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Mavragani A, Eysenbach G, Chang WJ, Lin TY, Jen GHH, Hsu CY, Wang ST, Dang H, Chen SLS. New Surveillance Metrics for Alerting Community-Acquired Outbreaks of Emerging SARS-CoV-2 Variants Using Imported Case Data: Bayesian Markov Chain Monte Carlo Approach. JMIR Public Health Surveill 2022; 8:e40866. [PMID: 36265134 PMCID: PMC9746786 DOI: 10.2196/40866] [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: 07/08/2022] [Revised: 08/15/2022] [Accepted: 10/18/2022] [Indexed: 11/06/2022] Open
Abstract
BACKGROUND Global transmission from imported cases to domestic cluster infections is often the origin of local community-acquired outbreaks when facing emerging SARS-CoV-2 variants. OBJECTIVE We aimed to develop new surveillance metrics for alerting emerging community-acquired outbreaks arising from new strains by monitoring the risk of small domestic cluster infections originating from few imported cases of emerging variants. METHODS We used Taiwanese COVID-19 weekly data on imported cases, domestic cluster infections, and community-acquired outbreaks. The study period included the D614G strain in February 2020, the Alpha and Delta variants of concern (VOCs) in 2021, and the Omicron BA.1 and BA.2 VOCs in April 2022. The number of cases arising from domestic cluster infection caused by imported cases (Dci/Imc) per week was used as the SARS-CoV-2 strain-dependent surveillance metric for alerting local community-acquired outbreaks. Its upper 95% credible interval was used as the alert threshold for guiding the rapid preparedness of containment measures, including nonpharmaceutical interventions (NPIs), testing, and vaccination. The 2 metrics were estimated by using the Bayesian Monte Carlo Markov Chain method underpinning the directed acyclic graphic diagram constructed by the extra-Poisson (random-effect) regression model. The proposed model was also used to assess the most likely week lag of imported cases prior to the current week of domestic cluster infections. RESULTS A 1-week lag of imported cases prior to the current week of domestic cluster infections was considered optimal. Both metrics of Dci/Imc and the alert threshold varied with SARS-CoV-2 variants and available containment measures. The estimates were 9.54% and 12.59%, respectively, for D614G and increased to 14.14% and 25.10%, respectively, for the Alpha VOC when only NPIs and testing were available. The corresponding figures were 10.01% and 13.32% for the Delta VOC, but reduced to 4.29% and 5.19% for the Omicron VOC when NPIs, testing, and vaccination were available. The rapid preparedness of containment measures guided by the estimated metrics accounted for the lack of community-acquired outbreaks during the D614G period, the early Alpha VOC period, the Delta VOC period, and the Omicron VOC period between BA.1 and BA.2. In contrast, community-acquired outbreaks of the Alpha VOC in mid-May 2021, Omicron BA.1 VOC in January 2022, and Omicron BA.2 VOC from April 2022 onwards, were indicative of the failure to prepare containment measures guided by the alert threshold. CONCLUSIONS We developed new surveillance metrics for estimating the risk of domestic cluster infections with increasing imported cases and its alert threshold for community-acquired infections varying with emerging SARS-CoV-2 strains and the availability of containment measures. The use of new surveillance metrics is important in the rapid preparedness of containment measures for averting large-scale community-acquired outbreaks arising from emerging imported SARS-CoV-2 variants.
