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Aedes aegypti and Culex sp. is a vector of dengue, filariasis, and malaria in Indonesia. One of the alternative efforts to control the spread of mosquitoes that are safe and harmless to the environment and humans can be done by giving natural ingredients that have the potential as larvicides, namely bitter melon. The purpose of this study was to determine the effect and effectiveness of bitter melon as a natural larvicide for Aedes aegypti and Culex sp. The design of this study was experimental using samples of bitter melon and fourth instar larvae of Aedes aegypti and Culex sp. The treatments in this study included ethanol extract of bitter melon with concentrations of 250 ppm, 500 ppm, 750 ppm and 1000 ppm, abate 1% as a positive control and aquades as a negative control. All treatments were given to the fourth instar larvae of Aedes aegypti Culex sp. each containing 20 larvae. Data analysis in this study was carried out by One-Way ANNOVA test, Independent sample t-test, and probit analysis. The results showed that the lowest-highest percentage of Aedes aegypti larvae mortality ranged from 26% - 85%, while Culex sp. ranged from 17% - 65%. The results of the ANOVA test showed significant differences between the treatment groups (P0.05) between the Aedes aegypti and Culex sp. The results of the probit analysis showed that the LC50 value for Aedes aegypti was 728,789 ppm while Culex sp was 732,272 ppm. The conclusion of this study was that the administration of ethanol extract of bitter melon was more effective on the fourth instar larvae of Aedes aegypti than Culex sp.

Mosquito species are considered important vectors of many diseases in humans, companion animals, and livestock. There is a great need to understand their dynamics and to develop methods for predicting their abundances. However, the population dynamics of mosquitoes are often complex displaying non-linear dynamics and thus, making it difficult to be modeled using linear statistical approaches. In this project, we explored the seasonal population patterns of mosquito populations in a Mediterranean environment in Northern Greece using straightforward machine learning techniques such as Artificial Neural Networks (ANNs). To train, validate and test the network model we have used 2 years weekly counts of adult mosquito data including Culex sp., a major vector of the West Nile virus and related encephalitis diseases. The model training was performed in an open-loop (i.e., parallel series network architecture), including the validation and testing step and later on, after training, it was transformed to a closed-loop for the needs of a multistep-ahead mosquito abundance prediction. Determined by the autocorrelation function, one of the final models is using as inputs one week lagged values of mosquito abundances and was able to capture the adult seasonal mosquito patterns in most cases at acceptable levels. We conclude that ANNs suggest an important candidate for modeling and predicting the seasonal abundance of mosquito data since it is suitable for modeling noisy and incomplete ecological data, with no specific assumptions to be made about the underlying relationships and which are solely determined through data mining. However, we are also looking forward to improving the particular model performance using new data sets since it is of fundamental importance to choose an appropriate training set size and to provide representative coverage of all possible conditions to capture accurately the patterns of ecological time series. Nevertheless, despite the limitations of the current study, this work contributes to knowledge of the seasonal functioning of arthropod vector dynamics and contributes towards the development of decision tools to be used in the preventive management of the transmission cycle of vector-borne diseases.

Simple Summary Arthropod vectors are responsible for transmitting a large number of diseases, and for most, there are still not available effective vaccines. Vector disease control is mostly achieved by a sustained prediction of vector populations to maintain support for surveillance and control activities. Mathematical models may assist in predicting arthropod population dynamics. However, arthropod dynamics, and mosquitoes particularly, due their complex life cycle, often exhibit an abrupt and non-linear occurrence. Therefore, there is a growing interest in describing mosquito population dynamics using new methodologies. In this work, we made an effort to gain insights into the non-linear population dynamics of Culex sp. adults, aiming to introduce straightforward soft-computing techniques based on artificial neural networks (ANNs). We propose two kind of models, one autoregressive, handling temperature as an exogenous driver and population as an endogenous one, and a second based only on the exogenous factor. To the best of our knowledge, this is the first study using recurrent neural networks and the most influential environmental variable for prediction of the WNv vector Culex sp. population dynamics, providing a new framework to be used in arthropod decision-support systems. Abstract A central issue of public health strategies is the availability of decision tools to be used in the preventive management of the transmission cycle of vector-borne diseases. In this work, we present, for the first time, a soft system computing modeling approach using two dynamic artificial neural network (ANNs) models to describe and predict the non-linear incidence and time evolution of a medically important mosquito species, Culex sp., in Northern Greece. The first model is an exogenous non-linear autoregressive recurrent neural network (NARX), which is designed to take as inputs the temperature as an exogenous variable and mosquito abundance as endogenous variable. The second model is a focused time-delay neural network (FTD), which takes into account only the temperature variable as input to provide forecasts of the mosquito abundance as the target variable. Both models behaved well considering the non-linear nature of the adult mosquito abundance data. Although, the NARX model predicted slightly better (R = 0.623) compared to the FTD model (R = 0.534), the advantage of the FTD over the NARX neural network model is that it can be applied in the case where past values of the population system, here mosquito abundance, are not available for their forecasting.

