Search

Background: Small-incision lenticule extraction (SMILE) is a relatively novel refractive procedure introduced to correct myopia and myopic astigmatism using femtosecond technology. Aim: The aim of this study was to prospectively assess the predictability, efficacy and safety of SMILE surgery. Setting: The study took place in Alyamama Vision Refractive Center, Baghdad, Iraq. Methods: In this prospective, non-randomised, single-centre clinical study, all patients with myopia or myopic astigmatism underwent the SMILE procedure performed by one surgeon (H.S.S.) with a targeted post-operative refraction of 0 to −0.5 dioptres (D). The procedure was performed using the VisuMax femtosecond platform. Post-operative data, including slit-lamp examination and measurements of uncorrected visual acuity (UCVA), best corrected visual acuity (BCVA), objective and subjective refraction, and intraocular pressure (IOP), were recorded. Results: A total of 141 eyes of 76 patients (51 females and 25 males) were included in the study. The mean preoperative spherical equivalent (SE) (−4.37 D ± 1.65 D) ranged from −2.0 D to −10.25 D after 2 years of follow-up, and the mean post-operative SE (−0.06 D ± 0.18 D) ranged from −0.75 D to 0.63 D. A UCVA of 20/40 or better was noted in 98% of the eyes at the 2-year follow-up. Most of the surgical complications, which were seen in 30 eyes, did not result in permanent visual loss. Corneal abrasion was the most common surgical complication, constituting 10.6% of all complications, followed by adherence of the lenticule to the cap (3.5%). Conclusion: The SMILE procedure is effective with high safety and efficacy, and its complication rate will probably decrease with improvement in the surgeon experience level and refinement of the laser parameters.

Background. Currently, diabetic retinopathy (DR) has a wide recognition as a neurovascular rather than a microvascular diabetic complication with an increasing need for enhanced detection approaches. Pattern-reversal visual evoked potentials (PRVEPs) test, as an objective electrophysiological measure of the optic nerve and retinal function, can be of great value in the detection of diabetic retinal changes. Objectives. The use of two sizes of checkerboard PRVEPs testing to detect any neurological changes in persons with type 2 diabetes mellitus (T2DM) with and without a clinically detected DR. Also, to compare the results according to the candidate age, duration, and glycemic status of T2DM. Methods. This study included 50 candidates as group A with T2DM and did not have a clinically detected DR and 50 candidates as group B with T2DM and had a clinically detected early DR and 50 candidates as controls who were neither diabetic nor had any other medical or ophthalmic condition that might affect PRVEPs test results. The PRVEPs were recorded in the consultant unit of ophthalmology in Almawani Teaching Hospital. Monocular PRVEPs testing of both eyes was done by using large (60 min) and small (15 min) checks to measure N75 latency and P100 latency and amplitude. Results. There was a statistically significant P100 latency delay and P100 amplitude reduction in both groups A and B in comparison with the controls. The difference between groups A and B was also significant. In both test results of groups A and B, the proportions of abnormal P100 latency were higher than those of P100 amplitude with a higher abnormal proportions in 15 min test. Conclusions. The PRVEP test detected neurological changes, mainly as conductive alterations affecting mostly the foveal region prior to any overt DR clinical changes, and these alterations were heightened by the presence of DR clinical changes.

To better meet the qualitative and quantitative requirements of customers or relevant sector managers, workshop environments are implementing increasingly complex task management systems. The job shop scheduling problem (JSSP) involves assigning each task to a single machine while scheduling many tasks on different machines. Finding the best scheduling for machines is one of the challenging optimizations of difficult non-deterministic polynomial (NP) time problems. The fundamental goal of optimization is to shorten the makespan (total execution time of all tasks). This paper is interested in the joint resolution of scheduling and transport problems and more particularly the Job-shop problem with Routing (JSSPR) as opposed to the Job-shop problem with Transport (JSSPT). These two problems are modeled in the form of a disjunctive graph. For the JSSPT, the solution to the transport problem is not linked to any quality of service (QoS) criterion and the solution is therefore often semi-active. The Job-shop with Routing explicitly considers transport operations and uses algorithms from the transport community to solve the transport problem. It is shown that the routing part of the JSSPR is a problem of the vehicle routing family and of the Pickup and Delivery Problem family. QoS in the JSSPR is defined by the duration of tours, the duration of transport of parts and the waiting time for them. A new evaluation function – named Time-Lag Insertion Heuristic (TLH) – is proposed to evaluate a disjunctive graph by simultaneously minimizing the makespan and maximizing the quality of service. Thus, the solution obtained is not semi-active, but a compromise between the different criteria. This evaluation function is included in a metaheuristic. Our numerical evaluations demonstrate that, on the one hand, the TLH evaluation can find almost optimal solutions regarding the QoS criterion; and on the other hand, the TLH evaluation is not very sensitive to the order of insertion of the maximum time-lags during the different minimization steps.

