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Lim JS, Vergne T, Kim E, Guinat C, Dellicour S, Andraud M. A spatially-heterogeneous impact of fencing on the African swine fever wavefront in the Korean wild boar population. Vet Res 2024; 55:163. [PMID: 39696606 DOI: 10.1186/s13567-024-01422-7] [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: 12/07/2023] [Accepted: 09/15/2024] [Indexed: 12/20/2024] Open
Abstract
In October 2019, South Korea's first case of African swine fever (ASF) was reported in wild boar in the north of the country. Despite the implementation of a 2300 km-long fencing strategy, the ASF wavefront continued to invade southward. Our study aimed to investigate the ASF wavefront dynamics in different regions of South Korea, as well as to assess the effectiveness of the fencing measures on ASF dispersal and wavefront velocity. From the nationwide wild boar surveillance system, we extracted 2661 cases, starting from 2 October 2019 (first detection) to 15 September 2022. The cases were categorised into four main spatiotemporal clusters. The average wavefront velocity over the four clusters was estimated at 0.52 km/week, with the cluster in the eastern part of the Korean peninsula exhibiting the fastest velocity (0.99 km/week) compared to the other clusters (0.44, 0.31, and 0.15 km/week). We hypothesise that these differences are related to different wild boar densities due to heterogeneous habitat suitability. We also found that fencing significantly impacted ASF dispersal in only two of the four main clusters, with no evidence that fencing slowed down the spread of the wavefront in any of the clusters. We argue that this heterogeneity might result from fencing locations being misaligned with the true (and unobserved) wavefront.
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Affiliation(s)
- Jun-Sik Lim
- IHAP, Université de Toulouse, INRAE, ENVT, Toulouse, France
| | | | - Eutteum Kim
- Department of Companion Animal Industry, Daegu University, Gyeongsan-si, Republic of Korea
| | - Claire Guinat
- IHAP, Université de Toulouse, INRAE, ENVT, Toulouse, France
| | - Simon Dellicour
- Spatial Epidemiology Lab (SpELL), Université Libre de Bruxelles, Brussels, Belgium
- Department of Microbiology, Immunology and Transplantation, Laboratory for Clinical and Epidemiological Virology, Rega Institute, KU Louvain, University of Leuven, Louvain, Belgium
| | - Mathieu Andraud
- Epidemiology, Health and Welfare Research Unit, Ploufragan‑Plouzané‑Niort Laboratory, Anses, French Agency for Food, Environmental and Occupational Health & Safety, Ploufragan, France.
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2
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Thompson R, Hart W, Keita M, Fall I, Gueye A, Chamla D, Mossoko M, Ahuka-Mundeke S, Nsio-Mbeta J, Jombart T, Polonsky J. Using real-time modelling to inform the 2017 Ebola outbreak response in DR Congo. Nat Commun 2024; 15:5667. [PMID: 38971835 PMCID: PMC11227569 DOI: 10.1038/s41467-024-49888-5] [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: 02/12/2024] [Accepted: 06/19/2024] [Indexed: 07/08/2024] Open
Abstract
Important policy questions during infections disease outbreaks include: i) How effective are particular interventions?; ii) When can resource-intensive interventions be removed? We used mathematical modelling to address these questions during the 2017 Ebola outbreak in Likati Health Zone, Democratic Republic of the Congo (DRC). Eight cases occurred before 15 May 2017, when the Ebola Response Team (ERT; co-ordinated by the World Health Organisation and DRC Ministry of Health) was deployed to reduce transmission. We used a branching process model to estimate that, pre-ERT arrival, the reproduction number was R = 1.49 (95% credible interval ( 0.67, 2.81 ) ). The risk of further cases occurring without the ERT was estimated to be 0.97 (97%). However, no cases materialised, suggesting that the ERT's measures were effective. We also estimated the risk of withdrawing the ERT in real-time. By the actual ERT withdrawal date (2 July 2017), the risk of future cases without the ERT was only 0.01, indicating that the ERT withdrawal decision was safe. We evaluated the sensitivity of our results to the estimated R value and considered different criteria for determining the ERT withdrawal date. This research provides an extensible modelling framework that can be used to guide decisions about when to relax interventions during future outbreaks.
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Affiliation(s)
- R Thompson
- Mathematical Institute, University of Oxford, Oxford, UK.
