Changes in the incidence of invasive disease due to <i>Streptococcus pneumoniae, Haemophilus influenzae</i>, and <i>Neisseria meningitidis</i> during the COVID-19 pandemic in 26 countries and territories in the Invasive Respiratory Infection Surveillance Initiative: a prospective analysis of surveillance data

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Summary

Background

Streptococcus pneumoniae, Haemophilus influenzae, and Neisseria meningitidis, which are typically transmitted via respiratory droplets, are leading causes of invasive diseases, including bacteraemic pneumonia and meningitis, and of secondary infections subsequent to post-viral respiratory disease. The aim of this study was to investigate the incidence of invasive disease due to these pathogens during the early months of the COVID-19 pandemic.

Methods

In this prospective analysis of surveillance data, laboratories in 26 countries and territories across six continents submitted data on cases of invasive disease due to S pneumoniae, H influenzae, and N meningitidis from Jan 1, 2018, to May, 31, 2020, as part of the Invasive Respiratory Infection Surveillance (IRIS) Initiative. Numbers of weekly cases in 2020 were compared with corresponding data for 2018 and 2019. Data for invasive disease due to Streptococcus agalactiae, a non-respiratory pathogen, were collected from nine laboratories for comparison. The stringency of COVID-19 containment measures was quantified using the Oxford COVID-19 Government Response Tracker. Changes in population movements were assessed using Google COVID-19 Community Mobility Reports. Interrupted time-series modelling quantified changes in the incidence of invasive disease due to S pneumoniae, H influenzae, and N meningitidis in 2020 relative to when containment measures were imposed.

Findings

27 laboratories from 26 countries and territories submitted data to the IRIS Initiative for S pneumoniae (62 837 total cases), 24 laboratories from 24 countries submitted data for H influenzae (7796 total cases), and 21 laboratories from 21 countries submitted data for N meningitidis (5877 total cases). All countries and territories had experienced a significant and sustained reduction in invasive diseases due to S pneumoniae, H influenzae, and N meningitidis in early 2020 (Jan 1 to May 31, 2020), coinciding with the introduction of COVID-19 containment measures in each country. By contrast, no significant changes in the incidence of invasive S agalactiae infections were observed. Similar trends were observed across most countries and territories despite differing stringency in COVID-19 control policies. The incidence of reported S pneumoniae infections decreased by 68% at 4 weeks (incidence rate ratio 0·32 [95% CI 0·27–0·37]) and 82% at 8 weeks (0·18 [0·14–0·23]) following the week in which significant changes in population movements were recorded.

Interpretation

The introduction of COVID-19 containment policies and public information campaigns likely reduced transmission of S pneumoniae, H influenzae, and N meningitidis, leading to a significant reduction in life-threatening invasive diseases in many countries worldwide.

Funding

Wellcome Trust (UK), Robert Koch Institute (Germany), Federal Ministry of Health (Germany), Pfizer, Merck, Health Protection Surveillance Centre (Ireland), SpID-Net project (Ireland), European Centre for Disease Prevention and Control (European Union), Horizon 2020 (European Commission), Ministry of Health (Poland), National Programme of Antibiotic Protection (Poland), Ministry of Science and Higher Education (Poland), Agencia de Salut Pública de Catalunya (Spain), Sant Joan de Deu Foundation (Spain), Knut and Alice Wallenberg Foundation (Sweden), Swedish Research Council (Sweden), Region Stockholm (Sweden), Federal Office of Public Health of Switzerland (Switzerland), and French Public Health Agency (France).

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Most cited references 29

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An interactive web-based dashboard to track COVID-19 in real time

Ensheng Dong, Hongru Du, Lauren Gardner (2020)

