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Iyaniwura SA, Ringa N, Adu PA, Mak S, Janjua NZ, Irvine MA, Otterstatter M. Understanding the impact of mobility on COVID-19 spread: A hybrid gravity-metapopulation model of COVID-19. PLoS Comput Biol 2023; 19:e1011123. [PMID: 37172027 DOI: 10.1371/journal.pcbi.1011123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2022] [Revised: 05/24/2023] [Accepted: 04/24/2023] [Indexed: 05/14/2023] Open
Abstract
The outbreak of the severe acute respiratory syndrome coronavirus 2 started in Wuhan, China, towards the end of 2019 and spread worldwide. The rapid spread of the disease can be attributed to many factors including its high infectiousness and the high rate of human mobility around the world. Although travel/movement restrictions and other non-pharmaceutical interventions aimed at controlling the disease spread were put in place during the early stages of the pandemic, these interventions did not stop COVID-19 spread. To better understand the impact of human mobility on the spread of COVID-19 between regions, we propose a hybrid gravity-metapopulation model of COVID-19. Our modeling framework has the flexibility of determining mobility between regions based on the distances between the regions or using data from mobile devices. In addition, our model explicitly incorporates time-dependent human mobility into the disease transmission rate, and has the potential to incorporate other factors that affect disease transmission such as facemasks, physical distancing, contact rates, etc. An important feature of this modeling framework is its ability to independently assess the contribution of each factor to disease transmission. Using a Bayesian hierarchical modeling framework, we calibrate our model to the weekly reported cases of COVID-19 in thirteen local health areas in Metro Vancouver, British Columbia (BC), Canada, from July 2020 to January 2021. We consider two main scenarios in our model calibration: using a fixed distance matrix and time-dependent weekly mobility matrices. We found that the distance matrix provides a better fit to the data, whilst the mobility matrices have the ability to explain the variance in transmission between regions. This result shows that the mobility data provides more information in terms of disease transmission than the distances between the regions.
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Affiliation(s)
- Sarafa A Iyaniwura
- British Columbia Centre for Disease Control, Vancouver, British Columbia, Canada
- Department of Mathematics and Institute of Applied Mathematics, University of British Columbia, Vancouver, British Columbia, Canada
| | - Notice Ringa
- British Columbia Centre for Disease Control, Vancouver, British Columbia, Canada
- School of Population and Public Health, University of British Columbia, Vancouver, British Columbia, Canada
| | - Prince A Adu
- British Columbia Centre for Disease Control, Vancouver, British Columbia, Canada
- School of Population and Public Health, University of British Columbia, Vancouver, British Columbia, Canada
| | - Sunny Mak
- British Columbia Centre for Disease Control, Vancouver, British Columbia, Canada
| | - Naveed Z Janjua
- British Columbia Centre for Disease Control, Vancouver, British Columbia, Canada
- School of Population and Public Health, University of British Columbia, Vancouver, British Columbia, Canada
| | - Michael A Irvine
- British Columbia Centre for Disease Control, Vancouver, British Columbia, Canada
- Faculty of Health Sciences, Simon Fraser University, Burnaby, British Columbia, Canada
| | - Michael Otterstatter
- British Columbia Centre for Disease Control, Vancouver, British Columbia, Canada
- School of Population and Public Health, University of British Columbia, Vancouver, British Columbia, Canada
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Ridenti MA, Teles LK, Maranhão A, Teles VK. Mathematical modeling and investigation on the role of demography and contact patterns in social distancing measures effectiveness in COVID-19 dissemination. Math Med Biol 2023; 40:73-95. [PMID: 36373595 DOI: 10.1093/imammb/dqac015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Revised: 08/30/2022] [Accepted: 10/03/2022] [Indexed: 11/15/2022]
Abstract
In this article, we investigate the importance of demography and contact patterns in determining the spread of COVID-19 and to the effectiveness of social distancing policies. We investigate these questions proposing an augmented epidemiological model with an age-structured model, with the population divided into susceptible (S), exposed (E), asymptomatic infectious (A), hospitalized (H), symptomatic infectious (I) and recovered individuals (R), to simulate COVID-19 dissemination. The simulations were carried out using six combinations of four types of isolation policies (work restrictions, isolation of the elderly, community distancing and school closures) and four representative fictitious countries generated over alternative demographic transition stage patterns (aged developed, developed, developing and least developed countries). We concluded that the basic reproduction number depends on the age profile and the contact patterns. The aged developed country had the lowest basic reproduction number ($R0=1.74$) due to the low contact rate among individuals, followed by the least developed country ($R0=2.00$), the developing country ($R0=2.43$) and the developed country ($R0=2.64$). Because of these differences in the basic reproduction numbers, the same intervention policies had higher efficiencies in the aged and least developed countries. Of all intervention policies, the reduction in work contacts and community distancing were the ones that produced the highest decrease in the $R0$ value, prevalence, maximum hospitalization demand and fatality rate. The isolation of the elderly was more effective in the developed and aged developed countries. The school closure was the less effective intervention policy, though its effects were not negligible in the least developed and developing countries.
