This comprehensive overview focuses on the issues presented by the pandemic due to COVID-19, understanding its spread and the wide-ranging effects of government-imposed restrictions. The overview examines the utility of autoregressive integrated moving average (ARIMA) models, which are often overlooked in pandemic forecasting due to perceived limitations in handling complex and dynamic scenarios. Our work applies ARIMA models to a case study using data from Recife, the capital of Pernambuco, Brazil, collected between March and September 2020. The research provides insights into the implications and adaptability of predictive methods in the context of a global pandemic. The findings highlight the ARIMA models’ strength in generating accurate short-term forecasts, crucial for an immediate response to slow down the disease’s rapid spread. Accurate and timely predictions serve as the basis for evidence-based public health strategies and interventions, greatly assisting in pandemic management. Our model selection involves an automated process optimizing parameters by using autocorrelation and partial autocorrelation plots, as well as various precise measures. The performance of the chosen ARIMA model is confirmed when comparing its forecasts with real data reported after the forecast period. The study successfully forecasts both confirmed and recovered COVID-19 cases across the preventive plan phases in Recife. However, limitations in the model’s performance are observed as forecasts extend into the future. By the end of the study period, the model’s error substantially increased, and it failed to detect the stabilization and deceleration of cases. The research highlights challenges associated with COVID-19 data in Brazil, such as under-reporting and data recording delays. Despite these limitations, the study emphasizes the potential of ARIMA models for short-term pandemic forecasting while emphasizing the need for further research to enhance long-term predictions.

The focus of this paper is given to investigate the effect of different fibers on the pore pressure of fiber reinforced self-consolidating concrete under fire. The investigation on the pore pressure-time and temperature relationships at different depths of fiber reinforced self-consolidating concrete beams was carried out. The results indicated that micro PP fiber is more effective in mitigating the pore pressure than macro PP fiber and steel fiber. The composed use of steel fiber, micro PP fiber and macro PP fiber showed clear positive hybrid effect on the pore pressure reduction near the beam bottom subjected to fire. Compared to the effect of macro PP fiber with high dosages, the effect of micro PP fiber with low fiber contents on the pore pressure reduction is much stronger. The significant factor for reduction of pore pressure depends mainly on the number of PP fibers and not only on the fiber content. An empirical formula was proposed to predict the relative maximum pore pressure of fiber reinforced self-consolidating concrete exposed to fire by considering the moisture content, compressive strength and various fibers. The suggested model corresponds well with the experimental results of other research and tends to prove that the micro PP fiber can be the vital component for reduction in pore pressure, temperature as well spalling of concrete.National Natural Science Foundation of China (Grant: 51121005), DUT and Fundaçãopara a Ciência e a Tecnologia (SFRH/BPD/22680/2005), the FEDER Funds through “Programa Operacional Factores de Competitividade - COMPETE" and by Portuguese Funds through FCT-within the Projects PEst-CMAT/UI0013/2011 and PTDC/MAT/112273/2009

Poor immunological responders (PIR) are HIV-infected patients with virologic suppression upon antiretroviral therapy (ART) but persistently low CD4+ T cell counts. Early identification of PIR is important given their higher morbimortality compared to adequate immune responders (AIR). In this study, 33 patients severely lymphopenic at ART onset, were followed for at least 36 months, and classified as PIR or AIR using cluster analysis grounded on their CD4+ T cell count trajectories. Based on a variety of immunological parameters, we built predictive models of PIR/AIR outcome using logistic regression. All PIR had CD4+ T cell counts consistently below 500 cells/µL, while all AIR reached this threshold. AIR showed higher percentage of recent thymic emigrants among CD4+ T cells; higher numbers of sjTRECs and greater sj/βTREC ratios; and significant increases in thymic volume from baseline to 12 months of ART. We identified mathematical models that correctly predicted PIR/AIR outcome after 36 months of therapy in 77-87 % of cases, based on observations made until 2-6 months after ART onset. This study highlights the importance of thymic activity in the immune recovery of severely lymphopenic patients, and may help to select the patients that will benefit from closer follow-up or novel therapeutic approaches.