Public health impact of delaying second dose of BNT162b2 or mRNA-1273 covid-19 vaccine: simulation agent based modeling study

AbstractObjectiveTo estimate population health outcomes with delayed second dose versus standard schedule of SARS-CoV-2 mRNA vaccination.DesignSimulation agent based modeling study.SettingSimulated population based on real world US county.ParticipantsThe simulation included 100 000 agents, with a representative distribution of demographics and occupations. Networks of contacts were established to simulate potentially infectious interactions though occupation, household, and random interactions.InterventionsSimulation of standard covid-19 vaccination versus delayed second dose vaccination prioritizing the first dose. The simulation runs were replicated 10 times. Sensitivity analyses included first dose vaccine efficacy of 50%, 60%, 70%, 80%, and 90% after day 12 post-vaccination; vaccination rate of 0.1%, 0.3%, and 1% of population per day; assuming the vaccine prevents only symptoms but not asymptomatic spread (that is, non-sterilizing vaccine); and an alternative vaccination strategy that implements delayed second dose for people under 65 years of age, but not until all those above this age have been vaccinated.Main outcome measuresCumulative covid-19 mortality, cumulative SARS-CoV-2 infections, and cumulative hospital admissions due to covid-19 over 180 days.ResultsOver all simulation replications, the median cumulative mortality per 100 000 for standard dosing versus delayed second dose was 226 v 179, 233 v 207, and 235 v 236 for 90%, 80%, and 70% first dose efficacy, respectively. The delayed second dose strategy was optimal for vaccine efficacies at or above 80% and vaccination rates at or below 0.3% of the population per day, under both sterilizing and non-sterilizing vaccine assumptions, resulting in absolute cumulative mortality reductions between 26 and 47 per 100 000. The delayed second dose strategy for people under 65 performed consistently well under all vaccination rates tested.ConclusionsA delayed second dose vaccination strategy, at least for people aged under 65, could result in reduced cumulative mortality under certain conditions.

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The Public Health Impact of Delaying a Second Dose of the BNT162b2 or mRNA-1273 COVID-19 Vaccine

10.1101/2021.02.23.21252299 ◽

2021 ◽

Author(s):

Santiago Romero-Brufau ◽

Ayush Chopra ◽

Alex J Ryu ◽

Esma Gel ◽

Ramesh Raskar ◽

...

Keyword(s):

Vaccination Strategy ◽

Population Level ◽

Health Impact ◽

Vaccination Rate ◽

Agent Based Modeling ◽

Sensitivity Analyses ◽

Agent Based ◽

Cumulative Mortality ◽

Vaccination Rates ◽

The Public

AbstractObjectivesTo estimate population health outcomes under delayedsecond dose versus standard schedule SARS-CoV-2 mRNA vaccination.DesignAgent-based modeling on a simulated population of 100,000 based on a real-world US county. The simulation runs were replicated 10 times. To test the robustness of these findings, simulations were performed under different estimates for single-dose efficacy and vaccine administration rates, and under the possibility that a vaccine prevents only symptoms but not asymptomatic spread.Settingpopulation level simulation.Participants100,000 agents are included in the simulation, with a representative distribution of demographics and occupations. Networks of contacts are established to simulate potentially infectious interactions though occupation, household, and random interactionsInterventionswe simulate standard Covid-19 vaccination, versus delayed-second-dose vaccination prioritizing first dose. Sensitivity analyses include first-dose vaccine efficacy of 70%, 80% and 90% after day 12 post-vaccination; vaccination rate of 0.1%, 0.3%, and 1% of population per day; assuming the vaccine prevents only symptoms but not asymptomatic spread; and an alternative vaccination strategy that implements delayed-second-dose only for those under 65 years of age.Main outcome measurescumulative Covid-19 mortality over 180 days, cumulative infections and hospitalizations.ResultsOver all simulation replications, the median cumulative mortality per 100,000 for standard versus delayed second dose was 226 vs 179; 233 vs 207; and 235 vs 236; for 90%, 80% and 70% first-dose efficacy, respectively. The delayed-second-dose strategy was optimal for vaccine efficacies at or above 80%, and vaccination rates at or below 0.3% population per day, both under sterilizing and non-sterilizing vaccine assumptions, resulting in absolute cumulative mortality reductions between 26 and 47 per 100,000. The delayed-second-dose for those under 65 performed consistently well under all vaccination rates tested.ConclusionsA delayed-second-dose vaccination strategy, at least for those under 65, could result in reduced cumulative mortality under certain conditions.

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Beach Management Practices and Occupation Dynamics: An Agent-Based Modeling Study for the Coastal Town of Nags Head, NC, USA

Beach Management Tools - Concepts, Methodologies and Case Studies - Coastal Research Library ◽

10.1007/978-3-319-58304-4_19 ◽

2017 ◽

pp. 373-395

Author(s):

Ayse Karanci ◽

Liliana Velásquez-Montoya ◽

Juan F. Paniagua-Arroyave ◽

Peter N. Adams ◽

Margery F. Overton

Keyword(s):

Management Practices ◽

Agent Based Modeling ◽

Beach Management ◽

Agent Based ◽

Modeling Study

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Role of T Cell-To-Dendritic Cell Chemoattraction in T Cell Priming Initiation in the Lymph Node: An Agent-Based Modeling Study