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Affiliation(s)
- Marco A Ridenti
- Physics Department, Aeronautics Institute of Technology, Marechal Eduardo Gomes, 50 Vila das Acácias, 12228-900, SP, Brazil
| | - Lara K Teles
- Physics Department, Aeronautics Institute of Technology, Marechal Eduardo Gomes, 50 Vila das Acácias, 12228-900, SP, Brazil
| | - Alexandre Maranhão
- Physics Department, Aeronautics Institute of Technology, Marechal Eduardo Gomes, 50 Vila das Acácias, 12228-900, SP, Brazil
| | - Vladimir K Teles
- Sao Paulo School of Economics, FGV-SP, Rua Itapeva, 474 Bela Vista, 01332-000, SP, Brazil
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Wang S, Zhang Y, Zhang Q, Lu Q, Liu C, Yi F. A Strategy Formulation Framework for Efficient Screening during the Early Stage of a Pandemic. Trop Med Infect Dis 2023; 8:tropicalmed8020078. [PMID: 36828494 PMCID: PMC9960745 DOI: 10.3390/tropicalmed8020078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Revised: 01/16/2023] [Accepted: 01/18/2023] [Indexed: 01/25/2023] Open
Abstract
For viruses that can be transmitted by contacts of people, efficiently screening infected individuals is beneficial for controlling outbreaks rapidly and avoiding widespread diffusion, especially during the early stage of a pandemic. The process of virus transmission can be described as virus diffusion in complex networks such as trajectory networks. We propose a strategy formulation framework (SFF) for generating various screening strategies to identify influential nodes in networks. We propose two types of metrics to measure the nodes' influence and three types of screening modes. Then, we can obtain six combinations, i.e., six strategies. To verify the efficiencies of the strategies, we build a scenario model based on the multi-agent modelling. In this model, people can move according to their self-decisions, and a virtual trajectory network is generated by their contacts. We found that (1) screening people will have a better performance based on their contact paths if there is no confirmed case yet, and (2) if the first confirmed case has been discovered, it is better to screen people sequentially by their influences. The proposed SFF and strategies can provide support for decision makers, and the proposed scenario model can be applied to simulate and forecast the virus-diffusion process.
