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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] [What about the content of this article? (0)] [Affiliation(s)] [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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Murphy C, Lim WW, Mills C, Wong JY, Chen D, Xie Y, Li M, Gould S, Xin H, Cheung JK, Bhatt S, Cowling BJ, Donnelly CA. Effectiveness of social distancing measures and lockdowns for reducing transmission of COVID-19 in non-healthcare, community-based settings. Philos Trans A Math Phys Eng Sci 2023; 381:20230132. [PMID: 37611629 PMCID: PMC10446910 DOI: 10.1098/rsta.2023.0132] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Accepted: 05/23/2023] [Indexed: 08/25/2023]
Abstract
Social distancing measures (SDMs) are community-level interventions that aim to reduce person-to-person contacts in the community. SDMs were a major part of the responses first to contain, then to mitigate, the spread of SARS-CoV-2 in the community. Common SDMs included limiting the size of gatherings, closing schools and/or workplaces, implementing work-from-home arrangements, or more stringent restrictions such as lockdowns. This systematic review summarized the evidence for the effectiveness of nine SDMs. Almost all of the studies included were observational in nature, which meant that there were intrinsic risks of bias that could have been avoided were conditions randomly assigned to study participants. There were no instances where only one form of SDM had been in place in a particular setting during the study period, making it challenging to estimate the separate effect of each intervention. The more stringent SDMs such as stay-at-home orders, restrictions on mass gatherings and closures were estimated to be most effective at reducing SARS-CoV-2 transmission. Most studies included in this review suggested that combinations of SDMs successfully slowed or even stopped SARS-CoV-2 transmission in the community. However, individual effects and optimal combinations of interventions, as well as the optimal timing for particular measures, require further investigation. This article is part of the theme issue 'The effectiveness of non-pharmaceutical interventions on the COVID-19 pandemic: the evidence'.
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Affiliation(s)
- Caitriona Murphy
- 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, People's Republic of China
| | - Wey Wen Lim
- 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, People's Republic of China
| | - Cathal Mills
- Department of Statistics, University of Oxford, Oxford, UK
| | - Jessica Y. Wong
- 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, People's Republic of China
| | - Dongxuan Chen
- 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, People's Republic of China
- Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, New Territories, Hong Kong, People's Republic of China
| | - Yanmy Xie
- 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, People's Republic of China
| | - Mingwei Li
- 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, People's Republic of China
- Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, New Territories, Hong Kong, People's Republic of China
| | - Susan Gould
- Department of Clinical Sciences, Liverpool School of Tropical Medicine, Liverpool, UK
- Tropical and Infectious Disease Unit, Liverpool University Hospitals NHS Foundation Trust, Liverpool, UK
| | - Hualei Xin
- 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, People's Republic of China
| | - Justin K. Cheung
- 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, People's Republic of China
| | - Samir Bhatt
- Section of Epidemiology, Department of Public Health, University of Copenhagen, Kobenhavn, Denmark
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Faculty of Medicine, Imperial College London, London, UK
| | - 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, People's Republic of China
- Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, New Territories, Hong Kong, People's Republic of China
| | - Christl A. Donnelly
- Department of Statistics, University of Oxford, Oxford, UK
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Faculty of Medicine, Imperial College London, London, UK
- Pandemic Sciences Institute, University of Oxford, Oxford, UK
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Lal R, Huang W, Li Z, Prasad S. An assessment of transmission dynamics via time-varying reproduction number of the second wave of the COVID-19 epidemic in Fiji. R Soc Open Sci 2022; 9:220004. [PMID: 36061527 PMCID: PMC9428540 DOI: 10.1098/rsos.220004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Accepted: 08/16/2022] [Indexed: 06/15/2023]
Abstract
This study involves the estimation of a key epidemiological parameter for evaluating and monitoring the transmissibility of a disease. The time-varying reproduction number is the index for quantifying the transmissibility of infectious diseases. Accurate and timely estimation of the time-varying reproduction number is essential for optimizing non-pharmacological interventions and movement control orders during epidemics. The time-varying reproduction number for the second wave of the pandemic in Fiji is estimated using the popular EpiEstim R package and the publicly available COVID-19 data from 19 April 2021 to 1 December 2021. Our findings show that the non-pharmacological interventions and movement control orders introduced and enforced by the Fijian Government had a significant impact in preventing the spread of COVID-19. Moreover, the results show that many restrictions were either relaxed or eased when the time-varying reproduction number was below the threshold value of 1. The results have provided some information on the second wave of the COVID-19 pandemic that could be used in the future as a guide for public health policymakers in Fiji. Estimation of time-varying reproduction numbers would be helpful for continuous monitoring of the effectiveness of the current public health policies that are being implemented in Fiji.
