The Promotion of Hearing Health through Wikipedia Campaigns: Article Quality and Reach Assessment.

This case study examined the feasibility, reach, and potential impact of using Wikipedia as a tool for hearing health promotion. Activities involved editing existing Portuguese-language Wikipedia hearing health articles, as well as translating English-language hearing health articles to Portuguese during the Wiki4WorldHearingDay2019 and Wiki4YearOfSound2020 online campaigns. The Wikipedia efforts that took place in Brazil were carried out by 10 volunteer undergraduate students in Speech-Language Pathology and Audiology at the Federal University of Santa Catarina, in Brazil. Among new and existing Wikipedia articles, the group edited 37 articles, which attracted more than 220,000 views during the set tracking period. Students were responsible for 60% of the Portuguese-language edits during the Wiki4WorldHearingDay2019 campaign and more than 90% of the Portuguese-language edits during the first half of the Wiki4YearOfSound2020 campaign. Moreover, the quality indexes for pages either created or edited were improved in all situations by registering an increase rate ranging from 33% to 100%. Wikipedia-centered activities expanded the availability of quality scientific content, written in plain language, to the public. Students worked together in order to select topics, assess existing information, validate it, create new content, and share information-steps that contributed to the mission of health promotion and knowledge dissemination for the benefit of society.

Collective view: mapping Sargassum distribution along beaches.

The atypical arrival of pelagic Sargassum to the Mexican Caribbean beaches has caused considerable economic and ecological damage. Furthermore, it has raised new challenges for monitoring the coastlines. Historically, satellite remote-sensing has been used for Sargassum monitoring in the ocean; nonetheless, limitations in the temporal and spatial resolution of available satellite platforms do not allow for near real-time monitoring of this macro-algae on beaches. This study proposes an innovative approach for monitoring Sargassum on beaches using Crowdsourcing for imagery collection, deep learning for automatic classification, and geographic information systems for visualizing the results. We have coined this collaborative process "Collective View". It offers a geotagged dataset of images illustrating the presence or absence of Sargassum on beaches located along the northern and eastern regions in the Yucatan Peninsula, in Mexico. This new dataset is the largest of its kind in surrounding areas. As part of the design process for Collective View, three convolutional neural networks (LeNet-5, AlexNet and VGG16) were modified and retrained to classify images, according to the presence or absence of Sargassum. Findings from this study revealed that AlexNet demonstrated the best performance, achieving a maximum recall of 94%. These results are good considering that the training was carried out using a relatively small set of unbalanced images. Finally, this study provides a first approach to mapping the Sargassum distribution along the beaches using the classified geotagged images and offers novel insight into how we can accurately map the arrival of algal blooms along the coastline.

Health Sentinel: A mobile crowdsourcing platform for self-reported surveys provides early detection of COVID-19 clusters in San Luis Potosí, Mexico.

BACKGROUND: The Health Sentinel (Centinela de la Salud, CDS), a mobile crowdsourcing platform that includes the CDS app, was deployed to assess its utility as a tool for COVID-19 surveillance in San Luis Potosí, Mexico. METHODS: The CDS app allowed anonymized individual surveys of demographic features and COVID-19 risk of transmission and exacerbation factors from users of the San Luis Potosí Metropolitan Area (SLPMA). The platform's data processing pipeline computed and geolocalized the risk index of each user and enabled the analysis of the variables and their association. Point process analysis identified geographic clustering patterns of users at risk and these were compared with the patterns of COVID-19 cases confirmed by the State Health Services. RESULTS: A total of 1554 COVID-19 surveys were administered through the CDS app. Among the respondents, 50.4 % were men and 49.6 % women, with an average age of 33.5 years. Overall risk index frequencies were, in descending order: no-risk 77.8 %, low risk 10.6 %, respiratory symptoms 6.7 %, medium risk 1.4 %, high risk 2.0 %, very high risk 1.5 %. Comorbidity was the most frequent vulnerability category (32.4 %), followed by the inability to keep home lockdown (19.2 %). Statistically significant risk clusters identified at a spatial scale between 5 and 730 m coincided with those in neighborhoods containing substantial numbers of confirmed COVID-19 cases. CONCLUSIONS: The CDS platform enables the analysis of the sociodemographic features and spatial distribution of individual risk indexes of COVID-19 transmission and exacerbation. It is a useful epidemiological surveillance and early detection tool because it identifies statistically significant and consistent risk clusters in neighborhoods with a substantial number of confirmed COVID-19 cases.

