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Bhattacharya P, Machi D, Chen J, Hoops S, Lewis B, Mortveit H, Venkatramanan S, Wilson ML, Marathe A, Porebski P, Klahn B, Outten J, Vullikanti A, Xie D, Adiga A, Brown S, Barrett C, Marathe M. Novel multi-cluster workflow system to support real-time HPC-enabled epidemic science: Investigating the impact of vaccine acceptance on COVID-19 spread. JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING 2024; 191:104899. [PMID: 38774820 PMCID: PMC11105799 DOI: 10.1016/j.jpdc.2024.104899] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/24/2024]
Abstract
We present MacKenzie, a HPC-driven multi-cluster workflow system that was used repeatedly to configure and execute fine-grained US national-scale epidemic simulation models during the COVID-19 pandemic. Mackenzie supported federal and Virginia policymakers, in real-time, for a large number of "what-if" scenarios during the COVID-19 pandemic, and continues to be used to answer related questions as COVID-19 transitions to the endemic stage of the disease. MacKenzie is a novel HPC meta-scheduler that can execute US-scale simulation models and associated workflows that typically present significant big data challenges. The meta-scheduler optimizes the total execution time of simulations in the workflow, and helps improve overall human productivity. As an exemplar of the kind of studies that can be conducted using Mackenzie, we present a modeling study to understand the impact of vaccine-acceptance in controlling the spread of COVID-19 in the US. We use a 288 million node synthetic social contact network (digital twin) spanning all 50 US states plus Washington DC, comprised of 3300 counties, with 12 billion daily interactions. The highly-resolved agent-based model used for the epidemic simulations uses realistic information about disease progression, vaccine uptake, production schedules, acceptance trends, prevalence, and social distancing guidelines. Computational experiments show that, for the simulation workload discussed above, MacKenzie is able to scale up well to 10K CPU cores. Our modeling results show that, when compared to faster and accelerating vaccinations, slower vaccination rates due to vaccine hesitancy cause averted infections to drop from 6.7M to 4.5M, and averted total deaths to drop from 39.4K to 28.2K across the US. This occurs despite the fact that the final vaccine coverage is the same in both scenarios. We also find that if vaccine acceptance could be increased by 10% in all states, averted infections could be increased from 4.5M to 4.7M (a 4.4% improvement) and total averted deaths could be increased from 28.2K to 29.9K (a 6% improvement) nationwide.
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Affiliation(s)
| | - Dustin Machi
- Biocomplexity Institute, University of Virginia, Charlottesville, VA, USA
| | - Jiangzhuo Chen
- Biocomplexity Institute, University of Virginia, Charlottesville, VA, USA
| | - Stefan Hoops
- Biocomplexity Institute, University of Virginia, Charlottesville, VA, USA
| | - Bryan Lewis
- Biocomplexity Institute, University of Virginia, Charlottesville, VA, USA
| | - Henning Mortveit
- Biocomplexity Institute, University of Virginia, Charlottesville, VA, USA
- Department of Systems Engineering, University of Virginia, Charlottesville, VA, USA
| | | | - Mandy L Wilson
- Biocomplexity Institute, University of Virginia, Charlottesville, VA, USA
| | - Achla Marathe
- Biocomplexity Institute, University of Virginia, Charlottesville, VA, USA
- Department of Public Health Sciences, University of Virginia, Charlottesville, VA, USA
| | | | - Brian Klahn
- Biocomplexity Institute, University of Virginia, Charlottesville, VA, USA
| | - Joseph Outten
- Biocomplexity Institute, University of Virginia, Charlottesville, VA, USA
| | - Anil Vullikanti
- Biocomplexity Institute, University of Virginia, Charlottesville, VA, USA
- Department of Computer Science, University of Virginia, Charlottesville, VA, USA
| | - Dawen Xie
- Biocomplexity Institute, University of Virginia, Charlottesville, VA, USA
| | - Abhijin Adiga
- Biocomplexity Institute, University of Virginia, Charlottesville, VA, USA
| | | | | | - Madhav Marathe
- Biocomplexity Institute, University of Virginia, Charlottesville, VA, USA
- Department of Computer Science, University of Virginia, Charlottesville, VA, USA
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Ling L, Mondal WU, Ukkusuri SV. Cooperating Graph Neural Networks With Deep Reinforcement Learning for Vaccine Prioritization. IEEE J Biomed Health Inform 2024; 28:4891-4902. [PMID: 38691436 DOI: 10.1109/jbhi.2024.3392436] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/03/2024]
Abstract
This study explores the vaccine prioritization strategy to reduce the overall burden of the pandemic when the supply is limited. Existing vaccine distribution methods focus on macro-level or simplified micro-level assuming homogeneous behavior within populations without considering mobility patterns. Directly applying these models for micro-level vaccine allocation leads to sub-optimal solutions. To address the issue, we first proposed a Trans-vaccine-SEIR model to incorporate mobility heterogeneity in disease propagation. Then we develop a novel deep reinforcement learning to seek the optimal vaccine allocation strategy for the disease evolution system. The graph neural network is used to effectively capture the structural properties of the mobility network and extract disease features. In our evaluation, the proposed framework reduces 7%-10% of infections and deaths compared to the baseline strategies. Extensive evaluation shows that the proposed framework is robust to seek the optimal vaccine allocation with diverse mobility patterns. In particular, we find transit usage restriction is significantly more effective than restricting cross-zone mobility for the top 10% age-based and income-based zones under optimal vaccine allocation strategy. These results provide valuable insights for areas with limited vaccines and low logistic efficacy.
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Wang Q, Yang L, Xiu S, Shen Y, Jin H, Lin L. A Prediction Model for Identifying Seasonal Influenza Vaccination Uptake Among Children in Wuxi, China: Prospective Observational Study. JMIR Public Health Surveill 2024; 10:e56064. [PMID: 38885032 PMCID: PMC11217706 DOI: 10.2196/56064] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Revised: 05/07/2024] [Accepted: 05/15/2024] [Indexed: 06/18/2024] Open
Abstract
BACKGROUND Predicting vaccination behaviors accurately could provide insights for health care professionals to develop targeted interventions. OBJECTIVE The aim of this study was to develop predictive models for influenza vaccination behavior among children in China. METHODS We obtained data from a prospective observational study in Wuxi, eastern China. The predicted outcome was individual-level vaccine uptake and covariates included sociodemographics of the child and parent, parental vaccine hesitancy, perceptions of convenience to the clinic, satisfaction with clinic services, and willingness to vaccinate. Bayesian networks, logistic regression, least absolute shrinkage and selection operator (LASSO) regression, support vector machine (SVM), naive Bayes (NB), random forest (RF), and decision tree classifiers were used to construct prediction models. Various performance metrics, including area under the receiver operating characteristic curve (AUC), were used to evaluate the predictive performance of the different models. Receiver operating characteristic curves and calibration plots were used to assess model performance. RESULTS A total of 2383 participants were included in the study; 83.2% of these children (n=1982) were <5 years old and 6.6% (n=158) had previously received an influenza vaccine. More than half (1356/2383, 56.9%) the parents indicated a willingness to vaccinate their child against influenza. Among the 2383 children, 26.3% (n=627) received influenza vaccination during the 2020-2021 season. Within the training set, the RF model showed the best performance across all metrics. In the validation set, the logistic regression model and NB model had the highest AUC values; the SVM model had the highest precision; the NB model had the highest recall; and the logistic regression model had the highest accuracy, F1 score, and Cohen κ value. The LASSO and logistic regression models were well-calibrated. CONCLUSIONS The developed prediction model can be used to quantify the uptake of seasonal influenza vaccination for children in China. The stepwise logistic regression model may be better suited for prediction purposes.
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Affiliation(s)
- Qiang Wang
- Department of Epidemiology, School of Public Health, Fudan University, Shanghai, China
- Department of Epidemiology and Health Statistics, School of Public Health, Southeast University, Nanjing, China
| | - Liuqing Yang
- Department of Epidemiology and Health Statistics, School of Public Health, Southeast University, Nanjing, China
| | - Shixin Xiu
- Department of Immunization, Affiliated Wuxi Center for Disease Control and Prevention of Nanjing Medical University, Wuxi Center for Disease Control and Prevention, Wuxi, China
| | - Yuan Shen
- Department of Immunization, Affiliated Wuxi Center for Disease Control and Prevention of Nanjing Medical University, Wuxi Center for Disease Control and Prevention, Wuxi, China
| | - Hui Jin
- Department of Epidemiology and Health Statistics, School of Public Health, Southeast University, Nanjing, China
| | - Leesa Lin
- Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, United Kingdom
- Laboratory of Data Discovery for Health (D24H), Hong Kong Science Park, Hong Kong Special Administrative Region, China
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
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Kou Y, Zhang N. Implicit theories of health predict influenza vaccination intention among elder Chinese: The mediating role of anticipated regret. J Health Psychol 2024:13591053241253067. [PMID: 38801106 DOI: 10.1177/13591053241253067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/29/2024] Open
Abstract
Receiving influenza vaccines is the most effective public health strategy to protect people against seasonal flu infection. However, influenza vaccination rates are extremely low in China. This study investigated the association between implicit theories of health and influenza vaccination intention among elder Chinese when the vaccine is free (vs not free), and examined the mediating effect of anticipated regret. The results suggested that implicit theories of health, especially incremental theory of health, significantly predicted Chinese elders' influenza vaccination intention and this relationship was mediated by anticipated regret. Implications of the current research for promoting influenza vaccination among elder Chinese and directions for future research are discussed.
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Affiliation(s)
- Yun Kou
- School of Public Health and the Second Affiliated Hospital of Zhejiang University School of Medicine, China
| | - Ning Zhang
- School of Public Health and the Second Affiliated Hospital of Zhejiang University School of Medicine, China
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Reddinger JL, Charness G, Levine D. Vaccination as personal public good provision. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2022.04.21.22274110. [PMID: 35923323 PMCID: PMC9347278 DOI: 10.1101/2022.04.21.22274110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Vaccination against infectious diseases has both private and public benefits. We study whether social preferences-concerns for the well-being of other people-are associated with one's decision regarding vaccination. We measure these social preferences for 549 online subjects with a public-good game and an altruism game. To the extent that one gets vaccinated out of concern for the health of others, contribution in the public-good game is analogous to an individual's decision to obtain vaccination, while our altruism game provides a different measure of altruism, equity, and efficiency concerns. We proxy vaccine demand with how quickly a representative individual voluntarily took the initial vaccination for COVID-19 (after the vaccine was widely available). We collect COVID-19 vaccination history separately from the games to avoid experimenter-demand effects. We find a strong result: Contribution in the public-good game is associated with greater demand to voluntarily receive a first dose, and thus also to vaccinate earlier. Compared to a subject who contributes nothing, one who contributes the maximum ($4) is 58% more likely to obtain a first dose voluntarily in the four-month period that we study (April through August 2021). In short, people who are more pro-social are more likely to take a voluntary COVID-19 vaccination. Behavior in our altruism game does not predict vaccination. We recommend further research on the use of pro-social preferences to help motivate individuals to vaccinate for other transmissible diseases, such as the flu and HPV.
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Hamilton MA, Knight J, Mishra S. Examining the Influence of Imbalanced Social Contact Matrices in Epidemic Models. Am J Epidemiol 2024; 193:339-347. [PMID: 37715459 PMCID: PMC10840077 DOI: 10.1093/aje/kwad185] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Revised: 06/16/2023] [Accepted: 09/12/2023] [Indexed: 09/17/2023] Open
Abstract
Transmissible infections such as those caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spread according to who contacts whom. Therefore, many epidemic models incorporate contact patterns through contact matrices. Contact matrices can be generated from social contact survey data. However, the resulting matrices are often imbalanced, such that the total number of contacts reported by group A with group B do not match those reported by group B with group A. We examined the theoretical influence of imbalanced contact matrices on the estimated basic reproduction number (R0). We then explored how imbalanced matrices may bias model-based epidemic projections using an illustrative simulation model of SARS-CoV-2 with 2 age groups (<15 and ≥15 years). Models with imbalanced matrices underestimated the initial spread of SARS-CoV-2, had later time to peak incidence, and had smaller peak incidence. Imbalanced matrices also influenced cumulative infections observed per age group, as well as the estimated impact of an age-specific vaccination strategy. Stratified transmission models that do not consider contact balancing may generate biased projections of epidemic trajectory and the impact of targeted public health interventions. Therefore, modeling studies should implement and report methods used to balance contact matrices for stratified transmission models.
