Maximum Cluster Size Research Articles

Study on the Effect of Ce Doping Concentration on the Kinetics of Graphene Formation

The thermodynamic and kinetic processes of Ce-doped graphene were simulated by the method of first-principles molecular dynamics. The structural optimization and annealing, the kinetic properties of Ce-doped graphene composites were calculated by the classical mechanics Forcite module. The results show that with the increase of Ce doping concentration, the order of the radial distribution function in the system increases and presents a state of aggregation. According to the analysis of mean azimuth shift function, the mean square displacement (MSD) value of Ce doped graphene composite increases with the increase of doping concentration, and the corresponding diffusion coefficient also increases. The research of gyration radius and gyration evolution radius shows that with the increase of temperature and doping concentration, the gyration evolution radius of atoms becomes shorter and shorter, indicating that the interatomic force in the system is enhanced and the range of activity is shortened. Combined with the analysis of local cluster structure, it can be seen that the maximum cluster size and number of Ce doped graphene composites do not change significantly with the increase of doping concentration, indicating that Ce atom doping will improve the order in the system, increase the diffusion coefficient and enhance the interaction force between atoms. The paper aims to reduce the error between the theoretical properties and the actual properties, and provide a theoretical basis for the development of new materials with excellent properties.

Prospective Spatiotemporal Cluster Detection Using SaTScan: Tutorial for Designing and Fine-Tuning a System to Detect Reportable Communicable Disease Outbreaks.

Staff at public health departments have few training materials to learn how to design and fine-tune systems to quickly detect acute, localized, community-acquired outbreaks of infectious diseases. Since 2014, the Bureau of Communicable Disease at the New York City Department of Health and Mental Hygiene has analyzed reportable communicable diseases daily using SaTScan. SaTScan is a free software that analyzes data using scan statistics, which can detect increasing disease activity without a priori specification of temporal period, geographic location, or size. The Bureau of Communicable Disease's systems have quickly detected outbreaks of salmonellosis, legionellosis, shigellosis, and COVID-19. This tutorial details system design considerations, including geographic and temporal data aggregation, study period length, inclusion criteria, whether to account for population size, network location file setup to account for natural boundaries, probability model (eg, space-time permutation), day-of-week effects, minimum and maximum spatial and temporal cluster sizes, secondary cluster reporting criteria, signaling criteria, and distinguishing new clusters versus ongoing clusters with additional events. We illustrate how to support health equity by minimizing analytic exclusions of patients with reportable diseases (eg, persons experiencing homelessness who are unsheltered) and accounting for purely spatial patterns, such as adjusting nonparametrically for areas with lower access to care and testing for reportable diseases. We describe how to fine-tune the system when the detected clusters are too large to be of interest or when signals of clusters are delayed, missed, too numerous, or false. We demonstrate low-code techniques for automating analyses and interpreting results through built-in features on the user interface (eg, patient line lists, temporal graphs, and dynamic maps), which became newly available with the July 2022 release of SaTScan version 10.1. This tutorial is the first comprehensive resource for health department staff to design and maintain a reportable communicable disease outbreak detection system using SaTScan to catalyze field investigations as well as develop intuition for interpreting results and fine-tuning the system. While our practical experience is limited to monitoring certain reportable diseases in a dense, urban area, we believe that most recommendations are generalizable to other jurisdictions in the United States and internationally. Additional analytic technical support for detecting outbreaks would benefit state, tribal, local, and territorial public health departments and the populations they serve.

Prediction of wind energy location by parallel programming using MPI-based KMEANS clustering algorithm

