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Charniga K, Park SW, Akhmetzhanov AR, Cori A, Dushoff J, Funk S, Gostic KM, Linton NM, Lison A, Overton CE, Pulliam JRC, Ward T, Cauchemez S, Abbott S. Best practices for estimating and reporting epidemiological delay distributions of infectious diseases. PLoS Comput Biol 2024; 20:e1012520. [PMID: 39466727 DOI: 10.1371/journal.pcbi.1012520] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/30/2024] Open
Abstract
Epidemiological delays are key quantities that inform public health policy and clinical practice. They are used as inputs for mathematical and statistical models, which in turn can guide control strategies. In recent work, we found that censoring, right truncation, and dynamical bias were rarely addressed correctly when estimating delays and that these biases were large enough to have knock-on impacts across a large number of use cases. Here, we formulate a checklist of best practices for estimating and reporting epidemiological delays. We also provide a flowchart to guide practitioners based on their data. Our examples are focused on the incubation period and serial interval due to their importance in outbreak response and modeling, but our recommendations are applicable to other delays. The recommendations, which are based on the literature and our experience estimating epidemiological delay distributions during outbreak responses, can help improve the robustness and utility of reported estimates and provide guidance for the evaluation of estimates for downstream use in transmission models or other analyses.
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Affiliation(s)
- Kelly Charniga
- Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, Université Paris Cité, CNRS UMR 2000, Paris, France
| | - Sang Woo Park
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, New Jersey, United States of America
| | | | - Anne Cori
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, United Kingdom
| | - Jonathan Dushoff
- Departments of Mathematics & Statistics and Biology, McMaster University, Hamilton, Ontario, Canada
- Department of Biology, McMaster University, Hamilton, Ontario, Canada
- M. G. DeGroote Institute for Infectious Disease Research, McMaster University, Hamilton, Ontario, Canada
| | - Sebastian Funk
- Department of Infectious Disease Epidemiology and Dynamics, London School of Hygiene & Tropical Medicine, London, United Kingdom
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, United Kingdom
| | - Katelyn M Gostic
- Center for Forecasting and Outbreak Analytics, U.S. Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America
| | - Natalie M Linton
- Graduate School of Medicine, Hokkaido University, Sapporo-shi, Hokkaido, Japan
| | - Adrian Lison
- Department of Biosystems Science and Engineering, ETH Zurich, Zurich, Switzerland
| | - Christopher E Overton
- Department of Mathematical Sciences, University of Liverpool, Liverpool, United Kingdom
- All Hazards Intelligence, Infectious Disease Modelling Team, Data Analytics and Surveillance, UK Health Security Agency, United Kingdom
- Department of Mathematics, University of Manchester, Manchester, United Kingdom
| | - Juliet R C Pulliam
- Center for Forecasting and Outbreak Analytics, U.S. Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America
| | - Thomas Ward
- All Hazards Intelligence, Infectious Disease Modelling Team, Data Analytics and Surveillance, UK Health Security Agency, United Kingdom
| | - Simon Cauchemez
- Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, Université Paris Cité, CNRS UMR 2000, Paris, France
| | - Sam Abbott
- Department of Infectious Disease Epidemiology and Dynamics, London School of Hygiene & Tropical Medicine, London, United Kingdom
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, United Kingdom
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Cori A, Kucharski A. Inference of epidemic dynamics in the COVID-19 era and beyond. Epidemics 2024; 48:100784. [PMID: 39167954 DOI: 10.1016/j.epidem.2024.100784] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2024] [Revised: 06/25/2024] [Accepted: 07/11/2024] [Indexed: 08/23/2024] Open
Abstract
The COVID-19 pandemic demonstrated the key role that epidemiology and modelling play in analysing infectious threats and supporting decision making in real-time. Motivated by the unprecedented volume and breadth of data generated during the pandemic, we review modern opportunities for analysis to address questions that emerge during a major modern epidemic. Following the broad chronology of insights required - from understanding initial dynamics to retrospective evaluation of interventions, we describe the theoretical foundations of each approach and the underlying intuition. Through a series of case studies, we illustrate real life applications, and discuss implications for future work.
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Affiliation(s)
- Anne Cori
- MRC Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, United Kingdom.
| | - Adam Kucharski
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, United Kingdom.
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3
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Dhiman A, Yom-Tov E, Pellis L, Edelstein M, Pebody R, Hayward A, House T, Finnie T, Guzman D, Lampos V, Cox IJ. Estimating the household secondary attack rate and serial interval of COVID-19 using social media. NPJ Digit Med 2024; 7:194. [PMID: 39033238 PMCID: PMC11271293 DOI: 10.1038/s41746-024-01160-2] [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: 10/11/2023] [Accepted: 06/10/2024] [Indexed: 07/23/2024] Open
Abstract
We propose a method to estimate the household secondary attack rate (hSAR) of COVID-19 in the United Kingdom based on activity on the social media platform X, formerly known as Twitter. Conventional methods of hSAR estimation are resource intensive, requiring regular contact tracing of COVID-19 cases. Our proposed framework provides a complementary method that does not rely on conventional contact tracing or laboratory involvement, including the collection, processing, and analysis of biological samples. We use a text classifier to identify reports of people tweeting about themselves and/or members of their household having COVID-19 infections. A probabilistic analysis is then performed to estimate the hSAR based on the number of self or household, and self and household tweets of COVID-19 infection. The analysis includes adjustments for a reluctance of Twitter users to tweet about household members, and the possibility that the secondary infection was not acquired within the household. Experimental results for the UK, both monthly and weekly, are reported for the period from January 2020 to February 2022. Our results agree with previously reported hSAR estimates, varying with the primary variants of concern, e.g. delta and omicron. The serial interval (SI) is based on the time between the two tweets that indicate a primary and secondary infection. Experimental results, though larger than the consensus, are qualitatively similar. The estimation of hSAR and SI using social media data constitutes a new tool that may help in characterizing, forecasting and managing outbreaks and pandemics in a faster, affordable, and more efficient manner.
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Affiliation(s)
- Aarzoo Dhiman
- Department of Computer Science, University College London, London, UK.
- Centre of Excellence for Data Science, AI and Modelling, University of Hull, Hull, UK.
| | - Elad Yom-Tov
- Microsoft Research, Herzliya, Israel
- Department of Computer Science, Bar Ilan University, Ramat Gan, Israel
| | - Lorenzo Pellis
- Department of Mathematics, University of Manchester, Manchester, UK
| | | | - Richard Pebody
- UK Health Security Agency, 61 Collingdate Avenue, NW9 5EQ, London, UK
| | - Andrew Hayward
- UCL Collaborative Centre for Inclusion Health, UCL, London, UK
| | - Thomas House
- Department of Mathematics, University of Manchester, Manchester, UK
| | - Thomas Finnie
- UK Health Security Agency, 61 Collingdate Avenue, NW9 5EQ, London, UK
| | - David Guzman
- Department of Computer Science, University College London, London, UK
| | - Vasileios Lampos
- Department of Computer Science, University College London, London, UK.
| | - Ingemar J Cox
- Department of Computer Science, University College London, London, UK.
- Department of Computer Science, University of Copenhagen, Copenhagen, Denmark.
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4
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Ponce L, Linton NM, Toh WH, Cheng HY, Thompson RN, Akhmetzhanov AR, Dushoff J. Incubation Period and Serial Interval of Mpox in 2022 Global Outbreak Compared with Historical Estimates. Emerg Infect Dis 2024; 30:1173-1181. [PMID: 38781950 PMCID: PMC11138990 DOI: 10.3201/eid3006.231095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/25/2024] Open
Abstract
Understanding changes in the transmission dynamics of mpox requires comparing recent estimates of key epidemiologic parameters with historical data. We derived historical estimates for the incubation period and serial interval for mpox and contrasted them with pooled estimates from the 2022 outbreak. Our findings show the pooled mean infection-to-onset incubation period was 8.1 days for the 2022 outbreak and 8.2 days historically, indicating the incubation periods remained relatively consistent over time, despite a shift in the major mode of transmission. However, we estimated the onset-to-onset serial interval at 8.7 days using 2022 data, compared with 14.2 days using historical data. Although the reason for this shortening of the serial interval is unclear, it may be because of increased public health interventions or a shift in the mode of transmission. Recognizing such temporal shifts is essential for informed response strategies, and public health measures remain crucial for controlling mpox and similar future outbreaks.
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Shearer FM, McCaw JM, Ryan GE, Hao T, Tierney NJ, Lydeamore MJ, Wu L, Ward K, Ellis S, Wood J, McVernon J, Golding N. Estimating the impact of test-trace-isolate-quarantine systems on SARS-CoV-2 transmission in Australia. Epidemics 2024; 47:100764. [PMID: 38552550 DOI: 10.1016/j.epidem.2024.100764] [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: 07/02/2023] [Revised: 12/15/2023] [Accepted: 03/14/2024] [Indexed: 06/17/2024] Open
Abstract
BACKGROUND Australian states and territories used test-trace-isolate-quarantine (TTIQ) systems extensively in their response to the COVID-19 pandemic in 2020-2021. We report on an analysis of Australian case data to estimate the impact of test-trace-isolate-quarantine systems on SARS-CoV-2 transmission. METHODS Our analysis uses a novel mathematical modelling framework and detailed surveillance data on COVID-19 cases including dates of infection and dates of isolation. First, we directly translate an empirical distribution of times from infection to isolation into reductions in potential for onward transmission during periods of relatively low caseloads (tens to hundreds of reported cases per day). We then apply a simulation approach, validated against case data, to assess the impact of case-initiated contact tracing on transmission during a period of relatively higher caseloads and system stress (up to thousands of cases per day). RESULTS We estimate that under relatively low caseloads in the state of New South Wales (tens of cases per day), TTIQ contributed to a 54% reduction in transmission. Under higher caseloads in the state of Victoria (hundreds of cases per day), TTIQ contributed to a 42% reduction in transmission. Our results also suggest that case-initiated contact tracing can support timely quarantine in times of system stress (thousands of cases per day). CONCLUSION Contact tracing systems for COVID-19 in Australia were highly effective and adaptable in supporting the national suppression strategy from 2020-21, prior to the emergence of the Omicron variant in November 2021. TTIQ systems were critical to the maintenance of the strong suppression strategy and were more effective when caseloads were (relatively) low.
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Affiliation(s)
- Freya M Shearer
- Infectious Disease Dynamics Unit, Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Australia; Telethon Kids Institute, Perth, Australia.
| | - James M McCaw
- Infectious Disease Dynamics Unit, Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Australia; School of Mathematics and Statistics, The University of Melbourne, Melbourne, Australia
| | - Gerard E Ryan
- Infectious Disease Dynamics Unit, Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Australia; Telethon Kids Institute, Perth, Australia
| | - Tianxiao Hao
- Infectious Disease Dynamics Unit, Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Australia; Telethon Kids Institute, Perth, Australia
| | | | - Michael J Lydeamore
- Department of Econometrics and Business Statistics, Monash University, Melbourne, Australia
| | - Logan Wu
- Walter and Eliza Hall Institute, Melbourne, Australia
| | - Kate Ward
- Public Health Response Branch, NSW Ministry of Health, Australia
| | - Sally Ellis
- Public Health Response Branch, NSW Ministry of Health, Australia
| | - James Wood
- School of Population Health, The University of New South Wales, Sydney, Australia
| | - Jodie McVernon
- Department of Infectious Diseases at the Peter Doherty Institute for Infection and Immunity, The University of Melbourne, Melbourne, Australia; Victorian Infectious Diseases Reference Laboratory Epidemiology Unit at the Peter Doherty Institute for Infection and Immunity, The University of Melbourne, Melbourne, Australia
| | - Nick Golding
- Infectious Disease Dynamics Unit, Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Australia; Telethon Kids Institute, Perth, Australia; Curtin University, Perth, Australia.
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Schmidt PW. Inference under superspreading: Determinants of SARS-CoV-2 transmission in Germany. Stat Med 2024; 43:1933-1954. [PMID: 38422989 DOI: 10.1002/sim.10046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Revised: 01/11/2024] [Accepted: 02/10/2024] [Indexed: 03/02/2024]
Abstract
Superspreading, under-reporting, reporting delay, and confounding complicate statistical inference on determinants of disease transmission. A model that accounts for these factors within a Bayesian framework is estimated using German Covid-19 surveillance data. Compartments based on date of symptom onset, location, and age group allow to identify age-specific changes in transmission, adjusting for weather, reported prevalence, and testing and tracing. Several factors were associated with a reduction in transmission: public awareness rising, information on local prevalence, testing and tracing, high temperature, stay-at-home orders, and restaurant closures. However, substantial uncertainty remains for other interventions including school closures and mandatory face coverings. The challenge of disentangling the effects of different determinants is discussed and examined through a simulation study. On a broader perspective, the study illustrates the potential of surveillance data with demographic information and date of symptom onset to improve inference in the presence of under-reporting and reporting delay.
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Kehoe AD, Mallhi AK, Barton CR, Martin HM, Turner CM, Hua X, Kwok KO, Chowell G, Fung ICH. SARS-CoV-2 Transmission in Alberta, British Columbia, and Ontario, Canada, January 2020-January 2022. Emerg Infect Dis 2024; 30:956-967. [PMID: 38666622 PMCID: PMC11060455 DOI: 10.3201/eid3005.230482] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/02/2024] Open
Abstract
We estimated COVID-19 transmission potential and case burden by variant type in Alberta, British Columbia, and Ontario, Canada, during January 23, 2020-January 27, 2022; we also estimated the effectiveness of public health interventions to reduce transmission. We estimated time-varying reproduction number (Rt) over 7-day sliding windows and nonoverlapping time-windows determined by timing of policy changes. We calculated incidence rate ratios (IRRs) for each variant and compared rates to determine differences in burden among provinces. Rt corresponding with emergence of the Delta variant increased in all 3 provinces; British Columbia had the largest increase, 43.85% (95% credible interval [CrI] 40.71%-46.84%). Across the study period, IRR was highest for Omicron (8.74 [95% CrI 8.71-8.77]) and burden highest in Alberta (IRR 1.80 [95% CrI 1.79-1.81]). Initiating public health interventions was associated with lower Rt and relaxing restrictions and emergence of new variants associated with increases in Rt.
