1
|
Oduro MS, Arhin-Donkor S, Asiedu L, Kadengye DT, Iddi S. SARS-CoV-2 incidence monitoring and statistical estimation of the basic and time-varying reproduction number at the early onset of the pandemic in 45 sub-Saharan African countries. BMC Public Health 2024; 24:612. [PMID: 38409118 PMCID: PMC10895859 DOI: 10.1186/s12889-024-18184-8] [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: 11/05/2023] [Accepted: 02/22/2024] [Indexed: 02/28/2024] Open
Abstract
The world battled to defeat a novel coronavirus 2019 (SARS-CoV-2 or COVID-19), a respiratory illness that is transmitted from person to person through contacts with droplets from infected persons. Despite efforts to disseminate preventable messages and adoption of mitigation strategies by governments and the World Health Organization (WHO), transmission spread globally. An accurate assessment of the transmissibility of the coronavirus remained a public health priority for many countries across the world to fight this pandemic, especially at the early onset. In this paper, we estimated the transmission potential of COVID-19 across 45 countries in sub-Saharan Africa using three approaches, namely, [Formula: see text] based on (i) an exponential growth model (ii) maximum likelihood (ML) estimation and (iii) a time-varying basic reproduction number at the early onset of the pandemic. Using data from March 14, 2020, to May 10, 2020, sub-Saharan African countries were still grappling with COVID-19 at that point in the pandemic. The region's basic reproduction number ([Formula: see text]) was 1.89 (95% CI: 1.767 to 2.026) using the growth model and 1.513 (95% CI: 1.491 to 1.535) with the maximum likelihood method, indicating that, on average, infected individuals transmitted the virus to less than two secondary persons. Several countries, including Sudan ([Formula: see text]: 2.03), Ghana ([Formula: see text]: 1.87), and Somalia ([Formula: see text]: 1.85), exhibited high transmission rates. These findings highlighted the need for continued vigilance and the implementation of effective control measures to combat the pandemic in the region. It is anticipated that the findings in this study would not only function as a historical record of reproduction numbers during the COVID-19 pandemic in African countries, but can serve as a blueprint for addressing future pandemics of a similar nature.
Collapse
Affiliation(s)
- Michael Safo Oduro
- Pfizer Research & Development, PSSM Data Sciences, Pfizer, Inc, Groton, CT, USA.
- Department of Applied Statistics and Research Methods, University of Northern Colorado, Greeley, Colorado, USA.
| | - Seth Arhin-Donkor
- Market Finance Analysis - Sr - Prd - Regional, Humana Inc., Louisville, Kentucky, USA
| | - Louis Asiedu
- Department of Statistics and Actuarial Sciences, University of Ghana, Accra, Ghana
| | - Damazo T Kadengye
- Data Synergy and Evaluation, African Population and Health Research Center, Manga Close, Nairobi, Kenya
- Department of Economics and Statistics, Kabale University, Kabale, Uganda
| | - Samuel Iddi
- Department of Statistics and Actuarial Sciences, University of Ghana, Accra, Ghana
- Data Synergy and Evaluation, African Population and Health Research Center, Manga Close, Nairobi, Kenya
| |
Collapse
|
2
|
van Boven M, van Dorp CH, Westerhof I, Jaddoe V, Heuvelman V, Duijts L, Fourie E, Sluiter-Post J, van Houten MA, Badoux P, Euser S, Herpers B, Eggink D, de Hoog M, Boom T, Wildenbeest J, Bont L, Rozhnova G, Bonten MJ, Kretzschmar ME, Bruijning-Verhagen P. Estimation of introduction and transmission rates of SARS-CoV-2 in a prospective household study. PLoS Comput Biol 2024; 20:e1011832. [PMID: 38285727 PMCID: PMC10852262 DOI: 10.1371/journal.pcbi.1011832] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 02/08/2024] [Accepted: 01/16/2024] [Indexed: 01/31/2024] Open
Abstract
Household studies provide an efficient means to study transmission of infectious diseases, enabling estimation of susceptibility and infectivity by person-type. A main inclusion criterion in such studies is usually the presence of an infected person. This precludes estimation of the hazards of pathogen introduction into the household. Here we estimate age- and time-dependent household introduction hazards together with within household transmission rates using data from a prospective household-based study in the Netherlands. A total of 307 households containing 1,209 persons were included from August 2020 until March 2021. Follow-up of households took place between August 2020 and August 2021 with maximal follow-up per household mostly limited to 161 days. Almost 1 out of 5 households (59/307) had evidence of an introduction of SARS-CoV-2. We estimate introduction hazards and within-household transmission rates in our study population with penalized splines and stochastic epidemic models, respectively. The estimated hazard of introduction of SARS-CoV-2 in the households was lower for children (0-12 years) than for adults (relative hazard: 0.62; 95%CrI: 0.34-1.0). Estimated introduction hazards peaked in mid October 2020, mid December 2020, and mid April 2021, preceding peaks in hospital admissions by 1-2 weeks. Best fitting transmission models included increased infectivity of children relative to adults and adolescents, such that the estimated child-to-child transmission probability (0.62; 95%CrI: 0.40-0.81) was considerably higher than the adult-to-adult transmission probability (0.12; 95%CrI: 0.057-0.19). Scenario analyses indicate that vaccination of adults can strongly reduce household infection attack rates and that adding adolescent vaccination offers limited added benefit.
Collapse
Affiliation(s)
- Michiel van Boven
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
- Center for Complex Systems Studies (CCSS), Utrecht University, Utrecht, The Netherlands
| | - Christiaan H. van Dorp
- Department of Pathology & Cell Biology, Columbia University Irving Medical Center, New York, United States of America
| | - Ilse Westerhof
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | | | | | | | | | | | | | - Paul Badoux
- Regional Public Health Laboratory Kennemerland, Haarlem, the Netherlands
| | - Sjoerd Euser
- Regional Public Health Laboratory Kennemerland, Haarlem, the Netherlands
| | - Bjorn Herpers
- Regional Public Health Laboratory Kennemerland, Haarlem, the Netherlands
| | - Dirk Eggink
- National Institute for Public Health and the Environment, Bilthoven, the Netherlands
| | - Marieke de Hoog
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Trisja Boom
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Joanne Wildenbeest
- Department of Paediatric Infectious Diseases and Immunology, Wilhelmina Children’s hospital, University Medical Center Utrecht, the Netherlands
| | - Louis Bont
- Department of Paediatric Infectious Diseases and Immunology, Wilhelmina Children’s hospital, University Medical Center Utrecht, the Netherlands
| | - Ganna Rozhnova
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
- Center for Complex Systems Studies (CCSS), Utrecht University, Utrecht, The Netherlands
- BioISI—Biosystems & Integrative Sciences Institute, Faculdade de Ciências, Universidade de Lisboa, Lisbon, Portugal
| | - Marc J. Bonten
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Mirjam E. Kretzschmar
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
- Center for Complex Systems Studies (CCSS), Utrecht University, Utrecht, The Netherlands
| | - Patricia Bruijning-Verhagen
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| |
Collapse
|
3
|
Nielsen BF, Sneppen K, Simonsen L. The counterintuitive implications of superspreading diseases. Nat Commun 2023; 14:6954. [PMID: 37907452 PMCID: PMC10618522 DOI: 10.1038/s41467-023-42612-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Accepted: 10/16/2023] [Indexed: 11/02/2023] Open
Abstract
The superspreading that characterized SARS and now COVID-19 can be rapidly quantified; however, its implications for outbreak control were never well understood. Recent studies point to its profound impact on outbreak dynamics and prospects for effective control of a future Disease X. These insights necessitate research into the mechanisms, impact and different modes of superspreading more widely.
Collapse
Affiliation(s)
- Bjarke Frost Nielsen
- PandemiX Center of Excellence, Roskilde University, Roskilde, Denmark.
- High Meadows Environmental Institute, Princeton University, Princeton, NJ, USA.
| | - Kim Sneppen
- The Niels Bohr Institute, University of Copenhagen, Copenhagen, Denmark
| | - Lone Simonsen
- PandemiX Center of Excellence, Roskilde University, Roskilde, Denmark
| |
Collapse
|
4
|
Anderson TL, Nande A, Merenstein C, Raynor B, Oommen A, Kelly BJ, Levy MZ, Hill AL. Quantifying individual-level heterogeneity in infectiousness and susceptibility through household studies. Epidemics 2023; 44:100710. [PMID: 37556994 PMCID: PMC10594662 DOI: 10.1016/j.epidem.2023.100710] [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: 12/01/2022] [Revised: 03/17/2023] [Accepted: 07/18/2023] [Indexed: 08/11/2023] Open
Abstract
The spread of SARS-CoV-2, like that of many other pathogens, is governed by heterogeneity. "Superspreading," or "over-dispersion," is an important factor in transmission, yet it is hard to quantify. Estimates from contact tracing data are prone to potential biases due to the increased likelihood of detecting large clusters of cases, and may reflect variation in contact behavior more than biological heterogeneity. In contrast, the average number of secondary infections per contact is routinely estimated from household surveys, and these studies can minimize biases by testing all members of a household. However, the models used to analyze household transmission data typically assume that infectiousness and susceptibility are the same for all individuals or vary only with predetermined traits such as age. Here we develop and apply a combined forward simulation and inference method to quantify the degree of inter-individual variation in both infectiousness and susceptibility from observations of the distribution of infections in household surveys. First, analyzing simulated data, we show our method can reliably ascertain the presence, type, and amount of these heterogeneities given data from a sufficiently large sample of households. We then analyze a collection of household studies of COVID-19 from diverse settings around the world, and find strong evidence for large heterogeneity in both the infectiousness and susceptibility of individuals. Our results also provide a framework to improve the design of studies to evaluate household interventions in the presence of realistic heterogeneity between individuals.
Collapse
Affiliation(s)
- Thayer L Anderson
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD 21218, United States of America
| | - Anjalika Nande
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD 21218, United States of America
| | - Carter Merenstein
- Department of Microbiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States of America
| | - Brinkley Raynor
- Department of Biostatistics, Epidemiology, & Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States of America
| | - Anisha Oommen
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD 21218, United States of America; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, United States of America
| | - Brendan J Kelly
- Department of Microbiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States of America; Department of Biostatistics, Epidemiology, & Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States of America; Division of Infectious Diseases, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States of America
| | - Michael Z Levy
- Department of Biostatistics, Epidemiology, & Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States of America
| | - Alison L Hill
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD 21218, United States of America; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, United States of America.
| |
Collapse
|
5
|
Leiva V, Alcudia E, Montano J, Castro C. An Epidemiological Analysis for Assessing and Evaluating COVID-19 Based on Data Analytics in Latin American Countries. BIOLOGY 2023; 12:887. [PMID: 37372171 DOI: 10.3390/biology12060887] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Revised: 06/14/2023] [Accepted: 06/16/2023] [Indexed: 06/29/2023]
Abstract
This research provides a detailed analysis of the COVID-19 spread across 14 Latin American countries. Using time-series analysis and epidemic models, we identify diverse outbreak patterns, which seem not to be influenced by geographical location or country size, suggesting the influence of other determining factors. Our study uncovers significant discrepancies between the number recorded COVID-19 cases and the real epidemiological situation, emphasizing the crucial need for accurate data handling and continuous surveillance in managing epidemics. The absence of a clear correlation between the country size and the confirmed cases, as well as with the fatalities, further underscores the multifaceted influences on COVID-19 impact beyond population size. Despite the decreased real-time reproduction number indicating quarantine effectiveness in most countries, we note a resurgence in infection rates upon resumption of daily activities. These insights spotlight the challenge of balancing public health measures with economic and social activities. Our core findings provide novel insights, applicable to guiding epidemic control strategies and informing decision-making processes in combatting the pandemic.
Collapse
Affiliation(s)
- Víctor Leiva
- School of Industrial Engineering, Pontificia Universidad Católica de Valparaíso, Valparaíso 2362807, Chile
| | - Esdras Alcudia
- Faculty of Statistics and Informatics, Universidad Veracruzana, Xalapa 91140, Mexico
| | - Julia Montano
- Faculty of Statistics and Informatics, Universidad Veracruzana, Xalapa 91140, Mexico
| | - Cecilia Castro
- Centre of Mathematics, University of Minho, 4710-057 Braga, Portugal
| |
Collapse
|
6
|
van Boven M, van Dorp CH, Westerhof I, Jaddoe V, Heuvelman V, Duijts L, Fourie E, Sluiter-Post J, van Houten MA, Badoux P, Euser S, Herpers B, Eggink D, de Hoog M, Boom T, Wildenbeest J, Bont L, Rozhnov G, Bonten MJ, Kretzschmar ME, Bruijning-Verhagen P. Estimation of introduction and transmission rates of SARS-CoV-2 in a prospective household study. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.06.02.23290879. [PMID: 37333399 PMCID: PMC10275010 DOI: 10.1101/2023.06.02.23290879] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/20/2023]
Abstract
Household studies provide an efficient means to study transmission of infectious diseases, enabling estimation of individual susceptibility and infectivity. A main inclusion criterion in such studies is often the presence of an infected person. This precludes estimation of the hazards of pathogen introduction into the household. Here we use data from a prospective household-based study to estimate SARS-CoV-2 age- and time-dependent household introduction hazards together with within household transmission rates in the Netherlands from August 2020 to August 2021. Introduction hazards and within-household transmission rates are estimated with penalized splines and stochastic epidemic models, respectively. The estimated hazard of introduction of SARS-CoV-2 in the households was lower for children (0-12 years) than for adults (relative hazard: 0.62; 95%CrI: 0.34-1.0). Estimated introduction hazards peaked in mid October 2020, mid December 2020, and mid April 2021, preceding peaks in hospital admissions by 1-2 weeks. The best fitting transmission models include increased infectivity of children relative to adults and adolescents, such that the estimated child-to-child transmission probability (0.62; 95%CrI: 0.40-0.81) was considerably higher than the adult-to-adult transmission probability (0.12; 95%CrI: 0.057-0.19). Scenario analyses show that vaccination of adults could have strongly reduced infection attack rates in households and that adding adolescent vaccination would have offered limited added benefit.