In December, 2019, a local outbreak of pneumonia of initially unknown cause was detected in Wuhan (Hubei, China), and was quickly determined to be caused by a novel coronavirus, 1 namely severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The outbreak has since spread to every province of mainland China as well as 27 other countries and regions, with more than 70 000 confirmed cases as of Feb 17, 2020. 2 In response to this ongoing public health emergency, we developed an online interactive dashboard, hosted by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University, Baltimore, MD, USA, to visualise and track reported cases of coronavirus disease 2019 (COVID-19) in real time. The dashboard, first shared publicly on Jan 22, illustrates the location and number of confirmed COVID-19 cases, deaths, and recoveries for all affected countries. It was developed to provide researchers, public health authorities, and the general public with a user-friendly tool to track the outbreak as it unfolds. All data collected and displayed are made freely available, initially through Google Sheets and now through a GitHub repository, along with the feature layers of the dashboard, which are now included in the Esri Living Atlas. The dashboard reports cases at the province level in China; at the city level in the USA, Australia, and Canada; and at the country level otherwise. During Jan 22–31, all data collection and processing were done manually, and updates were typically done twice a day, morning and night (US Eastern Time). As the outbreak evolved, the manual reporting process became unsustainable; therefore, on Feb 1, we adopted a semi-automated living data stream strategy. Our primary data source is DXY, an online platform run by members of the Chinese medical community, which aggregates local media and government reports to provide cumulative totals of COVID-19 cases in near real time at the province level in China and at the country level otherwise. Every 15 min, the cumulative case counts are updated from DXY for all provinces in China and for other affected countries and regions. For countries and regions outside mainland China (including Hong Kong, Macau, and Taiwan), we found DXY cumulative case counts to frequently lag behind other sources; we therefore manually update these case numbers throughout the day when new cases are identified. To identify new cases, we monitor various Twitter feeds, online news services, and direct communication sent through the dashboard. Before manually updating the dashboard, we confirm the case numbers with regional and local health departments, including the respective centres for disease control and prevention (CDC) of China, Taiwan, and Europe, the Hong Kong Department of Health, the Macau Government, and WHO, as well as city-level and state-level health authorities. For city-level case reports in the USA, Australia, and Canada, which we began reporting on Feb 1, we rely on the US CDC, the government of Canada, the Australian Government Department of Health, and various state or territory health authorities. All manual updates (for countries and regions outside mainland China) are coordinated by a team at Johns Hopkins University. The case data reported on the dashboard aligns with the daily Chinese CDC 3 and WHO situation reports 2 for within and outside of mainland China, respectively (figure ). Furthermore, the dashboard is particularly effective at capturing the timing of the first reported case of COVID-19 in new countries or regions (appendix). With the exception of Australia, Hong Kong, and Italy, the CSSE at Johns Hopkins University has reported newly infected countries ahead of WHO, with Hong Kong and Italy reported within hours of the corresponding WHO situation report. Figure Comparison of COVID-19 case reporting from different sources Daily cumulative case numbers (starting Jan 22, 2020) reported by the Johns Hopkins University Center for Systems Science and Engineering (CSSE), WHO situation reports, and the Chinese Center for Disease Control and Prevention (Chinese CDC) for within (A) and outside (B) mainland China. Given the popularity and impact of the dashboard to date, we plan to continue hosting and managing the tool throughout the entirety of the COVID-19 outbreak and to build out its capabilities to establish a standing tool to monitor and report on future outbreaks. We believe our efforts are crucial to help inform modelling efforts and control measures during the earliest stages of the outbreak.

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A global panel database of pandemic policies (Oxford COVID-19 Government Response Tracker)

Thomas C. Hale, Noam Angrist, Rafael Goldszmidt … (2021)

COVID-19 has prompted unprecedented government action around the world. We introduce the Oxford COVID-19 Government Response Tracker (OxCGRT), a dataset that addresses the need for continuously updated, readily usable and comparable information on policy measures. From 1 January 2020, the data capture government policies related to closure and containment, health and economic policy for more than 180 countries, plus several countries' subnational jurisdictions. Policy responses are recorded on ordinal or continuous scales for 19 policy areas, capturing variation in degree of response. We present two motivating applications of the data, highlighting patterns in the timing of policy adoption and subsequent policy easing and reimposition, and illustrating how the data can be combined with behavioural and epidemiological indicators. This database enables researchers and policymakers to explore the empirical effects of policy responses on the spread of COVID-19 cases and deaths, as well as on economic and social welfare.

