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Teslya A, Rozhnova G, Pham TM, van Wees DA, Nunner H, Godijk NG, Bootsma M, Kretzschmar ME. The importance of sustained compliance with physical distancing during COVID-19 vaccination rollout. COMMUNICATIONS MEDICINE 2022; 2:146. [PMID: 36402924 PMCID: PMC9675824 DOI: 10.1038/s43856-022-00207-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Accepted: 10/26/2022] [Indexed: 11/21/2022] Open
Abstract
BACKGROUND Increasing vaccination coverage against SARS-CoV-2 enabled relaxation of lockdowns in many countries in Europe. As the vaccination rollouts progressed, the public health authorities were seeking recommendations on the continuation of physical distancing measures during ongoing vaccination rollouts. Compliance with these measures was declining while more transmissible virus variants have emerged. METHODS We used a SARS-CoV-2 transmission model to investigate the feedback between compliance, infection incidence, and vaccination coverage. We quantified our findings in terms of cumulative number of new hospitalisations three and six months after the start of vaccination. RESULTS Our results suggest that the combination of fast waning compliance in non-vaccinated individuals, low compliance in vaccinated individuals, low vaccine efficacy against infection and more transmissible virus variants may result in a higher cumulative number of new hospitalisations than in a situation without vaccination. These adverse effects can be alleviated by deploying behavioural interventions that should preferably target both vaccinated and non-vaccinated individuals. The choice of the most appropriate intervention depends on vaccination rate and vaccine efficacy against infection. CONCLUSIONS Supplementary behavioural interventions aiming to boost compliance to physical distancing measures can improve the outcome of vaccination programmes, until vaccination coverage is sufficiently high. For optimal results, these interventions should be selected based on the vaccine efficacy against infection and expected vaccination rate. While we considered the dynamics of SARS-CoV-2, the qualitative effects of the interplay between infectious disease spread and behavior on the outcomes of a vaccination programme can be used as guidance in a future similar pandemic.
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Affiliation(s)
- Alexandra Teslya
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.
| | - Ganna Rozhnova
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.
- BioISI-Biosystems & Integrative Sciences Institute, Faculdade de Ciências, Universidade de Lisboa, Lisboa, Portugal.
- Centre for Complex System Studies (CCSS), Utrecht University, Utrecht, The Netherlands.
| | - Thi Mui Pham
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Daphne A van Wees
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Hendrik Nunner
- Centre for Complex System Studies (CCSS), Utrecht University, Utrecht, The Netherlands
- Department of Sociology / ICS, Utrecht University, Utrecht, The Netherlands
| | - Noortje G Godijk
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Martin Bootsma
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
- Department of Mathematics, Faculty of Sciences, Utrecht University, Utrecht, The Netherlands
| | - Mirjam E Kretzschmar
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
- Centre for Complex System Studies (CCSS), Utrecht University, Utrecht, The Netherlands
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102
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Nguyen QD, Prokopenko M. A general framework for optimising cost-effectiveness of pandemic response under partial intervention measures. Sci Rep 2022; 12:19482. [PMID: 36376551 PMCID: PMC9662136 DOI: 10.1038/s41598-022-23668-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Accepted: 11/03/2022] [Indexed: 11/16/2022] Open
Abstract
The COVID-19 pandemic created enormous public health and socioeconomic challenges. The health effects of vaccination and non-pharmaceutical interventions (NPIs) were often contrasted with significant social and economic costs. We describe a general framework aimed to derive adaptive cost-effective interventions, adequate for both recent and emerging pandemic threats. We also quantify the net health benefits and propose a reinforcement learning approach to optimise adaptive NPIs. The approach utilises an agent-based model simulating pandemic responses in Australia, and accounts for a heterogeneous population with variable levels of compliance fluctuating over time and across individuals. Our analysis shows that a significant net health benefit may be attained by adaptive NPIs formed by partial social distancing measures, coupled with moderate levels of the society's willingness to pay for health gains (health losses averted). We demonstrate that a socially acceptable balance between health effects and incurred economic costs is achievable over a long term, despite possible early setbacks.
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Affiliation(s)
- Quang Dang Nguyen
- Centre for Complex Systems, Faculty of Engineering, University of Sydney, Darlington, NSW, 2008, Australia.
| | - Mikhail Prokopenko
- Centre for Complex Systems, Faculty of Engineering, University of Sydney, Darlington, NSW, 2008, Australia
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103
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Shi R, Zhang H, Morris Q. PAN-cODE: COVID-19 forecasting using conditional latent ODEs. J Am Med Inform Assoc 2022; 29:2089-2095. [PMID: 36047844 PMCID: PMC9667190 DOI: 10.1093/jamia/ocac160] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Revised: 07/22/2022] [Accepted: 08/31/2022] [Indexed: 01/07/2023] Open
Abstract
The coronavirus disease 2019 (COVID-19) pandemic has caused millions of deaths around the world and revealed the need for data-driven models of pandemic spread. Accurate pandemic caseload forecasting allows informed policy decisions on the adoption of non-pharmaceutical interventions (NPIs) to reduce disease transmission. Using COVID-19 as an example, we present Pandemic conditional Ordinary Differential Equation (PAN-cODE), a deep learning method to forecast daily increases in pandemic infections and deaths. By using a deep conditional latent variable model, PAN-cODE can generate alternative caseload trajectories based on alternate adoptions of NPIs, allowing stakeholders to make policy decisions in an informed manner. PAN-cODE also allows caseload estimation for regions that are unseen during model training. We demonstrate that, despite using less detailed data and having fully automated training, PAN-cODE's performance is comparable to state-of-the-art methods on 4-week-ahead and 6-week-ahead forecasting. Finally, we highlight the ability of PAN-cODE to generate realistic alternative outcome trajectories on select US regions.
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Affiliation(s)
- Ruian Shi
- Department of Computer Science, University of Toronto, Toronto, Canada
- Vector Institute for Artificial Intelligence, Toronto, Ontario, Canada
| | - Haoran Zhang
- Department of Computer Science, University of Toronto, Toronto, Canada
- Vector Institute for Artificial Intelligence, Toronto, Ontario, Canada
| | - Quaid Morris
- Department of Computer Science, University of Toronto, Toronto, Canada
- Vector Institute for Artificial Intelligence, Toronto, Ontario, Canada
- Computational and Systems Biology, Memorial Sloan Kettering Cancer Center, New York City, New York, USA
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104
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Faiões VDS, Póvoa HCC, Thurler BA, Chianca GC, Assaf AV, Iorio NLPP. Two years of COVID-19 pandemic: Framework of health interventions in a Brazilian city. Front Public Health 2022; 10:1025410. [PMID: 36388316 PMCID: PMC9650536 DOI: 10.3389/fpubh.2022.1025410] [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: 08/22/2022] [Accepted: 10/10/2022] [Indexed: 01/28/2023] Open
Abstract
The COVID-19 pandemic and its effects on public health have urgently demanded effective health policies to avoid the spread of COVID-19. Thus, public administrators have implemented non-pharmacological and pharmacological interventions to mitigate the pandemic's impacts and strengthen health services. The aim of this ecological study is to describe the scenario of COVID-19 pandemic in a Brazilian city, during 2 years. This ecological study was carried out in Nova Friburgo, a Brazilian city, for 105 weeks (two years), from March 29, 2020 (week 1) to April 02, 2022 (week 105). Data on COVID-19 cases and COVID-19 deaths, occupation of COVID-19 exclusive beds in hospitals, community mobility, vaccination, government regulation on the opening of city establishments and city risk assessment were collected from public datasets. Four waves of COVID-19 cases and deaths were observed during this period. The first case occurred in week 1 and first death in week 3 of this study. The highest peaks of cases and deaths were observed during the third wave with 1,131 cases (week 54) and 47 deaths (week 55) and where the highest occupation of COVID-19 exclusive beds in local hospitals occurred. Interventions from more restrictive to more flexible, were implemented throughout this study, including lockdown and gradual return in economic and social strata levels. Vaccination began on week 43 and at the end of this study 89.91% of the total population was vaccinated with at least one dose, being 83.22% fully vaccinated. A deep description of several interventions used to avoid COVID-19 spread in a Brazilian city during 2 years of this pandemic can help promote better decision-making in the future while it exposes the challenges of conducting public health policies in a pandemic scenario.
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Affiliation(s)
- Vanessa dos Santos Faiões
- Postgraduate Program in Dentistry, Instituto de Saúde de Nova Friburgo, Universidade Federal Fluminense (UFF), Nova Friburgo, Rio de Janeiro, Brazil,Department of Basic Science, Instituto de Saúde de Nova Friburgo, Universidade Federal Fluminense (UFF), Nova Friburgo, Rio de Janeiro, Brazil
| | - Helvécio Cardoso Corrêa Póvoa
- Department of Basic Science, Instituto de Saúde de Nova Friburgo, Universidade Federal Fluminense (UFF), Nova Friburgo, Rio de Janeiro, Brazil
| | - Bruna Alves Thurler
- Department of Basic Science, Instituto de Saúde de Nova Friburgo, Universidade Federal Fluminense (UFF), Nova Friburgo, Rio de Janeiro, Brazil
| | - Gabriela Ceccon Chianca
- School of Pharmacy, Universidade Estácio de Sá (UNESA), Nova Friburgo, Rio de Janeiro, Brazil
| | - Andréa Videira Assaf
- Postgraduate Program in Dentistry, Instituto de Saúde de Nova Friburgo, Universidade Federal Fluminense (UFF), Nova Friburgo, Rio de Janeiro, Brazil,Department of Specific Formation, Instituto de Saúde de Nova Friburgo, Universidade Federal Fluminense (UFF), Nova Friburgo, Rio de Janeiro, Brazil
| | - Natalia Lopes Pontes Póvoa Iorio
- Postgraduate Program in Dentistry, Instituto de Saúde de Nova Friburgo, Universidade Federal Fluminense (UFF), Nova Friburgo, Rio de Janeiro, Brazil,Department of Basic Science, Instituto de Saúde de Nova Friburgo, Universidade Federal Fluminense (UFF), Nova Friburgo, Rio de Janeiro, Brazil,*Correspondence: Natalia Lopes Pontes Póvoa Iorio
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105
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Aruffo E, Yuan P, Tan Y, Gatov E, Gournis E, Collier S, Ogden N, Bélair J, Zhu H. Community structured model for vaccine strategies to control COVID19 spread: A mathematical study. PLoS One 2022; 17:e0258648. [PMID: 36301932 PMCID: PMC9612529 DOI: 10.1371/journal.pone.0258648] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Accepted: 10/02/2021] [Indexed: 11/06/2022] Open
Abstract
Initial efforts to mitigate the COVID-19 pandemic have relied heavily on non-pharmaceutical interventions (NPIs), including physical distancing, hand hygiene, and mask-wearing. However, an effective vaccine is essential to containing the spread of the virus. We developed a compartmental model to examine different vaccine strategies for controlling the spread of COVID-19. Our framework accounts for testing rates, test-turnaround times, and vaccination waning immunity. Using reported case data from the city of Toronto, Canada between Mar-Dec, 2020 we defined epidemic phases of infection using contact rates as well as the probability of transmission upon contact. We investigated the impact of vaccine distribution by comparing different permutations of waning immunity, vaccine coverage and efficacy throughout various stages of NPI's relaxation in terms of cases and deaths. The basic reproduction number is also studied. We observed that widespread vaccine coverage substantially reduced the number of cases and deaths. Under phases with high transmission, an early or late reopening will result in new resurgence of the infection, even with the highest coverage. On the other hand, under phases with lower transmission, 60% of coverage is enough to prevent new infections. Our analysis of R0 showed that the basic reproduction number is reduced by decreasing the tests turnaround time and transmission in the household. While we found that household transmission can decrease following the introduction of a vaccine, public health efforts to reduce test turnaround times remain important for virus containment.
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Affiliation(s)
- Elena Aruffo
- Centre for Diseases Modeling (CDM), York University, Toronto, Ontario, Canada
- Department of Mathematics and Statistics, York University, Toronto, Ontario, Canada
| | - Pei Yuan
- Centre for Diseases Modeling (CDM), York University, Toronto, Ontario, Canada
- Department of Mathematics and Statistics, York University, Toronto, Ontario, Canada
| | - Yi Tan
- Centre for Diseases Modeling (CDM), York University, Toronto, Ontario, Canada
- Department of Mathematics and Statistics, York University, Toronto, Ontario, Canada
| | | | | | | | - Nick Ogden
- Public Health Agency of Canada, Ottawa, Ontario, Canada
| | - Jacques Bélair
- Centre for Diseases Modeling (CDM), York University, Toronto, Ontario, Canada
- Département de Mathématiques et de Statistique, Université de Montréal, Montréal, Québec, Canada
| | - Huaiping Zhu
- Centre for Diseases Modeling (CDM), York University, Toronto, Ontario, Canada
- Department of Mathematics and Statistics, York University, Toronto, Ontario, Canada
- * E-mail:
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106
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Jung SM, Jung J. The Possible Impact of Nationwide Vaccination on Outcomes of the COVID-19 Epidemic in North Korea: A Modelling Study. J Korean Med Sci 2022; 37:e300. [PMID: 36281488 PMCID: PMC9592938 DOI: 10.3346/jkms.2022.37.e300] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Accepted: 08/26/2022] [Indexed: 11/20/2022] Open
Abstract
BACKGROUND The Democratic People's Republic of Korea (North Korea) had successfully suppressed the coronavirus disease 2019 (COVID-19) epidemic via border closures. However, a rapid surge in incidence was reported due to the spread of the omicron variant (B.1.1.529), leading to a national emergency declaration in May 2022. Moreover, with the lack of vaccine accessibility and medical facilities, it is unclear how the disease burden may be exacerbated. Despite the limited epidemiological data, we aimed to project the COVID-19 transmissions in North Korea and quantify the potential impact of nationwide vaccination, comparing epidemiological outcomes via scenario analysis. METHODS A discrete-time deterministic compartmental model was used. The parameters were calibrated using empirical data. Numerical simulations incorporated nationwide COVID-19 vaccination into the proposed model with various asymptomatic proportions. RESULTS Our model suggested that the stringent public health and social measures (PHSMs) reduced the severe acute respiratory syndrome coronavirus 2 transmissibility by more than 80% in North Korea. Projections that explicitly incorporated vaccination indicated that nationwide vaccination would be necessary to suppress a huge resurgence in both COVID-19 cases and hospitalizations after the stringent PHSMs are eased. Moreover, vaccinating more than 80% of the population with two doses may keep the peak prevalence of hospitalizations below 1,500, averting more than 40,000 hospitalizations across all scenarios. CONCLUSION Nationwide vaccination would be essential to suppress the prevalence of COVID-19 hospitalizations in North Korea after the stringent PHSMs are lifted, especially in the case of a small asymptomatic proportion.
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Affiliation(s)
- Sung-Mok Jung
- Kyoto University School of Public Health, Kyoto, Japan
| | - Jaehun Jung
- Department of Preventive Medicine, Gachon University College of Medicine, Incheon, Korea.
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107
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Coccia M. COVID-19 Vaccination is not a Sufficient Public Policy to face Crisis Management of next Pandemic Threats. PUBLIC ORGANIZATION REVIEW 2022. [PMCID: PMC9574799 DOI: 10.1007/s11115-022-00661-6] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Indexed: 05/21/2023]
Abstract
This study reveals that a vast vaccination campaign is a necessary but not sufficient public policy to reduce the negative impact of Coronavirus Disease 2019 (COVID-19) pandemic crisis because manifold factors guide the spread of this new infectious disease and related mortality in society. Statistical evidence here, based on a worldwide sample of countries, shows a positive correlation between people fully vaccinated and COVID-19 mortality (r = + 0.65, p-value < 0.01). Multivariate regression, controlling income per capita, confirms this finding. Results suggest that the increasing share of people vaccinated against COVID-19 seems to be a necessary but not sufficient health policy to reduce mortality of COVID-19. The findings here can be explained with the role of Peltzman effect, new variants, environmental and socioeconomic factors that affect the diffusion and negative impact of COVID-19 pandemic in society. This study extends the knowledge in this research field to design effective public policies of crisis management for facing next pandemic threats.
