Automatic mapping of high-risk urban areas for Aedes aegypti infestation based on building facade image analysis.

BACKGROUND: Dengue, Zika, and chikungunya, whose viruses are transmitted mainly by Aedes aegypti, significantly impact human health worldwide. Despite the recent development of promising vaccines against the dengue virus, controlling these arbovirus diseases still depends on mosquito surveillance and control. Nonetheless, several studies have shown that these measures are not sufficiently effective or ineffective. Identifying higher-risk areas in a municipality and directing control efforts towards them could improve it. One tool for this is the premise condition index (PCI); however, its measure requires visiting all buildings. We propose a novel approach capable of predicting the PCI based on facade street-level images, which we call PCINet. METHODOLOGY: Our study was conducted in Campinas, a one million-inhabitant city in São Paulo, Brazil. We surveyed 200 blocks, visited their buildings, and measured the three traditional PCI components (building and backyard conditions and shading), the facade conditions (taking pictures of them), and other characteristics. We trained a deep neural network with the pictures taken, creating a computational model that can predict buildings' conditions based on the view of their facades. We evaluated PCINet in a scenario emulating a real large-scale situation, where the model could be deployed to automatically monitor four regions of Campinas to identify risk areas. PRINCIPAL FINDINGS: PCINet produced reasonable results in differentiating the facade condition into three levels, and it is a scalable strategy to triage large areas. The entire process can be automated through data collection from facade data sources and inferences through PCINet. The facade conditions correlated highly with the building and backyard conditions and reasonably well with shading and backyard conditions. The use of street-level images and PCINet could help to optimize Ae. aegypti surveillance and control, reducing the number of in-person visits necessary to identify buildings, blocks, and neighborhoods at higher risk from mosquito and arbovirus diseases.

Using Self-Organizing Maps to find spatial relationships between wildlife-vehicle crashes and land use classes.

The construction and expansion of roads cause significant impacts on the environment. The main potential impacts to biotic environment are vegetation suppression, reduction of the amount and composition of animal distribution due to forest fragmentation and increasing risks of animal (domestic and wildlife) vehicle collisions. The objective of this work was to establish a relationship between the different spatial patterns in wildlife-vehicle crash, by using spatial analysis and machine learning tools. Self-Organizing Maps (SOM), an artificial neural network (ANN), was selected to reorganize the multi-dimensional data according to the similarity between them. The results of the spatial pattern analysis were important to perceive that the point data pattern varies from an animal type to another. The events occur spatially clustered and are not uniformly distributed along the highway. SOM was able to analyze the relationship between multiple variables, linear and non-linear, such as ecological data, and established distinct spatial patterns per each animal type. In the studied area, most of the wildlife was run over very close to forest area and water bodies, and not so close to sugarcane fields, forestry and built environment. A considerable part of the wildlife-vehicle collisions occurred in areas with diverse landscape.

Ovipositional Reproduction of the Dengue Vector for Identifying High-Risk Urban Areas.

Identification and classification of high-risk areas for the presence of Aedes aegypti is not an easy task. To develop suitable methods to identify this areas is an essential task that will increase the efficiency and effectiveness of control measures and to optimize the use of resources. The objectives of this study were to identify high- risk areas for the presence of Ae. aegypti using mosquito traps and household visits to identify breeding sites; to identify and validate aspects of the remote sensing images that could characterize these areas; to evaluate the relationship between this spatial risk classification and the occurrence of Ae. aegypti; and provide a methodology to the health and control vector services and prioritize these areas for development of control measure. Information about the geographical coordinates of these traps will enable us to apply the kriging spatial analysis tool to generate maps with the predicted numbers of Ae. aegypti. Satellite images were used to identify the characteristic features the four areas, so that other areas could also be classified using only the sensing remote images. The developed methodology enables the identification of high-risk areas for Ae. aegypti and for the occurrence of Dengue, as well as Zika fever and Chikungunya fever using only sensing remote images. These results allow health and vector control services to prioritize these areas for developing surveillance and control measures. The use of the available resources can be optimized and potentially promote a decrease in the expected incidences of these diseases, particularly Dengue.