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Open-access bacterial population genomics: BIGSdb software, the PubMLST.org website and their applications

Keith Jolley, James E Bray, Martin Maiden … (2018)

The PubMLST.org website hosts a collection of open-access, curated databases that integrate population sequence data with provenance and phenotype information for over 100 different microbial species and genera. Although the PubMLST website was conceived as part of the development of the first multi-locus sequence typing (MLST) scheme in 1998 the software it uses, the Bacterial Isolate Genome Sequence database (BIGSdb, published in 2010), enables PubMLST to include all levels of sequence data, from single gene sequences up to and including complete, finished genomes. Here we describe developments in the BIGSdb software made from publication to June 2018 and show how the platform realises microbial population genomics for a wide range of applications. The system is based on the gene-by-gene analysis of microbial genomes, with each deposited sequence annotated and curated to identify the genes present and systematically catalogue their variation. Originally intended as a means of characterising isolates with typing schemes, the synthesis of sequences and records of genetic variation with provenance and phenotype data permits highly scalable (whole genome sequence data for tens of thousands of isolates) means of addressing a wide range of functional questions, including: the prediction of antimicrobial resistance; likely cross-reactivity with vaccine antigens; and the functional activities of different variants that lead to key phenotypes. There are no limitations to the number of sequences, genetic loci, allelic variants or schemes (combinations of loci) that can be included, enabling each database to represent an expanding catalogue of the genetic variation of the population in question. In addition to providing web-accessible analyses and links to third-party analysis and visualisation tools, the BIGSdb software includes a RESTful application programming interface (API) that enables access to all the underlying data for third-party applications and data analysis pipelines.

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All references

Author and article information

Contributors

Angela B Brueggemann

Journal

Journal ID (nlm-ta): Lancet Digit Health

Journal ID (iso-abbrev): Lancet Digit Health

Title: The Lancet. Digital Health

Publisher: Elsevier Ltd

ISSN (Electronic): 2589-7500

Publication date PMC-release: 24 May 2021

Publication date Collection: June 2021

Publication date (Electronic): 24 May 2021

Volume: 3

Issue: 6

Pages: e360-e370

Affiliations

[a ]National Reference Centre for Streptococcus pneumoniae, University Hospitals Leuven, Leuven, Belgium

[b ]Department of Microbiology, Immunology and Transplantation, KU Leuven, Leuven, Belgium

[c ]National Reference Centre for Haemophilus influenzae, Laboratoires des Hôpitaux Universitaires de Bruxelles, Universitaire Laboratorium Brussel, Brussels, Belgium

[d ]Faculté de Médecine et Pharmacie, Université de Mons, Mons, Belgium

[e ]National Reference Centre for Neisseria meningitidis, Sciensano, Brussels, Belgium

[f ]National Laboratory for Meningitis and Pneumococcal Infections, Center of Bacteriology, Institute Adolfo Lutz, São Paulo, Brazil

[g ]National Microbiology Laboratory, Public Health Agency of Canada, Winnipeg, MB, Canada

[h ]Department of Pulmonary and Critical Care Medicine, Center of Respiratory Medicine, China-Japan Friendship Hospital, Institute of Respiratory Medicine, Chinese Academy of Medical Sciences, National Clinical Research Center for Respiratory Diseases, Beijing, China

[i ]National Reference Laboratory for Haemophilus Infections, Centre for Epidemiology and Microbiology, National Institute of Public Health, Prague, Czech Republic

[j ]National Reference Laboratory for Meningococcal Infections, Centre for Epidemiology and Microbiology, National Institute of Public Health, Prague, Czech Republic

[k ]National Reference Laboratory for Streptococcal Infections, Centre for Epidemiology and Microbiology, National Institute of Public Health, Prague, Czech Republic

[l ]Department of Bacteria, Parasites and Fungi, Statens Serum Institut, Copenhagen, Denmark

[m ]Nuffield Department of Population Health, Big Data Institute, University of Oxford, Oxford, UK

[n ]Department of Zoology, University of Oxford, Oxford, UK

[o ]Blavatnik School of Government, University of Oxford, Oxford, UK

[p ]Immunisation and Countermeasures Division, National Infection Service, Public Health England, London, UK

[q ]Respiratory and Vaccine Preventable Bacteria Reference Unit, National Infection Service, Public Health England, London, UK

[r ]Meningococcal Reference Unit, National Infection Service, Public Health England, Manchester Royal Infirmary, Manchester, UK

[s ]Trinity College Dublin, Dublin, Ireland

[t ]Finnish Institute for Health and Welfare, Helsinki, Finland

[u ]Institut Pasteur, Invasive Bacterial Infections Unit and National Reference Centre for Meningococci and Haemophilus influenzae, Paris, France

[v ]Laboratory of Medical Biology and National Reference Centre for Pneumococci, Intercommunal Hospital of Créteil, Créteil, France

[w ]Department of Medical Microbiology, German National Reference Center for Streptococci, University Hospital RWTH Aachen, Aachen, Germany

