Complexity of COVID-19 Dynamics

With population explosion and globalization, the spread of infectious diseases has been a major concern. In 2019, a newly identified type of Coronavirus caused an outbreak of respiratory illness, popularly known as COVID-19, and became a pandemic. Although enormous efforts have been made to understand the spread of COVID-19, our knowledge of the COVID-19 dynamics still remains limited. The present study employs the concepts of chaos theory to examine the temporal dynamic complexity of COVID-19 around the world. The false nearest neighbor (FNN) method is applied to determine the dimensionality and, hence, the complexity of the COVID-19 dynamics. The methodology involves: (1) reconstruction of a single-variable COVID-19 time series in a multi-dimensional phase space to represent the underlying dynamics; and (2) identification of “false” neighbors in the reconstructed phase space and estimation of the dimension of the COVID-19 series. For implementation, COVID-19 data from 40 countries/regions around the world are studied. Two types of COVID-19 data are analyzed: (1) daily COVID-19 cases; and (2) daily COVID-19 deaths. The results for the 40 countries/regions indicate that: (1) the dynamics of COVID-19 cases exhibit low- to medium-level complexity, with dimensionality in the range 3 to 7; and (2) the dynamics of COVID-19 deaths exhibit complexity anywhere from low to high, with dimensionality ranging from 3 to 13. The results also suggest that the complexity of the dynamics of COVID-19 deaths is greater than or at least equal to that of the dynamics of COVID-19 cases for most (three-fourths) of the countries/regions. These results have important implications for modeling and predicting the spread of COVID-19 (and other infectious diseases), especially in the identification of the appropriate complexity of models.

Temporal dynamic and key species of the sarcosapropha­gous entomofauna in a Mediterranean natural environment

Los insectos pueden detectar la presencia de un cadáver a gran distancia, colonizándolo rápidamente y siendo los primeros en explotar este recurso. La composición y dinámica de la comunidad, junto con su presencia en un lugar específico, son clave para su posterior aplicación forense. Por este motivo, se estudió la fauna entomosarcosaprófaga asociada a un cadáver en una zona montañosa de la provincia de Murcia (SE, España). Las especies Chrysomya albiceps, Calliphora vicina y C. vomitoria fueron las especies más valiosas como indicadores estacionales. Estas especies, junto con Dermestes frischii, Camponotus sylvaticus y la superfamilia Chalcidoidea, definen un patrón claro de colonización que varía según la estación del año. Finalmente, la especies Iberoformica subrufa pueden tener valor potencial como indicador de hábitat del área estudiada. Insects can detect the presence of a cadaver at a great distance, colonizing it rapidly and being the first to exploit this resource. Its composition and dynamics, together with its presence in a specific location, are key to its subsequent forensic application. For this reason, it was studied the entomosarcosaprophagous fauna associated with a carcass in a wild mountainous area of the province of Murcia (SE, Spain). The species Chrysomya albiceps, Calliphora vicina and C. vomitoria were the most valuables species as seasonal indicators. These species, together with Dermestes frischii, Camponotus sylvaticus and those of the Chalcidoidea superfamily, define a clear pattern of colonization that varies according to the season of the year. Finally, the species Iberoformica subrufa may be of potential use as habitat indicator of the studied area.

More Than Just Static: Dynamic Functional Connectivity Changes of the Thalamic Nuclei to Cortex in Parkinson's Disease With Freezing of Gait

Background: The thalamus is not only a key relay node of the thalamocortical circuit but also a hub in the regulation of gait. Previous studies of resting-state functional magnetic resonance imaging (fMRI) have shown static functional connectivity (FC) between the thalamus and the cortex are disrupted in Parkinson's disease (PD) patients with freezing of gait (FOG). However, temporal dynamic FC between the thalamus and the cortex has not yet been characterized in these patients.Methods: Fifty PD patients, including 25 PD patients with FOG (PD-FOG) and 25 PD patients without FOG (PD-NFOG), and 25 healthy controls (HC) underwent resting-state fMRI. Seed-voxel-wise static and dynamic FC were calculated between each thalamic nuclei and other voxels across the brain using the 14 thalamic nuclei in both hemispheres as regions of interest. Associations between altered thalamic FC based on significant inter-group differences and severity of FOG symptoms were also examined in PD-FOG.Results: Both PD-FOG and PD-NFOG showed lower static FC between the right lateral posterior thalamic nuclei and right inferior parietal lobule (IPL) compared with HC. Altered FC dynamics between the thalamic nuclei and several cortical areas were identified in PD-FOG, as shown by temporal dynamic FC analyses. Specifically, relative to PD-NFOG or HC, PD-FOG showed greater fluctuations in FC between the left intralaminar (IL) nuclei and right IPL and between the left medial geniculate and left postcentral gyrus. Furthermore, the dynamics of FC between the left pulvinar anterior nuclei and left inferior frontal gyrus were upregulated in both PD-FOG and PD-NFOG. The dynamics of FC between the right ventral lateral nuclei and left paracentral lobule were elevated in PD-NFOG but were maintained in PD-FOG and HC. The quantitative variability of FC between the left IL nuclei and right IPL was positively correlated with the clinical scales scores in PD-FOG.Conclusions: Dynamic FC between the thalamic nuclei and relevant associative cortical areas involved in sensorimotor integration or cognitive function was disrupted in PD-FOG, which was reflected by greater temporal fluctuations. Abnormal dynamic FC between the left IL nuclei of the thalamus and right IPL was related to the severity of FOG.

Spatio-Temporal Dynamic Fields Estimating and Modeling of Missing Points in Data Sets Using a Flexible State-Space Model

Modelling and estimating spatio-temporal dynamic field are common challenges in much applied research. Most existing spatio-temporal interpolation methods require massive prior calculations and consistent observational data, resulting in low interpolation efficiency. This paper presents a flexible state-space model for iteratively fitting time-series from random missing points in data sets, namely Flexible Universal Kriging state-space model(FUKSS). In this work, a recursive method similar to Kalman filter is used to estimate the time-series, avoiding the problem of increasing data caused by Kriging space-time extension. Based on the statistical characteristics of Kriging, this method introduces a spatial selection matrix to make the different observation data and state vectors identical at different times, which solves the problem of missing data and reduces the calculation complexity. In addition, a dynamic linear autoregressive model is introduced to solve the problem that the universal Kriging state-space model cannot predict. We have demonstrated the superiority of our method by comparing it with different methods through experiments, and verified the effectiveness of this method through practical cases.