Ovipositional Reproduction of the Dengue Vector for Identifying High-Risk Urban Areas

Identification and classification of high-risk areas for the presence of Aedes aegypti is not an easy task. To develop suitable methods to identify this areas is an essential task that will increase the efficiency and effectiveness of control measures and to optimize the use of resources. The objectives of this study were to identify high- risk areas for the presence of Ae. aegypti using mosquito traps and household visits to identify breeding sites; to identify and validate aspects of the remote sensing images that could characterize these areas; to evaluate the relationship between this spatial risk classification and the occurrence of Ae. aegypti; and provide a methodology to the health and control vector services and prioritize these areas for development of control measure. Information about the geographical coordinates of these traps will enable us to apply the kriging spatial analysis tool to generate maps with the predicted numbers of Ae. aegypti. Satellite images were used to identify the characteristic features the four areas, so that other areas could also be classified using only the sensing remote images. The developed methodology enables the identification of high-risk areas for Ae. aegypti and for the occurrence of Dengue, as well as Zika fever and Chikungunya fever using only sensing remote images. These results allow health and vector control services to prioritize these areas for developing surveillance and control measures. The use of the available resources can be optimized and potentially promote a decrease in the expected incidences of these diseases, particularly Dengue.

Water tank and swimming pool detection based on remote sensing and deep learning: Relationship with socioeconomic level and applications in dengue control.

Studies have shown that areas with lower socioeconomic standings are often more vulnerable to dengue and similar deadly diseases that can be spread through mosquitoes. This study aims to detect water tanks installed on rooftops and swimming pools in digital images to identify and classify areas based on the socioeconomic index, in order to assist public health programs in the control of diseases linked to the Aedes aegypti mosquito. This study covers four regions of Campinas, São Paulo, characterized by different socioeconomic contexts. With mosaics of images obtained by a 12.1 MP Canon PowerShot S100 (5.2 mm focal length) carried by unmanned aerial vehicles, we developed deep learning algorithms in the scope of computer vision for the detection of water tanks and swimming pools. An object detection model, which was initially created for areas of Belo Horizonte, Minas Gerais, was enhanced using the transfer learning technique, and allowed us to detect objects in Campinas with fewer samples and more efficiency. With the detection of objects in digital images, the proportions of objects per square kilometer for each region studied were estimated by adopting a Chi-square distribution model. Thus, we found that regions with low socioeconomic status had more exposed water tanks, while regions with high socioeconomic levels had more exposed pools. Using deep learning approaches, we created a useful tool for Ae. aegypti control programs to utilize and direct disease prevention efforts. Therefore, we concluded that it is possible to detect objects directly related to the socioeconomic level of a given region from digital images, which encourages the practicality of this approach for studies aimed towards public health.

Comparative analysis of data reduction techniques for questionnaire validation using self-reported driver behaviors.

INTRODUCTION: Exploratory data reduction techniques, such as Factor Analysis (FA) and Principal Component Analysis (PCA), are widely used in questionnaire validation with ordinal data, such as Likert Scale data, even though both techniques are indicated to metric measures. In this context, this study presents an e-survey, conducted to obtain self-reported behaviors between Brazilian drivers (N = 1,354, 55.2% of males) and Portuguese drivers (N = 348, 46.6% of males) based on 20 items from the Driver Behavior Questionnaire (DBQ) on a five-point Likert Scale. This paper aimed to examine DBQ validation using FA and PCA compared to Categorical Principal Component Analysis (CATPCA) which is more indicative to use with Likert Scale data. RESULTS: The results from all techniques confirmed the most replicated factor structure of DBQ, distinguishing behaviors as errors, ordinary violations, and aggressive violation. However, after Varimax rotation, CATPCA explained 11% more variance compared to FA and 2% more than PCA. We identified cross-loadings among the component of the techniques. An item changed its dimension in the CATPCA results but did not change the structural interpretability. Individual scores from dimension 1 of CATPCA were significantly different from FA and PCA. Individual scores from factor 1 of CATPCA were significantly different from FA and PCA. Practical applications: The CATPCA seems to be more advantageous in order to represent the original data and considering data constrains. In addition to finding an interpretable factorial structure, the representation of the original data is regarded as relevant since the factor scores could be used for crash prediction in future analyses.

