Similar Threshold Values Research Articles

Analyzing breast cancer comorbidities: a network approach using community detection algorithms

Breast cancer is a prominent global health concern, as the data from the International Agency for Research on Cancer (IARC) shows that breast cancer is the leading cancer type with new cases in 2020 and among the Top 5 cancer types with the most deaths. To help improve the current breast cancer comorbidity identification by medical personnel and ultimately, lower the number of death cases from breast cancer comorbidity, this research aims to discover the breast cancer comorbidity community, do modularity and similarity-based evaluation, suggest the best semantic similarity measurement and threshold value, and validate the data of breast cancer comorbidities with several data from research papers. The Wang algorithm, with a threshold value of 0.5, is chosen to build the network. Leiden, Louvain, RBER Pots, RB Pots, and Walktrap are the best five community detection algorithms. Similarity measurements with the best three fitness functions (edges inside, scaled density, and size) suggest that the Leiden–Louvain algorithm and RBER Pots-RB Pots algorithm are two pairs of algorithms with similar results. Other similarity measurements with the V-measure heatmap suggest that Louvain–Leiden (0.99), RB Pots–Leiden (0.97), and RB Pots–RBER Pots (0.96) results are similar. Comorbidity is then evaluated using the best five community detection algorithms and four centrality algorithms. As a result, fourteen diseases are agreed upon by the best five community detection algorithms, five diseases are agreed by four algorithms, two diseases are agreed by three algorithms, a disease is agreed by two algorithms, and ten diseases are agreed by an algorithm.

Approach for Tattoo Detection and Identification Based on YOLOv5 and Similarity Distance

The large number of images in the different areas and the possibilities of technologies lead to various solutions in automatization using image data. In this paper, tattoo detection and identification were analyzed. The combination of YOLOv5 object detection methods and similarity measures was investigated. During the experimental research, various parameters have been investigated to determine the best combination of parameters for tattoo detection. In this case, the influence of data augmentation parameters, the size of the YOLOv5 models (n, s, m, l, x), and the three main hyperparameters of YOLOv5 were analyzed. Also, the efficiency of the most popular similarity distances cosine and Euclidean was analyzed in the tattoo identification process with the purpose of matching the detected tattoo with the person’s tattoo in the database. Experiments have been performed using the deMSI dataset, where images were manually labeled to be suitable for use by the YOLOv5 algorithm. To validate the results obtained, the newly collected tattoo dataset was used. The results have shown that the highest average accuracy of all tattoo detection experiments has been obtained using the YOLOv5l model, where mAP@0.5:0.95 is equal to 0.60, and mAP@0.5 is equal to 0.79. The accuracy for tattoo identification reaches 0.98, and the F-score is up to 0.52 when the highest cosine similarity tattoo is associated. Meanwhile, to ensure that no suspects will be missed, the cosine similarity threshold value of 0.15 should be applied. Then, photos with higher similarity scores should be analyzed only. This would lead to a 1.0 recall and would reduce the manual tattoo comparison by 20%.

Enhancing post-traumatic stress disorder patient assessment: leveraging natural language processing for research of domain criteria identification using electronic medical records