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Affiliation(s)
- Mario Coccia
- CNR -- NATIONAL RESEARCH COUNCIL OF ITALY, Collegio Carlo Alberto, Via Real Collegio, n. 30, 10024 Moncalieri (TO), Italy
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108
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Zilhadia Z, Ariyanti F, Nurmansyah MI, Iriani DU, Dwirahmadi F. Factors Associated with COVID-19 Vaccination Acceptance Among Muslim High School Students in Jakarta Metropolitan Area, Indonesia. J Multidiscip Healthc 2022; 15:2341-2352. [PMID: 36267849 PMCID: PMC9578469 DOI: 10.2147/jmdh.s380171] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Accepted: 09/30/2022] [Indexed: 11/16/2022] Open
Abstract
Purpose This study aims to identify the factors associated with COVID-19 vaccination uptake and the intention to receive the COVID-19 vaccine among Muslim high school students in Jakarta, Indonesia. Patients and Methods A cross-sectional study using an online survey was conducted for high school students. The population in this study were Muslim students grade 11 and 12 at secondary high school living and studying in Jakarta Metropolitan area. A total of 440 samples have been selected using non-probability sampling. In this study, the independent variables were factors associated with vaccination behaviors that were derived from the HBM and TPB theories, while the dependent variables were vaccination uptake and vaccination intention. Factors associated with the dependent variables have been identified using chi-square and Mann-Whitney tests. Results About 65% respondents had received COVID-19 vaccine and 72% of those who had not received COVID-19 vaccine had the intention to receive the vaccine. Some of the behavioral model variables such as the perceived susceptibility score, perceived severity score, perceived benefits score, perceived barriers score, self-efficacy score, attitude score, and social norms score were significantly associated with COVID-19 vaccine uptake among high school students. Furthermore, all of the behavioral model variables, ie, perceived susceptibility score, perceived severity score, perceived benefits score, perceived barriers score, cues to action score, self-efficacy score, attitude score, social norms score, and perceived behavioral control score were significantly associated with an intention of being vaccinated. Conclusion The vaccination for students can be set in more affordable locationsfor example, in schools. In addition, efforts to increase student knowledge regarding the effectiveness and safety of vaccines and the dangers of COVID-19 should be sustained.
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Affiliation(s)
- Zilhadia Zilhadia
- Department of Pharmacy, Faculty of Health Sciences, Universitas Islam Negeri Syarif Hidayatullah Jakarta, Tangerang Selatan, Banten, Indonesia,Correspondence: Zilhadia Zilhadia, Faculty of Health Sciences, Universitas Islam Negeri Syarif Hidayatullah Jakarta, Kertamukti Road, Tangerang Selatan, Banten, 15419, Indonesia, Tel/Fax +62 21 74716718, Email
| | - Fajar Ariyanti
- Department of Public Health, Faculty of Health Sciences, Universitas Islam Negeri Syarif Hidayatullah Jakarta, Tangerang Selatan, Banten, Indonesia
| | - Mochamad Iqbal Nurmansyah
- Department of Public Health, Faculty of Health Sciences, Universitas Islam Negeri Syarif Hidayatullah Jakarta, Tangerang Selatan, Banten, Indonesia
| | - Dewi Utami Iriani
- Department of Public Health, Faculty of Health Sciences, Universitas Islam Negeri Syarif Hidayatullah Jakarta, Tangerang Selatan, Banten, Indonesia
| | - Febi Dwirahmadi
- Center for Environment and Population Health, School of Medicine, Griffith University, Brisbane, QLD, Australia
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109
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Sardar T, Nadim SS, Rana S. Detection of multiple waves for COVID-19 and its optimal control through media awareness and vaccination: study based on some Indian states. NONLINEAR DYNAMICS 2022; 111:1903-1920. [PMID: 36246667 PMCID: PMC9540085 DOI: 10.1007/s11071-022-07887-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Accepted: 09/11/2022] [Indexed: 06/16/2023]
Abstract
UNLABELLED COVID-19 is a highly infectious disease, and in very recent times, it has shown a massive impact throughout the globe. Several countries faced the COVID-19 infection waves multiple times. These later waves are more aggressive than the first wave and drastically impact social and economic factors. We developed a mechanistic model with imperfect lockdown effect, reinfection, transmission variability between symptomatic & asymptomatic, and media awareness to focus on the early detection of multiple waves and their control measures. Using daily COVID-19 cases data from six states of India, we estimated several important model parameters. Moreover, we estimated the home quarantine, community, and basic reproduction numbers. We developed an algorithm to carry out global sensitivity analysis (Sobol) of the parameters that influence the number of COVID-19 waves ( W C ) and the average number of COVID-19 cases in a wave ( A W ). We have identified some critical controlling parameters that mainly influenced W C and A W . Our study also revealed the best COVID-19 control strategy/strategies among vaccination, media awareness, and their combination using an optimal cost-effective study. The detailed analysis suggests that the severity of asymptomatic transmission is around 10% to 29% of that of symptomatic transmission in all six locations. About 1% to 4% of the total population under lockdown may contribute to new COVID-19 infection in all six locations. Optimal cost-effective analysis based on interventions, namely only vaccination (VA), only media awareness (ME), and a combination of vaccination & media (VA+ME), are projected for the period March 14, 2020, to August 31, 2021, for all the six locations. We have found that a large percentage of the population (26% to 45%) must be vaccinated from February 13 to August 31, 2021, to avert an optimal number of COVID-19 cases in these six locations. SUPPLEMENTARY INFORMATION The online version contains supplementary material available at 10.1007/s11071-022-07887-5.
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Affiliation(s)
- Tridip Sardar
- Department of Mathematics, Dinabandhu Andrews College, Kolkata, India
| | - Sk Shahid Nadim
- Odum School of Ecology, University of Georgia, Athens, USA
- Department of Mathematics, Indian Institute of Technology, Roorkee, India
| | - Sourav Rana
- Department of Statistics, Visva-Bharati University, Santiniketan, West Bengal India
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110
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Sanz-Leon P, Hamilton LHW, Raison SJ, Pan AJX, Stevenson NJ, Stuart RM, Abeysuriya RG, Kerr CC, Lambert SB, Roberts JA. Modelling herd immunity requirements in Queensland: impact of vaccination effectiveness, hesitancy and variants of SARS-CoV-2. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2022; 380:20210311. [PMID: 35965469 PMCID: PMC9376720 DOI: 10.1098/rsta.2021.0311] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Accepted: 06/13/2022] [Indexed: 05/21/2023]
Abstract
Long-term control of SARS-CoV-2 outbreaks depends on the widespread coverage of effective vaccines. In Australia, two-dose vaccination coverage of above 90% of the adult population was achieved. However, between August 2020 and August 2021, hesitancy fluctuated dramatically. This raised the question of whether settings with low naturally derived immunity, such as Queensland where less than [Formula: see text] of the population is known to have been infected in 2020, could have achieved herd immunity against 2021's variants of concern. To address this question, we used the agent-based model Covasim. We simulated outbreak scenarios (with the Alpha, Delta and Omicron variants) and assumed ongoing interventions (testing, tracing, isolation and quarantine). We modelled vaccination using two approaches with different levels of realism. Hesitancy was modelled using Australian survey data. We found that with a vaccine effectiveness against infection of 80%, it was possible to control outbreaks of Alpha, but not Delta or Omicron. With 90% effectiveness, Delta outbreaks may have been preventable, but not Omicron outbreaks. We also estimated that a decrease in hesitancy from 20% to 14% reduced the number of infections, hospitalizations and deaths by over 30%. Overall, we demonstrate that while herd immunity may not be attainable, modest reductions in hesitancy and increases in vaccine uptake may greatly improve health outcomes. 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)
- Paula Sanz-Leon
- Brain Modelling Group, QIMR Berghofer Medical Research Institute, Brisbane, QLD 4006, Australia
| | - Lachlan H W Hamilton
- Brain Modelling Group, QIMR Berghofer Medical Research Institute, Brisbane, QLD 4006, Australia
| | - Sebastian J Raison
- Brain Modelling Group, QIMR Berghofer Medical Research Institute, Brisbane, QLD 4006, Australia
| | - Anna J X Pan
- Brain Modelling Group, QIMR Berghofer Medical Research Institute, Brisbane, QLD 4006, Australia
| | - Nathan J Stevenson
- Brain Modelling Group, QIMR Berghofer Medical Research Institute, Brisbane, QLD 4006, Australia
| | - Robyn M Stuart
- Department of Mathematical Sciences, University of Copenhagen, DK-2100 Copenhagen, Denmark
| | | | - Cliff C Kerr
- Institute for Disease Modeling, Bill and Melinda Gates Foundation, Seattle, WA 98109, USA
| | - Stephen B Lambert
- National Centre for Immunisation Research and Surveillance for Vaccine Preventable Diseases, Westmead, NSW 2145, Australia
| | - James A Roberts
- Brain Modelling Group, QIMR Berghofer Medical Research Institute, Brisbane, QLD 4006, Australia
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111
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Hinch R, Panovska-Griffiths J, Probert WJM, Ferretti L, Wymant C, Di Lauro F, Baya N, Ghafari M, Abeler-Dörner L, Fraser C. Estimating SARS-CoV-2 variant fitness and the impact of interventions in England using statistical and geo-spatial agent-based models. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2022. [PMID: 35965459 DOI: 10.6084/m9.figshare.c.6067650] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 05/07/2023]
Abstract
The SARS-CoV-2 epidemic has been extended by the evolution of more transmissible viral variants. In autumn 2020, the B.1.177 lineage became the dominant variant in England, before being replaced by the B.1.1.7 (Alpha) lineage in late 2020, with the sweep occurring at different times in each region. This period coincided with a large number of non-pharmaceutical interventions (e.g. lockdowns) to control the epidemic, making it difficult to estimate the relative transmissibility of variants. In this paper, we model the spatial spread of these variants in England using a meta-population agent-based model which correctly characterizes the regional variation in cases and distribution of variants. As a test of robustness, we additionally estimated the relative transmissibility of multiple variants using a statistical model based on the renewal equation, which simultaneously estimates the effective reproduction number R. Relative to earlier variants, the transmissibility of B.1.177 is estimated to have increased by 1.14 (1.12-1.16) and that of Alpha by 1.71 (1.65-1.77). The vaccination programme starting in December 2020 is also modelled. Counterfactual simulations demonstrate that the vaccination programme was essential for reopening in March 2021, and that if the January lockdown had started one month earlier, up to 30 k (24 k-38 k) deaths could have been prevented. 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)
- Robert Hinch
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Jasmina Panovska-Griffiths
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- The Queen's College, and, University of Oxford, Oxford, UK
| | - William J M Probert
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Luca Ferretti
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Chris Wymant
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Francesco Di Lauro
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Nikolas Baya
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Mahan Ghafari
- Department of Zoology, University of Oxford, Oxford, UK
| | - Lucie Abeler-Dörner
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Christophe Fraser
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK
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Hinch R, Panovska-Griffiths J, Probert WJM, Ferretti L, Wymant C, Di Lauro F, Baya N, Ghafari M, Abeler-Dörner L, Fraser C. Estimating SARS-CoV-2 variant fitness and the impact of interventions in England using statistical and geo-spatial agent-based models. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2022; 380:20210304. [PMID: 35965459 PMCID: PMC9376717 DOI: 10.1098/rsta.2021.0304] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Accepted: 02/22/2022] [Indexed: 05/04/2023]
Abstract
The SARS-CoV-2 epidemic has been extended by the evolution of more transmissible viral variants. In autumn 2020, the B.1.177 lineage became the dominant variant in England, before being replaced by the B.1.1.7 (Alpha) lineage in late 2020, with the sweep occurring at different times in each region. This period coincided with a large number of non-pharmaceutical interventions (e.g. lockdowns) to control the epidemic, making it difficult to estimate the relative transmissibility of variants. In this paper, we model the spatial spread of these variants in England using a meta-population agent-based model which correctly characterizes the regional variation in cases and distribution of variants. As a test of robustness, we additionally estimated the relative transmissibility of multiple variants using a statistical model based on the renewal equation, which simultaneously estimates the effective reproduction number R. Relative to earlier variants, the transmissibility of B.1.177 is estimated to have increased by 1.14 (1.12-1.16) and that of Alpha by 1.71 (1.65-1.77). The vaccination programme starting in December 2020 is also modelled. Counterfactual simulations demonstrate that the vaccination programme was essential for reopening in March 2021, and that if the January lockdown had started one month earlier, up to 30 k (24 k-38 k) deaths could have been prevented. 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)
- Robert Hinch
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Jasmina Panovska-Griffiths
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- The Queen's College, University of Oxford, Oxford, UK
| | - William J. M. Probert
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Luca Ferretti
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Chris Wymant
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Francesco Di Lauro
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Nikolas Baya
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Mahan Ghafari
- Department of Zoology, University of Oxford, Oxford, UK
| | - Lucie Abeler-Dörner
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | | | - Christophe Fraser
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK
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113
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Campillo-Funollet E, Wragg H, Van Yperen J, Duong DL, Madzvamuse A. Reformulating the susceptible-infectious-removed model in terms of the number of detected cases: well-posedness of the observational model. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2022; 380:20210306. [PMID: 35965462 PMCID: PMC9376718 DOI: 10.1098/rsta.2021.0306] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/05/2021] [Accepted: 02/23/2022] [Indexed: 06/15/2023]
Abstract
Compartmental models are popular in the mathematics of epidemiology for their simplicity and wide range of applications. Although they are typically solved as initial value problems for a system of ordinary differential equations, the observed data are typically akin to a boundary value-type problem: we observe some of the dependent variables at given times, but we do not know the initial conditions. In this paper, we reformulate the classical susceptible-infectious-recovered system in terms of the number of detected positive infected cases at different times to yield what we term the observational model. We then prove the existence and uniqueness of a solution to the boundary value problem associated with the observational model and present a numerical algorithm to approximate the solution. 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)
- Eduard Campillo-Funollet
- Department of Statistical Methodology and Applications, School of Mathematics, Statistics and Actuarial Science, University of Kent, Canterbury, Kent CT2 7PE, UK
| | - Hayley Wragg
- Department of Engineering Mathematics, School of Computer Science, Electrical and Electronic Engineering and Engineering Maths, University of Bristol, Bristol BS8 1TW, UK
- Department of Mathematics, School of Mathematical and Physical Sciences, University of Sussex, Brighton, East Sussex BN1 9QH, UK
| | - James Van Yperen
- Department of Mathematics, School of Mathematical and Physical Sciences, University of Sussex, Brighton, East Sussex BN1 9QH, UK
| | - Duc-Lam Duong
- Department of Mathematics, School of Mathematical and Physical Sciences, University of Sussex, Brighton, East Sussex BN1 9QH, UK
- School of Engineering Science, LUT University, Lappeenranta 53850, Finland
| | - Anotida Madzvamuse
- Department of Mathematics, School of Mathematical and Physical Sciences, University of Sussex, Brighton, East Sussex BN1 9QH, UK
- Department of Mathematics, University of Johannesburg, Johannesburg, South Africa
- University of British Columbia, Department of Mathematics, Vancouver, Canada
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114
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Costa GS, Cota W, Ferreira SC. Data-driven approach in a compartmental epidemic model to assess undocumented infections. CHAOS, SOLITONS, AND FRACTALS 2022; 163:112520. [PMID: 35996714 PMCID: PMC9385215 DOI: 10.1016/j.chaos.2022.112520] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Revised: 07/27/2022] [Accepted: 07/29/2022] [Indexed: 06/15/2023]
Abstract
Nowcasting and forecasting of epidemic spreading rely on incidence series of reported cases to derive the fundamental epidemiological parameters for a given pathogen. Two relevant drawbacks for predictions are the unknown fractions of undocumented cases and levels of nonpharmacological interventions, which span highly heterogeneously across different places and times. We describe a simple data-driven approach using a compartmental model including asymptomatic and pre-symptomatic contagions that allows to estimate both the level of undocumented infections and the value of effective reproductive number R t from time series of reported cases, deaths, and epidemiological parameters. The method was applied to epidemic series for COVID-19 across different municipalities in Brazil allowing to estimate the heterogeneity level of under-reporting across different places. The reproductive number derived within the current framework is little sensitive to both diagnosis and infection rates during the asymptomatic states. The methods described here can be extended to more general cases if data is available and adapted to other epidemiological approaches and surveillance data.