Biotic Yield Losses in the Southern Amazon, Brazil: Making Use of Smartphone-Assisted Plant Disease Diagnosis Data.

Pathogens and animal pests (P&A) are a major threat to global food security as they directly affect the quantity and quality of food. The Southern Amazon, Brazil's largest domestic region for soybean, maize and cotton production, is particularly vulnerable to the outbreak of P&A due to its (sub)tropical climate and intensive farming systems. However, little is known about the spatial distribution of P&A and the related yield losses. Machine learning approaches for the automated recognition of plant diseases can help to overcome this research gap. The main objectives of this study are to (1) evaluate the performance of Convolutional Neural Networks (ConvNets) in classifying P&A, (2) map the spatial distribution of P&A in the Southern Amazon, and (3) quantify perceived yield and economic losses for the main soybean and maize P&A. The objectives were addressed by making use of data collected with the smartphone application Plantix. The core of the app's functioning is the automated recognition of plant diseases via ConvNets. Data on expected yield losses were gathered through a short survey included in an "expert" version of the application, which was distributed among agronomists. Between 2016 and 2020, Plantix users collected approximately 78,000 georeferenced P&A images in the Southern Amazon. The study results indicate a high performance of the trained ConvNets in classifying 420 different crop-disease combinations. Spatial distribution maps and expert-based yield loss estimates indicate that maize rust, bacterial stalk rot and the fall armyworm are among the most severe maize P&A, whereas soybean is mainly affected by P&A like anthracnose, downy mildew, frogeye leaf spot, stink bugs and brown spot. Perceived soybean and maize yield losses amount to 12 and 16%, respectively, resulting in annual yield losses of approximately 3.75 million tonnes for each crop and economic losses of US$2 billion for both crops together. The high level of accuracy of the trained ConvNets, when paired with widespread use from following a citizen-science approach, results in a data source that will shed new light on yield loss estimates, e.g., for the analysis of yield gaps and the development of measures to minimise them.

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

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

Improving Signal-Strength Aggregation for Mobile Crowdsourcing Scenarios.

Due to its huge impact on the overall quality of service (QoS) of wireless networks, both academic and industrial research have actively focused on analyzing the received signal strength in areas of particular interest. In this paper, we propose the improvement of signal-strength aggregation with a special focus on Mobile Crowdsourcing scenarios by avoiding common issues related to the mishandling of log-scaled signal values, and by the proposal of a novel aggregation method based on interpolation. Our paper presents two clear contributions. First, we discuss the misuse of log-scaled signal-strength values, which is a persistent problem within the mobile computing community. We present the physical and mathematical formalities on how signal-strength values must be handled in a scientific environment. Second, we present a solution to the difficulties of aggregating signal strength in Mobile Crowdsourcing scenarios, as a low number of measurements and nonuniformity in spatial distribution. Our proposed method obtained consistently lower Root Mean Squared Error (RMSE) values than other commonly used methods at estimating the expected value of signal strength over an area. Both contributions of this paper are important for several recent pieces of research that characterize signal strength for an area of interest.

Risk perception: A study using dashcam videos and participants from different world regions.