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8
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Lee H, Lee G, Kim T, Kim S, Kim H, Lee S. Variability in the serial interval of COVID-19 in South Korea: a comprehensive analysis of age and regional influences. Front Public Health 2024; 12:1362909. [PMID: 38515590 PMCID: PMC10955094 DOI: 10.3389/fpubh.2024.1362909] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2023] [Accepted: 02/23/2024] [Indexed: 03/23/2024] Open
Abstract
Introduction Quantifying the transmissibility over time, particularly by region and age, using parameters such as serial interval and time-varying reproduction number, helps in formulating targeted interventions. Moreover, considering the impact of geographical factors on transmission provides valuable insights into the effectiveness of control measures. Methods Drawing on a comprehensive dataset of COVID-19 cases in South Korea, we analyzed transmission dynamics with a focus on age and regional variations. The dataset, compiled through the efforts of dedicated epidemiologists, includes information on symptom onset dates, enabling detailed investigations. The pandemic was divided into distinct phases, aligning with changes in policies, emergence of variants, and vaccination efforts. We analyzed various interventions such as social distancing, vaccination rates, school closures, and population density. Key parameters like serial interval, heatmaps, and time-varying reproduction numbers were used to quantify age and region-specific transmission trends. Results Analysis of transmission pairs within age groups highlighted the significant impact of school closure policies on the spread among individuals aged 0-19. This analysis also shed light on transmission dynamics within familial and educational settings. Changes in confirmed cases over time revealed a decrease in spread among individuals aged 65 and older, attributed to higher vaccination rates. Conversely, densely populated metropolitan areas experienced an increase in confirmed cases. Examination of time-varying reproduction numbers by region uncovered heterogeneity in transmission patterns, with regions implementing strict social distancing measures showing both increased confirmed cases and delayed spread, indicating the effectiveness of these policies. Discussion Our findings underscore the importance of evaluating and tailoring epidemic control policies based on key COVID-19 parameters. The analysis of social distancing measures, school closures, and vaccine impact provides valuable insights into controlling transmission. By quantifying the impact of these interventions on different age groups and regions, we contribute to the ongoing efforts to combat the COVID-19 pandemic effectively.
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Affiliation(s)
- Hyosun Lee
- Department of Applied Mathematics, Kyung Hee University, Yongin, Republic of Korea
| | - Gira Lee
- Humanitas College, Kyung Hee University, Seoul, Republic of Korea
| | - Tobhin Kim
- Department of Applied Mathematics, Kyung Hee University, Yongin, Republic of Korea
| | - Suhyeon Kim
- Department of Applied Mathematics, Kyung Hee University, Yongin, Republic of Korea
| | - Hyoeun Kim
- Department of Applied Mathematics, Kyung Hee University, Yongin, Republic of Korea
| | - Sunmi Lee
- Department of Applied Mathematics, Kyung Hee University, Yongin, Republic of Korea
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9
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Pung R, Russell TW, Kucharski AJ. Detecting changes in generation and serial intervals under varying pathogen biology, contact patterns and outbreak response. PLoS Comput Biol 2024; 20:e1011967. [PMID: 38517931 PMCID: PMC10990235 DOI: 10.1371/journal.pcbi.1011967] [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: 06/25/2023] [Revised: 04/03/2024] [Accepted: 03/04/2024] [Indexed: 03/24/2024] Open
Abstract
The epidemiological characteristics of SARS-CoV-2 transmission have changed over the pandemic due to emergence of new variants. A decrease in the generation or serial intervals would imply a shortened transmission timescale and, hence, outbreak response measures would need to expand at a faster rate. However, there are challenges in measuring these intervals. Alongside epidemiological changes, factors like varying delays in outbreak response, social contact patterns, dependence on the growth phase of an outbreak, and effects of exposure to multiple infectors can also influence measured generation or serial intervals. To guide real-time interpretation of variant data, we simulated concurrent changes in the aforementioned factors and estimated the statistical power to detect a change in the generation and serial interval. We compared our findings to the reported decrease or lack thereof in the generation and serial intervals of different SARS-CoV-2 variants. Our study helps to clarify contradictory outbreak observations and informs the required sample sizes under certain outbreak conditions to ensure that future studies of generation and serial intervals are adequately powered.
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Affiliation(s)
- Rachael Pung
- Ministry of Health, Singapore, Singapore
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Timothy W. Russell
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Adam J. Kucharski
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom
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Sunagawa J, Park H, Kim KS, Komorizono R, Choi S, Ramirez Torres L, Woo J, Jeong YD, Hart WS, Thompson RN, Aihara K, Iwami S, Yamaguchi R. Isolation may select for earlier and higher peak viral load but shorter duration in SARS-CoV-2 evolution. Nat Commun 2023; 14:7395. [PMID: 37989736 PMCID: PMC10663562 DOI: 10.1038/s41467-023-43043-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2023] [Accepted: 10/30/2023] [Indexed: 11/23/2023] Open
Abstract
During the COVID-19 pandemic, human behavior change as a result of nonpharmaceutical interventions such as isolation may have induced directional selection for viral evolution. By combining previously published empirical clinical data analysis and multi-level mathematical modeling, we find that the SARS-CoV-2 variants selected for as the virus evolved from the pre-Alpha to the Delta variant had earlier and higher peak in viral load dynamics but a shorter duration of infection. Selection for increased transmissibility shapes the viral load dynamics, and the isolation measure is likely to be a driver of these evolutionary transitions. In addition, we show that a decreased incubation period and an increased proportion of asymptomatic infection are also positively selected for as SARS-CoV-2 mutated to adapt to human behavior (i.e., Omicron variants). The quantitative information and predictions we present here can guide future responses in the potential arms race between pandemic interventions and viral evolution.
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Affiliation(s)
- Junya Sunagawa
- Department of Advanced Transdisciplinary Sciences, Hokkaido University, Sapporo, Hokkaido, Japan
| | - Hyeongki Park
- interdisciplinary Biology Laboratory (iBLab), Division of Natural Science, Graduate School of Science, Nagoya University, Nagoya, Japan
| | - Kwang Su Kim
- interdisciplinary Biology Laboratory (iBLab), Division of Natural Science, Graduate School of Science, Nagoya University, Nagoya, Japan
- Department of Scientific Computing, Pukyong National University, Busan, South Korea
- Department of Mathematics, Pusan National University, Busan, South Korea
| | - Ryo Komorizono
- Laboratory of RNA Viruses, Department of Virus Research, Institute for Life and Medical Sciences (LiMe), Kyoto University, Kyoto, Japan
| | - Sooyoun Choi
- interdisciplinary Biology Laboratory (iBLab), Division of Natural Science, Graduate School of Science, Nagoya University, Nagoya, Japan
- Department of Mathematics, Pusan National University, Busan, South Korea
| | - Lucia Ramirez Torres
- interdisciplinary Biology Laboratory (iBLab), Division of Natural Science, Graduate School of Science, Nagoya University, Nagoya, Japan
| | - Joohyeon Woo
- interdisciplinary Biology Laboratory (iBLab), Division of Natural Science, Graduate School of Science, Nagoya University, Nagoya, Japan
| | - Yong Dam Jeong
- interdisciplinary Biology Laboratory (iBLab), Division of Natural Science, Graduate School of Science, Nagoya University, Nagoya, Japan
- Department of Mathematics, Pusan National University, Busan, South Korea
| | - William S Hart
- Mathematical Institute, University of Oxford, Oxford, UK
| | - Robin N Thompson
- Mathematical Institute, University of Oxford, Oxford, UK
- Mathematics Institute, University of Warwick, Coventry, UK
- Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, University of Warwick, Coventry, UK
| | - Kazuyuki Aihara
- International Research Center for Neurointelligence, The University of Tokyo Institutes for Advanced Study, The University of Tokyo, Tokyo, Japan
| | - Shingo Iwami
- interdisciplinary Biology Laboratory (iBLab), Division of Natural Science, Graduate School of Science, Nagoya University, Nagoya, Japan.
- Institute of Mathematics for Industry, Kyushu University, Fukuoka, Japan.
- Institute for the Advanced Study of Human Biology (ASHBi), Kyoto University, Kyoto, Japan.
- Interdisciplinary Theoretical and Mathematical Sciences Program (iTHEMS), RIKEN, Saitama, Japan.
- NEXT-Ganken Program, Japanese Foundation for Cancer Research (JFCR), Tokyo, Japan.
- Science Groove Inc, Fukuoka, Japan.
| | - Ryo Yamaguchi
- Department of Advanced Transdisciplinary Sciences, Hokkaido University, Sapporo, Hokkaido, Japan.
- Department of Zoology & Biodiversity Research Centre, University of British Columbia, Vancouver, BC, Canada.
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11
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Meijers M, Ruchnewitz D, Eberhardt J, Łuksza M, Lässig M. Population immunity predicts evolutionary trajectories of SARS-CoV-2. Cell 2023; 186:5151-5164.e13. [PMID: 37875109 PMCID: PMC10964984 DOI: 10.1016/j.cell.2023.09.022] [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: 12/02/2022] [Revised: 08/26/2023] [Accepted: 09/21/2023] [Indexed: 10/26/2023]
Abstract
The large-scale evolution of the SARS-CoV-2 virus has been marked by rapid turnover of genetic clades. New variants show intrinsic changes, notably increased transmissibility, and antigenic changes that reduce cross-immunity induced by previous infections or vaccinations. How this functional variation shapes global evolution has remained unclear. Here, we establish a predictive fitness model for SARS-CoV-2 that integrates antigenic and intrinsic selection. The model is informed by tracking of time-resolved sequence data, epidemiological records, and cross-neutralization data of viral variants. Our inference shows that immune pressure, including contributions of vaccinations and previous infections, has become the dominant force driving the recent evolution of SARS-CoV-2. The fitness model can serve continued surveillance in two ways. First, it successfully predicts the short-term evolution of circulating strains and flags emerging variants likely to displace the previously predominant variant. Second, it predicts likely antigenic profiles of successful escape variants prior to their emergence.
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Affiliation(s)
- Matthijs Meijers
- Institute for Biological Physics, University of Cologne, Zülpicherstr. 77, 50937 Köln, Germany
| | - Denis Ruchnewitz
- Institute for Biological Physics, University of Cologne, Zülpicherstr. 77, 50937 Köln, Germany
| | - Jan Eberhardt
- Institute for Biological Physics, University of Cologne, Zülpicherstr. 77, 50937 Köln, Germany
| | - Marta Łuksza
- Tisch Cancer Institute, Departments of Oncological Sciences and Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Michael Lässig
- Institute for Biological Physics, University of Cologne, Zülpicherstr. 77, 50937 Köln, Germany.
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12
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Bonaldi C, Fouillet A, Sommen C, Lévy-Bruhl D, Paireau J. Monitoring the reproductive number of COVID-19 in France: Comparative estimates from three datasets. PLoS One 2023; 18:e0293585. [PMID: 37906577 PMCID: PMC10617725 DOI: 10.1371/journal.pone.0293585] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Accepted: 10/17/2023] [Indexed: 11/02/2023] Open
Abstract
BACKGROUND The effective reproduction number (Rt) quantifies the average number of secondary cases caused by one person with an infectious disease. Near-real-time monitoring of Rt during an outbreak is a major indicator used to monitor changes in disease transmission and assess the effectiveness of interventions. The estimation of Rt usually requires the identification of infected cases in the population, which can prove challenging with the available data, especially when asymptomatic people or with mild symptoms are not usually screened. The purpose of this study was to perform sensitivity analysis of Rt estimates for COVID-19 surveillance in France based on three data sources with different sensitivities and specificities for identifying infected cases. METHODS We applied a statistical method developed by Cori et al. to estimate Rt using (1) confirmed cases identified from positive virological tests in the population, (2) suspected cases recorded by a national network of emergency departments, and (3) COVID-19 hospital admissions recorded by a national administrative system to manage hospital organization. RESULTS Rt estimates in France from May 27, 2020, to August 12, 2022, showed similar temporal trends regardless of the dataset. Estimates based on the daily number of confirmed cases provided an earlier signal than the two other sources, with an average lag of 3 and 6 days for estimates based on emergency department visits and hospital admissions, respectively. CONCLUSION The COVID-19 experience confirmed that monitoring temporal changes in Rt was a key indicator to help the public health authorities control the outbreak in real time. However, gaining access to data on all infected people in the population in order to estimate Rt is not straightforward in practice. As this analysis has shown, the opportunity to use more readily available data to estimate Rt trends, provided that it is highly correlated with the spread of infection, provides a practical solution for monitoring the COVID-19 pandemic and indeed any other epidemic.
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Affiliation(s)
- Christophe Bonaldi
- Data Science Division, Santé Publique France, The French Public Health Agency, Saint Maurice, France
| | - Anne Fouillet
- Data Science Division, Santé Publique France, The French Public Health Agency, Saint Maurice, France
| | - Cécile Sommen
- Data Science Division, Santé Publique France, The French Public Health Agency, Saint Maurice, France
| | - Daniel Lévy-Bruhl
- Infectious Diseases Division, Santé Publique France, The French Public Health Agency, Saint Maurice, France
| | - Juliette Paireau
- Infectious Diseases Division, Santé Publique France, The French Public Health Agency, Saint Maurice, France
- Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, Université de Paris Cité, CNRS UMR 2000, Paris, France
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13
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Kremer C, Willem L, Boone J, Arrazola de Oñate W, Hammami N, Faes C, Hens N. Key performance indicators of COVID-19 contact tracing in Belgium from September 2020 to December 2021. PLoS One 2023; 18:e0292346. [PMID: 37862313 PMCID: PMC10588862 DOI: 10.1371/journal.pone.0292346] [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: 10/04/2022] [Accepted: 09/18/2023] [Indexed: 10/22/2023] Open
Abstract
The goal of tracing, testing, and quarantining contacts of infected individuals is to contain the spread of infectious diseases, a strategy widely used during the COVID-19 pandemic. However, limited research exists on the effectiveness of contact tracing, especially with regard to key performance indicators (KPIs), such as the proportion of cases arising from previously identified contacts. In our study, we analyzed contact tracing data from Belgium collected between September 2020 and December 2021 to assess the impact of contact tracing on SARS-CoV-2 transmission and understand its characteristics. Among confirmed cases involved in contact tracing in the Flemish and Brussels-Capital regions, 19.1% were previously identified as close contacts and were aware of prior exposure. These cases, referred to as 'known' to contact tracing operators, reported on average fewer close contacts compared to newly identified individuals (0.80 versus 1.05), resulting in fewer secondary cases (0.23 versus 0.28). Additionally, we calculated the secondary attack rate, representing infections per contact, which was on average lower for the 'known' cases (0.22 versus 0.25) between December 2020 and August 2021. These findings indicate the effectiveness of contact tracing in Belgium in reducing SARS-CoV-2 transmission. Although we were unable to quantify the exact number of prevented cases, our findings emphasize the importance of contact tracing as a public health measure. In addition, contact tracing data provide indications of potential shifts in transmission patterns among different age groups associated with emerging variants of concern and increasing vaccination rates.
