Search Index Research Articles

(208) GLOBAL WEB TRENDS ANALYSIS OF SEX TOYS

Abstract Introduction Pleasure is one of the most notable reasons people take part in sexual activity. Sex toys and devices may improve and broaden sexual pleasure while also adding alternatives and excitement to sexual intercourse. As a result, they are popular among women, men, and transgender individuals all around the world. Objective This study examined the trends about the most popular preferred sex toys by nation/region from 2009 using data from Google Trends (GT). Methods Since 2004, data on the global search traffic for a given Google query, including time period and category trends, has been provided by GT. GT can generate a “line graph” that displays how interest in a certain region has changed over time. The relative search volume (RSV), which ranges from 0 to 100, is used to index search values for certain terms. RSV 100 displays the largest search trends, while RSV 50 represents half of the searches. The top 5 sex toys used globally were chosen as search phrases. These were “dildo”, “vibrator”, “strap-on”, “anal plug” and “masturbator”. From 1 January 2009 to 30 June 2023, the data was included as “worldwide” under the “global” enquiry category. Using linear regression analysis, each polynomial trend line was evaluated. If the p-value was less than 0.05, the result was deemed significant. Results In the linear regression analysis, the trends by strength were sorted as follows: dildo, vibrator, strap-on, anal plug, and masturbator. In 2018, vibrator surpassed strap-on and came in second, while the regression analysis showed a positive trend for dildo between 2009 and 2016 (p = 0.014). It had a negative trend after 2016. Strap-on was the only one to have a downward trend (p = 0.029) Regression analysis revealed positive trends for vibrator and masturbator (p = 0.030, p = 0.045). The upward trends have continued uninterruptedly since 2009 (Table 1).In global trends, the dildo had strong trends in Northern Europe, vibrator in Eastern Europe, strap-on in the Aegean region, anal plug in South America, and masturbator in Central Europe (Fig. 1). Conclusions People from all around the world use the internet to acquire sex toys and devices with increasing interest in order to improve their sexual pleasure and add alternatives and excitement to sexual intercourse. In this sense, various industries can use web trends as a useful indicator for sales policies. Considering their compatibility with technological development, clinical use and side effect profiles, the most important trend increase was in favor of vibrators, followed by masturbators. Future studies should evaluate the balance of negative and positive effects of different types of sexual products and sales policies with objective scales in line with these results. Disclosure No.

Analysis of professional competency awareness based on visible network graphs

Introduction: With the advancement of society and technology, the importance of professional competency has been increasingly emphasized. This study aims to provide an in-depth analysis of the Baidu search index for “professional competency” from July 3, 2017, to October 6, 2023, across 34 provincial-level administrative regions in China.Methods: The study begins with a literature review to establish a theoretical framework. Subsequently, it delves into the fundamental concepts of complex networks, their parameters, and the principles of visible network graph algorithms. The application of system clustering and community detection algorithms is explored within this framework.Results: The empirical analysis starts with a provincial-level examination, involving the construction of visible network graphs, the exploration of scale-free networks, and discussions on regional system clustering. Following this, a nationwide analysis is conducted, focusing on the construction of visible network graphs to visualize the awareness of professional competency and studying community structures.Discussion: The findings provide insights into the awareness of professional competency across different regions. These insights are valuable for informing policy formulation and corporate decision-making, contributing to a deeper understanding of professional competency awareness and its implications.

The use of pretrained neural networks for solving the problem of reverse searching of X-ray images of prohibited items and substances