ABSTRACT Renewable energy resources, like the power of the wind, are the essential sources of energy in today’s world. To keep down the greenhouse energy degasification and stop global warming, it is very important to predict the exact location where maximum wind energy is generated. The total amount of electrical power produced by a turbine relies more on the speed of the wind, pressure created by the wind, and weather conditions. The proposed method predicts the maximum power generated in a particular location using the current conditions of the weather, pressure generated through wind, and its speed. The speed of the wind and pressure of the wind are clustered using the Message Passing Interface (MPI) based KMEANS clustering algorithm. The system minimizes the amount of time required for clustering and it is done by MPI. Clustering is formed with the help Euclidean distance of each point. The wind data is collected and formed into three clusters such as low, medium, and high based on the speed of the wind. The parameters for evaluation are considered as the speed of the wind, and the direction of the wind are determined. The results show that the speed of the wind varies up to 25 km/s with the power of 2000 watts. The proposed method compares the execution time of sequential and MPI-based KMEANS clustering for different numbers of clusters. The sequential KMEANS clustering algorithm takes 3% to 7% more time compared to MPI-based KMEANS clustering algorithm. For the maximum cluster size of 5, the MPI-based KMEANS clustering algorithm produces the result in 0.42 s.

Geospatial variation of exclusive breastfeeding and its determinants among mothers of infants under 6 months in Ethiopia: spatial and geographical weighted regression analysis

BackgroundExclusive breastfeeding (EBF) is a major public health problem in Ethiopia. However, the spatial variation of EBF and the associated factors have not been studied as much as we have...

Characterizing different motility-induced regimes in active matter with machine learning and noise.

We examine motility-induced phase separation (MIPS) in two-dimensional run-and-tumble disk systems using both machine learning and noise fluctuation analysis. Our measures suggest that within the MIPS state there are several distinct regimes as a function of density and run time, so that systems with MIPS transitions exhibit an active fluid, an active crystal, and a critical regime. The different regimes can be detected by combining an order parameter extracted from principal component analysis with a cluster stability measurement. The principal component-derived order parameter is maximized in the critical regime, remains low in the active fluid, and has an intermediate value in the active crystal regime. We demonstrate that machine learning can better capture dynamical properties of the MIPS regimes compared to more standard structural measures such as the maximum cluster size. The different regimes can also be characterized via changes in the noise power of the fluctuations in the average speed. In the critical regime, the noise power passes through a maximum and has a broad spectrum with a 1/f^{1.6} signature, similar to the noise observed near depinning transitions or for solids undergoing plastic deformation.

Clustering and Switching in Semantic Verbal Fluency: Their Development and Relationship with Word Productivity in Typically Developing Greek-Speaking Children and Adolescents.

Performance in semantic verbal fluency (SVF) tasks, mainly measured by the number of words of a particular semantic category produced within a limited time, is a widely accepted measure of cognitive functioning used in the neuropsychological assessment of children and adults. Two strategic processes, Clustering and Switching (C&S) have been proposed to underlie fluency processes and affect performance in the task. However, few studies have reported on the development of those cognitive strategies and their relationship with word productivity in typically developing children. Even fewer studies have covered a broad developmental period from preschool to adolescence or measured the effect of contextual factors in this relationship. Based on a sample of 472 typically developing Greek-speaking children aged 4;0 to 16;11 years, we investigated the development of SVF performance and reported on the degree to which it is affected by C&S strategies, children's sex, and level of parental education. Results revealed a large effect of age on word productivity and on the use of C&S strategies. Two switching factors (number of clusters and number of switches) and two clustering factors (mean cluster size and a novel measure, maximum cluster size), appeared to be significantly associated with word productivity, with the largest effect being attributed to the two switching factors. C&S factors, together with children's age and parental education, predicted 91.7% of the variance in the SVF score. Children's sex was not found to have a significant effect on either word productivity or C&S strategies. Results are discussed for their theoretical implications on the strategic processes underlying word production in typically developing children.

Two-Way Linear Probing Revisited

Linear probing continues to be one of the best practical hashing algorithms due to its good average performance, efficiency, and simplicity of implementation. However, the worst-case performance of linear probing seems to degrade with high load factors due to a primary-clustering tendency of one collision to cause more nearby collisions. It is known that the maximum cluster size produced by linear probing, and hence the length of the longest probe sequence needed to insert or search for a key in a hash table of size n, is Ω(logn), in probability. In this article, we introduce linear probing hashing schemes that employ two linear probe sequences to find empty cells for the keys. Our results show that two-way linear probing is a promising alternative to linear probing for hash tables. We show that two-way linear probing has an asymptotically almost surely O(loglogn) maximum cluster size when the load factor is constant. Matching lower bounds on the maximum cluster size produced by any two-way linear probing algorithm are obtained as well. Our analysis is based on a novel approach that uses the multiple-choice paradigm and witness trees.

