The Spatiotemporal Transmission Dynamics of COVID-19 among Multiple Regions: A Modeling Study in China

Current explosive outbreak of COVID-19 around the world is a complex spatiotemporal process with hidden interactions between viruses and humans. This study aims at clarifying the transmission patterns and the driving mechanism that contributed to the COVID-19 epidemics across the provinces of China. Thus a new dynamical transmission model is established by ordinary differential system. The model takes into account the hidden circulation of COVID-19 virus among/within humans, which incorporates the spatial diffusion of infection by parameterizing human mobility. Theoretical analysis indicates that the basic reproduction number is a unique epidemic threshold, which can unite infectivity in each region by human mobility, and can totally determine whether COVID-19 proceeds among multiple regions. By validating the model with real epidemic data in China, it is found that (1) if without any intervention, COVID-19 would overrun China within three months, resulting in more than 1.1 billion infections; (2) high frequency of human mobility can trigger COVID-19 diffusion across each province in China, no matter where the initial infection locates; (3) travel restrictions and other non-pharmaceutical interventions must be implemented simultaneously for disease control; and (4) infection sites in central and east (rather than west and northeast) of China would easily stimulate quick diffusion of COVID-19 in the whole country.

Applications of brain organoids in neurodevelopment and neurological diseases

A brain organoid is a self-organizing three-dimensional tissue derived from human embryonic stem cells or pluripotent stem cells and is able to simulate the architecture and functionality of the human brain. Brain organoid generation methods are abundant and continue to improve, and now, an in vivo vascularized brain organoid has been encouragingly reported. The combination of brain organoids with immune-staining and single-cell sequencing technology facilitates our understanding of brain organoids, including the structural organization and the diversity of cell types. Recent publications have reported that brain organoids can mimic the dynamic spatiotemporal process of early brain development, model various human brain disorders, and serve as an effective preclinical platform to test and guide personalized treatment. In this review, we introduce the current state of brain organoid differentiation strategies, summarize current progress and applications in the medical domain, and discuss the challenges and prospects of this promising technology.

Amtrak Rail Trespasser Analysis using a GIS Space-Time Approach

This research explores Amtrak trespass incident data from 2011 to 2019 using a GIS spatiotemporal process. The objective is to evaluate incident characteristics based on space, time, incident factors, and statistical significance. Incidents were first analyzed at the megaregional level, revealing Northern and Southern California as the highest trespassing risk in the country, followed by the Northeast and Great Lakes megaregions. A new standardized point density approach was applied to reveal incident clusters representing high-risk localities. Then, the optimized and emerging hot spot methods were applied to the top four megaregions. The results showed four Amtrak corridors as hot spots, including three along coastal California railways and the Philadelphia region. Trends for incident report factors were analyzed (e.g., pre-crash activity, time of day, location of impact). “Walking” prior to impact, occurrence in the “afternoon,” and crash location “on the tracks” were found to be the most prominent incident characteristics for those factors.

Fusion of Multisensor SSTs Based on the Spatiotemporal Hierarchical Bayesian Model

This study focuses on merging MODIS-mapped SSTs with 4-km spatial resolution and AMSR-E optimally interpolated SSTs at 25-km resolution. A new data fusion method was developed—the Spatiotemporal Hierarchical Bayesian Model (STHBM). This method, which is implemented through the Markov chain Monte Carlo technique utilized to extract inferential results, is specified hierarchically by decomposing the SST spatiotemporal process into three subprocesses, that is, the spatial trend process, the seasonal cycle process, and the spatiotemporal random effect process. Spatial-scale transformation and spatiotemporal variation are introduced into the fusion model through the data model and model parameters, respectively, with suitably selected link functions. Compared with two modern spatiotemporal statistical methods—the Bayesian maximum entropy and the robust fixed rank kriging—STHBM has the following strength: it can simultaneously meet the expression of uncertainties from data and model, seamless scale transformation, and SST spatiotemporal process simulation. Utilizing multisensors’ complementation, merged data with complete spatial coverage, high resolution (4 km), and fine spatial pattern lying in MODIS SSTs can be obtained through STHBM. The merged data are assessed for local spatial structure, overall accuracy, and local accuracy. The evaluation results illustrate that STHBM can provide spatially complete SST fields with reasonably good data values and acceptable errors, and that the merged SSTs collect fine spatial patterns lying in MODIS SSTs with fine resolution. The accuracy of merged SSTs is between MODIS and AMSR-E SSTs. The contribution to the accuracy and the spatial pattern of the merged SSTs from the original MODIS SSTs is stronger than that of the original AMSR-E SSTs.

Theoretical perspective on the route to turbulence in a pipe

The route to turbulence in pipe flow is a complex, nonlinear, spatiotemporal process for which an increasingly clear understanding has emerged in recent years. This paper presents a theoretical perspective on the problem, focusing on what can be understood from relatively few physical features and models that encompass these features. The paper proceeds step-by-step with increasing detail about the transition process, first discussing the relationship to phase transitions and then exploiting an even deeper connection between pipe flow and excitable and bistable media. In the end a picture emerges for all stages of the transition process, from transient turbulence, to the onset of sustained turbulence in a percolation transition, to the modest and then rapid expansion of turbulence, ultimately leading to fully turbulent pipe flow.