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Dengue is an emerging vector-borne viral disease across the world. The primary dengue mosquito vectors breed in containers with sufficient water and nutrition. Outdoor containers can be detected from geotagged images using state-of-the-art deep learning methods. In this study, we utilize such container information from street view images in developing a risk mapping model and determine the added value of including container information in predicting dengue risk. We developed seasonal-spatial models in which the target variable dengue incidence was explained using weather and container variable predictors. Linear mixed models with fixed and random effects are employed in our models to account for different characteristics of containers and weather variables. Using data from three provinces of Thailand between 2015 and 2018, the models are developed at the sub-district level resolution to facilitate the development of effective targeted intervention strategies. The performance of the models is evaluated with two baseline models: a classic linear model and a linear mixed model without container information. The performance evaluated with the correlation coefficients, R-squared, and AIC shows the proposed model with the container information outperforms both baseline models in all three provinces. Through sensitivity analysis, we investigate the containers that have a high impact on dengue risk. Our findings indicate that outdoor containers identified from street view images can be a useful data source in building effective dengue risk models and that the resulting models have potential in helping to target container elimination interventions. Author summary: The primary dengue mosquito vectors breed in containers with sufficient water and nutrition. Outdoor containers can be detected from geotagged images using state-of-the-art deep learning methods. Eight breeding site container types in Google street view images are detected using convolutional neural networks. We investigate the added value of including container information from geotagged images in predicting dengue risk. To explain the target variable dengue incidence, weather variables are added to complement the container variable predictors. Linear mixed-effects models are built to account for the effects of spatial and seasonal variation in weather and container variables on the dengue incidence. Evaluation is carried out over three provinces in Thailand: Bangkok, Nakhon Si Thammarat, and Krabi in comparison with classic linear models as well as the mixed effect models without container information. The proposed model with the container information outperforms both baseline models in all three provinces. We further perform sensitivity analysis to investigate the sensitivity of dengue incidence to the changes in the number of containers as well as the improvement in the model performance. This is the first work on dengue risk prediction models using container density information from geotagged images analysis. [ABSTRACT FROM AUTHOR]

Given the increased prevalence of cancer, respiratory diseases, and reproductive disorders, for which multifactorial origins are strongly suspected, the impact of the environment on the population represents a substantial public health challenge. Surveillance systems have become an essential public health decision-making tool. Networks have been constructed to facilitate the development of analyses of the multifactorial aspects of the relationships between occupational contexts and health. The aim of this study is to develop and present an approach for the optimal exploitation of observational databases to describe and improve the understanding of the (occupational) environment–health relationships, taking into account key multifactorial aspects. We have developed a spectral analysis (SA) approach that takes into account both the multi-exposure and dynamic natures of occupational health problems (OHPs) and related associations. The main results of this paper are to present the construction method of the “spectrum” and “spectrosome” of OHPs (range and structured list of occupational exposures) and describe the information contained therein with an illustrative example. The approach is illustrated using the case of non-Hodgkin lymphoma (NHL) from the French National Occupational Diseases Surveillance and Prevention Network database as a working example of an occupational disease. We found that the NHL spectrum includes 40 sets of occupational exposures characterized by important multi-exposures, especially solvent combinations or pesticide combinations, but also specific exposures such as polycyclic aromatic hydrocarbons, formaldehyde and ionizing radiation. These findings may be useful for surveillance and the assessment of occupational exposure related to health risks. [ABSTRACT FROM AUTHOR]