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Affiliation(s)
| | | | - Sharmistha Mishra
- Correspondence to Dr. Sharmistha Mishra, Department of Medicine, University of Toronto, Li Ka Shing Knowledge Institute, Unity Health Toronto, 209 Victoria Street, Toronto M5B 1T8, Canada (e-mail: )
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Andrade G, Abdelmonem KYA, Alqaderi N, Teir HJ, Elamin ABA, Bedewy D. Fear of Needles and Seasonal Influenza Vaccine Acceptance Amongst Adults in the United Arab Emirates: A Cross-Sectional Study and Implications for Nursing. SAGE Open Nurs 2024; 10:23779608241261622. [PMID: 38881678 PMCID: PMC11177738 DOI: 10.1177/23779608241261622] [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: 12/13/2023] [Revised: 05/13/2024] [Accepted: 05/27/2024] [Indexed: 06/18/2024] Open
Abstract
Introduction Seasonal influenza remains a challenge in the United Arab Emirates, and vaccination is an important preventive measure. However, fear of needles may be an obstacle in vaccination efforts. Objective The purpose of this study was to determine, in a non-clinical sample obtained from the UAE, how much of an impact fear of needles has on the decision to receive the seasonal influenza vaccine. Methods In the period September-December 2022, 372 participants were surveyed on their willingness to be vaccinated, their level of vaccine knowledge, and their level of fear of needles. Logistic regression models were used to assess the impact of fear of needles and vaccine knowledge on the decision to accept the seasonal influenza vaccine. Results Fear of needles and vaccine knowledge are significant predictors in the decision to receive the vaccine. There were no gender or ethnic differences in fear of needles, but there were differences in the decision to receive the vaccines, with women and non-Arabs being more hesitant. Conclusion Fear of needles may be an important variable to account for in public policies designed to improve vaccination rates in the UAE. For public health policy in the UAE, this implies that authorities must dedicate efforts to manage fear of needles in the general population. Efforts to address fear of vaccines in the general population must be made with proper training of nurses. Alternatively, authorities may need to seek oral alternatives for the administration of the seasonal influenza vaccine.
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Affiliation(s)
| | | | | | | | | | - Dalia Bedewy
- Ajman University, Ajman, UAE
- Tanta University, Tanta, Egypt
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Patón M, Acuña JM, Rodríguez J. Evaluation of vaccine rollout strategies for emerging infectious diseases: A model-based approach including protection attitudes. Infect Dis Model 2023; 8:1032-1049. [PMID: 37674584 PMCID: PMC10477745 DOI: 10.1016/j.idm.2023.07.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Revised: 07/28/2023] [Accepted: 07/30/2023] [Indexed: 09/08/2023] Open
Abstract
Vaccine allocation strategies become crucial during vaccine shortages, especially in the face of potential outbreaks of new infectious diseases, as witnessed during the COVID-19 pandemic. To address this, a specialized compartmental model is created, which simulates an emerging infectious disease similar to COVID-19. This model divides the population into different age groups and is used to compare various vaccine prioritisation approaches, aiming to minimize the total number of fatalities. The model is an improvement upon previous ones as it incorporates essential behavioural factors and is adapted to account for the protective effects of vaccination against both disease infection and transmission. It takes into account human behaviors such as mask-wearing and social distancing by utilizing specific parameters related to self-protection, awareness levels, and the frequency of daily person-to-person interactions within each age group. Furthermore, a novel method for dynamic vaccine prioritisation was introduced in this study. This approach is model-independent and relies on the dynamic R number. It is the first time such a method has been developed, offering a decision-making approach that is not tied to any specific model. This innovation provides a flexible and adaptable strategy for determining vaccine priorities based on real-time data and the current state of the outbreak. Our findings reveal crucial insights into vaccine allocation strategies. When the daily rollout rates are fast (0.75% or higher) and children are eligible for vaccination, prioritising groups with high daily person-to-person interactions can lead to substantial reductions in total fatalities (up to approximately 40% lower). On the other hand, if rollout rates are slower and overall vaccination coverage is high, focusing on vaccinating elders emerges as the most effective strategy, resulting in up to approximately 10% fewer fatalities. However, the scenario changes significantly when children are not eligible for vaccination, as they constitute a highly interactive population group. In this case, the differences between priority strategies become smaller. With fast daily rollout rates, prioritisation based on interactions achieves only a 7% reduction in total fatalities, while a slower rollout with vaccination of elders first leads to an approximately 11% reduction in fatalities compared to the scenario where children are eligible for vaccination. The impact of behavioural parameters is equally critical. When the self-protection levels exercised by the population are low, it significantly affects the optimal vaccine prioritisation strategy to be followed, making it essential to consider behavioural factors in decision-making.
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Affiliation(s)
- Mauricio Patón
- Department of Chemical Engineering, College of Engineering, Khalifa University, SAN Campus PO Box 127788, Abu Dhabi, United Arab Emirates
| | - Juan M. Acuña
- Department of Epidemiology and Public Health, College of Medicine. Khalifa University, PO Box 127788, Abu Dhabi, United Arab Emirates
| | - Jorge Rodríguez
- Department of Chemical Engineering, College of Engineering, Khalifa University, SAN Campus PO Box 127788, Abu Dhabi, United Arab Emirates
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Anderson TL, Nande A, Merenstein C, Raynor B, Oommen A, Kelly BJ, Levy MZ, Hill AL. Quantifying individual-level heterogeneity in infectiousness and susceptibility through household studies. Epidemics 2023; 44:100710. [PMID: 37556994 PMCID: PMC10594662 DOI: 10.1016/j.epidem.2023.100710] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 03/17/2023] [Accepted: 07/18/2023] [Indexed: 08/11/2023] Open
Abstract
The spread of SARS-CoV-2, like that of many other pathogens, is governed by heterogeneity. "Superspreading," or "over-dispersion," is an important factor in transmission, yet it is hard to quantify. Estimates from contact tracing data are prone to potential biases due to the increased likelihood of detecting large clusters of cases, and may reflect variation in contact behavior more than biological heterogeneity. In contrast, the average number of secondary infections per contact is routinely estimated from household surveys, and these studies can minimize biases by testing all members of a household. However, the models used to analyze household transmission data typically assume that infectiousness and susceptibility are the same for all individuals or vary only with predetermined traits such as age. Here we develop and apply a combined forward simulation and inference method to quantify the degree of inter-individual variation in both infectiousness and susceptibility from observations of the distribution of infections in household surveys. First, analyzing simulated data, we show our method can reliably ascertain the presence, type, and amount of these heterogeneities given data from a sufficiently large sample of households. We then analyze a collection of household studies of COVID-19 from diverse settings around the world, and find strong evidence for large heterogeneity in both the infectiousness and susceptibility of individuals. Our results also provide a framework to improve the design of studies to evaluate household interventions in the presence of realistic heterogeneity between individuals.
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Affiliation(s)
- Thayer L Anderson
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD 21218, United States of America
| | - Anjalika Nande
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD 21218, United States of America
| | - Carter Merenstein
- Department of Microbiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States of America
| | - Brinkley Raynor
- Department of Biostatistics, Epidemiology, & Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States of America
| | - Anisha Oommen
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD 21218, United States of America; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, United States of America
| | - Brendan J Kelly
- Department of Microbiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States of America; Department of Biostatistics, Epidemiology, & Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States of America; Division of Infectious Diseases, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States of America
| | - Michael Z Levy
- Department of Biostatistics, Epidemiology, & Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States of America
| | - Alison L Hill
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD 21218, United States of America; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, United States of America.
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Abell IR, McCaw JM, Baker CM. Understanding the impact of disease and vaccine mechanisms on the importance of optimal vaccine allocation. Infect Dis Model 2023; 8:539-550. [PMID: 37288288 PMCID: PMC10241858 DOI: 10.1016/j.idm.2023.05.003] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Revised: 05/17/2023] [Accepted: 05/17/2023] [Indexed: 06/09/2023] Open
Abstract
Vaccination is an important epidemic intervention strategy. However, it is generally unclear how the outcomes of different vaccine strategies change depending on population characteristics, vaccine mechanisms and allocation objective. In this paper we develop a conceptual mathematical model to simulate strategies for pre-epidemic vaccination. We extend the SEIR model to incorporate a range of vaccine mechanisms and disease characteristics. We then compare the outcomes of optimal and suboptimal vaccination strategies for three public health objectives (total infections, total symptomatic infections and total deaths) using numerical optimisation. Our comparison shows that the difference in outcomes between vaccinating optimally and suboptimally depends on vaccine mechanisms, disease characteristics, and objective considered. Our modelling finds vaccines that impact transmission produce better outcomes as transmission is reduced for all strategies. For vaccines that impact the likelihood of symptomatic disease or dying due to infection, the improvement in outcome as we decrease these variables is dependent on the strategy implemented. Through a principled model-based process, this work highlights the importance of designing effective vaccine allocation strategies. We conclude that efficient allocation of resources can be just as crucial to the success of a vaccination strategy as the vaccine effectiveness and/or amount of vaccines available.
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Affiliation(s)
- Isobel R. Abell
- School of Mathematics and Statistics, The University of Melbourne, Melbourne, Australia
- Melbourne Centre for Data Science, The University of Melbourne, Melbourne, Australia
| | - James M. McCaw
- School of Mathematics and Statistics, The University of Melbourne, Melbourne, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Australia
- Peter Doherty Institute for Infection and Immunity, The Royal Melbourne Hospital and the University of Melbourne, Melbourne, Australia
| | - Christopher M. Baker
- School of Mathematics and Statistics, The University of Melbourne, Melbourne, Australia
- Melbourne Centre for Data Science, The University of Melbourne, Melbourne, Australia
- Centre of Excellence for Biosecurity Risk Analysis, The University of Melbourne, Melbourne, Australia
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Dorélien AM, Venkateswaran N, Deng J, Searle K, Enns E, Alarcon Espinoza G, Kulasingam S. Quantifying social contact patterns in Minnesota during stay-at-home social distancing order. BMC Infect Dis 2023; 23:324. [PMID: 37189060 PMCID: PMC10184106 DOI: 10.1186/s12879-022-07968-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Accepted: 12/23/2022] [Indexed: 05/17/2023] Open
Abstract
SARS-CoV-2 is primarily transmitted through person-to-person contacts. It is important to collect information on age-specific contact patterns because SARS-CoV-2 susceptibility, transmission, and morbidity vary by age. To reduce the risk of infection, social distancing measures have been implemented. Social contact data, which identify who has contact with whom especially by age and place are needed to identify high-risk groups and serve to inform the design of non-pharmaceutical interventions. We estimated and used negative binomial regression to compare the number of daily contacts during the first round (April-May 2020) of the Minnesota Social Contact Study, based on respondent's age, gender, race/ethnicity, region, and other demographic characteristics. We used information on the age and location of contacts to generate age-structured contact matrices. Finally, we compared the age-structured contact matrices during the stay-at-home order to pre-pandemic matrices. During the state-wide stay-home order, the mean daily number of contacts was 5.7. We found significant variation in contacts by age, gender, race, and region. Adults between 40 and 50 years had the highest number of contacts. The way race/ethnicity was coded influenced patterns between groups. Respondents living in Black households (which includes many White respondents living in inter-racial households with black family members) had 2.7 more contacts than respondents in White households; we did not find this same pattern when we focused on individual's reported race/ethnicity. Asian or Pacific Islander respondents or in API households had approximately the same number of contacts as respondents in White households. Respondents in Hispanic households had approximately two fewer contacts compared to White households, likewise Hispanic respondents had three fewer contacts than White respondents. Most contacts were with other individuals in the same age group. Compared to the pre-pandemic period, the biggest declines occurred in contacts between children, and contacts between those over 60 with those below 60.
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Affiliation(s)
| | | | - Jiuchen Deng
- University of Minnesota, Minneapolis, MN, 55455, USA
| | - Kelly Searle
- University of Minnesota, Minneapolis, MN, 55455, USA
| | - Eva Enns
- University of Minnesota, Minneapolis, MN, 55455, USA
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Maity B, Banerjee S, Senapati A, Chattopadhyay J. Quantifying optimal resource allocation strategies for controlling epidemics. J R Soc Interface 2023; 20:20230036. [PMID: 37194270 PMCID: PMC10189312 DOI: 10.1098/rsif.2023.0036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2023] [Accepted: 04/25/2023] [Indexed: 05/18/2023] Open
Abstract
Frequent emergence of communicable diseases is a major concern worldwide. Lack of sufficient resources to mitigate the disease burden makes the situation even more challenging for lower-income countries. Hence, strategy development for disease eradication and optimal management of the social and economic burden has garnered a lot of attention in recent years. In this context, we quantify the optimal fraction of resources that can be allocated to two major intervention measures, namely reduction of disease transmission and improvement of healthcare infrastructure. Our results demonstrate that the effectiveness of each of the interventions has a significant impact on the optimal resource allocation in both long-term disease dynamics and outbreak scenarios. The optimal allocation strategy for long-term dynamics exhibits non-monotonic behaviour with respect to the effectiveness of interventions, which differs from the more intuitive strategy recommended in the case of outbreaks. Further, our results indicate that the relationship between investment in interventions and the corresponding increase in patient recovery rate or decrease in disease transmission rate plays a decisive role in determining optimal strategies. Intervention programmes with decreasing returns promote the necessity for resource sharing. Our study provides fundamental insights into determining the best response strategy when controlling epidemics in resource-constrained situations.