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Affiliation(s)
- Rajnesh Lal
- School of Mathematical and Computing Sciences, Fiji National University, Lautoka, Fiji
| | - Weidong Huang
- TD School, University of Technology Sydney, Ultimo, New South Wales 2007, Australia
| | - Zhenquan Li
- School of Computing and Mathematics, Charles Sturt University, Thurgoona, New South Wales 2640, Australia
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Ahmad NA, Mohd MH, Musa KI, Abdullah JM, Othman NA. Modelling COVID-19 Scenarios for the States and Federal Territories of Malaysia. Malays J Med Sci 2022; 28:1-9. [PMID: 35115883 PMCID: PMC8793970 DOI: 10.21315/mjms2021.28.5.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Accepted: 09/05/2021] [Indexed: 02/08/2023] Open
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes COVID-19 disease, which has become pandemic since December 2019. In the recent months, among five countries in the Southeast Asia, Malaysia has the highest per-capita daily new cases and daily new deaths. A mathematical modelling approach using a Singular Spectrum Analysis (SSA) technique was used to generate data-driven 30-days ahead forecasts for the number of daily cases in the states and federal territories in Malaysia at four consecutive time points between 27 July 2021 and 26 August 2021. Each forecast was produced using SSA prediction model of the current major trend at each time point. The objective is to understand the transition dynamics of COVID-19 in each state by analysing the direction of change of the major trends during the period of study. The states and federal territories in Malaysia were grouped in four categories based on the nature of the transition. Overall, it was found that the COVID-19 spread has progressed unevenly across states and federal territories. Major regions like Selangor, Kuala Lumpur, Putrajaya and Negeri Sembilan were in Group 3 (fast decrease in infectivity) and Labuan was in Group 4 (possible eradication of infectivity). Other states e.g. Pulau Pinang, Sabah, Sarawak, Kelantan and Johor were categorised in Group 1 (very high infectivity levels) with Perak, Kedah, Pahang, Terengganu and Melaka were classified in Group 2 (high infectivity levels). It is also cautioned that SSA provides a promising avenue for forecasting the transition dynamics of COVID-19; however, the reliability of this technique depends on the availability of good quality data.