| | - W Hart
- Mathematical Institute, University of Oxford, Oxford, UK
| | - M Keita
- World Health Organization, Regional Office for Africa, Brazzaville, Democratic Republic of the Congo
- Institute of Global Health, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - I Fall
- Global Neglected Tropical Diseases Programme, World Health Organization, Geneva, Switzerland
| | - A Gueye
- World Health Organization, Regional Office for Africa, Brazzaville, Democratic Republic of the Congo
| | - D Chamla
- World Health Organization, Regional Office for Africa, Brazzaville, Democratic Republic of the Congo
| | - M Mossoko
- Institut National de Santé Publique, Ministry of Public Health, Hygiene and Prevention, Kinshasa, Democratic Republic of the Congo
| | - S Ahuka-Mundeke
- Institut National de Recherche Biomédicale, Kinshasa, Democratic Republic of the Congo
| | - J Nsio-Mbeta
- Institut National de Santé Publique, Ministry of Public Health, Hygiene and Prevention, Kinshasa, Democratic Republic of the Congo
| | - T Jombart
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College, London, UK
| | - J Polonsky
- Geneva Centre of Humanitarian Studies, University of Geneva, Geneva, Switzerland
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3
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Hart WS, Buckingham JM, Keita M, Ahuka-Mundeke S, Maini PK, Polonsky JA, Thompson RN. Optimizing the timing of an end-of-outbreak declaration: Ebola virus disease in the Democratic Republic of the Congo. SCIENCE ADVANCES 2024; 10:eado7576. [PMID: 38959306 PMCID: PMC11221504 DOI: 10.1126/sciadv.ado7576] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Accepted: 05/23/2024] [Indexed: 07/05/2024]
Abstract
Following the apparent final case in an Ebola virus disease (EVD) outbreak, the decision to declare the outbreak over must balance societal benefits of relaxing interventions against the risk of resurgence. Estimates of the end-of-outbreak probability (the probability that no future cases will occur) provide quantitative evidence that can inform the timing of an end-of-outbreak declaration. An existing modeling approach for estimating the end-of-outbreak probability requires comprehensive contact tracing data describing who infected whom to be available, but such data are often unavailable or incomplete during outbreaks. Here, we develop a Markov chain Monte Carlo-based approach that extends the previous method and does not require contact tracing data. Considering data from two EVD outbreaks in the Democratic Republic of the Congo, we find that data describing who infected whom are not required to resolve uncertainty about when to declare an outbreak over.
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Affiliation(s)
- William S. Hart
- Wolfson Centre for Mathematical Biology, Mathematical Institute, University of Oxford, Oxford OX2 6GG, UK
| | - Jack M. Buckingham
- EPSRC Centre for Doctoral Training in Mathematics for Real-World Systems, Mathematics Institute, University of Warwick, Coventry CV4 7AL, UK
| | - Mory Keita
- World Health Organization, Regional Office for Africa, Brazzaville, Republic of the Congo
- Institute of Global Health, Faculty of Medicine, University of Geneva, Geneva 1202, Switzerland
| | - Steve Ahuka-Mundeke
- Institut National de Recherche Biomédicale, Kinshasa, Democratic Republic of the Congo
| | - Philip K. Maini
- Wolfson Centre for Mathematical Biology, Mathematical Institute, University of Oxford, Oxford OX2 6GG, UK
| | - Jonathan A. Polonsky
- Geneva Centre of Humanitarian Studies, University of Geneva, Geneva 1205, Switzerland
| | - Robin N. Thompson
- Wolfson Centre for Mathematical Biology, Mathematical Institute, University of Oxford, Oxford OX2 6GG, UK
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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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5
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Ogi-Gittins I, Hart WS, Song J, Nash RK, Polonsky J, Cori A, Hill EM, Thompson RN. A simulation-based approach for estimating the time-dependent reproduction number from temporally aggregated disease incidence time series data. Epidemics 2024; 47:100773. [PMID: 38781911 DOI: 10.1016/j.epidem.2024.100773] [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: 09/12/2023] [Revised: 02/29/2024] [Accepted: 05/13/2024] [Indexed: 05/25/2024] Open
Abstract
Tracking pathogen transmissibility during infectious disease outbreaks is essential for assessing the effectiveness of public health measures and planning future control strategies. A key measure of transmissibility is the time-dependent reproduction number, which has been estimated in real-time during outbreaks of a range of pathogens from disease incidence time series data. While commonly used approaches for estimating the time-dependent reproduction number can be reliable when disease incidence is recorded frequently, such incidence data are often aggregated temporally (for example, numbers of cases may be reported weekly rather than daily). As we show, commonly used methods for estimating transmissibility can be unreliable when the timescale of transmission is shorter than the timescale of data recording. To address this, here we develop a simulation-based approach involving Approximate Bayesian Computation for estimating the time-dependent reproduction number from temporally aggregated disease incidence time series data. We first use a simulated dataset representative of a situation in which daily disease incidence data are unavailable and only weekly summary values are reported, demonstrating that our method provides accurate estimates of the time-dependent reproduction number under such circumstances. We then apply our method to two outbreak datasets consisting of weekly influenza case numbers in 2019-20 and 2022-23 in Wales (in the United Kingdom). Our simple-to-use approach will allow accurate estimates of time-dependent reproduction numbers to be obtained from temporally aggregated data during future infectious disease outbreaks.
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Affiliation(s)
- I Ogi-Gittins
- Mathematics Institute, University of Warwick, Coventry CV4 7AL, UK; Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research (SBIDER), University of Warwick, Coventry CV4 7AL, UK
| | - W S Hart
- Mathematical Institute, University of Oxford, Oxford OX2 6GG, UK
| | - J Song
- Communicable Disease Surveillance Centre, Health Protection Division, Public Health Wales, Cardiff CF10 4BZ, UK
| | - R K Nash
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College, London W2 1PG, UK
| | - J Polonsky
- Geneva Centre of Humanitarian Studies, University of Geneva, Geneva 1205, Switzerland
| | - A Cori
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College, London W2 1PG, UK
| | - E M Hill
- Mathematics Institute, University of Warwick, Coventry CV4 7AL, UK; Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research (SBIDER), University of Warwick, Coventry CV4 7AL, UK
| | - R N Thompson
- Mathematical Institute, University of Oxford, Oxford OX2 6GG, UK.