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Affiliation(s)
- Shuangyan Wang
- Party School of the Central Committee of C.P.C. (National Academy of Governance), Beijing 100089, China
| | - Yuan Zhang
- School of Social Development and Public Policy, Beijing Normal University, Beijing 100875, China
| | - Qiang Zhang
- School of Social Development and Public Policy, Beijing Normal University, Beijing 100875, China
- Correspondence: ; Tel.: +86-18500084200; Fax: +86-10-58800366
| | - Qibin Lu
- School of Social Development and Public Policy, Beijing Normal University, Beijing 100875, China
| | - Chengcheng Liu
- School of Social Development and Public Policy, Beijing Normal University, Beijing 100875, China
| | - Fangxin Yi
- School of Social Development and Public Policy, Beijing Normal University, Beijing 100875, China
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Abstract
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been spreading worldwide for over two years, with millions of reported cases and deaths. The deployment of mathematical modeling in the fight against COVID-19 has recorded tremendous success. However, most of these models target the epidemic phase of the disease. The development of safe and effective vaccines against SARS-CoV-2 brought hope of safe reopening of schools and businesses and return to pre-COVID normalcy, until mutant strains like the Delta and Omicron variants, which are more infectious, emerged. A few months into the pandemic, reports of the possibility of both vaccine- and infection-induced immunity waning emerged, thereby indicating that COVID-19 may be with us for longer than earlier thought. As a result, to better understand the dynamics of COVID-19, it is essential to study the disease with an endemic model. In this regard, we developed and analyzed an endemic model of COVID-19 that incorporates the waning of both vaccine- and infection-induced immunities using distributed delay equations. Our modeling framework assumes that the waning of both immunities occurs gradually over time at the population level. We derived a nonlinear ODE system from the distributed delay model and showed that the model could exhibit either a forward or backward bifurcation depending on the immunity waning rates. Having a backward bifurcation implies that $ R_c < 1 $ is not sufficient to guarantee disease eradication, and that the immunity waning rates are critical factors in eradicating COVID-19. Our numerical simulations show that vaccinating a high percentage of the population with a safe and moderately effective vaccine could help in eradicating COVID-19.
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Affiliation(s)
- Sarafa A Iyaniwura
- Department of Mathematics and Institute of Applied Mathematics (IAM), University of British Columbia, Vancouver, British Columbia, Canada
| | - Rabiu Musa
- Faculty of Mathematics, Technion Israel Institute of Technology, Haifa 32000, Israel
| | - Jude D Kong
- Department of Mathematics and Statistics, York University, Toronto, Ontario, Canada
- Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC), York University, Toronto, Ontario, Canada
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Paul JN, Mbalawata IS, Mirau SS, Masandawa L. Mathematical modeling of vaccination as a control measure of stress to fight COVID-19 infections. Chaos Solitons Fractals 2023; 166:112920. [PMID: 36440088 PMCID: PMC9678855 DOI: 10.1016/j.chaos.2022.112920] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/26/2022] [Revised: 10/29/2022] [Accepted: 11/16/2022] [Indexed: 06/16/2023]
Abstract
The world experienced the life-threatening COVID-19 disease worldwide since its inversion. The whole world experienced difficult moments during the COVID-19 period, whereby most individual lives were affected by the disease socially and economically. The disease caused millions of illnesses and hundreds of thousands of deaths worldwide. To fight and control the COVID-19 disease intensity, mathematical modeling was an essential tool used to determine the potentiality and seriousness of the disease. Due to the effects of the COVID-19 disease, scientists observed that vaccination was the main option to fight against the disease for the betterment of human lives and the world economy. Unvaccinated individuals are more stressed with the disease, hence their body's immune system are affected by the disease. In this study, the S V E I H R deterministic model of COVID-19 with six compartments was proposed and analyzed. Analytically, the next-generation matrix method was used to determine the basic reproduction number ( R 0 ). Detailed stability analysis of the no-disease equilibrium ( E 0 ) of the proposed model to observe the dynamics of the system was carried out and the results showed that E 0 is stable if R 0 < 1 and unstable when R 0 > 1 . The Bayesian Markov Chain Monte Carlo (MCMC) method for the parameter identifiability was discussed. Moreover, the sensitivity analysis of R 0 showed that vaccination was an essential method to control the disease. With the presence of a vaccine in our S V E I H R model, the results showed that R 0 = 0 . 208 , which means COVID-19 is fading out of the community and hence minimizes the transmission. Moreover, in the absence of a vaccine in our model, R 0 = 1 . 7214 , which means the disease is in the community and spread very fast. The numerical simulations demonstrated the importance of the proposed model because the numerical results agree with the sensitivity results of the system. The numerical simulations also focused on preventing the disease to spread in the community.
