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Martignoni MM, Arino J, Hurford A. Is SARS-CoV-2 elimination or mitigation best? Regional and disease characteristics determine the recommended strategy. ROYAL SOCIETY OPEN SCIENCE 2024; 11:240186. [PMID: 39100176 PMCID: PMC11295893 DOI: 10.1098/rsos.240186] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Accepted: 05/01/2024] [Indexed: 08/06/2024]
Abstract
Public health responses to the COVID-19 pandemic varied across the world. Some countries (e.g. mainland China, New Zealand and Taiwan) implemented elimination strategies involving strict travel measures and periods of rigorous non-pharmaceutical interventions (NPIs) in the community, aiming to achieve periods with no disease spread; while others (e.g. many European countries and the USA) implemented mitigation strategies involving less strict NPIs for prolonged periods, aiming to limit community spread. Travel measures and community NPIs have high economic and social costs, and there is a need for guidelines that evaluate the appropriateness of an elimination or mitigation strategy in regional contexts. To guide decisions, we identify key criteria and provide indicators and visualizations to help answer each question. Considerations include determining whether disease elimination is: (1) necessary to ensure healthcare provision; (2) feasible from an epidemiological point of view and (3) cost-effective when considering, in particular, the economic costs of travel measures and treating infections. We discuss our recommendations by considering the regional and economic variability of Canadian provinces and territories, and the epidemiological characteristics of different SARS-CoV-2 variants. While elimination may be a preferable strategy for regions with limited healthcare capacity, low travel volumes, and few ports of entry, mitigation may be more feasible in large urban areas with dense infrastructure, strong economies, and with high connectivity to other regions.
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Affiliation(s)
- Maria M. Martignoni
- Department of Mathematics and Statistics, Memorial University of Newfoundland, St. John’s, Canada
- Department of Ecology, Evolution and Behavior, A. Silberman Institute of Life Sciences, Faculty of Sciences, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Julien Arino
- Department of Mathematics, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Amy Hurford
- Department of Mathematics and Statistics, Memorial University of Newfoundland, St. John’s, Canada
- Biology Department and Department of Mathematics and Statistics, Memorial University of Newfoundland, St. John’s, Canada
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2
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Parag KV, Cowling BJ, Lambert BC. Angular reproduction numbers improve estimates of transmissibility when disease generation times are misspecified or time-varying. Proc Biol Sci 2023; 290:20231664. [PMID: 37752839 PMCID: PMC10523088 DOI: 10.1098/rspb.2023.1664] [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: 07/25/2023] [Accepted: 09/04/2023] [Indexed: 09/28/2023] Open
Abstract
We introduce the angular reproduction number Ω, which measures time-varying changes in epidemic transmissibility resulting from variations in both the effective reproduction number R, and generation time distribution w. Predominant approaches for tracking pathogen spread infer either R or the epidemic growth rate r. However, R is biased by mismatches between the assumed and true w, while r is difficult to interpret in terms of the individual-level branching process underpinning transmission. R and r may also disagree on the relative transmissibility of epidemics or variants (i.e. rA > rB does not imply RA > RB for variants A and B). We find that Ω responds meaningfully to mismatches and time-variations in w while mostly maintaining the interpretability of R. We prove that Ω > 1 implies R > 1 and that Ω agrees with r on the relative transmissibility of pathogens. Estimating Ω is no more difficult than inferring R, uses existing software, and requires no generation time measurements. These advantages come at the expense of selecting one free parameter. We propose Ω as complementary statistic to R and r that improves transmissibility estimates when w is misspecified or time-varying and better reflects the impact of interventions, when those interventions concurrently change R and w or alter the relative risk of co-circulating pathogens.
