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Yang T, Deng W, Liu Y, Deng J. Comparison of health-oriented cross-regional allocation strategies for the COVID-19 vaccine: a mathematical modelling study. Ann Med 2022; 54:941-952. [PMID: 35393922 PMCID: PMC9004521 DOI: 10.1080/07853890.2022.2060522] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/02/2022] Open
Abstract
BACKGROUND Controlling the epidemic spread and establishing the immune barrier in a short time through accurate vaccine demand prediction and optimised vaccine allocation strategy are still urgent problems to be solved under the condition of frequent virus mutations. METHODS A cross-regional Susceptible-Exposed-Infected-Removed dynamic model was used for scenario simulation to systematically elaborate and compare the effects of different cross-regional vaccine allocation strategies on the future development of the epidemic in regions with different population sizes, prevention and control capabilities, and initial risk levels. Furthermore, the trajectory of the cross-regional vaccine allocation strategy, calculated using a particle swarm optimisation algorithm, was compared with the trajectories of other strategies. RESULTS By visualising the final effect of the particle swarm optimisation vaccine allocation strategy, this study revealed the important role of prevention and control (including the level of social distancing control, the speed of tracking and isolating exposed and infected individuals, and the initial frequency of mask-wearing) in determining the allocation of vaccine resources. Most importantly, it supported the idea of prioritising control in regions with a large population and low initial risk level, which broke the general view that high initial risk needs to be given priority and proposed that outbreak risk should be firstly considered instead. CONCLUSIONS This is the first study to use a particle swarm optimisation algorithm to study the cross-regional allocation of COVID-19 vaccines. These data provide a theoretical basis for countries and regions to develop more targeted and sustainable vaccination strategies.KEY MESSAGEThe innovative combination of particle swarm optimisation and cross-regional SEIR model to simulate the pandemic trajectory and predict the vaccine demand helped to speed up and stabilise the construction of the immune barrier, especially faced with new virus mutations.We proposed that priority should be given to regions where it is possible to prevent more infections rather than regions where it is at high initial risk, thus regional outbreak risk should be considered when making vaccine allocation decisions.An optimal health-oriented strategy for vaccine allocation in the COVID-19 pandemic is determined considering both pharmaceutical and non-pharmaceutical policy interventions, including speed of isolation, degree of social distancing control, and frequency of mask-wearing.
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Affiliation(s)
- Tianan Yang
- School of Management and Economics, Beijing Institute of Technology, Beijing, China.,Sustainable Development Research Institute for Economy and Society of Beijing, Beijing, China
| | - Wenhao Deng
- School of Management and Economics, Beijing Institute of Technology, Beijing, China.,Sustainable Development Research Institute for Economy and Society of Beijing, Beijing, China
| | - Yexin Liu
- School of Management and Economics, Beijing Institute of Technology, Beijing, China.,Sustainable Development Research Institute for Economy and Society of Beijing, Beijing, China
| | - Jianwei Deng
- School of Management and Economics, Beijing Institute of Technology, Beijing, China.,Sustainable Development Research Institute for Economy and Society of Beijing, Beijing, China
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Burns J, Movsisyan A, Stratil JM, Biallas RL, Coenen M, Emmert-Fees KM, Geffert K, Hoffmann S, Horstick O, Laxy M, Klinger C, Kratzer S, Litwin T, Norris S, Pfadenhauer LM, von Philipsborn P, Sell K, Stadelmaier J, Verboom B, Voss S, Wabnitz K, Rehfuess E. International travel-related control measures to contain the COVID-19 pandemic: a rapid review. Cochrane Database Syst Rev 2021; 3:CD013717. [PMID: 33763851 PMCID: PMC8406796 DOI: 10.1002/14651858.cd013717.pub2] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