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Affiliation(s)
- Guilherme S Costa
- Departamento de Física, Universidade Federal de Viçosa, 36570-900, Viçosa, MG, Brazil
| | - Wesley Cota
- Departamento de Física, Universidade Federal de Viçosa, 36570-900, Viçosa, MG, Brazil
| | - Silvio C Ferreira
- Departamento de Física, Universidade Federal de Viçosa, 36570-900, Viçosa, MG, Brazil
- National Institute of Science and technology for Complex Systems, Centro Brasileiro de Pesquisas Físicas, Rua Xavier Sigaud 150, 22290-180, Rio de Janeiro, Brazil
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115
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Kaleta M, Kęsik-Brodacka M, Nowak K, Olszewski R, Śliwiński T, Żółtowska I. Long-term spatial and population-structured planning of non-pharmaceutical interventions to epidemic outbreaks. COMPUTERS & OPERATIONS RESEARCH 2022; 146:105919. [PMID: 35755160 PMCID: PMC9212736 DOI: 10.1016/j.cor.2022.105919] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Revised: 04/01/2022] [Accepted: 06/10/2022] [Indexed: 06/15/2023]
Abstract
In this paper, we consider the problem of planning non-pharmaceutical interventions to control the spread of infectious diseases. We propose a new model derived from classical compartmental models; however, we model spatial and population-structure heterogeneity of population mixing. The resulting model is a large-scale non-linear and non-convex optimisation problem. In order to solve it, we apply a special variant of covariance matrix adaptation evolution strategy. We show that results obtained for three different objectives are better than natural heuristics and, moreover, that the introduction of an individual's mobility to the model is significant for the quality of the decisions. We apply our approach to a six-compartmental model with detailed Poland and COVID-19 disease data. The obtained results are non-trivialand sometimes unexpected; therefore, we believe that our model could be applied to support policy-makers in fighting diseases at the long-term decision-making level.
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Affiliation(s)
- Mariusz Kaleta
- Warsaw University of Technology, Pl. Politechniki 1, Warsaw 00-661, Poland
| | | | | | - Robert Olszewski
- Warsaw University of Technology, Pl. Politechniki 1, Warsaw 00-661, Poland
| | - Tomasz Śliwiński
- Warsaw University of Technology, Pl. Politechniki 1, Warsaw 00-661, Poland
| | - Izabela Żółtowska
- Warsaw University of Technology, Pl. Politechniki 1, Warsaw 00-661, Poland
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116
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Gavish N, Katriel G. Optimal vaccination at high reproductive numbers: sharp transitions and counterintuitive allocations. Proc Biol Sci 2022; 289:20221525. [PMID: 36168762 PMCID: PMC9515634 DOI: 10.1098/rspb.2022.1525] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Accepted: 09/01/2022] [Indexed: 11/12/2022] Open
Abstract
Optimization of vaccine allocations among different segments of a heterogeneous population is important for enhancing the effectiveness of vaccination campaigns in reducing the burden of epidemics. Intuitively, it would seem that allocations designed to minimize infections should prioritize those with the highest risk of being infected and infecting others. This prescription is well supported by vaccination theory, e.g. when the vaccination campaign aims to reach herd immunity. In this work, we show, however, that for vaccines providing partial protection (leaky vaccines) and for sufficiently high values of the basic reproduction number, intuition is overturned: the optimal allocation minimizing the number of infections prioritizes the vaccination of those who are least likely to be infected. The work combines numerical investigations, asymptotic analysis for a general model, and complete mathematical analysis in a two-group model. The results point to important considerations in managing vaccination campaigns for infections with high transmissibility.
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Affiliation(s)
- Nir Gavish
- Faculty of Mathematics, Technion—IIT, Haifa 32000, Israel
| | - Guy Katriel
- Department of Applied Mathematics, Braude College of Engineering, Karmiel 216100, Israel
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117
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He M, Huang Y, Wang Y, Liu J, Han M, Xiao Y, Zhang N, Gui H, Qiu H, Cao L, Jia W, Huang S. Metabolomics-based investigation of SARS-CoV-2 vaccination (Sinovac) reveals an immune-dependent metabolite biomarker. Front Immunol 2022; 13:954801. [PMID: 36248825 PMCID: PMC9554639 DOI: 10.3389/fimmu.2022.954801] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Accepted: 09/12/2022] [Indexed: 11/25/2022] Open
Abstract
SARS-CoV-2 and its mutant strains continue to rapidly spread with high infection and fatality. Large-scale SARS-CoV-2 vaccination provides an important guarantee for effective resistance to existing or mutated SARS-CoV-2 virus infection. However, whether the host metabolite levels respond to SARS-CoV-2 vaccine-influenced host immunity remains unclear. To help delineate the serum metabolome profile of SARS-CoV-2 vaccinated volunteers and determine that the metabolites tightly respond to host immune antibodies and cytokines, in this study, a total of 59 sera samples were collected from 30 individuals before SARS-CoV-2 vaccination and from 29 COVID-19 vaccines 2 weeks after the two-dose vaccination. Next, untargeted metabolomics was performed and a distinct metabolic composition was revealed between the pre-vaccination (VB) group and two-dose vaccination (SV) group by partial least squares-discriminant and principal component analyses. Based on the criteria: FDR < 0.05, absolute log2 fold change greater than 0.25, and VIP >1, we found that L-glutamic acid, gamma-aminobutyric acid (GABA), succinic acid, and taurine showed increasing trends from SV to VB. Furthermore, SV-associated metabolites were mainly annotated to butanoate metabolism and glutamate metabolism pathways. Moreover, two metabolite biomarkers classified SV from VB individuals with an area under the curve (AUC) of 0.96. Correlation analysis identified a positive association between four metabolites enriched in glutamate metabolism and serum antibodies in relation to IgG, IgM, and IgA. These results suggest that the contents of gamma-aminobutyric acid and indole in serum could be applied as biomarkers in distinguishing vaccinated volunteers from the unvaccinated. What’s more, metabolites such as GABA and taurine may serve as a metabolic target for adjuvant vaccines to boost the ability of the individuals to improve immunity.
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Affiliation(s)
- Maozhang He
- Department of Microbiology, The Key Laboratory of Microbiology and Parasitology of Anhui Province, The Key Laboratory of Zoonoses of High Institutions in Anhui, School of Basic Medical Sciences, Anhui Medical University, Hefei, China
| | - Yixuan Huang
- Department of Clinical Medicine, The First School of Clinical Medicine, Anhui Medical University, Hefei, China
| | - Yun Wang
- Department of Nosocomial Infection Control, Anhui No.2 Provincial People’s Hospital, Hefei, China
| | - Jiling Liu
- Department of Microbiology, The Key Laboratory of Microbiology and Parasitology of Anhui Province, The Key Laboratory of Zoonoses of High Institutions in Anhui, School of Basic Medical Sciences, Anhui Medical University, Hefei, China
| | - Maozhen Han
- School of Life Sciences, Anhui Medical University, Hefei, China
| | - Yixuan Xiao
- Department of Microbiology, The Key Laboratory of Microbiology and Parasitology of Anhui Province, The Key Laboratory of Zoonoses of High Institutions in Anhui, School of Basic Medical Sciences, Anhui Medical University, Hefei, China
| | - Na Zhang
- Department of Nosocomial Infection Control, Anhui No.2 Provincial People’s Hospital, Hefei, China
| | - Hongya Gui
- Department of Microbiology, The Key Laboratory of Microbiology and Parasitology of Anhui Province, The Key Laboratory of Zoonoses of High Institutions in Anhui, School of Basic Medical Sciences, Anhui Medical University, Hefei, China
| | - Huan Qiu
- School of Nursing, Anhui Medical University, Hefei, China
| | - Liqing Cao
- School of Life Sciences, Anhui Medical University, Hefei, China
| | - Weihua Jia
- School of Life Sciences, Anhui Medical University, Hefei, China
- *Correspondence: Shenghai Huang, ; Weihua Jia,
| | - Shenghai Huang
- Department of Microbiology, The Key Laboratory of Microbiology and Parasitology of Anhui Province, The Key Laboratory of Zoonoses of High Institutions in Anhui, School of Basic Medical Sciences, Anhui Medical University, Hefei, China
- School of Life Sciences, Anhui Medical University, Hefei, China
- *Correspondence: Shenghai Huang, ; Weihua Jia,
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118
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Ghazy RM, Abdou MS, Awaidy S, Sallam M, Elbarazi I, Youssef N, Fiidow OA, Mehdad S, Hussein MF, Adam MF, Abdullah FSA, Rebai WK, Raad EB, Hussein M, Shehata SF, Ismail II, Salam AA, Samhouri D. Acceptance of COVID-19 Vaccine Booster Doses Using the Health Belief Model: A Cross-Sectional Study in Low-Middle- and High-Income Countries of the East Mediterranean Region. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph191912136. [PMID: 36231447 PMCID: PMC9566578 DOI: 10.3390/ijerph191912136] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Revised: 09/18/2022] [Accepted: 09/20/2022] [Indexed: 05/07/2023]
Abstract
Coronavirus disease (COVID-19) booster doses decrease infection transmission and disease severity. This study aimed to assess the acceptance of COVID-19 vaccine booster doses in low, middle, and high-income countries of the East Mediterranean Region (EMR) and its determinants using the health belief model (HBM). In addition, we aimed to identify the causes of booster dose rejection and the main source of information about vaccination. Using the snowball and convince sampling technique, a bilingual, self-administered, anonymous questionnaire was used to collect the data from 14 EMR countries through different social media platforms. Logistic regression analysis was used to estimate the key determinants that predict vaccination acceptance among respondents. Overall, 2327 participants responded to the questionnaire. In total, 1468 received compulsory doses of vaccination. Of them, 739 (50.3%) received booster doses and 387 (26.4%) were willing to get the COVID-19 vaccine booster doses. Vaccine booster dose acceptance rates in low, middle, and high-income countries were 73.4%, 67.9%, and 83.0%, respectively (p < 0.001). Participants who reported reliance on information about the COVID-19 vaccination from the Ministry of Health websites were more willing to accept booster doses (79.3% vs. 66.6%, p < 0.001). The leading causes behind booster dose rejection were the beliefs that booster doses have no benefit (48.35%) and have severe side effects (25.6%). Determinants of booster dose acceptance were age (odds ratio (OR) = 1.02, 95% confidence interval (CI): 1.01-1.03, p = 0.002), information provided by the Ministry of Health (OR = 3.40, 95% CI: 1.79-6.49, p = 0.015), perceived susceptibility to COVID-19 infection (OR = 1.88, 95% CI: 1.21-2.93, p = 0.005), perceived severity of COVID-19 (OR = 2.08, 95% CI: 137-3.16, p = 0.001), and perceived risk of side effects (OR = 0.25, 95% CI: 0.19-0.34, p < 0.001). Booster dose acceptance in EMR is relatively high. Interventions based on HBM may provide useful directions for policymakers to enhance the population's acceptance of booster vaccination.
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Affiliation(s)
- Ramy Mohamed Ghazy
- Tropical Health Department, High Institute of Public Health, Alexandria University, Alexandria 21561, Egypt
- Correspondence:
| | - Marwa Shawky Abdou
- Department of Epidemiology, High Institute of Public Health, Alexandria University, Alexandria 21561, Egypt
| | - Salah Awaidy
- Health Affairs, Ministry of Health, Muscat 100, Oman
| | - Malik Sallam
- Department of Pathology, Microbiology and Forensic Medicine, School of Medicine, The University of Jordan, Amman 11942, Jordan
- Department of Clinical Laboratories and Forensic Medicine, Jordan University Hospital, Amman 11942, Jordan
- Department of Translational Medicine, Faculty of Medicine, Lund University, 22184 Malmö, Sweden
| | - Iffat Elbarazi
- Institute of Public Health, College of Medicine and Health Sciences, United Arab Emirates University, AlAin 15551, United Arab Emirates
| | - Naglaa Youssef
- Department of Medical-Surgical Nursing, College of Nursing, Princess Nourah bint Abdulrahman University, Riyadh 11671, Saudi Arabia
| | - Osman Abubakar Fiidow
- School of Public Health and Research, Somali National University, Mogadishu P.O. Box 15, Somalia
| | - Slimane Mehdad
- Physiology and Physiopathology Research Team, Research Centre of Human Pathology Genomics, Faculty of Sciences, Mohammed V University, Rabat BP 8007, Morocco
| | - Mohamed Fakhry Hussein
- Occupational Health and Industrial Medicine Department, High Institute of Public Health, Alexandria University, Alexandria 21526, Egypt
| | | | | | | | - Etwal Bou Raad
- Department of Epidemiology and Population Health, American University of Beirut, Beirut P.O. Box 110236, Lebanon
- School of Pharmacy, Lebanese International University, Beirut P.O. Box 146404, Lebanon
| | - Mai Hussein
- Clinical Research Administration, Alexandria Directorate of Health Affairs, Egyptian Ministry of Health and Population, Alexandria 21554, Egypt
- Harvard Medical School, Boston, MA 02115, USA
| | - Shehata F. Shehata
- Department of Family and Community Medicine, King Khalid University, Abha 62529, Saudi Arabia
- Biostatistics Department, High Institute of Public Health, Alexandria University, Alexandria 21561, Egypt
| | - Ismail Ibrahim Ismail
- Department of Neurology, Ibn Sina Hospital, Gamal Abdel Nasser Street, Sabah Medical Area, Safat 070001, Kuwait
| | | | - Dalia Samhouri
- Emergency Preparedness and International Health Regulations, WHO EMRO (DS), P.O. Box 7608, Naser City 11371, Egypt
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Abstract
The pandemic coronavirus disease 2019 (COVID-19) has rapidly spread to all countries worldwide. The emergence of its variants has exacerbated this problem. To date, many variants have been identified across the viral genome; the variants of concern are the focus of attention due to their higher transmissibility and resistance to vaccines, especially the delta variant. The delta variant has become the dominant severe acute respiratory syndrome novel coronavirus (SARS-CoV-2) variant worldwide, causing severe panic as it is highly infectious. A better understanding of these variants may help in the development of possible treatments and save more lives. In this study, we summarize the characteristics of the variants of concern. More importantly, we summarize the results of previous studies on the delta variant. The delta variant has a high transmissibility rate and increases the risk of hospitalization and death. However, it is partially sensitive to vaccines. In addition, nonpharmaceutical interventions are valuable during epidemics. These interventions can be used against the delta variant, but managing this variant should still be taken seriously.
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120
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Low skeletal muscle radiodensity and neutrophil-to-lymphocyte ratio as predictors of poor outcome in patients with COVID-19. Sci Rep 2022; 12:15718. [PMID: 36127500 PMCID: PMC9488878 DOI: 10.1038/s41598-022-20126-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Accepted: 09/08/2022] [Indexed: 12/15/2022] Open
Abstract
Inflammatory states and body composition changes are associated with a poor prognosis in many diseases, but their role in coronavirus disease 2019 (COVID-19) is not fully understood. To assess the impact of low skeletal muscle radiodensity (SMD), high neutrophil-to-lymphocyte ratio (NLR) and a composite score based on both variables, on complications, use of ventilatory support, and survival in patients with COVID-19. Medical records of patients hospitalized between May 1, 2020, and July 31, 2020, with a laboratory diagnosis of COVID-19 who underwent computed tomography (CT) were retrospectively reviewed. CT-derived body composition measurements assessed at the first lumbar vertebra level, and laboratory tests performed at diagnosis, were used to calculate SMD and NLR. Prognostic values were estimated via univariate and multivariate logistic regression analyses and the Kaplan–Meier curve. The study was approved by the local Institutional Review Board (CAAE 36276620.2.0000.5404). A total of 200 patients were included. Among the patients assessed, median age was 59 years, 58% were men and 45% required ICU care. A total of 45 (22.5%) patients died. Multivariate logistic analysis demonstrated that a low SMD (OR 2.94; 95% CI 1.13–7.66, P = 0.027), high NLR (OR 3.96; 95% CI 1.24–12.69, P = 0.021) and both low SMD and high NLR (OR 25.58; 95% CI 2.37–276.71, P = 0.008) combined, were associated with an increased risk of death. Patients who had both low SMD and high NLR required more mechanical ventilation (P < 0.001) and were hospitalized for a longer period (P < 0.001). Low SMD, high NLR and the composite score can predict poor prognosis in patients with COVID-19, and can be used as a tool for early identification of patients at risk. Systemic inflammation and low muscle radiodensity are useful predictors of poor prognosis, and the assessment of these factors in clinical practice should be considered.