Vector born disease account for about one third of all cases of emerging diseases. Culex sp., particularly, is one of the most important mosquito vectors transmitting important diseases such as the West Nile virus, filariasis and related encephalitis. Because there are no vaccines available the most effectual means to prevent infections from the above diseases, is to target mosquitos to prevent bites and disease transmission. However, to be effective such a strategy, it is important to predict the temporal change in mosquito abundance as well as to study how it is affected by weather conditions. This dissertation is devoted on the development of new methods to predict arthropod vector dynamics and with emphasis on the development of stochastic models and computational methods for predicting Culex sp. abundance in Northern Greece. The current dissertation is divided in three parts. The first part explores the non-trivial associations between Culex sp. mosquito abundance and weather variables using traditional and straightforward novel techniques. The information from the first part was a prerequisite for developing a series of stochastic prediction models based on the most detrimental factors affecting mosquito abundance. In the second part, a series of conventional and conditional stochastic Markov chain models are applied for the first time to predict the non-linear dynamics of Culex sp. adult abundance. In the third part of the dissertation a soft computing approach is introduced to model the population dynamics of Culex sp. and a series of autoregressive artificial neural networks are implied. Finally, the information of the models is extrapolated and a machine learning algorithm is proposed to be used for predicting arthropod vector dynamics having practical implications for public health decision making. Based on the current results there was a high and positive correlation between temperature and mosquito abundance during both observation years (r = 0.6). However, a very poor correlation was observed between rain and weekly mosquito abundances (r = 0.29), as well as between wind speed (r = 0.29), respectively. Additionally, according to the multiple linear regression model the effect of temperature, was significant. The continuous power spectrum of the mosquito abundance counts and mean temperatures depict in most cases similar power for periods which are close to 1 week, indicating the point of the lowest variance of the time series, although appearing on slightly different moments of time. The cross wavelet coherent analysis showed that inter weekly cycles with a period between 2 and 3 weeks between mosquito abundance and temperature were coherent mostly during the first and the last weeks of the season. Hence, the wavelet analysis shows a progressive oscillation in mosquito occurrences with time, which is higher at the start and the end of the season. Moreover, in contrast with standard methods of analysis, wavelets can provide useful insights into the time-resolved oscillation structure of mosquito data and accompanying revealing a non-stationary association with temperature. According to the correlation results a climate-conditioned Markov Chain (CMC) model was developed and applied for the first time to predict the dynamics of vectors of important medical diseases. Temporal changes in mosquito population profiles were generated to simulate the probabilities of a high population impact. The probabilities achieved from the trained model are very near to the observed data and the CMC model satisfactorily describes the temporal evolution of the mosquito population process. In general, our numerical results indicate that it is more likely for the population system to move into a state of high population level, when the former is a state of a low population level than the opponent. Field data on frequencies of successive mosquito population levels, which were not used for the data inferred MC modeling, were assembled to obtain an empirical intensity transition matrix and the observed frequencies. The findings match to a certain degree the empirical results in which the probabilities follow analogous patterns while no significant differences were observed between the transition matrices of the CMC model and the validation data (ChiSq=14.58013, df=24, p=0.9324451). Furter, a soft system computing modeling approach was followed to simulate and predict Culex sp. abundances. Three dynamic artificial neural network (ANNs) models were developed and applied to describe and predict the non-linear incidence and time evolution of a medical important mosquito species Culex sp. in Northern Greece. The first is a simple nonlinear autoregressive ANN model that used lagged population values as inputs, the second is an exogenous non-linear autoregressive recurrent neural network (NARX), which is designed to take as inputs the temperature as exogenous variable and mosquito abundance as endogenous. Finally, the third model is a focused time-delay neural network (FTD), which takes in to account only the temperature variable as input to provide forecasts of the mosquito abundance as target variable. All three models behaved well considering the non-linear nature of the adult mosquito abundance data. However, the NARX model, which takes in to account temperature, showed the best overall modelling performances. Nevertheless, although, the NARX model predicted slight better (R=0.623) compared to the FTD model (R=0.534), the advantage of the FTD over the NARX neural network model is that it can be applied in the case where past values of the population system, here mosquito abundance, are not available for their forecasting. This is very important considering that arthropod vector data are not always available as climatic data. Concluding, the proposed methods for simulating and predicting mosquito dynamics are recommended as viable for modeling vector disease population dynamics in order to make real-time recommendations utile for dynamic health policies decision making. The proposed stochastic models, as well as the current computational and machine learning techniques, of this work provide an accurate abstraction of the arthropod vector population progress observed within the dataset used for their generation. Nevertheless, the current study may consider also as a new entry point into the extensive literature of ecological modelling, medical entomology, as well as in simulating arthropod vector diseases epidemics. From a public health standpoint, the current models have the potential to be integrated into a decision support system allowing health policy makers in their planning to initiate specific management actions against the period of high activity of mosquito adults.