BackgroundExtracting research of domain criteria (RDoC) from high-risk populations like those with post-traumatic stress disorder (PTSD) is crucial for positive mental health improvements and policy enhancements. The intricacies of collecting, integrating, and effectively leveraging clinical notes for this purpose introduce complexities.MethodsIn our study, we created a natural language processing (NLP) workflow to analyze electronic medical record (EMR) data and identify and extract research of domain criteria using a pre-trained transformer-based natural language model, all-mpnet-base-v2. We subsequently built dictionaries from 100,000 clinical notes and analyzed 5.67 million clinical notes from 38,807 PTSD patients from the University of Pittsburgh Medical Center. Subsequently, we showcased the significance of our approach by extracting and visualizing RDoC information in two use cases: (i) across multiple patient populations and (ii) throughout various disease trajectories.ResultsThe sentence transformer model demonstrated high F1 macro scores across all RDoC domains, achieving the highest performance with a cosine similarity threshold value of 0.3. This ensured an F1 score of at least 80% across all RDoC domains. The study revealed consistent reductions in all six RDoC domains among PTSD patients after psychotherapy. We found that 60.6% of PTSD women have at least one abnormal instance of the six RDoC domains as compared to PTSD men (51.3%), with 45.1% of PTSD women with higher levels of sensorimotor disturbances compared to men (41.3%). We also found that 57.3% of PTSD patients have at least one abnormal instance of the six RDoC domains based on our records. Also, veterans had the higher abnormalities of negative and positive valence systems (60% and 51.9% of veterans respectively) compared to non-veterans (59.1% and 49.2% respectively). The domains following first diagnoses of PTSD were associated with heightened cue reactivity to trauma, suicide, alcohol, and substance consumption.ConclusionsThe findings provide initial insights into RDoC functioning in different populations and disease trajectories. Natural language processing proves valuable for capturing real-time, context dependent RDoC instances from extensive clinical notes.

The Optimization of the ARP Poisoning Attack Detection Model Using a Similar Approach Based on NetFlow Analysis

Information security and threats are a concern in the cyber era. Attacks can be malicious activities. One of them is known as ARP poisoning attack activity, which attacks by falsifying a computer's identity through illegal access to retrieve confidential information in a target computer. Besides, it has also caused service deadlocks in the network. Previous studies have been introduced for the ARP Attack Detection model using rule-based and mining-based. Still, they cannot show optimal detection performance and obtain high false positive results. This paper proposed a detection model for ARP poisoning attacks using a similarity measurement approach adopting cosine similarity. The goal is to obtain measurements of host activities similar to ARP poisoning attacks. The experiment results showed that the model got an accuracy of 97.25%, recall of 96.43%, and precision of 81% with a similarity threshold value of 0.488. Comparison results with previous studies showed higher detection accuracy than previous studies and some classification methods. It shows that the model can improve intrusion detection performance and facilitate network administrators to analyze ARP poisoning attacks in computer networks.

SARS-CoV-2 intra-host diversity, antibody response, and disease severity after reinfection by the variant of concern Gamma in Brazil

The rapid spread of the SARS-CoV-2 Variant of Concern (VOC) Gamma in Amazonas during early 2021 fueled a second large COVID-19 epidemic wave and raised concern about the potential role of reinfections. Very few cases of reinfection associated with the VOC Gamma have been reported to date, and their potential impact on clinical, immunological, and virological parameters remains largely unexplored. Here we describe 25 cases of SARS-CoV-2 reinfection in Brazil. SARS-CoV-2 genomic analysis confirmed that individuals were primo-infected with distinct viral lineages between March and December 2020 (B.1.1, B.1.1.28, B.1.1.33, B.1.195, and P.2) and reinfected with the VOC Gamma between 3 to 12 months after primo-infection. We found a similar mean cycle threshold (Ct) value and limited intra-host viral diversity in both primo-infection and reinfection samples. Sera of 14 patients tested 10–75 days after reinfection displayed detectable neutralizing antibodies (NAb) titers against SARS-CoV-2 variants that circulated before (B.1.*), during (Gamma), and after (Delta and Omicron) the second epidemic wave in Brazil. All individuals had milder or no symptoms after reinfection, and none required hospitalization. These findings demonstrate that individuals reinfected with the VOC Gamma may display relatively high RNA viral loads at the upper respiratory tract after reinfection, thus contributing to onward viral transmissions. Despite this, our study points to a low overall risk of severe Gamma reinfections, supporting that the abrupt increase in hospital admissions and deaths observed in Amazonas and other Brazilian states during the Gamma wave was mostly driven by primary infections. Our findings also indicate that most individuals analyzed developed a high anti-SARS-CoV-2 NAb response after reinfection that may provide some protection against reinfection or disease by different SARS-CoV-2 variants.