Use of an Extended Premise Condition Index for detection of priority areas for vector control actions.

The Premise Condition Index (PCI), proposed by Tun-Lin and colleagues in 1995, is a score that considers the conditions of a premise as well its yards and degree of shading. They hypothesized that the higher its value the greater the probability of the premise having the presence of Aedes aegypti. This study aimed to evaluate if there is a correspondence between PCI and Ae. aegypti infestation in four areas of a large city in the State of São Paulo, Brazil, if the inclusion of new categories related to the presence of animals in premises would increase the probability of detecting predictive areas for vector control actions and, if so, to propose an expanded PCI. The positivity of the premises for the presence of Ae. aegypti was modeled considering a Bernoulli probability distribution, in a Bayesian context using the Integrated Nested Laplace Approximation. The study showed that, in general, the higher the value of the PCI of a premise, the more likely it is to have the presence of Ae. aegypti, and the inclusion of information on the animals' presence can increase the discriminatory power of PCI. These results support the proposition of an extended PCI that would consider, in addition to the conditions of the premise, the presence of animals to classify it regarding the risk of the presence of Ae. aegypti.

Remote sensing for risk mapping of Aedes aegypti infestations: Is this a practical task?

Mosquito-borne diseases affect millions of individuals worldwide; the area of endemic transmission has been increasing due to several factors linked to globalization, urban sprawl, and climate change. The Aedes aegypti mosquito plays a central role in the dissemination of dengue, Zika, chikungunya, and urban yellow fever. Current preventive measures include mosquito control programs; however, identifying high-risk areas for mosquito infestation over a large geographic region based only on field surveys is labor-intensive and time-consuming. Thus, the objective of this study was to assess the potential of remote satellite images (WorldView) for determining land features associated with Ae. aegypti adult infestations in São José do Rio Preto/SP, Brazil. We used data from 60 adult mosquito traps distributed along four summers; the remote sensing images were classified by land cover types using a supervised classification method. We modeled the number of Ae. aegypti using a Poisson probability distribution with a geostatistical approach. The models were constructed in a Bayesian context using the Integrated nested Laplace Approximations and Stochastic Partial Differential Equation method. We showed that an infestation of Ae. aegypti adult mosquitoes was positively associated with the presence of asbestos roofing and roof slabs. This may be related to several other factors, such as socioeconomic or environmental factors. The usage of asbestos roofing may be more prevalent in socioeconomically poor areas, while roof slabs may retain rainwater and contribute to the generation of temporary mosquito breeding sites. Although preliminary, our results demonstrate the utility of satellite remote sensing in identifying landscape differences in urban environments using a geostatistical approach, and indicated directions for future research. Further analyses including other variables, such as land surface temperature, may reveal more complex relationships between urban mosquito micro-habitats and land cover features.

Use of an Extended Premise Condition Index for detection of priority areas for vector control actions

The Premise Condition Index (PCI), proposed by Tun-Lin and colleagues in 1995, is a score that considers the conditions of a premise as well its yards and degree of shading. They hypothesized that the higher its value the greater the probability of the premise having the presence of Aedes aegypti. This study aimed to evaluate if there is a correspondence between PCI and Ae. aegypti infestation in four areas of a large city in the State of São Paulo, Brazil, if the inclusion of new categories related to the presence of animals in premises would increase the probability of detecting predictive areas for vector control actions and, if so, to propose an expanded PCI. The positivity of the premises for the presence of Ae. aegypti was modeled considering a Bernoulli probability distribution, in a Bayesian context using the Integrated Nested Laplace Approximation. The study showed that, in general, the higher the value of the PCI of a premise, the more likely it is to have the presence of Ae. aegypti, and the inclusion of information on the animals' presence can increase the discriminatory power of PCI. These results support the proposition of an extended PCI that would consider, in addition to the conditions of the premise, the presence of animals to classify it regarding the risk of the presence of Ae. aegypti.