Objective: Research has shown that perceived risk is a vital variable in the understanding of road traffic safety. Having experience in a particular traffic environment can be expected to affect perceived risk. More specifically, drivers may readily recognize traffic hazards when driving in their own world region, resulting in high perceived risk (the expertise hypothesis). Oppositely, drivers may be desensitized to traffic hazards that are common in their own world region, resulting in low perceived risk (the desensitization hypothesis). This study investigated whether participants experienced higher or lower perceived risk for traffic situations from their region compared to traffic situations from other regions. Methods: In a crowdsourcing experiment, participants viewed dashcam videos from four regions: India, Venezuela, United States, and Western Europe. Participants had to press a key when they felt the situation was risky. Results: Data were obtained from 800 participants, with 52 participants from India, 75 from Venezuela, 79 from the United States, 32 from Western Europe, and 562 from other countries. The results provide support for the desensitization hypothesis. For example, participants from India perceived low risk for hazards (e.g., a stationary car on the highway) that were perceived as risky by participants from other regions. At the same time, support for the expertise hypothesis was obtained, as participants in some cases detected hazards that were specific to their own region (e.g., participants from Venezuela detected inconspicuous roadworks in a Venezuelan city better than did participants from other regions). Conclusion: We found support for the desensitization hypothesis and the expertise hypothesis. These findings have implications for cross-cultural hazard perception research.

Crowdsourcing as a Tool for Urban Emergency Management: Lessons from the Literature and Typology.

Recently, citizen involvement has been increasingly used in urban disaster prevention and management, taking advantage of new ubiquitous and collaborative technologies. This scenario has created a unique opportunity to leverage the work of crowds of volunteers. As a result, crowdsourcing approaches for disaster prevention and management have been proposed and evaluated. However, the articulation of citizens, tasks, and outcomes as a continuous flow of knowledge generation reveals a complex ecosystem that requires coordination efforts to manage interdependencies in crowd work. To tackle this challenging problem, this paper extends to the context of urban emergency management the results of a previous study that investigates how crowd work is managed in crowdsourcing platforms applied to urban planning. The goal is to understand how crowdsourcing techniques and quality control dimensions used in urban planning could be used to support urban emergency management, especially in the context of mining-related dam outages. Through a systematic literature review, our study makes a comparison between crowdsourcing tools designed for urban planning and urban emergency management and proposes a five-dimension typology of quality in crowdsourcing, which can be leveraged for optimizing urban planning and emergency management processes.

Clinical manifestations and quality of life in hidradenitis suppurativa patients: survey of participants from an internet support group

Abstract: Background: Hidradenitis suppurativa is a complex and infrequent autoinflammatory disease that impacts on quality of life. Its pathogenesis is not fully understood, which limits the development of curative treatments. Objectives: To evaluate clinical and quality of life aspects of hidradenitis suppurativa patients from a social group on the Internet. Methods: A cross-sectional, Internet-based survey study among participants in a discussion group (Facebook) of individuals with hidradenitis suppurativa. Patients were asked to answer a questionnaire about clinical-demographic aspects and quality of life (DLQI-BRA). Results: A total of 390 individuals agreed to participate in the study, 82% of them female, median age (p25-p75), of 31 (25-37) years old, disease onset at 15 (13-23) years, family member affected in 20% of cases, overweight (BMI 29 [25-33]) kg/m2 and severe impact on quality of life (DLQI 20 [13-25]). Regarding Hurley's classification, the participants provided information that enabled classification into: I (19%), II (52%) and III (29%). More severe cases were associated with males (OR = 1.69), higher weight (BMI: OR = 1.03) non-white color (OR = 1.43) and higher frequency of other autoinflammatory diseases (OR = 1.37). Study limitations: Voluntary adherence survey with self-completion of the questionnaire by 390 from about 1600 group members. Conclusions: Hidradenitis suppurativa patients who participated in a social network group had onset of the disease after puberty, with a predominance in females and overweight people, with great impact on the quality of life.

Development and Evaluation of a Crowdsourcing Platform for Education and Evidence-Based Medical Decision-Making / Desenvolvimento e Avaliação de uma Plataforma Colaborativa Digital para Educação e Tomada de Decisão Médica Baseada em Evidências