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Affiliation(s)
- James Nicodemus Paul
- School of Computational and Communication Science and Engineering, The Nelson Mandela African Institution of Science and Technology, P.O Box 447, Arusha, Tanzania
| | - Isambi Sailon Mbalawata
- African Institute for Mathematical Sciences, NEI Global Secretariat, Rue KG590 ST, Kigali, Rwanda
| | - Silas Steven Mirau
- School of Computational and Communication Science and Engineering, The Nelson Mandela African Institution of Science and Technology, P.O Box 447, Arusha, Tanzania
| | - Lemjini Masandawa
- School of Computational and Communication Science and Engineering, The Nelson Mandela African Institution of Science and Technology, P.O Box 447, Arusha, Tanzania
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Adu PA, Iyaniwura SA, Mahmood B, Jeong D, Makuza JD, Cua G, Binka M, García HAV, Ringa N, Wong S, Yu A, Irvine MA, Otterstatter M, Janjua NZ. Association between close interpersonal contact and vaccine hesitancy: Findings from a population-based survey in Canada. Front Public Health 2022; 10:971333. [PMID: 36267997 PMCID: PMC9577316 DOI: 10.3389/fpubh.2022.971333] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Accepted: 08/26/2022] [Indexed: 01/25/2023] Open
Abstract
Background Vaccine hesitancy threatens efforts to bring the coronavirus disease 2019 (COVID-19) pandemic to an end. Given that social or interpersonal contact is an important driver for COVID-19 transmission, understanding the relationship between contact rates and vaccine hesitancy may help identify appropriate targets for strategic intervention. The purpose of this study was to assess the association between interpersonal contact and COVID-19 vaccine hesitancy among a sample of unvaccinated adults in the Canadian province of British Columbia (BC). Methods Unvaccinated individuals participating in the BC COVID-19 Population Mixing Patterns Survey (BC-Mix) were asked to indicate their level of agreement to the statement, "I plan to get the COVID-19 vaccine." Multivariable multinomial logistic regression was used to assess the association between self-reported interpersonal contact and vaccine hesitancy, adjusting for age, sex, ethnicity, educational attainment, occupation, household size and region of residence. All analyses incorporated survey sampling weights based on age, sex, geography, and ethnicity. Results Results were based on survey responses collected between March 8, 2021 and December 6, 2021, by a total of 4,515 adults aged 18 years and older. Overall, 56.7% of respondents reported that they were willing to get the COVID-19 vaccine, 27.0% were unwilling and 16.3% were undecided. We found a dose-response association between interpersonal contact and vaccine hesitancy. Compared to individuals in the lowest quartile (least contact), those in the fourth quartile (highest contact), third quartile and second quartile groups were more likely to be vaccine hesitant, with adjusted odd ratios (aORs) of 2.85 (95% CI: 2.02, 4.00), 1.91(95% CI: 1.38, 2.64), 1.78 (95% CI: 1.13, 2.82), respectively. Conclusion Study findings show that among unvaccinated people in BC, vaccine hesitancy is greater among those who have high contact rates, and hence potentially at higher risk of acquiring and transmitting infection. This may also impact future uptake of booster doses.