Frontiers in Immunology ◽

10.3389/fimmu.2019.01289 ◽

2019 ◽

Vol 10 ◽

Cited By ~ 1

Author(s):

Ivan Azarov ◽

Kirill Peskov ◽

Gabriel Helmlinger ◽

Yuri Kosinsky

Keyword(s):

Lymph Node ◽

T Cell ◽

Dendritic Cell ◽

Agent Based Modeling ◽

Agent Based ◽

T Cell Priming ◽

Modeling Study

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Is Telehealth Better Used to Treat Patients or Help Other Physicians Treat Patients? An Agent-Based Modeling Study of Healthcare Provision

10.24251/hicss.2022.489 ◽

2022 ◽

Author(s):

Michael Chary

Keyword(s):

Agent Based Modeling ◽

Healthcare Provision ◽

Agent Based ◽

Modeling Study

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Does cost-effectiveness of influenza vaccine choice vary across the U.S.? An agent-based modeling study

Vaccine ◽

10.1016/j.vaccine.2017.05.093 ◽

2017 ◽

Vol 35 (32) ◽

pp. 3974-3981 ◽

Cited By ~ 4

Author(s):

Jay V. DePasse ◽

Mary Patricia Nowalk ◽

Kenneth J. Smith ◽

Jonathan M. Raviotta ◽

Eunha Shim ◽

...

Keyword(s):

Cost Effectiveness ◽

Influenza Vaccine ◽

Agent Based Modeling ◽

Agent Based ◽

The U.S ◽

Modeling Study

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Agent-based modeling to inform flood emergency planning and management

Journal of Emergency Management ◽

10.5055/jem.2011.0048 ◽

2011 ◽

Vol 9 (1) ◽

pp. 71 ◽

Cited By ~ 3

Author(s):

Manuela Di Mauro, BEng, MEng, PhD ◽

Darren Lumbroso, MEng, MSc, CEng ◽

Andy Tagg, MEng, CEng

Keyword(s):

Emergency Management ◽

Historical Data ◽

Management Strategies ◽

Agent Based Modeling ◽

Emergency Planning ◽

Sensitivity Analyses ◽

Robust Method ◽

Loss Of Life ◽

Building Collapse ◽

Agent Based

Objective: Agent-based modeling can provide powerful tools to inform flood emergency management and to provide an assessment of loss of life due to a flood event. The objective of this work is to study the suitability and robustness of this type of models for being applied in practice in managing flood emergencies.Design: This article describes the application of a prototype, agent-based Life Safety Model (LSM) to two populated areas in the Thames Estuary. Parameters sensitivity analyses have also been performed to assess the robustness and the applicability of this model as part of the actual emergency practice.Results: The model of the two areas resulted in the estimation of the number of fatalities for each scenario for different causes such as drowning, exhaustion, building collapse, and vehicles being swept away. The model was also successfully validated against historical data from the 1953 Canvey Island flood.Conclusions: The LSM offers a scientifically robust method of assessing injuries and lives lost, and it allows the comparison of different emergency management strategies that could assist in reducing the loss of life during future flood incidents.

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Prioritizing Healthcare Worker Vaccinations on the Basis of Social Network Analysis

Infection Control and Hospital Epidemiology ◽

10.1086/655466 ◽

2010 ◽

Vol 31 (9) ◽

pp. 893-900 ◽

Cited By ~ 36

Author(s):

Philip M. Polgreen ◽

Troy Leo Tassier ◽

Sriram Venkata Pemmaraju ◽

Alberto Maria Segre

Keyword(s):

Social Network ◽

Social Network Analysis ◽

Network Analysis ◽

Healthcare Workers ◽

Computational Simulation ◽

Vaccination Strategy ◽

Occupational Therapists ◽

Agent Based ◽

X Ray ◽

Vaccination Rates

Objective.To use social network analysis to design more effective strategies for vaccinating healthcare workers against influenza.Design.An agent-based simulation.Setting.A simulation based on a 700-bed hospital.Methods.We first observed human contacts (defined as approach within approximately 0.9 m) performed by 15 categories of healthcare workers (eg, floor nurses, intensive care unit nurses, staff physicians, phlebotomists, and respiratory therapists). We then constructed a series of contact graphs to represent the social network of the hospital and used these graphs to run agent-based simulations to model the spread of influenza. A targeted vaccination strategy that preferentially vaccinated more “connected” healthcare workers was compared with other vaccination strategies during simulations with various base vaccination rates, vaccine effectiveness, probability of transmission, duration of infection, and patient length of stay.Results.We recorded 6,654 contacts by 148 workers during 606 hours of observations from January through December 2006. Unit clerks, X-ray technicians, residents and fellows, transporters, and physical and occupational therapists had the most contacts. When repeated contacts with the same individual were excluded, transporters, unit clerks, X-ray technicians, physical and occupational therapists, and social workers had the most contacts. Preferentially vaccinating healthcare workers in more connected job categories yielded a substantially lower attack rate and fewer infections than a random vaccination strategy for all simulation parameters.Conclusions.Social network models can be used to derive more effective vaccination policies, which are crucial during vaccine shortages or in facilities with low vaccination rates. Local vaccination priorities can be determined in any healthcare facility with only a modest investment in collection of observational data on different types of healthcare workers. Our findings and methods (ie, social network analysis and computational simulation) have implications for the design of effective interventions to control a broad range of healthcare-associated infections.

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