan University, Wuhan 430071, China; (Y.J.); (W.-k.Z.)
| | - Ye-qing Tong
- Hubei Provincial Center for Disease Control and Prevention, Wuhan 430079, China; (Y.-q.T.); (B.F.)
| | - Bin Fang
- Hubei Provincial Center for Disease Control and Prevention, Wuhan 430079, China; (Y.-q.T.); (B.F.)
| | - Wen-kang Zhang
- State Key Laboratory of Virology, School of Public Health, Wuhan University, Wuhan 430071, China; (Y.J.); (W.-k.Z.)
| | - Xue-jie Yu
- State Key Laboratory of Virology, School of Public Health, Wuhan University, Wuhan 430071, China; (Y.J.); (W.-k.Z.)
- Correspondence:
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Dahlgren FS, Rossen LM, Fry AM, Reed C. Severity of the COVID-19 pandemic assessed with all-cause mortality in the United States during 2020. Influenza Other Respir Viruses 2022; 16:411-416. [PMID: 35044097 PMCID: PMC8983917 DOI: 10.1111/irv.12923] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Revised: 09/26/2021] [Accepted: 09/28/2021] [Indexed: 11/30/2022] Open
Abstract
BACKGROUND In the United States, infection with SARS-CoV-2 caused 380,000 reported deaths from March to December 2020. METHODS We adapted the Moving Epidemic Method to all-cause mortality data from the United States to assess the severity of the COVID-19 pandemic across age groups and all 50 states. By comparing all-cause mortality during the pandemic with intensity thresholds derived from recent, historical all-cause mortality, we categorized each week from March to December 2020 as either low severity, moderate severity, high severity, or very high severity. RESULTS Nationally for all ages combined, all-cause mortality was in the very high severity category for 9 weeks. Among people 18 to 49 years of age, there were 29 weeks of consecutive very high severity mortality. Forty-seven states, the District of Columbia, and New York City each experienced at least 1 week of very high severity mortality for all ages combined. CONCLUSIONS These periods of very high severity of mortality during March through December 2020 are likely directly or indirectly attributable to the COVID-19 pandemic. This method for standardized comparison of severity over time across different geographies and demographic groups provides valuable information to understand the impact of the COVID-19 pandemic and to identify specific locations or subgroups for deeper investigations into differences in severity.
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Affiliation(s)
- F Scott Dahlgren
- CDC COVID-19 Response Team, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
| | - Lauren M Rossen
- National Center for Health Statistics, Centers for Disease Control and Prevention, Hyattsville, Maryland, USA
| | - Alicia M Fry
- CDC COVID-19 Response Team, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
| | - Carrie Reed
- CDC COVID-19 Response Team, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
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Hernandez-Avila M, Tamayo-Ortiz M, Vieyra-Romero W, Gutierrez-Diaz H, Zepeda-Tello R, Barros-Sierra D, Velasco-Reyna R, Ramirez-Polanco E, Ortega-Alvarez M. Use of Private Sector Workforce Respiratory Disease Short-Term Disability Claims to Assess SARS-CoV-2, Mexico, 2020. Emerg Infect Dis 2022. [DOI: 10.3201/eid2801.21.1537] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
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31
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Hernandez-Avila M, Tamayo-Ortiz M, Vieyra-Romero W, Gutierrez-Diaz H, Zepeda-Tello R, Barros-Sierra D, Velasco-Reyna R, Ramirez-Polanco E, Ortega-Alvarez M. Use of Private Sector Workforce Respiratory Disease Short-Term Disability Claims to Assess SARS-CoV-2, Mexico, 2020. Emerg Infect Dis 2022; 28:214-218. [PMID: 34856113 PMCID: PMC8714224 DOI: 10.3201/eid2801.211357] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/04/2022] Open
Abstract
We examined respiratory disease short-term disability claims submitted to the Mexican Social Security Institute during 2020. A total of 1,631,587 claims were submitted by 19.1 million insured workers. Cumulative incidence (8.5%) was 3.6 times higher than that for January 2015‒December-2019. Workers in healthcare, social assistance, self-service, and retail stores were disproportionately affected.
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Domegan L, Garvey P, McEnery M, Fiegenbaum R, Brabazon E, Quintyne KI, O'Connor L, Cuddihy J, O'Donnell J. Establishing a COVID-19 pandemic severity assessment surveillance system in Ireland. Influenza Other Respir Viruses 2022; 16:172-177. [PMID: 34609049 PMCID: PMC8652866 DOI: 10.1111/irv.12890] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Revised: 07/02/2021] [Accepted: 07/11/2021] [Indexed: 11/27/2022] Open
Abstract
We developed a COVID-19 pandemic severity assessment (PSA) monitoring system in Ireland, in order to inform and improve public health preparedness, response and recovery. The system based on the World Health Organization (WHO) Pandemic Influenza Severity Assessment (PISA) project included a panel of surveillance parameters for the following indicators: transmissibility, impact and disease severity. Age-specific thresholds were established for each parameter and data visualised using heat maps. The findings from the first pandemic wave in Ireland have shown that the WHO PISA system can be adapted for COVID-19, providing a standardised tool for early warning and monitoring pandemic severity.
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Affiliation(s)
- Lisa Domegan
- Health Service ExecutiveHealth Protection Surveillance CentreDublinIreland
- European Programme for Intervention Epidemiology Training (EPIET)European Centre for Disease Prevention and Control (ECDC)StockholmSweden
| | - Patricia Garvey
- Health Service ExecutiveHealth Protection Surveillance CentreDublinIreland
| | - Maeve McEnery
- Health Service ExecutiveHealth Protection Surveillance CentreDublinIreland
| | - Rachel Fiegenbaum
- Health Service ExecutiveHealth Protection Surveillance CentreDublinIreland
| | - Elaine Brabazon
- Department of Public HealthHealth Service Executive North‐EastNavanIreland
| | - Keith Ian Quintyne
- Department of Public HealthHealth Service Executive North‐EastNavanIreland
| | - Lois O'Connor
- Health Service ExecutiveHealth Protection Surveillance CentreDublinIreland
| | - John Cuddihy
- Health Service ExecutiveHealth Protection Surveillance CentreDublinIreland
| | - Joan O'Donnell
- Health Service ExecutiveHealth Protection Surveillance CentreDublinIreland
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Akhtar Z, Chowdhury F, Rahman M, Ghosh PK, Ahmmed MK, Islam MA, Mott JA, Davis W. Seasonal influenza during the COVID-19 pandemic in Bangladesh. PLoS One 2021; 16:e0255646. [PMID: 34343203 PMCID: PMC8330950 DOI: 10.1371/journal.pone.0255646] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2021] [Accepted: 07/14/2021] [Indexed: 11/19/2022] Open
Abstract
INTRODUCTION During the 2019 novel coronavirus infectious disease (COVID-19) pandemic in 2020, limited data from several countries suggested reduced seasonal influenza viruses' circulation. This was due to community mitigation measures implemented to control the pandemic of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We used sentinel surveillance data to identify changes in the 2020 influenza season compared with previous seasons in Bangladesh. METHODS We used hospital-based influenza surveillance (HBIS) data of Bangladesh that are generated year-round and are population-representative severe acute respiratory infection (SARI) data for all age groups from seven public and two private tertiary care level hospitals data from 2016 to 2019. We applied the moving epidemic method (MEM) by using R language (v4.0.3), and MEM web applications (v2.14) on influenza-positive rates of SARI cases collected weekly to estimate an average seasonal influenza curve and establish epidemic thresholds. RESULTS The 2016-2019 average season started on epi week 18 (95% CI: 15-25) and lasted 12.5 weeks (95% CI: 12-14 weeks) until week 30.5. The 2020 influenza season started on epi week 36 and ended at epi week 41, lasting for only five weeks. Therefore, influenza epidemic started 18 weeks later, was 7.5 weeks shorter, and was less intense than the average epidemic of the four previous years. The 2020 influenza season started on the same week when COVID-19 control measures were halted, and 13 weeks after the measures were relaxed. CONCLUSION Our findings suggest that seasonal influenza circulation in Bangladesh was delayed and less intense in 2020 than in previous years. Community mitigation measures may have contributed to this reduction of seasonal influenza transmission. These findings contribute to a limited but growing body of evidence that influenza seasons were altered globally in 2020.