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Lin KY, Pan SC, Wang JT, Fang CT, Liao CH, Cheng CY, Tseng SH, Yang CH, Chen YC, Chang SC. Preventing and controlling intra-hospital spread of COVID-19 in Taiwan - Looking back and moving forward. J Formos Med Assoc 2024; 123 Suppl 1:S27-S38. [PMID: 37268473 PMCID: PMC10201313 DOI: 10.1016/j.jfma.2023.05.018] [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: 02/22/2023] [Accepted: 05/18/2023] [Indexed: 06/04/2023] Open
Abstract
COVID-19 has exposed major weaknesses in the healthcare settings. The surge in COVID-19 cases increases the demands of health care, endangers vulnerable patients, and threats occupational safety. In contrast to a hospital outbreak of SARS leading to a whole hospital quarantined, at least 54 hospital outbreaks following a COVID-19 surge in the community were controlled by strengthened infection prevention and control measures for preventing transmission from community to hospitals as well as within hospitals. Access control measures include establishing triage, epidemic clinics, and outdoor quarantine stations. Visitor access restriction is applied to inpatients to limit the number of visitors. Health monitoring and surveillance is applied to healthcare personnel, including self-reporting travel declaration, temperature, predefined symptoms, and test results. Isolation of the confirmed cases during the contagious period and quarantine of the close contacts during the incubation period are critical for containment. The target populations and frequency of SARS-CoV-2 PCR and rapid antigen testing depend on the level of transmission. Case investigation and contact tracing should be comprehensive to identify the close contacts to prevent further transmission. These facility-based infection prevention and control strategies help reduce hospital transmission of SARS-CoV-2 to a minimum in Taiwan.
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Affiliation(s)
- Kuan-Yin Lin
- Center for Infection Control, National Taiwan University Hospital, Taipei, Taiwan; Department of Internal Medicine, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan; Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan
| | - Sung-Ching Pan
- Center for Infection Control, National Taiwan University Hospital, Taipei, Taiwan; Department of Internal Medicine, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan
| | - Jann-Tay Wang
- Department of Internal Medicine, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan
| | - Chi-Tai Fang
- Department of Internal Medicine, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan; Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan
| | - Chun-Hsing Liao
- Department of Internal Medicine, Far Eastern Memorial Hospital, New Taipei City, Taiwan; School of Medicine, College of Medicine, National Yang Ming Chiao Tung University, Taipei City, Taiwan
| | - Chien-Yu Cheng
- Department of Infectious Diseases, Taoyuan General Hospital, Ministry of Health and Welfare, Taoyuan, Taiwan; Institute of Public Health, College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Shu-Hui Tseng
- Taiwan Centers for Disease Control, Ministry of Health and Welfare, Taipei, Taiwan
| | - Chin-Hui Yang
- Taiwan Centers for Disease Control, Ministry of Health and Welfare, Taipei, Taiwan
| | - Yee-Chun Chen
- Center for Infection Control, National Taiwan University Hospital, Taipei, Taiwan; Department of Internal Medicine, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan.
| | - Shan-Chwen Chang
- Department of Internal Medicine, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan
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7
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Chen YH, Fang CT. Achieving COVID-19 zero without lockdown, January 2020 to March 2022: The Taiwan model explained. J Formos Med Assoc 2024; 123 Suppl 1:S8-S16. [PMID: 37714769 DOI: 10.1016/j.jfma.2023.09.001] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Revised: 09/03/2023] [Accepted: 09/04/2023] [Indexed: 09/17/2023] Open
Abstract
Despite never imposing a lockdown, Taiwan achieved COVID-19 zero, with reporting only 56 local coronavirus disease 2019 (COVID-19) cases after testing 126,987 individuals in 2020, and further contained a large outbreak rapidly and successfully in 2021. At the onset of the COVID-19 pandemic, our infectious disease modeling results indicated that testing and contact tracing alone would fail to contain the pandemic. However, by supplementing this approach with general public surgical mask-wearing, the reproduction number (R0) could be suppressed to less than 1. This would effectively contain the virus's spread within Taiwan, particularly when combined with strict border control measures to prevent the overwhelming influx of imported COVID-19 cases and ensure the capacity of the medical and public health systems remains resilient. These modeling results became the theoretical basis behind the highly successful Taiwan model against COVID-19 during 2020-2021, supporting by negative excess mortality, seroepidemiological surveys, and molecular epidemiological analyses. This is a public health triumph demonstrating that a democratic and humane approach to the COVID-19 pandemic is not only feasible but highly effective. It also highlights the crucial role of infectious disease modeling in assisting the formulation of a successful national pandemic response.
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Affiliation(s)
- Yi-Hsuan Chen
- Epidemic Intelligence Center, Taiwan Centers for Disease Control, Taipei, Taiwan; Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan; Ministry of Health and Welfare and National Taiwan University Infectious Diseases Research and Education Center, Taipei, Taiwan
| | - Chi-Tai Fang
- Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan; Ministry of Health and Welfare and National Taiwan University Infectious Diseases Research and Education Center, Taipei, Taiwan; Epidemiology Advisor, Taiwan Central Epidemic Command Center for COVID-19, Taipei, Taiwan; Division of Infectious Diseases, Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan; Population Health Research Center, College of Public Health, National Taiwan University, Taipei, Taiwan.