ABSTRACT The medical education for clinical decision-making has undergone changes in recent years. Previously supported by printed material, problem solving in clinical practice has recently been aided by digital tools known as summaries platforms. Doctors and medical students have been using such tools from questions found in practice scenarios. These platforms have the advantage of high-quality, evidence-based and always up-to-date content. Its popularization was mainly due to the rise of the internet use and, more recently, of mobile devices such as tablets and smartphones, facilitating their use in clinical practice. Despite this platform is widely available, the most of them actually present several access barriers as costs, foreign language and not be able to Brazilian epidemiology. A free national platform of evidence-based medical summaries was proposed, using the crowdsourcing concept to resolve those barriers. Furthermore, concepts of gamification and content evaluation were implemented. Also, there is the possibility of evaluation by the users, who assigns note for each content created. The platform was built with modern technological tools and made available for web and mobile application. After development, an evaluation process was conducted by researchers to attest to the valid of content, usability, and user satisfying. Consolidated questionnaires and evaluation tools by the literature were applied. The process of developing the digital platform fostered interdisciplinarity, from the involvement of medical and information technology professionals. The work also allowed the reflection on the innovative educational processes, in which the learning from real life problems and the construction of knowledge in a collaborative way are integrated. The assessment results suggest that platform can be real alternative form the evidence-based medical decision-making.

RESUMO O processo de educação para tomada de decisão médica tem passado por mudanças nos últimos anos. Anteriormente suportada por material impresso, a resolução de problemas da prática clínica passou a contar recentemente com a ajuda de ferramentas digitais conhecidas como plataformas de sumários. Médicos e estudantes de Medicina têm utilizado tais ferramentas quando têm dúvidas encontradas nos cenários de prática. Essas plataformas apresentam como vantagem a presença de conteúdo de alta qualidade, baseado em evidências e sempre atualizado. Sua popularização deu-se sobretudo com a ascensão do uso da internet e, mais recentemente, de dispositivos móveis como tablets e smartphones, facilitando seu uso na prática clínica. Apesar de amplamente disponíveis, a maioria das plataformas atuais apresenta diversas barreiras de acesso, como custo, idioma estrangeiro e não ser adaptada à epidemiologia brasileira. Uma plataforma gratuita e totalmente nacional de sumários médicos baseados em evidências foi proposta, por meio do conceito da construção colaborativa, para contornar essas barreiras. Além disso, foram implementados conceitos de gamificação. Também há a possibilidade de avaliação pelos próprios usuários, que atribuem notas a cada conteúdo desenvolvido. A plataforma foi construída mediante ferramentas tecnológicas modernas e disponibilizada para web e aplicativo para dispositivos móveis. Após o desenvolvimento, um processo de avaliação foi conduzido pelos pesquisadores para atestar a validade do conteúdo, a usabilidade e a satisfação dos usuários. Foram aplicados questionários e ferramentas de avaliação consolidados na literatura. O processo de desenvolvimento da plataforma digital fomentou a interdisciplinaridade, por intermédio do envolvimento de profissionais da área médica e de tecnologia da informação. O trabalho também permitiu a reflexão sobre os processos educacionais inovadores, nos quais o aprendizado fundamentado em problemas da vida real e a construção de conhecimento de forma colaborativa estão integrados. Osresultados da avaliação apontam que a plataforma criada pode se tornar uma alternativa factível para tomada de decisão médica baseada em evidências.

Os fatores determinantes da participação na produção coletiva de ideias para solução de problemas públicos / Los factores determinantes de la participación en la producción colectiva de ideas para solución de problemas públicos / Determinant factors of participation in the co-production of ideas to solve public problems

Resumo Quais os fatores determinantes da participação dos cidadãos na produção coletiva de ideias para solução de problemas públicos? Para responder essa questão, 510 cidadãos, inscritos na plataforma de produção coletiva de ideias Prêmio Ideia, responderam a um questionário apontando o quanto os construtos extraídos da literatura como determinantes da participação em plataformas online seriam decisivos para seu interesse em participar. A análise de equações estruturais aponta que o retorno dado pela instituição pública aos cidadãos e a comodidade determinam o interesse em participar, mas que esse interesse não implica, necessariamente, a participação efetiva. Conclui-se que a aplicação das ideias geradas e o feedback aos participantes são determinantes para a participação social e sugerem-se pesquisas que abordem também as motivações das instituições proponentes em propor tais iniciativas.