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121
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Ben-Zuk N, Daon Y, Sasson A, Ben-Adi D, Huppert A, Nevo D, Obolski U. Assessing COVID-19 vaccination strategies in varied demographics using an individual-based model. Front Public Health 2022; 10:966756. [PMID: 36187701 PMCID: PMC9521355 DOI: 10.3389/fpubh.2022.966756] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2022] [Accepted: 08/15/2022] [Indexed: 01/24/2023] Open
Abstract
Background New variants of SARS-CoV-2 are constantly discovered. Administration of COVID-19 vaccines and booster doses, combined with the application of non-pharmaceutical interventions (NPIs), is often used to prevent outbreaks of emerging variants. Such outbreak dynamics are further complicated by the population's behavior and demographic composition. Hence, realistic simulations are needed to estimate the efficiency of proposed vaccination strategies in conjunction with NPIs. Methods We developed an individual-based model of COVID-19 dynamics that considers age-dependent parameters such as contact matrices, probabilities of symptomatic and severe disease, and households' age distribution. As a case study, we simulate outbreak dynamics under the demographic compositions of two Israeli cities with different household sizes and age distributions. We compare two vaccination strategies: vaccinate individuals in a currently prioritized age group, or dynamically prioritize neighborhoods with a high estimated reproductive number. Total infections and hospitalizations are used to compare the efficiency of the vaccination strategies under the two demographic structures, in conjunction with different NPIs. Results We demonstrate the effectiveness of vaccination strategies targeting highly infected localities and of NPIs actively detecting asymptomatic infections. We further show that different optimal vaccination strategies exist for each sub-population's demographic composition and that their application is superior to a uniformly applied strategy. Conclusion Our study emphasizes the importance of tailoring vaccination strategies to subpopulations' infection rates and to the unique characteristics of their demographics (e.g., household size and age distributions). The presented simulation framework and findings can help better design future responses against the following emerging variants.
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Affiliation(s)
- Noam Ben-Zuk
- School of Public Health, Tel Aviv University, Tel Aviv, Israel
- Porter School of the Environment and Earth Sciences, Tel Aviv University, Tel Aviv, Israel
| | - Yair Daon
- School of Public Health, Tel Aviv University, Tel Aviv, Israel
- Porter School of the Environment and Earth Sciences, Tel Aviv University, Tel Aviv, Israel
| | - Amit Sasson
- Department of Statistics and Operations Research, Tel Aviv University, Tel Aviv, Israel
| | - Dror Ben-Adi
- School of Public Health, Tel Aviv University, Tel Aviv, Israel
- Porter School of the Environment and Earth Sciences, Tel Aviv University, Tel Aviv, Israel
- Department of Statistics and Operations Research, Tel Aviv University, Tel Aviv, Israel
| | - Amit Huppert
- School of Public Health, Tel Aviv University, Tel Aviv, Israel
- The Bio-statistical and Bio-mathematical Unit, The Gertner Institute for Epidemiology and Health Policy Research, Tel Hashomer, Israel
| | - Daniel Nevo
- Department of Statistics and Operations Research, Tel Aviv University, Tel Aviv, Israel
| | - Uri Obolski
- School of Public Health, Tel Aviv University, Tel Aviv, Israel
- Porter School of the Environment and Earth Sciences, Tel Aviv University, Tel Aviv, Israel
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122
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Kim JE, Choi H, Choi Y, Lee CH. The economic impact of COVID-19 interventions: A mathematical modeling approach. Front Public Health 2022; 10:993745. [PMID: 36172208 PMCID: PMC9512395 DOI: 10.3389/fpubh.2022.993745] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Accepted: 08/23/2022] [Indexed: 01/26/2023] Open
Abstract
Prior to vaccination or drug treatment, non-pharmaceutical interventions were almost the only way to control the coronavirus disease 2019 (COVID-19) epidemic. After vaccines were developed, effective vaccination strategies became important. The prolonged COVID-19 pandemic has caused enormous economic losses worldwide. As such, it is necessary to estimate the economic effects of control policies, including non-pharmaceutical interventions and vaccination strategies. We estimated the costs associated with COVID-19 according to different vaccination rollout speeds and social distancing levels and investigated effective control strategies for cost minimization. Age-structured mathematical models were developed and used to study disease transmission epidemiology. Using these models, we estimated the actual costs due to COVID-19, considering costs associated with medical care, lost wages, death, vaccination, and gross domestic product (GDP) losses due to social distancing. The lower the social distancing (SD) level, the more important the vaccination rollout speed. SD level 1 was cost-effective under fast rollout speeds, but SD level 2 was more effective for slow rollout speeds. If the vaccine rollout rate is fast enough, even implementing SD level 1 will be cost effective and can control the number of critically ill patients and deaths. If social distancing is maintained at level 2 at the beginning and then relaxed when sufficient vaccinations have been administered, economic costs can be reduced while maintaining the number of patients with severe symptoms below the intensive care unit (ICU) capacity. Korea has wellequipped medical facilities and infrastructure for rapid vaccination, and the public's desire for vaccination is high. In this case, the speed of vaccine supply is an important factor in controlling the COVID-19 epidemic. If the speed of vaccination is fast, it is possible to maintain a low level of social distancing without a significant increase in the number of deaths and hospitalized patients with severe symptoms, and the corresponding costs can be reduced.
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Affiliation(s)
- Jung Eun Kim
- Department of Mathematical Sciences, Ulsan National Institute of Science and Technology, Ulsan, South Korea
| | - Heejin Choi
- Department of Mathematical Sciences, Ulsan National Institute of Science and Technology, Ulsan, South Korea
| | - Yongin Choi
- Busan Center for Medical Mathematics, National Institute of Mathematical Sciences, Daejeon, South Korea
| | - Chang Hyeong Lee
- Department of Mathematical Sciences, Ulsan National Institute of Science and Technology, Ulsan, South Korea,*Correspondence: Chang Hyeong Lee
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Althobaity Y, Wu J, Tildesley MJ. Non-pharmaceutical interventions and their relevance in the COVID-19 vaccine rollout in Saudi Arabia and Arab Gulf countries. Infect Dis Model 2022; 7:545-560. [PMID: 36035780 PMCID: PMC9391232 DOI: 10.1016/j.idm.2022.08.005] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Revised: 08/11/2022] [Accepted: 08/11/2022] [Indexed: 11/20/2022] Open
Abstract
In the early stages of the pandemic, Saudi Arabia and other countries in the Arab Gulf region relied on non-pharmaceutical therapies to limit the effect of the pandemic, much like other nations across the world. In comparison to other nations in the area or globally, these interventions were successful at lowering the healthcare burden. This was accomplished via the deterioration of the economy, education, and a variety of other societal activities. By the end of 2020, the promise of effective vaccinations against SARS-CoV-2 have been realized, and vaccination programs have begun in developed countries, followed by the rest of the world. Despite this, there is still a long way to go in the fight against the disease. In order to explore disease transmission, vaccine rollout and prioritisation, as well as behavioural dynamics, we relied on an age-structured compartmental model. We examine how individual and social behaviour changes in response to the initiation of vaccination campaigns and the relaxation of non-pharmacological treatments. Overall, vaccination remains the most effective method of containing the disease and resuming normal life. Additionally, we evaluate several vaccination prioritisation schemes based on age group, behavioural responses, vaccine effectiveness, and vaccination rollout speed. We applied our model to four Arab Gulf nations (Saudi Arabia, Bahrain, the United Arab Emirates, and Oman), which were chosen for their low mortality rate compared to other countries in the region or worldwide, as well as their demographic and economic settings. We fitted the model using actual pandemic data in these countries. Our results suggest that vaccinations focused on the elderly and rapid vaccine distribution are critical for reducing disease resurgence. Our result also reinforces the cautious note that early relaxation of safety measures may compromise the vaccine's short-term advantages.
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Affiliation(s)
- Yehya Althobaity
- The Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, School of Life Sciences and Mathematics Institute, University of Warwick, Coventry, CV4 7AL, United Kingdom
- Department of Mathematics, Taif University, Taif, P. O. Box 11099, Saudi Arabia
| | - Jianhong Wu
- Laboratory for Industrial and Applied Mathematics, Department of Mathematics and Statistics, York University, Toronto, Ontario, M3J 1P3, Canada
| | - Michael J. Tildesley
- The Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, School of Life Sciences and Mathematics Institute, University of Warwick, Coventry, CV4 7AL, United Kingdom
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Keeling MJ, Dyson L, Guyver-Fletcher G, Holmes A, Semple MG, Tildesley MJ, Hill EM. Fitting to the UK COVID-19 outbreak, short-term forecasts and estimating the reproductive number. Stat Methods Med Res 2022; 31:1716-1737. [PMID: 35037796 PMCID: PMC9465059 DOI: 10.1177/09622802211070257] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
The COVID-19 pandemic has brought to the fore the need for policy makers to receive timely and ongoing scientific guidance in response to this recently emerged human infectious disease. Fitting mathematical models of infectious disease transmission to the available epidemiological data provide a key statistical tool for understanding the many quantities of interest that are not explicit in the underlying epidemiological data streams. Of these, the effective reproduction number, [Formula: see text], has taken on special significance in terms of the general understanding of whether the epidemic is under control ([Formula: see text]). Unfortunately, none of the epidemiological data streams are designed for modelling, hence assimilating information from multiple (often changing) sources of data is a major challenge that is particularly stark in novel disease outbreaks. Here, focusing on the dynamics of the first wave (March-June 2020), we present in some detail the inference scheme employed for calibrating the Warwick COVID-19 model to the available public health data streams, which span hospitalisations, critical care occupancy, mortality and serological testing. We then perform computational simulations, making use of the acquired parameter posterior distributions, to assess how the accuracy of short-term predictions varied over the time course of the outbreak. To conclude, we compare how refinements to data streams and model structure impact estimates of epidemiological measures, including the estimated growth rate and daily incidence.
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Affiliation(s)
- Matt J Keeling
- The Zeeman Institute for Systems Biology & Infectious Disease Epidemiology Research, School of Life Sciences and Mathematics Institute, 2707University of Warwick, UK
- Joint Universities Pandemic and Epidemiological Research, https://maths.org/juniper/
| | - Louise Dyson
- The Zeeman Institute for Systems Biology & Infectious Disease Epidemiology Research, School of Life Sciences and Mathematics Institute, 2707University of Warwick, UK
- Joint Universities Pandemic and Epidemiological Research, https://maths.org/juniper/
| | - Glen Guyver-Fletcher
- The Zeeman Institute for Systems Biology & Infectious Disease Epidemiology Research, School of Life Sciences and Mathematics Institute, 2707University of Warwick, UK
- Midlands Integrative Biosciences Training Partnership, School of Life Sciences, 2707University of Warwick, UK
| | - Alex Holmes
- The Zeeman Institute for Systems Biology & Infectious Disease Epidemiology Research, School of Life Sciences and Mathematics Institute, 2707University of Warwick, UK
- Mathematics for Real World Systems Centre for Doctoral Training, Mathematics Institute, 2707University of Warwick, UK
| | - Malcolm G Semple
- NIHR Health Protection Research Unit in Emerging and Zoonotic Infections, Institute of Infection, Veterinary and Ecological Sciences, Faculty of Health and Life Sciences, 4591University of Liverpool, UK
- Respiratory Medicine, Alder Hey Children's Hospital, Institute in The Park, 4591University of Liverpool, Alder Hey Children's Hospital, Liverpool, UK
| | - Michael J Tildesley
- The Zeeman Institute for Systems Biology & Infectious Disease Epidemiology Research, School of Life Sciences and Mathematics Institute, 2707University of Warwick, UK
- Joint Universities Pandemic and Epidemiological Research, https://maths.org/juniper/
| | - Edward M Hill
- The Zeeman Institute for Systems Biology & Infectious Disease Epidemiology Research, School of Life Sciences and Mathematics Institute, 2707University of Warwick, UK
- Joint Universities Pandemic and Epidemiological Research, https://maths.org/juniper/
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125
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Schulenburg A, Cota W, Costa GS, Ferreira SC. Effects of infection fatality ratio and social contact matrices on vaccine prioritization strategies. CHAOS (WOODBURY, N.Y.) 2022; 32:093102. [PMID: 36182373 DOI: 10.1063/5.0096532] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Accepted: 08/01/2022] [Indexed: 06/16/2023]
Abstract
Effective strategies of vaccine prioritization are essential to mitigate the impacts of severe infectious diseases. We investigate the role of infection fatality ratio (IFR) and social contact matrices on vaccination prioritization using a compartmental epidemic model fueled by real-world data of different diseases and countries. Our study confirms that massive and early vaccination is extremely effective to reduce the disease fatality if the contagion is mitigated, but the effectiveness is increasingly reduced as vaccination beginning delays in an uncontrolled epidemiological scenario. The optimal and least effective prioritization strategies depend non-linearly on epidemiological variables. Regions of the epidemiological parameter space, in which prioritizing the most vulnerable population is more effective than the most contagious individuals, depend strongly on the IFR age profile being, for example, substantially broader for COVID-19 in comparison with seasonal influenza. Demographics and social contact matrices deform the phase diagrams but do not alter their qualitative shapes.
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Affiliation(s)
- Arthur Schulenburg
- Departamento de Física, Universidade Federal de Viçosa, Viçosa, Minas Gerais 36570-900, Brazil
| | - Wesley Cota
- Departamento de Física, Universidade Federal de Viçosa, Viçosa, Minas Gerais 36570-900, Brazil
| | - Guilherme S Costa
- Departamento de Física, Universidade Federal de Viçosa, Viçosa, Minas Gerais 36570-900, Brazil
| | - Silvio C Ferreira
- Departamento de Física, Universidade Federal de Viçosa, Viçosa, Minas Gerais 36570-900, Brazil
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PANTHA BUDDHI, MOHAMMED-AWEL JEMAL, VAIDYA NAVEENK. EFFECTS OF VACCINATION ON THE TRANSMISSION DYNAMICS OF COVID-19 IN DOUGHERTY COUNTY OF GEORGIA, USA. J BIOL SYST 2022. [DOI: 10.1142/s021833902250019x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Despite the significant progress in the development of vaccines, the COVID-19 pandemic still poses difficulty for its control because of many obstacles such as the proper implementation of vaccination, public hesitancy towards vaccines, dropping out from the second dose, and varying level of protection after the first and the second doses. In this study, we develop a novel mathematical model of COVID-19 transmission, including two separate vaccinated compartments (first dose and both doses). We parametrize and validate our model using data from Dougherty county of Georgia, USA, one of the most affected counties, where the transmission trend clearly is associated with various policies and public events. We analyze our model for stability of equilibria and persistence of the disease, and formulate expression for reproduction numbers. We estimate that the basic reproduction number in Dougherty county is 1.69, and the effective reproduction number during the study period ranges from 0.26 to 6.36. The number of daily undiagnosed cases peaked at 310 per day, resulting in the maximum number of active infectious individuals to be 2471. Our model predicts that in a high transmission scenario, the vaccination strategies should be combined with other non-pharmaceutical prevention strategies to ensure transmission control. Moreover, our results emphasize that completing both doses of vaccines on time is critical to achieve maximum benefits from the vaccination programs.
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Affiliation(s)
- BUDDHI PANTHA
- Department of Science and Mathematics, Abraham Baldwin Agricultural College, Tifton, GA 31793, USA
| | - JEMAL MOHAMMED-AWEL
- Department of Mathematics, Morgan State University, Baltimore, Maryland 21252, USA
| | - NAVEEN K. VAIDYA
- Department of Mathematics, San Diego State University, San Diego, California 92182, USA
- Computational Science Research Center, San Diego State University, San Diego, California 92182, USA
- Viral Information Institute, San Diego State University, San Diego, California 92182, USA
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127
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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] [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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Nia ZM, Ahmadi A, Bragazzi NL, Woldegerima WA, Mellado B, Wu J, Orbinski J, Asgary A, Kong JD. A cross-country analysis of macroeconomic responses to COVID-19 pandemic using Twitter sentiments. PLoS One 2022; 17:e0272208. [PMID: 36001531 PMCID: PMC9401163 DOI: 10.1371/journal.pone.0272208] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Accepted: 07/14/2022] [Indexed: 11/19/2022] Open
Abstract
The COVID-19 pandemic has had a devastating impact on the global economy. In this paper, we use the Phillips curve to compare and analyze the macroeconomics of three different countries with distinct income levels, namely, lower-middle (Nigeria), upper-middle (South Africa), and high (Canada) income. We aim to (1) find macroeconomic changes in the three countries during the pandemic compared to pre-pandemic time, (2) compare the countries in terms of response to the COVID-19 economic crisis, and (3) compare their expected economic reaction to the COVID-19 pandemic in the near future. An advantage to our work is that we analyze macroeconomics on a monthly basis to capture the shocks and rapid changes caused by on and off rounds of lockdowns. We use the volume and social sentiments of the Twitter data to approximate the macroeconomic statistics. We apply four different machine learning algorithms to estimate the unemployment rate of South Africa and Nigeria on monthly basis. The results show that at the beginning of the pandemic the unemployment rate increased for all the three countries. However, Canada was able to control and reduce the unemployment rate during the COVID-19 pandemic. Nonetheless, in line with the Phillips curve short-run, the inflation rate of Canada increased to a level that has never occurred in more than fifteen years. Nigeria and South Africa have not been able to control the unemployment rate and did not return to the pre-COVID-19 level. Yet, the inflation rate has increased in both countries. The inflation rate is still comparable to the pre-COVID-19 level in South Africa, but based on the Phillips curve short-run, it will increase further, if the unemployment rate decreases. Unfortunately, Nigeria is experiencing a horrible stagflation and a wild increase in both unemployment and inflation rates. This shows how vulnerable lower-middle-income countries could be to lockdowns and economic restrictions. In the near future, the main concern for all the countries is the high inflation rate. This work can potentially lead to more targeted and publicly acceptable policies based on social media content.