AN EXTENDED K-MEANS CLUSTER HEAD SELECTION ALGORITHM FOR EFFICIENT ENERGY CONSUMPTION IN WIRELESS SENSOR NETWORKS

Effective use of sensor nodes’ batteries in wireless sensor networks is critical since the batteries are difficult to recharge or replace. This is closely connected to the networks’ lifespan since once the battery is used up, the node is no longer useful. The entire network will not function if 60 to 80% of the nodes in it have completely depleted their energy. In order to minimize energy usage and sustain the network for a long time, many cluster head selection algorithms have been developed. However, the existing cluster head selection algorithms such as K-Means, particle swarm selection optimization (PSO), Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Fuzzy C-Means (FCM) cluster head election algorithm have not fully reduced the issue of energy usage in WSN. The objective of this paper was to develop an extended K Mean Cluster Head selection(CHS) algorithm that uses remaining energy, distance between node and base station, distance between nodes and neighbour nodes, node density, node degree Maximum Cluster size, received signal strength indicator (RSSI) and Signal to Noise Ratio. The algorithm developed was used to enhance the lifespan of WSNs. The performance of the simulated variants of LEACH routing protocols is measured and evaluated using the quantitative research methodology. Utilizing residual node energy, packet delivery ratio, throughput, network longevity, average energy usage, and the number of live and dead node, the suggested approach is contrasted to previous approaches. From the study we observed that the proposed approach outperforms existing actual LEACH, Mod-LEACH and TSILEACH approaches.

Dynamic pore-scale modeling of residual fluid configurations in disordered porous media

Fluid trapping in porous media is important in many subsurface flow processes such as enhanced oil recovery and geological sequestration of carbon dioxide. To achieve optimal performance in such applications, a fundamental understanding of residual trapping mechanisms at the pore scale is necessary. In this work, we present a computational study of fluid trapping behaviors in natural porous media under different flow regimes by employing a dynamic pore-network modeling approach. The model incorporates many advanced features that have not been collectively used in previous dynamic platforms. For instance, it rigorously solves for fluid pressure fields from two-phase mass balance equations in each pore element, incorporates a detailed description of pore-scale fluid displacement dynamics of piston-like advance, snap-off, and pore-body filling, and explicitly accounts for flow through wetting layers forming in corners and rough surfaces of pore spaces. Moreover, we extend the ability of our model by including contact angle hysteresis, which is often neglected in existing dynamic models. A heavily-parallelized implementation of this platform is further advanced to achieve an efficient computational performance. We first conduct primary drainage and imbibition simulations in pore networks representing Bentheimer and Berea sandstones. We show that the predicted two-phase relative permeability curves agree well with their experimental counterparts reported in the literature. Afterwards, the validated model is used to systematically probe fluid trapping behaviors in a core-sized pore network that is constructed from high-resolution micro-computed tomography images of a Berea sandstone core sample. The effects of dynamic flow conditions and fluid properties on core-scale two-phase displacement pattern, residual-fluid configuration, and residual oil saturations are examined in detail. Fluid trapping properties such as maximum and average residual-fluid cluster size and capillary-controlled invasion selectivity at the pore scale are analyzed under both capillaryand viscous-dominated flow regimes.

MRI Assessment of Cerebral Blood Flow in Nonhospitalized Adults Who Self-Isolated Due to COVID-19.

