Neurological and Neuroradiological Manifestations in Neonates Born to Mothers With Coronavirus Disease 2019.

BACKGROUND: To investigate the complications that occurred in neonates born to mothers with coronavirus disease 2019 (COVID-19), focusing on neurological and neuroradiological findings, and to compare differences associated with the presence of maternal symptoms. METHODS: Ninety neonates from 88 mothers diagnosed with coronavirus disease 2019 (COVID-19) during pregnancy were retrospectively reviewed. Neonates were divided into two groups: symptomatic (Sym-M-N, n = 34) and asymptomatic mothers (Asym-M-N, n = 56). The results of neurological physical examinations were compared between the groups. Data on electroencephalography, brain ultrasound, and magnetic resonance imaging abnormalities were collected for neonates with neurological abnormalities. RESULTS: Neurological abnormalities at birth were found in nine neonates (Sym-M-N, seven of 34, 20.6%). Decreased tone was the most common physical abnormality (n = 7). Preterm and very preterm birth (P < 0.01), very low birth weight (P < 0.01), or at least one neurological abnormality on physical examination (P = 0.049) was more frequent in Sym-M-N neonates. All infants with abnormalities on physical examination showed neuroradiological abnormalities. The most common neuroradiological abnormalities were intracranial hemorrhage (n = 5; germinal matrix, n = 2; parenchymal, n = 2; intraventricular, n = 1) and hypoxic brain injury (n = 3). CONCLUSIONS: Neonates born to mothers with symptomatic COVID-19 showed an increased incidence of neurological abnormalities. Most of the mothers (96.4%) were unvaccinated before the COVID-19 diagnosis. Our results highlight the importance of neurological and neuroradiological management in infants born to mothers with COVID-19 and the prevention of maternal COVID-19 infection.

Deadly Fungi: Invasive Fungal Rhinosinusitis in the Head and Neck.

Invasive fungal rhinosinusitis (IFRS) is a serious infection that is associated with high morbidity and mortality rates. The incidence of IFRS has been increasing, mainly because of the increased use of antibiotics and immunosuppressive drugs. Rhino-orbital cerebral mucormycosis has recently reemerged among patients affected by COVID-19 and has become a global concern. The detection of extrasinus involvement in its early stage contributes to improved outcomes; therefore, imaging studies are essential in establishing the degree of involvement and managing the treatment properly, especially in immunocompromised patients. The common sites of extrasinus fungal invasion are the intraorbital, cavernous sinus, and intracranial regions. Fungi spread directly to these regions along the blood vessels or nerves, causing devastating complications such as optic nerve ischemia or compression, optic neuritis or perineuritis, orbital cellulitis, cavernous sinus thrombosis, mycotic aneurysm, vasculitis, internal carotid arterial occlusion, cerebral infarction, cerebritis, and brain abscess. IFRS has a broad imaging spectrum, and familiarity with intra- and extrasinonasal imaging features, such as loss of contrast enhancement of the affected region, which indicates tissue ischemia due to angioinvasion of fungi, and the surrounding anatomy is essential for prompt diagnosis and management. The authors summarize the epidemiology, etiology, risk factors, and complications of IFRS and review the anatomy and key diagnostic imaging features of IFRS beyond the sinonasal regions. ©RSNA, 2022.

Identifying mitigation strategies for COVID-19 superspreading on flights using models that account for passenger movement.

BACKGROUND: Despite commercial airlines mandating masks, there have been multiple documented events of COVID-19 superspreading on flights. Conventional models do not adequately explain superspreading patterns on flights, with infection spread wider than expected from proximity based on passenger seating. An important reason for this is that models typically do not consider the movement of passengers during the flight, boarding, or deplaning. Understanding the risks for each of these aspects could provide insight into effective mitigation measures. METHODS: We modeled infection risk from seating and fine-grained movement patterns - boarding, deplaning, and inflight movement. We estimated infection model parameters from a prior superspreading event. We validated the model and the impact of interventions using available data from three flights, including cabin layout and seat locations of infected and uninfected passengers, to suggest interventions to mitigate COVID-19 superspreading events during air travel. Specifically, we studied: 1) London to Hanoi with 201 passengers, including 13 secondary infections among passengers; 2) Singapore to Hangzhou with 321 passengers, including 12 to 14 secondary infections; 3) a non-superspreading event on a private jet in Japan with 9 passengers and no secondary infections. RESULTS: Our results show that the inclusion of passenger movement better explains the infection spread patterns than conventional models do. We also found that FFP2/N95 mask usage would have reduced infection by 95-100%, while cloth masks would have reduced it by only 40-80%. Results indicate that leaving the middle seat vacant is effective in reducing infection, and the effectiveness increases when combined with good quality masks. However, with a good mask, the risk is quite low even without the middle seats being empty. CONCLUSIONS: Our results suggest the need for more stringent guidelines to reduce aviation-related superspreading events of COVID-19.