Collapse
Affiliation(s)
- Michiel van Boven
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
- Center for Complex Systems Studies (CCSS), Utrecht University, Utrecht, The Netherlands
| | - Christiaan H van Dorp
- Department of Pathology & Cell Biology, Columbia University Irving Medical Center, New York, United States
| | - Ilse Westerhof
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | | | | | | | | | | | | | - Paul Badoux
- Regional Public Health Laboratory Kennemerland, Haarlem, the Netherlands
| | - Sjoerd Euser
- Regional Public Health Laboratory Kennemerland, Haarlem, the Netherlands
| | - Bjorn Herpers
- Regional Public Health Laboratory Kennemerland, Haarlem, the Netherlands
| | - Dirk Eggink
- National Institute for Public Health, and the Environment, Bilthoven, the Netherlands
| | - Marieke de Hoog
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Trisja Boom
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Joanne Wildenbeest
- Department of Paediatric Infectious Diseases and Immunology, Wilhelmina Children's hospital, University Medical Center Utrecht, the Netherlands
| | - Louis Bont
- Department of Paediatric Infectious Diseases and Immunology, Wilhelmina Children's hospital, University Medical Center Utrecht, the Netherlands
| | - Ganna Rozhnov
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
- Center for Complex Systems Studies (CCSS), Utrecht University, Utrecht, The Netherlands
- BioISI-Biosystems & Integrative Sciences Institute, Faculdade de Ciências, Universidade de Lisboa, Lisbon, Portugal
| | - Marc J Bonten
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Mirjam E Kretzschmar
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
- Center for Complex Systems Studies (CCSS), Utrecht University, Utrecht, The Netherlands
| | - Patricia Bruijning-Verhagen
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| |
Collapse
|
7
|
Anderson TL, Nande A, Merenstein C, Raynor B, Oommen A, Kelly BJ, Levy MZ, Hill AL. Quantifying individual-level heterogeneity in infectiousness and susceptibility through household studies. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2022:2022.12.02.22281853. [PMID: 36523404 PMCID: PMC9753792 DOI: 10.1101/2022.12.02.22281853] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
The spread of SARS-CoV-2, like that of many other pathogens, is governed by heterogeneity. "Superspreading," or "over-dispersion," is an important factor in transmission, yet it is hard to quantify. Estimates from contact tracing data are prone to potential biases due to the increased likelihood of detecting large clusters of cases, and may reflect variation in contact behavior more than biological heterogeneity. In contrast, the average number of secondary infections per contact is routinely estimated from household surveys, and these studies can minimize biases by testing all members of a household. However, the models used to analyze household transmission data typically assume that infectiousness and susceptibility are the same for all individuals or vary only with predetermined traits such as age. Here we develop and apply a combined forward simulation and inference method to quantify the degree of inter-individual variation in both infectiousness and susceptibility from observations of the distribution of infections in household surveys. First, analyzing simulated data, we show our method can reliably ascertain the presence, type, and amount of these heterogeneities with data from a sufficiently large sample of households. We then analyze a collection of household studies of COVID-19 from diverse settings around the world, and find strong evidence for large heterogeneity in both the infectiousness and susceptibility of individuals. Our results also provide a framework to improve the design of studies to evaluate household interventions in the presence of realistic heterogeneity between individuals.
Collapse
Affiliation(s)
- Thayer L Anderson
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD 21218
| | - Anjalika Nande
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD 21218
| | - Carter Merenstein
- Department of Microbiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104
| | - Brinkley Raynor
- Department of Biostatistics, Epidemiology, & Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104
| | - Anisha Oommen
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD 21218
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218
| | - Brendan J Kelly
- Division of Infectious Diseases, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104
| | - Michael Z Levy
- Department of Biostatistics, Epidemiology, & Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104
| | - Alison L Hill
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD 21218
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218
| |
Collapse
|
8
|
Quantitatively evaluate the impact of domestic aviation control measures on the spread of COVID-19 in China. Sci Rep 2022; 12:17600. [PMID: 36266307 PMCID: PMC9584274 DOI: 10.1038/s41598-022-21355-5] [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: 07/25/2022] [Accepted: 09/26/2022] [Indexed: 01/13/2023] Open
Abstract
To quantitatively evaluate the impact of domestic aviation control measures on the spread of COVID-19 in China. The number of international flights from March to September 2019 simulated the number of flights from March to September 2020 without implementing aviation control measures. In addition, the proportion of asymptomatic persons and the delay in case reporting were adjusted to estimate the prevalence of each country during the same period and calculate the estimated imported cases. The estimated imported cases were assigned each day with weight, and the estimated daily reported cases were obtained based on the actual daily number of domestic cases in China. Effective Reproduction Number ([Formula: see text]) was calculated based on delayed distribution, Basic Reproductive Number ([Formula: see text]) distribution, and generation time distribution were reported in previous studies. Gaussian Process was used to estimate the effect of time-varying on [Formula: see text], and the estimated [Formula: see text] was compared with the actual [Formula: see text]. The estimated imported cases increased significantly compared with the actual number of imported cases. The estimated imported cases were mainly concentrated in North America and Europe from March to April and gradually increased in many East Asian countries from May to September. The difference between predicted [Formula: see text] and actual [Formula: see text] was statistically significant. The estimated imported cases and the estimated [Formula: see text] have increased compared to the actual situation. This paper quantitatively proves that Chinese aviation control measures significantly suppress the COVID-19 epidemic, which is conducive to promoting and applying this measure.
Collapse
|
9
|
Parag KV, Donnelly CA, Zarebski AE. Quantifying the information in noisy epidemic curves. NATURE COMPUTATIONAL SCIENCE 2022; 2:584-594. [PMID: 38177483 DOI: 10.1038/s43588-022-00313-1] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Accepted: 08/08/2022] [Indexed: 01/06/2024]
Abstract
Reliably estimating the dynamics of transmissible diseases from noisy surveillance data is an enduring problem in modern epidemiology. Key parameters are often inferred from incident time series, with the aim of informing policy-makers on the growth rate of outbreaks or testing hypotheses about the effectiveness of public health interventions. However, the reliability of these inferences depends critically on reporting errors and latencies innate to the time series. Here, we develop an analytical framework to quantify the uncertainty induced by under-reporting and delays in reporting infections, as well as a metric for ranking surveillance data informativeness. We apply this metric to two primary data sources for inferring the instantaneous reproduction number: epidemic case and death curves. We find that the assumption of death curves as more reliable, commonly made for acute infectious diseases such as COVID-19 and influenza, is not obvious and possibly untrue in many settings. Our framework clarifies and quantifies how actionable information about pathogen transmissibility is lost due to surveillance limitations.
Collapse
Affiliation(s)
- Kris V Parag
- NIHR Health Protection Research Unit in Behavioural Science and Evaluation, University of Bristol, Bristol, UK.
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK.
| | - Christl A Donnelly
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
- Department of Statistics, University of Oxford, Oxford, UK
| | | |
Collapse
|
10
|
House T, Riley H, Pellis L, Pouwels KB, Bacon S, Eidukas A, Jahanshahi K, Eggo RM, Sarah Walker A. Inferring risks of coronavirus transmission from community household data. Stat Methods Med Res 2022; 31:1738-1756. [PMID: 36112916 PMCID: PMC9465559 DOI: 10.1177/09622802211055853] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
The response of many governments to the COVID-19 pandemic has involved measures to control within- and between-household transmission, providing motivation to improve understanding of the absolute and relative risks in these contexts. Here, we perform exploratory, residual-based, and transmission-dynamic household analysis of the Office for National Statistics COVID-19 Infection Survey data from 26 April 2020 to 15 July 2021 in England. This provides evidence for: (i) temporally varying rates of introduction of infection into households broadly following the trajectory of the overall epidemic and vaccination programme; (ii) susceptible-Infectious transmission probabilities of within-household transmission in the 15-35% range; (iii) the emergence of the Alpha and Delta variants, with the former being around 50% more infectious than wildtype and 35% less infectious than Delta within households; (iv) significantly (in the range of 25-300%) more risk of bringing infection into the household for workers in patient-facing roles pre-vaccine; (v) increased risk for secondary school-age children of bringing the infection into the household when schools are open; (vi) increased risk for primary school-age children of bringing the infection into the household when schools were open since the emergence of new variants.
Collapse
Affiliation(s)
- Thomas House
- Department of Mathematics, 5292University of Manchester, Manchester UK
- IBM Research, Hartree Centre, Daresbury UK
- The Alan Turing Institute for Data Science and Artificial Intelligence, London UK
| | - Heather Riley
- Department of Mathematics, 5292University of Manchester, Manchester UK
| | - Lorenzo Pellis
- Department of Mathematics, 5292University of Manchester, Manchester UK
- The Alan Turing Institute for Data Science and Artificial Intelligence, London UK
| | - Koen B Pouwels
- 105596Nuffield Department of Medicine, University of Oxford, Oxford UK
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford UK
- Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, , Oxford UK
| | - Sebastian Bacon
- The DataLab, 12205Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford UK
| | | | | | - Rosalind M Eggo
- Centre for Mathematical Modelling of Infectious Diseases, 4906London School of Hygiene and Tropical Medicine, London UK
| | - A Sarah Walker
- 105596Nuffield Department of Medicine, University of Oxford, Oxford UK
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford UK
- The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford UK
- MRC Clinical Trials Unit at UCL, UCL, London UK
| |
Collapse
|
11
|
Biselli R, Nisini R, Lista F, Autore A, Lastilla M, De Lorenzo G, Peragallo MS, Stroffolini T, D’Amelio R. A Historical Review of Military Medical Strategies for Fighting Infectious Diseases: From Battlefields to Global Health. Biomedicines 2022; 10:2050. [PMID: 36009598 PMCID: PMC9405556 DOI: 10.3390/biomedicines10082050] [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: 07/27/2022] [Revised: 08/12/2022] [Accepted: 08/13/2022] [Indexed: 11/17/2022] Open
Abstract
The environmental conditions generated by war and characterized by poverty, undernutrition, stress, difficult access to safe water and food as well as lack of environmental and personal hygiene favor the spread of many infectious diseases. Epidemic typhus, plague, malaria, cholera, typhoid fever, hepatitis, tetanus, and smallpox have nearly constantly accompanied wars, frequently deeply conditioning the outcome of battles/wars more than weapons and military strategy. At the end of the nineteenth century, with the birth of bacteriology, military medical researchers in Germany, the United Kingdom, and France were active in discovering the etiological agents of some diseases and in developing preventive vaccines. Emil von Behring, Ronald Ross and Charles Laveran, who were or served as military physicians, won the first, the second, and the seventh Nobel Prize for Physiology or Medicine for discovering passive anti-diphtheria/tetanus immunotherapy and for identifying mosquito Anopheline as a malaria vector and plasmodium as its etiological agent, respectively. Meanwhile, Major Walter Reed in the United States of America discovered the mosquito vector of yellow fever, thus paving the way for its prevention by vector control. In this work, the military relevance of some vaccine-preventable and non-vaccine-preventable infectious diseases, as well as of biological weapons, and the military contributions to their control will be described. Currently, the civil-military medical collaboration is getting closer and becoming interdependent, from research and development for the prevention of infectious diseases to disasters and emergencies management, as recently demonstrated in Ebola and Zika outbreaks and the COVID-19 pandemic, even with the high biocontainment aeromedical evacuation, in a sort of global health diplomacy.
Collapse
Affiliation(s)
- Roberto Biselli
- Ispettorato Generale della Sanità Militare, Stato Maggiore della Difesa, Via S. Stefano Rotondo 4, 00184 Roma, Italy
| | - Roberto Nisini
- Dipartimento di Malattie Infettive, Istituto Superiore di Sanità, Viale Regina Elena 299, 00161 Roma, Italy
| | - Florigio Lista
- Dipartimento Scientifico, Policlinico Militare, Comando Logistico dell’Esercito, Via S. Stefano Rotondo 4, 00184 Roma, Italy
| | - Alberto Autore
- Osservatorio Epidemiologico della Difesa, Ispettorato Generale della Sanità Militare, Stato Maggiore della Difesa, Via S. Stefano Rotondo 4, 00184 Roma, Italy
| | - Marco Lastilla
- Istituto di Medicina Aerospaziale, Comando Logistico dell’Aeronautica Militare, Viale Piero Gobetti 2, 00185 Roma, Italy
| | - Giuseppe De Lorenzo
- Comando Generale dell’Arma dei Carabinieri, Dipartimento per l’Organizzazione Sanitaria e Veterinaria, Viale Romania 45, 00197 Roma, Italy
| | - Mario Stefano Peragallo
- Centro Studi e Ricerche di Sanità e Veterinaria, Comando Logistico dell’Esercito, Via S. Stefano Rotondo 4, 00184 Roma, Italy
| | - Tommaso Stroffolini
- Dipartimento di Malattie Infettive e Tropicali, Policlinico Umberto I, 00161 Roma, Italy
| | - Raffaele D’Amelio
- Dipartimento di Medicina Clinica e Molecolare, Sapienza Università di Roma, Via di Grottarossa 1035-1039, 00189 Roma, Italy
| |
Collapse
|
12
|
Nielsen BF, Eilersen A, Simonsen L, Sneppen K. Lockdowns exert selection pressure on overdispersion of SARS-CoV-2 variants. Epidemics 2022; 40:100613. [PMID: 35939969 PMCID: PMC9338171 DOI: 10.1016/j.epidem.2022.100613] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Revised: 06/23/2022] [Accepted: 07/20/2022] [Indexed: 11/22/2022] Open
Abstract
The SARS-CoV-2 ancestral strain has caused pronounced superspreading events, reflecting a disease characterized by overdispersion, where about 10% of infected people cause 80% of infections. New variants of the disease have different person-to-person variability in viral load, suggesting for example that the Alpha (B.1.1.7) variant is more infectious but relatively less prone to superspreading. Meanwhile, non-pharmaceutical mitigation of the pandemic has focused on limiting social contacts (lockdowns, regulations on gatherings) and decreasing transmission risk through mask wearing and social distancing. Using a mathematical model, we show that the competitive advantage of disease variants may heavily depend on the restrictions imposed. In particular, we find that lockdowns exert an evolutionary pressure which favours variants with lower levels of overdispersion. Our results suggest that overdispersion is an evolutionarily unstable trait, with a tendency for more homogeneously spreading variants to eventually dominate. Novel variants of SARS-CoV-2 appear to be less prone to superspreading. A new model shows that it is advantageous for the pathogen to spread homogeneously. Interventions exert a selective pressure towards developing homogeneous transmission. The results have implications for the assessment of novel variants. Adds to understanding of how behaviour and interventions shape pathogen evolution.