The paper considers the application of pretrained neural networks to solve the problem of reverse searching of X-ray images of prohibited items and substances. The purpose of the work is to conduct an analysis and substantiate ways to improve the efficiency of baggage and passenger hand luggage X-ray image recognition systems. An analysis of existing domestic and foreign works in the field of baggage and passenger hand luggage X-ray image recognition is presented. It has been revealed that, despite the achieved results in the development of algorithms for recognizing prohibited items and substances, they do not fully cope with such a complexity factor as the overlay of objects. To solve this problem, the paper proposes to additionally analyze X-ray images with low confidence in object recognition. This stage includes the following steps: image segmentation, extraction of features of segmented image elements; search for similar images in the database; decision-making on the class of segmented image elements. This article discusses the last three steps. Variants of approaches to feature extraction from images are analyzed, particularly those based on the application of convolutional autoencoders and pretrained neural networks. The approach based on the application of pretrained neural networks is chosen. The ResNet-50 architecture neural network, pretrained on the ImageNet collection, is used during the work. In order to apply this model to extract image feature vectors, the last classification layer was preliminarily removed. All the previous layers of the model encode the image into a vector. ResNet-50 generates a 2048-dimensional feature vector of images. The principal component analysis is used to reduce the dimensionality of the image feature vectors. The decision of whether the segmented image element is a prohibited item or substance is considered as a reverse search problem using the k-nearest neighbor algorithm. In this case, the class of the X-ray image element is the class most frequently encountered among the K nearest neighbors. In order to test the proposed approach, a training dataset, including 4,635 images of individual items and substances that may be encountered in baggage and passenger hand luggage, was generated. A comparative analysis of image indexing and image search under different algorithms and feature number is presented. A comparative analysis of the model accuracy is provided. It is concluded that the most acceptable is the “Brute force” algorithm in combination with the principal component analysis.

The efficacy and safety of GLP-1 receptor agonists in youth with type 2 diabetes: a meta-analysis

BackgroundGlucagon-like peptide 1 receptor agonists have been proven to be effective in adults with diabetes and children with obesity. However, children with type 2 diabetes constitute an underrepresented subpopulation with limited treatment options. This meta-analysis aimed to determine more precise estimates of the efficacy and safety of glucagon-like peptide-1 agonists in pediatric type 2 diabetes mellitus.MethodsThree databases were searched (PubMed, Embase, and Cochrane Central Register of Controlled Trials) for trials published until the end of March 2024. The search indexing terms included 3 categories: [1] type 2 diabetes mellitus [2], youth, and [3] glucagon-like peptide-1 receptor agonist (GLP-1 RA). Randomized controlled trials in youth with type 2 diabetes (age ≤ 18 years) that assessed anthropometric and metabolic parameters were included. A total of 1119 nonduplicate studies were retrieved, and 137 full-text articles were screened. The data were analyzed using mean differences (MDs) with 95% CIs and odds ratios (ORs) with 95% CIs. For outcomes with low heterogeneity, a fixed-effects model was used. Otherwise, we applied a random effects model. Our outcomes were Hb1Ac, fasting blood glucose (FBG), blood pressure, weight, and side effects.ResultsFive studies comprehending 415 children and adolescents were included. On average, GLP-1 RA reduced HbA1c levels (-1.01%; 95% CI, -1.26 to -0.76), fasting blood glucose levels (-1.88 mmol/L; 95% CI, -2.51 to -1.26), and body weight (-1.6 kg; 95% CI, -2.83 to -0.36). No significant reductions in systolic blood pressure (MD -0.19 mmHg; 95% CI, -3.9 to 3.52 mmHg) or diastolic blood pressure (MD 0.3 mmHg; 95% CI, -2.33 to 2.93 mmHg) were observed. Despite a higher incidence of side effects, withdrawal rates from the studies remained low.ConclusionsWithin this specific population, GLP-1 RAs exhibit a notable association with substantial reductions in HbA1c, FBG, and body weight. The administration of these medications is concurrent with an elevated incidence of side effects, which are predominantly gastrointestinal and tolerable.Trial registrationPROSPERO identifier: CRD42023393020.

Multivariable forecasting approach of high‐speed railway passenger demand based on residual term of Baidu search index and error correction

AbstractAccurate prior information of passenger flow demand on high‐speed railway is of great significance for the operation and the management of transportation systems. Various factors in modern social life have caused uncertainty at demand. Recently, individuals are increasingly depending on the online search results when choosing among different transportation modes, services, and destinations, which provide important basic information for forecasting the travel demand. This study employs Baidu search index to assist in capturing volatility of high‐speed railway passenger demands, offering insights into the travel inclinations and travelers' actions. Furthermore, we have given more in‐depth attention and analysis to their residual term accounting for the random nature caused by various factors. To this end, a sophisticated deep analysis mechanism based on data decomposition has been devised to extract and analyze the valuable information concealed within the residuals, so as to enhance the comprehension of the variability inherent in the high‐speed railway passenger flow. Meanwhile, an error correction strategy is implemented for all residual terms to improve further their forecasting accuracy. Experimental results from two real‐world datasets demonstrate the effectiveness and robustness of the developed hybrid approach across several popular evaluation indicators. Therefore, this study can function as a reliable instrument, provide sensible data‐driven guidance for resource allocation and make scientific decisions in the railway industry.

