Nonlinear time‐series forecasts for decision support: short‐term demand for ICU beds in Santiago, Chile, during the 2021 COVID‐19 pandemic

Quiroga, Bernardo F.; Vásquez, Cristián; Vicuña, M. Ignacia

doi:10.1111/itor.13222

Cited by 5 publications

(6 citation statements)

References 59 publications

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“…This section presents an overview of nine major global pandemic problem domains that analysts employ DES and ABS techniques to tackle (Hazarika and Gupta, 2020;He et al, 2020;Shim et al, 2020;Burgos and Ivanov, 2021;Akpan et al, 2023;Kim and Koo, 2021). The problems addressed using DES and ABS methods, as presented in Table 6, include epidemiological modeling, which involves forecasting COVID-19 infection and fatality rates (Hazarika and Gupta, 2020;Quiroga et al, 2021). Other pandemic challenges addressed involved evaluating the effectiveness of public health measures, contact tracing, and determining the optimal period for isolation.…”

Section: A Comparative Analysis Of Covid-19 Problems Tackled Using De...mentioning

confidence: 99%

“…Scientists scrambled for quick practical solutions to stem the tide. OR and MS techniques, including modeling and simulation, provided potent strategies to forecast and initiate infection control initiatives (Quiroga et al., 2021). The global pandemic problems offer an opportunity to examine the discourse about the relevance and potency of DES as an OR method.…”

Section: Introductionmentioning

confidence: 99%

“…provided potent strategies to forecast and initiate infection control initiatives (Quiroga et al, 2021). The global pandemic problems offer an opportunity to examine the discourse about the relevance and potency of DES as an OR method.…”

mentioning

confidence: 99%

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Discrete‐event simulation is still alive and strong: evidence from bibliometric performance evaluation of research during COVID‐19 global health pandemic

Akpan,

Shanker,

Offodile

2023

Int Trans Operational Res

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During the Annual Operational Research Society Simulation Workshop in Worchester, UK, in 2010, a panel discussion entitled “discrete‐event simulation (DES) is dead, long live agent‐based simulation (ABS)!” was held. Subsequent debates on the topic have captured the interest of many simulation professionals and researchers. This study lends credence to the discourse with evidence. In January 2020, a global health crisis named coronavirus disease (COVID‐19) occurred, infecting and killing millions of people globally, leading to community lockdowns and travel restrictions, and bringing the world to a standstill. Scientists scrambled for quick, practical solutions to stem the tide. Operational research (OR) and management science (MS) techniques, including modeling and simulation, provide effective methods to make COVID‐19 infection forecasts, assign/optimize intensive care unit beds, initiate healthcare management/control strategies, and decision‐making. The global pandemic problems offer an opportunity to evaluate the discourse on DES's continuous relevance and potency as an OR/MS technique. Utilizing bibliometric performance evaluation and text analytics of publications addressing various COVID‐19 pandemic problems, the results show an extensive use, relevance, and impact of the DES technique across multidisciplinary domains, including medical/healthcare, social and behavioral sciences, business, molecular biosciences, and more. The intellectual structure highlights strong multidisciplinary collaboration among sources in decision sciences, healthcare, natural, behavioral, and social sciences. The social network analysis of the publications shows the contributions and collaborations among authors, institutions, and countries. The study demonstrates that DES is still alive and robust, notwithstanding the hype about ABS.

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Section: A Comparative Analysis Of Covid-19 Problems Tackled Using De...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Discrete‐event simulation is still alive and strong: evidence from bibliometric performance evaluation of research during COVID‐19 global health pandemic

Akpan,

Shanker,

Offodile

2023

Int Trans Operational Res

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“…Hence, delayed discharge decisions may trickle down to emergency department admission delays, which are crucial for patient satisfaction (García‐Alfranca et al, 2018). Moreover, delays in emergency department admission may lead to emergency department crowding (Quiroga et al, 2022), which has been shown to further decrease patient satisfaction (Pines et al, 2008). Patient satisfaction is an important determinant of hospital revenue under the Value‐Based Purchasing program (Guadagnino, 2012).…”

Section: Countermeasures Against Externality From Economically Attrac...mentioning

confidence: 99%

Negative externality on service level across priority classes: Evidence from a radiology workflow platform

Lagzi

Quiroga

Romero

et al. 2023

J of Ops Management

Self Cite

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We study the potential negative impact of imbalanced compensation schemes on firm performance. We analyze data from a radiology workflow platform that connects off‐site radiologists with hospitals. These radiologists select tasks from a common pool, while service level is defined by priority‐specific turnaround time targets. However, imbalances between pay and workload of different tasks could result in higher priority tasks with low pay‐to‐workload ratio receiving poorer service. We investigate this hypothesis, showing turnaround time is decreasing in pay‐to‐workload for lower priority tasks, whereas it is increasing in workload for high‐priority tasks. Crucially, we find evidence of an externality effect: Having many economically attractive tasks with low priority can lead to longer turnaround times for higher priority tasks, increasing their likelihood of delay, thus partially defeating the purpose of the priority classes.