BACKGROUND In late 2019, the first cases of coronavirus disease 2019 (COVID-19) were reported in Wuhan, China, followed by a worldwide spread. Numerous countries have implemented control measures related to international travel, including border closures, travel restrictions, screening at borders, and quarantine of travellers. OBJECTIVES To assess the effectiveness of international travel-related control measures during the COVID-19 pandemic on infectious disease transmission and screening-related outcomes. SEARCH METHODS We searched MEDLINE, Embase and COVID-19-specific databases, including the Cochrane COVID-19 Study Register and the WHO Global Database on COVID-19 Research to 13 November 2020. SELECTION CRITERIA We considered experimental, quasi-experimental, observational and modelling studies assessing the effects of travel-related control measures affecting human travel across international borders during the COVID-19 pandemic. In the original review, we also considered evidence on severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS). In this version we decided to focus on COVID-19 evidence only. Primary outcome categories were (i) cases avoided, (ii) cases detected, and (iii) a shift in epidemic development. Secondary outcomes were other infectious disease transmission outcomes, healthcare utilisation, resource requirements and adverse effects if identified in studies assessing at least one primary outcome. DATA COLLECTION AND ANALYSIS Two review authors independently screened titles and abstracts and subsequently full texts. For studies included in the analysis, one review author extracted data and appraised the study. At least one additional review author checked for correctness of data. To assess the risk of bias and quality of included studies, we used the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool for observational studies concerned with screening, and a bespoke tool for modelling studies. We synthesised findings narratively. One review author assessed the certainty of evidence with GRADE, and several review authors discussed these GRADE judgements. MAIN RESULTS Overall, we included 62 unique studies in the analysis; 49 were modelling studies and 13 were observational studies. Studies covered a variety of settings and levels of community transmission. Most studies compared travel-related control measures against a counterfactual scenario in which the measure was not implemented. However, some modelling studies described additional comparator scenarios, such as different levels of stringency of the measures (including relaxation of restrictions), or a combination of measures. Concerns with the quality of modelling studies related to potentially inappropriate assumptions about the structure and input parameters, and an inadequate assessment of model uncertainty. Concerns with risk of bias in observational studies related to the selection of travellers and the reference test, and unclear reporting of certain methodological aspects. Below we outline the results for each intervention category by illustrating the findings from selected outcomes. Travel restrictions reducing or stopping cross-border travel (31 modelling studies) The studies assessed cases avoided and shift in epidemic development. We found very low-certainty evidence for a reduction in COVID-19 cases in the community (13 studies) and cases exported or imported (9 studies). Most studies reported positive effects, with effect sizes varying widely; only a few studies showed no effect. There was very low-certainty evidence that cross-border travel controls can slow the spread of COVID-19. Most studies predicted positive effects, however, results from individual studies varied from a delay of less than one day to a delay of 85 days; very few studies predicted no effect of the measure. Screening at borders (13 modelling studies; 13 observational studies) Screening measures covered symptom/exposure-based screening or test-based screening (commonly specifying polymerase chain reaction (PCR) testing), or both, before departure or upon or within a few days of arrival. Studies assessed cases avoided, shift in epidemic development and cases detected. Studies generally predicted or observed some benefit from screening at borders, however these varied widely. For symptom/exposure-based screening, one modelling study reported that global implementation of screening measures would reduce the number of cases exported per day from another country by 82% (95% confidence interval (CI) 72% to 95%) (moderate-certainty evidence). Four modelling studies predicted delays in epidemic development, although there was wide variation in the results between the studies (very low-certainty evidence). Four modelling studies predicted that the proportion of cases detected would range from 1% to 53% (very low-certainty evidence). Nine observational studies observed the detected proportion to range from 0% to 100% (very low-certainty evidence), although all but one study observed this proportion to be less than 54%. For test-based screening, one modelling study provided very low-certainty evidence for the number of cases avoided. It reported that testing travellers reduced imported or exported cases as well as secondary cases. Five observational studies observed that