Instrumental Guanxi Culture and Inbound Urban Migration in China: A Prefecture-level Analysis Using Online Search Data

AbstractThe socioeconomic role of guanxi networks among individuals has been widely recorded, yet macro-level analysis has been sparse in empirical research. This research fills that gap by presenting the first nationally representative evidence illustrating the connection between regional guanxi culture and population mobility among cities in China, with a particular focus on instrumental guanxi culture. To quantify guanxi culture, we employ online search indices related to gift giving, a measure which is challenging to capture through traditional survey data. Applying matched prefecture-level data spanning from 2011 to 2019, the panel model reveals a strong negative correlation between a city's instrumental guanxi culture and inbound migration, while sentimental guanxi culture exhibits a positive correlation with inbound mobility. This research not only adds to the existing theories by exploring the macro-level effects of both instrumental and sentimental guanxi practices but also introduces an innovative method for quantifying guanxi culture through big data analysis.

Implementation of A Blockchain-based Searchable Encryption for Securing Contact Tracing Data

<p>We developed a blockchain-based multi-keyword searchable encryption scheme for securing COVID-19 contact tracing data. In this scheme, we used AES-GCM to encrypt the contact tracing data, guaranteeing that only authorized users can perform decryption. Our scheme employed a Ciphertext-Policy Attribute-based searchable encryption to encrypt the search index, ensuring only users with appropriate attributes can perform search operations. The scheme supports dynamic updates of the search index. The blockchain-based storage with smart contract ensures immutability and non-repudiation of storage and retrieval. Overall, the evaluation of the scheme shows that it works efficiently without compromising the security goals. This is one of the first works to implement a solution for secure storage and search of contact tracing data with blockchain-based searchable encryption. Compared to the existing searchable contact tracing schemes, it provides more features and maintains efficiency even if a large search index is used.</p> <p> </p>

Exploring the Impact of China's Internal Circulation Strategy on Its Stock Market under Deglobalization

Abstract In May 2020, China formulated the Internal Circulation Strategy (ICS) to address the risks of global economic downturns caused by deglobalization. This study is the first to empirically examine the impacts of China's ICS on its stock market performance, focusing on investor behavior. Using data from the Baidu search index and the Shanghai and Shenzhen stock exchanges for the period 27 July 2020 to 5 May 2023, the results reveal that stock returns are negatively associated with investor attention to China's ICS, while stock volatilities are positively associated. This suggests that the ongoing and profound shift in economic strategy might have raised concerns among investors. Furthermore, our analysis of heterogeneity finds that the negative association with stock market returns is statistically significant only during times of high policy uncertainty or geopolitical risk. The positive association with stock market volatility is statistically significant only during times of high geopolitical risk. This indicates that the stability of the internal and external environment plays a crucial role in alleviating investor concerns. We also observe mixed impacts on different sectors of the stock markets, with some sectors unaffected while others primarily experiencing a decline in returns.

Silence breaking: sex crime reporting in the MeToo era

This paper introduces an index for assessing local attitudes toward women in the United States, leveraging the Google search index and a machine learning methodology. Exploiting the constructed measure of sexism, our investigation reveals that the #MeToo movement garnered greater attention in areas characterized by low measured sexism in the pre-MeToo era. Additionally, a substantial increase in reported sex crimes is observed in those areas post-MeToo compared to those with higher sexism measures. Further empirical findings indicate that the surge in documented sex crimes primarily stems from changes in reporting behavior rather than substantive shifts in actual incidents.