Resumen ¿Cuáles son los factores determinantes de la participación de los ciudadanos in la producción colectiva de ideas para la solución de problemas públicos? Para responder a la pregunta, 510 ciudadanos, inscritos en la plataforma de producción colectiva de ideas Premio Ideia, respondiendo a un cuestionario apuntando cuánto son constructos extraídos de la literatura como determinantes de la participación en plataformas en línea, serían decisivas para su interés en participar. El análisis de ecuaciones estructurales apunta que el retorno de una institución pública a los ciudadanos y una comodidad son factores que determinan el interés en participar, pero que ese interés no implica necesariamente en la participación efectiva. Se concluye que la aplicación de las ideas generadas y el feedback a los participantes son determinantes para la participación social y se sugieren investigaciones que aborden también las motivaciones de las empresas proponentes en proponer tales iniciativas.

Abstract What are the determinant factors of citizens' participation in the collective production of ideas to solve public problems? In order to answer this question, 510 citizens, enrolled in Prêmio Ideia, a platform of collective production of ideas, responded to a questionnaire pointing out determinant factors identified in literature about participation in online platforms that are decisive for their own interest in participating. The structural equation analysis highlights that the feedback given by a public institution to citizens and convenience are determinant factors for participation. This interest in participation, however, does not necessarily imply effective participation. It is concluded that the application of the ideas created through the platform and the feedback to the participants are determinants for social participation and the study suggests further research approaching the motivation of companies that propose such initiatives.

A Service-Oriented Middleware for Integrated Management of Crowdsourced and Sensor Data Streams in Disaster Management.

The increasing number of sensors used in diverse applications has provided a massive number of continuous, unbounded, rapid data and requires the management of distinct protocols, interfaces and intermittent connections. As traditional sensor networks are error-prone and difficult to maintain, the study highlights the emerging role of "citizens as sensors" as a complementary data source to increase public awareness. To this end, an interoperable, reusable middleware for managing spatial, temporal, and thematic data using Sensor Web Enablement initiative services and a processing engine was designed, implemented, and deployed. The study found that its approach provided effective sensor data-stream access, publication, and filtering in dynamic scenarios such as disaster management, as well as it enables batch and stream management integration. Also, an interoperability analytics testing of a flood citizen observatory highlighted even variable data such as those provided by the crowd can be integrated with sensor data stream. Our approach, thus, offers a mean to improve near-real-time applications.

Will Participatory Syndromic Surveillance Work in Latin America? Piloting a Mobile Approach to Crowdsource Influenza-Like Illness Data in Guatemala.

BACKGROUND: In many Latin American countries, official influenza reports are neither timely nor complete, and surveillance of influenza-like illness (ILI) remains thin in consistency and precision. Public participation with mobile technology may offer new ways of identifying nonmedically attended cases and reduce reporting delays, but no published studies to date have assessed the viability of ILI surveillance with mobile tools in Latin America. We implemented and assessed an ILI-tailored mobile health (mHealth) participatory reporting system. OBJECTIVE: The objectives of this study were to evaluate the quality and characteristics of electronically collected data, the user acceptability of the symptom reporting platform, and the costs of running the system and of identifying ILI cases, and to use the collected data to characterize cases of reported ILI. METHODS: We recruited the heads of 189 households comprising 584 persons during randomly selected home visits in Guatemala. From August 2016 to March 2017, participants used text messages or an app to report symptoms of ILI at home, the ages of the ILI cases, if medical attention was sought, and if medicines were bought in pharmacies. We sent weekly reminders to participants and compensated those who sent reports with phone credit. We assessed the simplicity, flexibility, acceptability, stability, timeliness, and data quality of the system. RESULTS: Nearly half of the participants (47.1%, 89/189) sent one or more reports. We received 468 reports, 83.5% (391/468) via text message and 16.4% (77/468) via app. Nine-tenths of the reports (93.6%, 438/468) were received within 48 hours of the transmission of reminders. Over a quarter of the reports (26.5%, 124/468) indicated that at least someone at home had ILI symptoms. We identified 202 ILI cases and collected age information from almost three-fifths (58.4%, 118/202): 20 were aged between 0 and 5 years, 95 were aged between 6 and 64 years, and three were aged 65 years or older. Medications were purchased from pharmacies, without medical consultation, in 33.1% (41/124) of reported cases. Medical attention was sought in 27.4% (34/124) of reported cases. The cost of identifying an ILI case was US $6.00. We found a positive correlation (Pearson correlation coefficient=.8) between reported ILI and official surveillance data for noninfluenza viruses from weeks 41 (2016) to 13 (2017). CONCLUSIONS: Our system has the potential to serve as a practical complement to respiratory virus surveillance in Guatemala. Its strongest attributes are simplicity, flexibility, and timeliness. The biggest challenge was low enrollment caused by people's fear of victimization and lack of phone credit. Authorities in Central America could test similar methods to improve the timeliness, and extend the breadth, of disease surveillance. It may allow them to rapidly detect localized or unusual circulation of acute respiratory illness and trigger appropriate public health actions.

