Canine serological survey and dog culling ant its relationship with human visceral leishmaniasis in an endemic urban area.

BACKGROUND: Visceral leishmaniasis is an important but neglected disease that is spreading and is highly lethal when left untreated. This study sought to measure the Leishmania infantum seroprevalence in dogs, the coverage of its control activities (identification of the canine reservoir by serological survey, dog culling and insecticide spraying) and to evaluate its relationship with the occurrence of the disease in humans in the municipalities of Araçatuba and Birigui, state of São Paulo, Brazil. METHODS: Information from 2006 to 2015 was georeferenced for each municipality and modeling was performed for the two municipalities together. To do this, latent Gaussian Bayesian models with the incorporation of a spatio-temporal structure and Poisson distribution were used. The Besag-York-Mollie models were applied for random spatial effects, as also were autoregressive models of order 1 for random temporal effects. The modeling was performed using the INLA (Integrated Nested Laplace Approximations) deterministic approach, considering both the numbers of cases as well as the coverage paired year by year and lagged at one and two years. RESULTS: Control activity coverage was observed to be generally low. The behavior of the temporal tendency in the human disease presented distinct patterns in the two municipalities, however, in both the tendency was to decline. The canine serological survey presented as a protective factor only in the two-year lag model. CONCLUSIONS: The canine serological coverage, even at low intensity, carried out jointly with the culling of the positive dogs, suggested a decreasing effect on the occurrence of the disease in humans, whose effects would be seen two years after it was carried out.

Bayesian modeling of hematologic cancer and vehicular air pollution among young people in the city of São Paulo, Brazil.

Traffic-related air pollution is being associated with hematologic cancer in young individuals. This study performed a spatial analysis of the hematologic cancer incidence and mortality among younger people, using a Bayesian approach, to associate with traffic density in the city of São Paulo, Brazil. Two databases were employed: incidence (2002-2011) and mortality (2002-2013). The relationships between the cases of hematologic cancer and the covariates - traffic density, the Municipal Human Development Index (MHDI), and population density - were evaluated using a Besag-York-Mollié ecological model with relative risks (RRs) estimates. Per 1-unit standard-deviation increase in traffic density, in the MHDI, and in population density, the RR for the incidence was 1.06 (95% CI: 0.97-1.14), 1.28 (95% CI: 1.16-1.42), and 1.01 (95% CI: 0.94-1.08), respectively. For mortality, no covariates were considered risk factors. Our findings suggest significant association between living in regions with better socioeconomic conditions, where traffic density is usually higher, and risk of hematologic cancer in younger people.

Seroprevalence for dengue virus in a hyperendemic area and associated socioeconomic and demographic factors using a cross-sectional design and a geostatistical approach, state of São Paulo, Brazil.

BACKGROUND: São José do Rio Preto is one of the cities of the state of São Paulo, Brazil, that is hyperendemic for dengue, with the presence of the four dengue serotypes. OBJECTIVES: to calculate dengue seroprevalence in a neighbourhood of São José do Rio Preto and identify if socioeconomic and demographic covariates are associated with dengue seropositivity. METHODS: A cohort study to evaluate dengue seroprevalence and incidence and associated factors on people aged 10 years or older, was assembled in Vila Toninho neighbourhood, São José do Rio Preto. The participant enrolment occurred from October 2015 to March 2016 (the first wave of the cohort study), when blood samples were collected for serological test (ELISA IgG anti-DENV) and questionnaires were administrated on socio-demographic variables. We evaluated the data collected in this first wave using a cross-sectional design. We considered seropositive the participants that were positive in the serological test (seronegative otherwise). We modelled the seroprevalence with a logistic regression in a geostatistical approach. The Bayesian inference was made using integrated nested Laplace approximations (INLA) coupled with the Stochastic Partial Differential Equation method (SPDE). RESULTS: We found 986 seropositive individuals for DENV in 1322 individuals surveyed in the study area in the first wave of the cohort study, corresponding to a seroprevalence of 74.6% (95%CI: 72.2-76.9). Between the population that said never had dengue fever, 68.4% (566/828) were dengue seropositive. Older people, non-white and living in a house (instead of in an apartment), were positively associated with dengue seropositivity. We adjusted for the other socioeconomic and demographic covariates, and accounted for residual spatial dependence between observations, which was found to present up to 800 m. CONCLUSIONS: Only one in four people aged 10 years or older did not have contact with any of the serotypes of dengue virus in Vila Toninho neighbourhood in São José do Rio Preto. Age, race and type of house were associated with the occurrence of the disease. The use of INLA in a geostatistical approach in a Bayesian context allowed us to take into account the spatial dependence between the observations and identify the associated covariates to dengue seroprevalence.