Childhood Factors Associated With Unnatural Death Through Midadulthood

Life expectancy is decreasing in the US. Without national efforts to address factors that support policies and programs directed at children living in areas of concentrated poverty, life expectancy will likely continue to decline while costs and suffering associated with unnatural deaths will increase. To identify which childhood factors are associated with death from unnatural causes through midadulthood. For this cohort study, longitudinal data on childhood characteristics came from a group-randomized intervention trial implemented in Baltimore City Public Schools, Baltimore, Maryland (baseline 1985-1986; all students entering first grade were selected to participate at age 6 years). Participants were followed up to midadulthood with a National Death Index search through December 31, 2020. Data analysis was performed from February to May 2023. Exposures included individual factors (ie, sociodemographic characteristics, teacher-reported aggressive behavior, self-reported depression, anxiety, early alcohol and cannabis use, and assaultive violence exposure), family and peer factors (ie, household structure and education level, deviant peer affiliation, and parental monitoring), and neighborhood factors (ie, rates of neighborhood assault and public assistance). The main outcome was unnatural death, defined as death due to unintentional injury, suicide, and homicide. A National Death Index search ascertained participants who died by age 41 to 42 years and cause of death. Multivariable Cox proportional hazards models were used to identify whether the exposures were independently associated with future mortality by unnatural causes. The initial trial included 2311 children, and longitudinal data were available for 2180 participants (median [IQR] age in first grade, 6.3 [6.0-6.5] years; 1090 female [50.0%]; 1461 Black [67.0%]; 1168 received free or reduced lunch in first grade [53.6%]). A total of 111 male participants (10.2%) and 29 female participants (2.7%) died; among those who died, 96 male participants (86.5%) and 14 female participants (48.3%) died of unnatural causes. Two factors remained significantly associated with mortality from unnatural causes: female sex was associated with reduced risk (hazard ratio, 0.13; 95% CI, 0.08-0.22), and neighborhood public assistance was associated with increased risk (hazard ratio, 1.89; 95% CI, 1.09-3.30). In this urban population-based cohort study, no modifiable risk factors of mortality at the level of the individual (eg, depression or anxiety and substance use) or the family (eg, household education level) were identified. However, the degree of neighborhood poverty in early childhood was significantly associated with death by unnatural causes in early adulthood, suggesting that economic policies are needed to advance health equity in relation to premature mortality.

The prediction of influenza-like illness using national influenza surveillance data and Baidu query data

BackgroundSeasonal influenza and other respiratory tract infections are serious public health problems that need to be further addressed and investigated. Internet search data are recognized as a valuable source for forecasting influenza or other respiratory tract infection epidemics. However, the selection of internet search data and the application of forecasting methods are important for improving forecasting accuracy. The aim of the present study was to forecast influenza epidemics based on the long short-term memory neural network (LSTM) method, Baidu search index data, and the influenza-like-illness (ILI) rate.MethodsThe official weekly ILI% data for northern and southern mainland China were obtained from the Chinese Influenza Center from 2018 to 2021. Based on the Baidu Index, search indices related to influenza infection over the corresponding time period were obtained. Pearson correlation analysis was performed to explore the association between influenza-related search queries and the ILI% of southern and northern mainland China. The LSTM model was used to forecast the influenza epidemic within the same week and at lags of 1–4 weeks. The model performance was assessed by evaluation metrics, including the mean square error (MSE), root mean square error (RMSE) and mean absolute error (MAE).ResultsIn total, 24 search queries in northern mainland China and 7 search queries in southern mainland China were found to be correlated and were used to construct the LSTM model, which included the same week and a lag of 1–4 weeks. The LSTM model showed that ILI% + mask with one lag week and ILI% + influenza name were good prediction modules, with reduced RMSE predictions of 16.75% and 4.20%, respectively, compared with the estimated ILI% for northern and southern mainland China.ConclusionsThe results illuminate the feasibility of using an internet search index as a complementary data source for influenza forecasting and the efficiency of using the LSTM model to forecast influenza epidemics.

Do college anti‐plagiarism/cheating policies have teeth in the age of AI? Exploratory evidence from the Internet

AbstractThe advent of artificial intelligence (AI) has challenged academic institutions to ensure ethical practices and reward/promote merit. Adding formal insights into the importance of maintaining academic integrity, this paper examines the association of anti‐plagiarism/anti‐cheating policies with resources that facilitate such behavior. Using unique internet search indices of policies and resources, we find that the two are positively associated. This association is robust when internet policies are restricted to news searches, and include course syllabi. The findings reinforce the view that policies to check plagiarism/cheating likely lack teeth and maybe a step behind the resources that facilitate unethical behavior.

Detecting potential cooperative network for tourist attractions in a destination using search data.