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Affiliation(s)
- Biplab Maity
- Agricultural and Ecological Research Unit, Indian Statistical Institute, 203, B. T. Road, Kolkata 700108, India
| | - Swarnendu Banerjee
- Agricultural and Ecological Research Unit, Indian Statistical Institute, 203, B. T. Road, Kolkata 700108, India
- Copernicus Institute of Sustainable Development, Utrecht University, PO Box 80115, Utrecht 3508 TC, The Netherlands
| | - Abhishek Senapati
- Agricultural and Ecological Research Unit, Indian Statistical Institute, 203, B. T. Road, Kolkata 700108, India
- Center for Advanced Systems Understanding (CASUS), Untermarkt 20, Goerlitz 02826, Germany
| | - Joydev Chattopadhyay
- Agricultural and Ecological Research Unit, Indian Statistical Institute, 203, B. T. Road, Kolkata 700108, India
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Nowak SA, Nascimento de Lima P, Vardavas R. Optimal non-pharmaceutical pandemic response strategies depend critically on time horizons and costs. Sci Rep 2023; 13:2416. [PMID: 36765151 PMCID: PMC9912209 DOI: 10.1038/s41598-023-28936-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Accepted: 01/27/2023] [Indexed: 02/12/2023] Open
Abstract
The COVID-19 pandemic has called for swift action from local governments, which have instated non-pharmaceutical interventions (NPIs) to curb the spread of the disease. The swift implementation of social distancing policies has raised questions about the costs and benefits of strategies that either aim to keep cases as low as possible (suppression) or aim to reach herd immunity quickly (mitigation) to tackle the COVID-19 pandemic. While curbing COVID-19 required blunt instruments, it is unclear whether a less-transmissible and less-deadly emerging pathogen would justify the same response. This paper illuminates this question using a parsimonious transmission model by formulating the social distancing lives vs. livelihoods dilemma as a boundary value problem using calculus of variations. In this setup, society balances the costs and benefits of social distancing contingent on the costs of reducing transmission relative to the burden imposed by the disease. We consider both single-objective and multi-objective formulations of the problem. To the best of our knowledge, our approach is distinct in the sense that strategies emerge from the problem structure rather than being imposed a priori. We find that the relative time-horizon of the pandemic (i.e., the time it takes to develop effective vaccines and treatments) and the relative cost of social distancing influence the choice of the optimal policy. Unsurprisingly, we find that the appropriate policy response depends on these two factors. We discuss the conditions under which each policy archetype (suppression vs. mitigation) appears to be the most appropriate.
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Affiliation(s)
- Sarah A Nowak
- Larner College of Medicine at the University of Vermont, Burlington, VT, USA.
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14
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Wen Z, Yue T, Chen W, Jiang G, Hu B. Optimizing COVID-19 vaccine allocation considering the target population. Front Public Health 2023; 10:1015133. [PMID: 36684954 PMCID: PMC9853449 DOI: 10.3389/fpubh.2022.1015133] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Accepted: 12/14/2022] [Indexed: 01/07/2023] Open
Abstract
Vaccine allocation strategy for COVID-19 is an emerging and important issue that affects the efficiency and control of virus spread. In order to improve the fairness and efficiency of vaccine distribution, this paper studies the optimization of vaccine distribution under the condition of limited number of vaccines. We pay attention to the target population before distributing vaccines, including attitude toward the vaccination, priority groups for vaccination, and vaccination priority policy. Furthermore, we consider inventory and budget indexes to maximize the precise scheduling of vaccine resources. A mixed-integer programming model is developed for vaccine distribution considering the target population from the viewpoint of fairness and efficiency. Finally, a case study is provided to verify the model and provide insights for vaccine distribution.
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Affiliation(s)
- Zongliang Wen
- School of Public Health, Xuzhou Medical University, Xuzhou, China
- Affiliated Hospital of Xuzhou Medical University, Xuzhou, China
- School of Management, Xuzhou Medical University, Xuzhou, China
| | - Tingyu Yue
- School of Public Health, Xuzhou Medical University, Xuzhou, China
| | - Wei Chen
- School of Public Health, Xuzhou Medical University, Xuzhou, China
| | - Guanhua Jiang
- School of Public Health, Xuzhou Medical University, Xuzhou, China
| | - Bin Hu
- School of Public Health, Xuzhou Medical University, Xuzhou, China
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15
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Blasioli E, Mansouri B, Tamvada SS, Hassini E. Vaccine Allocation and Distribution: A Review with a Focus on Quantitative Methodologies and Application to Equity, Hesitancy, and COVID-19 Pandemic. OPERATIONS RESEARCH FORUM 2023; 4:27. [PMCID: PMC10028329 DOI: 10.1007/s43069-023-00194-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/24/2023]
Abstract
This review focuses on vaccine distribution and allocation in the context of the current COVID-19 pandemic. The implications discussed are in the areas of equity in vaccine distribution and allocation (at a national level as well as worldwide), vaccine hesitancy, game-theoretic modeling to guide decision-making and policy-making at a governmental level, distribution and allocation barriers (in particular in low-income countries), and operations research (OR) mathematical models to plan and execute vaccine distribution and allocation. To conduct this review, we adopt a novel methodology that consists of three phases. The first phase deploys a bibliometric analysis; the second phase concentrates on a network analysis; and the last phase proposes a refined literature review based on the results obtained by the previous two phases. The quantitative techniques utilized to conduct the first two phases allow describing the evolution of the research in this area and its potential ramifications in future. In conclusion, we underscore the significance of operations research (OR)/management science (MS) research in addressing numerous challenges and trade-offs connected to the current pandemic and its strategic impact in future research.
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Affiliation(s)
- Emanuele Blasioli
- grid.25073.330000 0004 1936 8227DeGroote School of Business, McMaster University, Hamilton, Canada
| | - Bahareh Mansouri
- grid.412362.00000 0004 1936 8219Sobey School of Business, Saint Mary’s University, Halifax, Canada
| | - Srinivas Subramanya Tamvada
- grid.29857.310000 0001 2097 4281Department of Industrial and Manufacturing Engineering, Pennsylvania State University, State College, PA, USA, PennsyIvania, USA
| | - Elkafi Hassini
- grid.25073.330000 0004 1936 8227DeGroote School of Business, McMaster University, Hamilton, Canada
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16
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Anderson TL, Nande A, Merenstein C, Raynor B, Oommen A, Kelly BJ, Levy MZ, Hill AL. Quantifying individual-level heterogeneity in infectiousness and susceptibility through household studies. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2022:2022.12.02.22281853. [PMID: 36523404 PMCID: PMC9753792 DOI: 10.1101/2022.12.02.22281853] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
The spread of SARS-CoV-2, like that of many other pathogens, is governed by heterogeneity. "Superspreading," or "over-dispersion," is an important factor in transmission, yet it is hard to quantify. Estimates from contact tracing data are prone to potential biases due to the increased likelihood of detecting large clusters of cases, and may reflect variation in contact behavior more than biological heterogeneity. In contrast, the average number of secondary infections per contact is routinely estimated from household surveys, and these studies can minimize biases by testing all members of a household. However, the models used to analyze household transmission data typically assume that infectiousness and susceptibility are the same for all individuals or vary only with predetermined traits such as age. Here we develop and apply a combined forward simulation and inference method to quantify the degree of inter-individual variation in both infectiousness and susceptibility from observations of the distribution of infections in household surveys. First, analyzing simulated data, we show our method can reliably ascertain the presence, type, and amount of these heterogeneities with data from a sufficiently large sample of households. We then analyze a collection of household studies of COVID-19 from diverse settings around the world, and find strong evidence for large heterogeneity in both the infectiousness and susceptibility of individuals. Our results also provide a framework to improve the design of studies to evaluate household interventions in the presence of realistic heterogeneity between individuals.
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Affiliation(s)
- Thayer L Anderson
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD 21218
| | - Anjalika Nande
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD 21218
| | - Carter Merenstein
- Department of Microbiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104
| | - Brinkley Raynor
- Department of Biostatistics, Epidemiology, & Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104
| | - Anisha Oommen
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD 21218
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218
| | - Brendan J Kelly
- Division of Infectious Diseases, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104
| | - Michael Z Levy
- Department of Biostatistics, Epidemiology, & Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104
| | - Alison L Hill
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD 21218
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218
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17
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Pandey A, Galvani AP. Role of immunity landscape in global risk assessment of re-emerging diseases. THE LANCET. INFECTIOUS DISEASES 2022; 23:385-386. [PMID: 36455592 PMCID: PMC9704845 DOI: 10.1016/s1473-3099(22)00756-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Accepted: 11/07/2022] [Indexed: 11/30/2022]
Affiliation(s)
- Abhishek Pandey
- Center for Infectious Disease Modeling and Analysis (CIDMA), Yale School of Public Health, New Haven, CT 06520, USA
| | - Alison P Galvani
- Center for Infectious Disease Modeling and Analysis (CIDMA), Yale School of Public Health, New Haven, CT 06520, USA.
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18
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Hong Z, Li Y, Gong Y, Chen W. A data-driven spatially-specific vaccine allocation framework for COVID-19. ANNALS OF OPERATIONS RESEARCH 2022:1-24. [PMID: 36467001 PMCID: PMC9684883 DOI: 10.1007/s10479-022-05037-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 10/18/2022] [Indexed: 05/30/2023]
Abstract
Although coronavirus disease 2019 (COVID-19) vaccines have been introduced, their allocation is a challenging problem. We propose a data-driven, spatially-specific vaccine allocation framework that aims to minimize the number of COVID-19-related deaths or infections. The framework combines a regional risk-level classification model solved by a self-organizing map neural network, a spatially-specific disease progression model, and a vaccine allocation model that considers vaccine production capacity. We use data obtained from Wuhan and 35 other cities in China from January 26 to February 11, 2020, to avoid the effects of intervention. Our results suggest that, in region-wise distribution of vaccines, they should be allocated first to the source region of the outbreak and then to the other regions in order of decreasing risk whether the outcome measure is the number of deaths or infections. This spatially-specific vaccine allocation policy significantly outperforms some current allocation policies.
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Affiliation(s)
- Zhaofu Hong
- School of Management, Northwestern Polytechnical University, Xi’an, People’s Republic of China
| | - Yingjie Li
- School of Civil Engineering, Central South University, Changsha, People’s Republic of China
- School of Management, Lanzhou University, Lanzhou, People’s Republic of China
| | | | - Wanying Chen
- School of Management and E-Business, Zhejiang Gongshang University, Hangzhou, People’s Republic of China
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19
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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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20
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Wu H, Wang K, Xu L. How can age-based vaccine allocation strategies be optimized? A multi-objective optimization framework. Front Public Health 2022; 10:934891. [PMID: 36159290 PMCID: PMC9493087 DOI: 10.3389/fpubh.2022.934891] [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: 05/03/2022] [Accepted: 08/11/2022] [Indexed: 01/25/2023] Open
Abstract
Human life is deeply influenced by infectious diseases. A vaccine, when available, is one of the most effective ways of controlling the spread of an epidemic. However, vaccine shortage and uncertain vaccine effectiveness in the early stage of vaccine production make vaccine allocation a critical issue. To tackle this issue, we propose a multi-objective framework to optimize the vaccine allocation strategy among different age groups during an epidemic under vaccine shortage in this study. Minimizing total disease onsets and total severe cases are the two objectives of this vaccine allocation optimization problem, and the multistage feature of vaccine allocation are considered in the framework. An improved Strength Pareto Evolutionary Algorithm (SPEA2) is used to solve the optimization problem. To evaluate the two objectives under different strategies, a deterministic age-stratified extended SEIR model is developed. In the proposed framework, different combinations of vaccine effectiveness and vaccine production capacity are investigated, and it is identified that for COVID-19 the optimal strategy is highly related to vaccine-related parameters. When the vaccine effectiveness is low, allocating most of vaccines to 0-19 age group or 65+ age group is a better choice under a low production capacity, while allocating most of vaccines to 20-49 age group or 50-64 age group is a better choice under a relatively high production capacity. When the vaccine effectiveness is high, a better strategy is to allocate vaccines to 65+ age group under a low production capacity, while to allocate vaccines to 20-49 age group under a relatively high production capacity.