Collapse
|
13
|
Zhao S, Chong MKC, Ryu S, Guo Z, He M, Chen B, Musa SS, Wang J, Wu Y, He D, Wang MH. Characterizing superspreading potential of infectious disease: Decomposition of individual transmissibility. PLoS Comput Biol 2022; 18:e1010281. [PMID: 35759509 PMCID: PMC9269899 DOI: 10.1371/journal.pcbi.1010281] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2021] [Revised: 07/08/2022] [Accepted: 06/06/2022] [Indexed: 11/18/2022] Open
Abstract
In the context of infectious disease transmission, high heterogeneity in individual infectiousness indicates that a few index cases can generate large numbers of secondary cases, a phenomenon commonly known as superspreading. The potential of disease superspreading can be characterized by describing the distribution of secondary cases (of each seed case) as a negative binomial (NB) distribution with the dispersion parameter, k. Based on the feature of NB distribution, there must be a proportion of individuals with individual reproduction number of almost 0, which appears restricted and unrealistic. To overcome this limitation, we generalized the compound structure of a Poisson rate and included an additional parameter, and divided the reproduction number into independent and additive fixed and variable components. Then, the secondary cases followed a Delaporte distribution. We demonstrated that the Delaporte distribution was important for understanding the characteristics of disease transmission, which generated new insights distinct from the NB model. By using real-world dataset, the Delaporte distribution provides improvements in describing the distributions of COVID-19 and SARS cases compared to the NB distribution. The model selection yielded increasing statistical power with larger sample sizes as well as conservative type I error in detecting the improvement in fitting with the likelihood ratio (LR) test. Numerical simulation revealed that the control strategy-making process may benefit from monitoring the transmission characteristics under the Delaporte framework. Our findings highlighted that for the COVID-19 pandemic, population-wide interventions may control disease transmission on a general scale before recommending the high-risk-specific control strategies. Superspreading is one of the key transmission features of many infectious diseases and is considered a consequence of the heterogeneity in infectiousness of individual cases. To characterize the superspreading potential, we divided individual infectiousness into two independent and additive components, including a fixed baseline and a variable part. Such decomposition produced an improvement in the fit of the model explaining the distribution of real-world datasets of COVID-19 and SARS that can be captured by the classic statistical tests. Disease control strategies may be developed by monitoring the characteristics of superspreading. For the COVID-19 pandemic, population-wide interventions are suggested first to limit the transmission at a scale of general population, and then high-risk-specific control strategies are recommended subsequently to lower the risk of superspreading.
Collapse
Affiliation(s)
- Shi Zhao
- JC School of Public Health and Primary Care, Chinese University of Hong Kong, Hong Kong, China
- CUHK Shenzhen Research Institute, Shenzhen, China
- * E-mail: (SZ); (DH)
| | - Marc K. C. Chong
- JC School of Public Health and Primary Care, Chinese University of Hong Kong, Hong Kong, China
- CUHK Shenzhen Research Institute, Shenzhen, China
| | - Sukhyun Ryu
- Department of Preventive Medicine, Konyang University College of Medicine, Daejeon, South Korea
| | - Zihao Guo
- JC School of Public Health and Primary Care, Chinese University of Hong Kong, Hong Kong, China
| | - Mu He
- Department of Foundational Mathematics, Xi’an Jiaotong-Liverpool University, Suzhou, China
| | - Boqiang Chen
- Department of Applied Mathematics, Hong Kong Polytechnic University, Hong Kong, China
| | - Salihu S. Musa
- Department of Applied Mathematics, Hong Kong Polytechnic University, Hong Kong, China
- Department of Mathematics, Kano University of Science and Technology, Wudil, Nigeria
| | - Jingxuan Wang
- JC School of Public Health and Primary Care, Chinese University of Hong Kong, Hong Kong, China
| | - Yushan Wu
- JC School of Public Health and Primary Care, Chinese University of Hong Kong, Hong Kong, China
| | - Daihai He
- Department of Applied Mathematics, Hong Kong Polytechnic University, Hong Kong, China
- * E-mail: (SZ); (DH)
| | - Maggie H. Wang
- JC School of Public Health and Primary Care, Chinese University of Hong Kong, Hong Kong, China
- CUHK Shenzhen Research Institute, Shenzhen, China
| |
Collapse
|
14
|
Miranda MNS, Pingarilho M, Pimentel V, Torneri A, Seabra SG, Libin PJK, Abecasis AB. A Tale of Three Recent Pandemics: Influenza, HIV and SARS-CoV-2. Front Microbiol 2022; 13:889643. [PMID: 35722303 PMCID: PMC9201468 DOI: 10.3389/fmicb.2022.889643] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Accepted: 05/06/2022] [Indexed: 11/13/2022] Open
Abstract
Emerging infectious diseases are one of the main threats to public health, with the potential to cause a pandemic when the infectious agent manages to spread globally. The first major pandemic to appear in the 20th century was the influenza pandemic of 1918, caused by the influenza A H1N1 strain that is characterized by a high fatality rate. Another major pandemic was caused by the human immunodeficiency virus (HIV), that started early in the 20th century and remained undetected until 1981. The ongoing HIV pandemic demonstrated a high mortality and morbidity rate, with discrepant impacts in different regions around the globe. The most recent major pandemic event, is the ongoing pandemic of COVID-19, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which has caused over 5.7 million deaths since its emergence, 2 years ago. The aim of this work is to highlight the main determinants of the emergence, epidemic response and available countermeasures of these three pandemics, as we argue that such knowledge is paramount to prepare for the next pandemic. We analyse these pandemics’ historical and epidemiological contexts and the determinants of their emergence. Furthermore, we compare pharmaceutical and non-pharmaceutical interventions that have been used to slow down these three pandemics and zoom in on the technological advances that were made in the progress. Finally, we discuss the evolution of epidemiological modelling, that has become an essential tool to support public health policy making and discuss it in the context of these three pandemics. While these pandemics are caused by distinct viruses, that ignited in different time periods and in different regions of the globe, our work shows that many of the determinants of their emergence and countermeasures used to halt transmission were common. Therefore, it is important to further improve and optimize such approaches and adapt it to future threatening emerging infectious diseases.
Collapse
Affiliation(s)
- Mafalda N S Miranda
- Global Health and Tropical Medicine (GHTM), Instituto de Higiene e Medicina Tropical/Universidade Nova de Lisboa (IHMT/UNL), Lisboa, Portugal
| | - Marta Pingarilho
- Global Health and Tropical Medicine (GHTM), Instituto de Higiene e Medicina Tropical/Universidade Nova de Lisboa (IHMT/UNL), Lisboa, Portugal
| | - Victor Pimentel
- Global Health and Tropical Medicine (GHTM), Instituto de Higiene e Medicina Tropical/Universidade Nova de Lisboa (IHMT/UNL), Lisboa, Portugal
| | - Andrea Torneri
- Artificial Intelligence Lab, Department of Computer Science, Vrije Universiteit Brussel, Brussels, Belgium
| | - Sofia G Seabra
- Global Health and Tropical Medicine (GHTM), Instituto de Higiene e Medicina Tropical/Universidade Nova de Lisboa (IHMT/UNL), Lisboa, Portugal
| | - Pieter J K Libin
- Artificial Intelligence Lab, Department of Computer Science, Vrije Universiteit Brussel, Brussels, Belgium.,Interuniversity Institute of Biostatistics and Statistical Bioinformatics, Data Science Institute, Hasselt University, Hasselt, Belgium.,Department of Microbiology and Immunology, Rega Institute for Medical Research, KU Leuven, University of Leuven, Leuven, Belgium
| | - Ana B Abecasis
- Global Health and Tropical Medicine (GHTM), Instituto de Higiene e Medicina Tropical/Universidade Nova de Lisboa (IHMT/UNL), Lisboa, Portugal
| |
Collapse
|
15
|
Abstract
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) delta variant transmits much more rapidly than prior SARS-CoV-2 viruses. The primary mode of transmission is via short range aerosols that are emitted from the respiratory tract of an index case. There is marked heterogeneity in the spread of this virus, with 10% to 20% of index cases contributing to 80% of secondary cases, while most index cases have no subsequent transmissions. Vaccination, ventilation, masking, eye protection, and rapid case identification with contact tracing and isolation can all decrease the transmission of this virus.
Collapse
Affiliation(s)
- Eric A Meyerowitz
- Montefiore Medical Center, 111 East 210th Street, Bronx, NY 10467, USA.
| | - Aaron Richterman
- Hospital of the University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA 19104, USA
| |
Collapse
|
16
|
Waites W, Pearson CAB, Gaskell KM, House T, Pellis L, Johnson M, Gould V, Hunt A, Stone NRH, Kasstan B, Chantler T, Lal S, Roberts CH, Goldblatt D, Marks M, Eggo RM. Transmission dynamics of SARS-CoV-2 in a strictly-Orthodox Jewish community in the UK. Sci Rep 2022; 12:8550. [PMID: 35595824 PMCID: PMC9121858 DOI: 10.1038/s41598-022-12517-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Accepted: 05/12/2022] [Indexed: 11/22/2022] Open
Abstract
Some social settings such as households and workplaces, have been identified as high risk for SARS-CoV-2 transmission. Identifying and quantifying the importance of these settings is critical for designing interventions. A tightly-knit religious community in the UK experienced a very large COVID-19 epidemic in 2020, reaching 64.3% seroprevalence within 10 months, and we surveyed this community both for serological status and individual-level attendance at particular settings. Using these data, and a network model of people and places represented as a stochastic graph rewriting system, we estimated the relative contribution of transmission in households, schools and religious institutions to the epidemic, and the relative risk of infection in each of these settings. All congregate settings were important for transmission, with some such as primary schools and places of worship having a higher share of transmission than others. We found that the model needed a higher general-community transmission rate for women (3.3-fold), and lower susceptibility to infection in children to recreate the observed serological data. The precise share of transmission in each place was related to assumptions about the internal structure of those places. Identification of key settings of transmission can allow public health interventions to be targeted at these locations.
Collapse
Affiliation(s)
- William Waites
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, Keppel Street, London, UK.
- Department of Computer and Information Sciences, University of Strathclyde, Glasgow, Scotland, UK.
| | - Carl A B Pearson
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, Keppel Street, London, UK
| | - Katherine M Gaskell
- Department of Clinical Research, London School of Hygiene and Tropical Medicine, Keppel Street, London, UK
| | - Thomas House
- School of Mathematics, University of Manchester, Manchester, UK
| | - Lorenzo Pellis
- School of Mathematics, University of Manchester, Manchester, UK
| | - Marina Johnson
- Great Ormond Street Institute of Child Health Biomedical Research Centre, University College London, London, UK
| | - Victoria Gould
- Department of Clinical Research, London School of Hygiene and Tropical Medicine, Keppel Street, London, UK
| | - Adam Hunt
- Great Ormond Street Institute of Child Health Biomedical Research Centre, University College London, London, UK
| | - Neil R H Stone
- Department of Clinical Research, London School of Hygiene and Tropical Medicine, Keppel Street, London, UK
- Hospital for Tropical Diseases, University College London Hospital NHS Foundation Trust, London, UK
| | - Ben Kasstan
- Centre for Health, Law and Society, University of Bristol Law School, Bristol, UK
- Department of Sociology and Anthropology, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Tracey Chantler
- Department of Global Health and Development, London School of Hygiene and Tropical Medicine, Keppel Street, London, UK
| | - Sham Lal
- Department of Clinical Research, London School of Hygiene and Tropical Medicine, Keppel Street, London, UK
| | - Chrissy H Roberts
- Department of Clinical Research, London School of Hygiene and Tropical Medicine, Keppel Street, London, UK
| | - David Goldblatt
- Great Ormond Street Institute of Child Health Biomedical Research Centre, University College London, London, UK
| | - Michael Marks
- Department of Clinical Research, London School of Hygiene and Tropical Medicine, Keppel Street, London, UK
- Hospital for Tropical Diseases, University College London Hospital NHS Foundation Trust, London, UK
| | - Rosalind M Eggo
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, Keppel Street, London, UK
| |
Collapse
|
17
|
Holmes A, Tildesley M, Dyson L. Approximating steady state distributions for household structured epidemic models. J Theor Biol 2022; 534:110974. [PMID: 34852241 DOI: 10.1016/j.jtbi.2021.110974] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Revised: 11/13/2021] [Accepted: 11/16/2021] [Indexed: 11/29/2022]
Abstract
Household-structured infectious disease models consider the increased transmission potential between individuals of the same household when compared with two individuals in different households. Accounting for these heterogeneities in transmission enables control measures to be more effectively planned. Ideally, pre-control data may be used to fit such a household-structured model at an endemic steady state, before making dynamic forward-predictions under different proposed strategies. However, this requires the accurate calculation of the steady states for the full dynamic model. We observe that steady state SIS dynamics with household structure cannot necessarily be described by the master equation for a single household, instead requiring consideration of the full system. However, solving the full system of equations becomes increasingly computationally intensive, particularly for higher-dimensional models. We compare two approximations to the full system: the single household master equation; and a proposed alternative method, using the Fokker-Planck equation. Moment closure is another commonly used method, but for more complicated systems, the equations quickly become unwieldy and very difficult to derive. In comparison, using the master equation for a single household is easily implementable, however it can be quite inaccurate. In this paper we compare these methods in terms of accuracy and ease of implementation. We find that there are regions of parameter space in which each method outperforms the other, and that these regions of parameter space can be characterised by the infection prevalence, or by the correlation between household states.
Collapse
Affiliation(s)
- Alex Holmes
- The Zeeman Institute for Systems Biology & Infectious Disease Epidemiology Research, School of Life Sciences and Mathematics Institute, University of Warwick, Coventry CV4 7AL, United Kingdom; Mathematics for Real World Systems Centre for Doctoral Training, Mathematics Institute, University of Warwick, Coventry CV4 7AL, United Kingdom.
| | - Mike Tildesley
- The Zeeman Institute for Systems Biology & Infectious Disease Epidemiology Research, School of Life Sciences and Mathematics Institute, University of Warwick, Coventry CV4 7AL, United Kingdom
| | - Louise Dyson
- The Zeeman Institute for Systems Biology & Infectious Disease Epidemiology Research, School of Life Sciences and Mathematics Institute, University of Warwick, Coventry CV4 7AL, United Kingdom
| |
Collapse
|
18
|
Wang J, Chen X, Guo Z, Zhao S, Huang Z, Zhuang Z, Wong ELY, Zee BCY, Chong MKC, Wang MH, Yeoh EK. Superspreading and heterogeneity in transmission of SARS, MERS, and COVID-19: A systematic review. Comput Struct Biotechnol J 2021; 19:5039-5046. [PMID: 34484618 PMCID: PMC8409018 DOI: 10.1016/j.csbj.2021.08.045] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2021] [Revised: 08/28/2021] [Accepted: 08/28/2021] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND Severe acute respiratory syndrome (SARS), Middle East respiratory syndrome (MERS), and coronavirus disease 2019 (COVID-19) have caused substantial public health burdens and global health threats. Understanding the superspreading potentials of these viruses are important for characterizing transmission patterns and informing strategic decision-making in disease control. This systematic review aimed to summarize the existing evidence on superspreading features and to compare the heterogeneity in transmission within and among various betacoronavirus epidemics of SARS, MERS and COVID-19. METHODS PubMed, MEDLINE, and Embase databases were extensively searched for original studies on the transmission heterogeneity of SARS, MERS, and COVID-19 published in English between January 1, 2003, and February 10, 2021. After screening the articles, we extracted data pertaining to the estimated dispersion parameter (k) which has been a commonly-used measurement for superspreading potential. FINDINGS We included a total of 60 estimates of transmission heterogeneity from 26 studies on outbreaks in 22 regions. The majority (90%) of the k estimates were small, with values less than 1, indicating an over-dispersed transmission pattern. The point estimates of k for SARS and MERS ranged from 0.12 to 0.20 and from 0.06 to 2.94, respectively. Among 45 estimates of individual-level transmission heterogeneity for COVID-19 from 17 articles, 91% were derived from Asian regions. The point estimates of k for COVID-19 ranged between 0.1 and 5.0. CONCLUSIONS We detected a substantial over-dispersed transmission pattern in all three coronaviruses, while the k estimates varied by differences in study design and public health capacity. Our findings suggested that even with a reduced R value, the epidemic still has a high resurgence potential due to transmission heterogeneity.