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Affiliation(s)
- Noor Atinah Ahmad
- School of Mathematical Sciences, Universiti Sains Malaysia, Pulau Pinang, Malaysia
| | - Mohd Hafiz Mohd
- School of Mathematical Sciences, Universiti Sains Malaysia, Pulau Pinang, Malaysia
| | - Kamarul Imran Musa
- School of Medical Sciences, Universiti Sains Malaysia, Kubang Kerian, Kelantan, Malaysia
| | - Jafri Malin Abdullah
- School of Medical Sciences, Universiti Sains Malaysia, Kubang Kerian, Kelantan, Malaysia
| | - Nurul Ashikin Othman
- School of Mathematical Sciences, Universiti Sains Malaysia, Pulau Pinang, Malaysia
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Razak FA, Zamzuri ZH. Modelling Heterogeneity and Super Spreaders of the COVID-19 Spread through Malaysian Networks. Symmetry (Basel) 2021; 13:1954. [DOI: 10.3390/sym13101954] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Malaysia is multi-ethnic and diverse country. Heterogeneity, in terms of population interactions, is ingrained in the foundation of the country. Malaysian policies and social distancing measures are based on daily infections and R0 (average number of infections per infected person), estimated from the data. Models of the Malaysian COVID-19 spread are mostly based on the established SIR compartmental model and its variants. These models usually assume homogeneity and symmetrical full mixing in the population; thus, they are unable to capture super-spreading events which naturally occur due to heterogeneity. Moreover, studies have shown that when heterogeneity is present, R0 may be very different and even possibly misleading. The underlying spreading network is a crucial element, as it introduces heterogeneity for a more representative and realistic model of the spread through specific populations. Heterogeneity introduces more complexities in the modelling due to its asymmetrical nature of infection compared to the relatively symmetrical SIR compartmental model. This leads to a different way of calculating R0 and defining super-spreaders. Quantifying a super-spreader individual is related to the idea of importance in a network. The definition of a super-spreading individual depends on how super-spreading is defined. Even when the spreading is defined, it may not be clear that a single centrality always correlates with super-spreading, since centralities are network dependent. We proposed using a measure of super-spreading directly related to R0 and that will give a measure of ‘spreading’ regardless of the underlying network. We captured the vulnerability for varying degrees of heterogeneity and initial conditions by defining a measure to quantify the chances of epidemic spread in the simulations. We simulated the SIR spread on a real Malaysian network to illustrate the effects of this measure and heterogeneity on the number of infections. We also simulated super-spreading events (based on our definition) within the bounds of heterogeneity to demonstrate the effectiveness of the newly defined measure. We found that heterogeneity serves as a natural curve-flattening mechanism; therefore, the number of infections and R0 may be lower than expected. This may lead to a false sense of security, especially since heterogeneity makes the population vulnerable to super-spreading events.
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Md Zamri ASS, Singh S, Ghazali SM, Herng LC, Dass SC, Aris T, Ibrahim HM, Gill BS. Effectiveness of the movement control measures during the third wave of COVID-19 in Malaysia. Epidemiol Health 2021; 43:e2021073. [PMID: 34607399 PMCID: PMC8891114 DOI: 10.4178/epih.e2021073] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2021] [Accepted: 09/23/2021] [Indexed: 11/29/2022] Open
Abstract
OBJECTIVES Starting in March 2020, movement control measures were instituted across several phases in Malaysia to break the chain of transmission of coronavirus disease 2019 (COVID-19). In this study, we developed a susceptible-exposed-infected-recovered (SEIR) model to examine the effects of the various phases of movement control measures on disease transmissibility and the trend of cases during the third wave of the COVID-19 pandemic in Malaysia. METHODS Three SEIR models were developed using the R programming software ODIN interface based on COVID-19 case data from September 1, 2020, to March 29, 2021. The models were validated and subsequently used to provide forecasts of daily cases from October 14, 2020, to March 29, 2021, based on 3 phases of movement control measures. RESULTS We found that the reproduction rate (R-value) of COVID-19 decreased by 59.1% from an initial high of 2.2 during the nationwide Recovery Movement Control Order (RMCO) to 0.9 during the Movement Control Order (MCO) and Conditional MCO (CMCO) phases. In addition, the observed cumulative and daily highest numbers of cases were much lower than the forecasted cumulative and daily highest numbers of cases (by 64.4-98.9% and 68.8-99.8%, respectively). CONCLUSIONS The movement control measures progressively reduced the R-value during the COVID-19 pandemic. In addition, more stringent movement control measures such as the MCO and CMCO were effective for further lowering the R-value and case numbers during the third wave of the COVID-19 pandemic in Malaysia due to their higher stringency than the nationwide RMCO.
