Some of the key questions of interest during the COVID-19 pandemic (and all outbreaks) include where did the disease start, how is it spreading, who is at risk, and how to control the spread. There are a large number of complex factors driving the spread of pandemics, and, as a result, multiple modeling techniques play an increasingly important role in shaping public policy and decision making. As different countries and regions go through phases of the pandemic, the questions and data availability also changes. Especially of interest is aligning model development and data collection to support response efforts at each stage of the pandemic. The COVID-19 pandemic has been unprecedented in terms of real-time collection and dissemination of a number of diverse datasets, ranging from disease outcomes, to mobility, behaviors, and socio-economic factors. The data sets have been critical from the perspective of disease modeling and analytics to support policymakers in real-time.  https://www.selleckchem.com/products/lenalidomide-s1029.html  In this overview article, we survey the data landscape around COVID-19, with a focus on how such datasets have aided modeling and response through different stages so far in the pandemic. We also discuss some of the current challenges and the needs that will arise as we plan our way out of the pandemic.Mycobacterium tuberculosis (Mtb) is transmitted by aerosol and can cause serious bacterial infection in the lung that can be fatal if left untreated. Mtb is now the leading cause of death worldwide by an infectious agent. Characterizing the early events of in vivo infection following aerosol challenge is critical for understanding how innate immune cells respond to infection but is technically challenging due to the small number of bacteria that initially infect the lung. Previous studies either evaluated Mtb-infected cells at later stages of infection when the number of bacteria in the lung is much higher or used in vitro model systems to assess the response of myeloid cells to Mtb. Here, we describe a method that uses fluorescent bacteria, a high-dose aerosol infection model, and flow cytometry to track Mtb-infected cells in the lung immediately following aerosol infection and fluorescence-activated cell sorting (FACS) to isolate naïve, bystander, and Mtb-infected cells for downstream applications, including RNA-sequencing. This protocol provides the ability to monitor Mtb-infection and cell-specific responses within the context of the lung environment, which is known to modulate the function of both resident and recruited populations. Using this protocol, we discovered that alveolar macrophages respond to Mtb infection in vivo by up-regulating a cell protective transcriptional response that is regulated by the transcription factor Nrf2 and is detrimental to early control of the bacteria.Significance Cerebral blood flow is an important biomarker of brain health and function as it regulates the delivery of oxygen and substrates to tissue and the removal of metabolic waste products. Moreover, blood flow changes in specific areas of the brain are correlated with neuronal activity in those areas. Diffuse correlation spectroscopy (DCS) is a promising noninvasive optical technique for monitoring cerebral blood flow and for measuring cortex functional activation tasks. However, the current state-of-the-art DCS adoption is hindered by a trade-off between sensitivity to the cortex and signal-to-noise ratio (SNR). Aim We aim to develop a scalable method that increases the sensitivity of DCS instruments. Approach We report on a multispeckle DCS (mDCS) approach that is based on a 1024-pixel single-photon avalanche diode (SPAD) camera. Our approach is scalable to > 100,000 independent speckle measurements since large-pixel-count SPAD cameras are becoming available, owing to the investments in LiDAR technology for automotive and augmented reality applications. Results We demonstrated a 32-fold increase in SNR with respect to traditional single-speckle DCS. Conclusion A mDCS system that is based on a SPAD camera serves as a scalable method toward high-sensitivity DCS measurements, thus enabling both high sensitivity to the cortex and high SNR.Significance Isolating task-evoked brain signals from background physiological noise (e.g., cardiac, respiratory, and blood pressure fluctuations) poses a major challenge for the analysis of functional near-infrared spectroscopy (fNIRS) data. Aim The performance of several analytic methods to separate background physiological noise from brain activity including spatial and temporal filtering, regression, component analysis, and the use of short-separation (SS) measurements were quantitatively compared. Approach Using experimentally recorded background signals (breath-hold task), receiver operating characteristics simulations were performed by adding various levels of additive synthetic "brain" responses in order to examine the sensitivity and specificity of several previously proposed analytic approaches. Results We found that the use of SS fNIRS channels as regressors of no-interest within a linear regression model was the best performing approach examined. Furthermore, we found that the addition of all available SS data, including all recorded channels and both hemoglobin species, improved the method performance despite the additional degrees-of-freedom of the models. When SS data were not available, we found that principal component filtering using a separate baseline scan was the best alternative. Conclusions The use of multiple SS measurements as regressors of no interest implemented in a robust, iteratively prewhitened, general linear model has the best performance of the tested existing methods.Significance Functional near-infrared spectroscopy (fNIRS) uses surface-placed light sources and detectors to record underlying changes in the brain due to fluctuations in hemoglobin levels and oxygenation. Since these measurements are recorded from the surface of the scalp, the mapping from underlying regions-of-interest (ROIs) in the brain space to the fNIRS channel space measurements depends on the registration of the sensors, the anatomy of the head/brain, and the sensitivity of these diffuse measurements through the tissue. However, small displacements in the probe position can change the distribution of recorded brain activity across the fNIRS measurements. Aim We propose an approach using either individual or atlas-based brain-space anatomical information to define ROI-based statistical hypotheses to test the null involvement of specific regions, which allows us to test the analogous ROI across subjects while adjusting for fNIRS probe placement and sensitivity differences due to head size variations without a localizer task.