scholarly journals Spatio-Temporal Spread Pattern of COVID-19 in Italy

Mathematics ◽  
2021 ◽  
Vol 9 (19) ◽  
pp. 2454
Author(s):  
Nicoletta D’Angelo ◽  
Antonino Abbruzzo ◽  
Giada Adelfio
Keyword(s):  
Temporal Evolution ◽  
Virus Infections ◽  
Mobility Patterns ◽  
Modeling Framework ◽  
Spatial Trend ◽  
Italian Government ◽  
Italian Provinces ◽  
Spread Pattern ◽  
Spatio Temporal ◽  
Temporal Spread

This paper investigates the spatio-temporal spread pattern of COVID-19 in Italy, during the first wave of infections, from February to October 2020. Disease mappings of the virus infections by using the Besag–York–Mollié model and some spatio-temporal extensions are provided. This modeling framework, which includes a temporal component, allows the studying of the time evolution of the spread pattern among the 107 Italian provinces. The focus is on the effect of citizens’ mobility patterns, represented here by the three distinct phases of the Italian virus first wave, identified by the Italian government, also characterized by the lockdown period. Results show the effectiveness of the lockdown action and an inhomogeneous spatial trend that characterizes the virus spread during the first wave. Furthermore, the results suggest that the temporal evolution of each province’s cases is independent of the temporal evolution of the other ones, meaning that the contagions and temporal trend may be caused by some province-specific aspects rather than by the subjects’ spatial movements.

scholarly journals Inferring who-infected-whom-where in the 2016 Zika outbreak in Singapore—a spatio-temporal model

2019 ◽  
Vol 16 (155) ◽  
pp. 20180604 ◽  
Author(s):  
Kiesha Prem ◽  
Max S. Y. Lau ◽  
Clarence C. Tam ◽  
Marc Z. J. Ho ◽  
Lee-Ching Ng ◽  
...  
Keyword(s):  
Data Augmentation ◽  
Herd Immunity ◽  
Pathogen Transmission ◽  
Contact Tracing ◽  
Virus Infections ◽  
Temporal Model ◽  
Spatio Temporal ◽  
Distant Relative ◽  
Spatial Transmission ◽  
Temporal Spread

Singapore experienced its first known Zika outbreak in 2016. Given the lack of herd immunity, the suitability of the climate for pathogen transmission, and the year-round presence of the vector— Aedes aegypti —Zika had the potential to become endemic, like dengue. Guillain–Barré syndrome and microcephaly are severe complications associated elsewhere with Zika and the risk of these complications makes understanding its spread imperative. We investigated the spatio-temporal spread of locally transmitted Zika in Singapore and assessed the relevance of non-residential transmission of Zika virus infections, by inferring the possible infection tree (i.e. who-infected-whom-where ) and comparing inferences using geographically resolved data on cases' home, their work, or their home and work. We developed a spatio-temporal model using time of onset and both addresses of the Zika-confirmed cases between July and September 2016 to estimate the infection tree using Bayesian data augmentation. Workplaces were involved in a considerable fraction (64.2%) of infections, and homes and workplaces may be distant relative to the scale of transmission, allowing ambulant infected persons may act as the ‘vector’ infecting distant parts of the country. Contact tracing is a challenge for mosquito-borne diseases, but inferring the geographically structured transmission tree sheds light on the spatial transmission of Zika to immunologically naive regions of the country.

scholarly journals Assessing the Spatio-temporal Spread of COVID-19 via Compartmental Models with Diffusion in Italy, USA, and Brazil

2021 ◽  
Author(s):  
Malú Grave ◽  
Alex Viguerie ◽  
Gabriel F. Barros ◽  
Alessandro Reali ◽  
Alvaro L. G. A. Coutinho
Keyword(s):  
Differential Equation ◽  
Epidemiological Data ◽  
Compartmental Models ◽  
Modeling Framework ◽  
Differential Equation Models ◽  
The World ◽  
Spatio Temporal ◽  
Qualitative Characteristics ◽  
Good Agreement ◽  
Temporal Spread

AbstractThe outbreak of COVID-19 in 2020 has led to a surge in interest in the mathematical modeling of infectious diseases. Such models are usually defined as compartmental models, in which the population under study is divided into compartments based on qualitative characteristics, with different assumptions about the nature and rate of transfer across compartments. Though most commonly formulated as ordinary differential equation models, in which the compartments depend only on time, recent works have also focused on partial differential equation (PDE) models, incorporating the variation of an epidemic in space. Such research on PDE models within a Susceptible, Infected, Exposed, Recovered, and Deceased framework has led to promising results in reproducing COVID-19 contagion dynamics. In this paper, we assess the robustness of this modeling framework by considering different geometries over more extended periods than in other similar studies. We first validate our code by reproducing previously shown results for Lombardy, Italy. We then focus on the U.S. state of Georgia and on the Brazilian state of Rio de Janeiro, one of the most impacted areas in the world. Our results show good agreement with real-world epidemiological data in both time and space for all regions across major areas and across three different continents, suggesting that the modeling approach is both valid and robust.