The amount of data available to vehicles has become very large in the vehicular networks’ environment. Failures that mislead real-time data from vehicle sensors and other devices have become massive, and the need for automated techniques that can analyze data to detect malicious sources has become paramount. The application of machine learning techniques in the environment of vehicular ad hoc networks (VANET) is very promising and is beginning to show results in terms of applications designed and articles published. These techniques are increasingly accessible and used intensively, as many researchers are working to detect anomalous data. However, there is no universal, effective technique so far that can detect all abnormal data and then recover it. This work is an effort in that direction. We propose a smart model that uses multiple machine-learning classification methods. Our contribution also relates to a study of the attributes of interest for the algorithm used during the detection phase, namely the hierarchical temporal memory algorithm (HTM). The packets exchanged by the vehicle are grouped in instant description windows. These windows are then analyzed to extract a set of attributes. These are linked to the properties of network traffic such as flow or latency. They are subject to the process of detecting anomalies and intrusions carried out thanks to the algorithm with HTM. We propose the performance of fault detection and recovery at the level of the fog layer. The obtained simulation results demonstrate the efficiency of the learning methods and HTM for the detection of defects and errors in the IoV.

In this paper, we investigate a Bresse-Timoshenko-type system with a distributed delay term and second sound. Under suitable assumptions, we establish the global well-posedness of the initial and boundary value problem by using the Faedo-Galerkin approximations and some energy estimates.

In this study, the authors present a novel methodology adept at decoding multilingual topic dynamics and identifying communication trends during crises. We focus on dialogues within Tunisian social networks during the coronavirus pandemic and other notable themes like sports and politics. We start by aggregating a varied multilingual corpus of comments relevant to these subjects. This dataset undergoes rigorous refinement during data preprocessing. We then introduce our No-English-to-English Machine Translation approach to handle linguistic differences. Empirical tests of this method show high accuracy and F1 scores, highlighting its suitability for linguistically coherent tasks. Delving deeper, advanced modeling techniques, specifically LDA and HDP models, are employed to extract pertinent topics from the translated content. This leads to applying ARIMA time series analysis to decode evolving topic trends. Applying our method to a multilingual Tunisian dataset, we effectively identify key topics mirroring public sentiment. Such insights prove vital for organizations and governments striving to understand public perspectives during crises. Compared to standard approaches, our model outperforms, as confirmed by metrics like coherence score, U-mass, and topic coherence. Additionally, an in-depth assessment of the identified topics reveals notable thematic shifts in discussions, with the proposed trends’ identification indicating impressive accuracy, backed by RMSE-based analysis.

Abstract Background The prevalence of eosinophilic asthma in Lebanon, one of the most severe phenotypes among severe asthma, is not known. This study aimed at determining the prevalence of the eosinophilic phenotype defined as an eosinophil count ≥ 300 cells/mm3 among severe asthma patients in Lebanon. Methods The Lebanese Chapter of the PREPARE study was a national, multicenter, cross-sectional observational study. Patients aged ≥ 12 years with severe asthma were identified and prospectively enrolled during clinic visits and completed the Global Initiative for Asthma (GINA) assessment of asthma control questionnaire. Patients’ health characteristics were collected from medical records and blood samples were obtained for measurement of serum IgE levels and blood eosinophils count. Results Overall, 101 patients (with mean age of 46.3 ± 17.0 years and 73.27% females) with severe asthma were included and, among them, 37% had eosinophilic phenotype, 67.3% had atopic phenotype with IgE > 100 IU/mL and 25.7% patients had overlapping atopic and eosinophilic phenotypes. Close to 80% had late-onset asthma, beyond 12 years of age, and around 85% had at least one severe exacerbation in the 12 months prior to study enrolment. The majority of participants [64.4%] had uncontrolled asthma, 24.7% had partially controlled symptoms and 10.9% had controlled symptoms. 19.8% of participants were on chronic oral corticosteroids, 78.2% had short course treatment of corticosteroids and all were prescribed a combination of inhaled corticosteroids and long-acting beta-agonist. Conclusions The majority of patients with severe asthma were uncontrolled of which 37% present with an eosinophilic phenotype, which should be taken into consideration for better management of these patients in view of the novel phenotype-specific therapeutic options.