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Affiliation(s)
- Hao Wu
- Department of Industrial Engineering, Tsinghua University, Beijing, China
| | - Kaibo Wang
- Vanke School of Public Health, Tsinghua University, Beijing, China,*Correspondence: Kaibo Wang
| | - Lei Xu
- Vanke School of Public Health, Tsinghua University, Beijing, China
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21
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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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22
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Miró Pina V, Nava-Trejo J, Tóbiás A, Nzabarushimana E, González-Casanova A, González-Casanova I. The role of connectivity on COVID-19 preventive approaches. PLoS One 2022; 17:e0273906. [PMID: 36048855 PMCID: PMC9436065 DOI: 10.1371/journal.pone.0273906] [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: 05/25/2021] [Accepted: 08/17/2022] [Indexed: 11/19/2022] Open
Abstract
Preventive and modeling approaches to address the COVID-19 pandemic have been primarily based on the age or occupation, and often disregard the importance of heterogeneity in population contact structure and individual connectivity. To address this gap, we developed models based on Erdős-Rényi and a power law degree distribution that first incorporate the role of heterogeneity and connectivity and then can be expanded to make assumptions about demographic characteristics. Results demonstrate that variations in the number of connections of individuals within a population modify the impact of public health interventions such as lockdown or vaccination approaches. We conclude that the most effective strategy will vary depending on the underlying contact structure of individuals within a population and on timing of the interventions.
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Affiliation(s)
- Verónica Miró Pina
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- National Autonomous University of Mexico (UNAM), Mexico, Mexico
| | | | | | | | | | - Inés González-Casanova
- Indiana University Bloomington, Bloomington, Indiana, United States of America
- * E-mail: (AG-C); (IG-C)
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23
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Cluster Analysis of US COVID-19 Infected States for Vaccine Distribution. Healthcare (Basel) 2022; 10:healthcare10071235. [PMID: 35885762 PMCID: PMC9323689 DOI: 10.3390/healthcare10071235] [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: 06/06/2022] [Revised: 06/30/2022] [Accepted: 07/01/2022] [Indexed: 11/16/2022] Open
Abstract
Since December 2019, COVID-19 has been raging worldwide. To prevent the spread of COVID-19 infection, many countries have proposed epidemic prevention policies and quickly administered vaccines, However, under facing a shortage of vaccines, the United States did not put forward effective epidemic prevention policies in time to prevent the infection from expanding, resulting in the epidemic in the United States becoming more and more serious. Through “The COVID Tracking Project”, this study collects medical indicators for each state in the United States from 2020 to 2021, and through feature selection, each state is clustered according to the epidemic’s severity. Furthermore, through the confusion matrix of the classifier to verify the accuracy of the cluster analysis, the study results show that the Cascade K-means cluster analysis has the highest accuracy. This study also labeled the three clusters of the cluster analysis results as high, medium, and low infection levels. Policymakers could more objectively decide which states should prioritize vaccine allocation in a vaccine shortage to prevent the epidemic from continuing to expand. It is hoped that if there is a similar epidemic in the future, relevant policymakers can use the analysis procedure of this study to determine the allocation of relevant medical resources for epidemic prevention according to the severity of infection in each state to prevent the spread of infection.
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24
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Lemaitre JC, Pasetto D, Zanon M, Bertuzzo E, Mari L, Miccoli S, Casagrandi R, Gatto M, Rinaldo A. Optimal control of the spatial allocation of COVID-19 vaccines: Italy as a case study. PLoS Comput Biol 2022; 18:e1010237. [PMID: 35802755 PMCID: PMC9299324 DOI: 10.1371/journal.pcbi.1010237] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Revised: 07/20/2022] [Accepted: 05/23/2022] [Indexed: 12/16/2022] Open
Abstract
While campaigns of vaccination against SARS-CoV-2 are underway across the world, communities face the challenge of a fair and effective distribution of a limited supply of doses. Current vaccine allocation strategies are based on criteria such as age or risk. In the light of strong spatial heterogeneities in disease history and transmission, we explore spatial allocation strategies as a complement to existing approaches. Given the practical constraints and complex epidemiological dynamics, designing effective vaccination strategies at a country scale is an intricate task. We propose a novel optimal control framework to derive the best possible vaccine allocation for given disease transmission projections and constraints on vaccine supply and distribution logistics. As a proof-of-concept, we couple our framework with an existing spatially explicit compartmental COVID-19 model tailored to the Italian geographic and epidemiological context. We optimize the vaccine allocation on scenarios of unfolding disease transmission across the 107 provinces of Italy, from January to April 2021. For each scenario, the optimal solution significantly outperforms alternative strategies that prioritize provinces based on incidence, population distribution, or prevalence of susceptibles. Our results suggest that the complex interplay between the mobility network and the spatial heterogeneities implies highly non-trivial prioritization strategies for effective vaccination campaigns. Our work demonstrates the potential of optimal control for complex and heterogeneous epidemiological landscapes at country, and possibly global, scales.
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Affiliation(s)
- Joseph Chadi Lemaitre
- Laboratory of Ecohydrology, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Dipartimento di Scienze Ambientali, Informatica e Statistica, Università Ca’ Foscari Venezia, Venezia-Mestre, Italy
| | - Damiano Pasetto
- Dipartimento di Scienze Ambientali, Informatica e Statistica, Università Ca’ Foscari Venezia, Venezia-Mestre, Italy
| | | | - Enrico Bertuzzo
- Dipartimento di Scienze Ambientali, Informatica e Statistica, Università Ca’ Foscari Venezia, Venezia-Mestre, Italy
| | - Lorenzo Mari
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milan, Italy
| | - Stefano Miccoli
- Dipartimento di Meccanica, Politecnico di Milano, Milan, Italy
| | - Renato Casagrandi
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milan, Italy
| | - Marino Gatto
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milan, Italy
| | - Andrea Rinaldo
- Laboratory of Ecohydrology, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Dipartimento ICEA, Università di Padova, Padova, Italy
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25
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Gilani H, Sahebi H. A data-driven robust optimization model by cutting hyperplanes on vaccine access uncertainty in COVID-19 vaccine supply chain. OMEGA 2022; 110:102637. [PMID: 35291647 PMCID: PMC8913040 DOI: 10.1016/j.omega.2022.102637] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/28/2021] [Accepted: 03/08/2022] [Indexed: 05/22/2023]
Abstract
The worldwide COVID-19 pandemic sparked such a wave of concern that made access to vaccines more necessary than before. As the vaccine inaccessibility in developing countries has made pandemic eradication more difficult, this study has presented a mathematical model of a sustainable SC for the COVID-19 vaccine that covers the economic, environmental and social aspects and provides vaccine both domestically and internationally. It has also proposed a robust data-driven model based on a polyhedral uncertainty set to address the unjust worldwide vaccine distribution as an uncertain parameter. It is acceptably robust and is also less conservative than its classical counterparts. For validation, the model has been implemented in a real case in Iran, and the results have shown that it is 21% less conservative than its classical rivals (Box and Polyhedral convex uncertainty sets) in facing the uncertain parameter. As a result, the model proposes the construction of two domestic vaccine production centers, including Pasteur Institute and Razi Institute, and five foreign distributors in Tehran, Isfahan, Ahvaz, Kermanshah, and Bandar Abbas strategically.
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Affiliation(s)
- Hani Gilani
- School of Industrial Engineering, Iran University of Science & Technology, Tehran, Iran
| | - Hadi Sahebi
- School of Industrial Engineering, Iran University of Science & Technology, Tehran, Iran
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26
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Miranda MNS, Pingarilho M, Pimentel V, Torneri A, Seabra SG, Libin PJK, Abecasis AB. A Tale of Three Recent Pandemics: Influenza, HIV and SARS-CoV-2. Front Microbiol 2022; 13:889643. [PMID: 35722303 PMCID: PMC9201468 DOI: 10.3389/fmicb.2022.889643] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Accepted: 05/06/2022] [Indexed: 11/13/2022] Open
Abstract
Emerging infectious diseases are one of the main threats to public health, with the potential to cause a pandemic when the infectious agent manages to spread globally. The first major pandemic to appear in the 20th century was the influenza pandemic of 1918, caused by the influenza A H1N1 strain that is characterized by a high fatality rate. Another major pandemic was caused by the human immunodeficiency virus (HIV), that started early in the 20th century and remained undetected until 1981. The ongoing HIV pandemic demonstrated a high mortality and morbidity rate, with discrepant impacts in different regions around the globe. The most recent major pandemic event, is the ongoing pandemic of COVID-19, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which has caused over 5.7 million deaths since its emergence, 2 years ago. The aim of this work is to highlight the main determinants of the emergence, epidemic response and available countermeasures of these three pandemics, as we argue that such knowledge is paramount to prepare for the next pandemic. We analyse these pandemics’ historical and epidemiological contexts and the determinants of their emergence. Furthermore, we compare pharmaceutical and non-pharmaceutical interventions that have been used to slow down these three pandemics and zoom in on the technological advances that were made in the progress. Finally, we discuss the evolution of epidemiological modelling, that has become an essential tool to support public health policy making and discuss it in the context of these three pandemics. While these pandemics are caused by distinct viruses, that ignited in different time periods and in different regions of the globe, our work shows that many of the determinants of their emergence and countermeasures used to halt transmission were common. Therefore, it is important to further improve and optimize such approaches and adapt it to future threatening emerging infectious diseases.
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Affiliation(s)
- Mafalda N S Miranda
- Global Health and Tropical Medicine (GHTM), Instituto de Higiene e Medicina Tropical/Universidade Nova de Lisboa (IHMT/UNL), Lisboa, Portugal
| | - Marta Pingarilho
- Global Health and Tropical Medicine (GHTM), Instituto de Higiene e Medicina Tropical/Universidade Nova de Lisboa (IHMT/UNL), Lisboa, Portugal
| | - Victor Pimentel
- Global Health and Tropical Medicine (GHTM), Instituto de Higiene e Medicina Tropical/Universidade Nova de Lisboa (IHMT/UNL), Lisboa, Portugal
| | - Andrea Torneri
- Artificial Intelligence Lab, Department of Computer Science, Vrije Universiteit Brussel, Brussels, Belgium
| | - Sofia G Seabra
- Global Health and Tropical Medicine (GHTM), Instituto de Higiene e Medicina Tropical/Universidade Nova de Lisboa (IHMT/UNL), Lisboa, Portugal
| | - Pieter J K Libin
- Artificial Intelligence Lab, Department of Computer Science, Vrije Universiteit Brussel, Brussels, Belgium.,Interuniversity Institute of Biostatistics and Statistical Bioinformatics, Data Science Institute, Hasselt University, Hasselt, Belgium.,Department of Microbiology and Immunology, Rega Institute for Medical Research, KU Leuven, University of Leuven, Leuven, Belgium
| | - Ana B Abecasis
- Global Health and Tropical Medicine (GHTM), Instituto de Higiene e Medicina Tropical/Universidade Nova de Lisboa (IHMT/UNL), Lisboa, Portugal
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Lerch A, Ten Bosch QA, L'Azou Jackson M, Bettis AA, Bernuzzi M, Murphy GAV, Tran QM, Huber JH, Siraj AS, Bron GM, Elliott M, Hartlage CS, Koh S, Strimbu K, Walters M, Perkins TA, Moore SM. Projecting vaccine demand and impact for emerging zoonotic pathogens. BMC Med 2022; 20:202. [PMID: 35705986 PMCID: PMC9200440 DOI: 10.1186/s12916-022-02405-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Accepted: 05/14/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Despite large outbreaks in humans seeming improbable for a number of zoonotic pathogens, several pose a concern due to their epidemiological characteristics and evolutionary potential. To enable effective responses to these pathogens in the event that they undergo future emergence, the Coalition for Epidemic Preparedness Innovations is advancing the development of vaccines for several pathogens prioritized by the World Health Organization. A major challenge in this pursuit is anticipating demand for a vaccine stockpile to support outbreak response. METHODS We developed a modeling framework for outbreak response for emerging zoonoses under three reactive vaccination strategies to assess sustainable vaccine manufacturing needs, vaccine stockpile requirements, and the potential impact of the outbreak response. This framework incorporates geographically variable zoonotic spillover rates, human-to-human transmission, and the implementation of reactive vaccination campaigns in response to disease outbreaks. As proof of concept, we applied the framework to four priority pathogens: Lassa virus, Nipah virus, MERS coronavirus, and Rift Valley virus. RESULTS Annual vaccine regimen requirements for a population-wide strategy ranged from > 670,000 (95% prediction interval 0-3,630,000) regimens for Lassa virus to 1,190,000 (95% PrI 0-8,480,000) regimens for Rift Valley fever virus, while the regimens required for ring vaccination or targeting healthcare workers (HCWs) were several orders of magnitude lower (between 1/25 and 1/700) than those required by a population-wide strategy. For each pathogen and vaccination strategy, reactive vaccination typically prevented fewer than 10% of cases, because of their presently low R0 values. Targeting HCWs had a higher per-regimen impact than population-wide vaccination. CONCLUSIONS Our framework provides a flexible methodology for estimating vaccine stockpile needs and the geographic distribution of demand under a range of outbreak response scenarios. Uncertainties in our model estimates highlight several knowledge gaps that need to be addressed to target vulnerable populations more accurately. These include surveillance gaps that mask the true geographic distribution of each pathogen, details of key routes of spillover from animal reservoirs to humans, and the role of human-to-human transmission outside of healthcare settings. In addition, our estimates are based on the current epidemiology of each pathogen, but pathogen evolution could alter vaccine stockpile requirements.
