Of all countries in the Western Hemisphere, Brazil has the highest economic losses caused by dengue fever. We evaluated the cost-effectiveness of a novel system of vector surveillance and control, Monitoramento Inteligente da Dengue (Intelligent Dengue Monitoring System [MID]), which was implemented in 21 cities in Minas Gerais, Brazil. Traps for adult female mosquitoes were spaced at 300-m intervals throughout each city. In cities that used MID, vector control was conducted specifically at high-risk sites (indicated through daily updates by MID). In control cities, vector control proceeded according to guidelines of the Brazilian government. We estimated that MID prevented 27,191 cases of dengue fever and saved an average of $227 (median $58) per case prevented, which saved approximately $364,517 in direct costs (health care and vector control) and $7,138,940 in lost wages (societal effect) annually. MID was more effective in cities with stronger economies and more cost-effective in cities with higher levels of mosquito infestation.
BackgroundInfectious diseases are a leading threat to public health. Accurate and timely monitoring of disease risk and progress can reduce their impact. Mentioning a disease in social networks is correlated with physician visits by patients, and can be used to estimate disease activity. Dengue is the fastest growing mosquito-borne viral disease, with an estimated annual incidence of 390 million infections, of which 96 million manifest clinically. Dengue burden is likely to increase in the future owing to trends toward increased urbanization, scarce water supplies and, possibly, environmental change. The epidemiological dynamic of Dengue is complex and difficult to predict, partly due to costly and slow surveillance systems.Methodology / Principal findingsIn this study, we aimed to quantitatively assess the usefulness of data acquired by Twitter for the early detection and monitoring of Dengue epidemics, both at country and city level at a weekly basis. Here, we evaluated and demonstrated the potential of tweets modeling for Dengue estimation and forecast, in comparison with other available web-based data, Google Trends and Wikipedia access logs. Also, we studied the factors that might influence the goodness-of-fit of the model. We built a simple model based on tweets that was able to ‘nowcast’, i.e. estimate disease numbers in the same week, but also ‘forecast’ disease in future weeks. At the country level, tweets are strongly associated with Dengue cases, and can estimate present and future Dengue cases until 8 weeks in advance. At city level, tweets are also useful for estimating Dengue activity. Our model can be applied successfully to small and less developed cities, suggesting a robust construction, even though it may be influenced by the incidence of the disease, the activity of Twitter locally, and social factors, including human development index and internet access.ConclusionsTweets association with Dengue cases is valuable to assist traditional Dengue surveillance at real-time and low-cost. Tweets are able to successfully nowcast, i.e. estimate Dengue in the present week, but also forecast, i.e. predict Dengue at until 8 weeks in the future, both at country and city level with high estimation capacity.
The ongoing geographical expansion of dengue is inducing an epidemiological transition in many previously transmission-free urban areas, which are now prone to annual epidemics. To analyze the spatiotemporal dynamics of dengue in these settings, we reconstruct transmission chains in Porto Alegre, Brazil, by applying a Bayesian inference model to geo-located dengue cases from 2013 to 2016. We found that transmission clusters expand by linearly increasing their diameter with time, at an average rate of about 600 m month−1. The majority (70.4%, 95% CI: 58.2–79.8%) of individual transmission events occur within a distance of 500 m. Cluster diameter, duration, and epidemic size are proportionally smaller when control interventions were more timely and intense. The results suggest that a large proportion of cases are transmitted via short-distance human movement (<1 km) and a limited contribution of long distance commuting within the city. These results can assist the design of control policies, including insecticide spraying and strategies for active case finding.
Background Aedes aegypti is an important vector for arboviroses and widely distributed throughout the world. Climatic factors can influence vector population dynamics and, consequently, disease transmission. The aim of this study was to characterize the temporal dynamics of an Ae. aegypti population and dengue cases and to investigate the relationship between meteorological variables and mosquito infestation.MethodsWe monitored and analyzed the adult female Ae. aegypti population, the dengue-fever vector, in Porto Alegre, a subtropical city in Brazil using the MI-Dengue system (intelligent dengue monitoring). This system uses sticky traps to monitor weekly infestation indices. We fitted generalized additive models (GAM) with climate variables including precipitation, temperature and humidity, and a GAM that additionally included mosquito abundance in the previous week as an explanatory variable. Logistic regression was used to evaluate the effect of adult mosquito infestation on the probability of dengue occurrence.ResultsAdult mosquito abundance was strongly seasonal, with low infestation indices during the winters and high infestation during the summers. Weekly minimum temperatures above 18 °C were strongly associated with increased mosquito abundance, whereas humidity above 75% had a negative effect on abundance. The GAM model that included adult mosquito infestation in the previous week adjusted and predicted the observed data much better than the model which included only meteorological predictor variables. Dengue was also seasonal and 98% of all cases occurred at times of high adult Ae. aegypti infestation. The probability of dengue occurrence increased by 25%, when the mean number of adult mosquitos caught by monitoring traps increased by 0.1 mosquitoes per week.ConclusionsThe results suggest that continuous monitoring of dengue vector population allows for more reliable predictions of infestation indices. The adult mosquito infestation index was a good predictor of dengue occurrence. Weekly adult dengue vector monitoring is a helpful dengue control strategy in subtropical Brazilian cities.Electronic supplementary materialThe online version of this article (doi:10.1186/s13071-017-2025-8) contains supplementary material, which is available to authorized users.
BackgroundVector control remains the primary defense against dengue fever. Its success relies on the assumption that vector density is related to disease transmission. Two operational issues include the amount by which mosquito density should be reduced to minimize transmission and the spatio-temporal allotment of resources needed to reduce mosquito density in a cost-effective manner. Recently, a novel technology, MI-Dengue, was implemented city-wide in several Brazilian cities to provide real-time mosquito surveillance data for spatial prioritization of vector control resources. We sought to understand the role of city-wide mosquito density data in predicting disease incidence in order to provide guidance for prioritization of vector control work.MethodsWe used hierarchical Bayesian regression modeling to examine the role of city-wide vector surveillance data in predicting human cases of dengue fever in space and time. We used four years of weekly surveillance data from Vitoria city, Brazil, to identify the best model structure. We tested effects of vector density, lagged case data and spatial connectivity. We investigated the generality of the best model using an additional year of data from Vitoria and two years of data from other Brazilian cities: Governador Valadares and Sete Lagoas.ResultsWe found that city-wide, neighborhood-level averages of household vector density were a poor predictor of dengue-fever cases in the absence of accounting for interactions with human cases. Effects of city-wide spatial patterns were stronger than within-neighborhood or nearest-neighborhood effects. Readily available proxies of spatial relationships between human cases, such as economic status, population density or between-neighborhood roadway distance, did not explain spatial patterns in cases better than unweighted global effects.ConclusionsFor spatial prioritization of vector controls, city-wide spatial effects should be given more weight than within-neighborhood or nearest-neighborhood connections, in order to minimize city-wide cases of dengue fever. More research is needed to determine which data could best inform city-wide connectivity. Once these data become available, MI-dengue may be even more effective if vector control is spatially prioritized by considering city-wide connectivity between cases together with information on the location of mosquito density and infected mosquitos.Electronic supplementary materialThe online version of this article (doi:10.1186/s13071-015-0659-y) contains supplementary material, which is available to authorized users.
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