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Affiliation(s)
- Kris V. Parag
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
- NIHR Health Protection Research Unit in Behavioural Science and Evaluation, University of Bristol, Bristol, UK
| | - Benjamin J. Cowling
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, The University of Hong Kong, Hong Kong Hong Kong
| | - Ben C. Lambert
- Department of Mathematics, College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, UK
- Department of Statistics, University of Oxford, Oxford, UK
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3
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Sunahara AS, Pessa AAB, Perc M, Ribeiro HV. Complexity of the COVID-19 pandemic in Maringá. Sci Rep 2023; 13:12695. [PMID: 37542059 PMCID: PMC10403588 DOI: 10.1038/s41598-023-39815-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Accepted: 07/28/2023] [Indexed: 08/06/2023] Open
Abstract
While extensive literature exists on the COVID-19 pandemic at regional and national levels, understanding its dynamics and consequences at the city level remains limited. This study investigates the pandemic in Maringá, a medium-sized city in Brazil's South Region, using data obtained by actively monitoring the disease from March 2020 to June 2022. Despite prompt and robust interventions, COVID-19 cases increased exponentially during the early spread of COVID-19, with a reproduction number lower than that observed during the initial outbreak in Wuhan. Our research demonstrates the remarkable impact of non-pharmaceutical interventions on both mobility and pandemic indicators, particularly during the onset and the most severe phases of the emergency. However, our results suggest that the city's measures were primarily reactive rather than proactive. Maringá faced six waves of cases, with the third and fourth waves being the deadliest, responsible for over two-thirds of all deaths and overwhelming the local healthcare system. Excess mortality during this period exceeded deaths attributed to COVID-19, indicating that the burdened healthcare system may have contributed to increased mortality from other causes. By the end of the fourth wave, nearly three-quarters of the city's population had received two vaccine doses, significantly decreasing deaths despite the surge caused by the Omicron variant. Finally, we compare these findings with the national context and other similarly sized cities, highlighting substantial heterogeneities in the spread and impact of the disease.
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Affiliation(s)
- Andre S Sunahara
- Departamento de Física, Universidade Estadual de Maringá, Maringá, PR, 87020-900, Brazil
| | - Arthur A B Pessa
- Departamento de Física, Universidade Estadual de Maringá, Maringá, PR, 87020-900, Brazil
| | - Matjaž Perc
- Faculty of Natural Sciences and Mathematics, University of Maribor, Koroška cesta 160, 2000, Maribor, Slovenia.
- Department of Medical Research, China Medical University Hospital, China Medical University, Taichung, Taiwan.
- Alma Mater Europaea, Slovenska ulica 17, 2000, Maribor, Slovenia.
- Department of Physics, Kyung Hee University, 26 Kyungheedae-ro, Dongdaemun-gu, Seoul, Republic of Korea.
- Complexity Science Hub Vienna, Josefstädterstraße 39, 1080, Vienna, Austria.
| | - Haroldo V Ribeiro
- Departamento de Física, Universidade Estadual de Maringá, Maringá, PR, 87020-900, Brazil.
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4
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Bosse NI, Abbott S, Cori A, van Leeuwen E, Bracher J, Funk S. Scoring epidemiological forecasts on transformed scales. PLoS Comput Biol 2023; 19:e1011393. [PMID: 37643178 PMCID: PMC10495027 DOI: 10.1371/journal.pcbi.1011393] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Revised: 09/11/2023] [Accepted: 07/27/2023] [Indexed: 08/31/2023] Open
Abstract
Forecast evaluation is essential for the development of predictive epidemic models and can inform their use for public health decision-making. Common scores to evaluate epidemiological forecasts are the Continuous Ranked Probability Score (CRPS) and the Weighted Interval Score (WIS), which can be seen as measures of the absolute distance between the forecast distribution and the observation. However, applying these scores directly to predicted and observed incidence counts may not be the most appropriate due to the exponential nature of epidemic processes and the varying magnitudes of observed values across space and time. In this paper, we argue that transforming counts before applying scores such as the CRPS or WIS can effectively mitigate these difficulties and yield epidemiologically meaningful and easily interpretable results. Using the CRPS on log-transformed values as an example, we list three attractive properties: Firstly, it can be interpreted as a probabilistic version of a relative error. Secondly, it reflects how well models predicted the time-varying epidemic growth rate. And lastly, using arguments on variance-stabilizing transformations, it can be shown that under the assumption of a quadratic mean-variance relationship, the logarithmic transformation leads to expected CRPS values which are independent of the order of magnitude of the predicted quantity. Applying a transformation of log(x + 1) to data and forecasts from the European COVID-19 Forecast Hub, we find that it changes model rankings regardless of stratification by forecast date, location or target types. Situations in which models missed the beginning of upward swings are more strongly emphasised while failing to predict a downturn following a peak is less severely penalised when scoring transformed forecasts as opposed to untransformed ones. We conclude that appropriate transformations, of which the natural logarithm is only one particularly attractive option, should be considered when assessing the performance of different models in the context of infectious disease incidence.