Neurological symptoms associated with coronavirus disease 2019 (COVID-19), such as fatigue and smell/taste changes, persist beyond infection. However, little is known of brain physiology in the post-COVID-19 timeframe. To determine whether adults who experienced flu-like symptoms due to COVID-19 would exhibit cerebral blood flow (CBF) alterations in the weeks/months beyond infection, relative to controls who experienced flu-like symptoms but tested negative for COVID-19. Prospective observational. A total of 39 adults who previously self-isolated at home due to COVID-19 (41.9 ± 12.6 years of age, 59% female, 116.5 ± 62.2 days since positive diagnosis) and 11 controls who experienced flu-like symptoms but had a negative COVID-19 diagnosis (41.5 ± 13.4 years of age, 55% female, 112.1 ± 59.5 since negative diagnosis). A 3.0 T; T1-weighted magnetization-prepared rapid gradient and echo-planar turbo gradient-spin echo arterial spin labeling sequences. Arterial spin labeling was used to estimate CBF. A self-reported questionnaire assessed symptoms, including ongoing fatigue. CBF was compared between COVID-19 and control groups and between those with (n=11) and without self-reported ongoing fatigue (n=28) within the COVID-19 group. Between-group and within-group comparisons of CBF were performed in a voxel-wise manner, controlling for age and sex, at a family-wise error rate of 0.05. Relative to controls, the COVID-19 group exhibited significantly decreased CBF in subcortical regions including the thalamus, orbitofrontal cortex, and basal ganglia (maximum cluster size=6012 voxels and maximum t-statistic=5.21). Within the COVID-19 group, significant CBF differences in occipital and parietal regions were observed between those with and without self-reported on-going fatigue. These cross-sectional data revealed regional CBF decreases in the COVID-19 group, suggesting the relevance of brain physiology in the post-COVID-19 timeframe. This research may help elucidate the heterogeneous symptoms of the post-COVID-19 condition. 2. Stage 3.

Bayesian group sequential designs for cluster‐randomized trials

Flexible approaches have been proposed for individually randomized trials to save time or reduce the sample size. However, flexible designs for cluster‐randomized trials in which groups of participants rather than individuals are randomized to treatment arms are less common. Motivated by a cluster‐randomized trial designed to assess the effectiveness of a machine‐learning based clinical decision support system for physicians treating patients with depression, two Bayesian group sequential designs for cluster‐randomized trials are proposed to allow for early stopping for efficacy at pre‐planned interim analyses. The difference between the two designs lies in the way that participants are sequentially recruited. Given a maximum number of clusters as well as the maximum cluster size allowed in the trial, one design sequentially recruits clusters with the given maximum cluster size, while the other recruits all clusters at the beginning of the trial but sequentially enrolls individual participants until the trial is stopped early for efficacy or the final analysis has been reached. The design operating characteristics are explored via simulations for a variety of scenarios and two outcome types for the two designs. We make recommendations for Bayesian group sequential designs of cluster‐randomized trials based on the simulation results.

Size-independent scaling analysis for explosive percolation.

The Achlioptas process, a percolation algorithm on random network, shows a rapid second-order phase transition referred to as explosive percolation. To obtain the transition point and critical exponent β for percolations on a random network, especially for bond percolations, we propose a new scaling analysis that is independent of the system size. The transition point and critical exponent β are estimated for the product-rule (PR) and daCosta-Dorogovtsev-Goltsev-Mendes (dCDGM) (m=2) models of the Achlioptas process, as well as for the Erdős-Rényi (ER) model, which is a classical model in which the analytic values are known. The validity of the scaling analysis is confirmed, especially for the transition point. The estimations of β are also consistent with previously reported values for the ER and dCDGM(2) models, whereas the β estimation for the PR model deviates somewhat. By introducing a parameter representing the maximum cluster size, we develop an extrapolation scheme for the critical exponent β from the simulation just at the transition point in order to obtain a more accurate value. The estimated value of β is improved compared with that obtained by the scaling analysis for the ER model and is also consistent with the β value obtained for the dCDGM(2) model, whereas its deviation from the previously reported value is larger for the PR model. We discuss the accuracy of the present estimations and draw conclusions about their reliability.

The effects of methanol clustering on methanol-water nucleation.