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Affiliation(s)
- Zubair Akhtar
- International Center for Diarrheal Diseases, Bangladesh, (icddr,b) Programme for Emerging Infections, Dhaka, Bangladesh
| | - Fahmida Chowdhury
- International Center for Diarrheal Diseases, Bangladesh, (icddr,b) Programme for Emerging Infections, Dhaka, Bangladesh
| | - Mahmudur Rahman
- International Center for Diarrheal Diseases, Bangladesh, (icddr,b) Programme for Emerging Infections, Dhaka, Bangladesh
| | - Probir Kumar Ghosh
- International Center for Diarrheal Diseases, Bangladesh, (icddr,b) Programme for Emerging Infections, Dhaka, Bangladesh
| | - Md. Kaousar Ahmmed
- International Center for Diarrheal Diseases, Bangladesh, (icddr,b) Programme for Emerging Infections, Dhaka, Bangladesh
| | - Md Ariful Islam
- International Center for Diarrheal Diseases, Bangladesh, (icddr,b) Programme for Emerging Infections, Dhaka, Bangladesh
| | - Joshua A. Mott
- Influenza Division, Centers for Disease Control and Prevention Regional Influenza Program, Bangkok, Thailand
| | - William Davis
- Influenza Division, Centers for Disease Control and Prevention Regional Influenza Program, Bangkok, Thailand
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Alamo T, G Reina D, Millán Gata P, Preciado VM, Giordano G. Data-driven methods for present and future pandemics: Monitoring, modelling and managing. ANNUAL REVIEWS IN CONTROL 2021; 52:448-464. [PMID: 34220287 PMCID: PMC8238691 DOI: 10.1016/j.arcontrol.2021.05.003] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/30/2020] [Revised: 05/24/2021] [Accepted: 05/27/2021] [Indexed: 05/29/2023]
Abstract
This survey analyses the role of data-driven methodologies for pandemic modelling and control. We provide a roadmap from the access to epidemiological data sources to the control of epidemic phenomena. We review the available methodologies and discuss the challenges in the development of data-driven strategies to combat the spreading of infectious diseases. Our aim is to bring together several different disciplines required to provide a holistic approach to epidemic analysis, such as data science, epidemiology, and systems-and-control theory. A 3M-analysis is presented, whose three pillars are: Monitoring, Modelling and Managing. The focus is on the potential of data-driven schemes to address three different challenges raised by a pandemic: (i) monitoring the epidemic evolution and assessing the effectiveness of the adopted countermeasures; (ii) modelling and forecasting the spread of the epidemic; (iii) making timely decisions to manage, mitigate and suppress the contagion. For each step of this roadmap, we review consolidated theoretical approaches (including data-driven methodologies that have been shown to be successful in other contexts) and discuss their application to past or present epidemics, such as Covid-19, as well as their potential application to future epidemics.
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Affiliation(s)
- Teodoro Alamo
- Departamento de Ingeniería de Sistemas y Automática, Universidad de Sevilla, Escuela Superior de Ingenieros, Sevilla, Spain
| | - Daniel G Reina
- Departamento de Ingeniería Electrónica, Universidad de Sevilla, Escuela Superior de Ingenieros, Sevilla, Spain
| | - Pablo Millán Gata
- Departamento de Ingeniería, Universidad Loyola Andalucía, Seville, Spain
| | - Victor M Preciado
- Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, USA
| | - Giulia Giordano
- Department of Industrial Engineering, University of Trento, Trento, Italy
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Spreco A, Eriksson O, Dahlström Ö, Cowling BJ, Biggerstaff M, Ljunggren G, Jöud A, Istefan E, Timpka T. Nowcasting (Short-Term Forecasting) of Influenza Epidemics in Local Settings, Sweden, 2008-2019. Emerg Infect Dis 2021; 26:2669-2677. [PMID: 33079036 PMCID: PMC7588521 DOI: 10.3201/eid2611.200448] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/04/2022] Open
Abstract
The timing of influenza case incidence during epidemics can differ between regions within nations and states. We conducted a prospective 10-year evaluation (January 2008–February 2019) of a local influenza nowcasting (short-term forecasting) method in 3 urban counties in Sweden with independent public health administrations by using routine health information system data. Detection-of-epidemic-start (detection), peak timing, and peak intensity were nowcasted. Detection displayed satisfactory performance in 2 of the 3 counties for all nonpandemic influenza seasons and in 6 of 9 seasons for the third county. Peak-timing prediction showed satisfactory performance from the influenza season 2011–12 onward. Peak-intensity prediction also was satisfactory for influenza seasons in 2 of the counties but poor in 1 county. Local influenza nowcasting was satisfactory for seasonal influenza in 2 of 3 counties. The less satisfactory performance in 1 of the study counties might be attributable to population mixing with a neighboring metropolitan area.
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Assessing the appropriateness of the Moving Epidemic Method and WHO Average Curve Method for the syndromic surveillance of acute respiratory infection in Mauritius. PLoS One 2021; 16:e0252703. [PMID: 34081752 PMCID: PMC8174728 DOI: 10.1371/journal.pone.0252703] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2020] [Accepted: 05/20/2021] [Indexed: 11/19/2022] Open
Abstract
Introduction Mauritius introduced Acute respiratory infection (ARI) syndromic surveillance in 2007. The Moving Epidemic Method (MEM) and the World Health Organization Average Curve Method (WHO ACM) have been used widely in several countries to establish thresholds to determine the seasonality of acute respiratory infections. This study aimed to evaluate the appropriateness of these tools for ARI syndromic surveillance in Mauritius, where two or more waves are observed. Method The proportion of attendance due to acute respiratory infections was identified as the transmissibility indicator to describe seasonality using the Moving Epidemic Method and the WHO Average Curve Method. The proportion was obtained from weekly outpatient data between 2012 and 2018 collected from the sentinel acute respiratory infections surveillance. A cross-validation analysis was carried out. The resulting indicators of the goodness of fit model were used to assess the robustness of the seasonal/epidemic threshold of both the Moving Epidemic Method and WHO Average Curve Method. Additionally, a comparative analysis examined the integrity of the thresholds without the year 2017. Result The cross-validation analysis demonstrated no statistically significant differences between the means scores of the indicators when comparing the two waves/seasons curves of WHO ACM and MEM. The only exception being that the Wilcoxon sign rank test strongly supported that the specificity mean score of the two waves/seasons curve for WHO ACM outweighed that of its corresponding wave model for the MEM (P = 0.002). The comparative analysis with 2017 data showed the value of the epidemic threshold remained the same regardless of the methods and the number of seasonal waves. Conclusion The two waves models of the Moving Epidemic Method and WHO Average Curve Method could be deployed for acute respiratory infection syndromic surveillance in Mauritius, considering that two or more activity peaks are observed in a season.