the proportion of cases detected varied from 58% to 90% (very low-certainty evidence). Quarantine (12 modelling studies) The studies assessed cases avoided, shift in epidemic development and cases detected. All studies suggested some benefit of quarantine, however the magnitude of the effect ranged from small to large across the different outcomes (very low- to low-certainty evidence). Three modelling studies predicted that the reduction in the number of cases in the community ranged from 450 to over 64,000 fewer cases (very low-certainty evidence). The variation in effect was possibly related to the duration of quarantine and compliance. Quarantine and screening at borders (7 modelling studies; 4 observational studies) The studies assessed shift in epidemic development and cases detected. Most studies predicted positive effects for the combined measures with varying magnitudes (very low- to low-certainty evidence). Four observational studies observed that the proportion of cases detected for quarantine and screening at borders ranged from 68% to 92% (low-certainty evidence). The variation may depend on how the measures were combined, including the length of the quarantine period and days when the test was conducted in quarantine. AUTHORS' CONCLUSIONS With much of the evidence derived from modelling studies, notably for travel restrictions reducing or stopping cross-border travel and quarantine of travellers, there is a lack of 'real-world' evidence. The certainty of the evidence for most travel-related control measures and outcomes is very low and the true effects are likely to be substantially different from those reported here. Broadly, travel restrictions may limit the spread of disease across national borders. Symptom/exposure-based screening measures at borders on their own are likely not effective; PCR testing at borders as a screening measure likely detects more cases than symptom/exposure-based screening at borders, although if performed only upon arrival this will likely also miss a meaningful proportion of cases. Quarantine, based on a sufficiently long quarantine period and high compliance is likely to largely avoid further transmission from travellers. Combining quarantine with PCR testing at borders will likely improve effectiveness. Many studies suggest that effects depend on factors, such as levels of community transmission, travel volumes and duration, other public health measures in place, and the exact specification and timing of the measure. Future research should be better reported, employ a range of designs beyond modelling and assess potential benefits and harms of the travel-related control measures from a societal perspective.
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Affiliation(s)
- Jacob Burns
- Institute for Medical Information Processing, Biometry and Epidemiology (IBE), Chair of Public Health and Health Services Research, LMU Munich, Munich, Germany
- Pettenkofer School of Public Health, Munich, Germany
| | - Ani Movsisyan
- Institute for Medical Information Processing, Biometry and Epidemiology (IBE), Chair of Public Health and Health Services Research, LMU Munich, Munich, Germany
- Pettenkofer School of Public Health, Munich, Germany
| | - Jan M Stratil
- Institute for Medical Information Processing, Biometry and Epidemiology (IBE), Chair of Public Health and Health Services Research, LMU Munich, Munich, Germany
- Pettenkofer School of Public Health, Munich, Germany
| | - Renke Lars Biallas
- Institute for Medical Information Processing, Biometry and Epidemiology (IBE), Chair of Public Health and Health Services Research, LMU Munich, Munich, Germany
- Pettenkofer School of Public Health, Munich, Germany
| | - Michaela Coenen
- Institute for Medical Information Processing, Biometry and Epidemiology (IBE), Chair of Public Health and Health Services Research, LMU Munich, Munich, Germany
- Pettenkofer School of Public Health, Munich, Germany
| | - Karl Mf Emmert-Fees
- Institute of Health Economics and Health Care Management, Helmholtz Zentrum München, Munich, Germany
| | - Karin Geffert
- Institute for Medical Information Processing, Biometry and Epidemiology (IBE), Chair of Public Health and Health Services Research, LMU Munich, Munich, Germany
- Pettenkofer School of Public Health, Munich, Germany
| | - Sabine Hoffmann
- Institute for Medical Information Processing, Biometry and Epidemiology (IBE), Chair of Public Health and Health Services Research, LMU Munich, Munich, Germany
- Pettenkofer School of Public Health, Munich, Germany
| | - Olaf Horstick
- Heidelberg Institute of Global Health, Heidelberg University, Heidelberg, Germany
| | - Michael Laxy
- Institute of Health Economics and Health Care Management, Helmholtz Zentrum München, Munich, Germany
- Department of Sport and Health Sciences, Technical University of Munich, Munich, Germany