Collapse
Affiliation(s)
- Jingxuan Wang
- JC School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Xiao Chen
- School of Public Health, Zhejiang University, Hangzhou, China
| | - Zihao Guo
- JC School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Shi Zhao
- JC School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China
- Shenzhen Research Institute, The Chinese University of Hong Kong, Shenzhen, China
| | - Ziyue Huang
- Mianyang Maternal and Child Health Care Hospital, Mianyang, China
| | - Zian Zhuang
- Department of Biostatistics, University of California Los Angeles Fielding School of Public Health, Los Angeles, CA, USA
| | - Eliza Lai-yi Wong
- JC School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China
- CUHK Institute of Health Equity, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China
- Centre for Health Systems and Policy Research, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Benny Chung-Ying Zee
- JC School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China
- Shenzhen Research Institute, The Chinese University of Hong Kong, Shenzhen, China
| | - Marc Ka Chun Chong
- JC School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China
- Shenzhen Research Institute, The Chinese University of Hong Kong, Shenzhen, China
| | - Maggie Haitian Wang
- JC School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China
- Shenzhen Research Institute, The Chinese University of Hong Kong, Shenzhen, China
| | - Eng Kiong Yeoh
- JC School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China
- CUHK Institute of Health Equity, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China
- Centre for Health Systems and Policy Research, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China
| |
Collapse
|
19
|
Abstract
We show that sub-spreading events, i.e. transmission events in which an infection propagates to few or no individuals, can be surprisingly important for defining the lifetime of an infectious disease epidemic and hence its waiting time to elimination or fade-out, measured from the time-point of its last observed case. While limiting super-spreading promotes more effective control when cases are growing, we find that when incidence is waning, curbing sub-spreading is more important for achieving reliable elimination of the epidemic. Controlling super-spreading in this low-transmissibility phase offers diminishing returns over non-selective, population-wide measures. By restricting sub-spreading, we efficiently dampen remaining variations among the reproduction numbers of infectious events, which minimizes the risk of premature and late end-of-epidemic declarations. Because case-ascertainment or reporting rates can be modelled in exactly the same way as control policies, we concurrently show that the under-reporting of sub-spreading events during waning phases will engender overconfident assessments of epidemic elimination. While controlling sub-spreading may not be easily realized, the likely neglecting of these events by surveillance systems could result in unexpectedly risky end-of-epidemic declarations. Super-spreading controls the size of the epidemic peak but sub-spreading mediates the variability of its tail.
Collapse
Affiliation(s)
- Kris V Parag
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London W2 1PG, UK
| |
Collapse
|
20
|
Ørskov S, Nielsen BF, Føns S, Sneppen K, Simonsen L. The COVID-19 pandemic: key considerations for the epidemic and its control. APMIS 2021; 129:408-420. [PMID: 33932317 PMCID: PMC8239778 DOI: 10.1111/apm.13141] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Accepted: 04/07/2021] [Indexed: 12/23/2022]
Abstract
The response to the ongoing COVID‐19 pandemic has been characterized by draconian measures and far too many important unknowns, such as the true mortality risk, the role of children as transmitters and the development and duration of immunity in the population. More than a year into the pandemic much has been learned and insights into this novel type of pandemic and options for control are shaping up. Using a historical lens, we review what we know and still do not know about the ongoing COVID‐19 pandemic. A pandemic caused by a member of the coronavirus family is a new situation following more than a century of influenza A pandemics. However, recent pandemic threats such as outbreaks of the related and novel deadly coronavirus SARS in 2003 and of MERS since 2012 had put coronaviruses on WHOs blueprint list of priority diseases. Like pandemic influenza, SARS‐CoV‐2 is highly transmissible (R0 ~ 2.5). Furthermore, it can fly under the radar due to a broad clinical spectrum where asymptomatic and pre‐symptomatic infected persons also transmit the virus—including children. COVID‐19 is far more deadly than seasonal influenza; initial data from China suggested a case fatality rate of 2.3%—which would have been on par with the deadly 1918 Spanish influenza. But, while the Spanish influenza killed young, otherwise healthy adults, it is the elderly who are at extreme risk of dying of COVID‐19. We review available seroepidemiological evidence of infection rates and compute infection fatality rates (IFR) for Denmark (0.5%), Spain (0.85%), and Iceland (0.3%). We also deduce that population age structure is key. SARS‐CoV‐2 is characterized by superspreading, so that ~10% of infected individuals yield 80% of new infections. This phenomenon turns out to be an Achilles heel of the virus that may explain our ability to effectively mitigate outbreaks so far. How will this pandemic come to an end? Herd immunity has not been achieved in Europe due to intense mitigation by non‐pharmaceutical interventions; for example, only ~8% of Danes were infected across the 1st and 2nd wave. Luckily, we now have several safe and effective vaccines. Global vaccine control of the pandemic depends in great measure on our ability to keep up with current and future immune escape variants of the virus. We should thus be prepared for a race between vaccine updates and mutations of the virus. A permanent reopening of society highly depends on winning that race.
Collapse
Affiliation(s)
- Søren Ørskov
- Department of Science and Environment, Roskilde University, Roskilde, Denmark
| | | | - Sofie Føns
- Department of Science and Environment, Roskilde University, Roskilde, Denmark
| | - Kim Sneppen
- Niels Bohr Institute (NBI), University of Copenhagen, Copenhagen, Denmark
| | - Lone Simonsen
- Department of Science and Environment, Roskilde University, Roskilde, Denmark
| |
Collapse
|
21
|
Dorigatti I, Lavezzo E, Manuto L, Ciavarella C, Pacenti M, Boldrin C, Cattai M, Saluzzo F, Franchin E, Del Vecchio C, Caldart F, Castelli G, Nicoletti M, Nieddu E, Salvadoretti E, Labella B, Fava L, Guglielmo S, Fascina M, Grazioli M, Alvisi G, Vanuzzo MC, Zupo T, Calandrin R, Lisi V, Rossi L, Castagliuolo I, Merigliano S, Unwin HJT, Plebani M, Padoan A, Brazzale AR, Toppo S, Ferguson NM, Donnelly CA, Crisanti A. SARS-CoV-2 antibody dynamics and transmission from community-wide serological testing in the Italian municipality of Vo'. Nat Commun 2021; 12:4383. [PMID: 34282139 PMCID: PMC8289856 DOI: 10.1038/s41467-021-24622-7] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Accepted: 06/21/2021] [Indexed: 01/04/2023] Open
Abstract
In February and March 2020, two mass swab testing campaigns were conducted in Vo', Italy. In May 2020, we tested 86% of the Vo' population with three immuno-assays detecting antibodies against the spike and nucleocapsid antigens, a neutralisation assay and Polymerase Chain Reaction (PCR). Subjects testing positive to PCR in February/March or a serological assay in May were tested again in November. Here we report on the results of the analysis of the May and November surveys. We estimate a seroprevalence of 3.5% (95% Credible Interval (CrI): 2.8-4.3%) in May. In November, 98.8% (95% Confidence Interval (CI): 93.7-100.0%) of sera which tested positive in May still reacted against at least one antigen; 18.6% (95% CI: 11.0-28.5%) showed an increase of antibody or neutralisation reactivity from May. Analysis of the serostatus of the members of 1,118 households indicates a 26.0% (95% CrI: 17.2-36.9%) Susceptible-Infectious Transmission Probability. Contact tracing had limited impact on epidemic suppression.
Collapse
Affiliation(s)
- Ilaria Dorigatti
- MRC Centre for Global Infectious Disease Analysis and the Abdul Latif Jameel Institute for Disease and Emergency Analytics, School of Public Health, Imperial College London, London, UK.
| | - Enrico Lavezzo
- Department of Molecular Medicine, University of Padova, Padova, Italy.
| | - Laura Manuto
- Department of Molecular Medicine, University of Padova, Padova, Italy
| | - Constanze Ciavarella
- MRC Centre for Global Infectious Disease Analysis and the Abdul Latif Jameel Institute for Disease and Emergency Analytics, School of Public Health, Imperial College London, London, UK
| | | | | | | | - Francesca Saluzzo
- Department of Molecular Medicine, University of Padova, Padova, Italy
| | - Elisa Franchin
- Department of Molecular Medicine, University of Padova, Padova, Italy
| | | | - Federico Caldart
- Department of Molecular Medicine, University of Padova, Padova, Italy
| | - Gioele Castelli
- Department of Molecular Medicine, University of Padova, Padova, Italy
| | - Michele Nicoletti
- Department of Molecular Medicine, University of Padova, Padova, Italy
| | - Eleonora Nieddu
- Department of Molecular Medicine, University of Padova, Padova, Italy
| | | | - Beatrice Labella
- Department of Molecular Medicine, University of Padova, Padova, Italy
| | - Ludovico Fava
- Department of Molecular Medicine, University of Padova, Padova, Italy
| | - Simone Guglielmo
- Department of Molecular Medicine, University of Padova, Padova, Italy
| | | | - Marco Grazioli
- Department of Molecular Medicine, University of Padova, Padova, Italy
| | - Gualtiero Alvisi
- Department of Molecular Medicine, University of Padova, Padova, Italy
| | | | | | | | | | | | | | - Stefano Merigliano
- Department of Surgery, Oncology and Gastroenterology, University of Padova, Padova, Italy
| | - H Juliette T Unwin
- MRC Centre for Global Infectious Disease Analysis and the Abdul Latif Jameel Institute for Disease and Emergency Analytics, School of Public Health, Imperial College London, London, UK
| | - Mario Plebani
- Department of Medicine, University of Padova, Padova, Italy
| | - Andrea Padoan
- Department of Medicine, University of Padova, Padova, Italy
| | | | - Stefano Toppo
- Department of Molecular Medicine, University of Padova, Padova, Italy
- CRIBI Biotech Centre, University of Padova, Padova, Italy
| | - Neil M Ferguson
- MRC Centre for Global Infectious Disease Analysis and the Abdul Latif Jameel Institute for Disease and Emergency Analytics, School of Public Health, Imperial College London, London, UK
| | - Christl A Donnelly
- MRC Centre for Global Infectious Disease Analysis and the Abdul Latif Jameel Institute for Disease and Emergency Analytics, School of Public Health, Imperial College London, London, UK
- Department of Statistics, University of Oxford, Oxford, UK
| | - Andrea Crisanti
- Department of Molecular Medicine, University of Padova, Padova, Italy.
- Azienda Ospedale Padova, Padova, Italy.
- Department of Life Science Imperial College London, Exhibition Road, London, UK.
| |
Collapse
|
22
|
Jayaweera M, Dannangoda C, Dilshan D, Dissanayake J, Perera H, Manatunge J, Gunawardana B. Grappling with COVID-19 by imposing and lifting non-pharmaceutical interventions in Sri Lanka: A modeling perspective. Infect Dis Model 2021; 6:820-831. [PMID: 34250320 PMCID: PMC8261138 DOI: 10.1016/j.idm.2021.06.004] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Revised: 06/10/2021] [Accepted: 06/10/2021] [Indexed: 12/24/2022] Open
Abstract
The imposition and lifting of non-pharmaceutical interventions (NPIs) to avert the COVID-19 pandemic have gained popularity worldwide and will continue to be enforced until herd immunity is achieved. We developed a linear regression model to ascertain the nexus between the time-varying reproduction number averaged over a time window of six days (Rts) and seven NPIs: contact tracing, quarantine efforts, social distancing and health checks, hand hygiene, wearing of facemasks, lockdown and isolation, and health-related supports. Our analysis suggests that the second wave that emerged in Sri Lanka in early October 2020 continued despite numerous NPIs. The model indicates that the most effective single NPI was lockdown and isolation. Conversely, the least effective individual NPIs were hand hygiene and wearing of facemasks. The model also demonstrates that to mitigate the second wave to a satisfactory level (Rts<1), the best single NPI was the contact tracing with stringent imposition (% of improvement of Rts was 69.43 against the base case). By contrast, the best combination of two NPIs was the lockdown & isolation with health-related supports (% of improvement was 31.92 against the base case). As such, many health authorities worldwide can use this model to successfully strategize the imposition and lifting of NPIs for averting the COVID-19 pandemic.
Collapse
Affiliation(s)
- Mahesh Jayaweera
- Department of Civil Engineering, University of Moratuwa, Sri Lanka
| | - Chamath Dannangoda
- Department of Physics and Astronomy, University of Texas at Rio Grande Valley, Brownsville, TX, 78520, USA
| | - Dilum Dilshan
- Department of Civil Engineering, University of Moratuwa, Sri Lanka
| | - Janith Dissanayake
- Department of Civil & Environmental Engineering, College of Engineering, Seoul National University, 1, Gwanak-ro, Gwanak-gu, Seoul, 08826, Republic of Korea
| | - Hasini Perera
- Department of Forestry and Environmental Science, University of Sri Jayewardenepura, Sri Lanka
| | - Jagath Manatunge
- Department of Civil Engineering, University of Moratuwa, Sri Lanka
| | | |
Collapse
|
23
|
Bi Q, Lessler J, Eckerle I, Lauer SA, Kaiser L, Vuilleumier N, Cummings DAT, Flahault A, Petrovic D, Guessous I, Stringhini S, Azman AS. Insights into household transmission of SARS-CoV-2 from a population-based serological survey. Nat Commun 2021; 12:3643. [PMID: 34131124 PMCID: PMC8206123 DOI: 10.1038/s41467-021-23733-5] [Citation(s) in RCA: 42] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2021] [Accepted: 05/13/2021] [Indexed: 12/23/2022] Open
Abstract
Understanding the risk of infection from household- and community-exposures and the transmissibility of asymptomatic infections is critical to SARS-CoV-2 control. Limited previous evidence is based primarily on virologic testing, which disproportionately misses mild and asymptomatic infections. Serologic measures are more likely to capture all previously infected individuals. We apply household transmission models to data from a cross-sectional, household-based population serosurvey of 4,534 people ≥5 years from 2,267 households enrolled April-June 2020 in Geneva, Switzerland. We found that the risk of infection from exposure to a single infected household member aged ≥5 years (17.3%,13.7-21.7) was more than three-times that of extra-household exposures over the first pandemic wave (5.1%,4.5-5.8). Young children had a lower risk of infection from household members. Working-age adults had the highest extra-household infection risk. Seropositive asymptomatic household members had 69.4% lower odds (95%CrI,31.8-88.8%) of infecting another household member compared to those reporting symptoms, accounting for 14.5% (95%CrI, 7.2-22.7%) of all household infections.