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Choi H, Choi WS, Han E. Suggestion of a simpler and faster influenza-like illness surveillance system using 2014-2018 claims data in Korea. Sci Rep 2021; 11:11243. [PMID: 34045533 PMCID: PMC8159991 DOI: 10.1038/s41598-021-90511-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2020] [Accepted: 05/06/2021] [Indexed: 11/10/2022] Open
Abstract
Influenza is an important public health concern. We propose a new real-time influenza-like illness (ILI) surveillance system that utilizes a nationwide prospective drug utilization monitoring in Korea. We defined ILI-related claims as outpatient claims that contain both antipyretic and antitussive agents and calculated the weekly rate of ILI-related claims, which was compared to weekly ILI rates from clinical sentinel surveillance data during 2014-2018. We performed a cross-correlation analysis using Pearson's correlation, time-series analysis to explore actual correlations after removing any dubious correlations due to underlying non-stationarity in both data sets. We used the moving epidemic method (MEM) to estimate an absolute threshold to designate potential influenza epidemics for the weeks with incidence rates above the threshold. We observed a strong correlation between the two surveillance systems each season. The absolute thresholds for the 4-years were 84.64 and 86.19 cases per 1000claims for claims data and 12.27 and 16.82 per 1000 patients for sentinel data. The epidemic patterns were more similar in the 2016-2017 and 2017-2018 seasons than the 2014-2015 and 2015-2016 seasons. ILI claims data can be loaded to a drug utilization review system in Korea to make an influenza surveillance system.
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Affiliation(s)
- HeeKyoung Choi
- College of Pharmacy, Yonsei Institute of Pharmaceutical Research, Yonsei University, 162-1 Songdo-dong, Yeonsu-gu, Incheon, Seoul, Republic of Korea
- Division of Infectious Diseases, Department of Internal Medicine, National Health Insurance Service Ilsan Hospital, Ilsan, Republic of Korea
| | - Won Suk Choi
- Division of Infectious Diseases, Department of Internal Medicine, Ansan Hospital, Korea University College of Medicine, Ansan, Republic of Korea
| | - Euna Han
- College of Pharmacy, Yonsei Institute of Pharmaceutical Research, Yonsei University, 162-1 Songdo-dong, Yeonsu-gu, Incheon, Seoul, Republic of Korea.
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Impact of the COVID-19 nonpharmaceutical interventions on influenza and other respiratory viral infections in New Zealand. Nat Commun 2021; 12:1001. [PMID: 33579926 PMCID: PMC7881137 DOI: 10.1038/s41467-021-21157-9] [Citation(s) in RCA: 231] [Impact Index Per Article: 77.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2020] [Accepted: 01/13/2021] [Indexed: 02/08/2023] Open
Abstract
Stringent nonpharmaceutical interventions (NPIs) such as lockdowns and border closures are not currently recommended for pandemic influenza control. New Zealand used these NPIs to eliminate coronavirus disease 2019 during its first wave. Using multiple surveillance systems, we observed a parallel and unprecedented reduction of influenza and other respiratory viral infections in 2020. This finding supports the use of these NPIs for controlling pandemic influenza and other severe respiratory viral threats. New Zealand has been relatively successful in controlling COVID-19 due to implementation of strict non-pharmaceutical interventions. Here, the authors demonstrate a striking decline in reports of influenza and other non-influenza respiratory pathogens over winter months in which the interventions have been in place.
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Kang M, Tan X, Ye M, Liao Y, Song T, Tang S. The moving epidemic method applied to influenza surveillance in Guangdong, China. Int J Infect Dis 2021; 104:594-600. [PMID: 33515775 DOI: 10.1016/j.ijid.2021.01.058] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2020] [Revised: 01/20/2021] [Accepted: 01/22/2021] [Indexed: 12/14/2022] Open
Abstract
OBJECTIVES The moving epidemic method (MEM) has been well used for assessing seasonal influenza epidemics in temperate regions. This study used the MEM to establish epidemic threshold for influenza in Guangdong, a subtropical province in China. METHODS Influenza virology surveillance data from 2011/2012 to 2017/2018 seasons in Guangdong were used with the MEM to calculate the epidemic thresholds and timeously detect the 2018/2019 influenza season epidemic. The weekly positive proportion of influenza A(H1N1)pdm09, A(H3N2), B/Victoria-lineage and B/Yamagata-lineage were separately adapted to calculate the subtype-specific epidemic thresholds. The performance of MEM was evaluated using a cross-validation procedure. RESULTS For the 2018/2019 influenza season, the epidemic threshold of a weekly positive proportion was 15.08%. Epidemic detection for the 2018/2019 season was 1 week in advance. Influenza A(H1N1)pdm09, B/Yamagata-lineage and B/Victoria-lineage prevailed during winter and spring and their epidemic thresholds were 5.12%, 4.53% and 4.38%, respectively. Influenza A(H3N2) was active in the summer, with an epidemic threshold of 11.99%. CONCLUSIONS Using influenza virology surveillance data stratified by types of influenza virus, the MEM was effectively used in Guangdong, China. This study provided a practical way for subtropical regions to establish local influenza epidemic thresholds.
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Affiliation(s)
- Min Kang
- School of Public Health, Southern Medical University, Guangzhou, China; Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China.
| | - Xiaohua Tan
- Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China
| | - Meiyun Ye
- Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China
| | - Yu Liao
- Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China
| | - Tie Song
- Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China.
| | - Shixing Tang
- School of Public Health, Southern Medical University, Guangzhou, China.