Skill and lead time of vegetation drought impact forecasts based on soil moisture observations

Timely and skilful forecasts of vegetation drought impacts should enable more proactive drought preparedness, management, and mitigation. Numerous previous studies found a temporal correlation between soil moisture and vegetation condition. However, a correlation across the full range of soil moisture and vegetation condition does not automatically translate into skill in forecasting infrequent events such as agricultural droughts. Here, we develop a threshold- or impact-based forecasting framework to assess early warning capability (EWC). We analysed the skill and lead time achieved using soil moisture observations at multiple depths as predictors of subsequent vegetation drought impacts inferred from MODIS satellite observations at 93 sites across the United States. Forecast thresholds were expressed in terms of seasonally-adjusted standardised anomalies (z-scores) to distinguish climate-related impacts from any seasonal vegetation cycle. Different combinations of soil moisture integration depth, satellite vegetation indices and threshold levels were tested. Near-Infrared Reflectance of vegetation (NIRv) yielded a marginally better EWC than other indicators of vegetation drought impact. The optimal soil moisture integration depth varied between land cover types, from 0 to 10 cm for cropping systems to 0–100 cm for grassland and savanna. The greatest skill improvements were achieved using similar z-score threshold values for the soil moisture trigger and vegetation impact, producing typical lead times of two to four weeks. Further research is recommended to combine the framework developed here with spatially continuous soil moisture analyses or forecasts available from remote sensing and land surface models.

A method for analyzing suspect-filler similarity using convolutional neural networks

Eyewitness misidentifications are one of the leading factors in wrongful convictions. This study focuses on the structure of the lineups, which is one of the factors that cause misidentification, and the use of artificial intelligence (AI) technologies in the selection of fillers to be included in the lineups. In the study, AI-based face recognition systems are used to determine the level of similarity of fillers to the suspect. Using two different face recognition models with a Convolutional Neural Network (CNN) structure, similarity threshold values close to human performance were calculated (VGG Face and Cosine similarity = 0.383, FaceNet and Euclidean l2 = 1.16). In the second part of the study, the problems that are likely to be caused by facial recognition systems used in the selection of fillers are examined. The results of the study reveal that models responsible for facial recognition may not suffice alone in the selection of fillers and, an advanced structure using CNN models trained to recognize other attributes (race, gender, age, etc.) associated with similarity along with face recognition models would produce more accurate results. In the last part of the study, a Line-up application that can analyze attributes such as facial similarity, race, gender, age, and facial expression, is introduced.

An optimized technique for copy–move forgery localization using statistical features

Copy–Move Forgery Detection (CMFD) helps to detect copied and pasted areas in one image. It plays a crucial role in legal evidence, forensic investigation, defence, and many more places. In the proposed CMFD method, a two-step identification of forgery is presented. In step one, the suspected image will be classified into either one of two classes that are forged or authentic. Step two is carried out​ only if the suspected is classified as forged, then forged location will be identified using the block-matching procedure. Initially, the suspected image is decomposed into different orientations using Steerable Pyramid Transform (SPT); Grey Level Co-occurrence Matrix (GLCM) features are extracted from each orientation. These features are used to train Optimized Support Vector Machine (OSVM) as well as to classify. If the suspected image is categorized into forged, then the suspected grey image is converted into overlapping blocks, and from each block, GLCM features are extracted. The proper similarity threshold value and distance threshold value can locate the forged region using GLCM block features. The performance of the proposed method is tested using standard datasets CoMoFoD and CASIA Datasets. The proposed CMFD approach results are consistent, even the forged image suffered from attacks like JPEG compression, scaling, and rotation. The OSVM classifier is showing superiority over the Optimized Naive Bayes Classifier (ONBC), Extreme Learning Machine (ELM) and Support Vector Machine (SVM).

Comparison of Auto Sampling and Passive Sampling Methods for SARS-CoV-2 Detection in Wastewater.