Collapse
Affiliation(s)
- Qifang Bi
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Justin Lessler
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Isabella Eckerle
- Geneva Center for Emerging Viral Diseases and Laboratory of Virology, Geneva University Hospitals, Geneva, Switzerland
- Department of Microbiology and Molecular Medicine, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Stephen A Lauer
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Laurent Kaiser
- Geneva Center for Emerging Viral Diseases and Laboratory of Virology, Geneva University Hospitals, Geneva, Switzerland
- Division of Infectious Diseases, Geneva University Hospitals, Geneva, Switzerland
- Department of Medicine, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Nicolas Vuilleumier
- Department of Medicine, Faculty of Medicine, University of Geneva, Geneva, Switzerland
- Division of Laboratory Medicine, Geneva University Hospitals, Geneva, Switzerland
| | - Derek A T Cummings
- Department of Biology, University of Florida, Gainesville, FL, USA
- Emerging Pathogens Institute, University of Florida, Gainesville, FL, USA
| | - Antoine Flahault
- Division of Tropical and Humanitarian Medicine, Geneva University Hospitals, Geneva, Switzerland
- Department of Health and Community Medicine, Faculty of Medicine, University of Geneva, Geneva, Switzerland
- Institute of Global Health, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Dusan Petrovic
- Division of Primary Care Medicine, Geneva University Hospitals, Geneva, Switzerland
- University Centre for General Medicine and Public Health, University of Lausanne, Lausanne, Switzerland
- Centre for Environment and Health, School of Public Health, Department of Epidemiology and Biostatistics, Imperial College London, London, UK
| | - Idris Guessous
- Department of Health and Community Medicine, Faculty of Medicine, University of Geneva, Geneva, Switzerland
- Division of Primary Care Medicine, Geneva University Hospitals, Geneva, Switzerland
| | - Silvia Stringhini
- Department of Health and Community Medicine, Faculty of Medicine, University of Geneva, Geneva, Switzerland
- Division of Primary Care Medicine, Geneva University Hospitals, Geneva, Switzerland
- University Centre for General Medicine and Public Health, University of Lausanne, Lausanne, Switzerland
| | - Andrew S Azman
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
- Institute of Global Health, Faculty of Medicine, University of Geneva, Geneva, Switzerland.
- Division of Primary Care Medicine, Geneva University Hospitals, Geneva, Switzerland.
| |
Collapse
|
24
|
Czypionka T, Greenhalgh T, Bassler D, Bryant MB. Masks and Face Coverings for the Lay Public : A Narrative Update. Ann Intern Med 2021; 174:511-520. [PMID: 33370173 PMCID: PMC7774036 DOI: 10.7326/m20-6625] [Citation(s) in RCA: 42] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
Whether and when to mandate the wearing of facemasks in the community to prevent the spread of coronavirus disease 2019 remains controversial. Published literature across disciplines about the role of masks in mitigating severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission is summarized. Growing evidence that SARS-CoV-2 is airborne indicates that infection control interventions must go beyond contact and droplet measures (such as handwashing and cleaning surfaces) and attend to masking and ventilation. Observational evidence suggests that masks work mainly by source control (preventing infected persons from transmitting the virus to others), but laboratory studies of mask filtration properties suggest that they could also provide some protection to wearers (protective effect). Even small reductions in individual transmission could lead to substantial reductions in population spread. To date, only 1 randomized controlled trial has examined a community mask recommendation. This trial did not identify a significant protective effect and was not designed to evaluate source control. Filtration properties and comfort vary widely across mask types. Masks may cause discomfort and communication difficulties. However, there is no evidence that masks result in significant physiologic decompensation or that risk compensation and fomite transmission are associated with mask wearing. The psychological effects of masks are culturally shaped; they may include threats to autonomy, social relatedness, and competence. Evidence suggests that the potential benefits of wearing masks likely outweigh the potential harms when SARS-CoV-2 is spreading in a community. However, mask mandates involve a tradeoff with personal freedom, so such policies should be pursued only if the threat is substantial and mitigation of spread cannot be achieved through other means.
Collapse
Affiliation(s)
- Thomas Czypionka
- Institute for Advanced Studies, Vienna, Austria, and London School of Economics, London, United Kingdom (T.C.)
| | | | - Dirk Bassler
- University Hospital of Zurich and University of Zurich, Zurich, Switzerland (D.B., M.B.B.)
| | - Manuel B Bryant
- University Hospital of Zurich and University of Zurich, Zurich, Switzerland (D.B., M.B.B.)
| |
Collapse
|
25
|
Overdispersion in COVID-19 increases the effectiveness of limiting nonrepetitive contacts for transmission control. Proc Natl Acad Sci U S A 2021; 118:2016623118. [PMID: 33741734 PMCID: PMC8040586 DOI: 10.1073/pnas.2016623118] [Citation(s) in RCA: 52] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
Evidence indicates that superspreading plays a dominant role in COVID-19 transmission, so that a small fraction of infected people causes a large proportion of new COVID-19 cases. We developed an agent-based model that simulates a superspreading disease moving through a society with networks of both repeated contacts and nonrepeated, random contacts. The results indicate that superspreading is the virus’ Achilles’ heel: Reducing random contacts—such as those that occur at sporting events, restaurants, bars, and the like—can control the outbreak at population scales. Increasing evidence indicates that superspreading plays a dominant role in COVID-19 transmission. Recent estimates suggest that the dispersion parameter k for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is on the order of 0.1, which corresponds to about 10% of cases being the source of 80% of infections. To investigate how overdispersion might affect the outcome of various mitigation strategies, we developed an agent-based model with a social network that allows transmission through contact in three sectors: “close” (a small, unchanging group of mutual contacts as might be found in a household), “regular” (a larger, unchanging group as might be found in a workplace or school), and “random” (drawn from the entire model population and not repeated regularly). We assigned individual infectivity from a gamma distribution with dispersion parameter k. We found that when k was low (i.e., greater heterogeneity, more superspreading events), reducing random sector contacts had a far greater impact on the epidemic trajectory than did reducing regular contacts; when k was high (i.e., less heterogeneity, no superspreading events), that difference disappeared. These results suggest that overdispersion of COVID-19 transmission gives the virus an Achilles’ heel: Reducing contacts between people who do not regularly meet would substantially reduce the pandemic, while reducing repeated contacts in defined social groups would be less effective.
Collapse
|
26
|
Nielsen BF, Simonsen L, Sneppen K. COVID-19 Superspreading Suggests Mitigation by Social Network Modulation. PHYSICAL REVIEW LETTERS 2021; 126:118301. [PMID: 33798363 DOI: 10.1103/physrevlett.126.118301] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/01/2020] [Revised: 01/06/2021] [Accepted: 01/22/2021] [Indexed: 05/05/2023]
Abstract
Although COVID-19 has caused severe suffering globally, the efficacy of nonpharmaceutical interventions has been greater than typical models have predicted. Meanwhile, evidence is mounting that the pandemic is characterized by superspreading. Capturing this phenomenon theoretically requires modeling at the scale of individuals. Using a mathematical model, we show that superspreading drastically enhances mitigations which reduce the overall personal contact number and that social clustering increases this effect.
Collapse
Affiliation(s)
- Bjarke Frost Nielsen
- Niels Bohr Institute, University of Copenhagen, Blegdamsvej 17, 2100 Copenhagen, Denmark
| | - Lone Simonsen
- Department of Science and Environment, Roskilde University, Universitetsvej 1, 4000 Roskilde, Denmark
| | - Kim Sneppen
- Niels Bohr Institute, University of Copenhagen, Blegdamsvej 17, 2100 Copenhagen, Denmark
| |
Collapse
|
27
|
Eilersen A, Sneppen K. SARS-CoV-2 superspreading in cities vs the countryside. APMIS 2021; 129:401-407. [PMID: 33622024 PMCID: PMC8013868 DOI: 10.1111/apm.13120] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2020] [Accepted: 01/20/2021] [Indexed: 12/23/2022]
Abstract
The first wave of the COVID‐19 pandemic was characterized by an initial rapid rise in new cases followed by a peak and a more erratic behaviour that varies between regions. This is not easy to reproduce with traditional SIR models, which predict a more symmetric epidemic. Here, we argue that superspreaders and population heterogeneity would predict such behaviour even in the absence of restrictions on social life. We present an agent‐based lattice model of a disease spreading in a heterogeneous population. We predict that an epidemic driven by superspreaders will spread rapidly in cities, but not in the countryside where the sparse population limits the maximal number of secondary infections. This suggests that mitigation strategies should include restrictions on venues where people meet a large number of strangers. Furthermore, mitigating the epidemic in cities and in the countryside may require different levels of restrictions.
Collapse
Affiliation(s)
- Andreas Eilersen
- Niels Bohr Institute, University of Copenhagen, København Ø, Denmark
| | - Kim Sneppen
- Niels Bohr Institute, University of Copenhagen, København Ø, Denmark
| |
Collapse
|
28
|
Parag KV, Donnelly CA, Jha R, Thompson RN. An exact method for quantifying the reliability of end-of-epidemic declarations in real time. PLoS Comput Biol 2020; 16:e1008478. [PMID: 33253158 PMCID: PMC7717584 DOI: 10.1371/journal.pcbi.1008478] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2020] [Revised: 12/04/2020] [Accepted: 10/28/2020] [Indexed: 12/13/2022] Open
Abstract
We derive and validate a novel and analytic method for estimating the probability that an epidemic has been eliminated (i.e. that no future local cases will emerge) in real time. When this probability crosses 0.95 an outbreak can be declared over with 95% confidence. Our method is easy to compute, only requires knowledge of the incidence curve and the serial interval distribution, and evaluates the statistical lifetime of the outbreak of interest. Using this approach, we show how the time-varying under-reporting of infected cases will artificially inflate the inferred probability of elimination, leading to premature (false-positive) end-of-epidemic declarations. Contrastingly, we prove that incorrectly identifying imported cases as local will deceptively decrease this probability, resulting in delayed (false-negative) declarations. Failing to sustain intensive surveillance during the later phases of an epidemic can therefore substantially mislead policymakers on when it is safe to remove travel bans or relax quarantine and social distancing advisories. World Health Organisation guidelines recommend fixed (though disease-specific) waiting times for end-of-epidemic declarations that cannot accommodate these variations. Consequently, there is an unequivocal need for more active and specialised metrics for reliably identifying the conclusion of an epidemic.
Collapse
Affiliation(s)
- Kris V. Parag
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - Christl A. Donnelly
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
- Department of Statistics, University of Oxford, Oxford, UK
| | - Rahul Jha
- Department of Applied Math and Theoretical Physics, University of Cambridge, Cambridge, UK
| | | |
Collapse
|
29
|
Parag KV, Donnelly CA. Adaptive Estimation for Epidemic Renewal and Phylogenetic Skyline Models. Syst Biol 2020; 69:1163-1179. [PMID: 32333789 PMCID: PMC7584150 DOI: 10.1093/sysbio/syaa035] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2019] [Revised: 04/14/2020] [Accepted: 04/16/2020] [Indexed: 11/12/2022] Open
Abstract
Estimating temporal changes in a target population from phylogenetic or count data is an important problem in ecology and epidemiology. Reliable estimates can provide key insights into the climatic and biological drivers influencing the diversity or structure of that population and evidence hypotheses concerning its future growth or decline. In infectious disease applications, the individuals infected across an epidemic form the target population. The renewal model estimates the effective reproduction number, R, of the epidemic from counts of observed incident cases. The skyline model infers the effective population size, N, underlying a phylogeny of sequences sampled from that epidemic. Practically, R measures ongoing epidemic growth while N informs on historical caseload. While both models solve distinct problems, the reliability of their estimates depends on p-dimensional piecewise-constant functions. If p is misspecified, the model might underfit significant changes or overfit noise and promote a spurious understanding of the epidemic, which might misguide intervention policies or misinform forecasts. Surprisingly, no transparent yet principled approach for optimizing p exists. Usually, p is heuristically set, or obscurely controlled via complex algorithms. We present a computable and interpretable p-selection method based on the minimum description length (MDL) formalism of information theory. Unlike many standard model selection techniques, MDL accounts for the additional statistical complexity induced by how parameters interact. As a result, our method optimizes p so that R and N estimates properly and meaningfully adapt to available data. It also outperforms comparable Akaike and Bayesian information criteria on several classification problems, given minimal knowledge of the parameter space, and exposes statistical similarities among renewal, skyline, and other models in biology. Rigorous and interpretable model selection is necessary if trustworthy and justifiable conclusions are to be drawn from piecewise models. [Coalescent processes; epidemiology; information theory; model selection; phylodynamics; renewal models; skyline plots].
Collapse
Affiliation(s)
- Kris V Parag
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, W2 1PG, UK
| | - Christl A Donnelly
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, W2 1PG, UK
- Department of Statistics, University of Oxford, Oxford, OX1 3LB, UK
| |
Collapse
|
30
|
Hébert-Dufresne L, Althouse BM, Scarpino SV, Allard A. Beyond R0: heterogeneity in secondary infections and probabilistic epidemic forecasting. J R Soc Interface 2020; 17:20200393. [PMID: 33143594 PMCID: PMC7729039 DOI: 10.1098/rsif.2020.0393] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2020] [Accepted: 10/12/2020] [Indexed: 01/02/2023] Open
Abstract
The basic reproductive number, R0, is one of the most common and most commonly misapplied numbers in public health. Often used to compare outbreaks and forecast pandemic risk, this single number belies the complexity that different epidemics can exhibit, even when they have the same R0. Here, we reformulate and extend a classic result from random network theory to forecast the size of an epidemic using estimates of the distribution of secondary infections, leveraging both its average R0 and the underlying heterogeneity. Importantly, epidemics with lower R0 can be larger if they spread more homogeneously (and are therefore more robust to stochastic fluctuations). We illustrate the potential of this approach using different real epidemics with known estimates for R0, heterogeneity and epidemic size in the absence of significant intervention. Further, we discuss the different ways in which this framework can be implemented in the data-scarce reality of emerging pathogens. Lastly, we demonstrate that without data on the heterogeneity in secondary infections for emerging infectious diseases like COVID-19 the uncertainty in outbreak size ranges dramatically. Taken together, our work highlights the critical need for contact tracing during emerging infectious disease outbreaks and the need to look beyond R0.