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Redondo-Bravo L, Delgado-Sanz C, Oliva J, Vega T, Lozano J, Larrauri A, The Spanish Influenza Sentinel Surveillance System. Transmissibility of influenza during the 21st-century epidemics, Spain, influenza seasons 2001/02 to 2017/18. ACTA ACUST UNITED AC 2020; 25. [PMID: 32489178 PMCID: PMC7268270 DOI: 10.2807/1560-7917.es.2020.25.21.1900364] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
Abstract
BackgroundUnderstanding influenza seasonality is necessary for determining policies for influenza control.AimWe characterised transmissibility during seasonal influenza epidemics, including one influenza pandemic, in Spain during the 21th century by using the moving epidemic method (MEM) to calculate intensity levels and estimate differences across seasons and age groups.MethodsWe applied the MEM to Spanish Influenza Sentinel Surveillance System data from influenza seasons 2001/02 to 2017/18. A modified version of Goldstein's proxy was used as an epidemiological-virological parameter. We calculated the average starting week and peak, the length of the epidemic period and the length from the starting week to the peak of the epidemic, by age group and according to seasonal virus circulation.ResultsIndividuals under 15 years of age presented higher transmissibility, especially in the 2009 influenza A(H1N1) pandemic. Seasons with dominance/co-dominance of influenza A(H3N2) virus presented high intensities in older adults. The 2004/05 influenza season showed the highest influenza-intensity level for all age groups. In 12 seasons, the epidemic started between week 50 and week 3. Epidemics started earlier in individuals under 15 years of age (-1.8 weeks; 95% confidence interval (CI):-2.8 to -0.7) than in those over 64 years when influenza B virus circulated as dominant/co-dominant. The average time from start to peak was 4.3 weeks (95% CI: 3.6-5.0) and the average epidemic length was 8.7 weeks (95% CI: 7.9-9.6).ConclusionsThese findings provide evidence for intensity differences across seasons and age groups, and can be used guide public health actions to diminish influenza-related morbidity and mortality.
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Affiliation(s)
| | - Concepción Delgado-Sanz
- National Centre of Epidemiology, CIBER Epidemiología y Salud Pública (CIBERESP), Institute of Health Carlos III (ISCIII), Madrid, Spain
| | - Jesús Oliva
- National Centre of Epidemiology, CIBER Epidemiología y Salud Pública (CIBERESP), Institute of Health Carlos III (ISCIII), Madrid, Spain
| | - Tomás Vega
- Public Health Directorate, Castilla y León Regional Health Ministry, Valladolid, Spain
| | - Jose Lozano
- Public Health Directorate, Castilla y León Regional Health Ministry, Valladolid, Spain
| | - Amparo Larrauri
- National Centre of Epidemiology, CIBER Epidemiología y Salud Pública (CIBERESP), Institute of Health Carlos III (ISCIII), Madrid, Spain
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Huang QS, Wood T, Jelley L, Jennings T, Jefferies S, Daniells K, Nesdale A, Dowell T, Turner N, Campbell-Stokes P, Balm M, Dobinson HC, Grant CC, James S, Aminisani N, Ralston J, Gunn W, Bocacao J, Danielewicz J, Moncrieff T, McNeill A, Lopez L, Waite B, Kiedrzynski T, Schrader H, Gray R, Cook K, Currin D, Engelbrecht C, Tapurau W, Emmerton L, Martin M, Baker MG, Taylor S, Trenholme A, Wong C, Lawrence S, McArthur C, Stanley A, Roberts S, Ranama F, Bennett J, Mansell C, Dilcher M, Werno A, Grant J, van der Linden A, Youngblood B, Thomas PG, Webby RJ. Impact of the COVID-19 nonpharmaceutical interventions on influenza and other respiratory viral infections in New Zealand. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2020:2020.11.11.20228692. [PMID: 33200149 PMCID: PMC7668762 DOI: 10.1101/2020.11.11.20228692] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Stringent nonpharmaceutical interventions (NPIs) such as lockdowns and border closures are not currently recommended for pandemic influenza control. New Zealand used these NPIs to eliminate coronavirus disease 2019 during its first wave. Using multiple surveillance systems, we observed a parallel and unprecedented reduction of influenza and other respiratory viral infections in 2020. This finding supports the use of these NPIs for controlling pandemic influenza and other severe respiratory viral threats.
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Affiliation(s)
- Q Sue Huang
- Institute of Environmental Science and Research, Wellington, New Zealand
| | - Tim Wood
- Institute of Environmental Science and Research, Wellington, New Zealand
| | - Lauren Jelley
- Institute of Environmental Science and Research, Wellington, New Zealand
| | - Tineke Jennings
- Regional Public Health, Hutt Valley District Health Board, Wellington, New Zealand
| | - Sarah Jefferies
- Institute of Environmental Science and Research, Wellington, New Zealand
| | - Karen Daniells
- Institute of Environmental Science and Research, Wellington, New Zealand
| | - Annette Nesdale
- Regional Public Health, Hutt Valley District Health Board, Wellington, New Zealand
| | - Tony Dowell
- University of Otago, School of Medicine in Wellington, Wellington, New Zealand
| | | | | | - Michelle Balm
- Capital Coast District Health Board, Wellington, New Zealand
| | | | | | - Shelley James
- Capital Coast District Health Board, Wellington, New Zealand
| | - Nayyereh Aminisani
- Institute of Environmental Science and Research, Wellington, New Zealand
| | - Jacqui Ralston
- Institute of Environmental Science and Research, Wellington, New Zealand
| | - Wendy Gunn
- Institute of Environmental Science and Research, Wellington, New Zealand
| | - Judy Bocacao
- Institute of Environmental Science and Research, Wellington, New Zealand
| | | | - Tessa Moncrieff
- Institute of Environmental Science and Research, Wellington, New Zealand
| | - Andrea McNeill
- Institute of Environmental Science and Research, Wellington, New Zealand
| | - Liza Lopez
- Institute of Environmental Science and Research, Wellington, New Zealand
| | - Ben Waite
- Institute of Environmental Science and Research, Wellington, New Zealand
| | | | - Hannah Schrader
- Institute of Environmental Science and Research, Wellington, New Zealand
| | - Rebekah Gray
- Institute of Environmental Science and Research, Wellington, New Zealand
| | - Kayla Cook
- Institute of Environmental Science and Research, Wellington, New Zealand
| | - Danielle Currin
- Institute of Environmental Science and Research, Wellington, New Zealand
| | - Chaune Engelbrecht
- Regional Public Health, Hutt Valley District Health Board, Wellington, New Zealand
| | - Whitney Tapurau
- Regional Public Health, Hutt Valley District Health Board, Wellington, New Zealand
| | - Leigh Emmerton
- Regional Public Health, Hutt Valley District Health Board, Wellington, New Zealand
| | - Maxine Martin
- Regional Public Health, Hutt Valley District Health Board, Wellington, New Zealand
| | - Michael G Baker
- University of Otago, School of Medicine in Wellington, Wellington, New Zealand
| | - Susan Taylor
- Counties Manukau District Health Board, Auckland, New Zealand
| | | | - Conroy Wong
- Counties Manukau District Health Board, Auckland, New Zealand
| | | | | | | | - Sally Roberts
- Auckland District Health Board, Auckland, New Zealand
| | | | - Jenny Bennett
- Waikato District Health Board, Hamilton, New Zealand
| | - Chris Mansell
- Waikato District Health Board, Hamilton, New Zealand
| | - Meik Dilcher
- Canterbury District Health Board, Christchurch, New Zealand
| | - Anja Werno
- Canterbury District Health Board, Christchurch, New Zealand
| | | | | | - Ben Youngblood
- WHO Collaborating Centre, St Jude Children's Research Hospital, Memphis, USA
| | - Paul G Thomas
- WHO Collaborating Centre, St Jude Children's Research Hospital, Memphis, USA
| | - Richard J Webby
- WHO Collaborating Centre, St Jude Children's Research Hospital, Memphis, USA
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Dickson EM, Marques DFP, Currie S, Little A, Mangin K, Coyne M, Reynolds A, McMenamin J, Yirrell D. The experience of point-of-care testing for influenza in Scotland in 2017/18 and 2018/19 – no gain without pain. Euro Surveill 2020; 25. [PMID: 33153519 PMCID: PMC7645975 DOI: 10.2807/1560-7917.es.2020.25.44.1900419] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
Background During the 2017/18 and 2018/19 influenza seasons, molecular amplification-based point-of-care tests (mPOCT) were introduced in Scotland to aid triaging respiratory patients for hospital admission, yet communication of results to national surveillance was unaccounted for. Aim This retrospective study aims to describe steps taken to capture mPOCT data and assess impact on influenza surveillance. Methods Questionnaires determined mPOCT usage in 2017/18 and 2018/19. Searches of the Electronic Communication of Surveillance in Scotland (ECOSS) database were performed and compared with information stored in laboratory information management systems. Effect of incomplete data on surveillance was determined by comparing routine against enhanced data and assessing changes in influenza activity levels determined by the moving epidemic method. Results The number of areas employing mPOCT increased over the two seasons (6/14 in 2017/18 and 8/14 in 2018/19). Analysis of a small number of areas (n = 3) showed capture of positive mPOCT results in ECOSS improved between seasons and remained high (> 94%). However, capture of negative results was incomplete. Despite small discrepancies in weekly activity assessments, routine data were able to identify trend, start, peak and end of both influenza seasons. Conclusion This study has shown an improvement in capture of data from influenza mPOCT and has highlighted issues that need to be addressed for results to be accurately captured in national surveillance. With the clear benefit to patient management we suggest careful consideration should be given to the connectivity aspects of the technology in order to ensure minimal impact on national surveillance.