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Affiliation(s)
- Anita Lerch
- Department of Biological Sciences and Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN, USA
| | - Quirine A Ten Bosch
- Quantitative Veterinary Epidemiology, Wageningen University and Research, Wageningen, The Netherlands
| | | | - Alison A Bettis
- Coalition for Epidemic Preparedness Innovations (CEPI), Oslo, Norway
| | - Mauro Bernuzzi
- Coalition for Epidemic Preparedness Innovations (CEPI), London, UK
| | | | - Quan M Tran
- Department of Biological Sciences and Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN, USA
| | - John H Huber
- Department of Biological Sciences and Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN, USA
| | - Amir S Siraj
- Department of Biological Sciences and Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN, USA
| | - Gebbiena M Bron
- Quantitative Veterinary Epidemiology, Wageningen University and Research, Wageningen, The Netherlands
| | - Margaret Elliott
- Department of Biological Sciences and Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN, USA
| | - Carson S Hartlage
- Department of Biological Sciences and Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN, USA
| | - Sojung Koh
- Department of Biological Sciences and Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN, USA
| | - Kathyrn Strimbu
- Department of Biological Sciences and Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN, USA
| | - Magdalene Walters
- Department of Biological Sciences and Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN, USA
| | - T Alex Perkins
- Department of Biological Sciences and Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN, USA.
| | - Sean M Moore
- Department of Biological Sciences and Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN, USA.
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Zhang H, Yin L, Mao L, Mei S, Chen T, Liu K, Feng S. Combinational Recommendation of Vaccinations, Mask-Wearing, and Home-Quarantine to Control Influenza in Megacities: An Agent-Based Modeling Study With Large-Scale Trajectory Data. Front Public Health 2022; 10:883624. [PMID: 35719665 PMCID: PMC9204335 DOI: 10.3389/fpubh.2022.883624] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Accepted: 03/31/2022] [Indexed: 11/15/2022] Open
Abstract
The outbreak of COVID-19 stimulated a new round of discussion on how to deal with respiratory infectious diseases. Influenza viruses have led to several pandemics worldwide. The spatiotemporal characteristics of influenza transmission in modern cities, especially megacities, are not well-known, which increases the difficulty of influenza prevention and control for populous urban areas. For a long time, influenza prevention and control measures have focused on vaccination of the elderly and children, and school closure. Since the outbreak of COVID-19, the public's awareness of measures such as vaccinations, mask-wearing, and home-quarantine has generally increased in some regions of the world. To control the influenza epidemic and reduce the proportion of infected people with high mortality, the combination of these three measures needs quantitative evaluation based on the spatiotemporal transmission characteristics of influenza in megacities. Given that the agent-based model with both demographic attributes and fine-grained mobility is a key planning tool in deploying intervention strategies, this study proposes a spatially explicit agent-based influenza model for assessing and recommending the combinations of influenza control measures. This study considers Shenzhen city, China as the research area. First, a spatially explicit agent-based influenza transmission model was developed by integrating large-scale individual trajectory data and human response behavior. Then, the model was evaluated across multiple intra-urban spatial scales based on confirmed influenza cases. Finally, the model was used to evaluate the combined effects of the three interventions (V: vaccinations, M: mask-wearing, and Q: home-quarantining) under different compliance rates, and their optimal combinations for given control objectives were recommended. This study reveals that adults were a high-risk population with a low reporting rate, and children formed the lowest infected proportion and had the highest reporting rate in Shenzhen. In addition, this study systematically recommended different combinations of vaccinations, mask-wearing, and home-quarantine with different compliance rates for different control objectives to deal with the influenza epidemic. For example, the "V45%-M60%-Q20%" strategy can maintain the infection percentage below 5%, while the "V20%-M60%-Q20%" strategy can maintain the infection percentage below 15%. The model and policy recommendations from this study provide a tool and intervention reference for influenza epidemic management in the post-COVID-19 era.
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Affiliation(s)
- Hao Zhang
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Ling Yin
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Liang Mao
- Department of Geography, University of Florida, Gainesville, FL, United States
| | - Shujiang Mei
- Shenzhen Center for Disease Control and Prevention, Shenzhen, China
| | - Tianmu Chen
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, China
| | - Kang Liu
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
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Bubar KM, Middleton CE, Bjorkman KK, Parker R, Larremore DB. SARS-CoV-2 transmission and impacts of unvaccinated-only screening in populations of mixed vaccination status. Nat Commun 2022; 13:2777. [PMID: 35589681 PMCID: PMC9120147 DOI: 10.1038/s41467-022-30144-7] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Accepted: 04/19/2022] [Indexed: 11/17/2022] Open
Abstract
Screening programs that test only the unvaccinated population have been proposed and implemented to mitigate SARS-CoV-2 spread, implicitly assuming that the unvaccinated population drives transmission. To evaluate this premise and quantify the impact of unvaccinated-only screening programs, we introduce a model for SARS-CoV-2 transmission through which we explore a range of transmission rates, vaccine effectiveness scenarios, rates of prior infection, and screening programs. We find that, as vaccination rates increase, the proportion of transmission driven by the unvaccinated population decreases, such that most community spread is driven by vaccine-breakthrough infections once vaccine coverage exceeds 55% (omicron) or 80% (delta), points which shift lower as vaccine effectiveness wanes. Thus, we show that as vaccination rates increase, the transmission reductions associated with unvaccinated-only screening decline, identifying three distinct categories of impact on infections and hospitalizations. More broadly, these results demonstrate that effective unvaccinated-only screening depends on population immunity, vaccination rates, and variant.
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Affiliation(s)
- Kate M Bubar
- Department of Applied Mathematics, University of Colorado Boulder, Boulder, CO, USA
| | - Casey E Middleton
- Department of Computer Science, University of Colorado Boulder, Boulder, CO, USA
| | - Kristen K Bjorkman
- BioFrontiers Institute, University of Colorado Boulder, Boulder, CO, USA
| | - Roy Parker
- BioFrontiers Institute, University of Colorado Boulder, Boulder, CO, USA
- Department of Biochemistry, University of Colorado Boulder, Boulder, CO, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
| | - Daniel B Larremore
- Department of Computer Science, University of Colorado Boulder, Boulder, CO, USA.
- BioFrontiers Institute, University of Colorado Boulder, Boulder, CO, USA.
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Mauskopf J, Blake L, Eiden A, Roberts C, Hu T, Nyaku M. Economic Evaluation of Vaccination Programs: A Guide for Selecting Modeling Approaches. VALUE IN HEALTH : THE JOURNAL OF THE INTERNATIONAL SOCIETY FOR PHARMACOECONOMICS AND OUTCOMES RESEARCH 2022; 25:810-823. [PMID: 35221205 DOI: 10.1016/j.jval.2021.10.017] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/14/2021] [Revised: 09/08/2021] [Accepted: 10/20/2021] [Indexed: 06/14/2023]
Abstract
OBJECTIVES Illustrate 3 economic evaluation methods whose value measures may be useful to decision makers considering vaccination programs. METHODS Keyword searches identified example publications of cost-effectiveness analysis (CEA), fiscal health modeling (FHM), and constrained optimization (CO) for economic evaluation of a vaccination program in countries where at least 2 of the methods had been used. We examined the extent to which different value measures may be useful for decision makers considering adoption of a new vaccination program. With these findings, we created a guide for selecting modeling approaches illustrating the decision-maker contexts and policy objectives for which each method may be useful. RESULTS We identified 8 countries with published evaluations for vaccination programs using >1 method for 4 infections: influenza, human papilloma virus, rotavirus, and malaria. CEA studies targeted health system decision makers using a threshold to determine the efficiency of a new vaccination program. FHM studies targeted public sector spending decision makers estimating lifetime changes in government tax revenue net of transfer payments. CO studies targeted decision makers selecting from a mix of options for preventing an infectious disease within budget and feasibility constraints. Cost and utility inputs, epidemiologic models, comparators, and constraints varied by modeling method. CONCLUSIONS Although CEAs measures of incremental cost-effectiveness ratios are critical for understanding vaccination program efficiency for all decision makers determining access and reimbursement, FHMs provide measures of the program's impact on public spending for government officials, and COs provide measures of the optimal mix of all prevention interventions for public health officials.
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Affiliation(s)
- Josephine Mauskopf
- Department of Health Economics, RTI Health Solutions, Research Triangle Park, NC, USA
| | - Leslie Blake
- Department of Health Economics, RTI Health Solutions, Research Triangle Park, NC, USA
| | - Amanda Eiden
- Center for Observation and Real-World Evidence, Merck & Co, Inc, Kenilworth, NJ, USA
| | - Craig Roberts
- Center for Observation and Real-World Evidence, Merck & Co, Inc, Kenilworth, NJ, USA
| | - Tianyan Hu
- Center for Observation and Real-World Evidence, Merck & Co, Inc, Kenilworth, NJ, USA
| | - Mawuli Nyaku
- Center for Observation and Real-World Evidence, Merck & Co, Inc, Kenilworth, NJ, USA.
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31
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Fadaki M, Abareshi A, Far SM, Lee PTW. Multi-period vaccine allocation model in a pandemic: A case study of COVID-19 in Australia. TRANSPORTATION RESEARCH. PART E, LOGISTICS AND TRANSPORTATION REVIEW 2022; 161:102689. [PMID: 35431604 PMCID: PMC8995313 DOI: 10.1016/j.tre.2022.102689] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Revised: 01/21/2022] [Accepted: 03/24/2022] [Indexed: 05/26/2023]
Abstract
While the swift development and production of a COVID-19 vaccine has been a remarkable success, it is equally crucial to ensure that the vaccine is allocated and distributed in a timely and efficient manner. Prior research on pandemic supply chain has not fully incorporated the underlying factors and constraints in designing a vaccine allocation model. This study proposes an innovative vaccine allocation model to contain the spread of infectious diseases incorporating key contributing factors to the risk of uninoculated people including susceptibility rate and exposure risk. Analyses of the data collected from the state of Victoria in Australia show that a vaccine allocation model can deliver a superior performance in minimizing the risk of unvaccinated people when a multi-period approach is employed and augmenting operational mechanisms including transshipment between medical centers, capacity sharing, and mobile units being integrated into the vaccine allocation model.
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Affiliation(s)
- Masih Fadaki
- Department of Supply Chain and Logistics Management, RMIT University, Melbourne, VIC 3000, Australia
| | - Ahmad Abareshi
- Department of Supply Chain and Logistics Management, RMIT University, Melbourne, VIC 3000, Australia
| | - Shaghayegh Maleki Far
- Department of Supply Chain and Logistics Management, RMIT University, Melbourne, VIC 3000, Australia
| | - Paul Tae-Woo Lee
- Maritime Logistics and Free Trade Islands Research Center, Ocean College, Zhejiang University, Zhoushan, China
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32
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Jarumaneeroj P, Dusadeerungsikul PO, Chotivanich T, Nopsopon T, Pongpirul K. An epidemiology-based model for the operational allocation of COVID-19 vaccines: A case study of Thailand. COMPUTERS & INDUSTRIAL ENGINEERING 2022; 167:108031. [PMID: 35228772 PMCID: PMC8865938 DOI: 10.1016/j.cie.2022.108031] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Revised: 02/01/2022] [Accepted: 02/18/2022] [Indexed: 05/25/2023]
Abstract
This paper addresses a framework for the operational allocation and administration of COVID-19 vaccines in Thailand, based on both COVID-19 transmission dynamics and other vital operational restrictions that might affect the effectiveness of vaccination strategies in the early stage of vaccine rollout. In this framework, the SIQRV model is first developed and later combined with the COVID-19 Vaccine Allocation Problem (CVAP) to determine the optimal allocation/administration strategies that minimize total weighted strain on the whole healthcare system. According to Thailand's second pandemic wave data (17th January 2021, to 15th February 2021), we find that the epicenter-based strategy is surprisingly the worst allocation strategy, due largely to the negligence of provincial demographics, vaccine efficacy, and overall transmission dynamics that lead to higher number of infectious individuals. We also find that early vaccination seems to significantly contribute to the reduction in the number of infectious individuals, whose effects tend to increase with more vaccine supply. With these insights, healthcare policy-makers should therefore focus not only on the procurement of COVID-19 vaccines at strategic levels but also on the allocation and administration of such vaccines at operational levels for the best of their limited vaccine supply.