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Affiliation(s)
- Cécile Kremer
- Interuniversity Institute for Biostatistics and statistical Bioinformatics, Data Science Institute, Hasselt University, Hasselt, Belgium
| | - Lander Willem
- Centre for Health Economics Research and Modelling Infectious Diseases, Vaccine and Infectious Disease Institute, University of Antwerp, Antwerp, Belgium
- Family Medicine and Population Health, University of Antwerp, Antwerp, Belgium
| | - Jorden Boone
- KPMG Advisory, Public Sector Practice, Zaventem, Belgium
| | - Wouter Arrazola de Oñate
- Belgian Lung and Tuberculosis Association, Brussels, Belgium
- Flemish Association for Respiratory Health and Tuberculosis, Leuven, Belgium
| | - Naïma Hammami
- Department of Infectious Disease Prevention and Control, Department of Care, Flemish Region, Brussels, Belgium
| | - Christel Faes
- Interuniversity Institute for Biostatistics and statistical Bioinformatics, Data Science Institute, Hasselt University, Hasselt, Belgium
| | - Niel Hens
- Interuniversity Institute for Biostatistics and statistical Bioinformatics, Data Science Institute, Hasselt University, Hasselt, Belgium
- Centre for Health Economics Research and Modelling Infectious Diseases, Vaccine and Infectious Disease Institute, University of Antwerp, Antwerp, Belgium
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14
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Bhatia S, Parag KV, Wardle J, Nash RK, Imai N, Elsland SLV, Lassmann B, Brownstein JS, Desai A, Herringer M, Sewalk K, Loeb SC, Ramatowski J, Cuomo-Dannenburg G, Jauneikaite E, Unwin HJT, Riley S, Ferguson N, Donnelly CA, Cori A, Nouvellet P. Retrospective evaluation of real-time estimates of global COVID-19 transmission trends and mortality forecasts. PLoS One 2023; 18:e0286199. [PMID: 37851661 PMCID: PMC10584190 DOI: 10.1371/journal.pone.0286199] [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: 07/22/2022] [Accepted: 05/11/2023] [Indexed: 10/20/2023] Open
Abstract
Since 8th March 2020 up to the time of writing, we have been producing near real-time weekly estimates of SARS-CoV-2 transmissibility and forecasts of deaths due to COVID-19 for all countries with evidence of sustained transmission, shared online. We also developed a novel heuristic to combine weekly estimates of transmissibility to produce forecasts over a 4-week horizon. Here we present a retrospective evaluation of the forecasts produced between 8th March to 29th November 2020 for 81 countries. We evaluated the robustness of the forecasts produced in real-time using relative error, coverage probability, and comparisons with null models. During the 39-week period covered by this study, both the short- and medium-term forecasts captured well the epidemic trajectory across different waves of COVID-19 infections with small relative errors over the forecast horizon. The model was well calibrated with 56.3% and 45.6% of the observations lying in the 50% Credible Interval in 1-week and 4-week ahead forecasts respectively. The retrospective evaluation of our models shows that simple transmission models calibrated using routine disease surveillance data can reliably capture the epidemic trajectory in multiple countries. The medium-term forecasts can be used in conjunction with the short-term forecasts of COVID-19 mortality as a useful planning tool as countries continue to relax public health measures.
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Affiliation(s)
- Sangeeta Bhatia
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, United Kingdom
- NIHR Health Protection Research Unit in Modelling and Health Economics, Modelling & Economics Unit, UK Health Security Agency, London, United Kingdom
| | - Kris V. Parag
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, United Kingdom
| | - Jack Wardle
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, United Kingdom
| | - Rebecca K. Nash
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, United Kingdom
| | - Natsuko Imai
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, United Kingdom
| | - Sabine L. Van Elsland
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, United Kingdom
| | - Britta Lassmann
- ProMED-mail, International Society for Infectious Diseases, Brookline, MA, United States of America
| | - John S. Brownstein
- Boston Children’s Hospital, Computational Epidemiology Lab, Boston, MA, United States of America
| | - Angel Desai
- ProMED-mail, International Society for Infectious Diseases, Brookline, MA, United States of America
- Division of Infectious Diseases, Department of Internal Medicine, University of California Davis, Sacramento, California, United States of America
| | - Mark Herringer
- Healthsites.io, The Global Healthsites Mapping Project, London, United Kingdom
| | - Kara Sewalk
- Boston Children’s Hospital, Computational Epidemiology Lab, Boston, MA, United States of America
| | - Sarah Claire Loeb
- ProMED-mail, International Society for Infectious Diseases, Brookline, MA, United States of America
| | - John Ramatowski
- ProMED-mail, International Society for Infectious Diseases, Brookline, MA, United States of America
| | - Gina Cuomo-Dannenburg
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, United Kingdom
| | - Elita Jauneikaite
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, United Kingdom
| | - H. Juliette T. Unwin
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, United Kingdom
| | - Steven Riley
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, United Kingdom
| | - Neil Ferguson
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, United Kingdom
| | - Christl A. Donnelly
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, United Kingdom
- Department of Statistics, University of Oxford, Oxford, United Kingdom
| | - Anne Cori
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, United Kingdom
| | - Pierre Nouvellet
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, United Kingdom
- School of Life Sciences, University of Sussex, Brighton, United Kingdom
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15
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Xu X, Wu Y, Kummer AG, Zhao Y, Hu Z, Wang Y, Liu H, Ajelli M, Yu H. Assessing changes in incubation period, serial interval, and generation time of SARS-CoV-2 variants of concern: a systematic review and meta-analysis. BMC Med 2023; 21:374. [PMID: 37775772 PMCID: PMC10541713 DOI: 10.1186/s12916-023-03070-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Accepted: 09/05/2023] [Indexed: 10/01/2023] Open
Abstract
BACKGROUND After the first COVID-19 wave caused by the ancestral lineage, the pandemic has been fueled from the continuous emergence of new SARS-CoV-2 variants. Understanding key time-to-event periods for each emerging variant of concern is critical as it can provide insights into the future trajectory of the virus and help inform outbreak preparedness and response planning. Here, we aim to examine how the incubation period, serial interval, and generation time have changed from the ancestral SARS-CoV-2 lineage to different variants of concern. METHODS We conducted a systematic review and meta-analysis that synthesized the estimates of incubation period, serial interval, and generation time (both realized and intrinsic) for the ancestral lineage, Alpha, Beta, and Omicron variants of SARS-CoV-2. RESULTS Our study included 280 records obtained from 147 household studies, contact tracing studies, or studies where epidemiological links were known. With each emerging variant, we found a progressive shortening of each of the analyzed key time-to-event periods, although we did not find statistically significant differences between the Omicron subvariants. We found that Omicron BA.1 had the shortest pooled estimates for the incubation period (3.49 days, 95% CI: 3.13-4.86 days), Omicron BA.5 for the serial interval (2.37 days, 95% CI: 1.71-3.04 days), and Omicron BA.1 for the realized generation time (2.99 days, 95% CI: 2.48-3.49 days). Only one estimate for the intrinsic generation time was available for Omicron subvariants: 6.84 days (95% CrI: 5.72-8.60 days) for Omicron BA.1. The ancestral lineage had the highest pooled estimates for each investigated key time-to-event period. We also observed shorter pooled estimates for the serial interval compared to the incubation period across the virus lineages. When pooling the estimates across different virus lineages, we found considerable heterogeneities (I2 > 80%; I2 refers to the percentage of total variation across studies that is due to heterogeneity rather than chance), possibly resulting from heterogeneities between the different study populations (e.g., deployed interventions, social behavior, demographic characteristics). CONCLUSIONS Our study supports the importance of conducting contact tracing and epidemiological investigations to monitor changes in SARS-CoV-2 transmission patterns. Our findings highlight a progressive shortening of the incubation period, serial interval, and generation time, which can lead to epidemics that spread faster, with larger peak incidence, and harder to control. We also consistently found a shorter serial interval than incubation period, suggesting that a key feature of SARS-CoV-2 is the potential for pre-symptomatic transmission. These observations are instrumental to plan for future COVID-19 waves.
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Affiliation(s)
- Xiangyanyu Xu
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Yanpeng Wu
- Shanghai Institute of Infectious Disease and Biosecurity, Fudan University, Shanghai, China
| | - Allisandra G Kummer
- Laboratory of Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomington, IN, USA
| | - Yuchen Zhao
- Shanghai Institute of Infectious Disease and Biosecurity, Fudan University, Shanghai, China
| | - Zexin Hu
- Shanghai Institute of Infectious Disease and Biosecurity, Fudan University, Shanghai, China
| | - Yan Wang
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Hengcong Liu
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Marco Ajelli
- Laboratory of Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomington, IN, USA.
| | - Hongjie Yu
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China.
- Shanghai Institute of Infectious Disease and Biosecurity, Fudan University, Shanghai, China.
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16
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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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17
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Wang Z, Wu P, Wang L, Li B, Liu Y, Ge Y, Wang R, Wang L, Tan H, Wu CH, Laine M, Salje H, Song H. Marginal effects of public health measures and COVID-19 disease burden in China: A large-scale modelling study. PLoS Comput Biol 2023; 19:e1011492. [PMID: 37721947 PMCID: PMC10538769 DOI: 10.1371/journal.pcbi.1011492] [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: 05/23/2023] [Revised: 09/28/2023] [Accepted: 09/05/2023] [Indexed: 09/20/2023] Open
Abstract
China had conducted some of the most stringent public health measures to control the spread of successive SARS-CoV-2 variants. However, the effectiveness of these measures and their impacts on the associated disease burden have rarely been quantitatively assessed at the national level. To address this gap, we developed a stochastic age-stratified metapopulation model that incorporates testing, contact tracing and isolation, based on 419 million travel movements among 366 Chinese cities. The study period for this model began from September 2022. The COVID-19 disease burden was evaluated, considering 8 types of underlying health conditions in the Chinese population. We identified the marginal effects between the testing speed and reduction in the epidemic duration. The findings suggest that assuming a vaccine coverage of 89%, the Omicron-like wave could be suppressed by 3-day interval population-level testing (PLT), while it would become endemic with 4-day interval PLT, and without testing, it would result in an epidemic. PLT conducted every 3 days would not only eliminate infections but also keep hospital bed occupancy at less than 29.46% (95% CI, 22.73-38.68%) of capacity for respiratory illness and ICU bed occupancy at less than 58.94% (95% CI, 45.70-76.90%) during an outbreak. Furthermore, the underlying health conditions would lead to an extra 2.35 (95% CI, 1.89-2.92) million hospital admissions and 0.16 (95% CI, 0.13-0.2) million ICU admissions. Our study provides insights into health preparedness to balance the disease burden and sustainability for a country with a population of billions.
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Affiliation(s)
- Zengmiao Wang
- State Key Laboratory of Remote Sensing Science, Center for Global Change and Public Health, Faculty of Geographical Science, Beijing Normal University, Beijing, China
| | - Peiyi Wu
- State Key Laboratory of Remote Sensing Science, Center for Global Change and Public Health, Faculty of Geographical Science, Beijing Normal University, Beijing, China
| | - Lin Wang
- Department of Genetics, University of Cambridge, Cambridge, United Kingdom
| | - Bingying Li
- State Key Laboratory of Remote Sensing Science, Center for Global Change and Public Health, Faculty of Geographical Science, Beijing Normal University, Beijing, China
| | - Yonghong Liu
- Beijing Center for Disease Prevention and Control, Beijing, China
| | - Yuxi Ge
- State Key Laboratory of Remote Sensing Science, Center for Global Change and Public Health, Faculty of Geographical Science, Beijing Normal University, Beijing, China
| | - Ruixue Wang
- State Key Laboratory of Remote Sensing Science, Center for Global Change and Public Health, Faculty of Geographical Science, Beijing Normal University, Beijing, China
| | - Ligui Wang
- Center of Disease Control and Prevention, PLA, Beijing, China
| | - Hua Tan
- Translational and Functional Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Chieh-Hsi Wu
- Mathematical Sciences, University of Southampton, Southampton, United Kingdom
| | - Marko Laine
- Finnish Meteorological Institute, Meteorological Research Unit, Helsinki, Finland
| | - Henrik Salje
- Department of Genetics, University of Cambridge, Cambridge, United Kingdom
| | - Hongbin Song
- Center of Disease Control and Prevention, PLA, Beijing, China
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18
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Geismar C, Nguyen V, Fragaszy E, Shrotri M, Navaratnam AMD, Beale S, Byrne TE, Fong WLE, Yavlinsky A, Kovar J, Hoskins S, Braithwaite I, Aldridge RW, Hayward AC, White PJ, Jombart T, Cori A. Bayesian reconstruction of SARS-CoV-2 transmissions highlights substantial proportion of negative serial intervals. Epidemics 2023; 44:100713. [PMID: 37579586 DOI: 10.1016/j.epidem.2023.100713] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 07/25/2023] [Accepted: 07/31/2023] [Indexed: 08/16/2023] Open
Abstract
BACKGROUND The serial interval is a key epidemiological measure that quantifies the time between the onset of symptoms in an infector-infectee pair. It indicates how quickly new generations of cases appear, thus informing on the speed of an epidemic. Estimating the serial interval requires to identify pairs of infectors and infectees. Yet, most studies fail to assess the direction of transmission between cases and assume that the order of infections - and thus transmissions - strictly follows the order of symptom onsets, thereby imposing serial intervals to be positive. Because of the long and highly variable incubation period of SARS-CoV-2, this may not always be true (i.e an infectee may show symptoms before their infector) and negative serial intervals may occur. This study aims to estimate the serial interval of different SARS-CoV-2 variants whilst accounting for negative serial intervals. METHODS This analysis included 5 842 symptomatic individuals with confirmed SARS-CoV-2 infection amongst 2 579 households from September 2020 to August 2022 across England & Wales. We used a Bayesian framework to infer who infected whom by exploring all transmission trees compatible with the observed dates of symptoms, based on a wide range of incubation period and generation time distributions compatible with estimates reported in the literature. Serial intervals were derived from the reconstructed transmission pairs, stratified by variants. RESULTS We estimated that 22% (95% credible interval (CrI) 8-32%) of serial interval values are negative across all VOC. The mean serial interval was shortest for Omicron BA5 (2.02 days, 1.26-2.84) and longest for Alpha (3.37 days, 2.52-4.04). CONCLUSIONS This study highlights the large proportion of negative serial intervals across SARS-CoV-2 variants. Because the serial interval is widely used to estimate transmissibility and forecast cases, these results may have critical implications for epidemic control.