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Affiliation(s)
- Elizabeth M Dickson
- Health Protection Scotland, Public Health Scotland, Glasgow, United Kingdom
- European Public Health Microbiology Training Programme (EUPHEM), European Centre for Disease Prevention and Control (ECDC), Stockholm, Sweden
| | - Diogo FP Marques
- Health Protection Scotland, Public Health Scotland, Glasgow, United Kingdom
| | - Sandra Currie
- Health Protection Scotland, Public Health Scotland, Glasgow, United Kingdom
| | - Annette Little
- Health Protection Scotland, Public Health Scotland, Glasgow, United Kingdom
| | - Kirsty Mangin
- Health Protection Scotland, Public Health Scotland, Glasgow, United Kingdom
| | - Michael Coyne
- Health Protection Scotland, Public Health Scotland, Glasgow, United Kingdom
| | - Arlene Reynolds
- Health Protection Scotland, Public Health Scotland, Glasgow, United Kingdom
| | - Jim McMenamin
- Health Protection Scotland, Public Health Scotland, Glasgow, United Kingdom
| | - David Yirrell
- Department of Medical Microbiology, Ninewells Hospital, Dundee, United Kingdom
- Health Protection Scotland, Public Health Scotland, Glasgow, United Kingdom
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Marbus S, van der Hoek W, van Dissel J, van Gageldonk-Lafeber A. Experience of establishing severe acute respiratory surveillance in the Netherlands: Evaluation and challenges. PUBLIC HEALTH IN PRACTICE 2020; 1:100014. [PMID: 34171043 PMCID: PMC7260511 DOI: 10.1016/j.puhip.2020.100014] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2019] [Revised: 05/02/2020] [Accepted: 05/12/2020] [Indexed: 11/24/2022] Open
Abstract
The 2009 influenza A (H1N1) pandemic prompted the World Health Organization (WHO) to recommend countries to establish a national severe acute respiratory infections (SARI) surveillance system for preparedness and emergency response. However, setting up or maintaining a robust SARI surveillance system has been challenging. Similar to other countries, surveillance data on hospitalisations for SARI in the Netherlands are still limited, in contrast to the robust surveillance data in primary care. The objective of this narrative review is to provide an overview, evaluation, and challenges of already available surveillance systems or datasets in the Netherlands, which might be used for near real-time surveillance of severe respiratory infections. Seven available surveillance systems or datasets in the Netherlands were reviewed. The evaluation criteria, including data quality, timeliness, representativeness, simplicity, flexibility, acceptability and stability were based on United States Centers for Disease Control and Prevention (CDC) and European Centre for Disease Prevention and Control (ECDC) guidelines for public health surveillance. We added sustainability as additional evaluation criterion. The best evaluated surveillance system or dataset currently available for SARI surveillance is crude mortality monitoring, although it lacks specificity. In contrast to influenza-like illness (ILI) in primary care, there is currently no gold standard for SARI surveillance in the Netherlands. Based on our experience with sentinel SARI surveillance, a fully or semi-automated, passive surveillance system seems most suited for a sustainable SARI surveillance system. An important future challenge remains integrating SARI surveillance into existing hospital programs in order to make surveillance data valuable for public health, as well as hospital quality of care management and individual patient care.
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Affiliation(s)
- S.D. Marbus
- Centre for Infectious Disease Control, National Institute for Public Health and the Environment, Bilthoven, the Netherlands
| | - W. van der Hoek
- Centre for Infectious Disease Control, National Institute for Public Health and the Environment, Bilthoven, the Netherlands
| | - J.T. van Dissel
- Centre for Infectious Disease Control, National Institute for Public Health and the Environment, Bilthoven, the Netherlands
- Department of Infectious Diseases and Internal Medicine, Leiden University Medical Center, Leiden, the Netherlands
| | - A.B. van Gageldonk-Lafeber
- Centre for Infectious Disease Control, National Institute for Public Health and the Environment, Bilthoven, the Netherlands
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Sullivan SG, Arriola CS, Bocacao J, Burgos P, Bustos P, Carville KS, Cheng AC, Chilver MB, Cohen C, Deng YM, El Omeiri N, Fasce RA, Hellferscee O, Huang QS, Gonzalez C, Jelley L, Leung VK, Lopez L, McAnerney JM, McNeill A, Olivares MF, Peck H, Sotomayor V, Tempia S, Vergara N, von Gottberg A, Walaza S, Wood T. Heterogeneity in influenza seasonality and vaccine effectiveness in Australia, Chile, New Zealand and South Africa: early estimates of the 2019 influenza season. ACTA ACUST UNITED AC 2020; 24. [PMID: 31718744 PMCID: PMC6852316 DOI: 10.2807/1560-7917.es.2019.24.45.1900645] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
We compared 2019 influenza seasonality and vaccine effectiveness (VE) in four southern hemisphere countries: Australia, Chile, New Zealand and South Africa. Influenza seasons differed in timing, duration, intensity and predominant circulating viruses. VE estimates were also heterogeneous, with all-ages point estimates ranging from 7-70% (I2: 33%) for A(H1N1)pdm09, 4-57% (I2: 49%) for A(H3N2) and 29-66% (I2: 0%) for B. Caution should be applied when attempting to use southern hemisphere data to predict the northern hemisphere influenza season.