This study addresses the critical need for regional tourism integration and sustainable development by identifying cooperation opportunities among tourist attractions within a region. We introduce a novel methodology that combines association rule mining with complex network analysis and utilizes search index data as a dynamic and contemporary data source to reveal cooperative patterns among tourist attractions. Our approach delineates a potential cooperative network within the destination ecosystem, categorizing tourist attractions into three distinct communities: core, intermediary, and periphery. These communities correspond to high, medium, and low tourist demand scales, respectively. The study uncovers a self-organizing network structure, driven by congruences in internal tourist demand and variances in external tourist experiences. Functionally, there is a directed continuum of cooperation prospects among these communities. The core community, characterized by significant tourist demand, acts as a catalyst, boosting demand for other attractions. The intermediary community, central in the network, links the core and periphery, enhancing cooperative ties and influence. Peripheral attractions, representing latent growth areas within the destination matrix, benefit from associations with the core and intermediary communities. Our findings provide vital insights into the dynamics, systemic characteristics, and fundamental mechanisms of potential cooperation networks among tourist attractions. They enable tourism management organizations to employ our analytical framework for real-time monitoring of tourism demand and flow trends. Additionally, the study guides the macro-control of tourism flows based on the tourism network, thereby improving the tourist experience and promoting coordinated development among inter-regional tourist attractions.

Online public concern about allergic rhinitis and its association with COVID-19 and air quality in China: an informative epidemiological study using Baidu index

BackgroundAllergic rhinitis is a common health concern that affects quality of life. This study aims to examine the online search trends of allergic rhinitis in China before and after the COVID-19 epidemic and to explore the association between the daily air quality and online search volumes of allergic rhinitis in Beijing.MethodsWe extracted the online search data of allergic rhinitis-related keywords from the Baidu index database from January 23, 2017 to June 23, 2022. We analyzed and compared the temporal distribution of online search behaviors across different themes of allergic rhinitis before and after the COVID-19 pandemic in mainland China, using the Baidu search index (BSI). We also obtained the air quality index (AQI) data in Beijing and assessed its correlation with daily BSIs of allergic rhinitis.ResultsThe online search for allergic rhinitis in China showed significant seasonal variations, with two peaks each year in spring from March to May and autumn from August and October. The BSI of total allergic rhinitis-related searches increased gradually from 2017 to 2019, reaching a peak in April 2019, and declined after the COVID-19 pandemic, especially in the first half of 2020. The BSI for all allergic rhinitis themes was significantly lower after the COVID-19 pandemic than before (all p values < 0.05). The results also revealed that, in Beijing, there was a significant negative association between daily BSI and AQI for each allergic rhinitis theme during the original variant strain epidemic period and a significant positive correlation during the Omicron variant period.ConclusionBoth air quality and the interventions used for COVID-19 pandemic, including national and local quarantines and mask wearing behaviors, may have affected the incidence and public concern about allergic rhinitis in China. The online search trends can serve as a valuable tool for tracking real-time public concerns about allergic rhinitis. By complementing traditional disease monitoring systems of health departments, these search trends can also offer insights into the patterns of disease outbreaks. Additionally, they can provide references and suggestions regarding the public’s knowledge demands related to allergic rhinitis, which can further be instrumental in developing targeted strategies to enhance population-based disease education on allergic diseases.

Is Public Interest Associated with Real-World Management of Ankyloglossia?

Assess the relationship between public interest in ankyloglossia as determined by internet search volume and real-world medical claims data. Retrospective Cohort Study. This retrospective cohort study was conducted using claims data from the Merative™ Marketscan® Research Databases. The internet search data was collected from Google Trends. Annual Google Trends data were compiled using search terms associated with "ankyloglossia" and "frenotomy" for the years 2011 to 2021. We obtained incidence of ankyloglossia diagnoses and frenotomy procedures in children under 12 months from Marketscan relative to all infants enrolled. We compared associations between search and incidence data among US states and over time. Google search correlated with ankyloglossia incidence (r = 0.4104, P = .0031) and with frenotomy incidence (r = 0.4062, P = .0034) per state. Ankyloglossia diagnoses increased with Google search index (coefficient = 0.336, 95% confidence interval [CI] 0.284, 0.388) and year (coefficient = 0.028, 95% CI 0.025, 0.031). Similarly, frenotomy procedures increased with Google search index (coefficient = 0.371, 95% CI 0.313, 0.429) and year (coefficient = 0.027, 95% CI 0.024, 0.030). Associations between online ankyloglossia search trends and both diagnosis and treatment rates, persist across US regions and timeframes. Internet search trends are pivotal in shaping pediatric health care decisions, driving clinical consensus, and disseminating evidence-based information.