Spatio-temporal evolution and dynamic origin of Jurassic-Cretaceous magmatism in the South China Block

2021 ◽  
Vol 217 ◽  
pp. 103605
Author(s):  
Xianzhi Cao ◽  
Nicolas Flament ◽  
Sanzhong Li ◽  
R. Dietmar Müller
Keyword(s):  
South China ◽  
Temporal Evolution ◽  
South China Block ◽  
The South ◽  
Spatio Temporal ◽  
Cretaceous Magmatism

scholarly journals Spatio-temporal evolution and influencing mechanism of the COVID-19 epidemic in Shandong province, China

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jinlong Shi ◽  
Xing Gao ◽  
Shuyan Xue ◽  
Fengqing Li ◽  
Qifan Nie ◽  
...  
Keyword(s):  
Prevention And Control ◽  
Temporal Evolution ◽  
Geographical Location ◽  
Shandong Province ◽  
Control Measures ◽  
Middle Aged ◽  
Health Crisis ◽  
Aged People ◽  
Spatio Temporal ◽  
And Control

AbstractThe novel coronavirus pneumonia (COVID-19) outbreak that emerged in late 2019 has posed a severe threat to human health and social and economic development, and thus has become a major public health crisis affecting the world. The spread of COVID-19 in population and regions is a typical geographical process, which is worth discussing from the geographical perspective. This paper focuses on Shandong province, which has a high incidence, though the first Chinese confirmed case was reported from Hubei province. Based on the data of reported confirmed cases and the detailed information of cases collected manually, we used text analysis, mathematical statistics and spatial analysis to reveal the demographic characteristics of confirmed cases and the spatio-temporal evolution process of the epidemic, and to explore the comprehensive mechanism of epidemic evolution and prevention and control. The results show that: (1) the incidence rate of COVID-19 in Shandong is 0.76/100,000. The majority of confirmed cases are old and middle-aged people who are infected by the intra-province diffusion, followed by young and middle-aged people who are infected outside the province. (2) Up to February 5, the number of daily confirmed cases shows a trend of “rapid increase before slowing down”, among which, the changes of age and gender are closely related to population migration, epidemic characteristics and intervention measures. (3) Affected by the regional economy and population, the spatial distribution of the confirmed cases is obviously unbalanced, with the cluster pattern of “high–low” and “low–high”. (4) The evolution of the migration pattern, affected by the geographical location of Wuhan and Chinese traditional culture, is dominated by “cross-provincial” and “intra-provincial” direct flow, and generally shows the trend of “southwest → northeast”. Finally, combined with the targeted countermeasures of “source-flow-sink”, the comprehensive mechanism of COVID-19 epidemic evolution and prevention and control in Shandong is revealed. External and internal prevention and control measures are also figured out.

scholarly journals Adjoint-based direct data assimilation of GNSS time series for optimizing frictional parameters and predicting postseismic deformation following the 2003 Tokachi-oki earthquake

2020 ◽  
Vol 72 (1) ◽  
Author(s):  
Masayuki Kano ◽  
Shin’ichi Miyazaki ◽  
Yoichi Ishikawa ◽  
Kazuro Hirahara
Keyword(s):  
Time Series ◽  
Data Assimilation ◽  
Evaluation Method ◽  
Temporal Evolution ◽  
Recovery Process ◽  
Postseismic Deformation ◽  
Coseismic Slip ◽  
Megathrust Earthquakes ◽  
Spatio Temporal ◽  
Gnss Time Series

Abstract Postseismic Global Navigation Satellite System (GNSS) time series followed by megathrust earthquakes can be interpreted as a result of afterslip on the plate interface, especially in its early phase. Afterslip is a stress release process accumulated by adjacent coseismic slip and can be considered a recovery process for future events during earthquake cycles. Spatio-temporal evolution of afterslip often triggers subsequent earthquakes through stress perturbation. Therefore, it is important to quantitatively capture the spatio-temporal evolution of afterslip and related postseismic crustal deformation and to predict their future evolution with a physics-based simulation. We developed an adjoint data assimilation method, which directly assimilates GNSS time series into a physics-based model to optimize the frictional parameters that control the slip behavior on the fault. The developed method was validated with synthetic data. Through the optimization of frictional parameters, the spatial distributions of afterslip could roughly (but not in detail) be reproduced if the observation noise was included. The optimization of frictional parameters reproduced not only the postseismic displacements used for the assimilation, but also improved the prediction skill of the following time series. Then, we applied the developed method to the observed GNSS time series for the first 15 days following the 2003 Tokachi-oki earthquake. The frictional parameters in the afterslip regions were optimized to A–B ~ O(10 kPa), A ~ O(100 kPa), and L ~ O(10 mm). A large afterslip is inferred on the shallower side of the coseismic slip area. The optimized frictional parameters quantitatively predicted the postseismic GNSS time series for the following 15 days. These characteristics can also be detected if the simulation variables can be simultaneously optimized. The developed data assimilation method, which can be directly applied to GNSS time series following megathrust earthquakes, is an effective quantitative evaluation method for assessing risks of subsequent earthquakes and for monitoring the recovery process of megathrust earthquakes.

scholarly journals Glimpse: A Gaze-Based Measure of Temporal Salience

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3099
Author(s):  
V. Javier Traver ◽  
Judith Zorío ◽  
Luis A. Leiva
Keyword(s):  
Visual Attention ◽  
Computational Models ◽  
Temporal Evolution ◽  
Temporal Consistency ◽  
Visual Salience ◽  
Temporal Dimension ◽  
Spatial Perspective ◽  
Spatio Temporal ◽  
Scoring Algorithms ◽  
Over Time

Temporal salience considers how visual attention varies over time. Although visual salience has been widely studied from a spatial perspective, its temporal dimension has been mostly ignored, despite arguably being of utmost importance to understand the temporal evolution of attention on dynamic contents. To address this gap, we proposed Glimpse, a novel measure to compute temporal salience based on the observer-spatio-temporal consistency of raw gaze data. The measure is conceptually simple, training free, and provides a semantically meaningful quantification of visual attention over time. As an extension, we explored scoring algorithms to estimate temporal salience from spatial salience maps predicted with existing computational models. However, these approaches generally fall short when compared with our proposed gaze-based measure. Glimpse could serve as the basis for several downstream tasks such as segmentation or summarization of videos. Glimpse’s software and data are publicly available.

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