Agent-based model of the impact of higher influenza vaccine efficacy on seasonal influenza burden

Krauland, Mary G.; Zimmerman, Richard K.; Williams, Katherine V.; Raviotta, Jonathan M.; Harrison, Lee H.; Williams, John; Roberts, Mark S.

doi:10.1016/j.jvacx.2022.100249

Vaccine: X

2023

DOI: 10.1016/j.jvacx.2022.100249

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Agent-based model of the impact of higher influenza vaccine efficacy on seasonal influenza burden

Mary G. Krauland

Richard K. Zimmerman

Katherine V. Williams

et al.

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Cited by 3 publications

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“…FRED has been extensively used to model influenza and other respiratory diseases. [20][21][22][23] The FRED COVID-19 model was calibrated to reasonably match the burden of deaths due to COVID-19 from January 2020 through April 2021 in Pennsylvania. Deaths represent the most reliable disease burden parameter, as case ascertainment was uncertain and varied widely during the pandemic due to early unavailability of testing and to the large number of mild and asymptomatic cases.…”

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confidence: 99%

Modeling the Impact of COVID-19 Mitigation Strategies in Pennsylvania, USA

Krauland,

Roberts

2024

MDM Policy & Practice

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Purpose. To estimate the impact on mortality of nonpharmaceutical interventions (NPIs) implemented early in the COVID-19 pandemic. Methods. We implemented an agent-based modified SEIR model of COVID-19, calibrated to match death numbers reported in Pennsylvania from January 2020 to April 2021 and including representations of NPIs implemented in Pennsylvania. To investigate the impact of these strategies, we ran the calibrated model with no interventions and with varying combinations, timings, and levels of interventions. Results. The model closely replicated death outcomes data for Pennsylvania. Without NPIs, deaths in the early months of the pandemic were estimated to be much higher (67,718 deaths compared to actual 6,969). Voluntary interventions alone were relatively ineffective at decreasing mortality. Delaying implementation of interventions led to higher deaths (∼9,000 more deaths with just a 1-week delay). School closure was insufficient as a single intervention but was an important part of a combined intervention strategy. Conclusions. NPIs were effective at reducing deaths early in the COVID-19 pandemic. Agent-based models can incorporate substantial detail on infectious disease spread and the impact of mitigations. Policy Implications. The model supports the importance and effectiveness of NPIs to decrease morbidity from respiratory pathogens. This is particularly important for emerging pathogens for which no vaccines or treatments exist, but such strategies are applicable to a variety of respiratory pathogens. Highlights Nonpharmaceutical interventions were used extensively during the early period of the COVID-19 pandemic, but their use has remained controversial. Agent-based modeling of the impact of these mitigation strategies early in the COVID-19 pandemic supports the effectiveness of nonpharmaceutical interventions at decreasing mortality. Since such interventions are not specific to a particular pathogen, they can be used to protect against any respiratory pathogen, known or emerging. They can be applied rapidly when conditions warrant.

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confidence: 99%

Modeling the Impact of COVID-19 Mitigation Strategies in Pennsylvania, USA

Krauland,

Roberts

2024

MDM Policy & Practice

Self Cite

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Quantitative measurement of influenza virus transmission in animal model: an overview of current state

Onkhonova,

Gudymo,

Kosenko

et al. 2023

Biophys Rev

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Limitations in creating artificial populations in agent-based epidemic modeling: a systematic review

Maslova,

Manolov,

Glushchenko

et al. 2024

Journal of microbiology, epidemiology and immunobiology

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Introduction. The key step in agent-based modeling of epidemics, which allows researchers to take into account individual characteristics of people, is the creation of an artificial population. The main difficulty of this procedure is finding a balance between the detail of the population description and the computational efficiency of the calculations. The aim and objectives of the review: Critically analyze and summarize the current evidence on how to create artificial populations; evaluate the limitations and advantages of available approaches in solving various problems in epidemiology. Materials and methods. An analysis of literature sources devoted to agent-based modeling has been performed. The analysis is focused on algorithms for creating an artificial population with a given level of detail for modeling human respiratory infections. Results. The approaches to the creation of artificial populations are generalized. The main principles of realization of interaction between agents are revealed: by means of networks of contacts between agents and on the basis of taking into account the movement of agents between locations. The first approach is the most computationally efficient and simple; the second approach allows to better take into account the change in the behavior of agents during the development of the epidemic process. Conclusion. Agent-based modeling is an optimal tool for selecting the best scenario for epidemic control and investigating the role of individual characteristics of people in the development of epidemics. When creating an artificial population, it is important to include in the model factors that can be targeted for control. A significant limitation is the lack of factual data on population structure, but this can be overcome by using indirect data.

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Agent-based model of the impact of higher influenza vaccine efficacy on seasonal influenza burden

Cited by 3 publications

References 42 publications

Modeling the Impact of COVID-19 Mitigation Strategies in Pennsylvania, USA

Modeling the Impact of COVID-19 Mitigation Strategies in Pennsylvania, USA

Quantitative measurement of influenza virus transmission in animal model: an overview of current state

Limitations in creating artificial populations in agent-based epidemic modeling: a systematic review

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