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Affiliation(s)
- Sheena G Sullivan
- World Health Organization (WHO) Collaborating Centre for Reference and Research on Influenza, Royal Melbourne Hospital, and Doherty Department, University of Melbourne, at the Peter Doherty Institute for Infection and Immunity, Melbourne, Australia
| | - Carmen S Arriola
- Influenza Division, Centers for Disease Control and Prevention, Atlanta, United States
| | - Judy Bocacao
- National Influenza Centre, Institute of Environmental Science and Research, Wellington, New Zealand
| | - Pamela Burgos
- Programa Nacional de Inmunizaciones, Ministerio de Salud, Santiago, Chile
| | - Patricia Bustos
- Sección de Virus Respiratorios y Exantematicos, Instituto de Salud Publica de Chile, Santiago, Chile
| | - Kylie S Carville
- Victorian Infectious Diseases Reference Laboratory, Royal Melbourne Hospital, at the Peter Doherty Institute for Infection and Immunity, Melbourne, Australia
| | - Allen C Cheng
- Department of Infectious Diseases, Alfred Health, and Central Clinical School, Monash University, Melbourne, Australia.,School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
| | - Monique Bm Chilver
- Discipline of General Practice, University of Adelaide, Adelaide, Australia
| | - Cheryl Cohen
- National Institute for Communicable Diseases, Johannesburg, South Africa
| | - Yi-Mo Deng
- WHO Collaborating Centre for Reference and Research on Influenza, Royal Melbourne Hospital, at the Peter Doherty Institute for Reference and Research on Influenza, Melbourne, Australia
| | - Nathalie El Omeiri
- Pan American Health Organization(PAHO)/WHO Regional Office for the Americas, Washington, United States
| | - Rodrigo A Fasce
- Subdepartamento de Enfermedades Virales, Instituto de Salud Publica de Chile, Santiago, Chile
| | | | - Q Sue Huang
- National Influenza Centre, Institute of Environmental Science and Research, Wellington, New Zealand
| | - Cecilia Gonzalez
- Programa Nacional de Inmunizaciones, Ministerio de Salud, Santiago, Chile
| | - Lauren Jelley
- National Influenza Centre, Institute of Environmental Science and Research, Wellington, New Zealand
| | - Vivian Ky Leung
- World Health Organization (WHO) Collaborating Centre for Reference and Research on Influenza, Royal Melbourne Hospital, and Doherty Department, University of Melbourne, at the Peter Doherty Institute for Infection and Immunity, Melbourne, Australia
| | - Liza Lopez
- Health Intelligence Team, Institute of Environmental Science and Research, Wellington, New Zealand
| | | | - Andrea McNeill
- Health Intelligence Team, Institute of Environmental Science and Research, Wellington, New Zealand
| | - Maria F Olivares
- Departamento de Epidemiologia, Ministerio de Salud, Santiago, Chile
| | - Heidi Peck
- WHO Collaborating Centre for Reference and Research on Influenza, Royal Melbourne Hospital, at the Peter Doherty Institute for Reference and Research on Influenza, Melbourne, Australia
| | | | - Stefano Tempia
- MassGenics, Duluth, United States.,Influenza Program, Centers for Disease Control and Prevention, Pretoria, South Africa.,National Institute for Communicable Diseases, Johannesburg, South Africa.,Influenza Division, Centers for Disease Control and Prevention, Atlanta, United States
| | - Natalia Vergara
- Departamento de Epidemiologia, Ministerio de Salud, Santiago, Chile
| | - Anne von Gottberg
- National Institute for Communicable Diseases, Johannesburg, South Africa
| | - Sibongile Walaza
- National Institute for Communicable Diseases, Johannesburg, South Africa
| | - Timothy Wood
- Health Intelligence Team, Institute of Environmental Science and Research, Wellington, New Zealand
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Baltrusaitis K, Reed C, Sewalk K, Brownstein JS, Crawley AW, Biggerstaff M. Health-care seeking behavior for respiratory illness among Flu Near You participants in the United States during the 2015-16 through 2018-19 influenza season. J Infect Dis 2020; 226:270-277. [PMID: 32761050 PMCID: PMC9400452 DOI: 10.1093/infdis/jiaa465] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2020] [Accepted: 07/27/2020] [Indexed: 11/14/2022] Open
Abstract
Background Flu Near You (FNY) is an online participatory syndromic surveillance system that collects health-related information. In this article, we summarized the healthcare-seeking behavior of FNY participants who reported influenza-like illness (ILI) symptoms. Methods We applied inverse probability weighting to calculate age-adjusted estimates of the percentage of FNY participants in the United States who sought health care for ILI symptoms during the 2015–2016 through 2018–2019 influenza season and compared seasonal trends across different demographic and regional subgroups, including age group, sex, census region, and place of care using adjusted χ 2 tests. Results The overall age-adjusted percentage of FNY participants who sought healthcare for ILI symptoms varied by season and ranged from 22.8% to 35.6%. Across all seasons, healthcare seeking was highest for the <18 and 65+ years age groups, women had a greater percentage compared with men, and the South census region had the largest percentage while the West census region had the smallest percentage. Conclusions The percentage of FNY participants who sought healthcare for ILI symptoms varied by season, geographical region, age group, and sex. FNY compliments existing surveillance systems and informs estimates of influenza-associated illness by adding important real-time insights into healthcare-seeking behavior.
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Affiliation(s)
- Kristin Baltrusaitis
- Center for Biostatistics in AIDS Research, Harvard T.H. Chan School of Public Health, Boston, MA, United States
| | - Carrie Reed
- Influenza Division, National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, Atlanta, GA, United States
| | - Kara Sewalk
- Computational Epidemiology Lab, Boston Children's Hospital, Boston, MA, United States
| | - John S Brownstein
- Computational Epidemiology Lab, Boston Children's Hospital, Boston, MA, 02115 United States; Department of Pediatrics, Harvard Medical School, Boston, MA, 02115, United States; Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
| | | | - Matthew Biggerstaff
- Influenza Division, National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, Atlanta, GA, United States
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Vos LM, Teirlinck AC, Lozano JE, Vega T, Donker GA, Hoepelman AI, Bont LJ, Oosterheert JJ, Meijer A. Use of the moving epidemic method (MEM) to assess national surveillance data for respiratory syncytial virus (RSV) in the Netherlands, 2005 to 2017. ACTA ACUST UNITED AC 2020; 24. [PMID: 31115311 PMCID: PMC6530251 DOI: 10.2807/1560-7917.es.2019.24.20.1800469] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
Background To control respiratory syncytial virus (RSV), which causes acute respiratory infections, data and methods to assess its epidemiology are important. Aim We sought to describe RSV seasonality, affected age groups and RSV-type distribution over 12 consecutive seasons in the Netherlands, as well as to validate the moving epidemic method (MEM) for monitoring RSV epidemics. Methods We used 2005−17 laboratory surveillance data and sentinel data. For RSV seasonality evaluation, epidemic thresholds (i) at 1.2% of the cumulative number of RSV-positive patients per season and (ii) at 20 detections per week (for laboratory data) were employed. We also assessed MEM thresholds. Results In laboratory data RSV was reported 25,491 times (no denominator). In sentinel data 5.6% (767/13,577) of specimens tested RSV positive. Over 12 seasons, sentinel data showed percentage increases of RSV positive samples. The average epidemic length was 18.0 weeks (95% confidence intervals (CI): 16.3–19.7) and 16.5 weeks (95% CI: 14.0–18.0) for laboratory and sentinel data, respectively. Epidemics started on average in week 46 (95% CI: 45–48) and 47 (95% CI: 46–49), respectively. The peak was on average in the first week of January in both datasets. MEM showed similar results to the other methods. RSV incidence was highest in youngest (0–1 and >1–2 years) and oldest (>65–75 and > 75 years) age groups, with age distribution remaining stable over time. RSV-type dominance alternated every one or two seasons. Conclusions Our findings provide baseline information for immunisation advisory groups. The possibility of employing MEM to monitor RSV epidemics allows prospective, nearly real-time use of surveillance data.