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Affiliation(s)
| | | | | | | | | | - Tahir Aris
- Institute for Medical Research, SHAH ALAM, Malaysia
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Dalawi I, Chen XW, Isa MR, Azhar ZI, Mohd Nor F. Assessment of Knowledge, Attitude, Practise and Health Literacy (KAPH) Towards COVID-19 in Post-COVID-19 New Reality: The Need and Its Challenges in Malaysia. Front Public Health 2021; 9:704115. [PMID: 34368066 PMCID: PMC8342846 DOI: 10.3389/fpubh.2021.704115] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2021] [Accepted: 06/21/2021] [Indexed: 11/17/2022] Open
Affiliation(s)
- Izzaty Dalawi
- Department of Public Health Medicine, Faculty of Medicine, Universiti Teknologi MARA, Sungai Buloh Campus, Sungai Buloh, Malaysia.,Ministry of Health, Putrajaya, Malaysia
| | - Xin Wee Chen
- Department of Public Health Medicine, Faculty of Medicine, Universiti Teknologi MARA, Sungai Buloh Campus, Sungai Buloh, Malaysia
| | - Mohamad Rodi Isa
- Department of Public Health Medicine, Faculty of Medicine, Universiti Teknologi MARA, Sungai Buloh Campus, Sungai Buloh, Malaysia
| | - Zahir Izuan Azhar
- Department of Public Health Medicine, Faculty of Medicine, Universiti Teknologi MARA, Sungai Buloh Campus, Sungai Buloh, Malaysia
| | - Fadzilah Mohd Nor
- Department of Medical Microbiology and Parasitology, Faculty of Medicine, Universiti Teknologi MARA, Sungai Buloh Campus, Sungai Buloh, Malaysia.,Integrative Pharmacogenomics Institute (iPROMISE), Universiti Teknologi MARA, Puncak Alam Campus, Selangor, Malaysia
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Salman AM, Ahmed I, Mohd MH, Jamiluddin MS, Dheyab MA. Scenario analysis of COVID-19 transmission dynamics in Malaysia with the possibility of reinfection and limited medical resources scenarios. Comput Biol Med 2021; 133:104372. [PMID: 33864970 PMCID: PMC8024227 DOI: 10.1016/j.compbiomed.2021.104372] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2021] [Revised: 03/30/2021] [Accepted: 03/30/2021] [Indexed: 12/11/2022]
Abstract
COVID-19 is a major health threat across the globe, which causes severe acute respiratory syndrome (SARS), and it is highly contagious with significant mortality. In this study, we conduct a scenario analysis for COVID-19 in Malaysia using a simple universality class of the SIR system and extensions thereof (i.e., the inclusion of temporary immunity through the reinfection problems and limited medical resources scenarios leads to the SIRS-type model). This system has been employed in order to provide further insights on the long-term outcomes of COVID-19 pandemic. As a case study, the COVID-19 transmission dynamics are investigated using daily confirmed cases in Malaysia, where some of the epidemiological parameters of this system are estimated based on the fitting of the model to real COVID-19 data released by the Ministry of Health Malaysia (MOH). We observe that this model is able to mimic the trend of infection trajectories of COVID-19 pandemic in Malaysia and it is possible for transmission dynamics to be influenced by the reinfection force and limited medical resources problems. A rebound effect in transmission could occur after several years and this situation depends on the intensity of reinfection force. Our analysis also depicts the existence of a critical value in reinfection threshold beyond which the infection dynamics persist and the COVID-19 outbreaks are rather hard to eradicate. Therefore, understanding the interplay between distinct epidemiological factors using mathematical modelling approaches could help to support authorities in making informed decisions so as to control the spread of this pandemic effectively.
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Affiliation(s)
- Amer M Salman
- School of Mathematical Sciences, Universiti Sains Malaysia, 11800 USM, Penang, Malaysia
| | - Issam Ahmed
- School of Mathematical Sciences, Universiti Sains Malaysia, 11800 USM, Penang, Malaysia
| | - Mohd Hafiz Mohd
- School of Mathematical Sciences, Universiti Sains Malaysia, 11800 USM, Penang, Malaysia.
| | | | - Mohammed Ali Dheyab
- Nano-Optoelectronics Research and Technology Lab (NORLab), School of Physics, Universiti Sains Malaysia, 11800 USM, Penang, Malaysia
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