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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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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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Alpers R, Kühne L, Truong HP, Zeeb H, Westphal M, Jäckle S. Evaluation of the EsteR Toolkit for COVID-19 Decision Support: Sensitivity Analysis and Usability Study. JMIR Form Res 2023; 7:e44549. [PMID: 37368487 DOI: 10.2196/44549] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 05/14/2023] [Accepted: 05/15/2023] [Indexed: 06/28/2023] Open
Abstract
BACKGROUND During the COVID-19 pandemic, local health authorities were responsible for managing and reporting current cases in Germany. Since March 2020, employees had to contain the spread of COVID-19 by monitoring and contacting infected persons as well as tracing their contacts. In the EsteR project, we implemented existing and newly developed statistical models as decision support tools to assist in the work of the local health authorities. OBJECTIVE The main goal of this study was to validate the EsteR toolkit in two complementary ways: first, investigating the stability of the answers provided by our statistical tools regarding model parameters in the back end and, second, evaluating the usability and applicability of our web application in the front end by test users. METHODS For model stability assessment, a sensitivity analysis was carried out for all 5 developed statistical models. The default parameters of our models as well as the test ranges of the model parameters were based on a previous literature review on COVID-19 properties. The obtained answers resulting from different parameters were compared using dissimilarity metrics and visualized using contour plots. In addition, the parameter ranges of general model stability were identified. For the usability evaluation of the web application, cognitive walk-throughs and focus group interviews were conducted with 6 containment scouts located at 2 different local health authorities. They were first asked to complete small tasks with the tools and then express their general impressions of the web application. RESULTS The simulation results showed that some statistical models were more sensitive to changes in their parameters than others. For each of the single-person use cases, we determined an area where the respective model could be rated as stable. In contrast, the results of the group use cases highly depended on the user inputs, and thus, no area of parameters with general model stability could be identified. We have also provided a detailed simulation report of the sensitivity analysis. In the user evaluation, the cognitive walk-throughs and focus group interviews revealed that the user interface needed to be simplified and more information was necessary as guidance. In general, the testers rated the web application as helpful, especially for new employees. CONCLUSIONS This evaluation study allowed us to refine the EsteR toolkit. Using the sensitivity analysis, we identified suitable model parameters and analyzed how stable the statistical models were in terms of changes in their parameters. Furthermore, the front end of the web application was improved with the results of the conducted cognitive walk-throughs and focus group interviews regarding its user-friendliness.
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Affiliation(s)
- Rieke Alpers
- Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany
| | - Lisa Kühne
- Department of Prevention and Evaluation, Leibniz Institute for Prevention Research and Epidemiology - BIPS, Bremen, Germany
| | - Hong-Phuc Truong
- Fraunhofer Institute for Industrial Mathematics ITWM, Kaiserslautern, Germany
| | - Hajo Zeeb
- Department of Prevention and Evaluation, Leibniz Institute for Prevention Research and Epidemiology - BIPS, Bremen, Germany
| | - Max Westphal
- Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany
| | - Sonja Jäckle
- Fraunhofer Institute for Digital Medicine MEVIS, Lübeck, Germany
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Guo Z, Zhao S, Yam CHK, Li C, Jiang X, Chow TY, Chong KC, Yeoh EK. Estimating the serial intervals of SARS-CoV-2 Omicron BA.4, BA.5, and BA.2.12.1 variants in Hong Kong. Influenza Other Respir Viruses 2023; 17:e13105. [PMID: 36824395 PMCID: PMC9942273 DOI: 10.1111/irv.13105] [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: 11/18/2022] [Revised: 01/12/2023] [Accepted: 01/15/2023] [Indexed: 02/10/2023] Open
Abstract
Empirical evidence on the epidemiological characteristics of the emerged SARS-CoV-2 variants could shed light on the transmission potential of the virus and strategic outbreak control planning. In this study, by using contact tracing data collected during an Omicron-predominant epidemic phase in Hong Kong, we estimated the mean serial interval of SARS-CoV-2 Omicron BA.4, BA.5, and BA.2.12.1 variants at 2.8 days (95% credible interval [CrI]: 1.5, 6.7), 2.7 days (95% CrI: 2.1, 3.6), and 4.4 days (95% CrI: 2.6, 7.5), respectively, with adjustment for right truncation and sampling bias. The short serial interval for the current circulating variant indicated that outbreak mitigations through contact tracing and case isolation would be quite challenging.