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Affiliation(s)
- Laura M Vos
- University Medical Centre Utrecht, Utrecht University, Department of Internal Medicine and Infectious Diseases, Utrecht, the Netherlands
| | - Anne C Teirlinck
- Centre for infectious Disease Control Bilthoven, Centre for Infectious Diseases, Epidemiology and Surveillance, National Institute for Public Health and the Environment (RIVM), Bilthoven, the Netherlands
| | - José E Lozano
- Dirección General de Salud Pública, Consejería de Sanidad, Valladolid, Spain
| | - Tomás Vega
- Dirección General de Salud Pública, Consejería de Sanidad, Valladolid, Spain
| | - Gé A Donker
- NIVEL Primary Care Database - Sentinel Practices, Utrecht, the Netherlands
| | - Andy Im Hoepelman
- University Medical Centre Utrecht, Utrecht University, Department of Internal Medicine and Infectious Diseases, Utrecht, the Netherlands
| | - Louis J Bont
- Wilhelmina Children's Hospital, Utrecht University, Department of Paediatric Infectious Diseases, Utrecht, the Netherlands
| | - Jan Jelrik Oosterheert
- University Medical Centre Utrecht, Utrecht University, Department of Internal Medicine and Infectious Diseases, Utrecht, the Netherlands
| | - Adam Meijer
- Centre for infectious Disease Control Bilthoven, Centre for Infectious Diseases Research, Diagnostics and laboratory Surveillance, National Institute for Public Health and the Environment (RIVM), Bilthoven, the Netherlands
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Gianino MM, Kakaa O, Politano G, Scarmozzino A, Benso A, Zotti CM. Severe and moderate seasonal influenza epidemics among Italian healthcare workers: A comparison of the excess of absenteeism. Influenza Other Respir Viruses 2020; 15:81-90. [PMID: 32666696 PMCID: PMC7767959 DOI: 10.1111/irv.12777] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2019] [Revised: 06/05/2020] [Accepted: 06/06/2020] [Indexed: 12/03/2022] Open
Abstract
Background This study aims to quantify the excess of sickness absenteeism among healthcare workers (HCWs), to estimate the impact of a severe versus moderate influenza season and to determine whether the vaccination rates are associated with reduced sickness absence. Methods We investigated the excess absenteeism that occurred in a large Italian hospital, 5300 HCWs, during the severe influenza season of 2017/2018 and compared it with three moderate flu seasons (2010/2013). Data on influenza vaccinations and absenteeism were obtained from the hospital's databases. The data were split into two periods: the epidemic, from 42 to 17 weeks, and non‐epidemic, defined as 18 to 41 weeks, which was used as the baseline. We stratified the absenteeism among HCWs in multiple variables. Results Our study showed an increased absenteeism among HCWs during the epidemic period of severe season in comparison with non‐epidemic periods, the absolute increase correlated with a relative increase of 70% (from 4.05 to 6.68 days/person). Vaccinated HCWs had less excess of absenteeism in comparison with non‐vaccinated HCWs (1.74 vs 2.71 days/person). The comparison with the moderate seasons showed a stronger impact on HCW sick absenteeism in the severe season (+0.747days/person, P = .03), especially among nurses and HCWs in contact with patients (+1.53 P < .01; +1.19 P < .01). Conclusions In conclusion, a severe influenza epidemic has greater impacts on the absenteeism among HCWs than a moderate one. Although at a low rate, a positive effect of vaccination on absenteeism is present, it may support healthcare facilities to recommend vaccinations for their workers.
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Affiliation(s)
- Maria Michela Gianino
- Department of Sciences of Public Health and Pediatrics, University of Torino, Torino, Italy
| | - Omar Kakaa
- Department of Sciences of Public Health and Pediatrics, University of Torino, Torino, Italy
| | - Gianfranco Politano
- Department of Control and Computer Engineering, Politecnico di Torino, Torino, Italy
| | | | - Alfredo Benso
- Department of Control and Computer Engineering, Politecnico di Torino, Torino, Italy
| | - Carla Maria Zotti
- Department of Sciences of Public Health and Pediatrics, University of Torino, Torino, Italy
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Grilc E, Prosenc Trilar K, Lajovic J, Sočan M. Determining the seasonality of respiratory syncytial virus in Slovenia. Influenza Other Respir Viruses 2020; 15:56-63. [PMID: 32656961 PMCID: PMC7767947 DOI: 10.1111/irv.12779] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2019] [Revised: 06/09/2020] [Accepted: 06/12/2020] [Indexed: 11/29/2022] Open
Abstract
Background In Slovenia, the respiratory syncytial virus (RSV) surveillance is based on national laboratory data. The weeks with more than 10% of samples tested positive compose RSV epidemic season. The use of real‐time multiplex PCR, which identifies other respiratory pathogens in parallel with RSV, caused more testing but the percentage of RSV positives lowered. The 10% threshold was reached with delay, which raised concern about its suitability for defining RSV seasonality. Methods To describe the seasonality of RSV, the onset, offset and duration of the RSV epidemic season across 10 years (from week 40, 2008/2009 to week 39, 2017/2018), four calculative methods were deployed including moving epidemic method, MEM, and epidemiological parameters were compared. Results In 10 years, 10 969 (12%) out of 90 264 samples tested positive for RSV. The number of tested samples increased remarkably from the first to last season with a drop in the percentage of positive samples from 23% to 10%. The onset of RSV epidemic varied considerably regardless of the calculative method used (from 10 to 13 weeks). The unevenness in the RSV epidemic season end was also observed. The average duration of RSV epidemic season was the shortest when moving epidemic method has been used (15.7 weeks) and longest with ≥3% method (22.9 weeks). Conclusion The ≥3% calculative method could be used as an early warning of the RSV season. However, ≥7% calculative method was found to be reliable enough to define the epidemiological parameters of an ongoing season and to support public health response. The potential of the moving epidemic method should be further explored.