The formation of subcritical methanol clusters in the vapor phase is known to complicate the analysis of nucleation measurements. Here, we investigate how this process affects the onset of binary nucleation as dilute water-methanol mixtures in nitrogen carrier gas expand in a supersonic nozzle. These are the first reported data for water-methanol nucleation in an expansion device. We start by extending an older monomer-dimer-tetramer equilibrium model to include larger clusters, relying on Helmholtz free energy differences derived from Monte Carlo simulations. The model is validated against the pressure/temperature measurements of Laksmono et al. [Phys. Chem. Chem. Phys. 13, 5855 (2011)] for dilute methanol-nitrogen mixtures expanding in a supersonic flow prior to the appearance of liquid droplets. These data are well fit when the maximum cluster size imax is 6-12. The extended equilibrium model is then used to analyze the current data. On the addition of small amounts of water, heat release prior to particle formation is essentially unchanged from that for pure methanol, but liquid formation proceeds at much higher temperatures. Once water comprises more than ∼24 mol% of the condensable vapor, droplet formation begins at temperatures too high for heat release from subcritical cluster formation to perturb the flow. Comparing the experimental results to binary nucleation theory is challenged by the need to extrapolate data to the subcooled region and by the inapplicability of explicit cluster models that require a minimum of 12 molecules in the critical cluster.

An Algorithmic Approach Based on Data Trees and Genetic Algorithms to Understanding Charged and Neutral Metal Nanocluster Growth.

Metal nanocluster growth pathways are challenging to study due to the number of possible species involved and the branching nature of possible mechanisms. We present a data tree-based approach to mapping these reaction pathways based on structures and energies obtained from Density Functional Theory (DFT) computations and including positive, negative, and neutral clusters in a continuum solvent of water. We also develop a genetic algorithm to study the relative stability of the clusters, which we combine with the data tree method to determine the effects that favorable decomposition reactions have on the reaction pathways. Introducing data tree pruning based on the exothermicity of each reaction, and including the first and second most important paths for each cluster up to five atoms including positive, neutral, and negative clusters, is then implemented to determine the cluster growth paths. These most favorable Ag-Ag reaction pathways are in agreement with more limited prior theoretical and experimental results, but they provide more systematic results that include predictions about the importance of clusters not previously identified. A key feature of the data tree approach is that it provides a comprehensive sampling of possible clusters, but without needing to generate the entire reaction tree. Additionally, the data tree-based approach allows for flexibility in the analysis based on restricting reactant charge or size, selecting a starting species to mimic an experimental precursor, and choosing a maximum allowable final cluster size or charge of interest.

Abstract 5907: Hot and cold spots of young-onset colorectal cancer mortality in U.S. counties, 1999-2019

Abstract Colorectal cancer mortality rates among those diagnosed under age 50 have been rising. Geospatial patterns of young-onset colorectal cancer (yoCRC) mortality rates in the U.S. have received limited attention, and prior studies were limited by a lack of adjustment for demographic factors, a focus only on hot spots, and a lack of cluster-specific relative risks (RRs). Adjustment allows clusters to represent areas where modifiable factors may be driving anomalous mortality rates. Aggregated 1999-2019 yoCRC mortality data for 3,036 counties was obtained from CDC WONDER Underlying Cause of Death, and demographics were obtained from 2005-2019 census variables and 2015 Robert Graham Center data. Mortality rates were stabilized via spatial smoothing, then a quasi-Poisson model was fit with median age, sex, race/ethnicity, and social deprivation. Adjusted yoCRC death counts were utilized in a Poisson circular spatial scan to identify Gini hot/cold spots at a maximum cluster size of 6% of the population at risk. Resulting RRs signified clusters where adjusted deaths were higher or lower than expected based on population and adjusted total deaths. Three statistically significant hot spots and five statistically significant cold spots were identified. The cluster with the largest log-likelihood ratio was a southern hot spot region encompassing counties horizontally from eastern Texas to central Georgia and vertically from southern Louisiana to southern Kentucky (RR: 1.26; p<0.0001). Other notable clusters included hot spots centered in North Carolina (RR: 1.18; p<0.0001) and Ohio (RR: 1.18; p<0.0001), and large cold spots in western counties (Table 1). Our results reiterate southern and Appalachian hot spots from prior literature and provide new insights into a notable Ohio hot spot along with vast western cold spots. Future work is needed to identify established and potentially novel factors that may be driving yoCRC mortality clustering patterns, such as obesity, diet, or physician access. Table 1. Poisson spatial scan results adjusted for median age, sex, race, and social deprivation Gini cluster Classification States in cluster Relative risk Log-likelihood ratio Monte Carlo p-value 1 Hot spot Alabama, Arkansas, Florida, Georgia, Louisiana, Mississippi, Tennessee, Texas 1.28 139.03 p<0.0001 2 Hot spot Georgia, Kentucky, North Carolina, South Carolina, Tennessee, Virginia, West Virginia 1.18 63.31 p<0.0001 3 Cold spot Arizona, California, Colorado, Idaho, Nevada, New Mexico, Utah, Wyoming 0.83 61.95 p<0.0001 4 Hot spot Kentucky, Michigan, Ohio, Pennsylvania, West Virginia 1.16 40.14 p<0.0001 5 Cold spot California, Oregon, Washington 0.86 39.10 p<0.0001 6 Cold spot California 0.87 35.53 p<0.0001 7 Cold spot Texas 0.86 29.91 p<0.0001 8 Cold spot Colorado, Iowa, Kansas, Minnesota, Missouri, Montana, Nebraska, North Dakota, Wisconsin, Wyoming 0.90 19.56 p<0.0001 Citation Format: R. Blake Buchalter, Alok A. Khorana, Shimoli Barot, David Liska, Stephanie L. Schmit. Hot and cold spots of young-onset colorectal cancer mortality in U.S. counties, 1999-2019 [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5907.

