OBJECTIVE To estimate the required number of public beds for adults in intensive care units in the state of Rio de Janeiro to meet the existing demand and compare results with recommendations by the Brazilian Ministry of Health.METHODS The study uses a hybrid model combining time series and queuing theory to predict the demand and estimate the number of required beds. Four patient flow scenarios were considered according to bed requests, percentage of abandonments and average length of stay in intensive care unit beds. The results were plotted against Ministry of Health parameters. Data were obtained from the State Regulation Center from 2010 to 2011.RESULTS There were 33,101 medical requests for 268 regulated intensive care unit beds in Rio de Janeiro. With an average length of stay in regulated ICUs of 11.3 days, there would be a need for 595 active beds to ensure system stability and 628 beds to ensure a maximum waiting time of six hours. Deducting current abandonment rates due to clinical improvement (25.8%), these figures fall to 441 and 417. With an average length of stay of 6.5 days, the number of required beds would be 342 and 366, respectively; deducting abandonment rates, 254 and 275. The Brazilian Ministry of Health establishes a parameter of 118 to 353 beds. Although the number of regulated beds is within the recommended range, an increase in beds of 122.0% is required to guarantee system stability and of 134.0% for a maximum waiting time of six hours.CONCLUSIONS Adequate bed estimation must consider reasons for limited timely access and patient flow management in a scenario that associates prioritization of requests with the lowest average length of stay.

ABSTRACT. Critical Care is a medical specialty which addresses the life-saving and lifesustaining management of patients at risk of imminent death. The number of Intensive Care Unit (ICU) beds has an impact on patient's prognosis. This paper aims to determine the optimal number of ICU beds to reduce patient's waiting time. Time series was applied to predict demand making use of information on the daily patient's requests for ICU beds to obtain a demand forecast by means of exponential smoothing and Box-Jenkins models, which provided the input of a Queuing model. The outputs were the optimal number of ICU beds, in different scenarios, based on demand rate and patient's length of stay (LOS). A maximum waiting time in the queue of 6 hours was proposed and compared to government recommendation (118-353 beds). The need for ICU beds varied from 345 to 592 for a 6-hour waiting time (for a LOS of 6.5 to 11.2 days, respectively). The results show that managing demand and discharge timing could control the queue. Moreover, they also suggest that the current recommendation is inadequate for the demand.

The oil price is a relevant variable for economic policy makers in countries where this commodity is the main energy source as well as in other countries where crude oil is not the only energy source. The sudden variations in the crude oil price cause direct influence in the national economies bringing changes in foreign trade, investments and productive activities. Therefore, the crude oil market is very important for the economic development. Furthermore, crude oil is directly or indirectly present in all productive activities. This way the crude oil market is related to the industrial production indicators. Many researches aim at establishing the stochastic process that can represent the movements of macroeconomic indicators through the oil price returns or variations that have been done in recent years. The purpose of this work is to study the relationship between crude oil prices and selected industrial production indicators of the Brazilian economy. To do that this work carried out cointegration and causality tests, from VAR estimations, and impulse response analysis. The data used in this study is monthly macroeconomic indicators, mentioned above, and the Brent crude oil type price negotiated in the London Market. All data used is in US$. The period of the sample used is from

This work aims to estimate the idiosyncratic risk of Latin American economies and emerging economies using heteroscedastic conditional models to verify the impact of the Covid-19 pandemic on the risk associated with productive projects. The methodology used is based on the portfolio theory to estimate the idiosyncratic risk. The results highlight that Latin American economies are more susceptible to sanitary crises, such as the current pandemic, than emerging economies. The inability of emerging countries to generate the necessary savings to provide for their development imposes the need to attract resources for project financing and investment. Thus, determining the specific risk of Latin American countries is fundamental for international investors giving them another parameter when deciding on investment or financing on the continent. Originally, this work demonstrates how the sanitary crisis deriving from the Covid-19 pandemic affected the idiosyncratic or specific risk of Latin American economies using their capital market indicators. This study contributes to the assessment of Latin American economies specific risk or country risk at the beginning of the pandemic.

The purpose of this work is to identify variables that are relevant to the copper price setting in the international market. Thus statistical hypothesis tests and statistical tools that help to identify historical relevance and to measure the intensity of the impact of each variable on the copper price on several time horizons were applied. At the end, a regression model that aims to assess the combined effect of the considered time series was estimated. The global industrial production and the aluminum price showed the greatest evidences of being relevant to the copper price. The results suggest that copper stocks, foreign exchange rates and crude oil price should also be considered.