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Affiliation(s)
- Cyril Geismar
- MRC Centre for Global Infectious Disease Analysis and NIHR Health Protection Research Unit in Modelling and Health Economics, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, UK; Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK.
| | - Vincent Nguyen
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Ellen Fragaszy
- Institute of Epidemiology and Health Care, University College London, London, UK; Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - Madhumita Shrotri
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Annalan M D Navaratnam
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Sarah Beale
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK; Institute of Epidemiology and Health Care, University College London, London, UK
| | - Thomas E Byrne
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Wing Lam Erica Fong
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Alexei Yavlinsky
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Jana Kovar
- Institute of Epidemiology and Health Care, University College London, London, UK
| | - Susan Hoskins
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Isobel Braithwaite
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Robert W Aldridge
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK
| | - Andrew C Hayward
- Institute of Epidemiology and Health Care, University College London, London, UK
| | - Peter J White
- MRC Centre for Global Infectious Disease Analysis and NIHR Health Protection Research Unit in Modelling and Health Economics, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, UK
| | - Thibaut Jombart
- MRC Centre for Global Infectious Disease Analysis and NIHR Health Protection Research Unit in Modelling and Health Economics, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, UK
| | - Anne Cori
- MRC Centre for Global Infectious Disease Analysis and NIHR Health Protection Research Unit in Modelling and Health Economics, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, UK
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19
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Nash RK, Bhatt S, Cori A, Nouvellet P. Estimating the epidemic reproduction number from temporally aggregated incidence data: A statistical modelling approach and software tool. PLoS Comput Biol 2023; 19:e1011439. [PMID: 37639484 PMCID: PMC10491397 DOI: 10.1371/journal.pcbi.1011439] [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: 12/14/2022] [Revised: 09/08/2023] [Accepted: 08/18/2023] [Indexed: 08/31/2023] Open
Abstract
The time-varying reproduction number (Rt) is an important measure of epidemic transmissibility that directly informs policy decisions and the optimisation of control measures. EpiEstim is a widely used opensource software tool that uses case incidence and the serial interval (SI, time between symptoms in a case and their infector) to estimate Rt in real-time. The incidence and the SI distribution must be provided at the same temporal resolution, which can limit the applicability of EpiEstim and other similar methods, e.g. for contexts where the time window of incidence reporting is longer than the mean SI. In the EpiEstim R package, we implement an expectation-maximisation algorithm to reconstruct daily incidence from temporally aggregated data, from which Rt can then be estimated. We assess the validity of our method using an extensive simulation study and apply it to COVID-19 and influenza data. For all datasets, the influence of intra-weekly variability in reported data was mitigated by using aggregated weekly data. Rt estimated on weekly sliding windows using incidence reconstructed from weekly data was strongly correlated with estimates from the original daily data. The simulation study revealed that Rt was well estimated in all scenarios and regardless of the temporal aggregation of the data. In the presence of weekend effects, Rt estimates from reconstructed data were more successful at recovering the true value of Rt than those obtained from reported daily data. These results show that this novel method allows Rt to be successfully recovered from aggregated data using a simple approach with very few data requirements. Additionally, by removing administrative noise when daily incidence data are reconstructed, the accuracy of Rt estimates can be improved.
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Affiliation(s)
- Rebecca K. Nash
- MRC Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, London, United Kingdom
| | - Samir Bhatt
- MRC Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, London, United Kingdom
- Section of Epidemiology, Department of Public Health, University of Copenhagen, Copenhagen, Denmark
| | - Anne Cori
- MRC Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, London, United Kingdom
| | - Pierre Nouvellet
- MRC Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, London, United Kingdom
- School of Life Sciences, University of Sussex, Brighton, United Kingdom
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20
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Lippiello E, Petrillo G, Baccari S, de Arcangelis L. Estimating generation time of SARS-CoV-2 variants in Italy from the daily incidence rate. Sci Rep 2023; 13:11543. [PMID: 37460598 DOI: 10.1038/s41598-023-38327-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Accepted: 07/06/2023] [Indexed: 07/20/2023] Open
Abstract
The identification of the transmission parameters of a virus is fundamental to identify the optimal public health strategy. These parameters can present significant changes over time caused by genetic mutations or viral recombination, making their continuous monitoring fundamental. Here we present a method, suitable for this task, which uses as unique information the daily number of reported cases. The method is based on a time since infection model where transmission parameters are obtained by means of an efficient maximization procedure of the likelihood. Applying the method to SARS-CoV-2 data in Italy, we find an average generation time [Formula: see text] days, during the temporal window when the majority of infections can be attributed to the Omicron variants. At the same time we find a significantly larger value [Formula: see text] days, in the temporal window when spreading was dominated by the Delta variant. We are also able to show that the presence of the Omicron variant, characterized by a shorter [Formula: see text], was already detectable in the first weeks of December 2021, in full agreement with results provided by sequences of SARS-CoV-2 genomes reported in national databases. Our results therefore show that the novel approach can indicate the existence of virus variants, resulting particularly useful in situations when information about genomic sequencing is not yet available. At the same time, we find that the standard deviation of the generation time does not significantly change among variants.
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Affiliation(s)
- Eugenio Lippiello
- Department of Mathematics and Physics, University of Campania "L. Vanvitelli", Viale Lincoln, 5, 81100, Caserta, Italy.
| | - Giuseppe Petrillo
- The Institute of Statistical Mathematics, Research Organization of Information and Systems, Tokyo, Japan
| | - Silvio Baccari
- Department of Mathematics and Physics, University of Campania "L. Vanvitelli", Viale Lincoln, 5, 81100, Caserta, Italy
| | - Lucilla de Arcangelis
- Department of Mathematics and Physics, University of Campania "L. Vanvitelli", Viale Lincoln, 5, 81100, Caserta, Italy
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21
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Kim T, Lee H, Kim S, Kim C, Son H, Lee S. Improved time-varying reproduction numbers using the generation interval for COVID-19. Front Public Health 2023; 11:1185854. [PMID: 37457248 PMCID: PMC10348824 DOI: 10.3389/fpubh.2023.1185854] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Accepted: 06/08/2023] [Indexed: 07/18/2023] Open
Abstract
Estimating key epidemiological parameters, such as incubation period, serial interval (SI), generation interval (GI) and latent period, is essential to quantify the transmissibility and effects of various interventions of COVID-19. These key parameters play a critical role in quantifying the basic reproduction number. With the hard work of epidemiological investigators in South Korea, estimating these key parameters has become possible based on infector-infectee surveillance data of COVID-19 between February 2020 and April 2021. Herein, the mean incubation period was estimated to be 4.9 days (95% CI: 4.2, 5.7) and the mean generation interval was estimated to be 4.3 days (95% CI: 4.2, 4.4). The mean serial interval was estimated to be 4.3, with a standard deviation of 4.2. It is also revealed that the proportion of presymptomatic transmission was ~57%, which indicates the potential risk of transmission before the disease onset. We compared the time-varying reproduction number based on GI and SI and found that the time-varying reproduction number based on GI may result in a larger estimation of Rt, which refers to the COVID-19 transmission potential around the rapid increase of cases. This highlights the importance of considering presymptomatic transmission and generation intervals when estimating the time-varying reproduction number.
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Affiliation(s)
- Tobhin Kim
- Department of Applied Mathematics, Kyung Hee University, Yongin, Republic of Korea
| | - Hyojung Lee
- Department of Statistics, Kyungpook National University, Daegu, Republic of Korea
| | - Sungchan Kim
- Department of Applied Mathematics, Kyung Hee University, Yongin, Republic of Korea
| | - Changhoon Kim
- Department of Preventive Medicine, College of Medicine, Pusan National University, Busan, Republic of Korea
- Busan Center for Infectious Disease Control and Prevention, Pusan National University Hospital, Busan, Republic of Korea
| | - Hyunjin Son
- Busan Center for Infectious Disease Control and Prevention, Pusan National University Hospital, Busan, Republic of Korea
- Department of Preventive Medicine, College of Medicine, Dong-A University, Busan, Republic of Korea
| | - Sunmi Lee
- Department of Applied Mathematics, Kyung Hee University, Yongin, Republic of Korea
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Madewell ZJ, Yang Y, Longini IM, Halloran ME, Vespignani A, Dean NE. Rapid review and meta-analysis of serial intervals for SARS-CoV-2 Delta and Omicron variants. BMC Infect Dis 2023; 23:429. [PMID: 37365505 PMCID: PMC10291789 DOI: 10.1186/s12879-023-08407-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Accepted: 06/20/2023] [Indexed: 06/28/2023] Open
Abstract
BACKGROUND The serial interval is the period of time between symptom onset in the primary case and symptom onset in the secondary case. Understanding the serial interval is important for determining transmission dynamics of infectious diseases like COVID-19, including the reproduction number and secondary attack rates, which could influence control measures. Early meta-analyses of COVID-19 reported serial intervals of 5.2 days (95% CI: 4.9-5.5) for the original wild-type variant and 5.2 days (95% CI: 4.87-5.47) for Alpha variant. The serial interval has been shown to decrease over the course of an epidemic for other respiratory diseases, which may be due to accumulating viral mutations and implementation of more effective nonpharmaceutical interventions. We therefore aggregated the literature to estimate serial intervals for Delta and Omicron variants. METHODS This study followed Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines. A systematic literature search was conducted of PubMed, Scopus, Cochrane Library, ScienceDirect, and preprint server medRxiv for articles published from April 4, 2021, through May 23, 2023. Search terms were: ("serial interval" or "generation time"), ("Omicron" or "Delta"), and ("SARS-CoV-2" or "COVID-19"). Meta-analyses were done for Delta and Omicron variants using a restricted maximum-likelihood estimator model with a random effect for each study. Pooled average estimates and 95% confidence intervals (95% CI) are reported. RESULTS There were 46,648 primary/secondary case pairs included for the meta-analysis of Delta and 18,324 for Omicron. Mean serial interval for included studies ranged from 2.3-5.8 days for Delta and 2.1-4.8 days for Omicron. The pooled mean serial interval for Delta was 3.9 days (95% CI: 3.4-4.3) (20 studies) and Omicron was 3.2 days (95% CI: 2.9-3.5) (20 studies). Mean estimated serial interval for BA.1 was 3.3 days (95% CI: 2.8-3.7) (11 studies), BA.2 was 2.9 days (95% CI: 2.7-3.1) (six studies), and BA.5 was 2.3 days (95% CI: 1.6-3.1) (three studies). CONCLUSIONS Serial interval estimates for Delta and Omicron were shorter than ancestral SARS-CoV-2 variants. More recent Omicron subvariants had even shorter serial intervals suggesting serial intervals may be shortening over time. This suggests more rapid transmission from one generation of cases to the next, consistent with the observed faster growth dynamic of these variants compared to their ancestors. Additional changes to the serial interval may occur as SARS-CoV-2 continues to circulate and evolve. Changes to population immunity (due to infection and/or vaccination) may further modify it.
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Affiliation(s)
- Zachary J Madewell
- Department of Biostatistics, University of Florida, Gainesville, FL, USA.
| | - Yang Yang
- Department of Statistics, University of Georgia, Athens, GA, USA
| | - Ira M Longini
- Department of Biostatistics, University of Florida, Gainesville, FL, USA
| | - M Elizabeth Halloran
- Department of Biostatistics, University of Washington, Seattle, WA, USA
- Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Alessandro Vespignani
- Laboratory for the Modeling of Biological and Socio-Technical Systems, Northeastern University, Boston, MA, USA
| | - Natalie E Dean
- Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA, USA
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23
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Gozzi N, Chinazzi M, Dean NE, Longini IM, Halloran ME, Perra N, Vespignani A. Estimating the impact of COVID-19 vaccine inequities: a modeling study. Nat Commun 2023; 14:3272. [PMID: 37277329 DOI: 10.1038/s41467-023-39098-w] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Accepted: 05/25/2023] [Indexed: 06/07/2023] Open
Abstract
Access to COVID-19 vaccines on the global scale has been drastically hindered by structural socio-economic disparities. Here, we develop a data-driven, age-stratified epidemic model to evaluate the effects of COVID-19 vaccine inequities in twenty lower middle and low income countries (LMIC) selected from all WHO regions. We investigate and quantify the potential effects of higher or earlier doses availability. In doing so, we focus on the crucial initial months of vaccine distribution and administration, exploring counterfactual scenarios where we assume the same per capita daily vaccination rate reported in selected high income countries. We estimate that more than 50% of deaths (min-max range: [54-94%]) that occurred in the analyzed countries could have been averted. We further consider scenarios where LMIC had similarly early access to vaccine doses as high income countries. Even without increasing the number of doses, we estimate an important fraction of deaths (min-max range: [6-50%]) could have been averted. In the absence of the availability of high-income countries, the model suggests that additional non-pharmaceutical interventions inducing a considerable relative decrease of transmissibility (min-max range: [15-70%]) would have been required to offset the lack of vaccines. Overall, our results quantify the negative impacts of vaccine inequities and underscore the need for intensified global efforts devoted to provide faster access to vaccine programs in low and lower-middle-income countries.
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Affiliation(s)
- Nicolò Gozzi
- Networks and Urban Systems Centre, University of Greenwich, London, UK
- ISI Foundation, Turin, Italy
| | - Matteo Chinazzi
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA, USA
| | - Natalie E Dean
- Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA, USA
| | - Ira M Longini
- Department of Biostatistics, College of Public Health and Health Professions, University of Florida, Gainesville, FL, USA
| | - M Elizabeth Halloran
- Fred Hutchinson Cancer Center, Seattle, WA, USA
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Nicola Perra
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA, USA.