[x ]German National Reference Center for Meningococci and Haemophilus influenzae, Institute for Hygiene and Microbiology, University of Würzburg, Würzburg, Germany

[y ]Department of Health, Microbiology Division, Public Health Laboratory Services Branch, Centre for Health Protection, Hong Kong Special Administrative Region, China

[z ]Department of Microbiology, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China

[aa ]Department of Clinical Microbiology, Landspitali-The National University Hospital of Iceland, Reykjavik, Iceland

[ab ]Irish Meningitis and Sepsis Reference Laboratory, Children's Health Ireland at Temple Street, Dublin, Ireland

[ac ]Department of Clinical Microbiology, Beaumont Hospital, Dublin, Ireland

[ad ]Department of Clinical Microbiology, Beaumont Hospital, Dublin, Ireland

[ae ]Royal College of Surgeons in Ireland, Beaumont Hospital, Dublin, Ireland

[af ]Government Central Laboratories, Ministry of Health, Jerusalem, Israel

[ag ]Laboratoire National de Sante, Dudelange, Luxembourg

[ah ]Department of Medical Microbiology and Infection Prevention, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, Netherlands

[ai ]Netherlands Reference Laboratory for Bacterial Meningitis, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, Netherlands

[aj ]Meningococcal Reference Laboratory, Institute of Environmental Science and Research Limited, Porirua, New Zealand

[ak ]Streptococcal Reference Laboratory, Institute of Environmental Science and Research Limited, Porirua, New Zealand

[al ]Department of Pathology and Biomedical Science, University of Otago, Christchurch, New Zealand

[am ]Public Health Agency, Belfast, Northern Ireland

[an ]National Reference Centre for Bacterial Meningitis, National Medicines Institute, Warsaw, Poland

[ao ]Bacterial Respiratory Infection Service, Scottish Microbiology Reference Laboratories, Glasgow, UK

[ap ]Centre for Respiratory Diseases and Meningitis, National Institute for Communicable Diseases, Division of the National Health Laboratory Service, Johannesburg, South Africa

[aq ]Division of Public Health Surveillance and Response, National Institute for Communicable Diseases, Division of the National Health Laboratory Service, Johannesburg, South Africa

[ar ]Department of Pediatrics, Seoul National University College of Medicine, Seoul, South Korea

[as ]Department of Internal Medicine, Division of Infectious Diseases, Korea University Guro Hospital, Korea University College of Medicine, Seoul, South Korea

[at ]Instituto de Recerca Pediatrica, Hospital Sant Joan de Deu, Barcelona, Spain

[au ]Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, Stockholm, Sweden

[av ]Department of Clinical Microbiology, Karolinska University Hospital, Stockholm, Sweden

[aw ]Department of Laboratory Medicine, National Reference Laboratory for Neisseria meningitidis, Clinical Microbiology, Faculty of Medicine and Health, Örebro University, Örebro, Sweden

[ax ]Public Health Agency of Sweden, Solna, Sweden

[ay ]Swiss National Reference Centre for invasive Pneumococci, Institute for Infectious Diseases, University of Bern, Bern, Switzerland

[az ]Public Health Wales, Cardiff, UK

Author notes

[* ]Correspondence to: Prof Angela B Brueggemann, Nuffield Department of Population Health, Big Data Institute, University of Oxford, Oxford OX37LF, UK angela.brueggemann@ 123456ndph.ox.ac.uk

Article

Publisher Item ID: S2589-7500(21)00077-7

DOI: 10.1016/S2589-7500(21)00077-7

PMC ID: 8166576

PubMed ID: 34045002

SO-VID: 80608af7-b207-43af-a120-795b2792367b

License:

This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Changes in the incidence of invasive disease due to Streptococcus pneumoniae, Haemophilus influenzae, and Neisseria meningitidis during the COVID-19 pandemic in 26 countries and territories in the Invasive Respiratory Infection Surveillance Initiative: a prospective analysis of surveillance data

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Summary

Background

Methods

Findings

Interpretation

Funding

Related collections

Novel Coronavirus Disease COVID-19

Most cited references 29

An interactive web-based dashboard to track COVID-19 in real time

A global panel database of pandemic policies (Oxford COVID-19 Government Response Tracker)

Open-access bacterial population genomics: BIGSdb software, the PubMLST.org website and their applications

Author and article information

Contributors

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Author notes

Article

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