A Predictive Model for Patient Census and Ventilator Requirements at Individual Hospitals During the Coronavirus Disease 2019 (COVID-19) Pandemic: A Preliminary Technical Report

Epstein, Richard H.; Dexter, Franklin

doi:10.7759/cureus.8501

Cited by 6 publications

(11 citation statements)

References 11 publications

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“…Another study [ 62 ] provided an analytical model for predicting patient census and estimating ventilator needs for a given hospital during the COVID-19 pandemic. Through this study, it was noticed that the estimation of the bed and ventilator needs is influenced by the length of hospital stay, and the number of days of inpatient ventilator use.…”

Section: Applications Of Data Analytics In Covid-19mentioning

confidence: 99%

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Applications of Big Data Analytics to Control COVID-19 Pandemic

Alsunaidi

Almuhaideb

Ibrahim

et al. 2021

Sensors

100

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The COVID-19 epidemic has caused a large number of human losses and havoc in the economic, social, societal, and health systems around the world. Controlling such epidemic requires understanding its characteristics and behavior, which can be identified by collecting and analyzing the related big data. Big data analytics tools play a vital role in building knowledge required in making decisions and precautionary measures. However, due to the vast amount of data available on COVID-19 from various sources, there is a need to review the roles of big data analysis in controlling the spread of COVID-19, presenting the main challenges and directions of COVID-19 data analysis, as well as providing a framework on the related existing applications and studies to facilitate future research on COVID-19 analysis. Therefore, in this paper, we conduct a literature review to highlight the contributions of several studies in the domain of COVID-19-based big data analysis. The study presents as a taxonomy several applications used to manage and control the pandemic. Moreover, this study discusses several challenges encountered when analyzing COVID-19 data. The findings of this paper suggest valuable future directions to be considered for further research and applications.

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Section: Applications Of Data Analytics In Covid-19mentioning

confidence: 99%

“…Furthermore, we found that several tools are used to implement models proposed in the reviewed studies, including R language [ 43 , 44 , 62 ], R language with Python [ 42 , 67 ], MATLAB [ 40 , 57 , 66 ], MS Excel [ 54 , 62 ], IBM SPSS [ 47 , 54 , 61 ], and GraphPad Prism [ 50 ].…”

Section: Findings Challenges and Future Directionsmentioning

confidence: 99%

Applications of Big Data Analytics to Control COVID-19 Pandemic

Alsunaidi

Almuhaideb

Ibrahim

et al. 2021

Sensors

100

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“…In the case of having more information to estimate scale and shape, future research could investigate the viability of a Weibull distribution instead. (Celeux et al, 2006;Epstein & Dexter, 2020;Mun, 2008).…”

Section: Advanced Model Option Bmentioning

confidence: 99%

“…These ranged from the development of vaccines to tools for forecasting infections and the impact of political mitigation measures using advanced artificial intelligence techniques. However, the management of hospitals and their resources did not seem to receive as much attention (Epstein & Dexter, 2020). Each country, each region, and even each hospital came to manage its critical resources (beds and healthcare personnel) locally, without the existence of coordination mechanisms and tools to anticipate and mitigate the consequences of the waves, which were usually produced by the aggregation of local infection clusters.…”

Section: Introductionmentioning

confidence: 99%

“…Los ámbitos han sido múltiples, desde el desarrollo de vacunas, hasta herramientas de previsión de infecciones y del impacto de medidas políticas de mitigación utilizando las técnicas más avanzadas de inteligencia artificial. Sin embargo, la gestión de hospitales y de sus recursos no parece haber recibido tanta atención (Epstein & Dexter, 2020). Se puede observar que cada país, cada región y hasta cada hospital ha llegado a gestionar sus recursos críticos (camas y personal sanitario) de manera local, sin que existan mecanismos de coordinación y herramientas para anticipar y mitigar las consecuencias de las olas, que normalmente se producen por agregación de clústeres locales de infecciones.…”

Section: Introductionunclassified

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A data generator for covid-19 patients’ care requirements inside hospitals

Marín-García

Ruiz²,

Maheut

et al. 2021

Work. pap. oper. manag.

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This paper presents the generation of a plausible data set related to the needs of COVID-19 patients with severe or critical symptoms. Possible illness’ stages were proposed within the context of medical knowledge as of January 2021. The parameters chosen in this data set were customized to fit the population data of the Valencia region (Spain) with approximately 2.5 million inhabitants. They were based on the evolution of the pandemic between September 2020 and March 2021, a period that included two complete waves of the pandemic.Contrary to expectation and despite the European and national transparency laws (BOE-A2013-12887, 2013; European Parliament and Council of the European Union, 2019), the actual COVID-19 pandemic-related data, at least in Spain, took considerable time to be updated and made available (usually a week or more). Moreover, some relevant data necessary to develop and validate hospital bed management models were not publicly accessible. This was either because these data were not collected, because public agencies failed to make them public (despite having them indexed in their databases), the data were processed within indicators and not shown as raw data, or they simply published the data in a format that was difficult to process (e.g., PDF image documents versus CSV tables). Despite the potential of hospital information systems, there were still data that were not adequately captured within these systems.Moreover, the data collected in a hospital depends on the strategies and practices specific to that hospital or health system. This limits the generalization of "real" data, and it encourages working with "realistic" or plausible data that are clean of interactions with local variables or decisions (Gunal, 2012; Marin-Garcia et al., 2020). Besides, one can parameterize the model and define the data structure that would be necessary to run the model without delaying till the real data become available. Conversely, plausible data sets can be generated from publicly available information and, later, when real data become available, the accuracy of the model can be evaluated (Garcia-Sabater and Maheut, 2021).This work opens lines of future research, both theoretical and practical. From a theoretical point of view, it would be interesting to develop machine learning tools that, by analyzing specific data samples in real hospitals, can identify the parameters necessary for the automatic prototyping of generators adapted to each hospital. Regarding the lines of research applied, it is evident that the formalism proposed for the generation of sound patients is not limited to patients affected by SARS-CoV-2 infection. The generation of heterogeneous patients can represent the needs of a specific population and serve as a basis for studying complex health service delivery systems.

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