Liquefaction of granular materials in constant-volume cyclic shearing: Transition between solid-like and fluid-like states

By means of 3D-DEM cyclic simple shear simulations at constant volume, this paper analyzes the evolution of the internal structure during cyclic liquefaction of dense granular media. Upon liquefaction, the granular system undergoes a fast transition to a regime where the fluid-like shear strain develops. This regime is characterized by low shear modulus, decreasing dilatancy, and reduced shear viscosity. We analyze the internal structure by means of several micro- and meso-scale descriptors, including coordination number, fabric anisotropy, number of clusters, a fraction of maximum cluster size, and a percolation index. We select a typical post-liquefaction cycle in which we highlight several particular states that naturally divide the cycle into different periods. During liquefaction, the system deforms significantly with an enhanced number of binary collisions initially and particle clusters subsequently. Both the coordination number and percolation index increase while fabric anisotropy oscillates. The fluid-like to solid-like transition is characterized by a well-defined value of the coordination number (3.6) when the particles percolate across the system. From the analysis of the micro- and meso-scale behaviors, we further discuss two possible criteria for exiting the fluid-like state based on the excess pore pressure.

A prior for record linkage based on allelic partitions

In database management, record linkage aims to identify multiple records that correspond to the same individual. Record linkage can be treated as a clustering problem in which one or more noisy database records are associated with a unique latent entity. In contrast to traditional clustering applications, a large number of clusters with a few observations per cluster is expected in this context. Hence, a new class of prior distributions based on allelic partitions is proposed for the small cluster setting of record linkage. The proposed prior facilitates the introduction of information about the cluster size distribution at different scales, and naturally enforces sublinear growth of the maximum cluster size – known as the microclustering property. In addition, a set of novel microclustering conditions are introduced in order to impose further constraints on the cluster sizes a priori. The performance of the proposed class of priors is evaluated using simulated data and three official statistics data sets. Moreover, different loss functions for optimal point estimation of the partitions are compared using decision-theoretical based approaches recently proposed in the literature.

Optimization of garbage removal within a territorial community

This paper proposes an algorithm for optimizing the garbage collection route in a local community (or a separate settlement). The study was conducted for one garbage truck. To achieve the maximum efficiency of the algorithm, it has been assumed that the points of discharge of collected waste by a garbage truck could be arranged along the way between the proposed clusters of garbage collection points. The optimization of the built routes has been proven, taking into consideration the above assumptions. The study's results could be used to reduce the budget expenditures by territorial community authorities for the collection and disposal of waste. The reported solutions could significantly shorten the garbage collection time, which would improve the environmental and aesthetic situation within the study area. The use of a new algorithm makes it possible to display the results both in quantitative and qualitative forms. An improved k-means algorithm with a maximum cluster size was selected for clustering. Each cluster was built on the basis of the maximal value of garbage truck tonnage. That means that the size of the cluster would be determined by the value of the maximum amount of waste that can be removed by a garbage truck in one run. A task of the traveling salesman was applied to find the shortest path between representatives of one cluster (garbage collection points) calling at all its points and to establish the optimal path between all the clusters formed for a territorial community. The issue related to efficient waste disposal in local communities tends to aggravate rapidly while the task to optimize garbage collection and removal is becoming increasingly acute. This is because at present, along with the increase in the global population, all types of production are increasing their volumes, which, in turn, leads to an increase in the amount of waste, in particular, household.

