Temporal abundance of Aedes aegypti in Manaus, Brazil, measured by two trap types for adult mosquitoes

A longitudinal study was conducted in Manaus, Brazil, to monitor changes of adult Aedes aegypti (L.) abundance. The objectives were to compare mosquito collections of two trap types, to characterise temporal changes of the mosquito population, to investigate the influence of meteorological variables on mosquito collections and to analyse the association between mosquito collections and dengue incidence. Mosquito monitoring was performed fortnightly using MosquiTRAPs (MQT) and BG-Sentinel (BGS) traps between December 2008-June 2010. The two traps revealed opposing temporal infestation patterns, with highest mosquito collections of MQTs during the dry season and highest collections of BGS during the rainy seasons. Several meteorological variables were significant predictors of mosquito collections in the BGS. The best predictor was the relative humidity, lagged two weeks (in a positive relationship). For MQT, only the number of rainy days in the previous week was significant (in a negative relationship). The correlation between monthly dengue incidence and mosquito abundance in BGS and MQT was moderately positive and negative, respectively. Catches of BGS traps reflected better the dynamic of dengue incidence. The findings help to understand the effects of meteorological variables on mosquito infestation indices of two different traps for adult dengue vectors in Manaus

Dengue prediction by the web: Tweets are a useful tool for estimating and forecasting Dengue at country and city level

<div><p>Background</p><p>Infectious 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.</p><p>Methodology / Principal findings</p><p>In 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.</p><p>Conclusions</p><p>Tweets association with Dengue cases is valuable to assist traditional Dengue surveillance at real-time and low-cost. Tweets are able to successfully <i>nowcast</i>, i.e. estimate Dengue in the present week, but also <i>forecast</i>, i.e. predict Dengue at until 8 weeks in the future, both at country and city level with high estimation capacity.</p></div

General description of Dengue activity for Twitter dataset in Brazil (Kind et al, 2016).

<p>General description of Dengue activity for Twitter dataset in Brazil (Kind et al, 2016).</p

Dengue estimation capacity by tweets.

<p>Frequency distribution of cities considering the deviance explained result of the model applied to each city.</p

Correlation matrix.

<p>Correlation between different possible explanatory factors and the goodness-of-fit (explained deviance) of the final model. The variables are: population (Pop), gross internal product <i>per capita</i> (GDP), mean human development index (IDHM), human development index for income (IDHMI), human development index for longevity (IDHML), human development index for education (IDHME), coverage of houses with personal computer (PC), coverage of houses with internet access (Net), total Dengue cases (DenTot), total Dengue incidence in cases per 100,000 inhabitants (DenInc), total Dengue cases in logarithmic scale (DenLog), total tweets (TwTot), total tweets incidence in activity per 100,000 inhabitants (TwInc), total tweets in logarithmic scale (TwLog), and deviance explained by the model (Model). Total tweets and Dengue cases were calculated as the sum of occurrences from September, 2012 to October, 2016.</p

Tweets signal can be obtained at city level.

<p>Spatial distribution of evaluated cities in Brazil and their intensity of Dengue-related tweeting activity (A), and their incidence of Dengue cases (B). The data were aggregated from September, 2012 to October, 2016 and presented as cases or activity per 100,000 inhabitants.</p

Forecast analysis.

<p>Capacity of the tweets to predict Dengue up to 8 weeks in advance. The model selected (<a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0005729#pntd.0005729.t002" target="_blank">Table 2</a>) was adjusted to different time lags between tweets and Dengue cases. The lines indicate the model result of Dengue estimated in 1 to 8 weeks in advance of tweets.</p

Heat map indicating the goodness-of-fit of the tweets model at city level.

<p>Deviance explained index resulting from the prediction model is shown. Cities that had too few data to be analyzed by the model are represented with grey circles. Cities with higher indices are mostly clustered at the southeastern region of the country.</p