Objective: To identify the source of eyes infections during coronavirus disease-2019 pandemic. Method: The cross-sectional study was conducted at the Al-Basrah Teaching Hospital, Iraq from March 2 to September 24, 2020, and comprised eye swabs from patients having confirmed diagnosis of coronavirus disease-2019. The swabs were subjected to microbiological and molecular examinations. They were cultivated on blood agar and savoured dextrose agar plates to detect the types of microbes and then confirmed by genetic analysis using polymerase chain reaction. Chi-square was used to identify the differences between samples who had symptoms or those who had no symptoms p Less than or Equal to 0.05 was considered significant. Results: Of the 213 patients quarantined with coronavirus disease-2019, there were 98(46%) males and 115(54%) females. Of them, 22(10.3%) had red eyes; 10(45.45%) males and 12(54.54%) females, with overall age ranging 18-74 years. Streptococcus pneumonia was detected in 8(36.6%) patients, followed by staphylococcus aureus and candida albicans 5(22.7%) each, and haemophilus influenzae 3(13.6%). Conclusion: Red eyes of some patients with coronavirus disease-2019 may have been caused by secondary infection. Keywords: Agar, Staphylococcus aureus, Photophobia, Polymerase chain reaction, Glucose, Pruritus.

Nowadays, transportation networks depend heavily on the technology known as vehicular ad hoc networks (VANETs). VANETs enhance traffic control and road safety while also enabling vehicle-to-vehicle communication using basic safety messages (BSM), which are susceptible to different kinds of attacks. This study focuses on techniques for detecting and classifying misbehavior in VANETs while dealing with unbalanced data. In order to ensure equal treatment of minority and majority categories, we provide a novel method called One vs. All Binary Tree (OVA-BT). This approach separates binary classifiers for each kind of misbehavior and provides specific assessment metrics for each kind of misbehavior. We evaluate our experiment using five-fold cross-validation with six individual models of ML and an ensemble classifier. The findings demonstrated that the use of OVA-BT enhances the classification accuracy when compared to a traditional single multi-class model and that the classifier ensemble’s classification performance is greater than the best individual model on the testing set.

INTRODUCTION: Waterpipe smoking is increasing in popularity, yet the evidence implicating waterpipe smoking in lung disease is limited. We hypothesized that impulse oscillometry (IOS) would detect airway abnormalities in waterpipe smokers (WPS). METHODS: We studied 210 participants, 40 years or older, from the community, of whom 92 were exclusive WPS and 118 were never-smokers. Waterpipe smoking history was assessed using a validated questionnaire. All participants underwent spirometry, and IOS and absolute and percentage predicted results (for age, sex, height, and weight) were compared between WPS and nonsmokers. The association of IOS parameters with waterpipe smoking duration and extent (waterpipe smoked/day * smoking duration) was evaluated using linear regression. RESULTS: WPS smoked on average 1.8 ± 1.2 waterpipes/day, over an average duration of 23.3 ± 39.8 years. WPS and nonsmokers were largely asymptomatic and had similar age, body mass index, sex distribution, and spirometric values. Nevertheless, WPS had higher IOS measured resistance at 5Hz compared to nonsmokers, (0.53 ± 0.2 vs. 0.48 ± 0.2 kPa/L/s, P = 0.03) and higher percentage-predicted resistance (124.5 ± 36.3 vs. 115.7% ± 35.6%, P = 0.04). Waterpipe smoking duration was also associated with resistance (β = 0.04 kPa/L/s/year, P = 0.01) and with percentage-predicted resistance (β = 0.05/year, P = 0.02). Waterpipe smoking extent was associated with resistance (β = 0.009 kPa/L/s/waterpipe-year, P = 0.04), while the association with percentage-predicted resistance was near significance (β = 0.009/waterpipe-year, P = 0.07). CONCLUSIONS: Waterpipe smoking is associated with increased airway resistance assessed by IOS but not by spirometry in largely asymptomatic individuals from the community.