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Affiliation(s)
- Nikos I. Bosse
- Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, United Kingdom
- Centre for the Mathematical Modelling of Infectious Diseases, London, United Kingdom
- NIHR Health Protection Research Unit in Modelling & Health Economics
| | - Sam Abbott
- Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, United Kingdom
- Centre for the Mathematical Modelling of Infectious Diseases, London, United Kingdom
| | - Anne Cori
- MRC Centre for Outbreak Analysis and Modelling, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, United Kingdom
| | - Edwin van Leeuwen
- Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, United Kingdom
- NIHR Health Protection Research Unit in Modelling & Health Economics
- Modelling & Economics Unit and NIHR Health Protection Research Unit in Modelling & Health Economics, UK Health Security Agency, London, United Kingdom
| | - Johannes Bracher
- Chair of Statistical Methods and Econometrics, Karlsruhe Institute of Technology, Karlsruhe, Germany
- Computational Statistics Group, Heidelberg Institute for Theoretical Studies, Heidelberg, Germany
| | - Sebastian Funk
- Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, United Kingdom
- Centre for the Mathematical Modelling of Infectious Diseases, London, United Kingdom
- NIHR Health Protection Research Unit in Modelling & Health Economics
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5
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Inward RP, Jackson F, Dasgupta A, Lee G, Battle AL, Parag KV, Kraemer MU. Impact of spatiotemporal heterogeneity in COVID-19 disease surveillance on epidemiological parameters and case growth rates. Epidemics 2022; 41:100627. [PMID: 36099708 PMCID: PMC9443927 DOI: 10.1016/j.epidem.2022.100627] [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: 03/31/2022] [Revised: 08/04/2022] [Accepted: 09/03/2022] [Indexed: 02/08/2023] Open
Abstract
SARS-CoV-2 case data are primary sources for estimating epidemiological parameters and for modelling the dynamics of outbreaks. Understanding biases within case-based data sources used in epidemiological analyses is important as they can detract from the value of these rich datasets. This raises questions of how variations in surveillance can affect the estimation of epidemiological parameters such as the case growth rates. We use standardised line list data of COVID-19 from Argentina, Brazil, Mexico and Colombia to estimate delay distributions of symptom-onset-to-confirmation, -hospitalisation and -death as well as hospitalisation-to-death at high spatial resolutions and throughout time. Using these estimates, we model the biases introduced by the delay from symptom-onset-to-confirmation on national and state level case growth rates (rt) using an adaptation of the Richardson-Lucy deconvolution algorithm. We find significant heterogeneities in the estimation of delay distributions through time and space with delay difference of up to 19 days between epochs at the state level. Further, we find that by changing the spatial scale, estimates of case growth rate can vary by up to 0.13 d-1. Lastly, we find that states with a high variance and/or mean delay in symptom-onset-to-diagnosis also have the largest difference between the rt estimated from raw and deconvolved case counts at the state level. We highlight the importance of high-resolution case-based data in understanding biases in disease reporting and how these biases can be avoided by adjusting case numbers based on empirical delay distributions. Code and openly accessible data to reproduce analyses presented here are available.
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Affiliation(s)
- Rhys P.D. Inward
- Department of Biology, University of Oxford, United Kingdom,Corresponding author
| | - Felix Jackson
- Department of Biology, University of Oxford, United Kingdom,Department of Computer Science, University of Oxford, United Kingdom
| | - Abhishek Dasgupta
- Department of Biology, University of Oxford, United Kingdom,Department of Computer Science, University of Oxford, United Kingdom
| | - Graham Lee
- Department of Biology, University of Oxford, United Kingdom,Department of Computer Science, University of Oxford, United Kingdom
| | | | - Kris V. Parag
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, United Kingdom,NIHR Health Protection Research Unit in Behavioural Science and Evaluation, University of Bristol, Bristol, United Kingdom
| | - Moritz U.G. Kraemer
- Department of Biology, University of Oxford, United Kingdom,Reuben College, University of Oxford, United Kingdom,Corresponding author at: Department of Biology, University of Oxford, United Kingdom
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6
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Creswell R, Augustin D, Bouros I, Farm HJ, Miao S, Ahern A, Robinson M, Lemenuel-Diot A, Gavaghan DJ, Lambert BC, Thompson RN. Heterogeneity in the onwards transmission risk between local and imported cases affects practical estimates of the time-dependent reproduction number. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2022; 380:20210308. [PMID: 35965464 PMCID: PMC9376709 DOI: 10.1098/rsta.2021.0308] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Accepted: 05/04/2022] [Indexed: 05/02/2023]
Abstract
During infectious disease outbreaks, inference of summary statistics characterizing transmission is essential for planning interventions. An important metric is the time-dependent reproduction number (Rt), which represents the expected number of secondary cases generated by each infected individual over the course of their infectious period. The value of Rt varies during an outbreak due to factors such as varying population immunity and changes to interventions, including those that affect individuals' contact networks. While it is possible to estimate a single population-wide Rt, this may belie differences in transmission between subgroups within the population. Here, we explore the effects of this heterogeneity on Rt estimates. Specifically, we consider two groups of infected hosts: those infected outside the local population (imported cases), and those infected locally (local cases). We use a Bayesian approach to estimate Rt, made available for others to use via an online tool, that accounts for differences in the onwards transmission risk from individuals in these groups. Using COVID-19 data from different regions worldwide, we show that different assumptions about the relative transmission risk between imported and local cases affect Rt estimates significantly, with implications for interventions. This highlights the need to collect data during outbreaks describing heterogeneities in transmission between different infected hosts, and to account for these heterogeneities in methods used to estimate Rt. This article is part of the theme issue 'Technical challenges of modelling real-life epidemics and examples of overcoming these'.