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Affiliation(s)
| | | | - Wei-Jung Chang
- Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan
| | - Ting-Yu Lin
- Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan
| | - Grace Hsiao-Hsuan Jen
- School of Oral Hygiene, College of Oral Medicine, Taipei Medical University, Taipei, Taiwan
| | - Chen-Yang Hsu
- Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan.,Daichung Hospital, Miaoli, Taiwan
| | - Sen-Te Wang
- Department of Family Medicine, Taipei Medical University Hospital, Taipei, Taiwan.,Department of Family Medicine, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
| | - Huong Dang
- Department of Economics and Finance, University of Canterbury, Christchurch, New Zealand
| | - Sam Li-Sheng Chen
- School of Oral Hygiene, College of Oral Medicine, Taipei Medical University, Taipei, Taiwan.,Research Center of Cancer Translational Medicine, Taipei Medical University, Taipei, Taiwan
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Jen GHH, Yen AMF, Hsu CY, Chen SLS, Chen THH. A pre-symptomatic incubation model for precision strategies of screening, quarantine, and isolation based on imported COVID-19 cases in Taiwan. Sci Rep 2022; 12:6053. [PMID: 35411061 PMCID: PMC8998162 DOI: 10.1038/s41598-022-09863-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Accepted: 03/21/2022] [Indexed: 12/31/2022] Open
Abstract
Facing the emerging COVID viral variants and the uneven distribution of vaccine worldwide, imported pre-symptomatic COVID-19 cases play a pivotal role in border control strategies. A stochastic disease process and computer simulation experiments with Bayesian underpinning was therefore developed to model pre-symptomatic disease progression during incubation period on which we were based to provide precision strategies for containing the resultant epidemic caused by imported COVID-19 cases. We then applied the proposed model to data on 1051 imported COVID-19 cases among inbound passengers to Taiwan between March 2020 and April 2021. The overall daily rate (per 100,000) of pre-symptomatic COVID-19 cases was estimated as 106 (95% credible interval (CrI): 95-117) in March-June 2020, fell to 37 (95% CrI: 28-47) in July-September 2020 (p < 0.0001), resurged to 141 (95% CrI: 118-164) in October-December 2020 (p < 0.0001), and declined to 90 (95% CrI: 73-108) in January-April 2021 (p = 0.0004). Given the median dwelling time, over 82% cases would progress from pre-symptomatic to symptomatic phase in 5-day quarantine. The time required for quarantine given two real-time polymerase chain reaction (RT-PCR) tests depends on the risk of departing countries, testing and quarantine strategies, and whether the passengers have vaccine jabs. Our proposed four-compartment stochastic process and computer simulation experiments design underpinning Bayesian MCMC algorithm facilitated the development of precision strategies for imported COVID-19 cases.
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Affiliation(s)
- Grace Hsiao-Hsuan Jen
- School of Oral Hygiene, College of Oral Medicine, Taipei Medical University, Taipei, Taiwan
| | - Amy Ming-Fang Yen
- School of Oral Hygiene, College of Oral Medicine, Taipei Medical University, Taipei, Taiwan
| | - Chen-Yang Hsu
- Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan
- Department of Emergency, Dachung Hospital, Miaoli, Taiwan
| | - Sam Li-Sheng Chen
- School of Oral Hygiene, College of Oral Medicine, Taipei Medical University, Taipei, Taiwan
| | - Tony Hsiu-Hsi Chen
- Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan.
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Chen CL, Lai CC, Luh DL, Chuang SY, Yang KC, Yeh YP, Ming-Fang Yen A, Chang KJ, Chang RE, Li-Sheng Chen S. Review of epidemic, containment strategies, clinical management, and economic evaluation of COVID-19 pandemic. J Formos Med Assoc 2021; 120 Suppl 1:S6-S18. [PMID: 34116896 PMCID: PMC8156902 DOI: 10.1016/j.jfma.2021.05.022] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2021] [Revised: 05/17/2021] [Accepted: 05/19/2021] [Indexed: 01/08/2023] Open
Abstract
The spread of the emerging pathogen, named as SARS-CoV-2, has led to an unprecedented COVID-19 pandemic since 1918 influenza pandemic. This review first sheds light on the similarity on global transmission, surges of pandemics, and the disparity of prevention between two pandemics. Such a brief comparison also provides an insight into the potential sequelae of COVID-19 based on the inference drawn from the fact that a cascade of successive influenza pandemic occurred after 1918 and also the previous experience on the epidemic of SARS and MERS occurring in 2003 and 2015, respectively. We then propose a systematic framework for elucidating emerging infectious disease (EID) such as COVID-19 with a panorama viewpoint from natural infection and disease process, public health interventions (non-pharmaceutical interventions (NPIs) and vaccine), clinical treatments and therapies (antivirals), until global aspects of health and economic loss, and economic evaluation of interventions with emphasis on mass vaccination. This review not only concisely delves for evidence-based scientific literatures from the origin of outbreak, the spread of SARS-CoV-2 to three surges of pandemic, and NPIs and vaccine uptakes but also provides a new insight into how to apply big data analytics to identify unprecedented discoveries through COVID-19 pandemic scenario embracing from biomedical to economic viewpoints.