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“…Twitter data have been analyzed to understand the population-level spread of disease [ 29 - 31 ]. Furthermore, forecasting models have been created to track demand for ICU capacity planning in countries such as Chile [ 32 , 33 ], Brazil [ 34 ], Colombia [ 35 ], the United States [ 36 ], India [ 37 ], and China [ 38 ]. Previous studies have applied convergent cross-mapping (CCM) analysis to explore possible relationships involving antiepidemic measure–related tweets [ 39 ], the dynamics of misleading news on Twitter [ 40 ], and the identification of the global drivers of influenza [ 41 ].…”

Section: Introductionmentioning

confidence: 99%

A Novel Approach for the Early Detection of Medical Resource Demand Surges During Health Care Emergencies: Infodemiology Study of Tweets

Kaur,

Cargill,

Hui

et al. 2024

JMIR Form Res

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Background The COVID-19 pandemic has highlighted gaps in the current handling of medical resource demand surges and the need for prioritizing scarce medical resources to mitigate the risk of health care facilities becoming overwhelmed. Objective During a health care emergency, such as the COVID-19 pandemic, the public often uses social media to express negative sentiment (eg, urgency, fear, and frustration) as a real-time response to the evolving crisis. The sentiment expressed in COVID-19 posts may provide valuable real-time information about the relative severity of medical resource demand in different regions of a country. In this study, Twitter (subsequently rebranded as X) sentiment analysis was used to investigate whether an increase in negative sentiment COVID-19 tweets corresponded to a greater demand for hospital intensive care unit (ICU) beds in specific regions of the United States, Brazil, and India. Methods Tweets were collected from a publicly available data set containing COVID-19 tweets with sentiment labels and geolocation information posted between February 1, 2020, and March 31, 2021. Regional medical resource shortage data were gathered from publicly available data sets reporting a time series of ICU bed demand across each country. Negative sentiment tweets were analyzed using the Granger causality test and convergent cross-mapping (CCM) analysis to assess the utility of the time series of negative sentiment tweets in forecasting ICU bed shortages. Results For the United States (30,742,934 negative sentiment tweets), the results of the Granger causality test (for whether negative sentiment COVID-19 tweets forecast ICU bed shortage, assuming a stochastic system) were significant (P<.05) for 14 (28%) of the 50 states that passed the augmented Dickey-Fuller test at lag 2, and the results of the CCM analysis (for whether negative sentiment COVID-19 tweets forecast ICU bed shortage, assuming a dynamic system) were significant (P<.05) for 46 (92%) of the 50 states. For Brazil (3,004,039 negative sentiment tweets), the results of the Granger causality test were significant (P<.05) for 6 (22%) of the 27 federative units, and the results of the CCM analysis were significant (P<.05) for 26 (96%) of the 27 federative units. For India (4,199,151 negative sentiment tweets), the results of the Granger causality test were significant (P<.05) for 6 (23%) of the 26 included regions (25 states and the national capital region of Delhi), and the results of the CCM analysis were significant (P<.05) for 26 (100%) of the 26 included regions. Conclusions This study provides a novel approach for identifying the regions of high hospital bed demand during a health care emergency scenario by analyzing Twitter sentiment data. Leveraging analyses that take advantage of natural language processing–driven tweet extraction systems has the potential to be an effective method for the early detection of medical resource demand surges.

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Nonlinear time‐series forecasts for decision support: short‐term demand for ICU beds in Santiago, Chile, during the 2021 COVID‐19 pandemic

Cited by 5 publications

References 59 publications

Discrete‐event simulation is still alive and strong: evidence from bibliometric performance evaluation of research during COVID‐19 global health pandemic

Discrete‐event simulation is still alive and strong: evidence from bibliometric performance evaluation of research during COVID‐19 global health pandemic

Negative externality on service level across priority classes: Evidence from a radiology workflow platform

A Novel Approach for the Early Detection of Medical Resource Demand Surges During Health Care Emergencies: Infodemiology Study of Tweets

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