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Affiliation(s)
- Pisit Jarumaneeroj
- Department of Industrial Engineering, Chulalongkorn University, Thailand
- Regional Centre for Manufacturing Systems Engineering, Chulalongkorn University, Thailand
| | | | - Tharin Chotivanich
- Department of Industrial Engineering, Chulalongkorn University, Thailand
| | - Tanawin Nopsopon
- Department of Preventive and Social Medicine, Chulalongkorn University, Thailand
| | - Krit Pongpirul
- Department of Preventive and Social Medicine, Chulalongkorn University, Thailand
- Bumrungrad International Hospital, Bangkok, Thailand
- Department of International Health, Johns Hopkins Bloomberg School of Public Health, USA
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33
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Nganmeni Z, Pongou R, Tchantcho B, Tondji J. Vaccine and inclusion. JOURNAL OF PUBLIC ECONOMIC THEORY 2022; 24:JPET12590. [PMID: 35600414 PMCID: PMC9115285 DOI: 10.1111/jpet.12590] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Revised: 03/18/2022] [Accepted: 03/26/2022] [Indexed: 05/12/2023]
Abstract
In majoritarian democracies, popular policies may not be inclusive, and inclusive policies may not be popular. This dilemma raises the crucial question of when it is possible to design a policy that is both inclusive and popular. We address this question in the context of vaccine allocation in a polarized economy facing a pandemic. In such an economy, individuals are organized around distinct networks and groups and have in-group preferences. We provide a complete characterization of the set of inclusive and popular vaccine allocations. The findings imply that the number of vaccine doses necessary to generate an inclusive and popular vaccine allocation is greater than the one necessary to obtain an allocation that is only popular. The analysis further reveals that it is always possible to design the decision-making rule of the economy to implement an inclusive and popular vaccine allocation. Under such a rule, the composition of any group endowed with the veto power should necessarily reflect the diversity of the society.
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Affiliation(s)
- Zéphirin Nganmeni
- UFR AES ‐ Economics and Management, Dionysian Economics Laboratory (L.E.D.)Université Paris 8Saint‐DenisFrance
| | - Roland Pongou
- Department of EconomicsUniversity of OttawaOttawaOntarioCanada
- Harvard Center for African StudiesHarvard UniversityCambridgeMassachusettsUnited States
| | - Bertrand Tchantcho
- Department of MathematicsAdvanced Teachers' Training College, University of Yaounde IYaoundeCameroon
| | - Jean‐Baptiste Tondji
- Department of EconomicsThe University of Texas Rio Grande Valley, Robert C. Vackar College of Business and EntrepreneurshipEdinburgTexasUSA
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Leung T, Eaton J, Matrajt L. Optimizing one-dose and two-dose cholera vaccine allocation in outbreak settings: A modeling study. PLoS Negl Trop Dis 2022; 16:e0010358. [PMID: 35442958 PMCID: PMC9060364 DOI: 10.1371/journal.pntd.0010358] [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: 12/01/2021] [Revised: 05/02/2022] [Accepted: 03/25/2022] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND A global stockpile of oral cholera vaccine (OCV) was established in 2013 for use in outbreak response and are licensed as two-dose regimens. Vaccine availability, however, remains limited. Previous studies have found that a single dose of OCV may provide substantial protection against cholera. METHODS Using a mathematical model with two age groups paired with optimization algorithms, we determine the optimal vaccination strategy with one and two doses of vaccine to minimize cumulative overall infections, symptomatic infections, and deaths. We explore counterfactual vaccination scenarios in three distinct settings: Maela, the largest refugee camp in Thailand, with high in- and out-migration; N'Djamena, Chad, a densely populated region; and Haiti, where departments are connected by rivers and roads. RESULTS Over the short term under limited vaccine supply, the optimal strategies for all objectives prioritize one dose to the older age group (over five years old), irrespective of setting and level of vaccination coverage. As more vaccine becomes available, it is optimal to administer a second dose for long-term protection. With enough vaccine to cover the whole population with one dose, the optimal strategies can avert up to 30% to 90% of deaths and 36% to 92% of symptomatic infections across the three settings over one year. The one-dose optimal strategies can avert 1.2 to 1.8 times as many cases and deaths compared to the standard two-dose strategy. CONCLUSIONS In an outbreak setting, speedy vaccination campaigns with a single dose of OCV is likely to avert more cases and deaths than a two-dose pro-rata campaign under a limited vaccine supply.
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Affiliation(s)
- Tiffany Leung
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
| | - Julia Eaton
- School of Interdisciplinary Arts and Sciences, University of Washington, Tacoma, Washington, United States of America
| | - Laura Matrajt
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
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35
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Bubar KM, Middleton CE, Bjorkman KK, Parker R, Larremore DB. SARS-CoV-2 Transmission and Impacts of Unvaccinated-Only Screening in Populations of Mixed Vaccination Status. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2022:2021.10.19.21265231. [PMID: 34909778 PMCID: PMC8669845 DOI: 10.1101/2021.10.19.21265231] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Community testing programs focused on the unvaccinated population are being enacted in populations with mixed vaccination status to mitigate SARS-CoV-2 spread. Presumably, these policies assume that the unvaccinated are driving transmission, though it is not well understood how viral spread occurs in mixed-status populations. Here, we analyze a model of transmission in which a variable fraction of the population is vaccinated, with unvaccinated individuals proactively screened for infection. By exploring a range of transmission rates, vaccine effectiveness (VE) scenarios, and rates of prior infection, this analysis reveals principles of viral spread in communities of mixed vaccination status, with implications for screening policies. As vaccination rates increase, the proportion of transmission driven by the unvaccinated population decreases, such that most community spread is driven by breakthrough infections once vaccine coverage exceeds 55% (omicron) or 80% (delta), with additional variation dependent on waning or boosted VE. More broadly, the potential impacts of unvaccinated-only screening fall into three distinct parameter regions: (I) "flattening the curve" with little impact on cumulative infections, (II) effectively suppressing transmission, and (III) negligible impact because herd immunity is reached without screening. By evaluating a wide range of scenarios, this work finds broadly that effective mitigation of SARS-CoV-2 transmission by unvaccinated-only screening is highly dependent on vaccination rate, population-level immunity, screening compliance, and vaccine effectiveness against the current variant.
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Affiliation(s)
- Kate M Bubar
- Department of Applied Mathematics, University of Colorado Boulder
| | | | | | - Roy Parker
- Department of Biochemistry, University of Colorado Boulder
- BioFrontiers Institute, University of Colorado Boulder
- Howard Hughes Medical Institute
| | - Daniel B Larremore
- Department of Computer Science, University of Colorado Boulder
- BioFrontiers Institute, University of Colorado Boulder
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Yadav AK, Kumar D. A fuzzy decision framework of lean-agile-green (LAG) practices for sustainable vaccine supply chain. INTERNATIONAL JOURNAL OF PRODUCTIVITY AND PERFORMANCE MANAGEMENT 2022. [DOI: 10.1108/ijppm-10-2021-0590] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
PurposeThe already-strained vaccine supply chain (VSC) of the expanded program for immunization (EPI) require a more robust and structured distribution network for pandemic/outbreak vaccination due to huge volume demand and time constraint. In this paper, a lean-agile-green (LAG) practices approach is proposed to improve the operational, economic and environmental efficiency of the VSC.Design/methodology/approachA fuzzy decision framework of importance performance analysis (IPA)–analytical hierarchy process (AHP)–technique for order for preference by similarity in ideal solution (TOPSIS) has been presented in this paper to prioritize the LAG practices on the basis of the influence on performance indicators. Sensitivity analysis is carried out to check the robustness of the presented model.FindingsThe derived result indicates that sustainable packaging, coordination among supply chain stakeholders and cold chain technology improvement are among the top practices affecting most of the performance parameters of VSC. The sensitivity analysis reveals that the priority of practices is highly dependent on the weightage of performance indicators.Practical implicationsThis study's finding will help policymakers reframe strategies for sustainable VSC (SVSC) by including new management practices that can handle regular immunization programs as well as emergency mass vaccination.Originality/valueTo the best of the authors' knowledge, this is the first study that proposes the LAG framework for SVSC. The IPA–Fuzzy AHP (FAHP)–Fuzyy TOPSIS (FTOPSIS) is also a novel combination in decision-making.
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Bertsimas D, Digalakis Jr V, Jacquillat A, Li ML, Previero A. Where to locate COVID-19 mass vaccination facilities? NAVAL RESEARCH LOGISTICS 2022; 69:179-200. [PMID: 38607841 PMCID: PMC8441649 DOI: 10.1002/nav.22007] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/21/2021] [Revised: 04/26/2021] [Accepted: 04/27/2021] [Indexed: 05/12/2023]
Abstract
The outbreak of COVID-19 led to a record-breaking race to develop a vaccine. However, the limited vaccine capacity creates another massive challenge: how to distribute vaccines to mitigate the near-end impact of the pandemic? In the United States in particular, the new Biden administration is launching mass vaccination sites across the country, raising the obvious question of where to locate these clinics to maximize the effectiveness of the vaccination campaign. This paper tackles this question with a novel data-driven approach to optimize COVID-19 vaccine distribution. We first augment a state-of-the-art epidemiological model, called DELPHI, to capture the effects of vaccinations and the variability in mortality rates across age groups. We then integrate this predictive model into a prescriptive model to optimize the location of vaccination sites and subsequent vaccine allocation. The model is formulated as a bilinear, nonconvex optimization model. To solve it, we propose a coordinate descent algorithm that iterates between optimizing vaccine distribution and simulating the dynamics of the pandemic. As compared to benchmarks based on demographic and epidemiological information, the proposed optimization approach increases the effectiveness of the vaccination campaign by an estimated 20%, saving an extra 4000 extra lives in the United States over a 3-month period. The proposed solution achieves critical fairness objectives-by reducing the death toll of the pandemic in several states without hurting others-and is highly robust to uncertainties and forecast errors-by achieving similar benefits under a vast range of perturbations.
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Affiliation(s)
- Dimitris Bertsimas
- Sloan School of Management and Operations Research CenterMassachusetts Institute of TechnologyCambridgeMassachusettsUSA
| | - Vassilis Digalakis Jr
- Sloan School of Management and Operations Research CenterMassachusetts Institute of TechnologyCambridgeMassachusettsUSA
| | - Alexander Jacquillat
- Sloan School of Management and Operations Research CenterMassachusetts Institute of TechnologyCambridgeMassachusettsUSA
| | - Michael Lingzhi Li
- Sloan School of Management and Operations Research CenterMassachusetts Institute of TechnologyCambridgeMassachusettsUSA
| | - Alessandro Previero
- Sloan School of Management and Operations Research CenterMassachusetts Institute of TechnologyCambridgeMassachusettsUSA
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38
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Shahparvari S, Hassanizadeh B, Mohammadi A, Kiani B, Lau KH, Chhetri P, Abbasi B. A decision support system for prioritised COVID-19 two-dosage vaccination allocation and distribution. TRANSPORTATION RESEARCH. PART E, LOGISTICS AND TRANSPORTATION REVIEW 2022; 159:102598. [PMID: 35185357 PMCID: PMC8843424 DOI: 10.1016/j.tre.2021.102598] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/21/2021] [Revised: 08/23/2021] [Accepted: 12/23/2021] [Indexed: 05/12/2023]
Abstract
This study proposes a decision support system (DSS) that integrates GIS, analytics, and simulation methods to help develop a priority-based distribution of COVID-19 vaccines in a large urban setting. The methodology applies novel hierarchical heuristic-simulation procedures to create a holistic algorithm for prioritising the process of demand allocation and optimising vaccine distribution. The Melbourne metropolitan area in Australia with a population of over five million is used as a case study. Three vaccine supply scenarios, namely limited, excessive, and disruption, were formulated to operationalise a two-dose vaccination program. Vaccine distribution with hard constraints were simulated and then further validated with sensitivity analyses. The results show that vaccines can be prioritised to society's most vulnerable segments and distributed using the current logistics network with 10 vehicles. Compared with other vaccine distribution plans with no prioritisation, such as equal allocation of vaccines to local government areas based on population size or one on a first-come-first-serve basis, the plans generated by the proposed DSS ensure prioritised vaccination of the most needed and vulnerable population. The aim is to curb the spread of the infection and reduce mortality rate more effectively. They also achieve vaccination of the entire population with less logistical resources required. As such, this study contributes to knowledge and practice in pandemic vaccine distribution and enables governments to make real-time decisions and adjustments in daily distribution plans. In this way any unforeseen disruptions in the vaccine supply chain can be coped with.