Influence of strategic points in the dispersion of Aedes aegypti in infested areas.

OBJECTIVE: To evaluate whether sites with large amount of potential breeding sites for immature forms of Aedes aegypti, called strategic points, influence in the active vector's dispersion into properties in their surroundings. METHODS: We selected four areas in the municipality of Campinas, three of them with strategic points classified as high, moderate, and low risk according to infestation and a control area, without strategic points. Between October 2015 and September 2016, we monthly installed oviposition traps and evaluated the infestation by Ae. aegypti in all properties of each selected area. To verify if there was vector dispersion from each strategic point, based on its location, we investigated the formation of clusters with excess of eggs or larvae or pupae containers, using the Gi spatial statistics. RESULTS: The amount of eggs collected in the ovitraps and the number of positive containers for Ae. aegypti did not show clusters of high values concerning its distance from the strategic point. Both presented random distribution not spatially associated with the positioning of strategic points in the area. CONCLUSIONS: Strategic points are not confirmed as responsible for the vector's dispersion for properties in their surroundings. We highlight the importance of reviewing the current strategy of the vector control program in Brazil, seeking a balance from the technical, operational, and economic point of view, without disregarding the role of strategic points as major producers of mosquitoes and their importance in the dissemination of arboviruses in periods of transmission.

Influence of strategic points in the dispersion of Aedes aegypti in infested areas / Influência de pontos estratégicos na dispersão de Aedes aegypti em áreas infestadas

ABSTRACT OBJECTIVE: To evaluate whether sites with large amount of potential breeding sites for immature forms of Aedes aegypti, called strategic points, influence in the active vector's dispersion into properties in their surroundings. METHODS: We selected four areas in the municipality of Campinas, three of them with strategic points classified as high, moderate, and low risk according to infestation and a control area, without strategic points. Between October 2015 and September 2016, we monthly installed oviposition traps and evaluated the infestation by Ae. aegypti in all properties of each selected area. To verify if there was vector dispersion from each strategic point, based on its location, we investigated the formation of clusters with excess of eggs or larvae or pupae containers, using the Gi spatial statistics. RESULTS: The amount of eggs collected in the ovitraps and the number of positive containers for Ae. aegypti did not show clusters of high values concerning its distance from the strategic point. Both presented random distribution not spatially associated with the positioning of strategic points in the area. CONCLUSIONS: Strategic points are not confirmed as responsible for the vector's dispersion for properties in their surroundings. We highlight the importance of reviewing the current strategy of the vector control program in Brazil, seeking a balance from the technical, operational, and economic point of view, without disregarding the role of strategic points as major producers of mosquitoes and their importance in the dissemination of arboviruses in periods of transmission.

RESUMO OBJETIVO: Avaliar se locais com grande quantidade de potenciais criadouros de formas imaturas de Aedes aegypti, denominados pontos estratégicos, influenciam a dispersão ativa do vetor aos imóveis no seu entorno. MÉTODOS: Foram selecionadas quatro áreas no município de Campinas, três delas com pontos estratégicos classificados como alto, médio e baixo risco segundo a infestação e uma área controle, sem ponto estratégico. Entre outubro de 2015 e setembro de 2016, instalaram-se mensalmente armadilhas de oviposição e avaliou-se a infestação por Ae. aegypti em todos os imóveis de cada área selecionada. Para verificar se houve dispersão do vetor a partir de cada ponto estratégico, com base em sua localização, investigou-se a formação de aglomerados com excesso de ovos ou de recipientes com larvas ou pupas, utilizando a estatística espacial Gi. RESULTADOS: o número de ovos coletados nas ovitrampas e o número de recipientes positivos para Ae. aegypti não apresentaram aglomerados de altos valores relativos à sua distância do ponto estratégico. Ambos apresentaram distribuição aleatória não associada espacialmente com o posicionamento dos pontos estratégicos na área. CONCLUSÕES: Pontos estratégicos não se confirmaram como responsáveis pela dispersão do vetor para os imóveis no seu entorno. Destaca-se a importância de rever a estratégia atual do programa de controle de vetores do Brasil, buscando um equilíbrio do ponto de vista técnico, operacional e econômico, sem desconsiderar o papel dos pontos estratégicos como grandes produtores de mosquitos e sua importância na disseminação de arboviroses em momentos de transmissão.