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Affiliation(s)
- R. Creswell
- Department of Computer Science, University of Oxford, Oxford OX1 3QD, UK
| | - D. Augustin
- Department of Computer Science, University of Oxford, Oxford OX1 3QD, UK
| | - I. Bouros
- Department of Computer Science, University of Oxford, Oxford OX1 3QD, UK
| | - H. J. Farm
- Department of Computer Science, University of Oxford, Oxford OX1 3QD, UK
| | - S. Miao
- Mathematical Institute, University of Oxford, Oxford OX2 6GG, UK
| | - A. Ahern
- Mathematical Institute, University of Oxford, Oxford OX2 6GG, UK
| | - M. Robinson
- Department of Computer Science, University of Oxford, Oxford OX1 3QD, UK
| | - A. Lemenuel-Diot
- Roche Pharmaceutical Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, Basel CH-4070, Switzerland
| | - D. J. Gavaghan
- Department of Computer Science, University of Oxford, Oxford OX1 3QD, UK
| | - B. C. Lambert
- Department of Computer Science, University of Oxford, Oxford OX1 3QD, UK
| | - R. N. Thompson
- Mathematics Institute, University of Warwick, Coventry CV4 7AL, UK
- Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, University of Warwick, Coventry CV4 7AL, UK
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7
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Inward RPD, Parag KV, Faria NR. Using multiple sampling strategies to estimate SARS-CoV-2 epidemiological parameters from genomic sequencing data. Nat Commun 2022; 13:5587. [PMID: 36151084 PMCID: PMC9508174 DOI: 10.1038/s41467-022-32812-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Accepted: 08/16/2022] [Indexed: 11/09/2022] Open
Abstract
The choice of viral sequences used in genetic and epidemiological analysis is important as it can induce biases that detract from the value of these rich datasets. This raises questions about how a set of sequences should be chosen for analysis. We provide insights on these largely understudied problems using SARS-CoV-2 genomic sequences from Hong Kong, China, and the Amazonas State, Brazil. We consider multiple sampling schemes which were used to estimate Rt and rt as well as related R0 and date of origin parameters. We find that both Rt and rt are sensitive to changes in sampling whilst R0 and the date of origin are relatively robust. Moreover, we find that analysis using unsampled datasets result in the most biased Rt and rt estimates for both our Hong Kong and Amazonas case studies. We highlight that sampling strategy choices may be an influential yet neglected component of sequencing analysis pipelines.
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Affiliation(s)
| | - Kris V Parag
- MRC Centre of Global Infectious Disease Analysis, Jameel Institute for Disease and Emergency Analytics, Imperial College London, London, UK.
- NIHR Health Protection Research Unit in Behavioural Science and Evaluation, University of Bristol, Bristol, UK.
| | - Nuno R Faria
- Department of Zoology, University of Oxford, Oxford, UK.
- MRC Centre of Global Infectious Disease Analysis, Jameel Institute for Disease and Emergency Analytics, Imperial College London, London, UK.
- Instituto de Medicina Tropical, Faculdade de Medicina da Universidade de Sao Paulo, Sao Paulo, Brazil.