Wastewater-based surveillance is emerging as an important tool for the COVID-19 pandemic trending. Current methods of wastewater collection, such as grab and auto-composite sampling, have drawbacks that impede effective surveillance, especially from small catchments with limited accessibility. Passive samplers, which are more cost-effective and require fewer resources to process, are promising candidates for monitoring wastewater for SARS-CoV-2. Here, we compared traditional auto sampling with passive sampling for SARS-CoV-2 detection in wastewater. A torpedo-style 3D-printed passive sampler device containing both cotton swabs and electronegative filter membranes was used. Between April and June 2021, fifteen passive samplers were placed at a local hospital’s wastewater outflow alongside an autosampler. Reverse transcription and quantitative polymerase chain reaction (RT-qPCR) was used to detect SARS-CoV-2 in the samples after processing and RNA extraction. The swab and membrane of the passive sampler showed similar detection rates and cycle threshold (Ct) values for SARS-CoV-2 RNA for the N1 and N2 gene targets. The passive method performed as well as the grab/auto sampling, with no significant differences between N1 and N2 Ct values. There were discrepant results on two days with negative grab/auto samples and positive passive samples, which might be related to the longer duration of passive sampling in the study. Overall, the passive sampler was rapid, reliable, and cost-effective, and could be used as an alternative sampling method for the detection of SARS-CoV-2 in wastewater.

Evaluation of a parasite-density based pooled targeted amplicon deep sequencing (TADS) method for molecular surveillance of Plasmodium falciparum drug resistance genes in Haiti.

Sequencing large numbers of individual samples is often needed for countrywide antimalarial drug resistance surveillance. Pooling DNA from several individual samples is an alternative cost and time saving approach for providing allele frequency (AF) estimates at a population level. Using 100 individual patient DNA samples of dried blood spots from a 2017 nationwide drug resistance surveillance study in Haiti, we compared codon coverage of drug resistance-conferring mutations in four Plasmodium falciparum genes (crt, dhps, dhfr, and mdr1), for the same deep sequenced samples run individually and pooled. Samples with similar real-time PCR cycle threshold (Ct) values (+/- 1.0 Ct value) were combined with ten samples per pool. The sequencing success for samples in pools were higher at a lower parasite density than the individual samples sequence method. The median codon coverage for drug resistance-associated mutations in all four genes were greater than 3-fold higher in the pooled samples than in individual samples. The overall codon coverage distribution for pooled samples was wider than the individual samples. The sample pools with < 40 parasites/μL blood showed more discordance in AF calls for dhfr and mdr1 between the individual and pooled samples. This discordance in AF estimation may be due to low amounts of parasite DNA, which could lead to variable PCR amplification efficiencies. Grouping samples with an estimated ≥ 40 parasites/μL blood prior to pooling and deep sequencing yielded the expected population level AF. Pooling DNA samples based on estimates of > 40 parasites/μL prior to deep sequencing can be used for rapid genotyping of a large number of samples for these four genes and possibly other drug resistant markers in population-based studies. As Haiti is a low malaria transmission country with very few mixed infections and continued chloroquine sensitivity, the pooled sequencing approach can be used for routine national molecular surveillance of resistant parasites.

An international, interlaboratory ring trial confirms the feasibility of an extraction-less "direct" RT-qPCR method for reliable detection of SARS-CoV-2 RNA in clinical samples.

Reverse transcription–quantitative polymerase chain reaction (RT-qPCR) is used worldwide to test and trace the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). “Extraction-less” or “direct” real time–reverse transcription polymerase chain reaction (RT-PCR) is a transparent and accessible qualitative method for SARS-CoV-2 detection from nasopharyngeal or oral pharyngeal samples with the potential to generate actionable data more quickly, at a lower cost, and with fewer experimental resources than full RT-qPCR. This study engaged 10 global testing sites, including laboratories currently experiencing testing limitations due to reagent or equipment shortages, in an international interlaboratory ring trial. Participating laboratories were provided a common protocol, common reagents, aliquots of identical pooled clinical samples, and purified nucleic acids and used their existing in-house equipment. We observed 100% concordance across laboratories in the correct identification of all positive and negative samples, with highly similar cycle threshold values. The test also performed well when applied to locally collected patient nasopharyngeal samples, provided the viral transport media did not contain charcoal or guanidine, both of which appeared to potently inhibit the RT-PCR reaction. Our results suggest that direct RT-PCR assay methods can be clearly translated across sites utilizing readily available equipment and expertise and are thus a feasible option for more efficient COVID-19 coronavirus disease testing as demanded by the continuing pandemic.