Valparaiso's 2014 Fire: Evaluation of Environmental and Epidemiological Risk Factors During the Emergency Through a Crowdsourcing Tool.

OBJECTIVE: To describe and relate the main environmental risk factors in the emergency process after a large urban fire in Valparaiso, Chile, in April 2014. METHODS: An observational, cross-sectional descriptive study was performed. All 243 reports from an ad hoc web/mobile website created on the Ushahidi/Crowdmap platform were reviewed. Reports were recorded in a new database with dichotomist variables based on either the presence or absence of the relevant category in each report. RESULTS: Almost one-third of the reports presented data about garbage (30%) and chemical toilets (29%). Reports related to water, infrastructural damage, and garbage had significant associations with 4 categories by chi-square test. In the logistic regression model for chemical toilets, only the variable of water was significant (P value=0.00; model P value: 0.00; R2: 11.7%). The "garbage" category confirmed infrastructural damage (P value: 0.00), water (P value: 0.028), and vectors (P value: 0.00) as predictors (model P value: 0.00; R2: 23.09%). CONCLUSIONS: Statistically significant evidence was found for the statistical dependence of 7 out of 10 studied variables. The most frequent environmental risk factors in the reports were garbage, chemical toilets, and donation centers. The highest correlation found was for damaged infrastructure, vectors, and garbage. (Disaster Med Public Health Preparedness. 2017;11:239-243).

Crowdsourcing: a valid alternative to expert evaluation of robotic surgery skills.

BACKGROUND: Robotic-assisted gynecologic surgery is common, but requires unique training. A validated assessment tool for evaluating trainees' robotic surgery skills is Robotic-Objective Structured Assessments of Technical Skills. OBJECTIVE: We sought to assess whether crowdsourcing can be used as an alternative to expert surgical evaluators in scoring Robotic-Objective Structured Assessments of Technical Skills. STUDY DESIGN: The Robotic Training Network produced the Robotic-Objective Structured Assessments of Technical Skills, which evaluate trainees across 5 dry lab robotic surgical drills. Robotic-Objective Structured Assessments of Technical Skills were previously validated in a study of 105 participants, where dry lab surgical drills were recorded, de-identified, and scored by 3 expert surgeons using the Robotic-Objective Structured Assessments of Technical Skills checklist. Our methods-comparison study uses these previously obtained recordings and expert surgeon scores. Mean scores per participant from each drill were separated into quartiles. Crowdworkers were trained and calibrated on Robotic-Objective Structured Assessments of Technical Skills scoring using a representative recording of a skilled and novice surgeon. Following this, 3 recordings from each scoring quartile for each drill were randomly selected. Crowdworkers evaluated the randomly selected recordings using Robotic-Objective Structured Assessments of Technical Skills. Linear mixed effects models were used to derive mean crowdsourced ratings for each drill. Pearson correlation coefficients were calculated to assess the correlation between crowdsourced and expert surgeons' ratings. RESULTS: In all, 448 crowdworkers reviewed videos from 60 dry lab drills, and completed a total of 2517 Robotic-Objective Structured Assessments of Technical Skills assessments within 16 hours. Crowdsourced Robotic-Objective Structured Assessments of Technical Skills ratings were highly correlated with expert surgeon ratings across each of the 5 dry lab drills (r ranging from 0.75-0.91). CONCLUSION: Crowdsourced assessments of recorded dry lab surgical drills using a validated assessment tool are a rapid and suitable alternative to expert surgeon evaluation.