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Affiliation(s)
- Shahrooz Shahparvari
- School of Accounting Information Systems & Supply Chain, RMIT University, Melbourne, VIC, Australia
| | | | - Alireza Mohammadi
- Department of Geography & Planning, Faculty of Social Sciences, University of Mohaghegh Ardabili, Ardabil, Iran
| | - Behzad Kiani
- Department of Medical Informatics, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Kwok Hung Lau
- School of Accounting Information Systems & Supply Chain, RMIT University, Melbourne, VIC, Australia
| | - Prem Chhetri
- School of Accounting Information Systems & Supply Chain, RMIT University, Melbourne, VIC, Australia
| | - Babak Abbasi
- School of Accounting Information Systems & Supply Chain, RMIT University, Melbourne, VIC, Australia
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Health and economic impact of seasonal influenza mass vaccination strategies in European settings: A mathematical modelling and cost-effectiveness analysis. Vaccine 2022; 40:1306-1315. [PMID: 35109968 PMCID: PMC8861572 DOI: 10.1016/j.vaccine.2022.01.015] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Revised: 01/07/2022] [Accepted: 01/11/2022] [Indexed: 11/21/2022]
Abstract
Seasonal influenza vaccine programmes usually target at-risk and older individuals. We used an age-structured dynamic-transmission model for eight European settings. Older people benefit from adjuvanted or high-dose trivalent or quadrivalent vaccines. Adopting mass paediatric influenza vaccination is also likely to be cost-effective. Results rest on vaccine costs, willingness to vaccinate and unknown long-term effects.
Introduction Despite seasonal influenza vaccination programmes in most countries targeting individuals aged ≥ 65 (or ≥ 55) years and high risk-groups, significant disease burden remains. We explored the impact and cost-effectiveness of 27 vaccination programmes targeting the elderly and/or children in eight European settings (n = 205.8 million). Methods We used an age-structured dynamic-transmission model to infer age- and (sub-)type-specific seasonal influenza virus infections calibrated to England, France, Ireland, Navarra, The Netherlands, Portugal, Scotland, and Spain between 2010/11 and 2017/18. The base-case vaccination scenario consisted of non-adjuvanted, non-high dose trivalent vaccines (TV) and no universal paediatric vaccination. We explored i) moving the elderly to “improved” (i.e., adjuvanted or high-dose) trivalent vaccines (iTV) or non-adjuvanted non-high-dose quadrivalent vaccines (QV); ii) adopting mass paediatric vaccination with TV or QV; and iii) combining the elderly and paediatric strategies. We estimated setting-specific costs and quality-adjusted life years (QALYs) gained from the healthcare perspective, and discounted QALYs at 3.0%. Results In the elderly, the estimated numbers of infection per 100,000 population are reduced by a median of 261.5 (range across settings: 154.4, 475.7) when moving the elderly to iTV and by 150.8 (77.6, 262.3) when moving them to QV. Through indirect protection, adopting mass paediatric programmes with 25% uptake achieves similar reductions in the elderly of 233.6 using TV (range: 58.9, 425.6) or 266.5 using QV (65.7, 477.9), with substantial health gains from averted infections across ages. At €35,000/QALY gained, moving the elderly to iTV plus adopting mass paediatric QV programmes provides the highest mean net benefits and probabilities of being cost-effective in all settings and paediatric coverage levels. Conclusion Given the direct and indirect protection, and depending on the vaccine prices, model results support a combination of having moved the elderly to an improved vaccine and adopting universal paediatric vaccination programmes across the European settings.
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Abstract
Mathematical models have been used to guide strategies for COVID-19 vaccine distribution. But with the emergence of new SARS-CoV-2 variants and waning immunity, how should vaccine distribution be prioritized?
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Gavish N, Katriel G. The role of childrens' vaccination for COVID-19-Pareto-optimal allocations of vaccines. PLoS Comput Biol 2022; 18:e1009872. [PMID: 35213541 PMCID: PMC8906616 DOI: 10.1371/journal.pcbi.1009872] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Revised: 03/09/2022] [Accepted: 01/27/2022] [Indexed: 11/18/2022] Open
Abstract
COVID-19 vaccines have been approved for children of age five and older in many countries. However, there is an ongoing debate as to whether children should be vaccinated and at what priority. In this work, we use mathematical modeling and optimization to study how vaccine allocations to different age groups effect epidemic outcomes. In particular, we consider the effect of extending vaccination campaigns to include the vaccination of children. When vaccine availability is limited, we consider Pareto-optimal allocations with respect to competing measures of the number of infections and mortality and systematically study the trade-offs among them. In the scenarios considered, when some weight is given to the number of infections, we find that it is optimal to allocate vaccines to adolescents in the age group 10-19, even when they are assumed to be less susceptible than adults. We further find that age group 0-9 is included in the optimal allocation for sufficiently high values of the basic reproduction number.
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Affiliation(s)
- Nir Gavish
- Faculty of Mathematics, Technion - IIT, Haifa, Israel
| | - Guy Katriel
- Department of Applied Mathematics, ORT Braude College of Engineering, Karmiel, Israel
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Gandon S, Lion S. Targeted vaccination and the speed of SARS-CoV-2 adaptation. Proc Natl Acad Sci U S A 2022; 119:e2110666119. [PMID: 35031567 PMCID: PMC8784131 DOI: 10.1073/pnas.2110666119] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Accepted: 11/18/2021] [Indexed: 12/16/2022] Open
Abstract
The limited supply of vaccines against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) raises the question of targeted vaccination. Many countries have opted to vaccinate older and more sensitive hosts first to minimize the disease burden. However, what are the evolutionary consequences of targeted vaccination? We clarify the consequences of different vaccination strategies through the analysis of the speed of viral adaptation measured as the rate of change of the frequency of a vaccine-adapted variant. We show that such a variant is expected to spread faster if vaccination targets individuals who are likely to be involved in a higher number of contacts. We also discuss the pros and cons of dose-sparing strategies. Because delaying the second dose increases the proportion of the population vaccinated with a single dose, this strategy can both speed up the spread of the vaccine-adapted variant and reduce the cumulative number of deaths. Hence, strategies that are most effective at slowing viral adaptation may not always be epidemiologically optimal. A careful assessment of both the epidemiological and evolutionary consequences of alternative vaccination strategies is required to determine which individuals should be vaccinated first.
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Affiliation(s)
- Sylvain Gandon
- CEFE, CNRS, Univ Montpellier, EPHE, IRD, Montpellier, France
| | - Sébastien Lion
- CEFE, CNRS, Univ Montpellier, EPHE, IRD, Montpellier, France
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Patterson SJ, Clutton-Brock TH, Pfeiffer DU, Drewe JA. Trait-Based Vaccination of Individual Meerkats (Suricata suricatta) against Tuberculosis Provides Evidence to Support Targeted Disease Control. Animals (Basel) 2022; 12:ani12020192. [PMID: 35049814 PMCID: PMC8772857 DOI: 10.3390/ani12020192] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Revised: 01/05/2022] [Accepted: 01/06/2022] [Indexed: 12/14/2022] Open
Abstract
Simple Summary There is evidence to show that, within a population, some individuals are more likely to spread infections than others. When trying to protect a population against infection, most strategies aim to vaccinate as many individuals as possible. However, vaccinating wildlife is difficult because individuals are difficult to find and capture. For wildlife therefore, the ideal strategy would involve targeting vaccinations at those individuals most likely to transmit infection, thus gaining maximum benefit from capturing a small number of individuals. Whilst this seems a very attractive solution, very few studies have attempted to provide evidence to support this theory. This study focuses on a population of meerkats with a history of tuberculosis. Previous work has suggested that socially dominant individuals are most likely to transmit infection, with subordinates most likely to become infected. Therefore, whilst some social groups were left untreated as a baseline, in others, either dominants or subordinates were vaccinated. All groups were monitored for two years, after which time the infection data was analysed. Groups in which vaccinations had been used showed reduced infection rates suggesting that the targeted approach had reduced transmission. A targeted approach may therefore offer an efficient option for vaccinating wildlife in the future. Abstract Individuals vary in their potential to acquire and transmit infections, but this fact is currently underexploited in disease control strategies. We trialled a trait-based vaccination strategy to reduce tuberculosis in free-living meerkats by targeting high-contact meerkats (socially dominant individuals) in one study arm, and high-susceptibility individuals (young subordinates) in a second arm. We monitored infection within vaccinated groups over two years comparing the results with untreated control groups. Being a member of a high-contact group had a protective effect on individuals’ survival times (Hazard Ratio = 0.5, 95% Confidence Interval, CI: 0.29–0.88, p = 0.02) compared to control groups. Over the study, odds of testing positive for tuberculosis increased more than five-fold in control groups (Odds Ratio = 5.40, 95% CI = 0.94–30.98, p = 0.058); however, no increases were observed in either of the treatment arms. Targeted disease control approaches, such as the one described in this study, allow for reduced numbers of interventions. Here, trait-based vaccination was associated with reduced infection rates and thus has the potential to offer more efficient alternatives to traditional mass-vaccination policies. Such improvements in efficiency warrant further study and could make infectious disease control more practically achievable in both animal (particularly wildlife) and human populations.
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Affiliation(s)
- Stuart J. Patterson
- Veterinary Epidemiology, Economics and Public Health Group, Royal Veterinary College, University of London, Hawkshead Lane, Hatfield, Hertfordshire AL9 7TA, UK; (D.U.P.); (J.A.D.)
- Correspondence:
| | - Tim H. Clutton-Brock
- Large Animal Research Group, Department of Zoology, University of Cambridge, Downing Street, Cambridge CB2 3EJ, UK;
- Mammal Research Institute, University of Pretoria, Hatfield, Pretoria 0028, South Africa
| | - Dirk U. Pfeiffer
- Veterinary Epidemiology, Economics and Public Health Group, Royal Veterinary College, University of London, Hawkshead Lane, Hatfield, Hertfordshire AL9 7TA, UK; (D.U.P.); (J.A.D.)
- Department of Infectious Diseases and Public Health, Jockey Club College of Veterinary Medicine and Life Sciences, City University of Hong Kong, Hong Kong, China
| | - Julian A. Drewe
- Veterinary Epidemiology, Economics and Public Health Group, Royal Veterinary College, University of London, Hawkshead Lane, Hatfield, Hertfordshire AL9 7TA, UK; (D.U.P.); (J.A.D.)
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Zou Y, Yang W, Lai J, Hou J, Lin W. Vaccination and Quarantine Effect on COVID-19 Transmission Dynamics Incorporating Chinese-Spring-Festival Travel Rush: Modeling and Simulations. Bull Math Biol 2022; 84:30. [PMID: 35006388 PMCID: PMC8743760 DOI: 10.1007/s11538-021-00958-5] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Accepted: 10/20/2021] [Indexed: 12/18/2022]
Abstract
The COVID-19 pandemic has adversely affected the entire world. The effective implementation of vaccination strategy is critical to prevent the resurgence of the pandemic, especially during large-scale population migration. We establish a multiple patch coupled model based on the transportation network among the 31 provinces in China, under the combined strategies of vaccination and quarantine during large-scale population migration. Based on the model, we derive a critical quarantine rate to control the pandemic transmission and a vaccination rate to achieve herd immunity. Furthermore, we evaluate the influence of passenger flow on the effective reproduction number during the Chinese-Spring-Festival travel rush. Meanwhile, the spread of the COVID-19 pandemic is investigated for different control strategies, viz. global control and local control. The impact of vaccine-related parameters, such as the number, the effectiveness and the immunity period of vaccine, are explored. It is believed that the articulated models as well as the presented simulation results could be beneficial to design of feasible strategies for preventing COVID-19 transmission during the Chinese-Spring-Festival travel rush or the other future events involving large-scale population migration.
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Affiliation(s)
- Yukun Zou
- Research Institute of Intelligent Complex Systems, Fudan University, Shanghai, 200433, China.,Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, 200433, China
| | - Wei Yang
- Research Institute of Intelligent Complex Systems, Fudan University, Shanghai, 200433, China.
| | - Junjie Lai
- Research Institute of Intelligent Complex Systems, Fudan University, Shanghai, 200433, China.,Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, 200433, China
| | - Jiawen Hou
- Research Institute of Intelligent Complex Systems, Fudan University, Shanghai, 200433, China.,Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, 200433, China
| | - Wei Lin
- Research Institute of Intelligent Complex Systems, Fudan University, Shanghai, 200433, China.,School of Mathematical Sciences, SCMS, and SCAM, Fudan University, Shanghai, 200433, China.,State Key Laboratory of Medical Neurobiology, and MOE Frontiers Center for Brain Science, Institutes of Brain Science, Fudan University, Shanghai, 200032, China
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Lopes JM, Morales CC, Alvarado M, Melo VAZC, Paiva LB, Dias EM, Pardalos PM. Optimization methods for large-scale vaccine supply chains: a rapid review. ANNALS OF OPERATIONS RESEARCH 2022; 316:699-721. [PMID: 35531563 PMCID: PMC9059697 DOI: 10.1007/s10479-022-04720-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 03/28/2022] [Indexed: 05/15/2023]
Abstract
Global vaccine revenues are projected at $59.2 billion, yet large-scale vaccine distribution remains challenging for many diseases in countries around the world. Poor management of the vaccine supply chain can lead to a disease outbreak, or at worst, a pandemic. Fortunately, a large number of those challenges, such as decision-making for optimal allocation of resources, vaccination strategy, inventory management, among others, can be improved through optimization approaches. This work aims to understand how optimization has been applied to vaccine supply chain and logistics. To achieve this, we conducted a rapid review and searched for peer-reviewed journal articles, published between 2009 and March 2020, in four scientific databases. The search resulted in 345 articles, of which 25 unique studies met our inclusion criteria. Our analysis focused on the identification of article characteristics such as research objectives, vaccine supply chain stage addressed, the optimization method used, whether outbreak scenarios were considered, among others. Approximately 64% of the studies dealt with vaccination strategy, and the remainder dealt with logistics and inventory management. Only one addressed market competition (4%). There were 14 different types of optimization methods used, but control theory, linear programming, mathematical model and mixed integer programming were the most common (12% each). Uncertainties were considered in the models of 44% of the studies. One resulting observation was the lack of studies using optimization for vaccine inventory management and logistics. The results provide an understanding of how optimization models have been used to address challenges in large-scale vaccine supply chains.