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Affiliation(s)
- Prince A. Adu
- British Columbia Centre for Disease Control, Vancouver, BC, Canada,School of Population and Public Health, University of British Columbia, Vancouver, BC, Canada
| | - Sarafa A. Iyaniwura
- British Columbia Centre for Disease Control, Vancouver, BC, Canada,Department of Mathematics, University of British Columbia, Vancouver, BC, Canada
| | - Bushra Mahmood
- Department of Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Dahn Jeong
- British Columbia Centre for Disease Control, Vancouver, BC, Canada,School of Population and Public Health, University of British Columbia, Vancouver, BC, Canada
| | - Jean Damascene Makuza
- British Columbia Centre for Disease Control, Vancouver, BC, Canada,School of Population and Public Health, University of British Columbia, Vancouver, BC, Canada
| | - Georgine Cua
- British Columbia Centre for Disease Control, Vancouver, BC, Canada,School of Population and Public Health, University of British Columbia, Vancouver, BC, Canada
| | - Mawuena Binka
- British Columbia Centre for Disease Control, Vancouver, BC, Canada,School of Population and Public Health, University of British Columbia, Vancouver, BC, Canada
| | - Héctor A. Velásquez García
- British Columbia Centre for Disease Control, Vancouver, BC, Canada,School of Population and Public Health, University of British Columbia, Vancouver, BC, Canada
| | - Notice Ringa
- British Columbia Centre for Disease Control, Vancouver, BC, Canada,School of Population and Public Health, University of British Columbia, Vancouver, BC, Canada
| | - Stanley Wong
- British Columbia Centre for Disease Control, Vancouver, BC, Canada
| | - Amanda Yu
- British Columbia Centre for Disease Control, Vancouver, BC, Canada
| | - Mike A. Irvine
- British Columbia Centre for Disease Control, Vancouver, BC, Canada
| | - Michael Otterstatter
- British Columbia Centre for Disease Control, Vancouver, BC, Canada,School of Population and Public Health, University of British Columbia, Vancouver, BC, Canada
| | - Naveed Z. Janjua
- British Columbia Centre for Disease Control, Vancouver, BC, Canada,School of Population and Public Health, University of British Columbia, Vancouver, BC, Canada,Centre for Health Evaluation & Outcome Sciences, St. Paul's Hospital, Vancouver, BC, Canada,*Correspondence: Naveed Z. Janjua
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Duan M, Jin Z. The heterogeneous mixing model of COVID-19 with interventions. J Theor Biol 2022; 553:111258. [PMID: 36041504 PMCID: PMC9420055 DOI: 10.1016/j.jtbi.2022.111258] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 08/17/2022] [Accepted: 08/18/2022] [Indexed: 12/15/2022]
Abstract
The emergence of mutant strains of COVID-19 reduces the effectiveness of vaccines in preventing infection, but remains effective in preventing severe illness and death. This paper established a heterogeneous mixing model of age groups with pharmaceutical and non-pharmaceutical interventions by analyzing the transmission mechanism of breakthrough infection caused by the heterogeneity of protection period under the action of vaccine-preventable infection with the original strain. The control reproduction number Rc of the system is analyzed, and the existence and stability of equilibrium are given by the comparison principle. Numerical simulation was conducted to evaluate the vaccination program and intervention measures in the customized scenario, demonstrating that the group-3 coverage rate p3 plays a key role in Rc. It is proposed that accelerating the rate of admission and testing is conducive to epidemic control by further fitting data of COVID-19 transmission in real scenarios. The findings provide a general modeling idea for the emergence of new vaccines to prevent infection by mutant strains, as well as a solid theoretical foundation for mainland China to formulate future vaccination strategies for new vaccines. This manuscript was submitted as part of a theme issue on “Modelling COVID-19 and Preparedness for Future Pandemics”.
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Affiliation(s)
- Moran Duan
- School of Data Science and Technology, North University of China, Taiyuan 030051, Shanxi, China; Complex Systems Research Center, Shanxi University, Taiyuan 030006, Shanxi, China
| | - Zhen Jin
- Complex Systems Research Center, Shanxi University, Taiyuan 030006, Shanxi, China; Shanxi Key Laboratory of Mathematical Technique and Big Data Analysis on Disease Control and Prevention, Taiyuan 030006, Shanxi, China.