Gamificação e crowdsourcing para melhoria do combate ao Aedes aegypti / Gamification and crowdsourcing for strengthening the combat against Aedes aegypti

OBJETIVOS: Criar um sistema baseado em conceitos de gamificação e crowdsourcing, para auxiliar no combate/prevenção ao Aedes aegypti. Com ênfase nos indicadores: número de notificações de focos do mosquito; tempo médio para gerar relatórios utilizados no planejamento e tempo médio entre a notificação e o recebimento da mesma pelo agente de campo. MÉTODOS: Trata-se de um estudo quantitativo-exploratório em parceria com as vigilâncias para a idealização, especificação, implementação e teste piloto do sistema proposto. RESULTADOS: Foi produzido um aplicativo móvel para a população realizar notificações que alimentam um sistema de informação na web, georreferenciado e usado pela VA/VE para apoio à gestão de seus serviços. No piloto foi verificado uma melhoria significativa nos indicadores considerados. CONCLUSÃO: O sistema poderá funcionar como um novo canal de denúncia, assim como auxiliar os processos e serviços da VA/VE para um combate mais eficiente e eficaz ao mosquito e às doenças por ele transmitidas.

OBJECTIVES: Develop and apply a gamified and crowdsourcing information system to speed up and improvedecision making by sanitary and health agencies ("VA/VE") as they attempt to prevent the spread of the zika, dengue, and chikungunya viruses transmitted by the Aedes aegypti mosquito. METHODS: Carry out a quantitative-exploratory study in partnership with VA/VE to specify, implement and (pilot) test the proposed system. RESULTS: The work produceda gamified, crowdsourced mobile app for the population to feed information on Aedes aegypti´s infestation into a georeferenced web information system. VA/VE use this web IS to manage their operations. The pilot test provided evidence that the partner VA/VE was able to make faster and better decisions. CONCLUSION: The proposed IS may serve as a newAedes aegypti infestation notification channel for the population and as a decision support system for VA/VE for more efficient and effective combat against the mosquito and related diseases.

Crowdsourcing the General Public for Large Scale Molecular Pathology Studies in Cancer.

BACKGROUND: Citizen science, scientific research conducted by non-specialists, has the potential to facilitate biomedical research using available large-scale data, however validating the results is challenging. The Cell Slider is a citizen science project that intends to share images from tumors with the general public, enabling them to score tumor markers independently through an internet-based interface. METHODS: From October 2012 to June 2014, 98,293 Citizen Scientists accessed the Cell Slider web page and scored 180,172 sub-images derived from images of 12,326 tissue microarray cores labeled for estrogen receptor (ER). We evaluated the accuracy of Citizen Scientist's ER classification, and the association between ER status and prognosis by comparing their test performance against trained pathologists. FINDINGS: The area under ROC curve was 0.95 (95% CI 0.94 to 0.96) for cancer cell identification and 0.97 (95% CI 0.96 to 0.97) for ER status. ER positive tumors scored by Citizen Scientists were associated with survival in a similar way to that scored by trained pathologists. Survival probability at 15 years were 0.78 (95% CI 0.76 to 0.80) for ER-positive and 0.72 (95% CI 0.68 to 0.77) for ER-negative tumors based on Citizen Scientists classification. Based on pathologist classification, survival probability was 0.79 (95% CI 0.77 to 0.81) for ER-positive and 0.71 (95% CI 0.67 to 0.74) for ER-negative tumors. The hazard ratio for death was 0.26 (95% CI 0.18 to 0.37) at diagnosis and became greater than one after 6.5 years of follow-up for ER scored by Citizen Scientists, and 0.24 (95% CI 0.18 to 0.33) at diagnosis increasing thereafter to one after 6.7 (95% CI 4.1 to 10.9) years of follow-up for ER scored by pathologists. INTERPRETATION: Crowdsourcing of the general public to classify cancer pathology data for research is viable, engages the public and provides accurate ER data. Crowdsourced classification of research data may offer a valid solution to problems of throughput requiring human input.