Parameter Space Exploration in Pedestrian Queue Design to Mitigate Infectious Disease Spread.

Reducing the interactions between pedestrians in crowded environments can potentially curb the spread of infectious diseases including COVID-19. The mixing of susceptible and infectious individuals in many high-density man-made environments such as waiting queues involves pedestrian movement, which is generally not taken into account in modeling studies of disease dynamics. In this paper, a social force-based pedestrian-dynamics approach is used to evaluate the contacts among proximate pedestrians which are then integrated with a stochastic epidemiological model to estimate the infectious disease spread in a localized outbreak. Practical application of such multiscale models to real-life scenarios can be limited by the uncertainty in human behavior, lack of data during early stage epidemics, and inherent stochasticity in the problem. We parametrize the sources of uncertainty and explore the associated parameter space using a novel high-efficiency parameter sweep algorithm. We show the effectiveness of a low-discrepancy sequence (LDS) parameter sweep in reducing the number of simulations required for effective parameter space exploration in this multiscale problem. The algorithms are applied to a model problem of infectious disease spread in a pedestrian queue similar to that at an airport security check point. We find that utilizing the low-discrepancy sequence-based parameter sweep, even for one component of the multiscale model, reduces the computational requirement by an order of magnitude.

Architecture-Aware Modeling of Pedestrian Dynamics.

The spread of infectious diseases arises from complex interactions between disease dynamics and human behavior. Predicting the outcome of this complex system is difficult. Consequently, there has been a recent emphasis on comparing the relative risks of different policy options rather than precise predictions. Here, one performs a parameter sweep to generate a large number of possible scenarios for human behavior under different policy options and identifies the relative risks of different decisions regarding policy or design choices. In particular, this approach has been used to identify effective approaches to social distancing in crowded locations, with pedestrian dynamics used to simulate the movement of individuals. This incurs a large computational load, though. The traditional approach of optimizing the implementation of existing mathematical models on parallel systems leads to a moderate improvement in computational performance. In contrast, we show that when dealing with human behavior, we can create a model from scratch that takes computer architectural features into account, yielding much higher performance without requiring complicated parallelization efforts. Our solution is based on two key observations. (i) Models do not capture human behavior as precisely as models for scientific phenomena describe natural processes. Consequently, there is some leeway in designing a model to suit the computational architecture. (ii) The result of a parameter sweep, rather than a single simulation, is the semantically meaningful result. Our model leverages these features to perform efficiently on CPUs and GPUs. We obtain a speedup factor of around 60 using this new model on two Xeon Platinum 8280 CPUs and a factor 125 speedup on 4 NVIDIA Quadro RTX 5000 GPUs over a parallel implementation of the existing model. The careful design of a GPU implementation makes it fast enough for real-time decision-making. We illustrate it on an application to COVID-19.

Multiscale model for the optimal design of pedestrian queues to mitigate infectious disease spread.

There is direct evidence for the spread of infectious diseases such as influenza, SARS, measles, and norovirus in locations where large groups of people gather at high densities e.g. theme parks, airports, etc. The mixing of susceptible and infectious individuals in these high people density man-made environments involves pedestrian movement which is generally not taken into account in modeling studies of disease dynamics. We address this problem through a multiscale model that combines pedestrian dynamics with stochastic infection spread models. The pedestrian dynamics model is utilized to generate the trajectories of motion and contacts between infected and susceptible individuals. We incorporate this information into a stochastic infection dynamics model with infection probability and contact radius as primary inputs. This generic model is applicable for several directly transmitted diseases by varying the input parameters related to infectivity and transmission mechanisms. Through this multiscale framework, we estimate the aggregate numbers and probabilities of newly infected people for different winding queue configurations. We find that the queue configuration has a significant impact on disease spread for a range of infection radii and transmission probabilities. We quantify the effectiveness of wall separators in suppressing the disease spread compared to rope separators. Further, we find that configurations with short aisles lower the infection spread when rope separators are used.