Spatiotemporal Analysis of COVID-19 Incidence Data

(1) Background: A better understanding of COVID-19 dynamics in terms of interactions among individuals would be of paramount importance to increase the effectiveness of containment measures. Despite this, the research lacks spatiotemporal statistical and mathematical analysis based on large datasets. We describe a novel methodology to extract useful spatiotemporal information from COVID-19 pandemic data. (2) Methods: We perform specific analyses based on mathematical and statistical tools, like mathematical morphology, hierarchical clustering, parametric data modeling and non-parametric statistics. These analyses are here applied to the large dataset consisting of about 19,000 COVID-19 patients in the Veneto region (Italy) during the entire Italian national lockdown. (3) Results: We estimate the COVID-19 cumulative incidence spatial distribution, significantly reducing image noise. We identify four clusters of connected provinces based on the temporal evolution of the incidence. Surprisingly, while one cluster consists of three neighboring provinces, another one contains two provinces more than 210 km apart by highway. The survival function of the local spatial incidence values is modeled here by a tapered Pareto model, also used in other applied fields like seismology and economy in connection to networks. Model’s parameters could be relevant to describe quantitatively the epidemic. (4) Conclusion: The proposed methodology can be applied to a general situation, potentially helping to adopt strategic decisions such as the restriction of mobility and gatherings.

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Spatiotemporal Analysis of COVID-19 Spread with Emerging Hotspot Analysis and Space–Time Cube Models in East Java, Indonesia

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10030133 ◽

2021 ◽

Vol 10 (3) ◽

pp. 133

Author(s):

Purwanto Purwanto ◽

Sugeng Utaya ◽

Budi Handoyo ◽

Syamsul Bachri ◽

Ike Sari Astuti ◽

...

Keyword(s):

Three Dimensional ◽

Distribution Patterns ◽

Spatiotemporal Analysis ◽

Space Time ◽

Road Density ◽

Hotspot Analysis ◽

Spatiotemporal Information ◽

Depth Analysis ◽

Analysis Models ◽

Oscillating Patterns

In this research, we analyzed COVID-19 distribution patterns based on hotspots and space–time cubes (STC) in East Java, Indonesia. The data were collected based on the East Java COVID-19 Radar report results from a four-month period, namely March, April, May, and June 2020. Hour, day, and date information were used as the basis of the analysis. We used two spatial analysis models: the emerging hotspot analysis and STC. Both techniques allow us to identify the hotspot cluster temporally. Three-dimensional visualizations can be used to determine the direction of spread of COVID-19 hotspots. The results showed that the spread of COVID-19 throughout East Java was centered in Surabaya, then mostly spread towards suburban areas and other cities. An emerging hotspot analysis was carried out to identify the patterns of COVID-19 hotspots in each bin. Both cities featured oscillating patterns and sporadic hotspots that accumulated over four months. This pattern indicates that newly infected patients always follow the recovery of previous COVID-19 patients and that the increase in the number of positive patients is higher when compared to patients who recover. The monthly hotspot analysis results yielded detailed COVID-19 spatiotemporal information and facilitated more in-depth analysis of events and policies in each location/time bin. The COVID-19 hotspot pattern in East Java, visually speaking, has an amoeba-like pattern. Many positive cases tend to be close to the city, in places with high road density, near trade and business facilities, financial storage, transportation, entertainment, and food venues. Determining the spatial and temporal resolution for the STC model is crucial because it affects the level of detail for the information of endemic disease distribution and is important for the emerging hotspot analysis results. We believe that similar research is still rare in Indonesia, although it has been done elsewhere, in different contexts and focuses.

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Traffic Congestion Analysis Based on a Web-GIS and Data Mining of Traffic Events from Twitter

Sensors ◽

10.3390/s21092964 ◽

2021 ◽

Vol 21 (9) ◽

pp. 2964

Author(s):

Juan Salazar-Carrillo ◽

Miguel Torres-Ruiz ◽

Clodoveu A. Davis ◽

Rolando Quintero ◽

Marco Moreno-Ibarra ◽

...