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8
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Chen YY, Yang MH, Lai JZ, Chen JW, Wang YL, Wei ST, Hou SM, Chen CJ, Wu HS. Seroprevalence of Anti-SARS-CoV-2 Remained Extremely Low in Taiwan Until the Vaccination Program Was Implemented. Open Forum Infect Dis 2024; 11:ofad614. [PMID: 38192381 PMCID: PMC10773475 DOI: 10.1093/ofid/ofad614] [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: 05/26/2023] [Accepted: 12/13/2023] [Indexed: 01/10/2024] Open
Abstract
Background The Taiwanese government made a concerted effort to contain a coronavirus disease 2019 (COVID-19) nosocomial outbreak of variant B.1.429, shortly before universal vaccination program implementation. This study aimed to investigate seroprevalence in the highest-risk regions. Methods Between January and February 2021, we retrieved 10 000 repository serum samples from blood donors to examine for antibodies against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) nucleocapsid (N) and spike (S) antigens. A positive result was confirmed if anti-N and anti-S antibodies were positive. Overall, 2000 donors residing in the highest-risk district and donating blood in January 2021 were further examined for SARS-CoV-2 RNA. We estimated seroprevalence and compared the epidemic curve between confirmed COVID-19 cases and blood donors with positive antibodies or viral RNA. Results Twenty-one cases with COVID-19 were confirmed in the nosocomial cluster, with an incidence of 1.27/100 000 in the COVID-affected districts. Among 4888 close contacts of the nosocomial cases, 20 (0.4%) became confirmed cases during isolation. Anti-SARS-CoV-2 was detected in 2 of the 10000 blood donors, showing a seroprevalence of 2/10000 (95% CI, 0.55-7.29). None of the 2000 donors who underwent tests for SARS-CoV-2 RNA were positive. The SARS-CoV-2 infection epidemic curve was observed sporadically in blood donors compared with the nosocomial cluster. Conclusions In early 2021, an extremely low anti-SARS-CoV-2 seroprevalence among blood donors was observed. Epidemic control measures through precise close contact tracing, testing, and isolation effectively contained SARS-CoV-2 transmission before universal vaccination program implementation.
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Affiliation(s)
| | | | | | - Jen-Wei Chen
- Taiwan Blood Services Foundation, Taipei, Taiwan
| | | | | | - Sheng-Mou Hou
- Taiwan Blood Services Foundation, Taipei, Taiwan
- Shin Kong Wu Ho-Su Memorial Hospital, Taipei, Taiwan
| | | | - Ho-Sheng Wu
- Hsinchu Blood Center, Hsinchu, Taiwan
- Taipei Medical University, Taipei, Taiwan
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9
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Chen WC, Huang ASE. COVID-19 public health surveillance and response in Taiwan: From containment to mitigation. J Formos Med Assoc 2024; 123 Suppl 1:S17-S26. [PMID: 37612159 DOI: 10.1016/j.jfma.2023.08.016] [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: 01/30/2023] [Revised: 08/07/2023] [Accepted: 08/10/2023] [Indexed: 08/25/2023] Open
Abstract
Taiwan learned from its 2003 SARS experience and established multiple surveillance systems to be able to detect and respond to COVID-19. With the find, test, trace, isolate, and support (FTTIS) strategy, Taiwan was successful in containing SARS-CoV-2 from spreading for two years. During the surge of the Omicron variant in the community, COVID-19 control strategy shifted from containment to mitigation in April 2022, to reduce morbidity and mortality. Lessons learned from COVID-19 response re-emphasizes the importance of having sensitive public health surveillance and linking surveillance with public health actions.
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Affiliation(s)
- Wan Chin Chen
- Taiwan Centers for Disease Control. No. 6, Linsen S. Rd., Jhongjheng District, Taipei City, Taiwan 10050
| | - Angela Song-En Huang
- Taiwan Centers for Disease Control. No. 6, Linsen S. Rd., Jhongjheng District, Taipei City, Taiwan 10050.
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10
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Bradbury NV, Hart WS, Lovell-Read FA, Polonsky JA, Thompson RN. Exact calculation of end-of-outbreak probabilities using contact tracing data. J R Soc Interface 2023; 20:20230374. [PMID: 38086402 PMCID: PMC10715912 DOI: 10.1098/rsif.2023.0374] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Accepted: 11/17/2023] [Indexed: 12/18/2023] Open
Abstract
A key challenge for public health policymakers is determining when an infectious disease outbreak has finished. Following a period without cases, an estimate of the probability that no further cases will occur in future (the end-of-outbreak probability) can be used to inform whether or not to declare an outbreak over. An existing quantitative approach (the Nishiura method), based on a branching process transmission model, allows the end-of-outbreak probability to be approximated from disease incidence time series, the offspring distribution and the serial interval distribution. Here, we show how the end-of-outbreak probability under the same transmission model can be calculated exactly if data describing who-infected-whom (the transmission tree) are also available (e.g. from contact tracing studies). In that scenario, our novel approach (the traced transmission method) is straightforward to use. We demonstrate this by applying the method to data from previous outbreaks of Ebola virus disease and Nipah virus infection. For both outbreaks, the traced transmission method would have determined that the outbreak was over earlier than the Nishiura method. This highlights that collection of contact tracing data and application of the traced transmission method may allow stringent control interventions to be relaxed quickly at the end of an outbreak, with only a limited risk of outbreak resurgence.
