A runtime alterable epidemic model with genetic drift, waning immunity and vaccinations

Wayne M. Getz; Richard Salter; Ludovica Luisa Vissat; James S. Koopman; Carl P. Simon

doi:10.1098/rsif.2021.0648

A runtime alterable epidemic model with genetic drift, waning immunity and vaccinations

Journal of The Royal Society Interface ◽

10.1098/rsif.2021.0648 ◽

2021 ◽

Vol 18 (184) ◽

Author(s):

Wayne M. Getz ◽

Richard Salter ◽

Ludovica Luisa Vissat ◽

James S. Koopman ◽

Carl P. Simon

Keyword(s):

Genetic Drift ◽

Environmental Persistence ◽

Vaccination Rates ◽

Contact Rates ◽

Waning Immunity ◽

Systems Models ◽

Model Platform ◽

Parameter Values ◽

Present Simulation ◽

Application Programs

We present methods for building a Java Runtime-Alterable-Model Platform (RAMP) of complex dynamical systems. We illustrate our methods by building a multivariant SEIR (epidemic) RAMP. Underlying our RAMP is an individual-based model that includes adaptive contact rates, pathogen genetic drift, waning and cross-immunity. Besides allowing parameter values, process descriptions and scriptable runtime drivers to be easily modified during simulations, our RAMP can used within R-Studio and other computational platforms. Process descriptions that can be runtime altered within our SEIR RAMP include pathogen variant-dependent host shedding, environmental persistence, host transmission and within-host pathogen mutation and replication. They also include adaptive social distancing and adaptive application of vaccination rates and variant-valency of vaccines. We present simulation results using parameter values and process descriptions relevant to the current COVID-19 pandemic. Our results suggest that if waning immunity outpaces vaccination rates, then vaccination rollouts may fail to contain the most transmissible variants, particularly if vaccine valencies are not adapted to deal with escape mutations. Our SEIR RAMP is designed for easy use by others. More generally, our RAMP concept facilitates construction of highly flexible complex systems models of all types, which can then be easily shared as stand-alone application programs.

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Adaptive vaccination may be needed to extirpate COVID-19: Results from a runtime-alterable strain-drift and waning-immunity model

10.1101/2021.06.07.21258504 ◽

2021 ◽

Author(s):

Wayne M Getz ◽

Richard Salter ◽

Ludovica Luisa Vissat ◽

James S Koopman ◽

Carl P Simon

Keyword(s):

Time Dependent ◽

Environmental Persistence ◽

Vaccination Programs ◽

Vaccination Rates ◽

Waning Immunity ◽

Monitoring And Surveillance ◽

Cross Immunity ◽

Model Platform ◽

Parameter Values ◽

Flexible Models

We developed an elaborated susceptible-infected-recovered (SIR) individual-based model (IBM) with pathogen strain drift, waning and cross immunity, implemented as a novel Java Runtime-Alterable-Model Platform (J-RAMP). This platform allows parameter values, process formulations, and scriptable runtime drivers to be easily added at the start of simulation. It includes facility for integration into the R statistical and other data analysis platforms. We selected a set of parameter values and process descriptions relevant to the current COVID-19 pandemic. These include pathogen-specific shedding, environmental persistence, host transmission and mortality, within-host pathogen mutation and replication, adaptive social distancing, and time dependent vaccine rate and strain valency specifications. Our simulations illustrate that if waning immunity outpaces vaccination rates, then vaccination rollouts may fail to contain the most transmissible strains. Our study highlights the need for adaptive vaccination rollouts, which depend on reliable real-time monitoring and surveillance of strain proliferation and reinfection data needed to ensure that vaccines target emerging strains and constrain escape mutations. Together with such data, our platform has the potential to inform the design of vaccination programs that extirpate rather than exacerbate local outbreaks. Finally, our RAMP concept promotes the development of highly flexible models that can be easily shared among researchers and policymakers not only addressing healthcare crises, but other types of environmental crises as well.

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The importance of sustained compliance with physical distancing during COVID-19 vaccination rollout

10.21203/rs.3.rs-390037/v1 ◽

2021 ◽

Author(s):

Alexandra Teslya ◽

Ganna Rozhnova ◽

Thi Mui Pham ◽

Daphne van Wees ◽

Hendrik Nunner ◽

...