Keyword(s):

Traffic Congestion ◽

Smart Cities ◽

Communication Technologies ◽

Spatiotemporal Analysis ◽

Good Alternative ◽

Web Gis ◽

Training Dataset ◽

Support Vector ◽

Machine Method ◽

Spatiotemporal Information

Smart cities are characterized by the use of massive information and digital communication technologies as well as sensor networks where the Internet and smart data are the core. This paper proposes a methodology to geocode traffic-related events that are collected from Twitter and how to use geocoded information to gather a training dataset, apply a Support Vector Machine method, and build a prediction model. This model produces spatiotemporal information regarding traffic congestions with a spatiotemporal analysis. Furthermore, a spatial distribution represented by heat maps is proposed to describe the traffic behavior of specific and sensed areas of Mexico City in a Web-GIS application. This work demonstrates that social media are a good alternative that can be leveraged to gather collaboratively Volunteered Geographic Information for sensing the dynamic of a city in which citizens act as sensors.

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Single-molecule imaging of microRNA-mediated gene silencing in cells

10.1101/2021.04.30.442050 ◽

2021 ◽

Author(s):

Hotaka Kobayashi ◽

Robert H Singer

Keyword(s):

Gene Silencing ◽

Single Molecule ◽

Nuclear Export ◽

Mrna Decay ◽

Ensemble Methods ◽

Spatiotemporal Analysis ◽

Translational Repression ◽

Single Molecule Level ◽

Spatiotemporal Information ◽

Spatiotemporal Regulation

MicroRNAs (miRNAs) are small non-coding RNAs, which regulate the expression of thousands of genes; miRNAs silence gene expression from complementary mRNAs through translational repression and mRNA decay. For decades, the function of miRNAs has been studied primarily by ensemble methods, where a bulk collection of molecules is measured outside cells. Thus, the behavior of individual molecules during miRNA-mediated gene silencing, as well as their spatiotemporal regulation inside cells, remains mostly unknown. Here we report single-molecule methods to visualize each step of miRNA-mediated gene silencing in situ inside cells. Simultaneous visualization of single mRNAs, translation, and miRNA-binding revealed that miRNAs preferentially bind to translated mRNAs rather than untranslated mRNAs. Spatiotemporal analysis based on our methods uncovered that miRNAs bind to mRNAs immediately after nuclear export. Subsequently, miRNAs induced translational repression and mRNA decay within 30 and 60 min, respectively, after the binding to mRNAs. This methodology provides a framework for studying mRNA regulation at the single-molecule level with spatiotemporal information inside cells.

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Assessing Changes in the Reproduction Number of COVID-19 in Cameroon

Asian Journal of Research in Infectious Diseases ◽

10.9734/ajrid/2021/v8i230234 ◽

2021 ◽

pp. 30-40

Author(s):

Solange Whegang Youdom ◽

Djam Chefor Alain ◽

Charles Kouanfack

Keyword(s):