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Affiliation(s)
- Zahra Movahedi Nia
- Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC), York University, Toronto, Canada
- Laboratory for Industrial and Applied Mathematics, York University, Toronto, Canada
| | - Ali Ahmadi
- Faculty of Computer Engineering, K.N. Toosi University, Tehran, Iran
- Advanced Disaster, Emergency and Rapid-response Simulation (ADERSIM), York University, Toronto, Ontario, Canada
| | - Nicola L. Bragazzi
- Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC), York University, Toronto, Canada
- Laboratory for Industrial and Applied Mathematics, York University, Toronto, Canada
| | - Woldegebriel Assefa Woldegerima
- Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC), York University, Toronto, Canada
- Laboratory for Industrial and Applied Mathematics, York University, Toronto, Canada
| | - Bruce Mellado
- Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC), York University, Toronto, Canada
- School of Physics, Institute for Collider Particle Physics, University of the Witwatersrand, Johannesburg, South Africa
| | - Jianhong Wu
- Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC), York University, Toronto, Canada
- Laboratory for Industrial and Applied Mathematics, York University, Toronto, Canada
| | - James Orbinski
- Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC), York University, Toronto, Canada
- Dahdaleh Institute for Global Health Research, York University, Toronto, Canada
| | - Ali Asgary
- Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC), York University, Toronto, Canada
- Advanced Disaster, Emergency and Rapid-response Simulation (ADERSIM), York University, Toronto, Ontario, Canada
| | - Jude Dzevela Kong
- Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC), York University, Toronto, Canada
- Laboratory for Industrial and Applied Mathematics, York University, Toronto, Canada
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Williams LR, Ferguson NM, Donnelly CA, Grassly NC. Measuring Vaccine Efficacy Against Infection and Disease in Clinical Trials: Sources and Magnitude of Bias in Coronavirus Disease 2019 (COVID-19) Vaccine Efficacy Estimates. Clin Infect Dis 2022; 75:e764-e773. [PMID: 34698827 PMCID: PMC8586723 DOI: 10.1093/cid/ciab914] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2021] [Indexed: 01/19/2023] Open
Abstract
BACKGROUND Phase III trials have estimated coronavirus disease 2019 (COVID-19) vaccine efficacy (VE) against symptomatic and asymptomatic infection. We explore the direction and magnitude of potential biases in these estimates and their implications for vaccine protection against infection and against disease in breakthrough infections. METHODS We developed a mathematical model that accounts for natural and vaccine-induced immunity, changes in serostatus, and imperfect sensitivity and specificity of tests for infection and antibodies. We estimated expected biases in VE against symptomatic, asymptomatic, and any severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections and against disease following infection for a range of vaccine characteristics and measurement approaches, and the likely overall biases for published trial results that included asymptomatic infections. RESULTS VE against asymptomatic infection measured by polymerase chain reaction (PCR) or serology is expected to be low or negative for vaccines that prevent disease but not infection. VE against any infection is overestimated when asymptomatic infections are less likely to be detected than symptomatic infections and the vaccine protects against symptom development. A competing bias toward underestimation arises for estimates based on tests with imperfect specificity, especially when testing is performed frequently. Our model indicates considerable uncertainty in Oxford-AstraZeneca ChAdOx1 and Janssen Ad26.COV2.S VE against any infection, with slightly higher than published, bias-adjusted values of 59.0% (95% uncertainty interval [UI] 38.4-77.1) and 70.9% (95% UI 49.8-80.7), respectively. CONCLUSIONS Multiple biases are likely to influence COVID-19 VE estimates, potentially explaining the observed difference between ChAdOx1 and Ad26.COV2.S vaccines. These biases should be considered when interpreting both efficacy and effectiveness study results.
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Affiliation(s)
- Lucy R Williams
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, United Kingdom
| | - Neil M Ferguson
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, United Kingdom
| | - Christl A Donnelly
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, United Kingdom
- Department of Statistics, University of Oxford, Oxfordshire, United Kingdom
| | - Nicholas C Grassly
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, United Kingdom
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130
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Wen H, Shi F, Liu Y, Xie C, Qin G, Wang F, Liu X, Bai J, Hong Q, Ma R, Yu C. Non-pharmacological interventions of travel restrictions and cancelation of public events had a major reductive mortality affect during pre-vaccination coronavirus disease 2019 period. Front Med (Lausanne) 2022; 9:914732. [PMID: 36072943 PMCID: PMC9441752 DOI: 10.3389/fmed.2022.914732] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Accepted: 08/01/2022] [Indexed: 11/18/2022] Open
Abstract
Background The coronavirus disease 2019 (COVID-19) is a severe acute respiratory disease that poses a continuous threat to global public health. Many non-pharmacological interventions (NPIs) have been implemented to control the COVID-19 pandemic since the beginning. The aim of this study was to assess the impact of various NPIs on COVID-19 mortality during pre-vaccination and vaccination periods. Methods The COVID-19 data used in this study comes from Our World in Data, we used the Oxford Strict Index (OSI) and its five combination interventions as independent variables. The COVID-19 mortality date (MRT) was defined as a date when daily rate of 0.02 COVID-19 deaths per 100,000 population in a country was reached, and the COVID-19 vaccination date (VRT) was defined as people vaccinated reaching 70%. Linear regression and random forest models were used to estimate the impact of various NPI implementation interventions during pre-vaccination and vaccination periods. The performance of models was assessed among others with Shapley Additive Explanations (SHAP) explaining the prediction capability of the model. Results During the pre-vaccination period, the various NPIs had strong protective effect. When the COVID-19 MRT was reached, for every unit increase in OSI, the cumulative mortality as of June 30, 2020 decreased by 0.71 deaths per 100,000 people. Restrictions in travel (SHAP 1.68) and cancelation of public events and gatherings (1.37) had major reducing effect on COVID-19 mortality, while staying at home (0.26) and school and workplace closure (0.26) had less effect. Post vaccination period, the effects of NPI reduced significantly: cancelation of public events and gatherings (0.25), staying at home (0.22), restrictions in travel (0.14), and school and workplace closure (0.06). Conclusion Continued efforts are still needed to promote vaccination to build sufficient immunity to COVID-19 in the population. Until herd immunity is achieved, NPI is still important for COVID-19 prevention and control. At the beginning of the COVID-19 pandemic, the stringency of NPI implementation had a significant negative association with COVID-19 mortality; however, this association was no longer significant after the vaccination rate reached 70%. As vaccination progresses, “cancelation of public events and gatherings” become more important for COVID-19 mortality.
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Affiliation(s)
- Haoyu Wen
- Department of Epidemiology and Biostatistics, School of Public Health, Wuhan University, Wuhan, China
| | - Fang Shi
- Department of Epidemiology and Biostatistics, School of Public Health, Wuhan University, Wuhan, China
| | - Yan Liu
- Department of Epidemiology and Biostatistics, School of Public Health, Wuhan University, Wuhan, China
| | - Cong Xie
- Hubei Provincial Center for Disease Control and Prevention, Institute of Preventive Medicine Information, Wuhan, China
| | - Guiyu Qin
- Department of Epidemiology and Biostatistics, School of Public Health, Wuhan University, Wuhan, China
| | - Fang Wang
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, China
| | - Xiaoxue Liu
- Department of Epidemiology and Biostatistics, School of Public Health, Wuhan University, Wuhan, China
| | - Jianjun Bai
- Department of Epidemiology and Biostatistics, School of Public Health, Wuhan University, Wuhan, China
| | - Qiumian Hong
- Department of Epidemiology and Biostatistics, School of Public Health, Wuhan University, Wuhan, China
| | - Runxue Ma
- Department of Epidemiology and Biostatistics, School of Public Health, Wuhan University, Wuhan, China
| | - Chuanhua Yu
- Department of Epidemiology and Biostatistics, School of Public Health, Wuhan University, Wuhan, China
- China Global Health Institute, Wuhan University, Wuhan, China
- *Correspondence: Chuanhua Yu,
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131
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Redlin M. Differences in NPI strategies against COVID-19. JOURNAL OF REGULATORY ECONOMICS 2022; 62:1-23. [PMID: 36035787 PMCID: PMC9395806 DOI: 10.1007/s11149-022-09452-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 07/17/2022] [Indexed: 06/15/2023]
Abstract
Non-pharmaceutical interventions are an effective strategy to prevent and control COVID-19 transmission in the community. However, the timing and stringency to which these measures have been implemented varied between countries and regions. The differences in stringency can only to a limited extent be explained by the number of infections and the prevailing vaccination strategies. Our study aims to shed more light on the lockdown strategies and to identify the determinants underlying the differences between countries on regional, economic, institutional, and political level. Based on daily panel data for 173 countries and the period from January 2020 to October 2021 we find significant regional differences in lockdown strategies. Further, more prosperous countries implemented milder restrictions but responded more quickly, while poorer countries introduced more stringent measures but had a longer response time. Finally, democratic regimes and stronger manifested institutions alleviated and slowed down the introduction of lockdown measures.
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Affiliation(s)
- Margarete Redlin
- Department of Economics, Paderborn University, Warburger Str. 100, 33098 Paderborn, Germany
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132
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Keeling MJ, Dyson L, Tildesley MJ, Hill EM, Moore S. Comparison of the 2021 COVID-19 roadmap projections against public health data in England. Nat Commun 2022; 13:4924. [PMID: 35995764 PMCID: PMC9395530 DOI: 10.1038/s41467-022-31991-0] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Accepted: 07/13/2022] [Indexed: 12/13/2022] Open
Abstract
Control and mitigation of the COVID-19 pandemic in England has relied on a combination of vaccination and non-pharmaceutical interventions (NPIs). Some of these NPIs are extremely costly (economically and socially), so it was important to relax these promptly without overwhelming already burdened health services. The eventual policy was a Roadmap of four relaxation steps throughout 2021, taking England from lock-down to the cessation of all restrictions on social interaction. In a series of six Roadmap documents generated throughout 2021, models assessed the potential risk of each relaxation step. Here we show that the model projections generated a reliable estimation of medium-term hospital admission trends, with the data points up to September 2021 generally lying within our 95% prediction intervals. The greatest uncertainties in the modelled scenarios came from vaccine efficacy estimates against novel variants, and from assumptions about human behaviour in the face of changing restrictions and risk.
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Affiliation(s)
- Matt J Keeling
- The Zeeman Institute for Systems Biology & Infectious Disease Epidemiology Research, University of Warwick, Coventry, UK.
- Joint Universities Pandemic and Epidemiological Research, .
| | - Louise Dyson
- The Zeeman Institute for Systems Biology & Infectious Disease Epidemiology Research, University of Warwick, Coventry, UK
- Joint Universities Pandemic and Epidemiological Research
| | - Michael J Tildesley
- The Zeeman Institute for Systems Biology & Infectious Disease Epidemiology Research, University of Warwick, Coventry, UK
- Joint Universities Pandemic and Epidemiological Research
| | - Edward M Hill
- The Zeeman Institute for Systems Biology & Infectious Disease Epidemiology Research, University of Warwick, Coventry, UK
- Joint Universities Pandemic and Epidemiological Research
| | - Samuel Moore
- The Zeeman Institute for Systems Biology & Infectious Disease Epidemiology Research, University of Warwick, Coventry, UK
- Joint Universities Pandemic and Epidemiological Research
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Uludağ E, Serçekuş P, Yıldırım DF, Özkan S. A qualitative study of pregnant women's opinions on COVID-19 vaccines in Turkey. Midwifery 2022; 114:103459. [PMID: 36029559 PMCID: PMC9385582 DOI: 10.1016/j.midw.2022.103459] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Revised: 07/18/2022] [Accepted: 08/11/2022] [Indexed: 11/29/2022]
Abstract
Objectives to examine pregnant Turkish women's opinions on COVID-19 vaccines. Design a qualitative approach was used to gather data through semi-structured interviews. Participants and setting 16 women about to receive a vaccine during their pregnancy and who did or did not experience vaccine hesitancy participated. Analysis qualitative content analysis. Findings three main themes emerged regarding the pregnant women's opinions on COVID-19 vaccines: fear, security/insecurity and social support. Key conclusions and implications for practice pregnant women mostly recalled their babies and fears about COVID-19 vaccines. Although the fear of vaccines created vaccine hesitancy during pregnancy, the fear of contracting COVID-19 led to a positive attitude to the vaccines. It is critical to provide pregnant women with information about COVID-19 and vaccines for the disease in order to enhance vaccination rates among pregnant women.
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Affiliation(s)
- Elif Uludağ
- Pamukkale University, Faculty of Health Sciences, Denizli, Turkey
| | - Pınar Serçekuş
- Pamukkale University, Faculty of Health Sciences, Denizli, Turkey.
| | | | - Sevgi Özkan
- Pamukkale University, Faculty of Health Sciences, Denizli, Turkey
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134
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Devi MB, Devi A, Gupta PK, Tripathi D. Response of vaccination on community transmission of COVID-19: a dynamical approach. THE EUROPEAN PHYSICAL JOURNAL. SPECIAL TOPICS 2022; 231:3749-3765. [PMID: 35991944 PMCID: PMC9380690 DOI: 10.1140/epjs/s11734-022-00652-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Accepted: 07/26/2022] [Indexed: 06/15/2023]
Abstract
Due to the severity of COVID-19, vaccination campaigns have been or are underway in most parts of the world. In the current circumstances, it is obligatory to examine the response of vaccination on transmission of the SARS-CoV-2 virus when there are many vaccines available. Considering the importance of vaccination, a dynamic model has been proposed to provide an insight in the same direction. A mathematical model has been developed where six population compartments viz. susceptible, infected, vaccinated, home-isolated, hospitalized and recovered population are considered. Moreover, two novel parameters are included in the model to ascertain the effectiveness and speed of the vaccination campaign. Reproduction number and local stability of both the disease-free and endemic equilibrium points are studied to examine the nature of population dynamics. Graphical results for the community stage of COVID-19 infection are simulated and compared with real data to ascertain the validity of our model. The data is then studied to understand the impact of vaccination. These numerical results evidently demonstrate that home isolation and hospitalization should continue for the infected people until the transmission of the virus from person to person reduces sufficiently after completely vaccinating every nation. This model also recommends that all type of prevention measures should still be taken to avoid any type of critical situation due to infection and also reduce the death rate.