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Affiliation(s)
- Eva Grilc
- National Institute of Public Health, Ljubljana, Slovenia
| | | | | | - Maja Sočan
- National Institute of Public Health, Ljubljana, Slovenia
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Rakocevic B, Grgurevic A, Trajkovic G, Mugosa B, Sipetic Grujicic S, Medenica S, Bojovic O, Lozano Alonso JE, Vega T. Influenza surveillance: determining the epidemic threshold for influenza by using the Moving Epidemic Method (MEM), Montenegro, 2010/11 to 2017/18 influenza seasons. ACTA ACUST UNITED AC 2020; 24. [PMID: 30914080 PMCID: PMC6440585 DOI: 10.2807/1560-7917.es.2019.24.12.1800042] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
Abstract
Background: In 2009, an improved influenza surveillance system was implemented and weekly reporting to the World Health Organization on influenza-like illness (ILI) began. The goals of the surveillance system are to monitor and analyse the intensity of influenza activity, to provide timely information about circulating strains and to help in establishing preventive and control measures. In addition, the system is useful for comparative analysis of influenza data from Montenegro with other countries. Aim: We aimed to evaluate the performance and usefulness of the Moving Epidemic Method (MEM), for use in the influenza surveillance system in Montenegro. Methods: Historical ILI data from 2010/11 to 2017/18 influenza seasons were modelled with MEM. Epidemic threshold for Montenegro 2017/18 season was calculated using incidence rates from 2010/11–2016/17 influenza seasons. Results: Pre-epidemic ILI threshold per 100,000 population was 19.23, while the post-epidemic threshold was 17.55. Using MEM, we identified an epidemic of 10 weeks’ duration. The sensitivity of the MEM epidemic threshold in Montenegro was 89% and the warning signal specificity was 99%. Conclusions: Our study marks the first attempt to determine the pre/post-epidemic threshold values for the epidemic period in Montenegro. The findings will allow a more detailed examination of the influenza-related epidemiological situation, timely detection of epidemic and contribute to the development of more efficient measures for disease prevention and control aimed at reducing the influenza-associated morbidity and mortality.
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Affiliation(s)
- Bozidarka Rakocevic
- These authors contributed equally to this work.,Center for Disease Control and Prevention, Institute of Public Health, Podgorica, Montenegro
| | - Anita Grgurevic
- Institute of Epidemiology, Faculty of Medicine, University of Belgrade, Belgrade, Serbia.,These authors contributed equally to this work
| | - Goran Trajkovic
- Institute for Medical Statistics and Informatics, Faculty of Medicine, University of Belgrade, Belgrade, Serbia
| | - Boban Mugosa
- Center for Disease Control and Prevention, Institute of Public Health, Podgorica, Montenegro
| | | | - Sanja Medenica
- Center for Disease Control and Prevention, Institute of Public Health, Podgorica, Montenegro
| | - Olivera Bojovic
- Department for Tuberculosis, Hospital for Lung Disease and Tuberculosis Brezovik, Niksic, Montenegro
| | | | - Tomas Vega
- Public Health Directorate, Castilla y León Regional Health Ministry, Valladolid, Spain
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Marbus SD, Schweitzer VA, Groeneveld GH, Oosterheert JJ, Schneeberger PM, van der Hoek W, van Dissel JT, van Gageldonk-Lafeber AB, Mangen MJ. Incidence and costs of hospitalized adult influenza patients in The Netherlands: a retrospective observational study. THE EUROPEAN JOURNAL OF HEALTH ECONOMICS : HEPAC : HEALTH ECONOMICS IN PREVENTION AND CARE 2020; 21:775-785. [PMID: 32180069 PMCID: PMC7095032 DOI: 10.1007/s10198-020-01172-1] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/10/2019] [Accepted: 02/25/2020] [Indexed: 05/14/2023]
Abstract
OBJECTIVE Influenza virus infections cause a high disease and economic burden during seasonal epidemics. However, there is still a need for reliable disease burden estimates to provide a more detailed picture of the impact of influenza. Therefore, the objectives of this study is to estimate the incidence of hospitalisation for influenza virus infection and associated hospitalisation costs in adult patients in the Netherlands during two consecutive influenza seasons. METHODS We conducted a retrospective study in adult patients with a laboratory confirmed influenza virus infection in three Dutch hospitals during respiratory seasons 2014-2015 and 2015-2016. Incidence was calculated as the weekly number of hospitalised influenza patients divided by the total population in the catchment populations of the three hospitals. Arithmetic mean hospitalisation costs per patient were estimated and included costs for emergency department consultation, diagnostics, general ward and/or intensive care unit admission, isolation, antibiotic and/or antiviral treatment. These hospitalisation costs were extrapolated to national level and expressed in 2017 euros. RESULTS The study population consisted of 380 hospitalised adult influenza patients. The seasonal cumulative incidence was 3.5 cases per 10,000 persons in respiratory season 2014-2015, compared to 1.8 cases per 10,000 persons in 2015-2016. The arithmetic mean hospitalisation cost per influenza patient was €6128 (95% CI €4934-€7737) per patient in 2014-2015 and €8280 (95% CI €6254-€10,665) in 2015-2016, potentially reaching total hospitalisation costs of €28 million in 2014-2015 and €20 million in 2015-2016. CONCLUSIONS Influenza virus infections lead to 1.8-3.5 hospitalised patients per 10,000 persons, with mean hospitalisation costs of €6100-€8300 per adult patient, resulting in 20-28 million euros annually in The Netherlands. The highest arithmetic mean hospitalisation costs per patient were found in the 45-64 year age group. These influenza burden estimates could be used for future influenza cost-effectiveness and impact studies.
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Affiliation(s)
- Sierk D. Marbus
- Centre for Infectious Diseases Epidemiology and Surveillance, Centre for Infectious Disease Control (CIb), National Institute for Public Health and the Environment (RIVM), PO Box 1, 3720 BA Bilthoven, The Netherlands
| | - Valentijn A. Schweitzer
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Geert H. Groeneveld
- Department of Infectious Diseases and Internal Medicine, Leiden University Medical Center, Leiden, The Netherlands
| | - Jan J. Oosterheert
- Department of Internal Medicine and Infectious Diseases, University Medical Centre Utrecht, Utrecht, The Netherlands
| | - Peter M. Schneeberger
- Regional Laboratory for Medical Microbiology and Infection Prevention, ‘s-Hertogenbosch, The Netherlands
| | - Wim van der Hoek
- Centre for Infectious Diseases Epidemiology and Surveillance, Centre for Infectious Disease Control (CIb), National Institute for Public Health and the Environment (RIVM), PO Box 1, 3720 BA Bilthoven, The Netherlands
| | - Jaap T. van Dissel
- Centre for Infectious Diseases Epidemiology and Surveillance, Centre for Infectious Disease Control (CIb), National Institute for Public Health and the Environment (RIVM), PO Box 1, 3720 BA Bilthoven, The Netherlands
- Department of Infectious Diseases and Internal Medicine, Leiden University Medical Center, Leiden, The Netherlands
| | - Arianne B. van Gageldonk-Lafeber
- Centre for Infectious Diseases Epidemiology and Surveillance, Centre for Infectious Disease Control (CIb), National Institute for Public Health and the Environment (RIVM), PO Box 1, 3720 BA Bilthoven, The Netherlands
| | - Marie-Josée Mangen
- Centre for Infectious Diseases Epidemiology and Surveillance, Centre for Infectious Disease Control (CIb), National Institute for Public Health and the Environment (RIVM), PO Box 1, 3720 BA Bilthoven, The Netherlands
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