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Affiliation(s)
- N. V. Bradbury
- Mathematics Institute, University of Warwick, Coventry CV4 7AL, UK
- Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research (SBIDER), University of Warwick, Coventry CV4 7AL, UK
| | - W. S. Hart
- Mathematical Institute, University of Oxford, Oxford OX2 6GG, UK
| | | | - J. A. Polonsky
- Geneva Centre of Humanitarian Studies, University of Geneva, Geneva 1205, Switzerland
| | - R. N. Thompson
- Mathematical Institute, University of Oxford, Oxford OX2 6GG, UK
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11
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Valenzuela-Fernández A, Cabrera-Rodriguez R, Ciuffreda L, Perez-Yanes S, Estevez-Herrera J, González-Montelongo R, Alcoba-Florez J, Trujillo-González R, García-Martínez de Artola D, Gil-Campesino H, Díez-Gil O, Lorenzo-Salazar JM, Flores C, Garcia-Luis J. Nanomaterials to combat SARS-CoV-2: Strategies to prevent, diagnose and treat COVID-19. Front Bioeng Biotechnol 2022; 10:1052436. [PMID: 36507266 PMCID: PMC9732709 DOI: 10.3389/fbioe.2022.1052436] [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: 09/23/2022] [Accepted: 11/09/2022] [Indexed: 11/26/2022] Open
Abstract
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and the associated coronavirus disease 2019 (COVID-19), which severely affect the respiratory system and several organs and tissues, and may lead to death, have shown how science can respond when challenged by a global emergency, offering as a response a myriad of rapid technological developments. Development of vaccines at lightning speed is one of them. SARS-CoV-2 outbreaks have stressed healthcare systems, questioning patients care by using standard non-adapted therapies and diagnostic tools. In this scenario, nanotechnology has offered new tools, techniques and opportunities for prevention, for rapid, accurate and sensitive diagnosis and treatment of COVID-19. In this review, we focus on the nanotechnological applications and nano-based materials (i.e., personal protective equipment) to combat SARS-CoV-2 transmission, infection, organ damage and for the development of new tools for virosurveillance, diagnose and immune protection by mRNA and other nano-based vaccines. All the nano-based developed tools have allowed a historical, unprecedented, real time epidemiological surveillance and diagnosis of SARS-CoV-2 infection, at community and international levels. The nano-based technology has help to predict and detect how this Sarbecovirus is mutating and the severity of the associated COVID-19 disease, thereby assisting the administration and public health services to make decisions and measures for preparedness against the emerging variants of SARS-CoV-2 and severe or lethal COVID-19.
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Affiliation(s)
- Agustín Valenzuela-Fernández
- Laboratorio de Inmunología Celular y Viral, Unidad de Farmacología, Sección de Medicina, Facultad de Ciencias de la Salud, Universidad de La Laguna, San Cristóbal de La Laguna, Spain
| | - Romina Cabrera-Rodriguez
- Laboratorio de Inmunología Celular y Viral, Unidad de Farmacología, Sección de Medicina, Facultad de Ciencias de la Salud, Universidad de La Laguna, San Cristóbal de La Laguna, Spain
| | - Laura Ciuffreda
- Research Unit, Hospital Universitario N. S. de Candelaria, Santa Cruz de Tenerife, Spain
| | - Silvia Perez-Yanes
- Laboratorio de Inmunología Celular y Viral, Unidad de Farmacología, Sección de Medicina, Facultad de Ciencias de la Salud, Universidad de La Laguna, San Cristóbal de La Laguna, Spain
| | - Judith Estevez-Herrera
- Laboratorio de Inmunología Celular y Viral, Unidad de Farmacología, Sección de Medicina, Facultad de Ciencias de la Salud, Universidad de La Laguna, San Cristóbal de La Laguna, Spain
| | | | - Julia Alcoba-Florez
- Servicio de Microbiología, Hospital Universitario N. S. de Candelaria, Santa Cruz de Tenerife, Spain
| | - Rodrigo Trujillo-González
- Laboratorio de Inmunología Celular y Viral, Unidad de Farmacología, Sección de Medicina, Facultad de Ciencias de la Salud, Universidad de La Laguna, San Cristóbal de La Laguna, Spain
- Departamento de Análisis Matemático, Facultad de Ciencias, Universidad de La Laguna, Santa Cruz de Tenerife, Spain
| | | | - Helena Gil-Campesino
- Servicio de Microbiología, Hospital Universitario N. S. de Candelaria, Santa Cruz de Tenerife, Spain
| | - Oscar Díez-Gil
- Servicio de Microbiología, Hospital Universitario N. S. de Candelaria, Santa Cruz de Tenerife, Spain
| | - José M. Lorenzo-Salazar
- Genomics Division, Instituto Tecnológico y de Energías Renovables, Santa Cruz de Tenerife, Spain
| | - Carlos Flores
- Research Unit, Hospital Universitario N. S. de Candelaria, Santa Cruz de Tenerife, Spain
- Genomics Division, Instituto Tecnológico y de Energías Renovables, Santa Cruz de Tenerife, Spain
- CIBER de Enfermedades Respiratorias, Instituto de Salud Carlos III, Madrid, Spain
- Faculty of Health Sciences, University of Fernando Pessoa Canarias, Las Palmas de Gran Canaria, Spain
| | - Jonay Garcia-Luis
- Laboratorio de Inmunología Celular y Viral, Unidad de Farmacología, Sección de Medicina, Facultad de Ciencias de la Salud, Universidad de La Laguna, San Cristóbal de La Laguna, Spain
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12
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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: 1.3] [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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13
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Akhmetzhanov AR, Cheng HY, Linton NM, Ponce L, Jian SW, Lin HH. Transmission Dynamics and Effectiveness of Control Measures during COVID-19 Surge, Taiwan, April-August 2021. Emerg Infect Dis 2022; 28:2051-2059. [PMID: 36104202 PMCID: PMC9514361 DOI: 10.3201/eid2810.220456] [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] [Indexed: 12/13/2022] Open
Abstract
An unprecedented surge of COVID-19 cases in Taiwan in May 2021 led the government to implement strict nationwide control measures beginning May 15. During the surge, the government was able to bring the epidemic under control without a complete lockdown despite the cumulative case count reaching >14,400 and >780 deaths. We investigated the effectiveness of the public health and social measures instituted by the Taiwan government by quantifying the change in the effective reproduction number, which is a summary measure of the ability of the pathogen to spread through the population. The control measures that were instituted reduced the effective reproduction number from 2.0-3.3 to 0.6-0.7. This decrease was correlated with changes in mobility patterns in Taiwan, demonstrating that public compliance, active case finding, and contact tracing were effective measures in preventing further spread of the disease.