1442. Spatiotemporal Clusters of Varicella and the Regional Risks through Bayesian Approach: A National Five-year Cohort Analysis

Abstract Background Since varicella epidemics repeatedly occurred in Korea, it is essential to control varicella outbreaks preemptively in the targeted region. Therefore, we aimed to reveal spatiotemporal clusters of varicella and the regional risk factor of varicella incidence at the national level. Methods All varicella cases (defined as ICD-10 codes, B01-B09) from 2013 to 2017 in Korea were extracted from National Health Insurance Service. Of the total, 566,978 cases were realigned spatially by 250 administrative districts of Korea and temporally by a week. Spatial autocorrelation was tested using the global Moran’s I statistics using Monte Carlo simulation. Kulldorff’s prospective space-time scan statistics were used to reveal space-time clusters of varicella. Possible risk factors were extracted from the Korean Statistical Information Service and Community Health Survey of Korea, including hand hygiene perceptions, alcohol and smoking status, the proportion of children under 15 years old, the number of households, and household income by regions. After selecting significant risk factors through non-spatial generalized linear models, a conditional autoregressive spatiotempoal model with Bayesian extension was applied to estimate the regional factors affecting varicella incidence. Results There was spatial autocorrelation using Global Moran’s I statistics (P< 0.01). When the maximum cluster size was limited to 10% of the population, 17 spatiotemporal clusters were detected in specific regions in Korea (figure 1). Low perception of hand hygiene, the high proportion of alcohol drinking and cigarette smoking, high children proportion, low number of familial member, and low household income were associated with varicella spatiotemporal incidence (odds ratio: 0.97, 1.01, 2.31, 1.10, 0.99, 0.99, respectively; 95% credible intervals of all risk factors did not include 1). Figure 1. Space-time prospective clusters of varicella in Korea using varicella incidence from 2013 to 2017. Relative risks ratio of each cluster is described at the point. Conclusion Varicella incidence shows spatiotemporal clustering patterns in specific regions. Since regional factors such as the perception rate of hand hygiene, child proportion, alcohol drinking, cigarette smoking, and low household income affect varicella’s spatiotemporal incidence, strategies for targeted control of high-risk regions are strongly recommended. Disclosures All Authors: No reported disclosures

Clustering of COVID-19 data for knowledge discovery using c-means and fuzzy c-means

In this work, the partitioning clustering of COVID-19 data using c-Means (cM) and Fuzy c-Means (Fc-M) algorithms is carried out. Based on the data available from January 2020 with respect to location, i.e., longitude and latitude of the globe, the confirmed daily cases, recoveries, and deaths are clustered. In the analysis, the maximum cluster size is treated as a variable and is varied from 5 to 50 in both algorithms to find out an optimum number. The performance and validity indices of the clusters formed are analyzed to assess the quality of clusters. The validity indices to understand all the COVID-19 clusters' quality are analysed based on the Zahid SC (Separation Compaction) index, Xie-Beni Index, Fukuyama–Sugeno Index, Validity function, PC (performance coefficient), and CE (entropy) indexes. The analysis results pointed out that five clusters were identified as a major centroid where the pandemic looks concentrated. Additionally, the observations revealed that mainly the pandemic is distributed easily at any global location, and there are several centroids of COVID-19, which primarily act as epicentres. However, the three main COVID-19 clusters identified are 1) cases with value <50,000, 2) cases with a value between 0.1 million to 2 million, and 3) cases above 2 million. These centroids are located in the US, Brazil, and India, where the rest of the small clusters of the pandemic look oriented. Furthermore, the Fc-M technique seems to provide a much better cluster than the c-M algorithm.