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Affiliation(s)
| | - Arpita Devi
- Department of Mathematics, National Institute of Technology Silchar, Cachar, 788010 Assam India
| | - Praveen Kumar Gupta
- Department of Mathematics, National Institute of Technology Silchar, Cachar, 788010 Assam India
| | - Dharmendra Tripathi
- Department of Mathematics, National Institute of Technology Uttarakhand, Srinagar, 246174 Uttarakhand India
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Alqahtani RT, Musa SS, Yusuf A. Unravelling the dynamics of the COVID-19 pandemic with the effect of vaccination, vertical transmission and hospitalization. RESULTS IN PHYSICS 2022; 39:105715. [PMID: 35720511 PMCID: PMC9192123 DOI: 10.1016/j.rinp.2022.105715] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Revised: 06/02/2022] [Accepted: 06/07/2022] [Indexed: 05/12/2023]
Abstract
The coronavirus disease 2019 (COVID-19) is caused by a newly emerged virus known as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), transmitted through air droplets from an infected person. However, other transmission routes are reported, such as vertical transmission. Here, we propose an epidemic model that considers the combined effect of vertical transmission, vaccination and hospitalization to investigate the dynamics of the virus's dissemination. Rigorous mathematical analysis of the model reveals that two equilibria exist: the disease-free equilibrium, which is locally asymptotically stable when the basic reproduction number ( R 0 ) is less than 1 (unstable otherwise), and an endemic equilibrium, which is globally asymptotically stable when R 0 > 1 under certain conditions, implying the plausibility of the disease to spread and cause large outbreaks in a community. Moreover, we fit the model using the Saudi Arabia cases scenario, which designates the incidence cases from the in-depth surveillance data as well as displays the epidemic trends in Saudi Arabia. Through Caputo fractional-order, simulation results are provided to show dynamics behaviour on the model parameters. Together with the non-integer order variant, the proposed model is considered to explain various dynamics features of the disease. Further numerical simulations are carried out using an efficient numerical technique to offer additional insight into the model's dynamics and investigate the combined effect of vaccination, vertical transmission, and hospitalization. In addition, a sensitivity analysis is conducted on the model parameters against the R 0 and infection attack rate to pinpoint the most crucial parameters that should be emphasized in controlling the pandemic effectively. Finally, the findings suggest that adequate vaccination coupled with basic non-pharmaceutical interventions are crucial in mitigating disease incidences and deaths.
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Affiliation(s)
- Rubayyi T Alqahtani
- Department of Mathematics and Statistics, College of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia
| | - Salihu S Musa
- Department of Applied Mathematics, Hong Kong Polytechnic University, Hong Kong, China
- Department of Mathematics, Near East University TRNC, Mersin 10, Nicosia 99138, Turkey
| | - Abdullahi Yusuf
- Department of Computer Engineering, Biruni University, Istanbul, Turkey
- Department of Mathematics, Federal University Dutse, Jigawa, Nigeria
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136
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Orangi S, Ojal J, Brand SP, Orlendo C, Kairu A, Aziza R, Ogero M, Agweyu A, Warimwe GM, Uyoga S, Otieno E, Ochola-Oyier LI, Agoti CN, Kasera K, Amoth P, Mwangangi M, Aman R, Ng'ang'a W, Adetifa IM, Scott JAG, Bejon P, Keeling MJ, Flasche S, Nokes DJ, Barasa E. Epidemiological impact and cost-effectiveness analysis of COVID-19 vaccination in Kenya. BMJ Glob Health 2022; 7:e009430. [PMID: 35914832 PMCID: PMC9344598 DOI: 10.1136/bmjgh-2022-009430] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Accepted: 06/22/2022] [Indexed: 11/09/2022] Open
Abstract
BACKGROUND A few studies have assessed the epidemiological impact and the cost-effectiveness of COVID-19 vaccines in settings where most of the population had been exposed to SARS-CoV-2 infection. METHODS We conducted a cost-effectiveness analysis of COVID-19 vaccine in Kenya from a societal perspective over a 1.5-year time frame. An age-structured transmission model assumed at least 80% of the population to have prior natural immunity when an immune escape variant was introduced. We examine the effect of slow (18 months) or rapid (6 months) vaccine roll-out with vaccine coverage of 30%, 50% or 70% of the adult (>18 years) population prioritising roll-out in those over 50-years (80% uptake in all scenarios). Cost data were obtained from primary analyses. We assumed vaccine procurement at US$7 per dose and vaccine delivery costs of US$3.90-US$6.11 per dose. The cost-effectiveness threshold was US$919.11. FINDINGS Slow roll-out at 30% coverage largely targets those over 50 years and resulted in 54% fewer deaths (8132 (7914-8373)) than no vaccination and was cost saving (incremental cost-effectiveness ratio, ICER=US$-1343 (US$-1345 to US$-1341) per disability-adjusted life-year, DALY averted). Increasing coverage to 50% and 70%, further reduced deaths by 12% (810 (757-872) and 5% (282 (251-317) but was not cost-effective, using Kenya's cost-effectiveness threshold (US$919.11). Rapid roll-out with 30% coverage averted 63% more deaths and was more cost-saving (ICER=US$-1607 (US$-1609 to US$-1604) per DALY averted) compared with slow roll-out at the same coverage level, but 50% and 70% coverage scenarios were not cost-effective. INTERPRETATION With prior exposure partially protecting much of the Kenyan population, vaccination of young adults may no longer be cost-effective.
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Affiliation(s)
- Stacey Orangi
- Health Economics Research Unit, KEMRI-Wellcome Trust Research Programme Nairobi, Nairobi, Kenya
- Institute of Healthcare Management, Strathmore University, Nairobi, Kenya
| | - John Ojal
- Kenya Medical Research Institute (KEMRI)-Wellcome Trust Research Programme, Kilifi, Kenya
- The Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, UK
| | - Samuel Pc Brand
- The Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research (SBIDER), University of Warwick, Coventry, UK
- School of Life Sciences, University of Warwick, Coventry, UK
| | - Cameline Orlendo
- Kenya Medical Research Institute (KEMRI)-Wellcome Trust Research Programme, Kilifi, Kenya
| | - Angela Kairu
- Health Economics Research Unit, KEMRI-Wellcome Trust Research Programme Nairobi, Nairobi, Kenya
| | - Rabia Aziza
- The Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research (SBIDER), University of Warwick, Coventry, UK
- School of Life Sciences, University of Warwick, Coventry, UK
| | - Morris Ogero
- Kenya Medical Research Institute (KEMRI)-Wellcome Trust Research Programme, Kilifi, Kenya
| | - Ambrose Agweyu
- Kenya Medical Research Institute (KEMRI)-Wellcome Trust Research Programme, Kilifi, Kenya
- Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - George M Warimwe
- Kenya Medical Research Institute (KEMRI)-Wellcome Trust Research Programme, Kilifi, Kenya
- Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Sophie Uyoga
- Kenya Medical Research Institute (KEMRI)-Wellcome Trust Research Programme, Kilifi, Kenya
| | - Edward Otieno
- Kenya Medical Research Institute (KEMRI)-Wellcome Trust Research Programme, Kilifi, Kenya
| | - Lynette I Ochola-Oyier
- Kenya Medical Research Institute (KEMRI)-Wellcome Trust Research Programme, Kilifi, Kenya
| | - Charles N Agoti
- Kenya Medical Research Institute (KEMRI)-Wellcome Trust Research Programme, Kilifi, Kenya
| | | | - Patrick Amoth
- Ministry of Health, Government of Kenya, Nairobi, Kenya
| | | | - Rashid Aman
- Ministry of Health, Government of Kenya, Nairobi, Kenya
| | - Wangari Ng'ang'a
- Presidential Policy & Strategy Unit, The Presidency, Government of Kenya, Nairobi, Kenya
| | - Ifedayo Mo Adetifa
- Kenya Medical Research Institute (KEMRI)-Wellcome Trust Research Programme, Kilifi, Kenya
- The Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, UK
| | - J Anthony G Scott
- Kenya Medical Research Institute (KEMRI)-Wellcome Trust Research Programme, Kilifi, Kenya
- The Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, UK
| | - Philip Bejon
- Kenya Medical Research Institute (KEMRI)-Wellcome Trust Research Programme, Kilifi, Kenya
- Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Matt J Keeling
- The Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research (SBIDER), University of Warwick, Coventry, UK
- School of Life Sciences, University of Warwick, Coventry, UK
- Mathematics Institute, University of Warwick, Coventry, UK
| | - Stefan Flasche
- The Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, UK
| | - D James Nokes
- Kenya Medical Research Institute (KEMRI)-Wellcome Trust Research Programme, Kilifi, Kenya
- The Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research (SBIDER), University of Warwick, Coventry, UK
- School of Life Sciences, University of Warwick, Coventry, UK
| | - Edwine Barasa
- Health Economics Research Unit, KEMRI-Wellcome Trust Research Programme Nairobi, Nairobi, Kenya
- Institute of Healthcare Management, Strathmore University, Nairobi, Kenya
- Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK
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Liang JB, Yuan HY, Li KK, Wei WI, Wong SYS, Tang A, Riley S, Kwok KO. Path to normality: Assessing the level of social-distancing measures relaxation against antibody-resistant SARS-CoV-2 variants in a partially-vaccinated population. Comput Struct Biotechnol J 2022; 20:4052-4059. [PMID: 35935805 PMCID: PMC9338450 DOI: 10.1016/j.csbj.2022.07.048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Revised: 07/28/2022] [Accepted: 07/28/2022] [Indexed: 02/09/2023] Open
Abstract
Introduction Two years into the coronavirus 2019 (COVID-19) pandemic, populations with less built-up immunity continued to devise ways to optimize social distancing measures (SDMs) relaxation levels for outbreaks triggered by SARS-CoV-2 and its variants to resume minimal economics activities while avoiding hospital system collapse. Method An age-stratified compartmental model featuring social mixing patterns was first fitted the incidence data in second wave in Hong Kong. Hypothetical scenario analysis was conducted by varying population mobility and vaccination coverages (VCs) to predict the number of hospital and intensive-care unit admissions in outbreaks initiated by ancestral strain and its variants (Alpha, Beta, Gamma, Delta and Omicron). Scenarios were "unsustainable" if either of admissions was larger than the maximum of its occupancy. Results At VC of 65%, scenarios of full SDMs relaxation (mean daily social encounters prior to COVID-19 pandemic = 14.1 contacts) for outbreaks triggered by ancestral strain, Alpha and Beta were sustainable. Restricting levels of SDMs was required such that the optimal population mobility had to be reduced to 0.9, 0.65 and 0.37 for Gamma, Delta and Omicron associated outbreaks respectively. VC improvement from 65% to 75% and 95% allowed complete SDMs relaxation in Gamma-, and Delta-driven epidemic respectively. However, this was not supported for Omicron-triggered epidemic. Discussion To seek a path to normality, speedy vaccine and booster distribution to the majority across all age groups is the first step. Gradual or complete SDMs lift could be considered if the hybrid immunity could be achieved due to high vaccination coverage and natural infection rate among vaccinated or the COVID-19 case fatality rate could be reduced similar to that for seasonal influenza to secure hospital system sustainability.
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Affiliation(s)
- Jing-Bo Liang
- Department of Biomedical Sciences, Jockey Club College of Veterinary Medicine and Life Sciences, City University of Hong Kong, Hong Kong Special administrative regions, China
| | - Hsiang-Yu Yuan
- Department of Biomedical Sciences, Jockey Club College of Veterinary Medicine and Life Sciences, City University of Hong Kong, Hong Kong Special administrative regions, China
- Centre for Applied One Health Research and Policy Advice, City University of Hong Kong, Hong Kong Special administrative regions, China
| | - Kin-Kit Li
- Department of Social and Behavioral Sciences, City University of Hong Kong, Hong Kong Special administrative regions, China
| | - Wan-In Wei
- JC School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong Special administrative regions, China
| | - Samuel Yeung Shan Wong
- JC School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong Special administrative regions, China
| | - Arthur Tang
- College of Computing and Informatics, Sungkyunkwan University, Seoul, Republic of Korea
| | - Steven Riley
- School of Public Health, Imperial College London, GB, UK
| | - Kin On Kwok
- JC School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong Special administrative regions, China
- Stanley Ho Centre for Emerging Infectious Diseases, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China
- Hong Kong Institute of Asia-Pacific Studies, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China
- Shenzhen Research Institute of the Chinese University of Hong Kong, Shenzhen, China
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138
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Swart LA, Taliep N, Ismail G, van Niekerk A. The converging influence of social, economic and psychological factors on public responsiveness to the COVID-19 pandemic in South Africa. BMC Public Health 2022; 22:1451. [PMID: 35907812 PMCID: PMC9338645 DOI: 10.1186/s12889-022-13845-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2022] [Accepted: 07/18/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND This study assessed the influence of social, economic, and psychological factors on South African's responsiveness to the COVID-19 pandemic. Although the South African government responded quickly to manage the pandemic, the strict lockdown placed a significant burden on the population. Understanding the converging influence of social, economic, and psychological factors on the population's responsiveness is important for improving people's cooperation in controlling COVID-19 and for supporting individuals and communities during the ongoing and future pandemics. METHODS Using data collected from a national telephonic survey (December 2020 to March 2021), we assessed whether selected social, economic and psychological factors were related to: 1) adoption of COVID-19 behavioural measures (hand hygiene, wearing of face masks, and physical distancing), and 2) adherence to government restrictions on movement. RESULTS South Africans were highly responsive to the pandemic with respondents generally reporting that they very often engaged in the protective behaviours and often to very often adhered to government restriction on movement. However, those from the white population group; with a higher education; living in uncrowded households; who perceived less vulnerability to contracting COVID-19; supported the measures; trusted the scientists; thought the measures by government were implemented fairly and fairly enforced by the police; felt more anxious, sad, hopeless, isolated, angry or had trouble sleeping; inclined to engage in coping behaviour, were more likely to adopt COVID-19 protective behaviours. Furthermore, females, those with a lower education, those less likely to have experienced poverty since the beginning of lockdown; who perceived greater vulnerability to COVID-19, trusted government, and were more supportive of the behavioural measures were more likely to adhere to the restrictions of movement. CONCLUSIONS Strengthening the South African population's responsiveness to the pandemic requires supporting those living in poor socioeconomic circumstances, promoting trust in the scientific evidence, and ensuring that the measures by government are perceived to be fairly implemented and fairly enforced by the police. Due to the impact on livelihoods, restrictions of movement should only be considered if necessary, and this will require trust and confidence in government and strategies to support those experiencing financial hardship.
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Affiliation(s)
- Lu-Anne Swart
- Institute for Social and Health Sciences, University of South Africa, Johannesburg, South Africa.
- Masculinity and Health Research Unit, South African Medical Research Council and University of South Africa, Cape Town, South Africa.
| | - Naiema Taliep
- Institute for Social and Health Sciences, University of South Africa, Johannesburg, South Africa
- Masculinity and Health Research Unit, South African Medical Research Council and University of South Africa, Cape Town, South Africa
| | - Ghouwa Ismail
- Institute for Social and Health Sciences, University of South Africa, Johannesburg, South Africa
- Masculinity and Health Research Unit, South African Medical Research Council and University of South Africa, Cape Town, South Africa
| | - Ashley van Niekerk
- Institute for Social and Health Sciences, University of South Africa, Johannesburg, South Africa
- Masculinity and Health Research Unit, South African Medical Research Council and University of South Africa, Cape Town, South Africa
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139
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Marchetti S, Borin A, Conteduca FP, Ilardi G, Guzzetta G, Poletti P, Pezzotti P, Bella A, Stefanelli P, Riccardo F, Merler S, Brandolini A, Brusaferro S. An epidemic model for SARS-CoV-2 with self-adaptive containment measures. PLoS One 2022; 17:e0272009. [PMID: 35877667 PMCID: PMC9312378 DOI: 10.1371/journal.pone.0272009] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Accepted: 07/12/2022] [Indexed: 11/25/2022] Open
Abstract
During the COVID-19 pandemic, several countries have resorted to self-adaptive mechanisms that tailor non-pharmaceutical interventions to local epidemiological and health care indicators. These mechanisms reinforce the mutual influence between containment measures and the evolution of the epidemic. To account for such interplay, we develop an epidemiological model that embeds an algorithm mimicking the self-adaptive policy mechanism effective in Italy between November 2020 and March 2022. This extension is key to tracking the historical evolution of health outcomes and restrictions in Italy. Focusing on the epidemic wave that started in mid-2021 after the diffusion of Delta, we compare the functioning of alternative mechanisms to show how the policy framework may affect the trade-off between health outcomes and the restrictiveness of mitigation measures. Mechanisms based on the reproduction number are generally highly responsive to early signs of a surging wave but entail severe restrictions. The emerging trade-off varies considerably depending on specific conditions (e.g., vaccination coverage), with less-reactive mechanisms (e.g., those based on occupancy rates) becoming more appealing in favorable contexts.