Collapse
Affiliation(s)
- Laurent Hébert-Dufresne
- Vermont Complex Systems Center, University of Vermont, Burlington, VT 05405, USA
- Department of Computer Science, University of Vermont, Burlington, VT 05405, USA
- Département de physique, de génie physique et d’optique, Université Laval, Québec, Canada G1V 0A6
| | - Benjamin M. Althouse
- Institute for Disease Modeling, Bellevue, WA 98005, USA
- Information School, University of Washington, Seattle, WA 98195-2840, USA
- Department of Biology, New Mexico State University, Las Cruces, NM 88003, USA
| | - Samuel V. Scarpino
- Network Science Institute, Northeastern University, Boston, MA 02115, USA
- Department of Marine and Environmental Sciences, Northeastern University, Boston, MA 02115, USA
- Department of Physics, Northeastern University, Boston, MA 02115, USA
- Department of Health Sciences, Northeastern University, Boston, MA 02115, USA
- ISI Foundation, Turin 10126, Italy
- Santa Fe Institute, Santa Fe, NM 87501, USA
| | - Antoine Allard
- Département de physique, de génie physique et d’optique, Université Laval, Québec, Canada G1V 0A6
- Centre interdisciplinaire en modélisation mathématique, Université Laval, Québec, Canada G1V 0A6
| |
Collapse
|
31
|
Huber G, Kamb M, Kawagoe K, Li LM, Veytsman B, Yllanes D, Zigmond D. A minimal model for household effects in epidemics. Phys Biol 2020; 17:065010. [PMID: 33085650 DOI: 10.1088/1478-3975/abb209] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Shelter-in-place and other confinement strategies implemented in the current COVID-19 pandemic have created stratified patterns of contacts between people: close contacts within households and more distant contacts between the households. The epidemic transmission dynamics is significantly modified as a consequence. We introduce a minimal model that incorporates these household effects in the framework of mean-field theory and numerical simulations. We show that the reproduction number R 0 depends on the household size in a surprising way: linearly for relatively small households, and as a square root of size for larger households. We discuss the implications of the findings for the lockdown, test, tracing, and isolation policies.
Collapse
Affiliation(s)
- Greg Huber
- Chan Zuckerberg Biohub, San Francisco, CA 94158, United States of America
| | | | | | | | | | | | | |
Collapse
|
32
|
He D, Zhao S, Xu X, Lin Q, Zhuang Z, Cao P, Wang MH, Lou Y, Xiao L, Wu Y, Yang L. Low dispersion in the infectiousness of COVID-19 cases implies difficulty in control. BMC Public Health 2020; 20:1558. [PMID: 33066755 PMCID: PMC7565242 DOI: 10.1186/s12889-020-09624-2] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2020] [Accepted: 09/29/2020] [Indexed: 12/23/2022] Open
Abstract
The individual infectiousness of coronavirus disease 2019 (COVID-19), quantified by the number of secondary cases of a typical index case, is conventionally modelled by a negative-binomial (NB) distribution. Based on patient data of 9120 confirmed cases in China, we calculated the variation of the individual infectiousness, i.e., the dispersion parameter k of the NB distribution, at 0.70 (95% confidence interval: 0.59, 0.98). This suggests that the dispersion in the individual infectiousness is probably low, thus COVID-19 infection is relatively easy to sustain in the population and more challenging to control. Instead of focusing on the much fewer super spreading events, we also need to focus on almost every case to effectively reduce transmission.
Collapse
Affiliation(s)
- Daihai He
- Department of Applied Mathematics, Hong Kong Polytechnic University, Hong Kong, China.
| | - Shi Zhao
- JC School of Public Health and Primary Care, Chinese University of Hong Kong, Hong Kong, China.,CUHK Shenzhen Research Institute, Shenzhen, China
| | - Xiaoke Xu
- College of Information and Communication Engineering, Dalian Minzu University, Dalian, 116600, China
| | - Qiangying Lin
- Michigan Institute for Data Science at University of Michigan, Ann Arbor, MI, USA
| | - Zian Zhuang
- Department of Applied Mathematics, Hong Kong Polytechnic University, Hong Kong, China
| | - Peihua Cao
- Clinical Research Center, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Maggie H Wang
- JC School of Public Health and Primary Care, Chinese University of Hong Kong, Hong Kong, China.,CUHK Shenzhen Research Institute, Shenzhen, China
| | - Yijun Lou
- Department of Applied Mathematics, Hong Kong Polytechnic University, Hong Kong, China
| | - Li Xiao
- College of Medical Information Engineering, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Ye Wu
- Computational Communication Research Center, Beijing Normal University, Zhuhai, 519087, China. .,School of Journalism and Communication, Beijing Normal University, Beijing, 100875, China.
| | - Lin Yang
- School of Nursing, Hong Kong Polytechnic University, Hong Kong, China.
| |
Collapse
|
33
|
He D, Zhao S, Li Y, Cao P, Gao D, Lou Y, Yang L. Comparing COVID-19 and the 1918-19 influenza pandemics in the United Kingdom. Int J Infect Dis 2020; 98:67-70. [PMID: 32599281 PMCID: PMC7318986 DOI: 10.1016/j.ijid.2020.06.075] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2020] [Revised: 06/20/2020] [Accepted: 06/23/2020] [Indexed: 11/15/2022] Open
Abstract
We compared the COVID-19 and 1918-19 influenza pandemics in the United Kingdom. We found that the ongoing COVID-19 wave of infection matched the major wave of the 1918-19 influenza pandemic surprisingly well, with both reaching similar magnitudes (in terms of estimated weekly new infections) and spending the same duration with over five cases per 1000 inhabitants over the previous two months. We also discussed the similarities in epidemiological characteristics between these two pandemics.
Collapse
Affiliation(s)
- Daihai He
- Department of Applied Mathematics, Hong Kong Polytechnic University, Hong Kong, China.
| | - Shi Zhao
- JC School of Public Health and Primary Care, Chinese University of Hong Kong, Hong Kong, China; CUHK Shenzhen Research Institute, Shenzhen, China
| | - Yingke Li
- College of Mathematics and Physics, Xinjiang Agriculture University, Urumqi, China
| | - Peihua Cao
- Clinical Research Center, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Daozhou Gao
- College of Mathematics and Science, Shanghai Normal University, Shanghai, China
| | - Yijun Lou
- Department of Applied Mathematics, Hong Kong Polytechnic University, Hong Kong, China
| | - Lin Yang
- School of Nursing, Hong Kong Polytechnic University, Hong Kong, China.
| |
Collapse
|
34
|
Parag KV, Donnelly CA. Using information theory to optimise epidemic models for real-time prediction and estimation. PLoS Comput Biol 2020; 16:e1007990. [PMID: 32609732 PMCID: PMC7360089 DOI: 10.1371/journal.pcbi.1007990] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2020] [Revised: 07/14/2020] [Accepted: 05/27/2020] [Indexed: 01/31/2023] Open
Abstract
The effective reproduction number, Rt, is a key time-varying prognostic for the growth rate of any infectious disease epidemic. Significant changes in Rt can forewarn about new transmissions within a population or predict the efficacy of interventions. Inferring Rt reliably and in real-time from observed time-series of infected (demographic) data is an important problem in population dynamics. The renewal or branching process model is a popular solution that has been applied to Ebola and Zika virus disease outbreaks, among others, and is currently being used to investigate the ongoing COVID-19 pandemic. This model estimates Rt using a heuristically chosen piecewise function. While this facilitates real-time detection of statistically significant Rt changes, inference is highly sensitive to the function choice. Improperly chosen piecewise models might ignore meaningful changes or over-interpret noise-induced ones, yet produce visually reasonable estimates. No principled piecewise selection scheme exists. We develop a practical yet rigorous scheme using the accumulated prediction error (APE) metric from information theory, which deems the model capable of describing the observed data using the fewest bits as most justified. We derive exact posterior prediction distributions for infected population size and integrate these within an APE framework to obtain an exact and reliable method for identifying the piecewise function best supported by available epidemic data. We find that this choice optimises short-term prediction accuracy and can rapidly detect salient fluctuations in Rt, and hence the infected population growth rate, in real-time over the course of an unfolding epidemic. Moreover, we emphasise the need for formal selection by exposing how common heuristic choices, which seem sensible, can be misleading. Our APE-based method is easily computed and broadly applicable to statistically similar models found in phylogenetics and macroevolution, for example. Our results explore the relationships among estimate precision, forecast reliability and model complexity. Understanding how the population of infected individuals (which may be humans, animals or plants) fluctuates in size over the course of an epidemic is an important problem in epidemiology and ecology. The effective reproduction number, R, provides an intuitive and useful way of describing these fluctuations by characterising the growth rate of the infected population. An R > 1 signifies a burgeoning epidemic whereas R < 1 indicates a declining one. Public health agencies often use R to inform or corroborate vaccination and quarantine policies. However, popular approaches to inferring R from epidemic data make heuristic choices, which may lead to visually reasonable estimates that are deceptive or unreliable. By adapting mathematical tools from information theory, we develop a general and principled scheme for estimating R in a data-justified way. Our method exposes the pitfalls of heuristic estimates and provides an easily computable correction that also maximises our ability to predict upcoming population fluctuations. Our work is widely applicable to similar inference problems found in evolution and genetics, demonstrably useful for reliably analysing emerging epidemics in real time and highlights how abstract mathematical concepts can inspire novel and practical biological solutions, showcasing the importance of multidisciplinary research.
Collapse
Affiliation(s)
- Kris V. Parag
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, W2 1PG, United Kingdom
- * E-mail:
| | - Christl A. Donnelly
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, W2 1PG, United Kingdom
- Department of Statistics, University of Oxford, Oxford, OX1 3LB, United Kingdom
| |
Collapse
|
35
|
Donsimoni JR, Glawion R, Hartl T, Plachter B, Timmer J, Wälde K, Weber E, Weiser C. Covid-19 in Deutschland – Erklärung, Prognose und Einfluss gesundheitspolitischer Maßnahmen. ACTA ACUST UNITED AC 2020. [DOI: 10.1515/pwp-2020-0019] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
Zusammenfassung
Die Autoren erklären den bisherigen Verlauf von Covid-19 in Deutschland durch Regressionsanalysen und epidemiologische Modelle. Sie beschreiben und quantifizieren den Effekt der gesundheitspolitischen Maßnahmen (GPM), die bis zum 19. April in Kraft waren. Sie berechnen den erwarteten Verlauf der Covid-19-Epidemie in Deutschland, wenn es diese Maßnahmen nicht gegeben hätte, und zeigen, dass die GPM einen erheblichen Beitrag zur Reduktion der Infektionszahlen geleistet haben. Die seit 20. April gelockerten GPM sind zwischen den Bundesländern relativ heterogen, was ein Glücksfall für die Wissenschaft ist. Mittels einer Analyse dieser Heterogenität kann aufgedeckt werden, welche Maßnahmen für eine Bekämpfung einer eventuellen zweiten Infektionswelle besonders hilfreich und besonders schädlich sind.
Collapse
Affiliation(s)
- Jean Roch Donsimoni
- Johannes Gutenberg Universität , Lehrstuhl für VWL, insb. Makroökonomik , Jakob-Welder-Weg 4 , Mainz Germany
| | - René Glawion
- Universität Hamburg , Fakultät für Wirtschafts- und Sozialwissenschaften, Volkswirtschaftslehre , Von-Melle-Park 5 , Hamburg Germany
| | - Tobias Hartl
- Universität Regensburg , Lehrstuhl für Ökonometrie , Regensburg Germany
| | - Bodo Plachter
- Universitätsmedizin Mainz , Institut für Virologie , Obere Zahlbacher Str. 67 , Mainz Germany
| | - Jens Timmer
- Universität Freiburg , Physikalisches Institut , CIBSS – Centre for Integrative Biological Signaling Studies, Hermann-Herder Str. 3 , Freiburg Germany
| | - Klaus Wälde
- Johannes Gutenberg Universität , Lehrstuhl für VWL, insb. Makroökonomik, Visiting Research Fellow IZA , Jakob-Welder-Weg 4 , Mainz Germany
| | - Enzo Weber
- Institut für Arbeitsmarkt- und Berufsforschung (IAB) der Bundesagentur für Arbeit (BA) , Regensburger Straße 104 , Nürnberg Germany
| | - Constantin Weiser
- Johannes Gutenberg Universität , Fachbereich Rechts- & Wirtschaftswissenschaften, Quantitative Methodenlehre , Jakob-Welder-Weg 4 , Mainz Germany
| |
Collapse
|
36
|
Riou J, Althaus CL. Pattern of early human-to-human transmission of Wuhan 2019 novel coronavirus (2019-nCoV), December 2019 to January 2020. ACTA ACUST UNITED AC 2020; 25. [PMID: 32019669 PMCID: PMC7001239 DOI: 10.2807/1560-7917.es.2020.25.4.2000058] [Citation(s) in RCA: 732] [Impact Index Per Article: 183.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
Since December 2019, China has been experiencing a large outbreak of a novel coronavirus (2019-nCoV) which can cause respiratory disease and severe pneumonia. We estimated the basic reproduction number R0 of 2019-nCoV to be around 2.2 (90% high density interval: 1.4–3.8), indicating the potential for sustained human-to-human transmission. Transmission characteristics appear to be of similar magnitude to severe acute respiratory syndrome-related coronavirus (SARS-CoV) and pandemic influenza, indicating a risk of global spread.
Collapse
Affiliation(s)
- Julien Riou
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
| | - Christian L Althaus
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
| |
Collapse
|
37
|
Pellis L, Cauchemez S, Ferguson NM, Fraser C. Systematic selection between age and household structure for models aimed at emerging epidemic predictions. Nat Commun 2020; 11:906. [PMID: 32060265 PMCID: PMC7021781 DOI: 10.1038/s41467-019-14229-4] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2018] [Accepted: 12/20/2019] [Indexed: 01/13/2023] Open
Abstract
Numerous epidemic models have been developed to capture aspects of human contact patterns, making model selection challenging when they fit (often-scarce) early epidemic data equally well but differ in predictions. Here we consider the invasion of a novel directly transmissible infection and perform an extensive, systematic and transparent comparison of models with explicit age and/or household structure, to determine the accuracy loss in predictions in the absence of interventions when ignoring either or both social components. We conclude that, with heterogeneous and assortative contact patterns relevant to respiratory infections, the model’s age stratification is crucial for accurate predictions. Conversely, the household structure is only needed if transmission is highly concentrated in households, as suggested by an empirical but robust rule of thumb based on household secondary attack rate. This work serves as a template to guide the simplicity/accuracy trade-off in designing models aimed at initial, rapid assessment of potential epidemic severity. Models of emerging epidemics can be exceedingly helpful in planning the response, but early on model selection is a difficult task. Here, the authors explore the joint contribution of age stratification and household structure on epidemic spread, and provides a rule of thumb to guide model choice.