Menthol-Based Topical Analgesic Induces Similar Upper and Lower Body Pain Pressure Threshold Values: A Randomized Trial.

Both health professionals and consumers use menthol-based topical analgesics extensively for the temporary relief of pain from musculoskeletal ailments or injury. However, there are no reports of differences in the pain pressure threshold (PPT) or the relative effectiveness of topical analgesics to reduce pain in the upper and lower body muscles and tendons. The objective of this study was to investigate whether differences existed in PPT and relative pain attenuation associated with a menthol-based topical analgesic over a variety of upper and lower body muscles and tendons. Randomized allocation, controlled, intervention study. Sixteen participants (10 females and 6 males) were tested on their dominant or nondominant side. The order of specific muscle/tendon testing was also randomized, which included upper body (middle deltoid, biceps brachii, and lateral epicondylar tendon) and lower body locations (quadriceps, hamstrings, gastrocnemius, lumbosacral erector spinae muscles, and patellar and Achilles tendons). The PPT was monitored before and 15minutes following the application of a menthol-based topical analgesic. A menthol-based topical analgesic increased PPT (decreased pain sensitivity) overall (P = .05; 11.6% [2.4%]; d = 1.05) and PPT was higher (P < .0001; 31.5%-44.2%; d = 1.03-1.8) for lower versus upper body locations. Health professionals and the public can be assured of similar reductions in pain sensitivity independent of the location of application of a menthol-based topical analgesic.

Development of a multiplex qPCR for the quantification of three protozoan parasites of the eastern oyster Crassostrea virginica.

A multiplex quantitative PCR (qPCR) assay for the simultaneous detection of 3 eastern oyster Crassostrea virginica parasites, Perkinsus marinus, Haplosporidium nelsoni, and H. costale, was developed using 3 different fluorescently labeled hydrolysis probes. The primers and probe from a previously validated singleplex qPCR for P. marinus detection were combined with newly designed primers and probes specific for H. nelsoni and H. costale. The functionality of the multiplex assay was demonstrated on 2 different platforms by the linear relationship of the standard curves and similar cycle threshold (CT) values between parasites. Efficiency of the multiplex qPCR assay on the Roche and BioRad platforms ranged between 93 and 101%. The sensitivity of detection ranged between 10 and 100 copies of plasmid DNA for P. marinus and Haplosporidium spp., respectively. The concordance between the Roche and BioRad platforms in the identification of the parasites P. marinus, H. nelsoni, and H. costale was 91, 97, and 97%, respectively, with a 10-fold increase in the sensitivity of detection of Haplosporidium spp. on the BioRad thermocycler. The concordance between multiplex qPCR and histology for P. marinus, H. nelsoni, and H. costale was 54, 57, and 87%, respectively. Discordances between detection methods were largely related to localized or low levels of infections in oyster tissues, and qPCR was the more sensitive diagnostic. The multiplex qPCR developed here is a sensitive diagnostic tool for the quantification and surveillance of single and mixed infections in the eastern oyster.

Emergence of SARS-CoV-2 B.1.1.7 Lineage at Outpatient Testing Site, Berlin, Germany, January-March 2021.

Within 5 weeks in 2021, B.1.1.7 became the dominant severe acute respiratory syndrome coronavirus 2 lineage at an outpatient testing site in Berlin, Germany. Compared with outpatients with wild-type virus infection, patients with B.1.1.7 had similar cycle threshold values, more frequent sore throat and travel history, and less frequent anosmia/ageusia.

Sensitive and Prolonged Detection of Dengue Virus RNA in Whole Blood.