Background: Leptospirosis is a worldwide zoonotic bacterial disease caused by infection with leptospires. Leptospirosis in humans and livestock is an endemic and epidemic disease in Thailand. Livestock may act as reservoirs for leptospires and source for human infection. Methodology/Principal findings: Data on leptospirosis infection in humans and livestock (Buffaloes, Cattle, and Pigs) species during 2010 to 2015 were analyzed. Serum samples were examined using Microscopic Agglutination Test (MAT) to identify antibodies against Leptospira serovars using a cut-off titer ≥ 1:100. The seroprevalence was 23.7% in humans, 24.8% in buffaloes, 28.1% in cattle, and 11.3% in pigs. Region specific prevalence among humans and livestock was found in a wide range. The most predominant serovars were Shermani, followed by Bratislava, Panama, and Sejroe in human, Shermani, Ranarum, and Tarassovi in buffaloes, and Shermani and Ranarum in cattle and pigs. Equally highest MAT titers against multiple serovars per one sample were found mainly in buffaloes and cattle showing equally titers against Ranarum and Shermani. The correlations of distribution of serovars across Thailand’s regions were found to be similar in pattern for cattle but not for buffaloes. In humans, the serovar distribution in the south differed from other regions. By logistic regression, the results indicated that livestock is more susceptible to infection by serovar Shermani when compared to humans. Conclusions/Significance: This study gives a detailed picture of the predominance of Leptospira serovars in relation to region, humans and typical livestock. The broad spatial distribution of seroprevalence was analyzed across and within species as well as regions in Thailand. Our finding may guide public health policy makers to implement appropriate control measures and help to reduce the impact of leptospirosis in Thailand. [ABSTRACT FROM AUTHOR]

Background: Urban leptospirosis has increasingly been reported in both developing and developed countries. The control of the disease is limited because our understanding of basic aspects of the epidemiology, including the transmission routes of leptospires among rat populations, remains incomplete. Through the ability to distinguish among Leptospira strains in rats, multispacer sequence typing (MST) could provide a modern understanding of Leptospira epidemiology; however, to our knowledge, the distribution of Leptospira strains among urban rat colonies has not been investigated using MST. Aims and Methodology: The objective of this study was to identify the Leptospira strains present in rats (Rattus norvegicus) in Lyon (France) using MST and to characterize their spatial distribution. Kidneys and urine were collected from rats trapped live in seven locations in the city and in one suburban location. Each location was considered to represent a rat colony. Bacterial cultures and quantitative polymerase chain reaction (qPCR) assays were performed, and the L. interrogans DNA identified was then genotyped using MST. The distributions of Leptospira strains were spatially described. Key Results: Among 84 wild rats, MST profiles were obtained in 35 of 37 rats that had a positive result for L. interrogans by bacterial culture and/or qPCR analyses. All of the MST profiles were related to reference strains previously isolated from human patients that belong to the serogroup Icterohaemorrhagiae and the serovars [strain(s)] Copenhageni [Wijinberg or M20] (n = 26), Icterohaemorrhagiae [CHU Réunion] (n = 7), Icterohaemorrhagiae [R1] (n = 1) and Copenhageni [Shibaura 9] (n = 1). Each colony was infected with leptospires having the same MST profile. Major Conclusions: This study demonstrated that MST could be used for the purpose of field studies, either on culture isolates or on DNA extracted from kidneys and urine, to distinguish among L. interrogans isolates in rats. MST could thus be used to monitor their distributions in urban rats from the same city, thereby providing new knowledge that could be applied to explore the circulation of L. interrogans infection in rat colonies. Because the strains are related to those previously found in humans, this application of MST could aid in the source tracking of human leptospirosis, and the findings would be relevant for public health purposes according to the One Health principle. [ABSTRACT FROM AUTHOR]

Design, sampling and data interpretation constitute an important challenge for wildlife surveillance of avian influenza viruses (AIV). The aim of this study was to construct a model to improve and enhance identification in both different periods and locations of avian species likely at high risk of contact with AIV in a specific wetland. This study presents an individual- based stochastic model for the Ebre Delta as an example of this appliance. Based on the Monte-Carlo method, the model simulates the dynamics of the spread of AIV among wild birds in a natural park following introduction of an infected bird. Data on wild bird species population, apparent AIV prevalence recorded in wild birds during the period of study, and ecological information on factors such as behaviour, contact rates or patterns of movements of waterfowl were incorporated as inputs of the model. From these inputs, the model predicted those species that would introduce most of AIV in different periods and those species and areas that would be at high risk as a consequence of the spread of these AIV incursions. This method can serve as a complementary tool to previous studies to optimize the allocation of the limited AI surveillance resources in a local complex ecosystem. However, this study indicates that in order to predict the evolution of the spread of AIV at the local scale, there is a need for further research on the identification of host factors involved in the interspecies transmission of AIV. [ABSTRACT FROM AUTHOR]