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8
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Anzai A, Nishiura H. Doubling time of infectious diseases. J Theor Biol 2022; 554:111278. [PMID: 36113624 PMCID: PMC9477213 DOI: 10.1016/j.jtbi.2022.111278] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 08/18/2022] [Accepted: 09/07/2022] [Indexed: 01/14/2023]
Abstract
The concept of doubling time has been increasingly used since the onset of the coronavirus disease 2019 (COVID-19) pandemic, but its characteristics are not well understood, especially as applied to infectious disease epidemiology. The present study aims to be a practical guide to monitoring the doubling time of infectious diseases. Via simulation exercise, we clarify the epidemiological characteristics of doubling time, allowing possible interpretations. We show that the commonly believed relationship between the doubling time and intrinsic growth rate in population ecology does not strictly apply to infectious diseases, and derive the correct relationship between the two. We examined the impact of varying (i) the growth rate, (ii) the starting point of counting cumulative number of cases, and (iii) the length of observation on statistical estimation of doubling time. It was difficult to recover values of growth rate from doubling time, especially when the growth rate was small. Starting time period is critical when the statistical estimation of doubling time occurs during the course of an epidemic. The length of observation was critical in determining the overall magnitude of doubling time, and when only the latest 1-2 weeks' data were used, the resulting doubling time was very short, regardless of the intrinsic growth rate r. We suggest that doubling time estimates of infectious disease epidemics should at a minimum be accompanied by descriptions of (i) the starting time at which the cumulative count is initiated and (ii) the length of observation.
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Affiliation(s)
- Asami Anzai
- Kyoto University School of Public Health, Yoshida-Konoe, Sakyo-ku, Kyoto 606-8601, Japan
| | - Hiroshi Nishiura
- Kyoto University School of Public Health, Yoshida-Konoe, Sakyo-ku, Kyoto 606-8601, Japan.
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9
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Pekar JE, Magee A, Parker E, Moshiri N, Izhikevich K, Havens JL, Gangavarapu K, Malpica Serrano LM, Crits-Christoph A, Matteson NL, Zeller M, Levy JI, Wang JC, Hughes S, Lee J, Park H, Park MS, Ching KZY, Lin RTP, Mat Isa MN, Noor YM, Vasylyeva TI, Garry RF, Holmes EC, Rambaut A, Suchard MA, Andersen KG, Worobey M, Wertheim JO. The molecular epidemiology of multiple zoonotic origins of SARS-CoV-2. Science 2022; 377:960-966. [PMID: 35881005 PMCID: PMC9348752 DOI: 10.1126/science.abp8337] [Citation(s) in RCA: 73] [Impact Index Per Article: 36.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Accepted: 07/18/2022] [Indexed: 01/08/2023]
Abstract
Understanding the circumstances that lead to pandemics is important for their prevention. We analyzed the genomic diversity of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) early in the coronavirus disease 2019 (COVID-19) pandemic. We show that SARS-CoV-2 genomic diversity before February 2020 likely comprised only two distinct viral lineages, denoted "A" and "B." Phylodynamic rooting methods, coupled with epidemic simulations, reveal that these lineages were the result of at least two separate cross-species transmission events into humans. The first zoonotic transmission likely involved lineage B viruses around 18 November 2019 (23 October to 8 December), and the separate introduction of lineage A likely occurred within weeks of this event. These findings indicate that it is unlikely that SARS-CoV-2 circulated widely in humans before November 2019 and define the narrow window between when SARS-CoV-2 first jumped into humans and when the first cases of COVID-19 were reported. As with other coronaviruses, SARS-CoV-2 emergence likely resulted from multiple zoonotic events.
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Affiliation(s)
- Jonathan E. Pekar
- Bioinformatics and Systems Biology Graduate Program, University of California San Diego, La Jolla, CA 92093, USA
- Department of Biomedical Informatics, University of California San Diego, La Jolla, CA 92093, USA
| | - Andrew Magee
- Department of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Edyth Parker
- Department of Immunology and Microbiology, The Scripps Research Institute, La Jolla, CA 92037, USA
| | - Niema Moshiri
- Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA 92093, USA
| | - Katherine Izhikevich
- Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA 92093, USA
- Department of Mathematics, University of California San Diego, La Jolla, CA 92093, USA
| | - Jennifer L. Havens
- Bioinformatics and Systems Biology Graduate Program, University of California San Diego, La Jolla, CA 92093, USA
| | - Karthik Gangavarapu
- Department of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA 90095, USA