Keyword(s):

Vaccination Coverage ◽

Vaccine Coverage ◽

Transmission Model ◽

Cumulative Number ◽

Vaccination Rates ◽

Contact Rates ◽

Health Authorities ◽

Public Health Authorities ◽

Vaccination Campaigns ◽

The Impact

Abstract Mass vaccination campaigns against SARS-CoV-2 are under way in many countries with the hope that increasing vaccination coverage will enable reducing current physical distancing measures. Compliance with these measures is waning, while more transmissible virus variants such as B.1.1.7 have emerged. Using SARS-CoV-2 transmission model we investigated the impact of the feedback between compliance, the incidence of infection, and vaccination coverage on the success of a vaccination programme in the population where waning of compliance depends on vaccine coverage. Our results suggest that the combination of fast waning compliance, slow vaccination rates, and more transmissible variants may result in a higher cumulative number of infections than in a situation without vaccination. These adverse effects can be alleviated if vaccinated individuals do not revert to pre-pandemic contact rates, and if non-vaccinated individuals remain compliant with physical distancing measures. Both require convincing, clear and appropriately targeted communication strategies by public health authorities.

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Factorial Design for Improving Influenza Vaccination Among Employees of a Large Health System

Infection Control and Hospital Epidemiology ◽

10.1086/598343 ◽

2009 ◽

Vol 30 (7) ◽

pp. 691-697 ◽

Cited By ~ 27

Author(s):

Richard Kent Zimmerman ◽

Mary Patricia Nowalk ◽

Chyongchiou J. Lin ◽

Mahlon Raymund ◽

Dwight E. Fox ◽

...

Keyword(s):

Influenza Vaccination ◽

Health System ◽

Factorial Design ◽

Healthcare Personnel ◽

Patient Contact ◽

Administrative Positions ◽

Healthcare Facilities ◽

Vaccination Rates ◽

Contact Rates ◽

Acute Care Facilities

Objective.As healthcare personnel (HCP) influenza vaccination becomes a quality indicator for healthcare facilities, effective inter¬ventions are needed. This study was designed to test a factorial design to improve HCP vaccination rates.Design.A before-after trial with education, publicity, and free and easily accessible influenza vaccines used a factorial design to determine the effect of mobile vaccination carts and incentives on vaccination rates of HCP, who were divided into groups on the basis of their level of patient contact (ie, business and/or administrative role, indirect patient contact, and direct patient contact).Setting.Eleven acute care facilities in a large health system.Participants.More than 26,000 nonphysician employees.Results.Influenza vaccination rates increased significantly in most facilities and increased system-wide from 32.4% to 39.6% (P < .001). In the baseline year, business unit employee vaccination rates were significantly higher than among HCP with patient contact; rates did not differ significantly across groups in the intervention year. In logistic regression that accounted for demographic characteristics, intervention year, and other factors, the use of incentives and/or mobile carts that provided access to vaccine at the work unit significantly increased the likelihood of vaccination among HCP with direct and indirect patient contact, compared with control sites.Conclusions.Interventions to improve vaccination rates are differentially effective among HCP with varying levels of patient contact. Mobile carts appear to remove access barriers, whereas incentives may motivate HCP to be vaccinated. Education and publicity may be sufficient for workers in business or administrative positions. Interventions tailored by worker type are likely to be most successful for improving HCP vaccination rates.

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A strategy for "constraint-based" parameter specification for environmental models

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-10-14857-2013 ◽

2013 ◽

Vol 10 (12) ◽

pp. 14857-14871 ◽

Cited By ~ 3

Author(s):

S. Gharari ◽

M. Shafiei ◽

M. Hrachowitz ◽

F. Fenicia ◽

H. V. Gupta ◽

...

Keyword(s):

Expert Knowledge ◽

Solution Space ◽

State Variables ◽

Rainfall Runoff ◽

Environmental Models ◽

Output Time ◽

Systems Models ◽

Parameter Values ◽

Parameter Constraints ◽

Process Constraints

Abstract. Many environmental systems models, such as conceptual rainfall-runoff models, rely on model calibration for parameter identification. For this, an observed output time series (such as runoff) is needed, but frequently not available. Here, we explore another way to constrain the parameter values of semi-distributed conceptual models, based on two types of restrictions derived from prior (or expert) knowledge. The first, called "parameter constraints", restrict the solution space based on realistic relationships that must hold between the different parameters of the model while the second, called "process constraints" require that additional realism relationships between the fluxes and state variables must be satisfied. Specifically, we propose a strategy for finding parameter sets that simultaneously satisfy all such constraints, based on stepwise sampling of the parameter space. Such parameter sets have the desirable property of being consistent with the modeler's intuition of how the catchment functions, and can (if necessary) serve as prior information for further investigations by reducing the prior uncertainties associated with both calibration and prediction.

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