Molecular detection of dengue virus (DENV) RNA from serum or plasma provides an accurate acute-phase diagnostic (< 7 days after symptom onset). Detection may be prolonged in whole blood, although data are limited. We tested for DENV by real-time reverse transcription-PCR in 345 paired acute-phase plasma and whole blood samples from individuals with a Flavivirus-like illness in southwestern Guatemala. In 18/18 cases with detectable DENV RNA in plasma, whole blood samples were positive and yielded similar cycle threshold values. In seven individuals with convalescent samples obtained 2-3 weeks later, DENV RNA remained detectable in whole blood but not plasma. In three additional cases, DENV RNA was only detectable in whole blood at the acute visit. In two cases, whole blood detection was linked to a virologically confirmed DENV infection 6-11 weeks earlier. Whole blood DENV RNA detection is sensitive for acute dengue infection and may remain positive for weeks to months.

Severe Acute Respiratory Syndrome Coronavirus 2 Testing in Children in a Large Regional US Health System During the Coronavirus Disease 2019 Pandemic.

The objective was to evaluate patterns of pediatric coronavirus disease 2019 testing in a large health system throughout the pandemic, before and after school reopening. This was a cross-sectional time-series study of clinical virology results from children tested for severe acute respiratory syndrome coronavirus 2 in Southern Connecticut and areas of New York and Rhode Island. Data collected include demographics, hospital admission, changes in percent positive tests over time, detection intervals in persistently positive children and cycle threshold values. The setting was the Yale New Haven Health System has 6 hospitals at 4 Connecticut locations, 1 hospital in Rhode Island and ambulatory locations in Connecticut, Rhode Island and New York. Participants included twenty-three-thousand one-hundred thirty-seven children ≤ 18 years of age, tested for coronavirus disease 2019 at an ambulatory testing site, the emergency department or on an inpatient unit within the Yale New Haven Health System. Among all tests, 3.2% were positive. Older children consistently made up the larger portion of positive pediatric cases, regardless of community prevalence. Increased pediatric cases later in the pandemic when prevalence in adults was relatively low correlates with a higher number of tests performed in children and not with an increased positivity rate. No significant changes in trends of positivity were detected after the reopening of schools. Symptomatic and asymptomatic children had similar cycle threshold values regardless of age, and a subset of children demonstrated persistent viral detection, some for as long as 6 weeks. An increase in pediatric cases documented in the late summer was predominately due to increased access to testing for children. The percent positivity in children did not change in the first 3 weeks after school opened. A subset of children has detectable severe acute respiratory syndrome coronavirus 2 RNA in the upper respiratory tract for weeks after the initial infection.

Developing thresholds for intervention recommendation and treatment success after orthopedic surgery

Abstract Background Patient reported outcome measures (PROMs) are increasingly used to monitor recovery paths and the outcome of orthopedic procedures. One of the focus areas is endoprosthetic joint care. Unfavorable post-surgery PROM scores might indicate adverse recovery paths. As of now, an adequate definition of thresholds for recommending an adaptation of therapy does not exist. The same is true for thresholds defining treatment success. Methods To address this research gap, minimal clinical improvement differences and patient acceptable symptom states are estimated using anchor-based methods. Moreover, to better capture the variance in patients' recovery paths, we calculate thresholds for subgroups of patients exhibiting similar pre-operative PROM scores. For this purpose, we use patient data from two German hospitals, covering roughly 6,000 total hip and knee replacement patients treated between 2016 and 2018. Patients answered standard PROM (WOMAC, EQ-5D) and tailored patient satisfaction questionnaires prior to surgery as well as three and twelve months after surgery. Results Whereas we found similar threshold values for total hip and knee replacement patients at the 12 months mark, threshold values after 3 months are systematically better for hip patients than for knee patients. Moreover, we find that thresholds depend on pre-surgery PROM scores. Patients with low pre-surgery scores recover slower, patients in mid-quartiles show large score improvements, and patients in the highest quartile show only mild improvements. Conclusions The study shows that when defining thresholds, it should not only be discriminated by indication but also by patient's pre-surgery condition. This pre-surgery condition can best be described by pre-surgery PROM scores. Further, to implement thresholds into standard care, policy makers need to collaborate with public sickness funds and hospitals to refine thresholds based on nation-wide data. Key messages An adequate definition of PROM score thresholds is necessary to be able to evaluate intervention recommendations and treatment success after surgery. So far, existing research is limited. Recovery paths and therefore PROM score thresholds differ between indications and are dependent on patients’ pre-surgery condition, best described by pre-surgery PROM scores.