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Affiliation(s)
- Zihao Guo
- JC School of Public Health and Primary Care, Faculty of MedicineChinese University of Hong KongHong KongChina
| | - Shi Zhao
- JC School of Public Health and Primary Care, Faculty of MedicineChinese University of Hong KongHong KongChina,Centre for Health Systems and Policy Research, Faculty of MedicineChinese University of Hong KongHong KongChina
| | - Carrie Ho Kwan Yam
- JC School of Public Health and Primary Care, Faculty of MedicineChinese University of Hong KongHong KongChina,Centre for Health Systems and Policy Research, Faculty of MedicineChinese University of Hong KongHong KongChina
| | - Conglu Li
- JC School of Public Health and Primary Care, Faculty of MedicineChinese University of Hong KongHong KongChina
| | - Xiaoting Jiang
- JC School of Public Health and Primary Care, Faculty of MedicineChinese University of Hong KongHong KongChina
| | - Tsz Yu Chow
- JC School of Public Health and Primary Care, Faculty of MedicineChinese University of Hong KongHong KongChina,Centre for Health Systems and Policy Research, Faculty of MedicineChinese University of Hong KongHong KongChina
| | - Ka Chun Chong
- JC School of Public Health and Primary Care, Faculty of MedicineChinese University of Hong KongHong KongChina,Centre for Health Systems and Policy Research, Faculty of MedicineChinese University of Hong KongHong KongChina
| | - Eng Kiong Yeoh
- JC School of Public Health and Primary Care, Faculty of MedicineChinese University of Hong KongHong KongChina,Centre for Health Systems and Policy Research, Faculty of MedicineChinese University of Hong KongHong KongChina
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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] [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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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] [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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Pons-Salort M, John J, Watson OJ, Brazeau NF, Verity R, Kang G, Grassly NC. Reassessing Reported Deaths and Estimated Infection Attack Rate during the First 6 Months of the COVID-19 Epidemic, Delhi, India. Emerg Infect Dis 2022; 28:759-766. [PMID: 35213800 DOI: 10.1101/2021.03.23.21254092] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/23/2023] Open
Abstract
India reported >10 million coronavirus disease (COVID-19) cases and 149,000 deaths in 2020. To reassess reported deaths and estimate incidence rates during the first 6 months of the epidemic, we used a severe acute respiratory syndrome coronavirus 2 transmission model fit to data from 3 serosurveys in Delhi and time-series documentation of reported deaths. We estimated 48.7% (95% credible interval 22.1%-76.8%) cumulative infection in the population through the end of September 2020. Using an age-adjusted overall infection fatality ratio based on age-specific estimates from mostly high-income countries, we estimated that just 15.0% (95% credible interval 9.3%-34.0%) of COVID-19 deaths had been reported, indicating either substantial underreporting or lower age-specific infection-fatality ratios in India than in high-income countries. Despite the estimated high attack rate, additional epidemic waves occurred in late 2020 and April-May 2021. Future dynamics will depend on the duration of natural and vaccine-induced immunity and their effectiveness against new variants.
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Pons-Salort M, John J, Watson OJ, Brazeau NF, Verity R, Kang G, Grassly NC. Reassessing Reported Deaths and Estimated Infection Attack Rate during the First 6 Months of the COVID-19 Epidemic, Delhi, India. Emerg Infect Dis 2022; 28:759-766. [PMID: 35213800 PMCID: PMC8962916 DOI: 10.3201/eid2804.210879] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023] Open
Abstract
India reported >10 million coronavirus disease (COVID-19) cases and 149,000 deaths in 2020. To reassess reported deaths and estimate incidence rates during the first 6 months of the epidemic, we used a severe acute respiratory syndrome coronavirus 2 transmission model fit to data from 3 serosurveys in Delhi and time-series documentation of reported deaths. We estimated 48.7% (95% credible interval 22.1%-76.8%) cumulative infection in the population through the end of September 2020. Using an age-adjusted overall infection fatality ratio based on age-specific estimates from mostly high-income countries, we estimated that just 15.0% (95% credible interval 9.3%-34.0%) of COVID-19 deaths had been reported, indicating either substantial underreporting or lower age-specific infection-fatality ratios in India than in high-income countries. Despite the estimated high attack rate, additional epidemic waves occurred in late 2020 and April-May 2021. Future dynamics will depend on the duration of natural and vaccine-induced immunity and their effectiveness against new variants.
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