| | - Carmen Klinger
- Institute for Medical Information Processing, Biometry and Epidemiology (IBE), Chair of Public Health and Health Services Research, LMU Munich, Munich, Germany
- Pettenkofer School of Public Health, Munich, Germany
| | - Suzie Kratzer
- Institute for Medical Information Processing, Biometry and Epidemiology (IBE), Chair of Public Health and Health Services Research, LMU Munich, Munich, Germany
- Pettenkofer School of Public Health, Munich, Germany
| | - Tim Litwin
- Institute for Medical Biometry and Statistics (IMBI), Freiburg Center for Data Analysis and Modeling (FDM), Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Susan Norris
- Institute for Medical Information Processing, Biometry and Epidemiology (IBE), Chair of Public Health and Health Services Research, LMU Munich, Munich, Germany
- Pettenkofer School of Public Health, Munich, Germany
- Oregon Health & Science University, Portland, OR, USA
| | - Lisa M Pfadenhauer
- Institute for Medical Information Processing, Biometry and Epidemiology (IBE), Chair of Public Health and Health Services Research, LMU Munich, Munich, Germany
- Pettenkofer School of Public Health, Munich, Germany
| | - Peter von Philipsborn
- Institute for Medical Information Processing, Biometry and Epidemiology (IBE), Chair of Public Health and Health Services Research, LMU Munich, Munich, Germany
- Pettenkofer School of Public Health, Munich, Germany
| | - Kerstin Sell
- Institute for Medical Information Processing, Biometry and Epidemiology (IBE), Chair of Public Health and Health Services Research, LMU Munich, Munich, Germany
- Pettenkofer School of Public Health, Munich, Germany
| | - Julia Stadelmaier
- Institute for Evidence in Medicine, Medical Center, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Ben Verboom
- Institute for Medical Information Processing, Biometry and Epidemiology (IBE), Chair of Public Health and Health Services Research, LMU Munich, Munich, Germany
- Pettenkofer School of Public Health, Munich, Germany
| | - Stephan Voss
- Institute for Medical Information Processing, Biometry and Epidemiology (IBE), Chair of Public Health and Health Services Research, LMU Munich, Munich, Germany
- Pettenkofer School of Public Health, Munich, Germany
| | - Katharina Wabnitz
- Institute for Medical Information Processing, Biometry and Epidemiology (IBE), Chair of Public Health and Health Services Research, LMU Munich, Munich, Germany
- Pettenkofer School of Public Health, Munich, Germany
| | - Eva Rehfuess
- Institute for Medical Information Processing, Biometry and Epidemiology (IBE), Chair of Public Health and Health Services Research, LMU Munich, Munich, Germany
- Pettenkofer School of Public Health, Munich, Germany
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Khan MT, Ali S, Khan AS, Muhammad N, Khalil F, Ishfaq M, Irfan M, Al-Sehemi AG, Muhammad S, Malik A, Khan TA, Wei DQ. SARS-CoV-2 Genome from the Khyber Pakhtunkhwa Province of Pakistan. ACS OMEGA 2021; 6:6588-6599. [PMID: 33748571 PMCID: PMC7944396 DOI: 10.1021/acsomega.0c05163] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/23/2020] [Accepted: 01/05/2021] [Indexed: 05/08/2023]
Abstract
Among viral outbreaks, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is one of the deadliest ones, and it has triggered the global COVID-19 pandemic. In Pakistan, until 5th September 2020, a total of 6342 deaths have been reported, of which 1255 were from the Khyber Pakhtunkhwa (KPK) province. To understand the disease progression and control and also to produce vaccines and therapeutic efforts, whole genome sequence analysis is important. In the current investigation, we sequenced a single sample of SARS-CoV-2 genomes (accession no. MT879619) from a male suspect from Peshawar, the KPK capital city, during the first wave of infection. The local SARS-CoV-2 strain shows some unique characteristics compared to neighboring Iranian and Chinese isolates in phylogenetic tree and mutations. The circulating strains of SARS-CoV-2 represent an intermediate evolution from China and Iran. Furthermore, eight complete whole genome sequences, including the current Pakistani isolates which have been submitted to Global Initiative on Sharing All Influenza Data (GSAID), were also investigated for specific mutations and characters. Some novel mutations [NSP2 (D268del), NSP5 (N228K), and NS3 (F105S)] and specific characters have been detected in the coding regions, which may affect viral transmission, epidemiology, and disease severity. The computational modeling revealed that a majority of these mutations may have a stabilizing effect on the viral protein structure. In conclusion, the genome sequencing of local strains is important for better understanding the pathogenicity, immunogenicity, and epidemiology of causative agents.