Incidence and mortality for respiratory cancer and traffic-related air pollution in São Paulo, Brazil.

BACKGROUND: Multiple lines of evidence have associated exposure to ambient air pollution with an increased risk of respiratory malignancies. However, there is a dearth of evidence from low-middle income countries, including those within South America, where the social inequalities are more marked. OBJECTIVES: To quantify the association between exposures to traffic related air pollution and respiratory cancer incidence and mortality within São Paulo, Brazil. Further, we aim to investigate the role of socioeconomic status (SES) upon these outcomes. METHODS: Cancer incidence between 2002 and 2011 was derived from the population-based cancer registry. Mortality data (between 2002 and 2013) was derived from the Municipal Health Department. A traffic density database and an annual nitrogen dioxide (NO2) land use regression model were used as markers of exposure. Age-adjusted Binomial Negative Regression models were developed, stratifying by SES and gender. RESULTS: We observed an increased rate of respiratory cancer incidence and mortality in association with increased traffic density and NO2 concentrations, which was higher among those regions with the lowest SES. For cancer mortality and traffic exposure, those in the most deprived region, had an incidence rate ratio (IRR) of 2.19 (95% CI: 1.70, 2.82) when comparing the highest exposure centile (top 90%) to the lowest (lowest 25%). By contrast, in the least deprived area, the IRR for the same exposure contrast was.1.07 (95% CI: 0.95, 1.20). For NO2 in the most deprived regions, the IRR for cancer mortality in the highest exposed group was 1.44 (95% CI: 1.10, 1.88) while in the least deprived area, the IRR for the highest exposed group was 1.11 (95% CI: 1.01, 1.23). CONCLUSIONS: Traffic density and NO2 were associated with an increased rate of respiratory cancer incidence and mortality in São Paulo. Residents from poor regions may suffer more from the impact of traffic air pollution.

Canine visceral leishmaniasis in Araçatuba, state of São Paulo, Brazil, and its relationship with characteristics of dogs and their owners: a cross-sectional and spatial analysis using a geostatistical approach.

BACKGROUND: The incidence of visceral leishmaniasis (VL), one of the most important neglected diseases worldwide, is increasing in Brazil. The objectives of this study were to determine the canine VL (CanL) seroprevalence in an urban area of Araçatuba municipality and to evaluate its relationship with the characteristics of dogs and their owners. RESULTS: The CanL seroprevalence in the study area was 0.081 (95% credible interval [CI]: 0.068-0.096). The following covariates/categories were positively associated with the occurrence of a seropositive dog: more than 10 dogs that had lived in the house (odds ratio [OR] = 2.36; 95% CI: 1.03-5.43) (baseline: 0-10 dogs); house with dogs that previously died of VL (OR = 4.85; 95% CI: 2.65-8.86) or died of causes other than old age (OR = 2.26; 95% CI: 1.12-4.46) (baseline: natural or no deaths); dogs that spent the day in a sheltered backyard (OR = 2.14; 95% CI: 1.05-4.40); dogs that spent the day in an unsheltered backyard or the street (OR = 2.67; 95% CI: 1.28-5.57) (baseline: inside home). Spatial dependence among observations occurred within about 45.7 m. CONCLUSIONS: The number of dogs that had lived in the house, previous deaths by VL or other cause, and the place the dog stayed during the day were associated with the occurrence of a VL seropositive dog. The short-distance spatial dependence could be related to the vector characteristics, producing a local neighbourhood VL transmission pattern. The geostatistical approach in a Bayesian context using integrated nested Laplace approximation (INLA) allowed to identify the covariates associated with VL, including its spatially dependent transmission pattern.