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14
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Du Z, Wang C, Liu C, Bai Y, Pei S, Adam DC, Wang L, Wu P, Lau EHY, Cowling BJ. Systematic review and meta-analyses of superspreading of SARS-CoV-2 infections. Transbound Emerg Dis 2022; 69:e3007-e3014. [PMID: 35799321 PMCID: PMC9349569 DOI: 10.1111/tbed.14655] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Revised: 07/03/2022] [Accepted: 07/04/2022] [Indexed: 11/29/2022]
Abstract
Superspreading, or overdispersion in transmission, is a feature of SARS-CoV-2 transmission which results in surging epidemics and large clusters of infection. The dispersion parameter is a statistical parameter used to characterize and quantify heterogeneity. In the context of measuring transmissibility, it is analogous to measures of superspreading potential among populations by assuming that collective offspring distribution follows a negative-binomial distribution. We conducted a systematic review and meta-analysis on globally reported dispersion parameters of SARS-CoV-2 infection. All searches were carried out on 10 September 2021 in PubMed for articles published from 1 January 2020 to 10 September 2021. Multiple estimates of the dispersion parameter have been published for 17 studies, which could be related to where and when the data were obtained, in 8 countries (e.g. China, the United States, India, Indonesia, Israel, Japan, New Zealand and Singapore). High heterogeneity was reported among the included studies. The mean estimates of dispersion parameters range from 0.06 to 2.97 over eight countries, the pooled estimate was 0.55 (95% CI: 0.30, 0.79), with changing means over countries and decreasing slightly with the increasing reproduction number. The expected proportion of cases accounting for 80% of all transmissions is 19% (95% CrI: 7, 34) globally. The study location and method were found to be important drivers for diversity in estimates of dispersion parameters. While under high potential of superspreading, larger outbreaks could still occur with the import of the COVID-19 virus by traveling even when an epidemic seems to be under control.
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Affiliation(s)
- Zhanwei Du
- Li Ka Shing Faculty of MedicineWHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, The University of Hong KongHong Kong Special Administrative RegionChina
- Laboratory of Data Discovery for HealthHong Kong Science and Technology ParkHong Kong Special Administrative RegionChina
| | - Chunyu Wang
- Li Ka Shing Faculty of MedicineWHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, The University of Hong KongHong Kong Special Administrative RegionChina
- Laboratory of Data Discovery for HealthHong Kong Science and Technology ParkHong Kong Special Administrative RegionChina
| | - Caifen Liu
- Li Ka Shing Faculty of MedicineWHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, The University of Hong KongHong Kong Special Administrative RegionChina
- Laboratory of Data Discovery for HealthHong Kong Science and Technology ParkHong Kong Special Administrative RegionChina
| | - Yuan Bai
- Li Ka Shing Faculty of MedicineWHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, The University of Hong KongHong Kong Special Administrative RegionChina
- Laboratory of Data Discovery for HealthHong Kong Science and Technology ParkHong Kong Special Administrative RegionChina
| | - Sen Pei
- Department of Environmental Health Sciences, Mailman School of Public HealthColumbia UniversityNew YorkNew York
| | - Dillon C. Adam
- Li Ka Shing Faculty of MedicineWHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, The University of Hong KongHong Kong Special Administrative RegionChina
| | - Lin Wang
- Department of GeneticsUniversity of CambridgeCambridgeUK
| | - Peng Wu
- Li Ka Shing Faculty of MedicineWHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, The University of Hong KongHong Kong Special Administrative RegionChina
- Laboratory of Data Discovery for HealthHong Kong Science and Technology ParkHong Kong Special Administrative RegionChina
| | - Eric H. Y. Lau
- Li Ka Shing Faculty of MedicineWHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, The University of Hong KongHong Kong Special Administrative RegionChina
- Laboratory of Data Discovery for HealthHong Kong Science and Technology ParkHong Kong Special Administrative RegionChina
| | - Benjamin J. Cowling
- Li Ka Shing Faculty of MedicineWHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, The University of Hong KongHong Kong Special Administrative RegionChina
- Laboratory of Data Discovery for HealthHong Kong Science and Technology ParkHong Kong Special Administrative RegionChina
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15
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Yuan B, Liu R, Tang S. A quantitative method to project the probability of the end of an epidemic: Application to the COVID-19 outbreak in Wuhan, 2020. J Theor Biol 2022; 545:111149. [PMID: 35500676 PMCID: PMC9055421 DOI: 10.1016/j.jtbi.2022.111149] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Revised: 04/21/2022] [Accepted: 04/25/2022] [Indexed: 02/06/2023]
Abstract
The end-of-outbreak declaration is an important part of epidemic control, marking the relaxation or cancellation of prevention and control measures. We propose a probability model to retrospectively quantify the confidence of giving the end-of-outbreak declaration during the COVID-19 epidemic in early 2020 in Wuhan. By using the linear spline, we firstly estimates the time-varying proportion of cases who miss the nonpharmaceutical interventions (NPIs) among all reported cases. Assuming the reproduction numbers being 1.5, 2.0, 3.0, 4.0, 5.0 and 6.0, the respective probability of the end of the COVID-19 outbreak with time after the last reported case can be iteratively computed. Consequently, the varying reproduction numbers produce slightly different increasing patterns of NPI effectiveness, and the end-of-outbreak declarations with 95% confidence are projected consistently earlier than the day when the lockdown was actually lifted. The reason for the timing discrepancy is discussed as well.