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Affiliation(s)
- Sabina Marchetti
- Directorate General for Economics, Statistics and Research, Bank of Italy, Rome, Italy
| | - Alessandro Borin
- Directorate General for Economics, Statistics and Research, Bank of Italy, Rome, Italy
| | | | - Giuseppe Ilardi
- Directorate General for Economics, Statistics and Research, Bank of Italy, Rome, Italy
| | - Giorgio Guzzetta
- Center for Health Emergencies, Bruno Kessler Foundation (FBK), Trento, Italy
| | - Piero Poletti
- Center for Health Emergencies, Bruno Kessler Foundation (FBK), Trento, Italy
| | - Patrizio Pezzotti
- Department of Infectious Diseases, Italian National Institute of Health (Istituto Superiore di Sanità), Rome, Italy
| | - Antonino Bella
- Department of Infectious Diseases, Italian National Institute of Health (Istituto Superiore di Sanità), Rome, Italy
| | - Paola Stefanelli
- Department of Infectious Diseases, Italian National Institute of Health (Istituto Superiore di Sanità), Rome, Italy
| | - Flavia Riccardo
- Department of Infectious Diseases, Italian National Institute of Health (Istituto Superiore di Sanità), Rome, Italy
| | - Stefano Merler
- Center for Health Emergencies, Bruno Kessler Foundation (FBK), Trento, Italy
| | - Andrea Brandolini
- Directorate General for Economics, Statistics and Research, Bank of Italy, Rome, Italy
| | - Silvio Brusaferro
- Italian National Institute of Health (Istituto Superiore di Sanità), Rome, Italy
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140
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Qi F, Barragan D, Rodriguez MG, Lu J. Evaluating spatial accessibility to COVID-19 vaccine resources in diversely populated counties in the United States. Front Public Health 2022; 10:895538. [PMID: 35958838 PMCID: PMC9358221 DOI: 10.3389/fpubh.2022.895538] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Accepted: 06/27/2022] [Indexed: 12/05/2022] Open
Abstract
This study examines the accessibility to COVID-19 vaccination resources in two counties surrounding Newark, NJ in the New York Metropolitan Area, United States. The study area represents diverse population makeups. COVID-19 vaccines were made available by different types of vaccination sites including county mass vaccination sites, medical facilities and pharmacies, and a FEMA community vaccination center in spring 2021. We used the two-step floating catchment area method to measure accessibility and calculated the average accessibility scores of different population groups. We examined the patterns and tested the significance of the differences in accessibility across population groups. The results showed clear spatial heterogeneity in the accessibility to vaccine resources with the existing infrastructure (medical/pharmacy vaccine sites). Accessibility patterns changed with the introduction of county mass sites and the FEMA community site. The county mass vaccination sites in one county greatly increased accessibilities for populations of minority and poverty. The FEMA community site in the other county accomplished the same. Both the local health department and the federal government played an important role in mitigating pre-existing inequalities during the vaccination campaign. Our study shows that social determinants of health need to be addressed and taken into explicit consideration when planning resource distribution during the pandemic.
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Affiliation(s)
- Feng Qi
- School of Environmental and Sustainability Sciences, Kean University, Union, NJ, United States
- *Correspondence: Feng Qi
| | - Daniela Barragan
- School of Environmental and Sustainability Sciences, Kean University, Union, NJ, United States
| | | | - Jiongcheng Lu
- Gobal Business School for Health, University College London, London, United Kingdom
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141
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Herrero M, Ciruela P, Mallafré-Larrosa M, Mendoza S, Patsi-Bosch G, Martínez-Solanas È, Mendioroz J, Jané M. SARS-CoV-2 Catalonia contact tracing program: evaluation of key performance indicators. BMC Public Health 2022; 22:1397. [PMID: 35858841 PMCID: PMC9299963 DOI: 10.1186/s12889-022-13695-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Accepted: 06/20/2022] [Indexed: 11/11/2022] Open
Abstract
Background Guidance on SARS-CoV-2 contact tracing indicators have been recently revised by international public health agencies. The aim of the study is to describe and analyse contact tracing indicators based on Catalonia’s (Spain) real data and proposing to update them according to recommendations. Methods Retrospective cohort analysis including Catalonia’s contact tracing dataset from 20 May until 31 December 2020. Descriptive statistics are performed including sociodemographic stratification by age, and differences are assessed over the study period. Results We analysed 923,072 contacts from 301,522 SARS-CoV-2 cases with identified contacts (67.1% contact tracing coverage). The average number of contacts per case was 4.6 (median 3, range 1–243). A total of 403,377 contacts accepted follow-up through three phone calls over a 14-day quarantine period (84.5% of contacts requiring follow-up). The percentage of new cases declared as contacts 14 days prior to diagnosis evolved from 33.9% in May to 57.9% in November. All indicators significantly improved towards the target over time (p < 0.05 for all four indicators). Conclusions Catalonia’s SARS-CoV-2 contact tracing indicators improved over time despite challenging context. The critical revision of the indicator’s framework aims to provide essential information in control policies, new indicators proposed will improve system delay’s follow-up. The study provides information on COVID-19 indicators framework experience from country’s real data, allowing to improve monitoring tools in 2021–2022. With the SARS-CoV-2 pandemic being so harmful to health systems and globally, is important to analyse and share contact tracing data with the scientific community. Supplementary Information The online version contains supplementary material available at 10.1186/s12889-022-13695-8.
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Affiliation(s)
- Mercè Herrero
- Sub-Directorate General of Surveillance and Response to Public Health Emergencies, Public Health Agency of Catalonia, Generalitat of Catalonia, 08005, Barcelona, Spain.
| | - Pilar Ciruela
- Sub-Directorate General of Surveillance and Response to Public Health Emergencies, Public Health Agency of Catalonia, Generalitat of Catalonia, 08005, Barcelona, Spain.,CIBER Epidemiologia y Salud Pública (CIBERESP), Instituto Salud Carlos III, 28029, Madrid, Spain
| | - Meritxell Mallafré-Larrosa
- Sub-Directorate General of Surveillance and Response to Public Health Emergencies, Public Health Agency of Catalonia, Generalitat of Catalonia, 08005, Barcelona, Spain
| | - Sergi Mendoza
- Sub-Directorate General of Surveillance and Response to Public Health Emergencies, Public Health Agency of Catalonia, Generalitat of Catalonia, 08005, Barcelona, Spain
| | - Glòria Patsi-Bosch
- Sub-Directorate General of Surveillance and Response to Public Health Emergencies, Public Health Agency of Catalonia, Generalitat of Catalonia, 08005, Barcelona, Spain
| | - Èrica Martínez-Solanas
- Sub-Directorate General of Surveillance and Response to Public Health Emergencies, Public Health Agency of Catalonia, Generalitat of Catalonia, 08005, Barcelona, Spain
| | - Jacobo Mendioroz
- Sub-Directorate General of Surveillance and Response to Public Health Emergencies, Public Health Agency of Catalonia, Generalitat of Catalonia, 08005, Barcelona, Spain.,Research Support Unit of Central Catalonia, University Institute for Research in Primary Health Care Jordi Gol i Gurina, 08272, Sant Fruitós de Bages, Spain
| | - Mireia Jané
- Sub-Directorate General of Surveillance and Response to Public Health Emergencies, Public Health Agency of Catalonia, Generalitat of Catalonia, 08005, Barcelona, Spain.,CIBER Epidemiologia y Salud Pública (CIBERESP), Instituto Salud Carlos III, 28029, Madrid, Spain.,Public Health, Barcelona University, Barcelona, Spain
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Aruffo E, Yuan P, Tan Y, Gatov E, Moyles I, Bélair J, Watmough J, Collier S, Arino J, Zhu H. Mathematical modelling of vaccination rollout and NPIs lifting on COVID-19 transmission with VOC: a case study in Toronto, Canada. BMC Public Health 2022; 22:1349. [PMID: 35841012 PMCID: PMC9283818 DOI: 10.1186/s12889-022-13597-9] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Accepted: 05/23/2022] [Indexed: 01/09/2023] Open
Abstract
BACKGROUND Since December 2020, public health agencies have implemented a variety of vaccination strategies to curb the spread of SARS-CoV-2, along with pre-existing Nonpharmaceutical Interventions (NPIs). Initial strategies focused on vaccinating the elderly to prevent hospitalizations and deaths, but with vaccines becoming available to the broader population, it became important to determine the optimal strategy to enable the safe lifting of NPIs while avoiding virus resurgence. METHODS We extended the classic deterministic SIR compartmental disease-transmission model to simulate the lifting of NPIs under different vaccine rollout scenarios. Using case and vaccination data from Toronto, Canada between December 28, 2020, and May 19, 2021, we estimated transmission throughout past stages of NPI escalation/relaxation to compare the impact of lifting NPIs on different dates on cases, hospitalizations, and deaths, given varying degrees of vaccine coverages by 20-year age groups, accounting for waning immunity. RESULTS We found that, once coverage among the elderly is high enough (80% with at least one dose), the main age groups to target are 20-39 and 40-59 years, wherein first-dose coverage of at least 70% by mid-June 2021 is needed to minimize the possibility of resurgence if NPIs are to be lifted in the summer. While a resurgence was observed for every scenario of NPI lifting, we also found that under an optimistic vaccination coverage (70% coverage by mid-June, along with postponing reopening from August 2021 to September 2021) can reduce case counts and severe outcomes by roughly 57% by December 31, 2021. CONCLUSIONS Our results suggest that focusing the vaccination strategy on the working-age population can curb the spread of SARS-CoV-2. However, even with high vaccination coverage in adults, increasing contacts and easing protective personal behaviours is not advisable since a resurgence is expected to occur, especially with an earlier reopening.
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Affiliation(s)
- Elena Aruffo
- Centre for Disease Modelling (CDM), York University, 4700 Keele Street, Toronto, ON, M3J1P3, Canada
- Department of Mathematics and Statistics, York University, 4700 Keele Street, Toronto, ON, M3J1P3, Canada
| | - Pei Yuan
- Centre for Disease Modelling (CDM), York University, 4700 Keele Street, Toronto, ON, M3J1P3, Canada
- Department of Mathematics and Statistics, York University, 4700 Keele Street, Toronto, ON, M3J1P3, Canada
| | - Yi Tan
- Centre for Disease Modelling (CDM), York University, 4700 Keele Street, Toronto, ON, M3J1P3, Canada
- Department of Mathematics and Statistics, York University, 4700 Keele Street, Toronto, ON, M3J1P3, Canada
| | - Evgenia Gatov
- Toronto Public Health, City of Toronto, Toronto, ON, Canada
| | - Iain Moyles
- Centre for Disease Modelling (CDM), York University, 4700 Keele Street, Toronto, ON, M3J1P3, Canada
- Department of Mathematics and Statistics, York University, 4700 Keele Street, Toronto, ON, M3J1P3, Canada
| | - Jacques Bélair
- Centre for Disease Modelling (CDM), York University, 4700 Keele Street, Toronto, ON, M3J1P3, Canada
- Département de Mathématiques Et de Statistique, Université de Montréal, Montréal, Québec, Canada
| | - James Watmough
- Centre for Disease Modelling (CDM), York University, 4700 Keele Street, Toronto, ON, M3J1P3, Canada
- Department of Mathematics and Statistics, University of New Brunswick, Fredericton, New Brunswick, Canada
| | - Sarah Collier
- Toronto Public Health, City of Toronto, Toronto, ON, Canada
| | - Julien Arino
- Centre for Disease Modelling (CDM), York University, 4700 Keele Street, Toronto, ON, M3J1P3, Canada
- Department of Mathematics, University of Manitoba, Winnipeg, MB, Canada
| | - Huaiping Zhu
- Centre for Disease Modelling (CDM), York University, 4700 Keele Street, Toronto, ON, M3J1P3, Canada.
- Department of Mathematics and Statistics, York University, 4700 Keele Street, Toronto, ON, M3J1P3, Canada.
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143
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Spiliotis K, Koutsoumaris CC, Reppas AI, Papaxenopoulou LA, Starke J, Hatzikirou H. Optimal vaccine roll-out strategies including social distancing for pandemics. iScience 2022; 25:104575. [PMID: 35720194 PMCID: PMC9197569 DOI: 10.1016/j.isci.2022.104575] [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] [Subscribe] [Scholar Register] [Received: 09/26/2021] [Revised: 03/05/2022] [Accepted: 06/07/2022] [Indexed: 12/14/2022] Open
Abstract
Non-pharmacological interventions (NPIs), principally social distancing, in combination with effective vaccines, aspire to develop a protective immunity shield against pandemics and particularly against the COVID-19 pandemic. In this study, an agent-based network model with small-world topology is employed to find optimal policies against pandemics, including social distancing and vaccination strategies. The agents' states are characterized by a variation of the SEIR model (susceptible, exposed, infected, recovered). To explore optimal policies, an equation-free method is proposed to solve the inverse problem of calibrating an agent's infection rate with respect to the vaccination efficacy. The results show that prioritizing the first vaccine dose in combination with mild social restrictions, is sufficient to control the pandemic, with respect to the number of deaths. Moreover, for the same mild number of social contacts, we find an optimal vaccination ratio of 0.85 between older people of ages > 65 compared to younger ones.
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Affiliation(s)
| | - Constantinos Chr. Koutsoumaris
- Department of Research, Development and Innovation Statistics, National Documentation Centre, 48 Vas. Konstantinou St, Athens 11635, Greece
| | - Andreas I. Reppas
- Universität Berlin and Humboldt- Universität zu Berlin, Charitéplatz 1, 10117 Berlin, Germany
| | - Lito A. Papaxenopoulou
- Department of Systems Immunology and Braunschweig Integrated Centre of Systems Biology, Helmholtz Centre for Infection Research, Rebenring 56, 38106 Braunschweig, Germany
| | - Jens Starke
- Institute of Mathematics, University of Rostock, 18057 Rostock, Germany
| | - Haralampos Hatzikirou
- Centre for Information Services and High Performance Computing, Technische Universität Dresden, Nöthnitzer Straße 46, 01062 Dresden, Germany
- Mathematics Department, Khalifa University of Science and Technology, P.O. Box 127788, Abu Dhabi, United Arab Emirates
- Corresponding author
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144
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Liu W, Guo Z, Abudunaibi B, Ouyang X, Wang D, Yang T, Deng B, Huang J, Zhao B, Su Y, Su C, Chen T. Model-Based Evaluation of Transmissibility and Intervention Measures for a COVID-19 Outbreak in Xiamen City, China. Front Public Health 2022; 10:887146. [PMID: 35910883 PMCID: PMC9326243 DOI: 10.3389/fpubh.2022.887146] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Accepted: 05/30/2022] [Indexed: 12/24/2022] Open
Abstract
Background In September 2021, there was an outbreak of coronavirus disease 2019 (COVID-19) in Xiamen, China. Various non-pharmacological interventions (NPIs) and pharmacological interventions (PIs) have been implemented to prevent and control the spread of the disease. This study aimed to evaluate the effectiveness of various interventions and to identify priorities for the implementation of prevention and control measures. Methods The data of patients with COVID-19 were collected from 8 to 30 September 2021. A Susceptible-Exposed-Infectious-Recovered (SEIR) dynamics model was developed to fit the data and simulate the effectiveness of interventions (medical treatment, isolation, social distancing, masking, and vaccination) under different scenarios. The effective reproductive number (Reff) was used to assess the transmissibility and transmission risk. Results A total of 236 cases of COVID-19 were reported in Xiamen. The epidemic curve was divided into three phases (Reff = 6.8, 1.5, and 0). Notably, the cumulative number of cases was reduced by 99.67% due to the preventive and control measures implemented by the local government. In the effective containment stage, the number of cases could be reduced to 115 by intensifying the implementation of interventions. The total number of cases (TN) could be reduced by 29.66–95.34% when patients voluntarily visit fever clinics. When only two or three of these measures are implemented, the simulated TN may be greater than the actual number. As four measures were taken simultaneously, the TN may be <100, which is 57.63% less than the actual number. The simultaneous implementation of five interventions could rapidly control the transmission and reduce the number of cases to fewer than 25. Conclusion With the joint efforts of the government and the public, the outbreak was controlled quickly and effectively. Authorities could promptly cut the transmission chain and control the spread of the disease when patients with fever voluntarily went to the hospital. The ultimate effect of controlling the outbreak through only one intervention was not obvious. The combined community control and mask wearing, along with other interventions, could lead to rapid control of the outbreak and ultimately lower the total number of cases. More importantly, this would mitigate the impact of the outbreak on society and socioeconomics.