Spatial and temporal epidemiology of malaria in extra-Amazonian regions of Brazil.

BACKGROUND: Mosquitoes, Plasmodium parasites, and humans live in sympatry in some extra-Amazonian regions of Brazil. Recent migrations of people from Amazonia and other countries to extra-Amazonian regions have led to many malaria outbreaks. Lack of relevant expertise among health professionals in non-endemic areas can lead to a neglect of the disease, which can be dangerous given its high fatality rate. Therefore, understanding the spatial and temporal epidemiology of malaria is essential for developing strategies for disease control and elimination. This study aimed to characterize imported (IMP) and autochthonous/introduced (AU/IN) cases in the extra-Amazonian regions and identify risk areas and groups. METHODS: Epidemiological data collected between 2007 and 2014 were obtained from the Notifiable Diseases Information System of the Ministry of Health (SINAN) and from the Department of the Unified Health System (DATASUS). High malaria risk areas were determined using the Local Indicator of Spatial Association. IMP and AU/IN malaria incidence rates were corrected by Local Empirical Bayesian rates. RESULTS: A total of 6092 malaria cases (IMP: 5416, 88.9 %; AU/IN: 676, 11.1 %) was recorded in the extra-Amazonian regions in 2007-2014. The highest numbers of IMP and AU/IN cases were registered in 2007 (n = 862) and 2010 (n = 149), respectively. IMP cases were more frequent than AU/IN cases in all states except for Espírito Santo. Piauí, Espírito Santo, and Paraná states had high incidences of AU/IN malaria. The majority of infections were by Plasmodium falciparum in northeast and southeast regions, while Plasmodium vivax was the predominant species in the south and mid-west showed cases of dual infection. AU/IN malaria cases were concentrated in the coastal region of Brazil, which contains the Atlantic Forest and hosts the Anopheles transmitters. Several malaria clusters were also associated with the Brazilian Pantanal biome and regions bordering the Amazonian biome. CONCLUSION: Malaria is widespread outside the Amazonian region of Brazil, including in more urbanized and industrialized states. This fact is concerning because these highly populated areas retain favourable conditions for spreading of the parasites and vectors. Control measures for both IMP and AU/IN malaria are essential in these high-risk areas.

Socio-economic and environmental effects influencing the development of leprosy in Bahia, north-eastern Brazil.

OBJECTIVES: To investigate spatial clusters and possible associations between relative risks of leprosy with socio-economic and environmental factors, taking into account diagnosed cases in children under 15 years old. METHODS: An ecological study was conceived using data aggregated by municipality to identify possible spatial clusters of leprosy from 2005 to 2011. Relative risks were calculated accounting for the respective covariate gender. The second stage of the analysis consisted of verifying possible associations between the relative risks of leprosy as a dependent variable, and socio-economic and environmental variables as independent. This was performed using a multivariate regression analysis according to a previously defined conceptual framework. RESULTS: Overall rates have decreased from 0.88/10 000 in 2005 to 0.52 in 2011. Spatial scan statistics identified 4 high-risk and 6 low-risk clusters. In the regression model, after allowing for spatial dependence, relative risks were associated with higher percentage of water bodies, higher Gini index, higher percentage of urban population, larger average number of dwellers by permanent residence and smaller percentage of residents born in Bahia. CONCLUSIONS: Although relative risks of leprosy in Bahia have been decreasing, they remain very high. The association between relative risks of leprosy and water bodies in the proposed geographic scale indicates that hypothesis linking M. leprae and humid environments cannot be discarded. Socio-economic conditions such as inequality, a greater number of dwellers by residence and migration are derived from the urbanisation process carried out in this State. Precarious settlements and poor living conditions in the cities would favour the continuity of leprosy transmission.