- School of Mathematical Sciences, Queen Mary University, London, UK.
| | - Alessandro Vespignani
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA, USA
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Schneckenreither G, Herrmann L, Reisenhofer R, Popper N, Grohs P. Assessing the heterogeneity in the transmission of infectious diseases from time series of epidemiological data. PLoS One 2023; 18:e0286012. [PMID: 37253038 PMCID: PMC10228818 DOI: 10.1371/journal.pone.0286012] [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: 10/06/2022] [Accepted: 05/05/2023] [Indexed: 06/01/2023] Open
Abstract
Structural features and the heterogeneity of disease transmissions play an essential role in the dynamics of epidemic spread. But these aspects can not completely be assessed from aggregate data or macroscopic indicators such as the effective reproduction number. We propose in this paper an index of effective aggregate dispersion (EffDI) that indicates the significance of infection clusters and superspreading events in the progression of outbreaks by carefully measuring the level of relative stochasticity in time series of reported case numbers using a specially crafted statistical model for reproduction. This allows to detect potential transitions from predominantly clustered spreading to a diffusive regime with diminishing significance of singular clusters, which can be a decisive turning point in the progression of outbreaks and relevant in the planning of containment measures. We evaluate EffDI for SARS-CoV-2 case data in different countries and compare the results with a quantifier for the socio-demographic heterogeneity in disease transmissions in a case study to substantiate that EffDI qualifies as a measure for the heterogeneity in transmission dynamics.
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Affiliation(s)
- Günter Schneckenreither
- Institute of Information Systems Engineering, TU Wien, Vienna, Austria
- dwh GmbH, Vienna, Austria
- Institute of Statistics and Mathematical Methods in Economics, TU Wien, Vienna, Austria
| | - Lukas Herrmann
- Johann Radon Institute for Computational and Applied Mathematics, Austrian Academy of Sciences, Linz, Austria
| | | | - Niki Popper
- Institute of Information Systems Engineering, TU Wien, Vienna, Austria
- dwh GmbH, Vienna, Austria
- Institute of Statistics and Mathematical Methods in Economics, TU Wien, Vienna, Austria
| | - Philipp Grohs
- Johann Radon Institute for Computational and Applied Mathematics, Austrian Academy of Sciences, Linz, Austria
- Faculty of Mathematics, University of Vienna, Vienna, Austria
- Research Network Data Science, University of Vienna, Vienna, Austria
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Shi H, Wang J, Cheng J, Qi X, Ji H, Struchiner CJ, Villela DAM, Karamov EV, Turgiev AS. Big data technology in infectious diseases modeling, simulation, and prediction after the COVID-19 outbreak. INTELLIGENT MEDICINE 2023; 3:85-96. [PMID: 36694623 PMCID: PMC9851724 DOI: 10.1016/j.imed.2023.01.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Revised: 12/06/2022] [Accepted: 01/04/2023] [Indexed: 06/17/2023]
Abstract
After the outbreak of COVID-19, the interaction of infectious disease systems and social systems has challenged traditional infectious disease modeling methods. Starting from the research purpose and data, researchers improved the structure and data of the compartment model or used agents and artificial intelligence based models to solve epidemiological problems. In terms of modeling methods, the researchers use compartment subdivision, dynamic parameters, agent-based model methods, and artificial intelligence related methods. In terms of factors studied, the researchers studied 6 categories: human mobility, nonpharmaceutical interventions (NPIs), ages, medical resources, human response, and vaccine. The researchers completed the study of factors through modeling methods to quantitatively analyze the impact of social systems and put forward their suggestions for the future transmission status of infectious diseases and prevention and control strategies. This review started with a research structure of research purpose, factor, data, model, and conclusion. Focusing on the post-COVID-19 infectious disease prediction simulation research, this study summarized various improvement methods and analyzes matching improvements for various specific research purposes.
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Affiliation(s)
- Honghao Shi
- School of Computer Science and Engineering, Beihang University, Beijing 100191, China
| | - Jingyuan Wang
- School of Computer Science and Engineering, Beihang University, Beijing 100191, China
| | - Jiawei Cheng
- School of Computer Science and Engineering, Beihang University, Beijing 100191, China
| | - Xiaopeng Qi
- Center for Global Public Health, Chinese Center for Disease Control and Prevention, Beijing 102211, China
| | - Hanran Ji
- Center for Global Public Health, Chinese Center for Disease Control and Prevention, Beijing 102211, China
| | - Claudio J Struchiner
- Fundação Getúlio Vargas, Rio de Janeiro, Brazil
- Instituto de Medicina Social Hesio Cordeiro, Universidade do Estado do Rio de Janeiro, Rio de Janeiro, Brazil
| | - Daniel AM Villela
- Programa de Computação Científica, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil
| | - Eduard V Karamov
- Gamaleya National Research Center for Epidemiology and Microbiology of the Russian Ministry of Health, Russia
- National Medical Research Center of Phthisiopulmonology and Infectious Diseases of the Russian Ministry of Health, Russia
| | - Ali S Turgiev
- Gamaleya National Research Center for Epidemiology and Microbiology of the Russian Ministry of Health, Russia
- National Medical Research Center of Phthisiopulmonology and Infectious Diseases of the Russian Ministry of Health, Russia
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26
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Wasnik RN, Vincze F, Földvári A, Pálinkás A, Sándor J. Effectiveness of and Inequalities in COVID-19 Epidemic Control Strategies in Hungary: A Nationwide Cross-Sectional Study. Healthcare (Basel) 2023; 11:healthcare11091220. [PMID: 37174762 PMCID: PMC10178097 DOI: 10.3390/healthcare11091220] [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: 03/24/2023] [Revised: 04/21/2023] [Accepted: 04/23/2023] [Indexed: 05/15/2023] Open
Abstract
INTRODUCTION Before the mass vaccination, epidemiological control measures were the only means of containing the COVID-19 epidemic. Their effectiveness determined the consequences of the COVID-19 epidemic. Our study evaluated the impact of sociodemographic, lifestyle, and clinical factors on patient-reported epidemiological control measures. METHODS A nationwide representative sample of 1008 randomly selected adults were interviewed in person between 15 March and 30 May 2021. The prevalence of test-confirmed SARS-CoV-2 infection was 12.1%, of testing was 33.7%, and of contact tracing among test-confirmed infected subjects was 67.9%. The vaccination coverage was 52.4%. RESULTS According to the multivariable logistic regression models, the occurrence of infection was not influenced by sociodemographic and lifestyle factors or by the presence of chronic disease. Testing was more frequent among middle-aged adults (aOR = 1.53, 95% CI 1.10-2.13) and employed adults (aOR = 2.06, 95% CI 1.42-3.00), and was more frequent among adults with a higher education (aORsecondary = 1.93, 95% CI 1.20-3.13; aORtertiary = 3.19, 95% CI 1.81-5.63). Contact tracing was more frequently implemented among middle-aged (aOR41-7y = 3.33, 95% CI 1.17-9.45) and employed (aOR = 4.58, 95% CI 1.38-15.22), and those with chronic diseases (aOR = 5.92, 95% CI 1.56-22.47). Positive correlation was observed between age groups and vaccination frequency (aOR41-70y = 2.94, 95% CI 2.09-4.15; aOR71+y = 14.52, 95% CI 7.33-28.77). Higher than primary education (aORsecondary = 1.69, 95% CI 1.08-2.63; aORtertiary = 4.36, 95% CI 2.46-7.73) and the presence of a chronic disease (aOR = 2.58, 95% CI 1.75-3.80) positively impacted vaccination. Regular smoking was inversely correlated with vaccination (aOR = 0.60; 95% CI 0.44-0.83). CONCLUSIONS The survey indicated that testing, contact tracing, and vaccination were seriously influenced by socioeconomic position; less so by chronic disease prevalence and very minimally by lifestyle. The etiological role of socioeconomic inequalities in epidemic measure implementation likely generated socioeconomic inequality in COVID-19-related complication and death rates.
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Affiliation(s)
- Rahul Naresh Wasnik
- Department of Public Health and Epidemiology, Faculty of Medicine, University of Debrecen, H-4002 Debrecen, Hungary
- Doctoral School of Health Sciences, University of Debrecen, H-4002 Debrecen, Hungary
| | - Ferenc Vincze
- Department of Public Health and Epidemiology, Faculty of Medicine, University of Debrecen, H-4002 Debrecen, Hungary
| | - Anett Földvári
- Doctoral School of Health Sciences, University of Debrecen, H-4002 Debrecen, Hungary
| | - Anita Pálinkás
- ELKH-DE Public Health Research Group, Department of Public Health and Epidemiology, Faculty of Medicine, University of Debrecen, H-4002 Debrecen, Hungary
| | - János Sándor
- Department of Public Health and Epidemiology, Faculty of Medicine, University of Debrecen, H-4002 Debrecen, Hungary
- ELKH-DE Public Health Research Group, Department of Public Health and Epidemiology, Faculty of Medicine, University of Debrecen, H-4002 Debrecen, Hungary
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Cheng K, Guo Z, Yan M, Fan Y, Liu X, Yang Y, Gao F, Xie F, Wang PP, Yao W, Wang Q, Wang W. The value of discharged case fatality rate in estimating the severity and epidemic trend of COVID-19 in China: a novel epidemiological study. ZEITSCHRIFT FUR GESUNDHEITSWISSENSCHAFTEN = JOURNAL OF PUBLIC HEALTH 2023:1-8. [PMID: 37361283 PMCID: PMC10069734 DOI: 10.1007/s10389-023-01895-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/12/2022] [Accepted: 03/17/2023] [Indexed: 04/05/2023]
Abstract
Aim The main objective of this study was to explore the value of the discharged case fatality rate (DCFR) in estimating the severity and epidemic trend of COVID-19 in China. Subjects and methods Epidemiological data on COVID-19 in China and Hubei Province were obtained from the National Health Commission of the People's Republic of China from January 20, 2020, to March 31, 2020. The number of daily new confirmed cases, daily confirmed deaths, daily recovered cases, the proportion of daily deaths and total deaths of discharged cases were collected, and the total discharge case fatality rate (tDCFR), daily discharge case fatality rate (dDCFR), and stage-discharge case fatality rate (sDCFR) were calculated. We used the R software (version 3.6.3, R core team) to apply a trimmed exact linear time method to search for changes in the mean and variance of dDCFR in order to estimate the pandemic phase from dDCFR. Results The tDCFR of COVID-19 in China was 4.16% until March 31, 2020. According to the pattern of dDCFR, the pandemic was divided into four phases: the transmission phase (from January 20 to February 2), the epidemic phase (from February 3 to February 14), the decline phase (from February 15 to February 22), and the sporadic phase (from February 23 to March 31). The sDCFR for these four phases was 43.18% (CI 39.82-46.54%), 13.23% (CI 12.52-13.94%), 5.86% (CI 5.49-6.22%), and 1.61% (CI 1.50-1.72%), respectively. Conclusion DCFR has great value in assessing the severity and epidemic trend of COVID-19. Supplementary Information The online version contains supplementary material available at 10.1007/s10389-023-01895-4.
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Affiliation(s)
- Kexuan Cheng
- Department of Occupational and Environmental Health, College of Public Health, Zhengzhou University, 100 Kexue Avenue, Zhengzhou, 450001 Henan China
- The Key Laboratory of Nano medicine and Health Inspection of Zhengzhou, Zhengzhou, 450001 Henan China
| | - Zhifeng Guo
- Department of Occupational and Environmental Health, College of Public Health, Zhengzhou University, 100 Kexue Avenue, Zhengzhou, 450001 Henan China
- The Key Laboratory of Nano medicine and Health Inspection of Zhengzhou, Zhengzhou, 450001 Henan China
| | - Mengqing Yan
- Department of Occupational and Environmental Health, College of Public Health, Zhengzhou University, 100 Kexue Avenue, Zhengzhou, 450001 Henan China
- The Key Laboratory of Nano medicine and Health Inspection of Zhengzhou, Zhengzhou, 450001 Henan China
| | - Yahui Fan
- Department of Occupational and Environmental Health, College of Public Health, Zhengzhou University, 100 Kexue Avenue, Zhengzhou, 450001 Henan China
- The Key Laboratory of Nano medicine and Health Inspection of Zhengzhou, Zhengzhou, 450001 Henan China
| | - Xiaohua Liu
- Department of Occupational and Environmental Health, College of Public Health, Zhengzhou University, 100 Kexue Avenue, Zhengzhou, 450001 Henan China
- The Key Laboratory of Nano medicine and Health Inspection of Zhengzhou, Zhengzhou, 450001 Henan China
| | - Yongli Yang
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, China
| | - Fuxiao Gao
- Center for New Immigrant Wellbeing, Markham, L3R 6G2 ON Canada
| | - Fangli Xie
- Durham Region Health Department, Durham, Ontario L1N0B7 Canada
| | - Peizhong Peter Wang
- Division of Community Health and Humanities, Faculty of Medicine, Memorial University of Newfoundland, St. John’s, NL A1C5S7 Canada
| | - Wu Yao
- Department of Occupational and Environmental Health, College of Public Health, Zhengzhou University, 100 Kexue Avenue, Zhengzhou, 450001 Henan China
| | - Qi Wang
- Department of Occupational and Environmental Health, College of Public Health, Zhengzhou University, 100 Kexue Avenue, Zhengzhou, 450001 Henan China
- Division of Community Health and Humanities, Faculty of Medicine, Memorial University of Newfoundland, St. John’s, NL A1C5S7 Canada
| | - Wei Wang
- Department of Occupational and Environmental Health, College of Public Health, Zhengzhou University, 100 Kexue Avenue, Zhengzhou, 450001 Henan China
- The Key Laboratory of Nano medicine and Health Inspection of Zhengzhou, Zhengzhou, 450001 Henan China
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Dai C, Zhou D, Gao B, Wang K. A new method for the joint estimation of instantaneous reproductive number and serial interval during epidemics. PLoS Comput Biol 2023; 19:e1011021. [PMID: 37000844 PMCID: PMC10096265 DOI: 10.1371/journal.pcbi.1011021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Revised: 04/12/2023] [Accepted: 03/09/2023] [Indexed: 04/03/2023] Open
Abstract
Although some methods for estimating the instantaneous reproductive number during epidemics have been developed, the existing frameworks usually require information on the distribution of the serial interval and/or additional contact tracing data. However, in the case of outbreaks of emerging infectious diseases with an unknown natural history or undetermined characteristics, the serial interval and/or contact tracing data are often not available, resulting in inaccurate estimates for this quantity. In the present study, a new framework was specifically designed for joint estimates of the instantaneous reproductive number and serial interval. Concretely, a likelihood function for the two quantities was first introduced. Then, the instantaneous reproductive number and the serial interval were modeled parametrically as a function of time using the interpolation method and a known traditional distribution, respectively. Using the Bayesian information criterion and the Markov Chain Monte Carlo method, we ultimately obtained their estimates and distribution. The simulation study revealed that our estimates of the two quantities were consistent with the ground truth. Seven data sets of historical epidemics were considered and further verified the robust performance of our method. Therefore, to some extent, even if we know only the daily incidence, our method can accurately estimate the instantaneous reproductive number and serial interval to provide crucial information for policymakers to design appropriate prevention and control interventions during epidemics.