| | | | - Alexander Crits-Christoph
- W. Harry Feinstone Department of Molecular Microbiology and Immunology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland 21205, USA
| | - Nathaniel L. Matteson
- Department of Immunology and Microbiology, The Scripps Research Institute, La Jolla, CA 92037, USA
| | - Mark Zeller
- Department of Immunology and Microbiology, The Scripps Research Institute, La Jolla, CA 92037, USA
| | - Joshua I. Levy
- Department of Immunology and Microbiology, The Scripps Research Institute, La Jolla, CA 92037, USA
| | - Jade C. Wang
- New York City Public Health Laboratory, New York City Department of Health and Mental Hygiene, New York, NY 11101, USA
| | - Scott Hughes
- New York City Public Health Laboratory, New York City Department of Health and Mental Hygiene, New York, NY 11101, USA
| | - Jungmin Lee
- Department of Microbiology, Institute for Viral Diseases, Biosafety Center, College of Medicine, Korea University, Seoul, South Korea
| | - Heedo Park
- Department of Microbiology, Institute for Viral Diseases, Biosafety Center, College of Medicine, Korea University, Seoul, South Korea
- BK21 Graduate Program, Department of Biomedical Sciences, Korea University College of Medicine, Seoul, 02841, Republic of Korea
| | - Man-Seong Park
- Department of Microbiology, Institute for Viral Diseases, Biosafety Center, College of Medicine, Korea University, Seoul, South Korea
- BK21 Graduate Program, Department of Biomedical Sciences, Korea University College of Medicine, Seoul, 02841, Republic of Korea
| | | | - Raymond Tzer Pin Lin
- National Public Health Laboratory, National Centre for Infectious Diseases, Singapore
| | - Mohd Noor Mat Isa
- Malaysia Genome and Vaccine Institute, Jalan Bangi, 43000 Kajang, Selangor, Malaysia
| | - Yusuf Muhammad Noor
- Malaysia Genome and Vaccine Institute, Jalan Bangi, 43000 Kajang, Selangor, Malaysia
| | - Tetyana I. Vasylyeva
- Department of Medicine, University of California San Diego, La Jolla, CA 92093, USA
| | - Robert F. Garry
- Tulane University, School of Medicine, Department of Microbiology and Immunology, New Orleans, LA 70112, USA
- Zalgen Labs, LCC, Frederick, MD 21703 USA
- Global Virus Network (GVN), Baltimore, MD 21201, USA
| | - Edward C. Holmes
- Sydney Institute for Infectious Diseases, School of Life and Environmental Sciences and School of Medical Sciences, The University of Sydney, Sydney, NSW 2006, Australia
| | - Andrew Rambaut
- Institute of Evolutionary Biology, University of Edinburgh, King's Buildings, Edinburgh, EH9 3FL, UK
| | - Marc A. Suchard
- Department of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA 90095, USA
- Department of Biomathematics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA 90095, USA
- Department of Biostatistics, Fielding School of Public Health, University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Kristian G. Andersen
- Department of Immunology and Microbiology, The Scripps Research Institute, La Jolla, CA 92037, USA
- Scripps Research Translational Institute, La Jolla, CA 92037, USA
| | - Michael Worobey
- Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, AZ 85721, USA
| | - Joel O. Wertheim
- Department of Medicine, University of California San Diego, La Jolla, CA 92093, USA
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10
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Brouwer AF. Why the Spectral Radius? An intuition-building introduction to the basic reproduction number. Bull Math Biol 2022; 84:96. [PMID: 35930076 PMCID: PMC9355935 DOI: 10.1007/s11538-022-01057-9] [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: 01/04/2022] [Accepted: 07/21/2022] [Indexed: 11/26/2022]
Abstract
The basic reproduction number [Formula: see text] is a fundamental concept in mathematical epidemiology and infectious disease modeling. Loosely speaking, it describes the number of people that an infectious person is expected to infect. The basic reproduction number has profound implications for epidemic trajectories and disease control strategies. It is well known that the basic reproduction number can be calculated as the spectral radius of the next generation matrix, but why this is the case may not be intuitively obvious. Here, we walk through how the discrete, next generation process connects to the ordinary differential equation disease system of interest, linearized at the disease-free equilibrium. Then, we use linear algebra to develop a geometric explanation of why the spectral radius of the next generation matrix is an epidemic threshold. Finally, we work through a series of examples that help to build familiarity with the kinds of patterns that arise in parameter combinations produced by the next generation method. This article is intended to help new infectious disease modelers develop intuition for the form and interpretation of the basic reproduction number in their disease systems of interest.
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Affiliation(s)
- Andrew F Brouwer
- Department of Epidemiology, University of Michigan, 1415 Washington Heights, Ann Arbor, MI, 48109, USA.