Effects of the El Niño-Southern Oscillation and seasonal weather conditions on Aedes aegypti infestation in the State of São Paulo (Brazil): A Bayesian spatio-temporal study.

BACKGROUND: Seasonal fluctuations in weather are recognized as factors that affect both Aedes (Ae.) aegypti mosquitoes and the diseases they carry, such as dengue fever. The El Niño-Southern Oscillation (ENSO) is widely regarded as one of the most impactful atmospheric phenomena on Earth, characterized by the interplay of shifting ocean temperatures, trade wind intensity, and atmospheric pressure, resulting in extensive alterations in climate conditions. In this study, we investigate the influence of ENSO and local weather conditions on the spatio-temporal variability of Ae. aegypti infestation index. METHODS: We collected seasonal entomological survey data of immature forms of Ae. aegypti mosquitoes (Breteau index), as well as data on temperature, rainfall and the Oceanic Niño Index (ONI) for the period 2008-2018 over the 645 municipalities of the subtropical State of São Paulo (Brazil). We grounded our analytical approach on a Bayesian framework and we used a hierarchical spatio-temporal model to study the relationship between ENSO tracked by ONI, seasonal weather fluctuations and the larval index, while adjusting for population density and wealth inequalities. RESULTS: Our results showed a relevant positive effect for El Niño on the Ae. aegypti larval index. In particular, we found that the number of positive containers would be expected to increase by 1.30-unit (95% Credible Intervals (CI): 1.23 to 1.37) with El Niño events (i.e., ≥ 1°C, moderate to strong) respect to neutral (and weak) events. We also found that seasonal rainfall exceeding 153.12 mm appears to have a notable impact on vector index, leading potentially to the accumulation of ample water in outdoor discarded receptacles, supporting the aquatic phase of mosquito development. Additionally, seasonal temperature above 23.30°C was found positively associated to the larval index. Although the State of São Paulo as a whole has characteristics favourable to proliferation of the vector, there were specific areas with a greater tendency for mosquito infestation, since the most vulnerable areas are predominantly situated in the central and northern regions of the state, with hot spots of abundance in the south, especially during El Niño events. Our findings also indicate that social disparities present in the municipalities contributes to Ae. aegypti proliferation. CONCLUSIONS: Considering the anticipated rise in both the frequency and intensity of El Niño events in the forthcoming decades as a consequence of climate change, the urgency to enhance our ability to track and diminish arbovirus outbreaks is crucial.

Socioeconomic factors increase the risk of teenage pregnancy: spatial and temporal analysis in a Brazilian municipality.

OBJECTIVE: To evaluate the distribution of the proportion of teenage mothers (PTM) in time and space and its relationship with socioeconomic indicators and social vulnerability. METHODS: An ecological study was carried out with teenage mothers living in 322 census tracts in Foz do Iguaçu (state of Paraná, Brazil) between 2013 and 2019. Spatial clusters of teenage mothers were identified by spatial scanning and grouped into strata with different prevalence. The association between these strata and the individual social vulnerability of the mothers was evaluated using the Pearson's Chi-square test. Linear regression models were adjusted to evaluate the association between PTM and socioeconomic factors by census tract and temporal trend in PTM in different strata. RESULTS: We identified five high prevalence clusters in peripheral regions and six with low prevalence in the central region of the municipality. Proportionally, there were more teenage mothers with a worse vulnerability index in the high prevalence stratum than in the low prevalence stratum. Places with worse socioeconomic conditions present higher PTM, a profile that did not change over time. For the increase of one unit in the Brazilian Deprivation Index and proportion of women responsible for the household, the PTM increased, respectively, by 3.8 (95%CI 3.1-4.4) and 0.086% (95%CI 0.03-0.14). There was a reduction in the global PTM in part of the period, which occurred later in the higher prevalence strata, but the proportions were stable again in the last years of study. CONCLUSION: Teenage pregnancy is concentrated in regions with worse socioeconomic conditions and greater maternal vulnerability and its behavior over time occurred differently in these areas.

Spatiotemporal Bayesian modeling of the risk of congenital syphilis in São Paulo, SP, Brazil.