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Affiliation(s)
- Baoyin Yuan
- School of Mathematics, South China University of Technology, Guangzhou 510640, China
| | - Rui Liu
- School of Mathematics, South China University of Technology, Guangzhou 510640, China; Pazhou Lab, Guangzhou 510330, China.
| | - Sanyi Tang
- School of Mathematics and Statistics, Shaanxi Normal University, Xi'an 710119, China.
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16
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Ng CYH, Lim NA, Bao LXY, Quek AML, Seet RCS. Mitigating SARS-CoV-2 Transmission in Hospitals: A Systematic Literature Review. Public Health Rev 2022; 43:1604572. [PMID: 35296115 PMCID: PMC8906284 DOI: 10.3389/phrs.2022.1604572] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Accepted: 01/19/2022] [Indexed: 11/25/2022] Open
Abstract
Objectives: Hospital outbreaks of SARS-CoV-2 infection are dreaded but preventable catastrophes. We review the literature to examine the pattern of SARS-CoV-2 transmission in hospitals and identify potential vulnerabilities to mitigate the risk of infection. Methods: Three electronic databases (PubMed, Embase and Scopus) were searched from inception to July 27, 2021 for publications reporting SARS-CoV-2 outbreaks in hospital. Relevant articles and grey literature reports were hand-searched. Results: Twenty-seven articles that described 35 SARS-CoV-2 outbreaks were included. Despite epidemiological investigations, the primary case could not be identified in 37% of outbreaks. Healthcare workers accounted for 40% of primary cases (doctors 17%, followed by ancillary staff 11%). Mortality among infected patients was approximately 15%. By contrast, none of the infected HCWs died. Several concerning patterns were identified, including infections involving ancillary staff and healthcare worker infections from the community and household contacts. Conclusion: Continuous efforts to train-retrain and enforce correct personal protective equipment use and regular routine screening tests (especially among ancillary staff) are necessary to stem future hospital outbreaks of SARS-CoV-2.
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Affiliation(s)
- Chester Yan Hao Ng
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Nicole-Ann Lim
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Lena X. Y. Bao
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Amy M. L. Quek
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Raymond C. S. Seet
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Healthy Longevity Translational Research Program, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- *Correspondence: Raymond C. S. Seet,
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17
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Farhat A, Ben Hlima H, Khemakhem B, Ben Halima Y, Michaud P, Abdelkafi S, Fendri I. Apigenin analogues as SARS-CoV-2 main protease inhibitors: In-silico screening approach. Bioengineered 2022; 13:3350-3361. [PMID: 35048792 PMCID: PMC8974217 DOI: 10.1080/21655979.2022.2027181] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022] Open
Abstract
The COVID-19 new variants spread rapidly all over the world, and until now scientists strive to find virus-specific antivirals for its treatment. The main protease of SARS-CoV-2 (Mpro) exhibits high structural and sequence homology to main protease of SARS-CoV (93.23% sequence identity), and their sequence alignment indicated 12 mutated/variant residues. The sequence alignment of SARS-CoV-2 main protease led to identification of only one mutated/variant residue with no significant role in its enzymatic process. Therefore, Mpro was considered as a high-profile drug target in anti-SARS-CoV-2 drug discovery. Apigenin analogues to COVID-19 main protease binding were evaluated. The detailed interactions between the analogues of Apigenin and SARS-CoV-2 Mpro inhibitors were determined as hydrogen bonds, electronic bonds and hydrophobic interactions. The binding energies obtained from the molecular docking of Mpro with Boceprevir, Apigenin, Apigenin 7-glucoside-4’-p-coumarate, Apigenin 7-glucoside-4’-trans-caffeate and Apigenin 7-O-beta-d-glucoside (Cosmosiin) were found to be −6.6, −7.2, −8.8, −8.7 and −8.0 kcal/mol, respectively. Pharmacokinetic parameters and toxicological characteristics obtained by computational techniques and Virtual ADME studies of the Apigenin analogues confirmed that the Apigenin 7-glucoside-4’-p-coumarate is the best candidate for SARS-CoV-2 Mpro inhibition.