Smart meters are key elements of a smart grid. These data from Smart Meters can help us analyze energy consumption behaviour. The machine learning and deep learning approaches can be used for mining the hidden theft detection information in the smart meter data. However, it needs effective data extraction. This research presents a theft detection dataset (TDD2022) and a machine learning-based solution for automated theft identification in a smart grid environment. An effective theft generator is modelled and used for obtaining a multi-class theft detection dataset from publicly available consumer energy consumption data, owned by the “Open Energy Data Initiative” (OEDI) platform. This is an important and interesting phase to explore in the smart grid field. The proposed dataset can be used for benchmarking and comparative studies. We evaluated the proposed dataset using five different machine learning techniques: k-nearest neighbours (KNN), decision trees (DT), random forest (RF), bagging ensemble (BE), and artificial neural networks (ANN) with different evaluation alternatives (mechanisms). Overall, our best empirical results have been recorded to the theft detection-based RF model scoring an improvement in the performance metrics by 10% or more over the other developed models.

Data analysts behind the "Marketing" desk keep trying to find links between identified data, but they can't easily find patterns in complex and huge amount of data, in order to generate insights. “There is where Artificial Intelligence brightly shines and supports many more functions intelligently in marketing department ... in real time...” The main objective for these papers is to put the magnifying-glass above the underlined part in the Previous §, To examine scene for marketing academics, managers and activists. Within trying to develop a marketing understanding of (AI) and its current potentials, in the way where we highlight its latest use-cases in the marketing context.

Using anterior segment optical coherence tomography (AS-OCT) to compare the depth of corneal demarcation line (DL) after corneal collagen cross-linking (CXL) with subtotal versus customized corneal epithelium debridement. Design: Prospective multi centric case-control study. Materials and Methods: The study enrolled 18 patients with bilateral progressive keratoconus. Both eyes treated by CXL using 3 mW/cm2 / 30 minutes setting. One eye with subtotal epithelial debridement ( about 9 mm diameter) and the contralateral eye with customized debridement ( approximately 1 mm single horizontal scratch ). One month postoperatively, patients had AS-OCT imaging to detect and measure the depth of DL. Results: Patients’ age mean was 25.17 years +/- 4.81 SD . Epithelial healing completed with in 3-7 days in conventional treatment group and demarcation line was evident in 16 eyes ( 89% ) with a mean depth of 290.31 micro m while in customized epi-off treatment group, the epithelial healing lasted less than 24 hours and DL was detectable in 10 patients ( 55.5 % ) with a mean DL depth of 221 micro m with a statistically significant difference ( p < 0.05 ) Subjective postoperative pain graded as “moderate to severe” in about 77% of eyes underwent 9 mm epi-off CXL compared with 55% of those with customized treatment. Conclusions: Although the DL if found in majority cases, but the shallow location in customized corneal debridement cases questioned the efficacy of this technique despite the quick re-epithelialization and less postoperative pain that accompanied it.

This study aims to recover the residual energy of the water in the water treatment plant using turbines instead of wasting energy in the dissipator as it is currently done by means of load- breaker valves. Analysis of the technical efficiency of the project and an assessment of the hydroelectric potential available were carried out and different options for the installation of turbines were proposed. The estimated maximum amount of energy that can be recovered is around 7.44 GWh, and the maximum amount of carbon emissions saved are 446.210 tons. The evaluation of the profitability of our project integrating an energy recovery device is fair.

The present work aims to develop a dynamic Wind Turbine-Fuel Cell (WT-FC) hybrid power system under Simulink to meet the energy needs of dwellings in the south of Algeria. The Enercon E40 wind turbine model with a rated power of 500 kW was selected. Simulations were performed for four sites, namely Tinfouye, Belkbir, Tabelbala and Tindouf using daily wind data ranging between 2004 and 2018. The results indicated that Tindouf has a high mean speed of 5.57 m/s at 10 m, resulting in a power density of 194 W/m2. However, a low average speed of 4.81 m/s is recorded at the site of Tabelbala. The maximum energy production is recorded at Belkbir reaching 4183.96 MWh/year with 0.9141 C$/kWh. Therefore, a high-power density of 239 W/m² was noted in Tabelbala. In the environmental analysis, Belkbir was found to have the highest avoided carbon dioxide (CO2) emission rate of 1829.64 tons CO2/kWh.