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Kanatani T, Nakagawa K. Analysis of reporting lag in daily data of COVID-19 in Japan. LETTERS IN SPATIAL AND RESOURCE SCIENCES 2023; 16:12. [PMID: 36974275 PMCID: PMC10034910 DOI: 10.1007/s12076-023-00334-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Accepted: 02/26/2023] [Indexed: 06/18/2023]
Abstract
The daily announcement of positive COVID-19 cases had a major socioeconomic impact. In Japan, it is well known that the characteristic of this number as time series data is the weekly periodicity. We assume that this periodicity is generated by changes in the timing of reporting on the weekend. We analyze a lag structure that shows how congestion that occurs over the weekend affects the number of new confirmed cases at the beginning of the following week. We refer to this reporting delay as the weekend effect. Our study aims to describe the geographical heterogeneity found in the time series of reported positive cases. We use data on the number of new positives reported by the prefectures. Our results suggest that delays generally occur in prefectures with a population of more than 2 million, including Japan's three largest metropolitan areas, Tokyo, Osaka, and Nagoya. The number of new positives was higher in the more populated prefectures. This will explain the weekend effect.
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Affiliation(s)
- Taro Kanatani
- Department of Finance, Shiga University, Hikone, Shiga 522-8522 Japan
| | - Kuninori Nakagawa
- School of Economics and Management, University of Hyogo, Kobe, Hyogo 651-2197 Japan
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Pung R, Clapham HE, Russell TW, Lee VJ, Kucharski AJ. Relative role of border restrictions, case finding and contact tracing in controlling SARS-CoV-2 in the presence of undetected transmission: a mathematical modelling study. BMC Med 2023; 21:97. [PMID: 36927576 PMCID: PMC10019421 DOI: 10.1186/s12916-023-02802-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Accepted: 02/20/2023] [Indexed: 03/18/2023] Open
Abstract
BACKGROUND Understanding the overall effectiveness of non-pharmaceutical interventions to control the COVID-19 pandemic and reduce the burden of disease is crucial for future pandemic planning. However, quantifying the effectiveness of specific control measures and the extent of missed infections, in the absence of early large-scale serological surveys or random community testing, has remained challenging. METHODS Combining data on notified local COVID-19 cases with known and unknown sources of infections in Singapore with a branching process model, we reconstructed the incidence of missed infections during the early phase of the wild-type SARS-CoV-2 and Delta variant transmission. We then estimated the relative effectiveness of border control measures, case finding and contact tracing when there was no or low vaccine coverage in the population. We compared the risk of ICU admission and death between the wild-type SARS-CoV-2 and the Delta variant in notified cases and all infections. RESULTS We estimated strict border control measures were associated with 0.2 (95% credible intervals, CrI 0.04-0.8) missed imported infections per notified case between July and December 2020, a decline from around 1 missed imported infection per notified case in the early phases of the pandemic. Contact tracing was estimated to identify 78% (95% CrI 62-93%) of the secondary infections generated by notified cases before the partial lockdown in Apr 2020, but this declined to 63% (95% CrI 56-71%) during the lockdown and rebounded to 78% (95% CrI 58-94%) during reopening in Jul 2020. The contribution of contact tracing towards overall outbreak control also hinges on ability to find cases with unknown sources of infection: 42% (95% CrI 12-84%) of such cases were found prior to the lockdown; 10% (95% CrI 7-15%) during the lockdown; 47% (95% CrI 17-85%) during reopening, due to increased testing capacity and health-seeking behaviour. We estimated around 63% (95% CrI 49-78%) of the wild-type SARS-CoV-2 infections were undetected during 2020 and around 70% (95% CrI 49-91%) for the Delta variant in 2021. CONCLUSIONS Combining models with case linkage data enables evaluation of the effectiveness of different components of outbreak control measures, and provides more reliable situational awareness when some cases are missed. Using such approaches for early identification of the weakest link in containment efforts could help policy makers to better redirect limited resources to strengthen outbreak control.
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Affiliation(s)
- Rachael Pung
- Ministry of Health, Singapore, Singapore.
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, UK.
| | - Hannah E Clapham
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
| | - Timothy W Russell
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, UK
| | - Vernon J Lee
- Ministry of Health, Singapore, Singapore
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
| | - Adam J Kucharski
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, UK
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Guo Z, Zhao S, Lee SS, Hung CT, Wong NS, Chow TY, Yam CHK, Wang MH, Wang J, Chong KC, Yeoh EK. A statistical framework for tracking the time-varying superspreading potential of COVID-19 epidemic. Epidemics 2023; 42:100670. [PMID: 36709540 PMCID: PMC9872564 DOI: 10.1016/j.epidem.2023.100670] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2022] [Revised: 10/29/2022] [Accepted: 01/17/2023] [Indexed: 01/26/2023] Open
Abstract
Timely detection of an evolving event of an infectious disease with superspreading potential is imperative for territory-wide disease control as well as preventing future outbreaks. While the reproduction number (R) is a commonly-adopted metric for disease transmissibility, the transmission heterogeneity quantified by dispersion parameter k, a metric for superspreading potential is seldom tracked. In this study, we developed an estimation framework to track the time-varying risk of superspreading events (SSEs) and demonstrated the method using the three epidemic waves of COVID-19 in Hong Kong. Epidemiological contact tracing data of the confirmed COVID-19 cases from 23 January 2020 to 30 September 2021 were obtained. By applying branching process models, we jointly estimated the time-varying R and k. Individual-based outbreak simulations were conducted to compare the time-varying assessment of the superspreading potential with the typical non-time-varying estimate of k over a period of time. We found that the COVID-19 transmission in Hong Kong exhibited substantial superspreading during the initial phase of the epidemics, with only 1 % (95 % Credible interval [CrI]: 0.6-2 %), 5 % (95 % CrI: 3-7 %) and 10 % (95 % CrI: 8-14 %) of the most infectious cases generated 80 % of all transmission for the first, second and third epidemic waves, respectively. After implementing local public health interventions, R estimates dropped gradually and k estimates increased thereby reducing the risk of SSEs to approaching zero. Outbreak simulations indicated that the non-time-varying estimate of k may overlook the possibility of large outbreaks. Hence, an estimation of the time-varying k as a compliment of R as a monitoring of both disease transmissibility and superspreading potential, particularly when public health interventions were relaxed is crucial for minimizing the risk of future outbreaks.
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Affiliation(s)
- Zihao Guo
- Jockey Club School of Public Health and Primary Care, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, Hong Kong, China
| | - Shi Zhao
- Jockey Club School of Public Health and Primary Care, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, Hong Kong, China; Centre for Health Systems and Policy Research, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, Hong Kong, China
| | - Shui Shan Lee
- Jockey Club School of Public Health and Primary Care, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, Hong Kong, China; Stanley Ho Centre for Emerging Infectious Diseases, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, Hong Kong, China
| | - Chi Tim Hung
- Jockey Club School of Public Health and Primary Care, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, Hong Kong, China; Centre for Health Systems and Policy Research, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, Hong Kong, China
| | - Ngai Sze Wong
- Jockey Club School of Public Health and Primary Care, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, Hong Kong, China; Stanley Ho Centre for Emerging Infectious Diseases, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, Hong Kong, China
| | - Tsz Yu Chow
- Jockey Club School of Public Health and Primary Care, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, Hong Kong, China; Centre for Health Systems and Policy Research, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, Hong Kong, China
| | - Carrie Ho Kwan Yam
- Jockey Club School of Public Health and Primary Care, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, Hong Kong, China; Centre for Health Systems and Policy Research, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, Hong Kong, China
| | - Maggie Haitian Wang
- Jockey Club School of Public Health and Primary Care, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, Hong Kong, China
| | - Jingxuan Wang
- Jockey Club School of Public Health and Primary Care, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, Hong Kong, China
| | - Ka Chun Chong
- Jockey Club School of Public Health and Primary Care, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, Hong Kong, China; Centre for Health Systems and Policy Research, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, Hong Kong, China.
| | - Eng Kiong Yeoh
- Jockey Club School of Public Health and Primary Care, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, Hong Kong, China; Centre for Health Systems and Policy Research, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, Hong Kong, China
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32
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Ho F, Parag KV, Adam DC, Lau EHY, Cowling BJ, Tsang TK. Accounting for the Potential of Overdispersion in Estimation of the Time-varying Reproduction Number. Epidemiology 2023; 34:201-205. [PMID: 36722802 DOI: 10.1097/ede.0000000000001563] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Abstract
BACKGROUND The time-varying reproduction number, Rt, is commonly used to monitor the transmissibility of an infectious disease during an epidemic, but standard methods for estimating Rt seldom account for the impact of overdispersion on transmission. METHODS We developed a negative binomial framework to estimate Rt and a time-varying dispersion parameter (kt). We applied the framework to COVID-19 incidence data in Hong Kong in 2020 and 2021. We conducted a simulation study to compare the performance of our model with the conventional Poisson-based approach. RESULTS Our framework estimated an Rt peaking around 4 (95% credible interval = 3.13, 4.30), similar to that from the Poisson approach but with a better model fit. Our approach further estimated kt <0.5 at the start of both waves, indicating appreciable heterogeneity in transmission. We also found that kt decreased sharply to around 0.4 when a large cluster of infections occurred. CONCLUSIONS Our proposed approach can contribute to the estimation of Rt and monitoring of the time-varying dispersion parameters to quantify the role of superspreading.
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Affiliation(s)
- Faith Ho
- From the WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - 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
| | - Dillon C Adam
- From the WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Eric H Y Lau
- From the WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
- Laboratory of Data Discovery for Health Limited, Hong Kong Science Park, New Territories, Hong Kong
| | - Benjamin J Cowling
- From the WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
- Laboratory of Data Discovery for Health Limited, Hong Kong Science Park, New Territories, Hong Kong
| | - Tim K Tsang
- From the WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
- Laboratory of Data Discovery for Health Limited, Hong Kong Science Park, New Territories, Hong Kong
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Cauchemez S, Bosetti P, Cowling BJ. Managing sources of error during pandemics. Science 2023; 379:437-439. [PMID: 36730404 DOI: 10.1126/science.add3173] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
The COVID-19 pandemic has highlighted important considerations for modeling future pandemics.
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Affiliation(s)
- Simon Cauchemez
- Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, Université Paris Cité, CNRS UMR2000, Paris, France
| | - Paolo Bosetti
- Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, Université Paris Cité, CNRS UMR2000, Paris, France
| | - Benjamin J Cowling
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China.,Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, New Territories, Hong Kong Special Administrative Region, China
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34
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Ratra S, Pant B, Roy K, Manohar S, Kumar P, Singh S, Tumba K, Kumari K, Singh P. A review on synthesis of antiviral drugs, in silico studies and their toxicity. J INDIAN CHEM SOC 2023. [DOI: 10.1016/j.jics.2023.100936] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/12/2023]
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35
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Du Z, Luo W, Sippy R, Wang L. Editorial: Infectious Disease Epidemiology and Transmission Dynamics. Viruses 2023; 15:246. [PMID: 36680286 PMCID: PMC9863623 DOI: 10.3390/v15010246] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Accepted: 01/13/2023] [Indexed: 01/18/2023] Open
Abstract
Infectious diseases, such as COVID-19 [...].
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Affiliation(s)
- Zhanwei Du
- WHO Collaborating Center for Infectious Disease Epidemiology and Control, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
- Laboratory of Data Discovery for Health Limited, Hong Kong Science Park, Hong Kong SAR, China
| | - Wei Luo
- Department of Geography, National University of Singapore, Singapore 117570, Singapore
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore 117570, Singapore
| | - Rachel Sippy
- Department of Genetics, University of Cambridge, Cambridge CB2 3EH, UK
| | - Lin Wang
- Department of Genetics, University of Cambridge, Cambridge CB2 3EH, UK
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36
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Leung D, Rosa Duque JS, Yip KM, So HK, Wong WHS, Lau YL. Effectiveness of BNT162b2 and CoronaVac in children and adolescents against SARS-CoV-2 infection during Omicron BA.2 wave in Hong Kong. COMMUNICATIONS MEDICINE 2023; 3:3. [PMID: 36604522 PMCID: PMC9813885 DOI: 10.1038/s43856-022-00233-1] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Accepted: 12/19/2022] [Indexed: 01/06/2023] Open
Abstract
BACKGROUND The SARS-CoV-2 Omicron BA.2 subvariant replaced BA.1 globally in early 2022, and caused an unprecedented tsunami of cases in Hong Kong, resulting in the collapse of elimination strategy. Vaccine effectiveness (VE) of BNT162b2 and CoronaVac against BA.2 is unclear. METHODS We utilize an ecological design incorporating population-level vaccine coverage statistics and territory-wide case-level SARS-CoV-2 infection surveillance data, and investigate the VE against infection during the Omicron BA.2 wave between January 1 to April 19, 2022, in Hong Kong for children and adolescents. RESULTS We estimate VE to be 33.0% for 1 dose of BNT162b2 in children aged 5-11 and 40.8% for 2 doses of CoronaVac in children aged 3-11. We also estimate 54.9% VE for 2 doses of BNT162b2, and 55.0% VE for 2 doses of CoronaVac in adolescents aged 12-18. CONCLUSIONS Our findings support partly preserved VE against infection by variants of concerns for children and adolescents in settings with extremely low levels of prior SARS-CoV-2 circulation.
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Affiliation(s)
- Daniel Leung
- Department of Paediatrics and Adolescent Medicine, The University of Hong Kong, Hong Kong, China
| | - Jaime S Rosa Duque
- Department of Paediatrics and Adolescent Medicine, The University of Hong Kong, Hong Kong, China
| | - Ka Man Yip
- Department of Paediatrics and Adolescent Medicine, The University of Hong Kong, Hong Kong, China
| | - Hung Kwan So
- Department of Paediatrics and Adolescent Medicine, The University of Hong Kong, Hong Kong, China
| | - Wilfred H S Wong
- Department of Paediatrics and Adolescent Medicine, The University of Hong Kong, Hong Kong, China.
| | - Yu Lung Lau
- Department of Paediatrics and Adolescent Medicine, The University of Hong Kong, Hong Kong, China.