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Affiliation(s)
- Juliano Marçal Lopes
- Gaesi, Departament of Electric Energy and Automation Engineering, Polytechnic School, University of São Paulo, São Paulo, SP Brazil
| | - Coralys Colon Morales
- HEALTH-Engine Laboratory, Department of Industrial and Systems Engineering, University of Florida, Gainesville, FL USA
| | - Michelle Alvarado
- HEALTH-Engine Laboratory, Department of Industrial and Systems Engineering, University of Florida, Gainesville, FL USA
| | - Vidal Augusto Z. C. Melo
- Gaesi, Departament of Electric Energy and Automation Engineering, Polytechnic School, University of São Paulo, São Paulo, SP Brazil
| | - Leonardo Batista Paiva
- Gaesi, Departament of Electric Energy and Automation Engineering, Polytechnic School, University of São Paulo, São Paulo, SP Brazil
| | - Eduardo Mario Dias
- Gaesi, Departament of Electric Energy and Automation Engineering, Polytechnic School, University of São Paulo, São Paulo, SP Brazil
| | - Panos M. Pardalos
- HEALTH-Engine Laboratory, Department of Industrial and Systems Engineering, University of Florida, Gainesville, FL USA
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Miura F, Leung KY, Klinkenberg D, Ainslie KEC, Wallinga J. Optimal vaccine allocation for COVID-19 in the Netherlands: A data-driven prioritization. PLoS Comput Biol 2021; 17:e1009697. [PMID: 34898617 PMCID: PMC8699630 DOI: 10.1371/journal.pcbi.1009697] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Revised: 12/23/2021] [Accepted: 11/29/2021] [Indexed: 12/14/2022] Open
Abstract
For the control of COVID-19, vaccination programmes provide a long-term solution. The amount of available vaccines is often limited, and thus it is crucial to determine the allocation strategy. While mathematical modelling approaches have been used to find an optimal distribution of vaccines, there is an excessively large number of possible allocation schemes to be simulated. Here, we propose an algorithm to find a near-optimal allocation scheme given an intervention objective such as minimization of new infections, hospitalizations, or deaths, where multiple vaccines are available. The proposed principle for allocating vaccines is to target subgroups with the largest reduction in the outcome of interest. We use an approximation method to reconstruct the age-specific transmission intensity (the next generation matrix), and express the expected impact of vaccinating each subgroup in terms of the observed incidence of infection and force of infection. The proposed approach is firstly evaluated with a simulated epidemic and then applied to the epidemiological data on COVID-19 in the Netherlands. Our results reveal how the optimal allocation depends on the objective of infection control. In the case of COVID-19, if we wish to minimize deaths, the optimal allocation strategy is not efficient for minimizing other outcomes, such as infections. In simulated epidemics, an allocation strategy optimized for an outcome outperforms other strategies such as the allocation from young to old, from old to young, and at random. Our simulations clarify that the current policy in the Netherlands (i.e., allocation from old to young) was concordant with the allocation scheme that minimizes deaths. The proposed method provides an optimal allocation scheme, given routine surveillance data that reflect ongoing transmissions. This approach to allocation is useful for providing plausible simulation scenarios for complex models, which give a more robust basis to determine intervention strategies.
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Affiliation(s)
- Fuminari Miura
- Centre for Infectious Disease Control, National Institute for Public Health and the Environment (RIVM), Bilthoven, the Netherlands
- Center for Marine Environmental Studies (CMES), Ehime University, Ehime, Japan
- * E-mail:
| | - Ka Yin Leung
- Centre for Infectious Disease Control, National Institute for Public Health and the Environment (RIVM), Bilthoven, the Netherlands
| | - Don Klinkenberg
- Centre for Infectious Disease Control, National Institute for Public Health and the Environment (RIVM), Bilthoven, the Netherlands
| | - Kylie E. C. Ainslie
- Centre for Infectious Disease Control, National Institute for Public Health and the Environment (RIVM), Bilthoven, the Netherlands
- School of Public Health, Imperial College London, London, United Kingdom
- MRC Centre for Global Infectious Disease Analysis and Abdul Latif Jameel Institute for Disease and Emergency Analytics, Imperial College London, London, United Kingdom
| | - Jacco Wallinga
- Centre for Infectious Disease Control, National Institute for Public Health and the Environment (RIVM), Bilthoven, the Netherlands
- Department of Biomedical Data Sciences, Leiden University Medical Center (LUMC), Leiden, the Netherlands
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Pettoello-Mantovani M, Carrasco-Sanz A, Huss G, Mestrovic J, Vural M, Pop TL, Ferrara P, Somekh E, Mujkic A, Hoey H, Namazova-Baranova L. Viewpoint of the European Pediatric Societies over Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Vaccination in Children Younger Than Age 12 Years Amid Return to School and the Surging Virus Variants. J Pediatr 2021; 239:250-251.e2. [PMID: 34536493 PMCID: PMC8442301 DOI: 10.1016/j.jpeds.2021.09.013] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Accepted: 09/09/2021] [Indexed: 02/05/2023]
Affiliation(s)
- Massimo Pettoello-Mantovani
- European Paediatric Association/Union of National European Paediatric Societies and Associations (EPA/UNEPSA), Berlin, Germany.
| | - Angel Carrasco-Sanz
- European Paediatric Association/Union of National European Paediatric Societies and Associations (EPA/UNEPSA), Berlin, Germany; European Confederation of Primary Care Paediatrics, Lyon, France
| | - Gottfried Huss
- European Confederation of Primary Care Paediatrics, Lyon, France
| | - Julije Mestrovic
- European Paediatric Association/Union of National European Paediatric Societies and Associations (EPA/UNEPSA), Berlin, Germany; Paediatric Association of the Balkans, Istanbul, Turkey; Croatian Society of Paediatrics, Zagreb, Croatia
| | - Mehmet Vural
- European Paediatric Association/Union of National European Paediatric Societies and Associations (EPA/UNEPSA), Berlin, Germany; Turkish Paediatric Association, Istanbul, Turkey
| | - Tudor Lucian Pop
- European Paediatric Association/Union of National European Paediatric Societies and Associations (EPA/UNEPSA), Berlin, Germany; Paediatric Association of the Balkans, Istanbul, Turkey; Romanian Society of Social Paediatrics, Cluj-Napoca, Romania
| | - Pietro Ferrara
- European Paediatric Association/Union of National European Paediatric Societies and Associations (EPA/UNEPSA), Berlin, Germany; Italian Society of Paediatrics, Rome, Italy
| | - Eli Somekh
- European Paediatric Association/Union of National European Paediatric Societies and Associations (EPA/UNEPSA), Berlin, Germany; Israel Paediatric Association, Tel Aviv, Israel
| | - Aida Mujkic
- European Paediatric Association/Union of National European Paediatric Societies and Associations (EPA/UNEPSA), Berlin, Germany; Paediatric Association of the Balkans, Istanbul, Turkey; Croatian Society of Paediatrics, Zagreb, Croatia
| | - Hilary Hoey
- European Paediatric Association/Union of National European Paediatric Societies and Associations (EPA/UNEPSA), Berlin, Germany; Faculty of Paediatrics, Royal College of Physicians of Ireland, Dublin, Ireland
| | - Leyla Namazova-Baranova
- European Paediatric Association/Union of National European Paediatric Societies and Associations (EPA/UNEPSA), Berlin, Germany; Russian Academy of Paediatrics, Moscow, Russia
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Hartnett GS, Parker E, Gulden TR, Vardavas R, Kravitz D. Modelling the impact of social distancing and targeted vaccination on the spread of COVID-19 through a real city-scale contact network. JOURNAL OF COMPLEX NETWORKS 2021; 9:cnab042. [PMID: 35039781 PMCID: PMC8754788 DOI: 10.1093/comnet/cnab042] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Accepted: 10/25/2021] [Indexed: 05/07/2023]
Abstract
We use mobile device data to construct empirical interpersonal physical contact networks in the city of Portland, Oregon, both before and after social distancing measures were enacted during the COVID-19 pandemic. These networks reveal how social distancing measures and the public's reaction to the incipient pandemic affected the connectivity patterns within the city. We find that as the pandemic developed there was a substantial decrease in the number of individuals with many contacts. We further study the impact of these different network topologies on the spread of COVID-19 by simulating an SEIR epidemic model over these networks and find that the reduced connectivity greatly suppressed the epidemic. We then investigate how the epidemic responds when part of the population is vaccinated, and we compare two vaccination distribution strategies, both with and without social distancing. Our main result is that the heavy-tailed degree distribution of the contact networks causes a targeted vaccination strategy that prioritizes high-contact individuals to reduce the number of cases far more effectively than a strategy that vaccinates individuals at random. Combining both targeted vaccination and social distancing leads to the greatest reduction in cases, and we also find that the marginal benefit of a targeted strategy as compared to a random strategy exceeds the marginal benefit of social distancing for reducing the number of cases. These results have important implications for ongoing vaccine distribution efforts worldwide.
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Affiliation(s)
| | - Edward Parker
- RAND Corporation, 1776 Main St, Santa Monica, CA 90401, USA
| | | | | | - David Kravitz
- RAND Corporation, 1776 Main St, Santa Monica, CA 90401, USA
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Yemshanov D, Liu N, Thompson DK, Parisien MA, Barber QE, Koch FH, Reimer J. Detecting critical nodes in forest landscape networks to reduce wildfire spread. PLoS One 2021; 16:e0258060. [PMID: 34618859 PMCID: PMC8496796 DOI: 10.1371/journal.pone.0258060] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Accepted: 09/16/2021] [Indexed: 11/18/2022] Open
Abstract
Although wildfires are an important ecological process in forested regions worldwide, they can cause significant economic damage and frequently create widespread health impacts. We propose a network optimization approach to plan wildfire fuel treatments that minimize the risk of fire spread in forested landscapes under an upper bound for total treated area. We used simulation modeling to estimate the probability of fire spread between pairs of forest sites and formulated a modified Critical Node Detection (CND) model that uses these estimated probabilities to find a pattern of fuel reduction treatments that minimizes the likely spread of fires across a landscape. We also present a problem formulation that includes control of the size and spatial contiguity of fuel treatments. We demonstrate the approach with a case study in Kootenay National Park, British Columbia, Canada, where we investigated prescribed burn options for reducing the risk of wildfire spread in the park area. Our results provide new insights into cost-effective planning to mitigate wildfire risk in forest landscapes. The approach should be applicable to other ecosystems with frequent wildfires.
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Affiliation(s)
- Denys Yemshanov
- Natural Resources Canada, Canadian Forest Service, Great Lakes Forestry Centre, Sault Ste. Marie, Ontario, Canada
| | - Ning Liu
- Natural Resources Canada, Canadian Forest Service, Great Lakes Forestry Centre, Sault Ste. Marie, Ontario, Canada
| | - Daniel K Thompson
- Natural Resources Canada, Canadian Forest Service, Great Lakes Forestry Centre, Sault Ste. Marie, Ontario, Canada
| | - Marc-André Parisien
- Natural Resources Canada, Canadian Forest Service, Northern Forestry Centre, Edmonton, Alberta, Canada
| | - Quinn E Barber
- Natural Resources Canada, Canadian Forest Service, Northern Forestry Centre, Edmonton, Alberta, Canada
| | - Frank H Koch
- USDA Forest Service, Southern Research Station, Eastern Forest Environmental Threat Assessment Center, Research Triangle Park, North Carolina, United States of America
| | - Jonathan Reimer
- Capital Regional District, Victoria, British Columbia, United Kingdom
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Putty MS. A Whirlwind Tour of Complex Systems. J Indian Inst Sci 2021; 101:297-302. [PMID: 34642556 PMCID: PMC8496620 DOI: 10.1007/s41745-021-00264-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/19/2021] [Indexed: 11/29/2022]
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