Health care for burn patients is one of the most expensive aspects of current health care systems and, possibly, one of the least studied in terms of costs and results. Every year in the United States 1.25 million burn patients are treated, of whom at least 50 000 require hospitalization. Severe burns with mortality rates of 50% or higher continue to provide a challenge to science. In addition, burn centers are subject to the growing pressure of improving their services, with regional coordination of facilities and institutions and improvement of facilities and management.¹ The aim of burn treatment centers should change from trying to save lives to enabling patients to return to productive activity.²

Addressing surgical site infection (SSI) is accomplished, in part, through studies that attempt to clarify the nature of many essential factors in the control of SSI. We sought to examine the link between multiple risk factors, including environmental factors, and SSI for prevention management. We conducted a longitudinal prospective study to identify SSIs in all patients who underwent interventions in 2014 in 8 selected hospitals on the Mediterranean coast of Spain. Risk factors related to the operating theatre included level of fungi and bacterial contamination, temperature and humidity, air renewal and differential air pressure. Patient-related variables included age, sex, comorbidity, nutrition level and transfusion. Other factors were antibiotic prophylaxis, electric versus manual shaving, American Society of Anaesthesiologists physical status classification, type of intervention, duration of the intervention and preoperative stay. Superficial SSI was most often associated with environmental factors, such as environmental contamination by fungi (from 2 colony-forming units) and bacteria as well as surface contamination. When there was no contamination in the operating room, no SSI was detected. Factors that determined deep and organ/space SSI were more often associated with patient characteristics (age, sex, transfusion, nasogastric feeding and nutrition, as measured by the level of albumin in the blood), type of intervention and preoperative stay. Antibiotic prophylaxis and shaving with electric razor were protective factors for both types of infection, whereas the duration of the intervention and the classification of the intervention as "dirty" were shared risk factors. Our results suggest the importance of environmental and surface contamination control to prevent SSI.

Background: Forecasting the behavior of epidemic outbreaks is vital in public health. This makes it possible to anticipate the planning and organization of the health system, as well as possible restrictive or preventive measures. During the COVID-19 pandemic, this need for prediction has been crucial. This paper attempts to characterize the alternative models that were applied in the first wave of this pandemic context, trying to shed light that could help to understand them for future practical applications. Methods: A systematic literature search was performed in standardized bibliographic repertoires, using keywords and Boolean operators to refine the findings, and selecting articles according to the main PRISMA 2020 statement recommendations. Results: After identifying models used throughout the first wave of this pandemic (between March and June 2020), we begin by examining standard data-driven epidemiological models, including studies applying models such as SIR (Susceptible-Infected-Recovered), SQUIDER, SEIR, time-dependent SIR, and other alternatives. For data-driven methods, we identify experiences using autoregressive integrated moving average (ARIMA), evolutionary genetic programming machine learning, short-term memory (LSTM), and global epidemic and mobility models. Conclusions: The COVID-19 pandemic has led to intensive and evolving use of alternative infectious disease prediction models. At this point it is not easy to decide which prediction method is the best in a generic way. Moreover, although models such as the LSTM emerge as remarkably versatile and useful, the practical applicability of the alternatives depends on the specific context of the underlying variable and on the information of the target to be prioritized. In addition, the robustness of the assessment is conditioned by heterogeneity in the quality of information sources and differences in the characteristics of disease control interventions. Further comprehensive comparison of the performance of models in comparable situations, assessing their predictive validity, is needed. This will help determine the most reliable and practical methods for application in future outbreaks and eventual pandemics.