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37
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Guo Z, Zhao S, Sun S, He D, Chong KC, Yeoh EK. Estimation of the serial interval of monkeypox during the early outbreak in 2022. J Med Virol 2023; 95:e28248. [PMID: 36271480 DOI: 10.1002/jmv.28248] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 10/18/2022] [Accepted: 10/19/2022] [Indexed: 01/11/2023]
Abstract
With increased transmissibility and novel transmission mode, monkeypox poses new threats to public health globally in the background of the ongoing COVID-19 pandemic. Estimates of the serial interval, a key epidemiological parameter of infectious disease transmission, could provide insights into the virus transmission risks. As of October 2022, little was known about the serial interval of monkeypox due to the lack of contact tracing data. In this study, public-available contact tracing data of global monkeypox cases were collected and 21 infector-infectee transmission pairs were identified. We proposed a statistical method applied to real-world observations to estimate the serial interval of the monkeypox. We estimated a mean serial interval of 5.6 days with the right truncation and sampling bias adjusted and calculated the reproduction number of 1.33 for the early monkeypox outbreaks at a global scale. Our findings provided a preliminary understanding of the transmission potentials of the current situation of monkeypox outbreaks. We highlighted the need for continuous surveillance of monkeypox for transmission risk assessment.
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Affiliation(s)
- Zihao Guo
- JC School of Public Health and Primary Care, Faculty of Medicine, Chinese University of Hong Kong, Hong Kong, China
| | - Shi Zhao
- JC School of Public Health and Primary Care, Faculty of Medicine, Chinese University of Hong Kong, Hong Kong, China
| | - Shengzhi Sun
- Department of Epidemiology and Biostatistics, School of Public Health, Capital Medical University, Beijing, China
| | - Daihai He
- Department of Applied Mathematics, Hong Kong Polytechnic University, Hong Kong, China
| | - Ka Chun Chong
- JC School of Public Health and Primary Care, Faculty of Medicine, Chinese University of Hong Kong, Hong Kong, China.,Centre for Health Systems and Policy Research, Faculty of Medicine, Chinese University of Hong Kong, Hong Kong, China
| | - Eng Kiong Yeoh
- JC School of Public Health and Primary Care, Faculty of Medicine, Chinese University of Hong Kong, Hong Kong, China.,Centre for Health Systems and Policy Research, Faculty of Medicine, Chinese University of Hong Kong, Hong Kong, China
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38
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Sloane R, Pieper CF, Faldowski R, Wixted D, Neighbors CE, Woods CW, Kristin Newby L. COVID-19 Infection Risk Among Previously Uninfected Adults: Development of a Prognostic Model. Health Serv Res Manag Epidemiol 2023; 10:23333928231154336. [PMID: 37006334 PMCID: PMC10052611 DOI: 10.1177/23333928231154336] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 01/07/2023] [Accepted: 01/14/2023] [Indexed: 03/29/2023] Open
Abstract
Background Few models exist that incorporate measures from an array of individual characteristics to predict the risk of COVID-19 infection in the general population. The aim was to develop a prognostic model for COVID-19 using readily obtainable clinical variables. Methods Over 74 weeks surveys were periodically administered to a cohort of 1381 participants previously uninfected with COVID-19 (June 2020 to December 2021). Candidate predictors of incident infection during follow-up included demographics, living situation, financial status, physical activity, health conditions, flu vaccination history, COVID-19 vaccine intention, work/employment status, and use of COVID-19 mitigation behaviors. The final logistic regression model was created using a penalized regression method known as the least absolute shrinkage and selection operator. Model performance was assessed by discrimination and calibration. Internal validation was performed via bootstrapping, and results were adjusted for overoptimism. Results Of the 1381 participants, 154 (11.2%) had an incident COVID-19 infection during the follow-up period. The final model included six variables: health insurance, race, household size, and the frequency of practicing three mitigation behavior (working at home, avoiding high-risk situations, and using facemasks). The c-statistic of the final model was 0.631 (0.617 after bootstrapped optimism-correction). A calibration plot suggested that with this sample the model shows modest concordance with incident infection at the lowest risk. Conclusion This prognostic model can help identify which community-dwelling older adults are at the highest risk for incident COVID-19 infection and may inform medical provider counseling of their patients about the risk of incident COVID-19 infection.
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Affiliation(s)
- Richard Sloane
- Center for the Study of Aging and Human Development, Duke University Medical Center, Durham, North Carolina, USA
- Richard Sloane, Center for the Study of Aging and Human Development, Box 3003, Duke University Medical Center, Durham, NC 27710, USA.
| | - Carl F Pieper
- Center for the Study of Aging and Human Development, Duke University Medical Center, Durham, North Carolina, USA
- Department of Biostatistics and Bioinformatics, Duke University Medical Center, Durham, North Carolina, USA
| | - Richard Faldowski
- Center for the Study of Aging and Human Development, Duke University Medical Center, Durham, North Carolina, USA
| | - Douglas Wixted
- Duke Clinical and Translational Science Institute, Duke University, Durham, North Carolina, USA
| | - Coralei E Neighbors
- Hubert-Yeargan Center for Global Health, Duke University, Durham, North Carolina, USA
| | - Christopher W Woods
- Hubert-Yeargan Center for Global Health, Duke University, Durham, North Carolina, USA
- Departments of Medicine and Pathology, Duke University Medical Center, Durham, North Carolina, USA
| | - L Kristin Newby
- Duke Clinical and Translational Science Institute, Duke University, Durham, North Carolina, USA
- Duke Clinical Research Institute, Duke University, Durham, North Carolina, USA
- Division of Cardiology, Department of Medicine, Duke University Medical Center, Durham, North Carolina, USA
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39
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Zhangbo Y, Zheng C, Hui W. Contact network analysis of COVID-19 Delta variant outbreak in urban China -based on 2,050 confirmed cases in Xi'an, China. BMC Public Health 2022; 22:2408. [PMID: 36550467 PMCID: PMC9772588 DOI: 10.1186/s12889-022-14882-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Accepted: 12/16/2022] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND The purpose of this paper is to study how the Delta variant spread in a China city, and to what extent the non-pharmaceutical prevention measures of local government be effective by reviewing the contact network of COVID-19 cases in Xi'an, China. METHODS We organize the case reports of the Shaanxi Health Commission into a database by text coding and convert them into a network matrix. Then we construct a dynamic contact network for the corresponding analysis and calculate network indicators. we analyze the cases' dynamic contact network structure and intervals between diagnosis time and isolation time by using data visualization, network analysis method, and Ordinary Least Square (OLS) regression. RESULTS The contact network for this outbreak in Xi'an is very sparse, with a density of less than 0.0001. The contact network is a scale-free network. The average degree centrality is 0.741 and the average PageRank score is 0.0005. The network generated from a single source of infection contains 1371 components. We construct three variables of intervals and analyze the trend of intervals during the outbreak. The mean interval (interval 1) between case diagnosis time and isolation time is - 3.9 days. The mean of the interval (interval 2) between the infector's diagnosis time and the infectee's diagnosis time is 4.2 days. The mean of the interval (interval 3) between infector isolation time and infectee isolation time is 2.9 days. Among the three intervals, only interval 1 has a significant positive correlation with degree centrality. CONCLUSIONS By integrating COVID-19 case reports of a Chinese city, we construct a contact network to analyze the dispersion of the outbreak. The network is a scale-free network with multiple hidden pathways that are not detected. The intervals of patients in this outbreak decreased compared to the beginning of the outbreak in 2020. City lockdown has a significant effect on the intervals that can affect patients' network centrality. Our study highlights the value of case report text. By linking different reports, we can quickly analyze the spread of the epidemic in an urban area.
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Affiliation(s)
- Yang Zhangbo
- grid.43169.390000 0001 0599 1243School of Humanities and Social Science, Xi’an Jiaotong University, Xi’an, China ,grid.43169.390000 0001 0599 1243Institute for Empirical Social Science Research, Xi’an Jiaotong University, Xi’an, China
| | - Chen Zheng
- grid.43169.390000 0001 0599 1243School of Humanities and Social Science, Xi’an Jiaotong University, Xi’an, China
| | - Wang Hui
- grid.43169.390000 0001 0599 1243School of Management, Xi’an Jiaotong University, No.28 Xianningxi Road, Xi’an, 710049 China
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40
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Chen D, Lau YC, Xu XK, Wang L, Du Z, Tsang TK, Wu P, Lau EHY, Wallinga J, Cowling BJ, Ali ST. Inferring time-varying generation time, serial interval, and incubation period distributions for COVID-19. Nat Commun 2022; 13:7727. [PMID: 36513688 PMCID: PMC9747081 DOI: 10.1038/s41467-022-35496-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Accepted: 12/07/2022] [Indexed: 12/15/2022] Open
Abstract
The generation time distribution, reflecting the time between successive infections in transmission chains, is a key epidemiological parameter for describing COVID-19 transmission dynamics. However, because exact infection times are rarely known, it is often approximated by the serial interval distribution. This approximation holds under the assumption that infectors and infectees share the same incubation period distribution, which may not always be true. We estimated incubation period and serial interval distributions using 629 transmission pairs reconstructed by investigating 2989 confirmed cases in China in January-February 2020, and developed an inferential framework to estimate the generation time distribution that accounts for variation over time due to changes in epidemiology, sampling biases and public health and social measures. We identified substantial reductions over time in the serial interval and generation time distributions. Our proposed method provides more reliable estimation of the temporal variation in the generation time distribution, improving assessment of transmission dynamics.
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Affiliation(s)
- Dongxuan Chen
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
- Laboratory of Data Discovery for Health Limited, Hong Kong Science and Technology Park, New Territories, Hong Kong Special Administrative Region, China
| | - Yiu-Chung Lau
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
- Laboratory of Data Discovery for Health Limited, Hong Kong Science and Technology Park, New Territories, Hong Kong Special Administrative Region, China
| | - Xiao-Ke Xu
- College of Information and Communication Engineering, Dalian Minzu University, Dalian, 116600, China
| | - Lin Wang
- Department of Genetics, University of Cambridge, Cambridge, CB2 3EH, UK
| | - Zhanwei Du
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
- Laboratory of Data Discovery for Health Limited, Hong Kong Science and Technology Park, New Territories, Hong Kong Special Administrative Region, China
| | - Tim K Tsang
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
- Laboratory of Data Discovery for Health Limited, Hong Kong Science and Technology Park, New Territories, Hong Kong Special Administrative Region, China
| | - Peng Wu
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
- Laboratory of Data Discovery for Health Limited, Hong Kong Science and Technology Park, New Territories, Hong Kong Special Administrative Region, China
| | - Eric H Y Lau
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
- Laboratory of Data Discovery for Health Limited, Hong Kong Science and Technology Park, New Territories, Hong Kong Special Administrative Region, China
| | - Jacco Wallinga
- Center for Infectious Disease Control, National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands
- Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
| | - Benjamin J Cowling
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China.
- Laboratory of Data Discovery for Health Limited, Hong Kong Science and Technology Park, New Territories, Hong Kong Special Administrative Region, China.
| | - Sheikh Taslim Ali
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
- Laboratory of Data Discovery for Health Limited, Hong Kong Science and Technology Park, New Territories, Hong Kong Special Administrative Region, China
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41
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Luisa Vissat L, Horvitz N, Phillips RV, Miao Z, Mgbara W, You Y, Salter R, Hubbard AE, Getz WM. A comparison of COVID-19 outbreaks across US Combined Statistical Areas using new methods for estimating R 0 and social distancing behaviour. Epidemics 2022; 41:100640. [PMID: 36274569 PMCID: PMC9550289 DOI: 10.1016/j.epidem.2022.100640] [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: 11/15/2021] [Revised: 10/03/2022] [Accepted: 10/03/2022] [Indexed: 02/05/2023] Open
Abstract
We investigated the initial outbreak rates and subsequent social distancing behaviour over the initial phase of the COVID-19 pandemic across 29 Combined Statistical Areas (CSAs) of the United States. We used the Numerus Model Builder Data and Simulation Analysis (NMB-DASA) web application to fit the exponential phase of a SCLAIV+D (Susceptible, Contact, Latent, Asymptomatic infectious, symptomatic Infectious, Vaccinated, Dead) disease classes model to outbreaks, thereby allowing us to obtain an estimate of the basic reproductive number R0 for each CSA. Values of R0 ranged from 1.9 to 9.4, with a mean and standard deviation of 4.5±1.8. Fixing the parameters from the exponential fit, we again used NMB-DASA to estimate a set of social distancing behaviour parameters to compute an epidemic flattening index cflatten. Finally, we applied hierarchical clustering methods using this index to divide CSA outbreaks into two clusters: those presenting a social distancing response that was either weaker or stronger. We found cflatten to be more influential in the clustering process than R0. Thus, our results suggest that the behavioural response after a short initial exponential growth phase is likely to be more determinative of the rise of an epidemic than R0 itself.
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Affiliation(s)
- Ludovica Luisa Vissat
- Department of Environmental Science, Policy, and Management, UC Berkeley, CA 94720, USA
| | - Nir Horvitz
- Department of Environmental Science, Policy, and Management, UC Berkeley, CA 94720, USA
| | | | - Zhongqi Miao
- Department of Environmental Science, Policy, and Management, UC Berkeley, CA 94720, USA
| | - Whitney Mgbara
- Department of Environmental Science, Policy, and Management, UC Berkeley, CA 94720, USA
| | - Yue You
- Division Environmental Health Sciences, UC Berkeley, CA 94720, USA
| | - Richard Salter
- Computer Science Department, Oberlin College, Oberlin, Ohio, OH 44074, USA
| | - Alan E Hubbard
- Division Environmental Health Sciences, UC Berkeley, CA 94720, USA
| | - Wayne M Getz
- Department of Environmental Science, Policy, and Management, UC Berkeley, CA 94720, USA; Division Environmental Health Sciences, UC Berkeley, CA 94720, USA; School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, Durban 4000, South Africa.