In this article, we considered the one-dimensional swelling problem in porous elastic soils with microtemperatures effects in the case of fluid saturation. First, we showed that the system is well-posed in the sens of semigroup. Then, we constructed a suitable Lyapunov functional based on the energy method and we proved that the dissipation given only by the microtemperatures is strong enough to provoke an exponential stability for the solution irrespective of the wave speeds of the system.

ObjectiveBurnout is a widespread issue in healthcare for many years. Lebanon combatted political and economic crises before the coronavirus disease 2019 (COVID-19) pandemic, in addition to the port explosion in August 2020. The study aimed to identify the determinants of personal burnout, patient-related burnout, and work-related burnout among postgraduate medical trainees (PGMT) and evaluate its relationship with sociodemographic characteristics.DesignA cross-sectional study utilized the Copenhagen Burnout Inventory (CBI) involving electronic, voluntary, and anonymous survey. The survey was completed by 188 PGMT including residents and fellows from all specialties and all levels of training.ResultsThe prevalence rates are 68.6% for personal burnout, 63.3% for work-related burnout, and 35.1% for patient-related burnout.ConclusionResults improve our understanding of the phenomenon of burnout, and the role of program leadership in shaping the impact of burnout on training and promoting wellbeing of PGMT. Discussion focuses on providing potential wellbeing strategies for program directors to follow for mitigating burnout.

AbstractFinding word boundaries in continuous speech is challenging as there is little or no equivalent of a ‘space’ delimiter between words. Popular Bayesian non-parametric models for text segmentation (Goldwater et al., 2006, 2009) use a Dirichlet process to jointly segment sentences and build a lexicon of word types. We introduce DP-Parse, which uses similar principles but only relies on an instance lexicon of word tokens, avoiding the clustering errors that arise with a lexicon of word types. On the Zero Resource Speech Benchmark 2017, our model sets a new speech segmentation state-of-the-art in 5 languages. The algorithm monotonically improves with better input representations, achieving yet higher scores when fed with weakly supervised inputs. Despite lacking a type lexicon, DP-Parse can be pipelined to a language model and learn semantic and syntactic representations as assessed by a new spoken word embedding benchmark. 1

The detection of weld defects by using X-rays is an important task in the industry. It requires trained specialists with the expertise to conduct a timely inspection, which is costly and cumbersome. Moreover, the process can be erroneous due to fatigue and lack of concentration. In this context, this study proposes an automated approach to identify multi-class welding defects by processing the X-ray images. It is realized by an intelligent hybridization of the data augmentation techniques and convolutional neural network (CNN). The proposed data augmentation mainly performs random rotation, shearing, zooming, brightness adjustment, and horizontal flips on the intended images. This augmentation is beneficial for the realization of a generalized trained CNN model, which can process the multi-class dataset for the identification of welding defects. The effectiveness of the proposed method is confirmed by testing its performance in processing an industrial dataset. The intended dataset contains 4479 X-ray images and belongs to six groups: cavity, cracks, inclusion slag, lack of fusion, shape defects, and normal defects. The devised technique achieved an average accuracy of 92%. This indicates that the approach is promising and can be used in contemporary solutions for the automated detection and categorization of welding defects.

In this work, we consider a one-dimensional Timoshenko system of thermoelasticity of type III with past history and distributive delay. It is known that an arbitrarily small delay may be the source of instability. We establish the stability of the system for the cases of equal and nonequal speeds of wave propagation respectively. Our results show that the damping effect is strong enough to uniformly stabilize the system even in the presence of time delay under suitable conditions and improve the related results.