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Affiliation(s)
- Muhammad Tahir Khan
- Institute of Molecular
Biology and Biotechnology (IMBB), The University
of Lahore, KM Defence Road, Lahore 58810, Pakistan
- State Key Laboratory of Microbial Metabolism,
Shanghai−Islamabad−Belgrade Joint Innovation Center
on Antibacterial Resistances, Joint International Research Laboratory
of Metabolic & Developmental Sciences and School of Life Sciences
and Biotechnology, Shanghai Jiao Tong University, Shanghai 200030, P. R. China
- Peng Cheng Laboratory, Vanke Cloud City Phase I Building 8, Xili Street, Nashan District, Shenzhen, Guangdong 518055, P. R. China
| | - Sajid Ali
- Department of Microbiology, Quaid-i-Azam University Islamabad, Islamabad 45320, Pakistan
| | - Anwar Sheed Khan
- Department of Microbiology, Kohat University of Science and Technology, Bannu Road, Near Jarma Bridge, Kohat 26000, Pakistan
| | - Noor Muhammad
- Department of Microbiology, Kohat University of Science and Technology, Bannu Road, Near Jarma Bridge, Kohat 26000, Pakistan
| | - Faiza Khalil
- Department of Biochemistry, Khyber Medical
College, Peshawar 25160, Pakistan
- University
of Peshawar, Road No.
2, Rahat Abad, Peshawar 25120, Khyber Pakhtunkhwa, Pakistan
| | - Muhammad Ishfaq
- Centre for Omic Sciences, Islamia
College Peshawar. Grand Trunk Road, Rahat Abad, Peshawar 25120, Pakistan
| | - Muhammad Irfan
- Department
of Oral Biology, College of Dentistry, University
of Florida, Gainesville, Florida 32611, United States
| | - Abdullah G. Al-Sehemi
- Research Center for Advanced Materials
Science (RCAMS), King Khalid University, P.O. Box 9004, Abha 61413, Saudi Arabia
- Department of Chemistry, College of Science, King Khalid University, P.O. Box 9004, Abha 61413, Saudi Arabia
| | - Shabbir Muhammad
- Research Center for Advanced Materials
Science (RCAMS), King Khalid University, P.O. Box 9004, Abha 61413, Saudi Arabia
- Department of Chemistry, College of Science, King Khalid University, P.O. Box 9004, Abha 61413, Saudi Arabia
- State Key Laboratory of Microbial Metabolism,
Shanghai−Islamabad−Belgrade Joint Innovation Center
on Antibacterial Resistances, Joint International Research Laboratory
of Metabolic & Developmental Sciences and School of Life Sciences
and Biotechnology, Shanghai Jiao Tong University, Shanghai 200030, P. R. China
- Peng Cheng Laboratory, Vanke Cloud City Phase I Building 8, Xili Street, Nashan District, Shenzhen, Guangdong 518055, P. R. China
| | - Arif Malik
- Institute of Molecular
Biology and Biotechnology (IMBB), The University
of Lahore, KM Defence Road, Lahore 58810, Pakistan
| | - Taj Ali Khan
- Institute of Pathology and Diagnostic Medicine, Khyber Medical University, Phase V, Hayatabad, Peshawar, Khyber Pakhtunkhwa 25000, Pakistan
| | - Dong Qing Wei
- State Key Laboratory of Microbial Metabolism,
Shanghai−Islamabad−Belgrade Joint Innovation Center
on Antibacterial Resistances, Joint International Research Laboratory
of Metabolic & Developmental Sciences and School of Life Sciences
and Biotechnology, Shanghai Jiao Tong University, Shanghai 200030, P. R. China
- Peng Cheng Laboratory, Vanke Cloud City Phase I Building 8, Xili Street, Nashan District, Shenzhen, Guangdong 518055, P. R. China
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