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Affiliation(s)
- Weikang Liu
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, China
| | - Zhinan Guo
- Xiamen Center for Disease Control and Prevention, Xiamen, China
| | - Buasiyamu Abudunaibi
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, China
| | - Xue Ouyang
- Xiamen Center for Disease Control and Prevention, Xiamen, China
| | - Demeng Wang
- Xiamen Center for Disease Control and Prevention, Xiamen, China
| | - Tianlong Yang
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, China
| | - Bin Deng
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, China
| | - Jiefeng Huang
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, China
| | - Benhua Zhao
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, China
| | - Yanhua Su
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, China
| | - Chenghao Su
- Zhongshan Hospital, Fudan University (Xiamen Branch), Xiamen, China
- Chenghao Su
| | - Tianmu Chen
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, China
- *Correspondence: Tianmu Chen
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145
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Ko Y, Mendoza VM, Mendoza R, Seo Y, Lee J, Lee J, Kwon D, Jung E. Multi-Faceted Analysis of COVID-19 Epidemic in Korea Considering Omicron Variant: Mathematical Modeling-Based Study. J Korean Med Sci 2022; 37:e209. [PMID: 35790210 PMCID: PMC9259245 DOI: 10.3346/jkms.2022.37.e209] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Accepted: 06/07/2022] [Indexed: 12/16/2022] Open
Abstract
BACKGROUND The most recent variant of concern, omicron (B.1.1.529), has caused numerous cases worldwide including the Republic of Korea due to its fast transmission and reduced vaccine effectiveness. METHODS A mathematical model considering age-structure, vaccine, antiviral drugs, and influx of the omicron variant was developed. We estimated transmission rates among age groups using maximum likelihood estimation for the age-structured model. The impact of non-pharmaceutical interventions (NPIs; in community and border), quantified by a parameter μ in the force of infection, and vaccination were examined through a multi-faceted analysis. A theory-based endemic equilibrium study was performed to find the manageable number of cases according to omicron- and healthcare-related factors. RESULTS By fitting the model to the available data, the estimated values of μ ranged from 0.31 to 0.73, representing the intensity of NPIs such as social distancing level. If μ < 0.55 and 300,000 booster shots were administered daily from February 3, 2022, the number of severe cases was forecasted to exceed the severe bed capacity. Moreover, the number of daily cases is reduced as the timing of screening measures is delayed. If screening measure was intensified as early as November 24, 2021 and the number of overseas entrant cases was contained to 1 case per 10 days, simulations showed that the daily incidence by February 3, 2022 could have been reduced by 87%. Furthermore, we found that the incidence number in mid-December 2021 exceeded the theory-driven manageable number of daily cases. CONCLUSION NPIs, vaccination, and antiviral drugs influence the spread of omicron and number of severe cases in the Republic of Korea. Intensive and early screening measures during the emergence of a new variant is key in controlling the epidemic size. Using the endemic equilibrium of the model, a formula for the manageable daily cases depending on the severity rate and average length of hospital stay was derived so that the number of severe cases does not surpass the severe bed capacity.
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Affiliation(s)
- Youngsuk Ko
- Department of Mathematics, Konkuk University, Seoul, Korea
| | - Victoria May Mendoza
- Department of Mathematics, Konkuk University, Seoul, Korea
- Institute of Mathematics, University of the Philippines Diliman, Quezon City, Philippines
| | - Renier Mendoza
- Department of Mathematics, Konkuk University, Seoul, Korea
- Institute of Mathematics, University of the Philippines Diliman, Quezon City, Philippines
| | - Yubin Seo
- Division of Infectious Disease, Department of Internal Medicine, Kangnam Sacred Heart Hospital, Hallym University College of Medicine, Seoul, Korea
| | - Jacob Lee
- Division of Infectious Disease, Department of Internal Medicine, Kangnam Sacred Heart Hospital, Hallym University College of Medicine, Seoul, Korea
| | - Jonggul Lee
- Division of Public Health Emergency Response Research, Korea Disease Control and Prevention Agency, Cheongju, Korea
| | - Donghyok Kwon
- Division of Public Health Emergency Response Research, Korea Disease Control and Prevention Agency, Cheongju, Korea
| | - Eunok Jung
- Department of Mathematics, Konkuk University, Seoul, Korea.
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146
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Yang P, Yang Z, Zhao C, Li X, Shao Z, Liu K, Shang L. Vaccination and Government Stringent Control as Effective Strategies in Preventing SARS-CoV-2 Infections: A Global Perspective. Front Public Health 2022; 10:903511. [PMID: 35812484 PMCID: PMC9263831 DOI: 10.3389/fpubh.2022.903511] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Accepted: 05/19/2022] [Indexed: 11/24/2022] Open
Abstract
With the rapid implementation of global vaccination against the coronavirus disease 2019 (COVID-19), the threat posed by the disease has been mitigated, yet it remains a major global public health concern. Few studies have estimated the effects of vaccination and government stringent control measures on the disease transmission from a global perspective. To address this, we collected 216 countries' data on COVID-19 daily reported cases, daily vaccinations, daily government stringency indexes (GSIs), and the human development index (HDI) from the dataset of the World Health Organization (WHO) and the Our World in Data COVID-19 (OWID). We utilized the interrupted time series (ITS) model to examine how the incidence was affected by the vaccination and GSI at continental and country levels from 22 January 2020 to 13 February 2022. We found that the effectiveness of vaccination was better in Europe, North America, and Africa than in Asia, South America, and Oceania. The long-term effects outperformed the short-term effects in most cases. Countries with a high HDI usually had a high vaccination coverage, resulting in better vaccination effects. Nonetheless, some countries with high vaccination coverage did not receive a relatively low incidence due to the weaker GSI. The results suggest that in addition to increasing population vaccination coverage, it is crucial to maintain a certain level of government stringent measures to prevent and control the disease. The strategy is particularly appropriate for countries with low vaccination coverage at present.
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Affiliation(s)
- Peng Yang
- Department of Health Statistics, Ministry of Education Key Lab of Hazard Assessment and Control in Special Operational Environment, School of Public Health, Air Force Medical University, Xi'an, China
| | - Zhe Yang
- Department of Health Statistics, Ministry of Education Key Lab of Hazard Assessment and Control in Special Operational Environment, School of Public Health, Air Force Medical University, Xi'an, China
| | - Chenxi Zhao
- Department of Epidemiology, Ministry of Education Key Lab of Hazard Assessment and Control in Special Operational Environment, School of Public Health, Air Force Medical University, Xi'an, China
- School of Public Health, Baotou Medical College, Baotou, China
| | - Xinrui Li
- Department of Health Statistics, Ministry of Education Key Lab of Hazard Assessment and Control in Special Operational Environment, School of Public Health, Air Force Medical University, Xi'an, China
- School of Medicine, Northwest University, Xi'an, China
| | - Zhongjun Shao
- Department of Epidemiology, Ministry of Education Key Lab of Hazard Assessment and Control in Special Operational Environment, School of Public Health, Air Force Medical University, Xi'an, China
| | - Kun Liu
- Department of Epidemiology, Ministry of Education Key Lab of Hazard Assessment and Control in Special Operational Environment, School of Public Health, Air Force Medical University, Xi'an, China
- Kun Liu
| | - Lei Shang
- Department of Health Statistics, Ministry of Education Key Lab of Hazard Assessment and Control in Special Operational Environment, School of Public Health, Air Force Medical University, Xi'an, China
- *Correspondence: Lei Shang
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147
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Oliva G, Schlueter M, Munetomo M, Scala A. Dynamical intervention planning against COVID-19-like epidemics. PLoS One 2022; 17:e0269830. [PMID: 35700170 PMCID: PMC9197046 DOI: 10.1371/journal.pone.0269830] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Accepted: 05/30/2022] [Indexed: 11/28/2022] Open
Abstract
COVID-19 has got us to face a new situation where, for the lack of ready-to-use vaccines, it is necessary to support vaccination with complex non-pharmaceutical strategies. In this paper, we provide a novel Mixed Integer Nonlinear Programming formulation for fine-grained optimal intervention planning (i.e., at the level of the single day) against newborn epidemics like COVID-19, where a modified SIR model accounting for heterogeneous population classes, social distancing and several types of vaccines (each with its efficacy and delayed effects), allows us to plan an optimal mixed strategy (both pharmaceutical and non-pharmaceutical) that takes into account both the vaccine availability in limited batches at selected time instants and the need for second doses while keeping hospitalizations and intensive care occupancy below a threshold and requiring that new infections die out at the end of the planning horizon. In order to show the effectiveness of the proposed formulation, we analyze a case study for Italy with realistic parameters.
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Affiliation(s)
- Gabriele Oliva
- Unit of Automatic Control, Department of Engineering, Università Campus Bio-Medico di Roma, Rome, Italy
- * E-mail: (GO); (AS)
| | - Martin Schlueter
- Information Initiative Center, Hokkaido University, Sapporo, Japan
| | | | - Antonio Scala
- CNR-ISC, Applico Lab, Roma, Italy
- Centro Ricerche Enrico Fermi, Roma, Italy
- Big Data in Health Society, Roma, Italy
- Global Health Security Agenda - GHSA, Roma, Italy
- * E-mail: (GO); (AS)
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148
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Estimating the course of the COVID-19 pandemic in Germany via spline-based hierarchical modelling of death counts. Sci Rep 2022; 12:9784. [PMID: 35697761 PMCID: PMC9191534 DOI: 10.1038/s41598-022-13723-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Accepted: 05/26/2022] [Indexed: 12/13/2022] Open
Abstract
We consider a retrospective modelling approach for estimating effective reproduction numbers based on death counts during the first year of the COVID-19 pandemic in Germany. The proposed Bayesian hierarchical model incorporates splines to estimate reproduction numbers flexibly over time while adjusting for varying effective infection fatality rates. The approach also provides estimates of dark figures regarding undetected infections. Results for Germany illustrate that our estimates based on death counts are often similar to classical estimates based on confirmed cases; however, considering death counts allows to disentangle effects of adapted testing policies from transmission dynamics. In particular, during the second wave of infections, classical estimates suggest a flattening infection curve following the “lockdown light” in November 2020, while our results indicate that infections continued to rise until the “second lockdown” in December 2020. This observation is associated with more stringent testing criteria introduced concurrently with the “lockdown light”, which is reflected in subsequently increasing dark figures of infections estimated by our model. In light of progressive vaccinations, shifting the focus from modelling confirmed cases to reported deaths with the possibility to incorporate effective infection fatality rates might be of increasing relevance for the future surveillance of the pandemic.
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149
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Ge Y, Zhang WB, Wu X, Ruktanonchai CW, Liu H, Wang J, Song Y, Liu M, Yan W, Yang J, Cleary E, Qader SH, Atuhaire F, Ruktanonchai NW, Tatem AJ, Lai S. Untangling the changing impact of non-pharmaceutical interventions and vaccination on European COVID-19 trajectories. Nat Commun 2022; 13:3106. [PMID: 35661759 PMCID: PMC9166696 DOI: 10.1038/s41467-022-30897-1] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2021] [Accepted: 05/24/2022] [Indexed: 12/27/2022] Open
Abstract
Non-pharmaceutical interventions (NPIs) and vaccination are two fundamental approaches for mitigating the coronavirus disease 2019 (COVID-19) pandemic. However, the real-world impact of NPIs versus vaccination, or a combination of both, on COVID-19 remains uncertain. To address this, we built a Bayesian inference model to assess the changing effect of NPIs and vaccination on reducing COVID-19 transmission, based on a large-scale dataset including epidemiological parameters, virus variants, vaccines, and climate factors in Europe from August 2020 to October 2021. We found that (1) the combined effect of NPIs and vaccination resulted in a 53% (95% confidence interval: 42–62%) reduction in reproduction number by October 2021, whereas NPIs and vaccination reduced the transmission by 35% and 38%, respectively; (2) compared with vaccination, the change of NPI effect was less sensitive to emerging variants; (3) the relative effect of NPIs declined 12% from May 2021 due to a lower stringency and the introduction of vaccination strategies. Our results demonstrate that NPIs were complementary to vaccination in an effort to reduce COVID-19 transmission, and the relaxation of NPIs might depend on vaccination rates, control targets, and vaccine effectiveness concerning extant and emerging variants. Non-pharmaceutical interventions (NPIs) and COVID-19 vaccination have been implemented concurrently, making their relative effects difficult to measure. Here, the authors show that effects of NPIs reduced as vaccine coverage increased, but that NPIs could still be important in the context of more transmissible variants.
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Affiliation(s)
- Yong Ge
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China. .,College of Resources and Environment, University of Academy of Sciences, Beijing, China.
| | - Wen-Bin Zhang
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China.,College of Resources and Environment, University of Academy of Sciences, Beijing, China.,Lancaster Environment Center, Faculty of Science and Technology, Lancaster University, Lancaster, UK
| | - Xilin Wu
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China.,College of Resources and Environment, University of Academy of Sciences, Beijing, China
| | | | - Haiyan Liu
- Marine Data Center, South Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, China
| | - Jianghao Wang
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China.,College of Resources and Environment, University of Academy of Sciences, Beijing, China
| | - Yongze Song
- School of Design and the Built Environment, Curtin University, Perth, Australia
| | - Mengxiao Liu
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China.,College of Resources and Environment, University of Academy of Sciences, Beijing, China
| | - Wei Yan
- Respiratory Medicine Department, Peking University Third Hospital, Beijing, China
| | - Juan Yang
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China.,Shanghai Institute of Infectious Disease and Biosecurity, Fudan University, Shanghai, China
| | - Eimear Cleary
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, UK
| | - Sarchil H Qader
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, UK.,Natural Resources Department, College of Agricultural Engineering Sciences, University of Sulaimani; Sulaimani 334, Kurdistan Region, Sulaymaniyah, Iraq
| | - Fatumah Atuhaire
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, UK.,School of Mathematical Sciences, University of Southampton, Southampton, UK
| | | | - Andrew J Tatem
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, UK.
| | - Shengjie Lai
- Shanghai Institute of Infectious Disease and Biosecurity, Fudan University, Shanghai, China. .,WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, UK. .,Institute for Life Sciences, University of Southampton, Southampton, UK.
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150
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Park SW, Bolker BM, Funk S, Metcalf CJE, Weitz JS, Grenfell BT, Dushoff J. The importance of the generation interval in investigating dynamics and control of new SARS-CoV-2 variants. J R Soc Interface 2022; 19:20220173. [PMID: 35702867 PMCID: PMC9198506 DOI: 10.1098/rsif.2022.0173] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Accepted: 05/19/2022] [Indexed: 12/19/2022] Open
Abstract
Inferring the relative strength (i.e. the ratio of reproduction numbers) and relative speed (i.e. the difference between growth rates) of new SARS-CoV-2 variants is critical to predicting and controlling the course of the current pandemic. Analyses of new variants have primarily focused on characterizing changes in the proportion of new variants, implicitly or explicitly assuming that the relative speed remains fixed over the course of an invasion. We use a generation-interval-based framework to challenge this assumption and illustrate how relative strength and speed change over time under two idealized interventions: a constant-strength intervention like idealized vaccination or social distancing, which reduces transmission rates by a constant proportion, and a constant-speed intervention like idealized contact tracing, which isolates infected individuals at a constant rate. In general, constant-strength interventions change the relative speed of a new variant, while constant-speed interventions change its relative strength. Differences in the generation-interval distributions between variants can exaggerate these changes and modify the effectiveness of interventions. Finally, neglecting differences in generation-interval distributions can bias estimates of relative strength.
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Affiliation(s)
- Sang Woo Park
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA
| | - Benjamin M. Bolker
- Department of Biology, McMaster University, Hamilton, Ontario, Canada
- Department of Mathematics and Statistics, McMaster University, Hamilton, Ontario, Canada
- M. G. DeGroote Institute for Infectious Disease Research, McMaster University, Hamilton, Ontario, Canada
| | - Sebastian Funk
- Department for Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK
| | - C. Jessica E. Metcalf
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA
- Princeton School of Public and International Affairs, Princeton University, Princeton, NJ, USA
| | - Joshua S. Weitz
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, USA
- School of Physics, Georgia Institute of Technology, Atlanta, GA, USA
- Institut de Biologie, École Normale Supérieure, Paris, France
| | - Bryan T. Grenfell
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA
- Princeton School of Public and International Affairs, Princeton University, Princeton, NJ, USA
| | - Jonathan Dushoff
- Department of Biology, McMaster University, Hamilton, Ontario, Canada
- Department of Mathematics and Statistics, McMaster University, Hamilton, Ontario, Canada
- M. G. DeGroote Institute for Infectious Disease Research, McMaster University, Hamilton, Ontario, Canada
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