The aim of this study is to analyze the spatiotemporal risk of congenital syphilis (CS) in high-prevalence areas in the city of São Paulo, SP, Brazil, and to evaluate its relationship with socioeconomic, demographic, and environmental variables. An ecological study was conducted based on secondary CS data with spatiotemporal components collected from 310 areas between 2010 and 2016. The data were modeled in a Bayesian context using the integrated nested Laplace approximation (INLA) method. Risk maps showed an increasing CS trend over time and highlighted the areas that presented the highest and lowest risk in each year. The model showed that the factors positively associated with a higher risk of CS were the Gini index and the proportion of women aged 18-24 years without education or with incomplete primary education, while the factors negatively associated were the proportion of women of childbearing age and the mean per capita income.

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.

Human movement and environmental barriers shape the emergence of dengue.

Understanding how emerging infectious diseases spread within and between countries is essential to contain future pandemics. Spread to new areas requires connectivity between one or more sources and a suitable local environment, but how these two factors interact at different stages of disease emergence remains largely unknown. Further, no analytical framework exists to examine their roles. Here we develop a dynamic modelling approach for infectious diseases that explicitly models both connectivity via human movement and environmental suitability interactions. We apply it to better understand recently observed (1995-2019) patterns as well as predict past unobserved (1983-2000) and future (2020-2039) spread of dengue in Mexico and Brazil. We find that these models can accurately reconstruct long-term spread pathways, determine historical origins, and identify specific routes of invasion. We find early dengue invasion is more heavily influenced by environmental factors, resulting in patchy non-contiguous spread, while short and long-distance connectivity becomes more important in later stages. Our results have immediate practical applications for forecasting and containing the spread of dengue and emergence of new serotypes. Given current and future trends in human mobility, climate, and zoonotic spillover, understanding the interplay between connectivity and environmental suitability will be increasingly necessary to contain emerging and re-emerging pathogens.

Spatial Clusters of Cancer Mortality in Brazil: A Machine Learning Modeling Approach.

Objectives: Our aim was to test if machine learning algorithms can predict cancer mortality (CM) at an ecological level and use these results to identify statistically significant spatial clusters of excess cancer mortality (eCM). Methods: Age-standardized CM was extracted from the official databases of Brazil. Predictive features included sociodemographic and health coverage variables. Machine learning algorithms were selected and trained with 70% of the data, and the performance was tested with the remaining 30%. Clusters of eCM were identified using SatScan. Additionally, separate analyses were performed for the 10 most frequent cancer types. Results: The gradient boosting trees algorithm presented the highest coefficient of determination (R 2 = 0.66). For total cancer, all algorithms overlapped in the region of Bagé (27% eCM). For esophageal cancer, all algorithms overlapped in west Rio Grande do Sul (48%-96% eCM). The most significant cluster for stomach cancer was in Macapá (82% eCM). The most important variables were the percentage of the white population and residents with computers. Conclusion: We found consistent and well-defined geographic regions in Brazil with significantly higher than expected cancer mortality.

Zika, chikungunya and co-occurrence in Brazil: space-time clusters and associated environmental-socioeconomic factors.

Chikungunya and Zika have been neglected as emerging diseases. This study aimed to analyze the space-time patterns of their occurrence and co-occurrence and their associated environmental and socioeconomic factors. Univariate (individually) and multivariate (co-occurrence) scans were analyzed for 608,388 and 162,992 cases of chikungunya and Zika, respectively. These occurred more frequently in the summer and autumn. The clusters with the highest risk were initially located in the northeast, dispersed to the central-west and coastal areas of São Paulo and Rio de Janeiro (2018-2021), and then increased in the northeast (2019-2021). Chikungunya and Zika demonstrated decreasing trends of 13% and 40%, respectively, whereas clusters showed an increasing trend of 85% and 57%, respectively. Clusters with a high co-occurrence risk have been identified in some regions of Brazil. High temperatures are associated with areas at a greater risk of these diseases. Chikungunya was associated with low precipitation levels, more urbanized environments, and places with greater social inequalities, whereas Zika was associated with high precipitation levels and low sewage network coverage. In conclusion, to optimize the surveillance and control of chikungunya and Zika, this study's results revealed high-risk areas with increasing trends and priority months and the role of socioeconomic and environmental factors.