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Affiliation(s)
- Ameny Farhat
- Laboratory of Plant Biotechnology, Faculty of Sciences of Sfax, University of Sfax, Sfax, Tunisia
| | - Hajer Ben Hlima
- Laboratoire de Génie Enzymatique et Microbiologie, Equipe Biotechnologie des Algues, Ecole Nationale d'Ingénieurs de Sfax, Université de Sfax, Sfax, Tunisia
| | - Bassem Khemakhem
- Laboratory of Plant Biotechnology, Faculty of Sciences of Sfax, University of Sfax, Sfax, Tunisia
| | - Youssef Ben Halima
- Riadi Labs, National School of Computer Science, Manouba University, Manouba, Tunisia
| | - Philippe Michaud
- Institut Pascal, Université Clermont Auvergne, CNRS, Clermont Auvergne INP, Clermont-Ferrand, France
| | - Slim Abdelkafi
- Laboratoire de Génie Enzymatique et Microbiologie, Equipe Biotechnologie des Algues, Ecole Nationale d'Ingénieurs de Sfax, Université de Sfax, Sfax, Tunisia
| | - Imen Fendri
- Laboratory of Plant Biotechnology, Faculty of Sciences of Sfax, University of Sfax, Sfax, Tunisia
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18
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Sachak-Patwa R, Byrne HM, Dyson L, Thompson RN. The risk of SARS-CoV-2 outbreaks in low prevalence settings following the removal of travel restrictions. COMMUNICATIONS MEDICINE 2021; 1:39. [PMID: 35602220 PMCID: PMC9053223 DOI: 10.1038/s43856-021-00038-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Accepted: 09/03/2021] [Indexed: 12/23/2022] Open
Abstract
Background Countries around the world have introduced travel restrictions to reduce SARS-CoV-2 transmission. As vaccines are gradually rolled out, attention has turned to when travel restrictions and other non-pharmaceutical interventions (NPIs) can be relaxed. Methods Using SARS-CoV-2 as a case study, we develop a mathematical branching process model to assess the risk that, following the removal of NPIs, cases arriving in low prevalence settings initiate a local outbreak. Our model accounts for changes in background population immunity due to vaccination. We consider two locations with low prevalence in which the vaccine rollout has progressed quickly – specifically, the Isle of Man (a British crown dependency in the Irish Sea) and the country of Israel. Results We show that the outbreak risk is unlikely to be eliminated completely when travel restrictions and other NPIs are removed. This general result is the most important finding of this study, rather than exact quantitative outbreak risk estimates in different locations. It holds even once vaccine programmes are completed. Key factors underlying this result are the potential for transmission even following vaccination, incomplete vaccine uptake, and the recent emergence of SARS-CoV-2 variants with increased transmissibility. Conclusions Combined, the factors described above suggest that, when travel restrictions are relaxed, it may still be necessary to implement surveillance of incoming passengers to identify infected individuals quickly. This measure, as well as tracing and testing (and/or isolating) contacts of detected infected passengers, remains useful to suppress potential outbreaks while global case numbers are high. The effectiveness of public health measures against COVID-19 has varied between countries, with some experiencing many infections and others containing transmission successfully. As vaccines are deployed, an important challenge is deciding when to relax measures. Here, we consider locations with few cases, and investigate whether vaccination can ever eliminate the risk of COVID-19 outbreaks completely, allowing measures to be removed risk-free. Using a mathematical model, we demonstrate that there is still a risk that imported cases initiate outbreaks when measures are removed, even if most of the population is fully vaccinated. This highlights the need for continued vigilance in low prevalence settings to prevent imported cases leading to local transmission. Until case numbers are reduced globally, so that SARS-CoV-2 spread between countries is less likely, the risk of outbreaks in low prevalence settings will remain. Sachak-Patwa et al. estimate the risk of SARS-CoV-2 outbreaks in low prevalence settings following the removal of travel restrictions and other non-pharmaceutical interventions, with the Isle of Man and Israel as case studies. Using a branching process mathematical model, the authors show that even after a large proportion of the population is vaccinated, there remains a risk of local outbreaks from imported cases.
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19
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Moin A, Usmani SUR, Khan H. Impending disaster: rise of the epsilon variant in Pakistan and the way forward. Int Health 2021; 14:540-541. [PMID: 34618895 PMCID: PMC9450649 DOI: 10.1093/inthealth/ihab068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Revised: 09/16/2021] [Accepted: 09/20/2021] [Indexed: 11/13/2022] Open
Abstract
The constantly mutating severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) does not appear to be slowing down any time soon. All countries, particularly developing countries, must adapt and strategically plan their way of life around the pandemic, while doing everything possible to keep the mortality rate and spread of the newly emerging variants as low as possible, in order to avoid a further blow to the economy and way of life. Pakistan is one such developing country that is currently battling the dangerous delta strain of SARS-COV-2 with limited resources and has recently seen the emergence of an equally transmissible and highly infectious epsilon strain. This is a concerning situation considering that Pakistan's already overburdened health system and faltering economy cannot withstand another dangerous SARS-COV-2 variant attack. This article highlights some strategies for the country to fortify its defences to prevent the epsilon variant from spreading before it is too late, and emphasises that while identifying potential immune evasion mechanisms in SARS-COV-2 variants is critical in the fight against COVID-19, it is also critical to develop methods of efficient and cost-effective detection to identify an early outbreak and then vigilantly and systematically plan area lockdowns before any hope of conquering this pandemic is lost.
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Affiliation(s)
- Alina Moin
- Dow University of Health Sciences, Baba e Urdu Road, Karachi 74200, Pakistan
| | | | - Hashim Khan
- Dow University of Health Sciences, Baba e Urdu Road, Karachi 74200, Pakistan
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