Inferring change points in the COVID-19 spreading reveals the effectiveness of interventions

As COVID-19 is rapidly spreading across the globe, short-term modeling forecasts provide time-critical information for decisions on containment and mitigation strategies. A main challenge for short-term forecasts is the assessment of key epidemiological parameters and how they change when first interventions show an effect. By combining an established epidemiological model with Bayesian inference, we analyze the time dependence of the effective growth rate of new infections. Focusing on the COVID-19 spread in Germany, we detect change points in the effective growth rate that correlate well with the times of publicly announced interventions. Thereby, we can quantify the effect of interventions, and we can incorporate the corresponding change points into forecasts of future scenarios and case numbers. Our code is freely available and can be readily adapted to any country or region.Comment: 23 pages, 11 figures. Our code is freely available and can be readily adapted to any country or region ( https://github.com/Priesemann-Group/covid19_inference_forecast/

USO DE ANÃLISE ESTATÃSTICA MULTIVARIADA PARA TIPIFICAÇÃO DE PRODUTORES DE LEITE DE MINAS GERAIS

The present work had as its main objective to identify and characterize milk production systems through methods of multivaried statistical analysis. Factorial analysis, cluster, discriminant and canonical correlation were utilized. The production gradation and defrayal were the main classification criterium among the productors. Combining the results obtained with the cluster analysis and the canonical correlation analysis, the second classification analysis aggregates the grazing lands and the care with the flock health. Three productors groups were identified, among which the first one is prominent for aggregating about 90% of the total analyzed and is composed by relatively smaller productors. The discriminant analysis and the measuring among financial indicators confirm such classification.multivaried analysis, milk cattle raising, Minas Gerais., Livestock Production/Industries, Research Methods/ Statistical Methods,

Dynamic Adaptive Computation: Tuning network states to task requirements

Neural circuits are able to perform computations under very diverse conditions and requirements. The required computations impose clear constraints on their fine-tuning: a rapid and maximally informative response to stimuli in general requires decorrelated baseline neural activity. Such network dynamics is known as asynchronous-irregular. In contrast, spatio-temporal integration of information requires maintenance and transfer of stimulus information over extended time periods. This can be realized at criticality, a phase transition where correlations, sensitivity and integration time diverge. Being able to flexibly switch, or even combine the above properties in a task-dependent manner would present a clear functional advantage. We propose that cortex operates in a "reverberating regime" because it is particularly favorable for ready adaptation of computational properties to context and task. This reverberating regime enables cortical networks to interpolate between the asynchronous-irregular and the critical state by small changes in effective synaptic strength or excitation-inhibition ratio. These changes directly adapt computational properties, including sensitivity, amplification, integration time and correlation length within the local network. We review recent converging evidence that cortex in vivo operates in the reverberating regime, and that various cortical areas have adapted their integration times to processing requirements. In addition, we propose that neuromodulation enables a fine-tuning of the network, so that local circuits can either decorrelate or integrate, and quench or maintain their input depending on task. We argue that this task-dependent tuning, which we call "dynamic adaptive computation", presents a central organization principle of cortical networks and discuss first experimental evidence.Comment: 6 pages + references, 2 figure

A unified picture of neuronal avalanches arises from the understanding of sampling effects

To date, it is still impossible to sample the entire mammalian brain with single-neuron precision. This forces one to either use spikes (focusing on few neurons) or to use coarse-sampled activity (averaging over many neurons, e.g. LFP). Naturally, the sampling technique impacts inference about collective properties. Here, we emulate both sampling techniques on a spiking model to quantify how they alter observed correlations and signatures of criticality. We discover a general effect: when the inter-electrode distance is small, electrodes sample overlapping regions in space, which increases the correlation between the signals. For coarse-sampled activity, this can produce power-law distributions even for non-critical systems. In contrast, spikes enable one to distinguish the underlying dynamics. This explains why coarse measures and spikes have produced contradicting results in the past -- that are now all consistent with a slightly subcritical regime.Comment: 14 pages, 7 figures, Supp. Info. w/ 7 page