The ability of energy commodities to hedge the dynamic risk of epidemic black swans

The ability of energy commodities to hedge the dynamic risk of epidemic black swans

Correlation between night sweats and season fluctuation in China.

Night sweats are a condition in which an individual sweats excessively during sleep without awareness, and stops when they wake up. Prolonged episodes of night sweats might result in the depletion of trace elements and nutrients, affecting the growth and development of children. To investigate the relationship between sweat nights and season. The Internet search index for night sweats in Zhengzhou, China was obtained from the Baidu index during 2011-2022. Meteorological factors, including ambient temperature, humidity, pressure, precipitation and average wind speed in Zhengzhou were obtained from the website https://en.tutiempo.net/climate. A time series decomposition model was used to study the relationship between night sweats and seasonality. Continuous wavelet transform and cross wavelet transform were utilized to explore the relationship between night sweats and meteorological factors. A typical periodic pattern is evident in the seasonal trend. Specifically, following a peak in January each year, there is a rapid decline followed by a secondary peak, after which a trough occurs. The search index of night sweats increased rapidly in the first stage, slowed down in the second stage, and showed negative growth from 2011 to 2014. The correlation coefficients between the search index for night sweats and atmospheric pressure as well as average temperature, are 0.25 and-0.26, respectively. Night sweat was connected with season. Specifically, the number of night sweat search index increased in the cold season and declined in the summer season. Night sweat was negatively connected with temperature but favorably correlated with air pressure.

A study on muti-strategy predator algorithm for passenger traffic prediction with big data

Abstract In this paper, we study the big data multi-strategy predator algorithm for tourist flow prediction and explore the application of the algorithm in optimizing the tourist flow prediction model to improve the prediction accuracy and efficiency. An adversarial learning strategy extends the search space, an adaptive weighting factor balances the global and local search ability, and a variance operation combined with differential evolution is used to avoid local optimal traps. The experiment adopts variables such as network booking volume and search index as inputs for passenger flow prediction. The predator algorithm is trained by Extreme Learning Machine (ELM) to optimize the input weights and biases to build the FMMPAELM model. The results show that on the training samples, the FMMPA-ELM model predictions are highly consistent with the actual values, with a maximum prediction index of 200.On the test samples, although there are errors, the FMMPA-ELM model exhibits better prediction ability than the traditional ELM model. It is concluded that the FMMPAELM model can effectively improve the accuracy of passenger flow prediction and provide powerful decision support for the tourism industry.

DForest: A Minimal Dimensionality-Aware Indexing for High-Dimensional Exact Similarity Search

DForest: A Minimal Dimensionality-Aware Indexing for High-Dimensional Exact Similarity Search

Entomological indicators and food sources of triatomines in the Brazilian semi-arid region.

Triatomines are biological vectors of Trypanosoma cruzi, the etiological agent of Chagas Disease (CD) and have various mammalian hosts. This study evaluated the entomological indicators and food sources of triatomines in Petrolina in the semi-arid region of Brazil, where CD is endemic. Triatomines were captured indoors and outdoors through an active search and entomological indices (household and natural infections) were calculated. Parasitological analyses were performed through microscopic visualization using Giemsa-stained insect feces, and DNA sequencing was employed to identify food sources from the gut contents of 82 insects (9.05%) that were better preserved. We captured triatomines (906) in peridomicile (807) and intradomicile (99): Triatoma brasiliensis (84.7%, 767 specimens), Triatoma spp. (8.2%, 74 specimens), T. pseudomaculata (6.5%, 59 specimens), Rhodnius spp. (0.4%, four specimens), R. nasutus (0.1%, one specimen), and T. sordida (0.1%, one specimen). The household infestation index is 11.8%. Thirty-five triatomines were infected (33 T. brasiliensis and two T. pseudomaculata), corresponding to a natural infection index of 3.8%. The identified food sources were human T. pseudomaculata and T. brasiliensis, dogs for T. brasiliensis and rodents (Mus musculus) for T. brasiliensis. The results reinforce the need to intensify CD diagnosis, surveillance, and control actions, as an increase in entomological indices was recorded. Blood from humans and domestic and synanthropic animals was detected in the infected triatomines, suggesting a risk of CD vector transmission in Petrolina. As CD is a zoonosis, multidisciplinary and intersectoral CD surveillance must be conducted in the context of the One Health.