Reproduction Number ◽

Moving Average ◽

Model Parameters ◽

Time Varying ◽

Autoregressive Integrated Moving Average ◽

Social Distancing ◽

Vaccine Introduction ◽

Incidence Data ◽

A Value ◽

Containment Measures

Aim: The purpose of this work is to assess changes that occur on COVID-19 infection in Cameroon since the start of the epidemic. Study Design: We use a data-based analysis on longitudinal data of reported COVID-19 cases in Cameroon. Place and Duration: The data for the study were obtained from the reports of confirmed COVID-19 cases from an official website between March 7, 2020 to September 29, 2021. Methodology: A modified Susceptible-Infected-Recovered-Deceased (SIRD) model for the contagion was used to describe the cumulated cases of COVID-19 during different phases of the epidemic that correlated with highest spikes. The approach features several aspects: one is that model parameters can be time-varying, allowing us to capture possible changes of the epidemic behaviour, due for example to containment measures enforced by authorities or modifications of the epidemic characteristics, country events, and COVID-19 vaccine introduction; the second aspect is that the model accounts for a social distancing parameter. The time-varying parameters was handled using a phase-to-phase modelling in which initial parameters were the number of susceptible individuals at the end of each phase. In addition, daily incidence data were used to estimate daily reproduction number. Secondly, we used an Autoregressive Integrated Moving Average (ARIMA) approach to analyse the dynamic of the effective reproduction number R and forecast the new number of infected contacts. Results: There was less than 54% compliance of social distancing during all phases. The reproduction number was above 1 during each phase of the analysis. As of September 2021, it was 2.43 suggesting a constant increase of infection. Time-series of the reproduction number was unseasonal and stationary. Forecasting of R gave a value of more than 2, suggesting a continued rise in the number of infected cases in the Country in the next coming months. Conclusion: It is uncertain when the pandemic will last in the country. While social distancing is in decrease, prevention through vaccination is an option to reach more people and stop the propagation of the disease.

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What Is the Estimated COVID-19 Reproduction Number and the Proportion of the Population That Needs to Be Immunized to Achieve Herd Immunity in Malaysia? A Mathematical Epidemiology Synthesis

COVID ◽

10.3390/covid1010003 ◽

2021 ◽

Vol 1 (1) ◽

pp. 13-19

Author(s):

Kurubaran Ganasegeran ◽

Alan Swee Hock Ch’ng ◽

Irene Looi

Keyword(s):

Reproduction Number ◽

Herd Immunity ◽

Control Measures ◽

Threshold Estimate ◽

Mathematical Epidemiology ◽

Incidence Data ◽

Malaysian Population ◽

Containment Measures ◽

Efficacious Vaccine ◽

Over Time

We aimed to determine Malaysia’s COVID-19 reproduction number and herd immunity threshold through a mathematical epidemiology synthesis. Using time-series incidence data, the time-dependent reproduction number (Rt) was yielded over time during the COVID-19 containment measures in Malaysia. The value of Rt at the beginning of the epidemic and prior to any interventions in place was used to determine the proportion of the population that needs to be immunized to achieve herd immunity. Rt was strongly influenced by interventions being put in place. We established that at least 74% of the Malaysian population needs to be vaccinated to achieve herd immunity against COVID-19. This threshold estimate is somewhat influenced by the availability of an efficacious vaccine. A vaccine with 95% efficacy would approximately synthesize a herd immunity threshold of 78%. We conclude that Rt is a valid estimator to determine the effectiveness of control measures and a parameter of use to synthesize herd immunity thresholds in the current COVID-19 pandemic.

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Facet Topography and Dislocation Structure as a Possible Source for Electrical Heterogeneity in [001] TILT Bicrystals of YBa2Cu3O7-δ

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100164155 ◽

1996 ◽

Vol 54 ◽

pp. 338-339

Author(s):

I-Fei Tsu ◽

D.L. Kaiser ◽

S.E. Babcock

Keyword(s):

Grain Boundary ◽

Critical Current Density ◽

Critical Current ◽

Current Density ◽

Electromagnetic Properties ◽

Length Scale ◽

Density Values ◽

Current Densities ◽

Applied Fields ◽

A Current

A current theme in the study of the critical current density behavior of YBa2Cu3O7-δ (YBCO) grain boundaries is that their electromagnetic properties are heterogeneous on various length scales ranging from 10s of microns to ˜ 1 Å. Recently, combined electromagnetic and TEM studies on four flux-grown bicrystals have demonstrated a direct correlation between the length scale of the boundaries’ saw-tooth facet configurations and the apparent length scale of the electrical heterogeneity. In that work, enhanced critical current densities are observed at applied fields where the facet period is commensurate with the spacing of the Abrikosov flux vortices which must be pinned if higher critical current density values are recorded. To understand the microstructural origin of the flux pinning, the grain boundary topography and grain boundary dislocation (GBD) network structure of [001] tilt YBCO bicrystals were studied by TEM and HRTEM.

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