Related Experiment Video
Updated: Dec 7, 2025
An Educational Video Demonstration of How to Prone a Critically Ill Intubated Patient
Published on: November 30, 2022
Forecast UTI: application for predicting intensive care unit beds in the context of the COVID-19 pandemic
Luiz Leduíno de Salles Neto1, Camila Bertini Martins2, Antônio Augusto Chaves1
1Universidade Federal de São Paulo, Instituto de Ciência e Tecnologia, São José dos Campos, SP, Brasil.
Abstract:
In view of the need to manage and forecast the number of Intensive Care Unit (ICU) beds for critically ill COVID-19 patients, the Forecast UTI open access application was developed to enable hospital indicator monitoring based on past health data and the temporal dynamics of the Coronavirus epidemic. Forecast UTI also enables short-term forecasts of the number of beds occupied daily by COVID-19 patients and possible care scenarios to be established. This article presents the functions, mode of access and examples of uses of Forecast UTI, a computational tool intended to assist managers of public and private hospitals within the Brazilian National Health System by supporting quick, strategic and efficient decision-making.
More Related Videos
09:36Halogenated Agent Delivery in Porcine Model of Acute Respiratory Distress Syndrome via an Intensive Care Unit Type Device
Published on: September 24, 2020
10:38Observational Study Protocol for Repeated Clinical Examination and Critical Care Ultrasonography Within the Simple Intensive Care Studies
Published on: January 16, 2019
Related Concept Videos
01:18Steps in Outbreak Investigation
01:28Current Trends in Nursing I
01:17Urinary Tract Infection IV: Nursing Management
01:30Urinary Tract Infection III: Diagnostic Studies and Interprofessional Care
01:30Current Trends in Nursing II
01:03Prediction Intervals
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.