Collapse
Affiliation(s)
- Lorenzo Pellis
- Department of Mathematics, University of Manchester, Manchester, UK. .,Zeeman Institute and Warwick Mathematics Institute, University of Warwick, Warwick, UK. .,MRC Centre for Global Infectious Disease Analysis, J-IDEA, School of Public Health, Imperial College, London, UK.
| | - Simon Cauchemez
- Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, UMR2000, CNRS, 75015, Paris, France
| | - Neil M Ferguson
- MRC Centre for Global Infectious Disease Analysis, J-IDEA, School of Public Health, Imperial College, London, UK
| | - Christophe Fraser
- Oxford Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| |
Collapse
|
38
|
Hilton J, Keeling MJ. Incorporating household structure and demography into models of endemic disease. J R Soc Interface 2019; 16:20190317. [PMID: 31387486 PMCID: PMC6731502 DOI: 10.1098/rsif.2019.0317] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023] Open
Abstract
The spread of infectious diseases is intimately linked with the strength and type of contact between individuals. Multiple observational and modelling studies have highlighted the importance of two forms of social mixing: age structure, where the likelihood of interaction between two individuals is determined by their ages; and household structure, which recognizes the much stronger contacts and hence transmission potential within the family setting. Age structure has been ubiquitous in predictive models of both endemic and epidemic infections, in part due to the ease of assessing someone’s age. By contrast, although household structure is potentially the dominant heterogeneity, it has received less attention, in part due to an absence of the necessary methodology. Here, we develop the modelling framework necessary to predict the behaviour of endemic infections (which necessitates capturing demographic processes) in populations that possess both household and age structure. We compare two childhood infections, with measles-like and mumps-like parameters, and two populations with UK-like and Kenya-like characteristics, which allows us to disentangle the impact of epidemiology and demography. For this high-dimensional model, we predict complex nonlinear dynamics, where the dynamics of within-household outbreaks are tempered by historical waves of infection and the immunity of older individuals.
Collapse
Affiliation(s)
- Joe Hilton
- MathSys CDT, Mathematics Institute, University of Warwick, Coventry CV4 7AL, UK.,Zeeman Institute (SBIDER), University of Warwick, Coventry CV4 7AL, UK
| | - Matt J Keeling
- Zeeman Institute (SBIDER), University of Warwick, Coventry CV4 7AL, UK.,Mathematics Institute, University of Warwick, Coventry CV4 7AL, UK.,School of Life Sciences, University of Warwick, Coventry CV4 7AL, UK
| |
Collapse
|
39
|
Nikbakht R, Baneshi MR, Bahrampour A, Hosseinnataj A. Comparison of methods to Estimate Basic Reproduction Number ( R 0) of influenza, Using Canada 2009 and 2017-18 A (H1N1) Data. JOURNAL OF RESEARCH IN MEDICAL SCIENCES 2019; 24:67. [PMID: 31523253 PMCID: PMC6670001 DOI: 10.4103/jrms.jrms_888_18] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/18/2018] [Revised: 03/13/2019] [Accepted: 05/17/2019] [Indexed: 12/29/2022]
Abstract
Background The basic reproduction number (R 0) has a key role in epidemics and can be utilized for preventing epidemics. In this study, different methods are used for estimating R 0's and their vaccination coverage to find the formula with the best performance. Materials and Methods We estimated R 0 for cumulative cases count data from April 18 to July 6, 2009 and 35-2017 to 34-2018 weeks in Canada: maximum likelihood (ML), exponential growth rate (EG), time-dependent reproduction numbers (TD), attack rate (AR), gamma-distributed generation time (GT), and the final size of the epidemic. Gamma distribution with mean and standard deviation 3.6 ± 1.4 is used as GT. Results The AR method obtained a R 0 (95% confidence interval [CI]) value of 1.116 (1.1163, 1.1165) and an EG (95%CI) value of 1.46 (1.41, 1.52). The R 0 (95%CI) estimate was 1.42 (1.27, 1.57) for the obtained ML, 1.71 (1.12, 2.03) for the obtained TD, 1.49 (1.0, 1.97) for the gamma-distributed GT, and 1.00 (0.91, 1.09) for the final size of the epidemic. The minimum and maximum vaccination coverage were related to AR and TD methods, respectively, where the TD method has minimum mean squared error (MSE). Finally, the R 0 (95%CI) for 2018 data was 1.52 (1.11, 1.94) by TD method, and vaccination coverage was estimated as 34.2%. Conclusion For the purposes of our study, the estimation of TD was the most useful tool for computing the R 0, because it has the minimum MSE. The estimation R 0 > 1 indicating that the epidemic has occurred. Thus, it is required to vaccinate at least 41.5% to prevent and control the next epidemic.
Collapse
Affiliation(s)
- Roya Nikbakht
- HIV/STI Surveillance Research Center, and WHO Collaborating Center for HIV Surveillance, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Department of Biostatistics and Epidemiology, Faculty of Health Kerman, Iran
| | - Mohammad Reza Baneshi
- Department of Biostatistics and Epidemiology, Faculty of Health, Modeling in Health Research Center, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran
| | - Abbas Bahrampour
- Department of Biostatistics and Epidemiology, Faculty of Health, Modeling in Health Research Center, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran
| | - Abolfazl Hosseinnataj
- Department of Biostatistics and Epidemiology, Faculty of Health, Modeling in Health Research Center, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran
| |
Collapse
|
40
|
Cousien A, Abel S, Monthieux A, Andronico A, Calmont I, Cervantes M, Césaire R, Gallian P, de Lamballerie X, Laouénan C, Najioullah F, Pierre-François S, Pircher M, Salje H, ten Bosch QA, Cabié A, Cauchemez S. Assessing Zika Virus Transmission Within Households During an Outbreak in Martinique, 2015-2016. Am J Epidemiol 2019; 188:1389-1396. [PMID: 30995296 PMCID: PMC6601520 DOI: 10.1093/aje/kwz091] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2018] [Revised: 03/27/2019] [Accepted: 03/27/2019] [Indexed: 12/12/2022] Open
Abstract
Since 2015, Zika virus (ZIKV) has caused large epidemics in the Americas. Households are natural targets for control interventions, but quantification of the contribution of household transmission to overall spread is needed to guide policy. We developed a modeling framework to evaluate this contribution and key epidemic features of the ZIKV epidemic in Martinique in 2015-2016 from the joint analysis of a household transmission study (n = 68 households), a study among symptomatic pregnant women (n = 281), and seroprevalence surveys of blood donors (n = 457). We estimated that the probability of mosquito-mediated within-household transmission (from an infected member to a susceptible one) was 21% (95% credible interval (CrI): 5, 51), and the overall probability of infection from outside the household (i.e., in the community) was 39% (95% CrI: 27, 50). Overall, 50% (95% CrI: 43, 58) of the population was infected, with 22% (95% CrI: 5, 46) of infections acquired in households and 40% (95% CrI: 23, 56) being asymptomatic. The probability of presenting with Zika-like symptoms due to another cause was 16% (95% CrI: 10, 23). This study characterized the contribution of household transmission in ZIKV epidemics, demonstrating the benefits of integrating multiple data sets to gain more insight into epidemic dynamics.
Collapse
Affiliation(s)
- Anthony Cousien
- Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, Unité Mixte de Recherche 2000, Centre National de la Recherche Scientifique, Paris, France
| | - Sylvie Abel
- Service de Maladies Infectieuses et Tropicales, Centre Hospitalier Universitaire de Martinique, Fort-de-France, Martinique
| | - Alice Monthieux
- Service de Gynécologie Obstétrique, Centre Hospitalier Universitaire de Martinique, Fort-de-France, Martinique
| | - Alessio Andronico
- Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, Unité Mixte de Recherche 2000, Centre National de la Recherche Scientifique, Paris, France
| | - Isabelle Calmont
- Institut National de la Santé et de la Recherche Médicale Centre d’Investigation Clinique 1424, Fort-de-France, Martinique
| | - Minerva Cervantes
- Infection Antimicrobials Modelling Evolution, Unité Mixte de Recherche 1137, Institut National de la Santé et de la Recherche Médicale, Université Paris Diderot, Paris, France
- Département d’Épidémiologie, Biostatistique et Recherche Clinique, Assistance Publique-Hôpitaux de Paris, Hôpital Bichat Claude Bernard, Paris, France
| | - Raymond Césaire
- Laboratoire de Virologie, Centre Hospitalier Universitaire de Martinique, Fort-de-France, Martinique
| | - Pierre Gallian
- Unité Mixte de Recherche Émergence des Pathologies Virales, Aix-Marseille University, Institut de Recherche pour le Développement 190, Institut National de la Santé et de la Recherche Médicale 1207, École des Hautes Études en Santé Publique, Instituts Hospitalo-Universitaires Méditerranée Infection, Marseille, France
- Etablissement Français du Sang Provence Alpes Côte d’Azur et Corse, Marseille, France
| | - Xavier de Lamballerie
- Unité Mixte de Recherche Émergence des Pathologies Virales, Aix-Marseille University, Institut de Recherche pour le Développement 190, Institut National de la Santé et de la Recherche Médicale 1207, École des Hautes Études en Santé Publique, Instituts Hospitalo-Universitaires Méditerranée Infection, Marseille, France
| | - Cédric Laouénan
- Infection Antimicrobials Modelling Evolution, Unité Mixte de Recherche 1137, Institut National de la Santé et de la Recherche Médicale, Université Paris Diderot, Paris, France
- Département d’Épidémiologie, Biostatistique et Recherche Clinique, Assistance Publique-Hôpitaux de Paris, Hôpital Bichat Claude Bernard, Paris, France
| | - Fatiha Najioullah
- Laboratoire de Virologie, Centre Hospitalier Universitaire de Martinique, Fort-de-France, Martinique
| | - Sandrine Pierre-François
- Service de Maladies Infectieuses et Tropicales, Centre Hospitalier Universitaire de Martinique, Fort-de-France, Martinique
| | - Mathilde Pircher
- Service de Maladies Infectieuses et Tropicales, Centre Hospitalier Universitaire de Martinique, Fort-de-France, Martinique
| | - Henrik Salje
- Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, Unité Mixte de Recherche 2000, Centre National de la Recherche Scientifique, Paris, France
| | - Quirine A ten Bosch
- Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, Unité Mixte de Recherche 2000, Centre National de la Recherche Scientifique, Paris, France
| | - André Cabié
- Service de Maladies Infectieuses et Tropicales, Centre Hospitalier Universitaire de Martinique, Fort-de-France, Martinique
- Institut National de la Santé et de la Recherche Médicale Centre d’Investigation Clinique 1424, Fort-de-France, Martinique
- Equipe d’Accueil 4537, Université des Antilles, Fort-de-France, Martinique
| | - Simon Cauchemez
- Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, Unité Mixte de Recherche 2000, Centre National de la Recherche Scientifique, Paris, France
| |
Collapse
|
41
|
Cauchemez S, Hoze N, Cousien A, Nikolay B, Ten Bosch Q. How Modelling Can Enhance the Analysis of Imperfect Epidemic Data. Trends Parasitol 2019; 35:369-379. [PMID: 30738632 PMCID: PMC7106457 DOI: 10.1016/j.pt.2019.01.009] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2018] [Revised: 01/14/2019] [Accepted: 01/15/2019] [Indexed: 01/02/2023]
Abstract
Mathematical models play an increasingly important role in our understanding of the transmission and control of infectious diseases. Here, we present concrete examples illustrating how mathematical models, paired with rigorous statistical methods, are used to parse data of different levels of detail and breadth and estimate key epidemiological parameters (e.g., transmission and its determinants, severity, impact of interventions, drivers of epidemic dynamics) even when these parameters are not directly measurable, when data are limited, and when the epidemic process is only partially observed. Finally, we assess the hurdles to be taken to increase availability and applicability of these approaches in an effort to ultimately enhance their public health impact. Many data can be used to estimate the transmission potential of a pathogen, including descriptions of the transmission chains, human cluster sizes, sources of infection, and epidemic curves. An important agenda in public health is understanding the impact of control methods. However, the dynamic nature of epidemics makes this task challenging. Models can disentangle the natural course of outbreaks from the effect of external factors. In the absence of reliable surveillance data, models can reconstruct epidemic history by combining age-specific seroprevalence data with an understanding of the natural history of infection. Mechanisms of immunity are hard to observe at an individual level, yet they affect population-level dynamics. Models can tease out such signatures. Morbidity and mortality can be difficult to estimate when many infections are unobserved and severe infections are reported more often. Models can be used to correct for under-reporting and selection bias.
Collapse
Affiliation(s)
- Simon Cauchemez
- Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, UMR2000, CNRS, 75015 Paris, France; All the authors made equal contributions.
| | - Nathanaël Hoze
- Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, UMR2000, CNRS, 75015 Paris, France; All the authors made equal contributions
| | - Anthony Cousien
- Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, UMR2000, CNRS, 75015 Paris, France; All the authors made equal contributions
| | - Birgit Nikolay
- Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, UMR2000, CNRS, 75015 Paris, France; All the authors made equal contributions
| | - Quirine Ten Bosch
- Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, UMR2000, CNRS, 75015 Paris, France; All the authors made equal contributions
| |
Collapse
|
42
|
Libin PJK, Verstraeten T, Roijers DM, Grujic J, Theys K, Lemey P, Nowé A. Bayesian Best-Arm Identification for Selecting Influenza Mitigation Strategies. MACHINE LEARNING AND KNOWLEDGE DISCOVERY IN DATABASES 2019. [DOI: 10.1007/978-3-030-10997-4_28] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
|
43
|
Viboud C, Lessler J. The 1918 Influenza Pandemic: Looking Back, Looking Forward. Am J Epidemiol 2018; 187:2493-2497. [PMID: 30346477 PMCID: PMC6454441 DOI: 10.1093/aje/kwy207] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2018] [Accepted: 09/06/2018] [Indexed: 12/14/2022] Open
Abstract
In commemoration of the centennial of the 1918 influenza pandemic, the American Journal of Epidemiology has convened a collection of 12 articles that further illuminate the epidemiology of that pandemic and consider whether we would be more prepared if an equally deadly influenza virus were to emerge again. In the present commentary, we place these 12 articles in the context of a growing body of work on the archeo-epidemiology of past pandemics, the socioeconomic and geographic drivers of influenza mortality and natality impact, and renewed interest in immune imprinting mechanisms and the development of novel influenza vaccines. We also highlight persisting mysteries in the origins and severity of the 1918 pandemic and the need to preserve rapidly decaying information that may provide treasure troves for future generations.
