On the interplay between mobility and hospitalization capacity during the COVID-19 pandemic: The SEIRHUD model

Veloz, Tomás; Maldonado, P; S, Ropert; Ravello, Cesar; barrios, alejandra; mora, soraya; Valdenegro, C; Villaseca, Tomas; Pérez-Acle, Tomás

doi:10.1101/2020.06.10.20127613

Cited by 2 publications

(2 citation statements)

References 23 publications

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“…This model can be augmented with interesting variables such as rate of re-susceptibility (inverse of temporary immunity period) (Bjørnstad et al, 2020) or hospitalization capacity (Veloz et al, 2020), present in related papers. However, given the difficulty of accessing to reliable data to provide a rigorous analysis of these variables, we have decided to simplify our scope to the basic SEIR model and leave the introduction of further variables for future works.…”

Section: Methodsmentioning

confidence: 99%

Dynamic versus Continuous Interventions: Optimizing Lockdown Policies for COVID-19

Akinci

Fdez

Peña-Tapia

et al. 2021

Preprint

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In the context of the ongoing COVID-19 pandemic, while millions of people await the administration of a vaccine, social distancing remains the leading approach towards the effect commonly known as "flattening the curve" of infections. Over the last year, governmental administrations throughout the globe have implemented various lockdown policies in hopes of slowing down the transmission of the disease. However, the current lack of consensus on when and how these policies should be implemented reflects the need for further studies regarding these questions. In this paper, we tackle the issue of lockdown policy management, in particular in terms of lockdown placement (how often, when, and how long these periods should be), in order to minimize the peak of infections in a specific population. We introduce a novel combination of classic mathematical disease modelling using the equation-based SEIR model, and Evolutionary Strategies (ES) for optimizing the peak of infections. The method is evaluated using data collected in different countries, and a particular focus is placed on the study of the effect of specific model parameters on lockdown optimization, such as the transmission rate ($\beta$), of which 4 alternative modelling functions have been proposed and analyzed. Our results indicate that this transmission rate parameter significantly influences the resulting optimal strategies. In particular, the presence of a gradual decay of the rate of transmission during lockdown leads to longer, more sparsely placed confinement periods while an abrupt, instantaneous drop in the amount of contacts per person favors shorter but more frequent lockdowns. Although these results are limited by the scope of action provided by the simplicity of the SEIR model, they suggest that the influence of the evolution of the rate of transmission along the disease should be assessed in further studies with alternative optimization strategies (agent-based) and models (SEIRSHUD).

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Section: Methodsmentioning

confidence: 99%

Dynamic versus Continuous Interventions: Optimizing Lockdown Policies for COVID-19

Akinci

Fdez

Peña-Tapia

et al. 2021

Preprint

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“…A central element of our integrated model is the stochastic network model, which captures patient flow among different units in the hospital and predicts unit-level workload (patient census) in Section 3. In the context of workload modeling for COVID patients, several papers incorporate compartments on hospitalization in different units into their disease progression model, for example, Capistran et al (2020), Garrido et al (2020), Hill et al (2020), andVeloz et al (2020). Bartz-Beielstein et al (2020) develop a detailed discreteevent simulation model for patient flow and provide resource requirement prediction under various worst-case and bestcase scenarios.…”

Section: Hospital Workload Modelsmentioning

confidence: 99%

Operations (management) warp speed: Rapid deployment of hospital‐focused predictive/prescriptive analytics for the COVID‐19 pandemic

Shi

Helm

Chen

et al. 2023

Production and Operations Management

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At the onset of the COVID‐19 pandemic, hospitals were in dire need of data‐driven analytics to provide support for critical, expensive, and complex decisions. Yet, the majority of analytics being developed were targeted at state‐ and national‐level policy decisions, with little availability of actionable information to support tactical and operational decision‐making and execution at the hospital level. To fill this gap, we developed a multi‐method framework leveraging a parsimonious design philosophy that allows for rapid deployment of high‐impact predictive and prescriptive analytics in a time‐sensitive, dynamic, data‐limited environment, such as a novel pandemic. The product of this research is a workload prediction and decision support tool to provide mission‐critical, actionable information for individual hospitals. Our framework forecasts time‐varying patient workload and demand for critical resources by integrating disease progression models, tailored to data availability during different stages of the pandemic, with a stochastic network model of patient movements among units within individual hospitals. Both components employ adaptive tuning to account for hospital‐dependent, time‐varying parameters that provide consistently accurate predictions by dynamically learning the impact of latent changes in system dynamics. Our decision support system is designed to be portable and easily implementable across hospital data systems for expeditious expansion and deployment. This work was contextually grounded in close collaboration with IU Health, the largest health system in Indiana, which has 18 hospitals serving over one million residents. Our initial prototype was implemented in April 2020 and has supported managerial decisions, from the operational to the strategic, across multiple functionalities at IU Health.

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On the interplay between mobility and hospitalization capacity during the COVID-19 pandemic: The SEIRHUD model

Cited by 2 publications

References 23 publications

Dynamic versus Continuous Interventions: Optimizing Lockdown Policies for COVID-19

Dynamic versus Continuous Interventions: Optimizing Lockdown Policies for COVID-19

Operations (management) warp speed: Rapid deployment of hospital‐focused predictive/prescriptive analytics for the COVID‐19 pandemic

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