C-ITS (Cooperative Intelligent Transport Systems) est une nouvelle technologie qui contribue à la réduction des accidents de la circulation et à l'amélioration de la sécurité routière. VANET ( V ehicular A d hoc Networks) sont un système STI basé sur la communication inter-véhicules par la transmission de messages de sécurité de base (BSM), qui sont vulnérables à une variété de comportements inappropriés. Pour résoudre ce défi, nous avons développé dans cet article un système de détection de mauvais comportement (MDS) basé sur une approche d'apprentissage automatisé pour identifier et catégoriser les messages de mauvais comportement délivrés par un véhicule sur les VANET à l'aide de la base de données d'extension VeReMi. Cette étude examine différents types de classification : dans la classification binaire, toutes sortes d'inconduites ont été regroupées en une seule catégorie « inconduite » ; cependant, dans la classification multi-classes pour trois classes, la mauvaise conduite a été divisée en deux classes : les attaques et les fautes. Le classificateur a des problèmes substantiels lors de l'apprentissage à partir de données déséquilibrées wLorsque vous travaillez avec des problèmes multi-classes, cela devient considérablement plus complexe. Les relations entre les catégories ne sont plus bien définies et il est facile de perdre en efficacité dans une classe tout en s'améliorant dans une autre. En conséquence, les résultats ne sont pas cohérents dans l'approche d'apprentissage classique pour la classification multi-classes lors de la classification des comportements répréhensibles dans différents types de classes de comportements répréhensibles. Pour résoudre ce problème, nous avons développé une approche nouvelle et puissante appelée "Approche d'apprentissage guidé pour la classification multi-classes" pour réduire le nombre de classes en combinant des comportements inappropriés comparables en un seul. Selon les résultats, le classificateur Random Forest surpasse les autres classificateurs.

The study investigates the effects of pomegranate peel extracts on some virulence factors such as the production of some enzymes produced by Proteus spp isolated from different clinical samples. Among 310 different clinical samples, 48 isolates of proteus were obtained, 14 isolates (29.16%) were obtained from urine, 15 isolates (31.25%) were obtained from stool, 7 isolates (14.58%) from ear swabs, 4 isolates (8.33%) were obtained from wounds, 5 isolates (10.41% (from burns and 3 isolates) 6.25%) of the high cervical. The study showed that from 48 isolates of Proteus spp 25 isolates (52.08 %) identified as P.mirabilis, 15 isolates (31.25% )of P. penerri, while 8 isolates of P. vulgaris were obtained with a percentage of (16.66%) and it was found that 18 isolates (72%) of P.mirabilis isolates were positive for the production of beta-lactamase enzyme, 24 isolates (96%) are positive for proteases and 14 isolates (56%) produced lipase, while 13 isolates from P. penerri (86.66%) produced beta-lactamases and proteases and 10 isolates (66.66%) produced lipase, also found that 5 isolates (62.5%) of P. vulgaris produced Beta-lactamase and all the isolates (100%) produced protease while 7 isolates (87.5%) produced lipase. It was found that the hot aqueous extract of pomegranate peels inhibited the production of beta-lactamase enzyme in 4 out of 7 isolates that produced this enzyme, while all tested isolates did not produce lipase by using extracts of pomegranate peel extracts. The isolates lost their ability to produce protease enzyme, except for two isolates that were not affected by extracts of pomegranate peel fruits.

In the present work, we consider a one-dimensional Von kármán beam with infinite memory, we establish the well-posedness of the system using semigroup theory and prove the exponential stability under some conditions on the kernel of the infinite memory term.

Abstract Background The aim of this study was to characterize the transmission chains and clusters of COVID-19 infection in Tunisia. Methods All cases were confirmed by Reverse Transcriptase Polymerase Chain Reaction of a nasopharyngeal specimen. Contact tracing is undertaken for all confirmed cases in order to identify close contacts that will be systematically screened and quarantined. Transmission chains were identified based on field investigation, contact tracing, results of screening tests and by assessing all probable mode of transmission and interactions. Results As of May 18, 2020, 656 cases out of a total of 1043 confirmed cases of Coronavirus disease 2019 belong to 127 transmission chains identified during the epidemic (mean age 42.36 years, Standard deviation 19.56 and sex ratio 0.86). The virus transmission is the most concentrated in the governorate of Tunis (31.5%), Ariana (10.2%) and Ben Arous (10.2%). Virus transmission occurred 50 times (9.72% of secondary transmission events) between two different governorates. A maximum of seven generations of secondary infection was identified, whereas 62% of these secondary infections belong the first generation. A total of 11 “super spreader” cases were identified in this investigation. Four large clusters have been identified. The evolution of secondary cases highlighted two peaks: one in 2nd April and a second in 16 th April whereas imported cases caused local transmission of virus during the early phase of the epidemic. Conclusion Correct contact tracing and early active case finding is useful to identify transmission chains and source of infection in order to contain the widespread transmission in the community.