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11
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Park SW, Bolker BM, Funk S, Metcalf CJE, Weitz JS, Grenfell BT, Dushoff J. The importance of the generation interval in investigating dynamics and control of new SARS-CoV-2 variants. J R Soc Interface 2022; 19:20220173. [PMID: 35702867 PMCID: PMC9198506 DOI: 10.1098/rsif.2022.0173] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
Inferring the relative strength (i.e. the ratio of reproduction numbers) and relative speed (i.e. the difference between growth rates) of new SARS-CoV-2 variants is critical to predicting and controlling the course of the current pandemic. Analyses of new variants have primarily focused on characterizing changes in the proportion of new variants, implicitly or explicitly assuming that the relative speed remains fixed over the course of an invasion. We use a generation-interval-based framework to challenge this assumption and illustrate how relative strength and speed change over time under two idealized interventions: a constant-strength intervention like idealized vaccination or social distancing, which reduces transmission rates by a constant proportion, and a constant-speed intervention like idealized contact tracing, which isolates infected individuals at a constant rate. In general, constant-strength interventions change the relative speed of a new variant, while constant-speed interventions change its relative strength. Differences in the generation-interval distributions between variants can exaggerate these changes and modify the effectiveness of interventions. Finally, neglecting differences in generation-interval distributions can bias estimates of relative strength.
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Affiliation(s)
- Sang Woo Park
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA
| | - Benjamin M Bolker
- Department of Biology, McMaster University, Hamilton, Ontario, Canada.,Department of Mathematics and Statistics, McMaster University, Hamilton, Ontario, Canada.,M. G. DeGroote Institute for Infectious Disease Research, McMaster University, Hamilton, Ontario, Canada
| | - Sebastian Funk
- Department for Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK.,Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK
| | - C Jessica E Metcalf
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA.,Princeton School of Public and International Affairs, Princeton University, Princeton, NJ, USA
| | - Joshua S Weitz
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, USA.,School of Physics, Georgia Institute of Technology, Atlanta, GA, USA.,Institut de Biologie, École Normale Supérieure, Paris, France
| | - Bryan T Grenfell
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA.,Princeton School of Public and International Affairs, Princeton University, Princeton, NJ, USA
| | - Jonathan Dushoff
- Department of Biology, McMaster University, Hamilton, Ontario, Canada.,Department of Mathematics and Statistics, McMaster University, Hamilton, Ontario, Canada.,M. G. DeGroote Institute for Infectious Disease Research, McMaster University, Hamilton, Ontario, Canada
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12
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Parag KV, Thompson RN, Donnelly CA. Are epidemic growth rates more informative than reproduction numbers? JOURNAL OF THE ROYAL STATISTICAL SOCIETY. SERIES A, (STATISTICS IN SOCIETY) 2022; 185:RSSA12867. [PMID: 35942192 PMCID: PMC9347870 DOI: 10.1111/rssa.12867] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Accepted: 04/22/2022] [Indexed: 05/04/2023]
Abstract
statistics, often derived from simplified models of epidemic spread, inform public health policy in real time. The instantaneous reproduction number,R t , is predominant among these statistics, measuring the average ability of an infection to multiply. However,R t encodes no temporal information and is sensitive to modelling assumptions. Consequently, some have proposed the epidemic growth rate,r t , that is, the rate of change of the log-transformed case incidence, as a more temporally meaningful and model-agnostic policy guide. We examine this assertion, identifying if and when estimates ofr t are more informative than those ofR t . We assess their relative strengths both for learning about pathogen transmission mechanisms and for guiding public health interventions in real time.
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Affiliation(s)
- Kris V. Parag
- Department of Infectious Disease EpidemiologyMRC Centre for Global Infectious Disease AnalysisImperial College LondonLondonUK
| | - Robin N. Thompson
- Mathematics InstituteUniversity of WarwickCoventryUK
- The Zeeman Institute for Systems Biology and Infectious Disease Epidemiology ResearchUniversity of WarwickCoventryUK
| | - Christl A. Donnelly
- Department of Infectious Disease EpidemiologyMRC Centre for Global Infectious Disease AnalysisImperial College LondonLondonUK
- Department of StatisticsUniversity of OxfordOxfordUK
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13
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Zafar ZUA, Younas S, Zaib S, Tunç C. An efficient numerical simulation and mathematical modeling for the prevention of tuberculosis. INT J BIOMATH 2021. [DOI: 10.1142/s1793524522500152] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
The main purpose of this research is to use a fractional-mathematical model including Atangana–Baleanu derivatives to explore the clinical associations and dynamical behavior of the tuberculosis. Herein, we used a lately introduced fractional operator having Mittag-Leffler kernel. The existence and inimitability problems to the relevant model were examined through the fixed-point theory. To verify the significance of the arbitrary fractional-order derivative, numerical outcomes were explored from the biological and mathematical viewpoints using the values of model parameters. The graphical simulations show the comparison of the predictor–corrector method (PCM) and Caputo method (CM) for different fractional orders and the results indicated the significant preference of PCM over CM.