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Affiliation(s)
- Chi-Ling Chen
- Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan; Graduate Institute of Clinical Medicine, College of Medicine, National Taiwan University, Taipei, Taiwan; Department of Surgery, National Taiwan University Hospital, Taipei, Taiwan
| | - Chao-Chih Lai
- Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan; Emergency Department of Taipei City Hospital, Ren-Ai Branch, Taiwan
| | - Dih-Ling Luh
- Department of Public Health, Chung Shan Medical University, Taichung, Taiwan
| | - Shao-Yuan Chuang
- Institute of Population Health Sciences, National Health Research Institutes, Taiwan
| | - Kuen-Cheh Yang
- Department of Family Medicine, College of Medicine, National Taiwan University, Taipei, Taiwan
| | - Yen-Po Yeh
- Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan; Changhua County Public Health Bureau, Changhua, Taiwan
| | - Amy Ming-Fang Yen
- School of Oral Hygiene, College of Oral Medicine, Taipei Medical University, Taipei, Taiwan
| | - King-Jen Chang
- Department of Surgery, National Taiwan University Hospital, Taipei, Taiwan
| | - Ray-E Chang
- Institute of Health Policy and Management, College of Public Health, National Taiwan University, Taipei, Taiwan
| | - Sam Li-Sheng Chen
- School of Oral Hygiene, College of Oral Medicine, Taipei Medical University, Taipei, Taiwan.
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Wang WC, Lin TY, Chiu SYH, Chen CN, Sarakarn P, Ibrahim M, Chen SLS, Chen HH, Yeh YP. Classification of community-acquired outbreaks for the global transmission of COVID-19: Machine learning and statistical model analysis. J Formos Med Assoc 2021; 120 Suppl 1:S26-S37. [PMID: 34083090 PMCID: PMC8126178 DOI: 10.1016/j.jfma.2021.05.010] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Revised: 05/06/2021] [Accepted: 05/07/2021] [Indexed: 01/10/2023] Open
Abstract
Background As Coronavirus disease 2019 (COVID-19) pandemic led to the unprecedent large-scale repeated surges of epidemics worldwide since the end of 2019, data-driven analysis to look into the duration and case load of each episode of outbreak worldwide has been motivated. Methods Using open data repository with daily infected, recovered and death cases in the period between March 2020 and April 2021, a descriptive analysis was performed. The susceptible-exposed-infected-recovery model was used to estimate the effective productive number (Rt). The duration taken from Rt > 1 to Rt < 1 and case load were first modelled by using the compound Poisson method. Machine learning analysis using the K-means clustering method was further adopted to classify patterns of community-acquired outbreaks worldwide. Results The global estimated Rt declined after the first surge of COVID-19 pandemic but there were still two major surges of epidemics occurring in September 2020 and March 2021, respectively, and numerous episodes due to various extents of Nonpharmaceutical Interventions (NPIs). Unsupervised machine learning identified five patterns as “controlled epidemic”, “mutant propagated epidemic”, “propagated epidemic”, “persistent epidemic” and “long persistent epidemic” with the corresponding duration and the logarithm of case load from the lowest (18.6 ± 11.7; 3.4 ± 1.8)) to the highest (258.2 ± 31.9; 11.9 ± 2.4). Countries like Taiwan outside five clusters were classified as no community-acquired outbreak. Conclusion Data-driven models for the new classification of community-acquired outbreaks are useful for global surveillance of uninterrupted COVID-19 pandemic and provide a timely decision support for the distribution of vaccine and the optimal NPIs from global to local community.
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Affiliation(s)
- Wei-Chun Wang
- Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan
| | - Ting-Yu Lin
- Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan
| | - Sherry Yueh-Hsia Chiu
- Department of Health Care Management and Healthy Aging Research Center, Chang Gung University, Taoyuan, Taiwan
| | - Chiung-Nien Chen
- Center for Functional Image and Interventional Therapy, National Taiwan University, Taipei, Taiwan; Department of Surgery, National Taiwan University Hospital, Taipei, Taiwan
| | - Pongdech Sarakarn
- Epidemiology and Biostatistics Department, Faculty of Public Health, Khon Kaen University, Khon Kaen, Thailand
| | - Mohd Ibrahim
- Department of Community and Family Medicine, University Malaysia Sabah, Malaysia
| | - Sam Li-Sheng Chen
- School of Oral Hygiene, College of Oral Medicine, Taipei Medical University, Taipei, Taiwan
| | - Hsiu-Hsi Chen
- Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan
| | - Yen-Po Yeh
- Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan; Changhua Public Health Bureau, Changhua, Taiwan.
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