Collapse
Affiliation(s)
- Cécile Viboud
- Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, Maryland
| | - Justin Lessler
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore Maryland
| |
Collapse
|
44
|
Short KR, Kedzierska K, van de Sandt CE. Back to the Future: Lessons Learned From the 1918 Influenza Pandemic. Front Cell Infect Microbiol 2018; 8:343. [PMID: 30349811 PMCID: PMC6187080 DOI: 10.3389/fcimb.2018.00343] [Citation(s) in RCA: 145] [Impact Index Per Article: 24.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2018] [Accepted: 09/10/2018] [Indexed: 01/02/2023] Open
Abstract
2018 marks the 100-year anniversary of the 1918 influenza pandemic, which killed ~50 million people worldwide. The severity of this pandemic resulted from a complex interplay between viral, host, and societal factors. Here, we review the viral, genetic and immune factors that contributed to the severity of the 1918 pandemic and discuss the implications for modern pandemic preparedness. We address unresolved questions of why the 1918 influenza H1N1 virus was more virulent than other influenza pandemics and why some people survived the 1918 pandemic and others succumbed to the infection. While current studies suggest that viral factors such as haemagglutinin and polymerase gene segments most likely contributed to a potent, dysregulated pro-inflammatory cytokine storm in victims of the pandemic, a shift in case-fatality for the 1918 pandemic toward young adults was most likely associated with the host's immune status. Lack of pre-existing virus-specific and/or cross-reactive antibodies and cellular immunity in children and young adults likely contributed to the high attack rate and rapid spread of the 1918 H1N1 virus. In contrast, lower mortality rate in in the older (>30 years) adult population points toward the beneficial effects of pre-existing cross-reactive immunity. In addition to the role of humoral and cellular immunity, there is a growing body of evidence to suggest that individual genetic differences, especially involving single-nucleotide polymorphisms (SNPs), contribute to differences in the severity of influenza virus infections. Co-infections with bacterial pathogens, and possibly measles and malaria, co-morbidities, malnutrition or obesity are also known to affect the severity of influenza disease, and likely influenced 1918 H1N1 disease severity and outcomes. Additionally, we also discuss the new challenges, such as changing population demographics, antibiotic resistance and climate change, which we will face in the context of any future influenza virus pandemic. In the last decade there has been a dramatic increase in the number of severe influenza virus strains entering the human population from animal reservoirs (including highly pathogenic H7N9 and H5N1 viruses). An understanding of past influenza virus pandemics and the lessons that we have learnt from them has therefore never been more pertinent.
Collapse
Affiliation(s)
- Kirsty R. Short
- School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane, QLD, Australia
- Australian Infectious Diseases Research Centre, The University of Queensland, Brisbane, QLD, Australia
| | - Katherine Kedzierska
- Department of Microbiology and Immunology, The Peter Doherty Institute for Infection and Immunity, University of Melbourne, Parkville, VIC, Australia
| | - Carolien E. van de Sandt
- Department of Microbiology and Immunology, The Peter Doherty Institute for Infection and Immunity, University of Melbourne, Parkville, VIC, Australia
- Department of Hematopoiesis, Sanquin Research and Landsteiner Laboratory, Amsterdam, Netherlands
| |
Collapse
|
45
|
Dyson L, Marks M, Crook OM, Sokana O, Solomon AW, Bishop A, Mabey DCW, Hollingsworth TD. Targeted Treatment of Yaws With Household Contact Tracing: How Much Do We Miss? Am J Epidemiol 2018; 187:837-844. [PMID: 29140407 PMCID: PMC5888927 DOI: 10.1093/aje/kwx305] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2017] [Accepted: 08/22/2017] [Indexed: 11/13/2022] Open
Abstract
Yaws is a disabling bacterial infection found primarily in warm and humid tropical areas. The World Health Organization strategy mandates an initial round of total community treatment (TCT) with single-dose azithromycin followed either by further TCT or active case-finding and treatment of cases and their contacts (the Morges strategy). We sought to investigate the effectiveness of the Morges strategy. We employed a stochastic household model to study the transmission of infection using data collected from a pre-TCT survey conducted in the Solomon Islands. We used this model to assess the proportion of asymptomatic infections that occurred in households without active cases. This analysis indicated that targeted treatment of cases and their household contacts would miss a large fraction of asymptomatic infections (65%–100%). This fraction was actually higher at lower prevalences. Even assuming that all active cases and their households were successfully treated, our analysis demonstrated that at all prevalences present in the data set, up to 90% of (active and asymptomatic) infections would not be treated under household-based contact tracing. Mapping was undertaken as part of the study “Epidemiology of Yaws in the Solomon Islands and the Impact of a Trachoma Control Programme,” in September–October 2013.
Collapse
Affiliation(s)
- Louise Dyson
- Mathematics Institute, University of Warwick, Coventry, United Kingdom
- School of Life Sciences, University of Warwick, Coventry, United Kingdom
| | - Michael Marks
- Clinical Research Department, Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom
- Hospital for Tropical Diseases, University College London Hospitals NHS Trust, London, United Kingdom
| | - Oliver M Crook
- Mathematics Institute, University of Warwick, Coventry, United Kingdom
| | - Oliver Sokana
- Ministry of Health and Medical Services, Honiara, Solomon Islands
| | - Anthony W Solomon
- Clinical Research Department, Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom
- Hospital for Tropical Diseases, University College London Hospitals NHS Trust, London, United Kingdom
| | - Alex Bishop
- Mathematics Institute, University of Warwick, Coventry, United Kingdom
| | - David C W Mabey
- Clinical Research Department, Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom
- Hospital for Tropical Diseases, University College London Hospitals NHS Trust, London, United Kingdom
| | - T Déirdre Hollingsworth
- Mathematics Institute, University of Warwick, Coventry, United Kingdom
- School of Life Sciences, University of Warwick, Coventry, United Kingdom
| |
Collapse
|
46
|
Disparities in influenza mortality and transmission related to sociodemographic factors within Chicago in the pandemic of 1918. Proc Natl Acad Sci U S A 2016; 113:13839-13844. [PMID: 27872284 DOI: 10.1073/pnas.1612838113] [Citation(s) in RCA: 93] [Impact Index Per Article: 11.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023] Open
Abstract
Social factors have been shown to create differential burden of influenza across different geographic areas. We explored the relationship between potential aggregate-level social determinants and mortality during the 1918 influenza pandemic in Chicago using a historical dataset of 7,971 influenza and pneumonia deaths. Census tract-level social factors, including rates of illiteracy, homeownership, population, and unemployment, were assessed as predictors of pandemic mortality in Chicago. Poisson models fit with generalized estimating equations (GEEs) were used to estimate the association between social factors and the risk of influenza and pneumonia mortality. The Poisson model showed that influenza and pneumonia mortality increased, on average, by 32.2% for every 10% increase in illiteracy rate adjusted for population density, homeownership, unemployment, and age. We also found a significant association between transmissibility and population density, illiteracy, and unemployment but not homeownership. Lastly, analysis of the point locations of reported influenza and pneumonia deaths revealed fine-scale spatiotemporal clustering. This study shows that living in census tracts with higher illiteracy rates increased the risk of influenza and pneumonia mortality during the 1918 influenza pandemic in Chicago. Our observation that disparities in structural determinants of neighborhood-level health lead to disparities in influenza incidence in this pandemic suggests that disparities and their determinants should remain targets of research and control in future pandemics.
Collapse
|
47
|
Gostic KM, Ambrose M, Worobey M, Lloyd-Smith JO. Potent protection against H5N1 and H7N9 influenza via childhood hemagglutinin imprinting. Science 2016; 354:722-726. [PMID: 27846599 PMCID: PMC5134739 DOI: 10.1126/science.aag1322] [Citation(s) in RCA: 324] [Impact Index Per Article: 40.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2016] [Accepted: 10/03/2016] [Indexed: 01/02/2023]
Abstract
Two zoonotic influenza A viruses (IAV) of global concern, H5N1 and H7N9, exhibit unexplained differences in age distribution of human cases. Using data from all known human cases of these viruses, we show that an individual's first IAV infection confers lifelong protection against severe disease from novel hemagglutinin (HA) subtypes in the same phylogenetic group. Statistical modeling shows that protective HA imprinting is the crucial explanatory factor, and it provides 75% protection against severe infection and 80% protection against death for both H5N1 and H7N9. Our results enable us to predict age distributions of severe disease for future pandemics and demonstrate that a novel strain's pandemic potential increases yearly when a group-mismatched HA subtype dominates seasonal influenza circulation. These findings open new frontiers for rational pandemic risk assessment.
Collapse
MESH Headings
- Age Factors
- Animals
- Child
- Genomic Imprinting
- Global Health
- Hemagglutinin Glycoproteins, Influenza Virus/genetics
- Hemagglutinin Glycoproteins, Influenza Virus/immunology
- Humans
- Influenza A Virus, H5N1 Subtype/genetics
- Influenza A Virus, H5N1 Subtype/immunology
- Influenza A Virus, H7N9 Subtype/genetics
- Influenza A Virus, H7N9 Subtype/immunology
- Influenza, Human/epidemiology
- Influenza, Human/immunology
- Influenza, Human/virology
- Models, Statistical
- Pandemics/statistics & numerical data
- Risk Assessment
- Zoonoses/epidemiology
- Zoonoses/virology
Collapse
Affiliation(s)
- Katelyn M Gostic
- Department of Ecology and Evolutionary Biology, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Monique Ambrose
- Department of Ecology and Evolutionary Biology, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Michael Worobey
- Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, AZ 85721, USA.
| | - James O Lloyd-Smith
- Department of Ecology and Evolutionary Biology, University of California, Los Angeles, Los Angeles, CA 90095, USA.
- Fogarty International Center, National Institutes of Health, Bethesda, MD 20892, USA
| |
Collapse
|
48
|
Evaluation of Influenza Vaccination Efficacy: A Universal Epidemic Model. BIOMED RESEARCH INTERNATIONAL 2016; 2016:5952890. [PMID: 27668256 PMCID: PMC5030473 DOI: 10.1155/2016/5952890] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/31/2016] [Revised: 08/04/2016] [Accepted: 08/18/2016] [Indexed: 11/25/2022]
Abstract
By means of a designed epidemic model, we evaluated the influence of seasonal vaccination coverage as well as a potential universal vaccine with differing efficacy on the aftermath of seasonal and pandemic influenza. The results of the modeling enabled us to conclude that, to control a seasonal influenza epidemic with a reproduction coefficient R0 ≤ 1.5, a 35% vaccination coverage with the current seasonal influenza vaccine formulation is sufficient, provided that other epidemiology measures are regularly implemented. Increasing R0 level of pandemic strains will obviously require stronger intervention. In addition, seasonal influenza vaccines fail to confer protection against antigenically distinct pandemic influenza strains. Therefore, the necessity of a universal influenza vaccine is clear. The model predicts that a potential universal vaccine will be able to provide sufficient reliable (90%) protection against pandemic influenza only if its efficacy is comparable with the effectiveness of modern vaccines against seasonal influenza strains (70%–80%); given that at least 40% of the population has been vaccinated in advance, ill individuals have been isolated (observed), and a quarantine has been introduced. If other antiepidemic measures are absent, a vaccination coverage of at least 80% is required.
Collapse
|
49
|
Mamelund SE, Haneberg B, Mjaaland S. A Missed Summer Wave of the 1918-1919 Influenza Pandemic: Evidence From Household Surveys in the United States and Norway. Open Forum Infect Dis 2016; 3:ofw040. [PMID: 27006964 PMCID: PMC4800462 DOI: 10.1093/ofid/ofw040] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2015] [Accepted: 02/12/2016] [Indexed: 11/13/2022] Open
Abstract
Background. Reanalysis of influenza survey data from 1918 to 1919 was done to obtain new insights into the geographic and host factors responsible for the various waves. Methods. We analyzed the age- and sex-specific influenza morbidity, fatality, and mortality for the city of Baltimore and smaller towns and rural areas of Maryland and the city of Bergen (Norway), using survey data. The Maryland surveys captured the 1918 fall wave, whereas the Bergen survey captured 3 waves during 1918–1919. Results. Morbidity in rural areas of Maryland was higher than in the city of Baltimore during the fall of 1918, that was almost equal to that in Bergen during the summer of 1918. In Bergen, the morbidity in the fall was only half of that in the summer, with more females than males just above the age of 20 falling ill, as seen in both regions of Maryland. In contrast, more males than females fell ill during the summer wave in Bergen. Individuals <40 years had the highest morbidity, whereas school-aged children had the lowest fatality and mortality. Conclusion. A previously unrecognized pandemic summer wave may have hit the 2 regions of Maryland in 1918.
Collapse
Affiliation(s)
- Svenn-Erik Mamelund
- Work Research Institute; Oslo and Akershus University College of Applied Sciences
| | - Bjørn Haneberg
- Department of Bacteriology and Immunology, Division of Environmental Medicine and Infectious Disease Control , Norwegian Institute of Public Health
| | - Siri Mjaaland
- Department of Bacteriology and Immunology, Division of Environmental Medicine and Infectious Disease Control, Norwegian Institute of Public Health; KG Jebsen Centre for Influenza Vaccine Research, University of Oslo, Norway
| |
Collapse
|
50
|
Black AJ, Ross JV. Computation of epidemic final size distributions. J Theor Biol 2014; 367:159-165. [PMID: 25497476 DOI: 10.1016/j.jtbi.2014.11.029] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2014] [Revised: 11/24/2014] [Accepted: 11/27/2014] [Indexed: 11/17/2022]
Abstract
We develop a new methodology for the efficient computation of epidemic final size distributions for a broad class of Markovian models. We exploit a particular representation of the stochastic epidemic process to derive a method which is both computationally efficient and numerically stable. The algorithms we present are also physically transparent and so allow us to extend this method from the basic SIR model to a model with a phase-type infectious period and another with waning immunity. The underlying theory is applicable to many Markovian models where we wish to efficiently calculate hitting probabilities.
Collapse
Affiliation(s)
- Andrew J Black
- School of Mathematical Sciences, The University of Adelaide, Adelaide SA 5005, Australia.
| | - J V Ross
- School of Mathematical Sciences, The University of Adelaide, Adelaide SA 5005, Australia
| |
Collapse
|