Covid-19 hospital mortality using spatial hierarchical models: cohort design with 74,994 registers.

OBJECTIVE: To investigate the relationship between covid-19 hospital mortality and risk factors, innovating by considering contextual and individual factors and spatial dependency and using data from the city of São Paulo, Brazil. METHODS: The study was performed with a spatial hierarchical retrospective cohort design using secondary data (individuals and contextual data) from hospitalized patients and their geographic unit residences. The study period corresponded to the first year of the pandemic, from February 25, 2020 to February 24, 2021. Mortality was modeled with the Bayesian context, Bernoulli probability distribution, and the integrated nested Laplace approximations. The demographic, distal, medial, and proximal covariates were considered. RESULTS: We found that per capita income, a contextual covariate, was a protective factor (odds ratio: 0.76 [95% credible interval: 0.74-0.78]). After adjusting for income, the other adjustments revealed no differences in spatial dependence. Without income inequality in São Paulo, the spatial risk of death would be close to one in the city. Other factors associated with high covid-19 hospital mortality were male sex, advanced age, comorbidities, ventilation, treatment in public healthcare settings, and experiencing the first covid-19 symptoms between January 24 and February 24, 2021. CONCLUSIONS: Other than sex and age differences, geographic income inequality was the main factor responsible for the spatial differences in the risk of covid-19 hospital mortality. Investing in public policies to reduce socioeconomic inequities, infection prevention, and other intersectoral measures should focus on lower per capita income, to control covid-19 hospital mortality.

Spatiotemporal bayesian modelling of scorpionism and its risk factors in the state of São Paulo, Brazil.

BACKGROUND: Scorpion stings in Brazil represent a major public health problem due to their incidence and their potential ability to lead to severe and often fatal clinical outcomes. A better understanding of scorpionism determinants is essential for a precise comprehension of accident dynamics and to guide public policy. Our study is the first to model the spatio-temporal variability of scorpionism across municipalities in São Paulo (SP) and to investigate its relationship with demographic, socioeconomic, environmental, and climatic variables. METHODOLOGY: This ecological study analyzed secondary data on scorpion envenomation in SP from 2008 to 2021, using the Integrated Nested Laplace Approximation (INLA) to perform Bayesian inference for detection of areas and periods with the most suitable conditions for scorpionism. PRINCIPAL FINDINGS: From the spring of 2008 to 2021, the relative risk (RR) increased eight times in SP, from 0.47 (95%CI 0.43-0.51) to 3.57 (95%CI 3.36-3.78), although there has been an apparent stabilization since 2019. The western, northern, and northwestern parts of SP showed higher risks; overall, there was a 13% decrease in scorpionism during winters. Among the covariates considered, an increase of one standard deviation in the Gini index, which captures income inequality, was associated with a 11% increase in scorpion envenomation. Maximum temperatures were also associated with scorpionism, with risks doubling for temperatures above 36°C. Relative humidity displayed a nonlinear association, with a 50% increase in risk for 30-32% humidity and reached a minimum of 0.63 RR for 75-76% humidity. CONCLUSIONS: Higher temperatures, lower humidity, and social inequalities were associated with a higher risk of scorpionism in SP municipalities. By capturing local and temporal relationships across space and time, authorities can design more effective strategies that adhere to local and temporal considerations.

Physical Attributes of Tree Holes in the Atlantic Forest Edges: Evaluating Their Association with the Presence and Abundance of Immature Haemagogus leucocelaenus.

