An integrated framework for modelling respiratory disease transmission and designing surveillance networks using a sentinel index

Defining epidemiologically relevant placements for sentinel units is critical for establishing effective health surveillance systems. We propose a novel methodology to identify optimal sentinel unit locations using network approaches and metapopulation modelling. Disease transmission dynamics were modelled using syndromic data on respiratory diseases, integrated with road mobility data. A generalizable sentinel index is introduced as a metric that evaluates the suitability of a site to host a sentinel unit, based on topological metrics and metapopulation dynamics. A case study using syndromic data from primary health care attendances in Bahia, Brazil, validated the relevance of existing sentinel units while identifying opportunities for local re-designs to improve disease surveillance coverage

Combining machine learning and dynamic system techniques to early detection of respiratory outbreaks in routinely collected primary healthcare records

Background: Methods that enable early outbreak detection represent powerful tools in epidemiological surveillance, allowing adequate planning and timely response to disease surges. Syndromic surveillance data collected from primary healthcare encounters can be used as a proxy for the incidence of confirmed cases of respiratory diseases. Deviations from historical trends in encounter numbers can provide valuable insights into emerging diseases with the potential to trigger widespread outbreaks. Methods: Unsupervised machine learning methods and dynamical systems concepts were combined into the Mixed Model of Artificial Intelligence and Next-Generation (MMAING) ensemble, which aims to detect early signs of outbreaks based on primary healthcare encounters. We used data from 27 Brazilian health regions, which cover 41% of the country’s territory, from 2017-2023 to identify anomalous increases in primary healthcare encounters that could be associated with an epidemic onset. Our validation approach comprised (i) a comparative analysis across Brazilian capitals; (ii) an analysis of warning signs for the COVID-19 period; and (iii) a comparison with related surveillance methods (namely EARS C1, C2, C3) based on real and synthetic labeled data. Results: The MMAING ensemble demonstrated its effectiveness in early outbreak detection using both actual and synthetic data, outperforming other surveillance methods. It successfully detected early warning signals in synthetic data, achieving a probability of detection of 86%, a positive predictive value of 85%, and an average reliability of 79%. When compared to EARS C1, C2, and C3, it exhibited superior performance based on receiver operating characteristic (ROC) curve results on synthetic data. When evaluated on real-world data, MMAING performed on par with EARS C2. Notably, the MMAING ensemble accurately predicted the onset of the four waves of the COVID-19 period in Brazil, further validating its effectiveness in real-world scenarios. Conclusion: Identifying trends in time series data related to primary healthcare encounters indicated the possibility of developing a reliable method for the early detection of outbreaks. MMAING demonstrated consistent identification capabilities across various scenarios, outperforming established reference methods

Analysis of events with b-jets and a pair of leptons of the same charge in pp collisions at √s=8 TeV with the ATLAS detector

An analysis is presented of events containing jets including at least one b-tagged jet, sizeable missing transverse momentum, and at least two leptons including a pair of the same electric charge, with the scalar sum of the jet and lepton transverse momenta being large. A data sample with an integrated luminosity of 20.3 fb−1 of pp collisions at √s=8 TeV recorded by the ATLAS detector at the Large Hadron Collider is used. Standard Model processes rarely produce these final states, but there are several models of physics beyond the Standard Model that predict an enhanced rate of production of such events; the ones considered here are production of vector-like quarks, enhanced four-top-quark production, pair production of chiral b′-quarks, and production of two positively charged top quarks. Eleven signal regions are defined; subsets of these regions are combined when searching for each class of models. In the three signal regions primarily sensitive to positively charged top quark pair production, the data yield is consistent with the background expectation. There are more data events than expected from background in the set of eight signal regions defined for searching for vector-like quarks and chiral b′-quarks, but the significance of the discrepancy is less than two standard deviations. The discrepancy reaches 2.5 standard deviations in the set of five signal regions defined for searching for four-top-quark production. The results are used to set 95% CL limits on various models

Social and biological determinants of iron deficiency anemia

This cross-sectional study aimed to identify the social and biological determinants of anemia in children enrolled in the Brazilian Income Transfer Program (PBF). The study evaluated 446 children (69.1% of the total enrolled) ranging from 6 to 84 months of age, of whom 262 were receiving the income transfer (60.2% of the beneficiaries) and 184 were not (87.6% of the non-beneficiaries). Testing for anemia was performed with the Hemocue portable hemoglobinometer, and the cutoff points were set at 11.0 and 11.5g/dL, according to age bracket. The data were analyzed using Poisson hierarchical regression with robust variance for multivariate analysis. There was no difference in the anemia prevalence rates between the beneficiary and non-beneficiary groups. Risk factors for anemia were low paternal schooling, cesarean birth, consumption of untreated water, stunting, and age less than 24 months. Prevalence of anemia in the group of non-beneficiary children under two years of age was significantly higher than in the beneficiary group in the same age bracket, suggesting the importance of the PBF income transfer for preventing anemia in children.Neste estudo transversal, objetivou-se conhecer a determinação social e biológica da anemia em crianças cadastradas no Programa Bolsa Família (PBF). Foram avaliadas 446 crianças (69,1% do total cadastrado) com idade entre 6 e 84 meses, sendo que 262 (60,2%) recebiam o benefício, e 184 (87,6%) não recebiam. O teste de anemia foi realizado com o hemoglobinômetro portátil Hemocue, e os pontos de corte adotados foram 11,0 e 11,5g/dL, segundo a faixa etária. Utilizou-se regressão de Poisson hierarquizada com variância robusta para análise multivariada. Não houve diferença entre as prevalências de anemia entre os grupos beneficiários e não-beneficiários. Os fatores de risco para essa carência foram baixa escolaridade paterna, parto cesariano, consumo de água sem tratamento, baixa estatura e idade inferior a 24 meses. A prevalência de anemia no grupo de crianças menores de dois anos não-beneficiárias foi significantemente maior do que no grupo beneficiário de mesma idade, o que sugere a importância do benefício do PBF no combate à anemia em crianças

Experimental traumatic brain injury

Traumatic brain injury, a leading cause of death and disability, is a result of an outside force causing mechanical disruption of brain tissue and delayed pathogenic events which collectively exacerbate the injury. These pathogenic injury processes are poorly understood and accordingly no effective neuroprotective treatment is available so far. Experimental models are essential for further clarification of the highly complex pathology of traumatic brain injury towards the development of novel treatments. Among the rodent models of traumatic brain injury the most commonly used are the weight-drop, the fluid percussion, and the cortical contusion injury models. As the entire spectrum of events that might occur in traumatic brain injury cannot be covered by one single rodent model, the design and choice of a specific model represents a major challenge for neuroscientists. This review summarizes and evaluates the strengths and weaknesses of the currently available rodent models for traumatic brain injury