A Computationally Efficient Method for Probabilistic Parameter Threshold Analysis for Health Economic Evaluations.

Background. Threshold analysis is used to determine the threshold value of an input parameter at which a health care strategy becomes cost-effective. Typically, it is performed in a deterministic manner, in which inputs are varied one at a time while the remaining inputs are each fixed at their mean value. This approach will result in incorrect threshold values if the cost-effectiveness model is nonlinear or if inputs are correlated. Objective. To propose a probabilistic method for performing threshold analysis, which accounts for the joint uncertainty in all input parameters and makes no assumption about the linearity of the cost-effectiveness model. Methods. Three methods are compared: 1) deterministic threshold analysis (DTA); 2) a 2-level Monte Carlo approach, which is considered the gold standard; and 3) a regression-based method using a generalized additive model (GAM), which identifies threshold values directly from a probabilistic sensitivity analysis sample. Results. We applied the 3 methods to estimate the minimum probability of hospitalization for typhoid fever at which 3 different vaccination strategies become cost-effective in Uganda. The threshold probability of hospitalization at which routine vaccination at 9 months with catchup campaign to 5 years becomes cost-effective is estimated to be 0.060 and 0.061 (95% confidence interval [CI], 0.058–0.064), respectively, for 2-level and GAM. According to DTA, routine vaccination at 9 months with catchup campaign to 5 years would never become cost-effective. The threshold probability at which routine vaccination at 9 months with catchup campaign to 15 years becomes cost-effective is estimated to be 0.092 (DTA), 0.074 (2-level), and 0.072 (95% CI, 0.069–0.075) (GAM). GAM is 430 times faster than the 2-level approach. Conclusions. When the cost-effectiveness model is nonlinear, GAM provides similar threshold values to the 2-level Monte Carlo approach and is computationally more efficient. DTA provides incorrect results and should not be used.

Exploring the threshold of epidemic spreading for a stochastic SIR model with local and global contacts

The spread of an epidemic process is considered in the context of a spatial SIR stochastic model that includes a parameter 0≤p≤1 that assigns weights p and 1−p to global and local infective contacts respectively. The model was previously studied by other authors in different contexts. In this work we characterized the behavior of the system around the threshold for epidemic spreading. We first used a deterministic approximation of the stochastic model and checked the existence of a threshold value of p for exponential epidemic spread. An analytical expression, which defines a function of the quotient α between the transmission and recovery rates, is obtained to approximate this threshold. We then performed different analyzes based on intensive stochastic simulations and found that this expression is also a good estimate for a similar threshold value of p obtained in the stochastic model. The dynamics of the average number of infected individuals and the average size of outbreaks show a behavior across the threshold that is well described by the deterministic approximation. The distributions of the outbreak sizes at the threshold present common features for all the cases considered corresponding to different values of α>1. These features are otherwise already known to hold for the standard stochastic SIR model at its threshold, α=1: (i) the probability of having an outbreak of size n goes asymptotically as n−3∕2 for an infinite system, (ii) the maximal size of an outbreak scales as N2∕3 for a finite system of size N.

Duplicate File Detection and Elimination

The problem of detecting and eliminating duplicated file is one of the major problems in the broad area of data cleaning and data quality in system. Many times, the same logical real world entity may have multiple representations in the data warehouse. Duplicate elimination is hard because it is caused by several types of errors like typographical errors, and different representations of the same logical value. The main objective of this research work is to detect exact and inexact duplicates by using duplicate detection and elimination rules. This approach is used to improve the efficiency of the data. The importance of data accuracy and quality has increased with the explosion of data size. In the duplicate elimination step, only one copy of exact duplicated records or file are retained and eliminated other duplicate records or file. The elimination process is very important to produce a cleaned data. Before the elimination process, the similarity threshold values are calculated for all the records which are available in the data set. The similarity threshold values are important for the elimination process.