Evaluation of the new modular biogents BG-Pro mosquito trap in comparison to CDC, EVS, BG-Sentinel, and BG-Mosquitaire traps

Mosquito surveillance is an essential component of mosquito control and mosquito traps are a universally employed tool to monitor adult populations. The objective of this paper was to evaluate the new modular Biogents BG-Pro mosquito trap (BGP) and compare its performance to 4 widely used traps for adult mosquitoes: the BG-Sentinel (BGS), the BG Mosquitaire (BGM), the CDC miniature light trap (CDC), and the encephalitis vector survey trap (EVS). One semi-field and 9 field Latin square trials were performed in 7 countries. Results showed that the collection performance of the BGP was equivalent to or exceeded that of the BGS, BGM, CDC, and EVS traps in head-to-head comparisons. The BGP uses 35% less power than the CDC and 75% less than the BGS and BGM. This lower power consumption allows it to run at 5 V for 2 days using a small lightweight 10,000-mAh rechargeable power bank. The BG-Pro is an excellent alternative for the surveillance of mosquito species that are usually monitored with BG-Sentinel, CDC, or EVS traps

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

Additional file 4: Table S3. of Meteorological variables and mosquito monitoring are good predictors for infestation trends of Aedes aegypti, the vector of dengue, chikungunya and Zika

Comparison of AICs of logistic regression models with MFAI as predictor variable in varying time lags. (PDF 83 kb

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

Additional file 6: Figure S2. of Meteorological variables and mosquito monitoring are good predictors for infestation trends of Aedes aegypti, the vector of dengue, chikungunya and Zika

Observed and fitted by model: a gam (Aaefem ~ offset(lNtraps) + s(Tmin4) + s(hum4), family = nb ()). b gam (Aaefem ~ offset(lNtraps) + s(hum4), family = nb ()), c gam (Aaefem ~ offset(lNtraps) + s(Tmin4), family = nb ()). (PDF 275 kb