[Master sample and geoprocessing: technologies for household surveys]. / Amostra mestra e geoprocessamento: tecnologias para inquéritos domiciliares.

OBJECTIVE: To reduce cost and time associated with household sampling process and to assess the feasibility of shared use of address data file of census enumeration areas in several epidemiological surveys using updated information from the National Survey of Households (PNAD). METHODS: Address data file comprising 72 census enumeration areas was kept as primary sampling units for the city of S o Paulo. During the period 1995-2000, three distinct household samples were drawn using the two-stage cluster sampling procedure. Geographic Information System (GIS) technology allowed delimiting boundaries, blocks and streets for any primary sampling unit and printing updated maps for selected sub-samples. RESULTS: Twenty-five thousand dwellings made up the permanent address data file of the master sample. A cheaper and quicker selection of each sample, plus gathering information on demographic and topographical profiles of census enumeration areas were the main contribution of the study results. CONCLUSIONS: The master sample concept, integrated with GIS technology, is an advantageous alternative sampling design for household surveys in urban areas. Using the list of addresses from the PNAD updated yearly, although limiting its application to the most populated Brazilian cities, avoids the need of creating an independent sampling procedure for each individual survey carried out in the period between demographic censuses, and it is an important contribution for planning sampling surveys in public health.

Amostra mestra e geoprocessamento: tecnologias para inquéritos domiciliares / Master sample and geoprocessing: technologies for household surveys

OBJETIVO: Reduzir as despesas e o tempo associados ao processo de amostragem de domicílios e mostrar a viabilidade de uso compartilhado dos cadastros de endereços entre vários inquéritos epidemiológicos, a partir da reciclagem de material produzido pela Pesquisa Nacional de Amostras de Domicílios (PNAD). MÉTODOS: Cadastros permanentes de endereços foram construídos e atualizados para 72 setores censitários, e retidos como unidades primárias da amostra mestra do município de Säo Paulo, SP. No período 1995-2000, sortearam-se três diferentes amostras pelo processo de conglomerados, em dois estágios. Operaçöes de sorteio e mapeamento das amostras foram automatizadas por meio da aplicaçäo de tecnologia de geoprocessamento. RESULTADOS: Cerca de 25.000 domicílios compöem o cadastro permanente de endereços da amostra mestra. A produçäo ágil e barata de cada amostra, aliada ao conhecimento acumulado sobre os perfis demográficos e topográficos dos setores censitários, pode ser considerada a principal contribuiçäo dos resultados alcançados. Verificou-se que o sistema proposto contrasta com a seleçäo de amostras independentes para cada investigaçäo "ad hoc", o que ainda é prática comum nas agências ou grupos que realizam inquéritos domiciliares. CONCLUSOES: A reciclagem das listagens anuais de endereços da PNAD, apesar de restringir a abrangência de replicaçäo da experiência aos municípios mais populosos do País, evita a repetiçäo do processo de amostragem para cada inquérito; além disso, significa importante contribuiçäo ao planejamento de investigaçöes descritivas na área da saúde pública. Questöes conceituais ou teóricas, decorrentes do procedimento operacional, foram facilmente superadas pela introduçäo de técnicas estatísticas compensatórias