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Affiliation(s)
- Zain Ul Abadin Zafar
- Department of Mathematics, Faculty of Sciences, University of Central Punjab, Lahore, Pakistan
| | - Samina Younas
- Department of Zoology, Government College University, Lahore, Pakistan
| | - Sumera Zaib
- Department of Biochemistry, Faculty of Life Sciences, University of Central Punjab, Lahore, Pakistan
| | - Cemil Tunç
- Department of Mathematics, Faculty of Sciences, Van Yuzuncu Yil University, 65080, Campus, Van, Turkey
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14
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Pellis L, Scarabel F, Stage HB, Overton CE, Chappell LHK, Fearon E, Bennett E, Lythgoe KA, House TA, Hall I. Challenges in control of COVID-19: short doubling time and long delay to effect of interventions. Philos Trans R Soc Lond B Biol Sci 2021; 376:20200264. [PMID: 34053267 PMCID: PMC8165602 DOI: 10.1098/rstb.2020.0264] [Citation(s) in RCA: 65] [Impact Index Per Article: 21.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/21/2021] [Indexed: 12/20/2022] Open
Abstract
Early assessments of the growth rate of COVID-19 were subject to significant uncertainty, as expected with limited data and difficulties in case ascertainment, but as cases were recorded in multiple countries, more robust inferences could be made. Using multiple countries, data streams and methods, we estimated that, when unconstrained, European COVID-19 confirmed cases doubled on average every 3 days (range 2.2-4.3 days) and Italian hospital and intensive care unit admissions every 2-3 days; values that are significantly lower than the 5-7 days dominating the early published literature. Furthermore, we showed that the impact of physical distancing interventions was typically not seen until at least 9 days after implementation, during which time confirmed cases could grow eightfold. We argue that such temporal patterns are more critical than precise estimates of the time-insensitive basic reproduction number R0 for initiating interventions, and that the combination of fast growth and long detection delays explains the struggle in countries' outbreak response better than large values of R0 alone. One year on from first reporting these results, reproduction numbers continue to dominate the media and public discourse, but robust estimates of unconstrained growth remain essential for planning worst-case scenarios, and detection delays are still key in informing the relaxation and re-implementation of interventions. This article is part of the theme issue 'Modelling that shaped the early COVID-19 pandemic response in the UK'.
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Affiliation(s)
- Lorenzo Pellis
- Department of Mathematics, The University of Manchester, Manchester, UK
- Joint UNIversities Pandemic and Epidemiological Research, UK
- The Alan Turing Institute, London, UK
| | - Francesca Scarabel
- Department of Mathematics, The University of Manchester, Manchester, UK
- Joint UNIversities Pandemic and Epidemiological Research, UK
- LIAM - Laboratory of Industrial and Applied Mathematics, Department of Mathematics and Statistics, York University, Toronto, Ontario, Canada
- CDLab - Computational Dynamics Laboratory, Department of Mathematics, Computer Science and Physics, University of Udine, Italy
| | - Helena B. Stage
- Department of Mathematics, The University of Manchester, Manchester, UK
- Joint UNIversities Pandemic and Epidemiological Research, UK
| | - Christopher E. Overton
- Department of Mathematics, The University of Manchester, Manchester, UK
- Clinical Data Science Unit, Manchester University NHS Foundation Trust, Manchester, UK
- Joint UNIversities Pandemic and Epidemiological Research, UK
| | | | - Elizabeth Fearon
- Department of Global Health and Development, London School of Hygiene and Tropical Medicine, UK
- CMMID - Centre for the Mathematical Modelling of Infectious Disease, London School of Hygiene and Tropical Medicine, UK
| | - Emma Bennett
- Emergency Response Department, Public Health England, UK
| | - Katrina A. Lythgoe
- Big Data Institute, University of Oxford, UK
- Department of Zoology, University of Oxford, UK
| | - Thomas A. House
- Department of Mathematics, The University of Manchester, Manchester, UK
- Joint UNIversities Pandemic and Epidemiological Research, UK
- The Alan Turing Institute, London, UK
- IBM Research, Hartree Centre, SciTech Daresbury, Warrington, UK
| | - Ian Hall
- Department of Mathematics, The University of Manchester, Manchester, UK
- Joint UNIversities Pandemic and Epidemiological Research, UK
- The Alan Turing Institute, London, UK
- Emergency Response Department, Public Health England, UK
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