Traumatic brain injury (TBI): morbidity, mortality and economic implications

Traumatisms, in general, result in high costs for health systems worldwide. They consist of the leading cause of death in young adults, primarily males. Traumatic brain injury (TBI) represents good part of this spending, reaching globally significant mortality rate, around 1.5 million victims a year. Only in the United States (US) attendances related to traumatic brain injuries in emergency departments revolve around 1.35 million annually, plus about 275,000 hospitalizations and 52,000 deaths. In Brazil, only in 2012 was spent over one billion dollars with hospitalizations related to external causes, including TBI. Mild TBI (Glasgow Coma Scale (GCS) 14-15) occur in about 80% of the total demand, moderate (GCS 9-13) in 10% and serious (GCS 3-8) in 10 %. Regarding mortality rate, this is relatively low compared to the total number, since much of fatal outcomes fits in the moderate to severe groups. One of lesions a valuable prognostic factor related to the TBI is the subdural hematoma (SDH), responsible for complications in up to 45% of cases of TBI, expressing mortality between 60-80% depending on the implemented workup and may even reach 90% when in delay of appropriate treatment. The acute subdural hematoma (ASDH) thus represents a neurosurgical emergency, taking most of these patients to be subjected to urgent evacuation of the hematoma by craniotomy, which also is not without risks, with several reports in literature of new contralateral hematoma formation after craniotomy for evacuation of hematoma, further aggravating the patient's prognosis. For best results of the TBI is needed better understanding of the pathophysiology, identification of newer parameters of brain function and development of innovative therapeutic modalities. According to the Centers for Disease Control and Prevention (CDC), under the Department of Health and Human Services, population data on TBI are fundamental for understanding its impact on the society and know the profile of patients and the mechanisms trauma, to assist in the formulation of prevention strategies and in setting priorities for research and support services to patients living with traumatic brain injury

Rationale, study design, and analysis plan of the Alveolar Recruitment for ARDS Trial (ART): Study protocol for a randomized controlled trial

Background: Acute respiratory distress syndrome (ARDS) is associated with high in-hospital mortality. Alveolar recruitment followed by ventilation at optimal titrated PEEP may reduce ventilator-induced lung injury and improve oxygenation in patients with ARDS, but the effects on mortality and other clinical outcomes remain unknown. This article reports the rationale, study design, and analysis plan of the Alveolar Recruitment for ARDS Trial (ART). Methods/Design: ART is a pragmatic, multicenter, randomized (concealed), controlled trial, which aims to determine if maximum stepwise alveolar recruitment associated with PEEP titration is able to increase 28-day survival in patients with ARDS compared to conventional treatment (ARDSNet strategy). We will enroll adult patients with ARDS of less than 72 h duration. The intervention group will receive an alveolar recruitment maneuver, with stepwise increases of PEEP achieving 45 cmH(2)O and peak pressure of 60 cmH2O, followed by ventilation with optimal PEEP titrated according to the static compliance of the respiratory system. In the control group, mechanical ventilation will follow a conventional protocol (ARDSNet). In both groups, we will use controlled volume mode with low tidal volumes (4 to 6 mL/kg of predicted body weight) and targeting plateau pressure <= 30 cmH2O. The primary outcome is 28-day survival, and the secondary outcomes are: length of ICU stay; length of hospital stay; pneumothorax requiring chest tube during first 7 days; barotrauma during first 7 days; mechanical ventilation-free days from days 1 to 28; ICU, in-hospital, and 6-month survival. ART is an event-guided trial planned to last until 520 events (deaths within 28 days) are observed. These events allow detection of a hazard ratio of 0.75, with 90% power and two-tailed type I error of 5%. All analysis will follow the intention-to-treat principle. Discussion: If the ART strategy with maximum recruitment and PEEP titration improves 28-day survival, this will represent a notable advance to the care of ARDS patients. Conversely, if the ART strategy is similar or inferior to the current evidence-based strategy (ARDSNet), this should also change current practice as many institutions routinely employ recruitment maneuvers and set PEEP levels according to some titration method.Hospital do Coracao (HCor) as part of the Program 'Hospitais de Excelencia a Servico do SUS (PROADI-SUS)'Brazilian Ministry of Healt

The signal of an extracellular neuronal recording depends on neuronal morphologies, tissue filtering, and other factors, which all impact the coarse-sampling effect.

In effect, an important factor is the distance of the neuron to the electrode. Here, we show how the distance-dependence, with which a neuron’s activity contributes to an electrode, determines the collapse of avalanche distributions. A: Biophysically plausible distance dependence of LFP, reproduced from [38]. B: Sketch of a neuron’s contribution to an electrode at distance dik, as motivated by (A). The decay exponent γ characterizes the field of view. C–F: Avalanche-size distribution p(S) for coarse-sampling with the sketched electrode contributions. C, D: With a wide-field of view, distributions are hardly distinguishable between dynamic states. In contrast, for spiking activity the differences are clear (light shades in C). E, F: With a narrower field of view, distributions do not fully collapse on top of each other, but differences between reverberating and critical dynamics remain hard to identify. Parameters: Inter-electrode distance dE = 400 μm and time-bin size Δt = 8 ms. Other parameter combinations in Fig B in S1 Text.</p

Analysis pipeline for avalanches from sampled data.

I: Under coarse-sampling (LFP-like), the recording is demeaned and thresholded. II: The timestamps of events are extracted. Under sub-sampling (spikes), timestamps are obtained directly. III: Events from all channels are binned with time-bin size Δt and summed. The size S of each neuronal avalanche is calculated. IV: The probability of an avalanche size is given by the (normalized) count of its occurrences throughout the recording.</p