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42
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Linton NM, Akhmetzhanov AR, Nishiura H. Correlation between times to SARS-CoV-2 symptom onset and secondary transmission undermines epidemic control efforts. Epidemics 2022; 41:100655. [PMID: 36413921 PMCID: PMC9661582 DOI: 10.1016/j.epidem.2022.100655] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Revised: 10/12/2022] [Accepted: 11/12/2022] [Indexed: 11/16/2022] Open
Abstract
Severe acute respiratory coronavirus 2 (SARS-CoV-2) infections have been associated with substantial presymptomatic transmission, which occurs when the generation interval-the time between infection of an individual with a pathogen and transmission of the pathogen to another individual-is shorter than the incubation period-the time between infection and symptom onset. We collected a dataset of 257 SARS-CoV-2 transmission pairs in Japan during 2020 and jointly estimated the mean incubation period of infectors (4.8 days, 95 % CrI: 4.4-5.1 days), mean generation interval to when they infect others (4.3 days, 95 % credible interval [CrI]: 4.0-4.7 days), and the correlation (Kendall's tau: 0.5, 95 % CrI: 0.4-0.6) between these two epidemiological parameters. Our finding of a positive correlation and mean generation interval shorter than the mean infector incubation period indicates ample infectiousness before symptom onset and suggests that reliance on isolation of symptomatic COVID-19 cases as a focal point of control efforts is insufficient to address the challenges posed by SARS-CoV-2 transmission dynamics.
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Affiliation(s)
- Natalie M. Linton
- Kyoto University School of Public Health, Yoshidakonoe-cho, Sakyo-ku, Kyoto city, 606-8501, Japan,Graduate School of Medicine, Hokkaido University, Kita 15 Jo Nishi 7 Chome, Kita-ku, Sapporo-shi, Hokkaido 060-8638, Japan
| | - Andrei R. Akhmetzhanov
- College of Public Health, National Taiwan University, 17 Xu-Zhou Road, Taipei 10055, Taiwan
| | - Hiroshi Nishiura
- Kyoto University School of Public Health, Yoshidakonoe-cho, Sakyo-ku, Kyoto city, 606-8501, Japan,Core Research for Evolutional Science and Technology (CREST), Japan Science and Technology Agency, Saitama, Japan,Corresponding author at: Kyoto University School of Public Health, Yoshidakonoe-cho, Sakyo-ku, Kyoto city, 606-8501, Japan
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43
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Gozzi N, Chinazzi M, Dean NE, Longini IM, Halloran ME, Perra N, Vespignani A. Estimating the impact of COVID-19 vaccine allocation inequities: a modeling study. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2022:2022.11.18.22282514. [PMID: 36415459 PMCID: PMC9681050 DOI: 10.1101/2022.11.18.22282514] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/30/2023]
Abstract
Access to COVID-19 vaccines on the global scale has been drastically impacted by structural socio-economic inequities. Here, we develop a data-driven, age-stratified epidemic model to evaluate the effects of COVID-19 vaccine inequities in twenty lower middle and low income countries (LMIC) sampled from all WHO regions. We focus on the first critical months of vaccine distribution and administration, exploring counterfactual scenarios where we assume the same per capita daily vaccination rate reported in selected high income countries. We estimate that, in this high vaccine availability scenario, more than 50% of deaths (min-max range: [56% - 99%]) that occurred in the analyzed countries could have been averted. We further consider a scenario where LMIC had similarly early access to vaccine doses as high income countries; even without increasing the number of doses, we estimate an important fraction of deaths (min-max range: [7% - 73%]) could have been averted. In the absence of equitable allocation, the model suggests that considerable additional non-pharmaceutical interventions would have been required to offset the lack of vaccines (min-max range: [15% - 75%]). Overall, our results quantify the negative impacts of vaccines inequities and call for amplified global efforts to provide better access to vaccine programs in low and lower middle income countries.
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Epidemiology and Transmission Dynamics of Infectious Diseases and Control Measures. Viruses 2022; 14:v14112510. [PMID: 36423119 PMCID: PMC9695084 DOI: 10.3390/v14112510] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Revised: 11/10/2022] [Accepted: 11/10/2022] [Indexed: 11/16/2022] Open
Abstract
The epidemiology and transmission dynamics of infectious diseases must be understood at the individual and community levels to improve public health decision-making for real-time and integrated community-based control strategies. Herein, we explore the epidemiological characteristics for assessing the impact of public health interventions in the community setting and their applications. Computational statistical methods could advance research on infectious disease epidemiology and accumulate scientific evidence of the potential impacts of pharmaceutical/nonpharmaceutical measures to mitigate or control infectious diseases in the community. Novel public health threats from emerging zoonotic infectious diseases are urgent issues. Given these direct and indirect mitigating impacts at various levels to different infectious diseases and their burdens, we must consider an integrated assessment approach, 'One Health', to understand the dynamics and control of infectious diseases.
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45
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Sebastiani G, Spassiani I. New Insights into the Estimation of Reproduction Numbers during an Epidemic. Vaccines (Basel) 2022; 10:1788. [PMID: 36366299 PMCID: PMC9694736 DOI: 10.3390/vaccines10111788] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Revised: 10/18/2022] [Accepted: 10/18/2022] [Indexed: 09/10/2024] Open
Abstract
In this paper, we deal with the problem of estimating the reproduction number Rt during an epidemic, as it represents one of the most used indicators to study and control this phenomenon. In particular, we focus on two issues. First, to estimate Rt, we consider the use of positive test case data as an alternative to the first symptoms data, which are typically used. We both theoretically and empirically study the relationship between the two approaches. Second, we modify a method for estimating Rt during an epidemic that is widely used by public institutions in several countries worldwide. Our procedure is not affected by the problems deriving from the hypothesis of Rt local constancy, which is assumed in the standard approach. We illustrate the results obtained by applying the proposed methodologies to real and simulated SARS-CoV-2 datasets. In both cases, we also apply some specific methods to reduce systematic and random errors affecting the data. Our results show that the Rt during an epidemic can be estimated by using the positive test data, and that our estimator outperforms the standard estimator that makes use of the first symptoms data. It is hoped that the techniques proposed here could help in the study and control of epidemics, particularly the current SARS-CoV-2 pandemic.
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Affiliation(s)
- Giovanni Sebastiani
- Istituto per le Applicazioni del Calcolo Mauro Picone, Consiglio Nazionale delle Ricerche, Via dei Taurini 19, 00185 Rome, Italy
- Mathematics Department “Guido Castelnuovo”, Sapienza University of Rome, Piazzale Aldo Moro 5, 00185 Rome, Italy
- Department of Mathematics and Statistics, University of Tromsø, H. Hansens veg 18, 9019 Tromsø, Norway
- Istituto Nazionale di Geofisica e Vulcanologia (INGV), Via di Vigna Murata 605, 00143 Rome, Italy
| | - Ilaria Spassiani
- Istituto Nazionale di Geofisica e Vulcanologia (INGV), Via di Vigna Murata 605, 00143 Rome, Italy
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46
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Wang Z, Liu XF, Du Z, Wang L, Wu Y, Holme P, Lachmann M, Lin H, Wong ZS, Xu XK, Sun Y. Epidemiologic information discovery from open-access COVID-19 case reports via pretrained language model. iScience 2022; 25:105079. [PMID: 36093379 PMCID: PMC9441477 DOI: 10.1016/j.isci.2022.105079] [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: 03/12/2022] [Revised: 07/04/2022] [Accepted: 08/31/2022] [Indexed: 11/26/2022] Open
Abstract
Although open-access data are increasingly common and useful to epidemiological research, the curation of such datasets is resource-intensive and time-consuming. Despite the existence of a major source of COVID-19 data, the regularly disclosed case reports were often written in natural language with an unstructured format. Here, we propose a computational framework that can automatically extract epidemiological information from open-access COVID-19 case reports. We develop this framework by coupling a language model developed using deep neural networks with training samples compiled using an optimized data annotation strategy. When applied to the COVID-19 case reports collected from mainland China, our framework outperforms all other state-of-the-art deep learning models. The information extracted from our approach is highly consistent with that obtained from the gold-standard manual coding, with a matching rate of 80%. To disseminate our algorithm, we provide an open-access online platform that is able to estimate key epidemiological statistics in real time, with much less effort for data curation.
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Affiliation(s)
- Zhizheng Wang
- College of Computer Science and Technology, Dalian University of Technology, Haishan Building No.2 Linggong Road, Dalian, Liaoning 116023, China
| | - Xiao Fan Liu
- Web Mining Laboratory, Department of Media and Communication, City University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Zhanwei Du
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Lin Wang
- Department of Genetics, University of Cambridge, Cambridge CB2 3EH, UK
| | - Ye Wu
- Computational Communication Research Center and School of Journalism and Communication, Beijing Normal University, Beijing, China
| | - Petter Holme
- Tokyo Tech World Research Hub Initiative (WRHI), Institute of Innovative Research, Tokyo Institute of Technology, Tokyo, Japan
| | | | - Hongfei Lin
- College of Computer Science and Technology, Dalian University of Technology, Haishan Building No.2 Linggong Road, Dalian, Liaoning 116023, China
| | - Zoie S.Y. Wong
- Graduate School of Public Health, St. Luke’s International University, Tokyo, Japan
| | - Xiao-Ke Xu
- College of Information and Communication Engineering, Dalian Minzu University, Liaoning, China
| | - Yuanyuan Sun
- College of Computer Science and Technology, Dalian University of Technology, Haishan Building No.2 Linggong Road, Dalian, Liaoning 116023, China
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47
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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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48
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Ackland GJ, Ackland JA, Antonioletti M, Wallace DJ. Fitting the reproduction number from UK coronavirus case data and why it is close to 1. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2022; 380:20210301. [PMID: 35965470 PMCID: PMC9376721 DOI: 10.1098/rsta.2021.0301] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Accepted: 04/11/2022] [Indexed: 05/04/2023]
Abstract
We present a method for rapid calculation of coronavirus growth rates and [Formula: see text]-numbers tailored to publicly available UK data. We assume that the case data comprise a smooth, underlying trend which is differentiable, plus systematic errors and a non-differentiable noise term, and use bespoke data processing to remove systematic errors and noise. The approach is designed to prioritize up-to-date estimates. Our method is validated against published consensus [Formula: see text]-numbers from the UK government and is shown to produce comparable results two weeks earlier. The case-driven approach is combined with weight-shift-scale methods to monitor trends in the epidemic and for medium-term predictions. Using case-fatality ratios, we create a narrative for trends in the UK epidemic: increased infectiousness of the B1.117 (Alpha) variant, and the effectiveness of vaccination in reducing severity of infection. For longer-term future scenarios, we base future [Formula: see text] on insight from localized spread models, which show [Formula: see text] going asymptotically to 1 after a transient, regardless of how large the [Formula: see text] transient is. This accords with short-lived peaks observed in case data. These cannot be explained by a well-mixed model and are suggestive of spread on a localized network. 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)
| | - James A. Ackland
- Department of Psychology, University of Cambridge,Cambridge CB2 3EB, UK
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49
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Costa GS, Cota W, Ferreira SC. Data-driven approach in a compartmental epidemic model to assess undocumented infections. CHAOS, SOLITONS, AND FRACTALS 2022; 163:112520. [PMID: 35996714 PMCID: PMC9385215 DOI: 10.1016/j.chaos.2022.112520] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Revised: 07/27/2022] [Accepted: 07/29/2022] [Indexed: 06/15/2023]
Abstract
Nowcasting and forecasting of epidemic spreading rely on incidence series of reported cases to derive the fundamental epidemiological parameters for a given pathogen. Two relevant drawbacks for predictions are the unknown fractions of undocumented cases and levels of nonpharmacological interventions, which span highly heterogeneously across different places and times. We describe a simple data-driven approach using a compartmental model including asymptomatic and pre-symptomatic contagions that allows to estimate both the level of undocumented infections and the value of effective reproductive number R t from time series of reported cases, deaths, and epidemiological parameters. The method was applied to epidemic series for COVID-19 across different municipalities in Brazil allowing to estimate the heterogeneity level of under-reporting across different places. The reproductive number derived within the current framework is little sensitive to both diagnosis and infection rates during the asymptomatic states. The methods described here can be extended to more general cases if data is available and adapted to other epidemiological approaches and surveillance data.
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Affiliation(s)
- Guilherme S Costa
- Departamento de Física, Universidade Federal de Viçosa, 36570-900, Viçosa, MG, Brazil
| | - Wesley Cota
- Departamento de Física, Universidade Federal de Viçosa, 36570-900, Viçosa, MG, Brazil
| | - Silvio C Ferreira
- Departamento de Física, Universidade Federal de Viçosa, 36570-900, Viçosa, MG, Brazil
- National Institute of Science and technology for Complex Systems, Centro Brasileiro de Pesquisas Físicas, Rua Xavier Sigaud 150, 22290-180, Rio de Janeiro, Brazil
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50
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Banholzer N, Lison A, Özcelik D, Stadler T, Feuerriegel S, Vach W. The methodologies to assess the effectiveness of non-pharmaceutical interventions during COVID-19: a systematic review. Eur J Epidemiol 2022; 37:1003-1024. [PMID: 36152133 PMCID: PMC9510554 DOI: 10.1007/s10654-022-00908-y] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Accepted: 07/15/2022] [Indexed: 11/26/2022]
Abstract
Non-pharmaceutical interventions, such as school closures and stay-at-home orders, have been implemented around the world to control the spread of SARS-CoV-2. Their effectiveness in improving health-related outcomes has been the subject of numerous empirical studies. However, these studies show fairly large variation among methodologies in use, reflecting the absence of an established methodological framework. On the one hand, variation in methodologies may be desirable to assess the robustness of results; on the other hand, a lack of common standards can impede comparability among studies. To establish a comprehensive overview over the methodologies in use, we conducted a systematic review of studies assessing the effectiveness of non-pharmaceutical interventions between January 1, 2020 and January 12, 2021 (n = 248). We identified substantial variation in methodologies with respect to study setting, outcome, intervention, methodological approach, and effectiveness assessment. On this basis, we point to shortcomings of existing studies and make recommendations for the design of future studies.
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Affiliation(s)
- Nicolas Banholzer
- Department of Management, Technology, and Economics, ETH Zurich, Zurich, Switzerland.
| | - Adrian Lison
- Department of Biosystems Science and Engineering, ETH Zurich, Zurich, Switzerland.
| | - Dennis Özcelik
- Chemistry | Biology | Pharmacy Information Center, ETH Zurich, Zurich, Switzerland
| | - Tanja Stadler
- Department of Biosystems Science and Engineering, ETH Zurich, Zurich, Switzerland
| | - Stefan Feuerriegel
- Department of Management, Technology, and Economics, ETH Zurich, Zurich, Switzerland
- LMU Munich School of Management, LMU Munich, Munich, Germany
| | - Werner Vach
- Basel Academy for Quality and Research in Medicine, Basel, Switzerland
- Department of Environmental Sciences, University of Basel, Basel, Switzerland
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