Sylvatic yellow fever (SYF) was recently a health issue in Brazil (2016-2019) because transmission was facilitated by a high density of vectors, amplifying hosts, and low vaccine coverage of the human population, especially in urban forests in the Southeast Region of Brazil. Moreover, urban forest edges are more likely to have contact between human and sylvatic vector mosquito populations. Here, we show the association between abiotic and biotic features of tree holes as Haemagogus leucocelaenus rearing sites in Cantareira State Park in Atlantic Forest edges. The analyzed physical features of the tree holes were diameter at breast height, tree hole opening diameter, depth, trunk diameter, tree hole volume, collected volume, height (varying from 0.02 to 4.2 m above ground), and the presence of Culicidae species other than Hg. leucocelaenus. We analyzed 105 positive and 68 negative water samples for larval presence and found no differences between them, suggesting the lack of specific physical characteristics in these categories. Hg. leucocelaenus larval abundance was correlated with the collected volume and opening diameter of tree holes. The tree species that most represented negative breeding sites were Euplassa cantareirae, Guarea macrophylla, Psychotria suterella, and Tibouchina pulchra. Four significant clusters as areas with a high risk of SYV were identified by Get-Ordis spatial analysis. Although Hg. leucocelaenus larvae were found in tree holes with high water levels, their occurrence was regulated by that of other mosquito species. Our findings contribute to clarifying immature vector ecology in tree holes related to human exposure to SYF in urban forest edges.

First identification of Culex flavivirus (Flaviviridae) in Brazil.

INTRODUCTION: Culex flavivirus (CxFV) was first isolated in 2007 from Culex pipiens in Japan and then identified in several other countries. Characterization of the CxFV showed that all strains are related to the cell fusing agent virus. In this manuscript we report the first identification of CxFV in South America. MATERIAL AND METHODS: We have collected Culex sp. mosquitoes using BG-Sentinel traps and manual aspirators. They were pooled according to genus, species, sex and location. Viral RNA was extracted and multiplex nested PCR was performed to test the presence of Flavivirus. The positive samples were isolated in C6/36 cells and sequenced for phylogenetic analyses. RESULTS: 265 female Culex mosquitoes pooled in 83 pools were tested with specific CxFV, Saint Louis encephalitis virus (SLEV) and West Nile virus (WNV) primers. Our sequence data indicated maximum sequence similarity of 97% with CxFV. DISCUSSION: In this study we report the circulation of CxFV in an urban setting where SLEV had previously caused an outbreak. In terms of public health, this is an important finding due to the assumption that the previous exposition of mosquitoes to CxFV might lessen the susceptibility of these mosquitoes to other flaviviruses.

Control methods for Aedes aegypti: Have we lost the battle?

The past twenty years have been a period of remarkable innovation in Aedes vector control, and several methods with varying success rates have been used. Here we discussed the main intervention categories of vector control applied nowadays and their main weaknesses. It is urgent to have more efficient design and management of control programs, and the requirement for studies to evaluate and compare methods must be prioritised. The world must better articulate actions and cooperate with other sectors beyond health; it is necessary to work together with managers and entomologists in action plans and adapt them to the condition of each region.

Impact of climate change on West Nile virus distribution in South America.

BACKGROUND: West Nile virus (WNV) is a vector-borne pathogen of global relevance and is currently the most widely distributed flavivirus causing encephalitis worldwide. Climate conditions have direct and indirect impacts on vector abundance and virus dynamics within the mosquito. The significance of environmental variables as drivers in WNV epidemiology is increasing under the current climate change scenario. In this study we used a machine learning algorithm to model WNV distributions in South America. METHODS: Our model evaluated eight environmental variables for their contribution to the occurrence of WNV since its introduction in South America in 2004. RESULTS: Our results showed that environmental variables can directly alter the occurrence of WNV, with lower precipitation and higher temperatures associated with increased virus incidence. High-risk areas may be modified in the coming years, becoming more evident with high greenhouse gas emission levels. Countries such as Bolivia, Paraguay and several Brazilian areas, mainly in the northeast and midwest regions and the Pantanal biome, will be greatly affected, drastically changing the current WNV distribution. CONCLUSIONS: Understanding the linkages between climatological and ecological change as determinants of disease emergence and redistribution will help optimize preventive strategies. Increased virus surveillance, integrated modelling and the use of geographically based data systems will provide more anticipatory measures by the scientific community.