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Adu PA, Binka M, Mahmood B, Jeong D, Buller-Taylor T, Damascene MJ, Iyaniwura S, Ringa N, Velásquez García HA, Wong S, Yu A, Bartlett S, Wilton J, Irvine MA, Otterstatter M, Janjua NZ. Cohort profile: the British Columbia COVID-19 Population Mixing Patterns Survey (BC-Mix). BMJ Open 2022; 12:e056615. [PMID: 36002217 PMCID: PMC9412046 DOI: 10.1136/bmjopen-2021-056615] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
PURPOSE Several non-pharmaceutical interventions, such as physical distancing, handwashing, self-isolation, and school and business closures, were implemented in British Columbia (BC) following the first laboratory-confirmed case of COVID-19 on 26 January 2020, to minimise in-person contacts that could spread infections. The BC COVID-19 Population Mixing Patterns Survey (BC-Mix) was established as a surveillance system to measure behaviour and contact patterns in BC over time to inform the timing of the easing/re-imposition of control measures. In this paper, we describe the BC-Mix survey design and the demographic characteristics of respondents. PARTICIPANTS The ongoing repeated online survey was launched in September 2020. Participants are mainly recruited through social media platforms (including Instagram, Facebook, YouTube, WhatsApp). A follow-up survey is sent to participants 2-4 weeks after completing the baseline survey. Survey responses are weighted to BC's population by age, sex, geography and ethnicity to obtain generalisable estimates. Additional indices such as the Material and Social Deprivation Index, residential instability, economic dependency, and others are generated using census and location data. FINDINGS TO DATE As of 26 July 2021, over 61 000 baseline survey responses were received of which 41 375 were eligible for analysis. Of the eligible participants, about 60% consented to follow-up and about 27% provided their personal health numbers for linkage with healthcare databases. Approximately 83.5% of respondents were female, 58.7% were 55 years or older, 87.5% identified as white and 45.9% had at least a university degree. After weighting, approximately 50% were female, 39% were 55 years or older, 65% identified as white and 50% had at least a university degree. FUTURE PLANS Multiple papers describing contact patterns, physical distancing measures, regular handwashing and facemask wearing, modelling looking at impact of physical distancing measures and vaccine acceptance, hesitancy and uptake are either in progress or have been published.
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Affiliation(s)
- Prince A Adu
- BC Centre for Disease Control, Vancouver, British Columbia, Canada
- School of Population and Public Health, The University of British Columbia, Vancouver, British Columbia, Canada
| | - Mawuena Binka
- BC Centre for Disease Control, Vancouver, British Columbia, Canada
| | - Bushra Mahmood
- Department of Medicine, The University of British Columbia, Vancouver, British Columbia, Canada
| | - Dahn Jeong
- BC Centre for Disease Control, Vancouver, British Columbia, Canada
- School of Population and Public Health, The University of British Columbia, Vancouver, British Columbia, Canada
| | | | - Makuza Jean Damascene
- BC Centre for Disease Control, Vancouver, British Columbia, Canada
- School of Population and Public Health, The University of British Columbia, Vancouver, British Columbia, Canada
| | - Sarafa Iyaniwura
- BC Centre for Disease Control, Vancouver, British Columbia, Canada
- Department of Mathematics, The University of British Columbia, Vancouver, British Columbia, Canada
| | - Notice Ringa
- BC Centre for Disease Control, Vancouver, British Columbia, Canada
- School of Population and Public Health, The University of British Columbia, Vancouver, British Columbia, Canada
| | - Héctor A Velásquez García
- BC Centre for Disease Control, Vancouver, British Columbia, Canada
- School of Population and Public Health, The University of British Columbia, Vancouver, British Columbia, Canada
| | - Stanley Wong
- BC Centre for Disease Control, Vancouver, British Columbia, Canada
| | - Amanda Yu
- BC Centre for Disease Control, Vancouver, British Columbia, Canada
| | - Sofia Bartlett
- BC Centre for Disease Control, Vancouver, British Columbia, Canada
- Department of Pathology and Laboratory Medicine, The University of British Columbia, Vancouver, British Columbia, Canada
| | - James Wilton
- BC Centre for Disease Control, Vancouver, British Columbia, Canada
| | - Mike A Irvine
- BC Centre for Disease Control, Vancouver, British Columbia, Canada
- Faculty of Health Sciences, Simon Fraser University, Burnaby, British Columbia, Canada
| | - Michael Otterstatter
- BC Centre for Disease Control, Vancouver, British Columbia, Canada
- School of Population and Public Health, The University of British Columbia, Vancouver, British Columbia, Canada
| | - Naveed Zafar Janjua
- BC Centre for Disease Control, Vancouver, British Columbia, Canada
- School of Population and Public Health, The University of British Columbia, Vancouver, British Columbia, Canada
- Centre for Health Evaluation & Outcome Sciences, St. Paul's Hospital, Vancouver, British Columbia, Canada
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