Simple Summary Understanding and predicting vector population and related disease dynamics, is crucial for gaining insight into the abundance and dynamics of arthropod disease vectors, and for the design of effective vector control strategies. Several mathematical and standard epidemiological models have been proposed in studying vector transmitted infectious disease dynamics. However, most models are of deterministic nature and are not able to estimate other relevant metrics such as the probability of vector population emergence as well as the probability and expected time to reach certain population and/or infection state. Here we are focusing on stochastic modeling of mosquito abundance data using weather driven Markov chains (MCs) and are particularly interested in estimating transition probabilities (TPs) between different population levels. A MC model is based on the assumption that the future state of the variable is only dependent on the present state and is suitable in cases of short and noisy data characterized by a complex and random behavior. The aim is to introduce and generalize a formulation of conditional Markov chain models (CMSs) for predicting probability transition estimates of arthropod vector populations. In this context, first we present the basic principles and assumptions behind Markov chain modeling approach, with an intuitive interpretation of the integration of conditional Markov chains (CMCs) and then demonstrate the usefulness of the approach in predicting the abundance of Culex sp. We conclude that the conditional Markov chain technique is recommended as viable for modeling populations that explicit random dynamics and predict their future evolution. Although, the Markov models generated in this work provide an accurate abstraction of the vector disease progress observed within the dataset used for their generation, we envision the current approach as an entry point into the medical entomology literature and methods for predicting arthropod vector diseases dynamics. Understanding and predicting mosquito population dynamics is crucial for gaining insight into the abundance of arthropod disease vectors and for the design of effective vector control strategies. In this work, a climate-conditioned Markov chain (CMC) model was developed and applied for the first time to predict the dynamics of vectors of important medical diseases. Temporal changes in mosquito population profiles were generated to simulate the probabilities of a high population impact. The simulated transition probabilities of the mosquito populations achieved from the trained model are very near to the observed data transitions that have been used to parameterize and validate the model. Thus, the CMC model satisfactorily describes the temporal evolution of the mosquito population process. In general, our numerical results, when temperature is considered as the driver of change, indicate that it is more likely for the population system to move into a state of high population level when the former is a state of a lower population level than the opposite. Field data on frequencies of successive mosquito population levels, which were not used for the data inferred MC modeling, were assembled to obtain an empirical intensity transition matrix and the frequencies observed. Our findings match to a certain degree the empirical results in which the probabilities follow analogous patterns while no significant differences were observed between the transition matrices of the CMC model and the validation data (ChiSq = 14.58013, df = 24, p = 0.9324451). The proposed modeling approach is a valuable eco-epidemiological study. Moreover, compared to traditional Markov chains, the benefit of the current CMC model is that it takes into account the stochastic conditional properties of ecological-related climate variables. The current modeling approach could save costs and time in establishing vector eradication programs and mosquito surveillance programs.

Simple Summary Understanding and predicting vector population and related disease dynamics, is crucial for gaining insight into the abundance and dynamics of arthropod disease vectors, and for the design of effective vector control strategies. Several mathematical and standard epidemiological models have been proposed in studying vector transmitted infectious disease dynamics. However, most models are of deterministic nature and are not able to estimate other relevant metrics such as the probability of vector population emergence as well as the probability and expected time to reach certain population and/or infection state. Here we are focusing on stochastic modeling of mosquito abundance data using weather driven Markov chains (MCs) and are particularly interested in estimating transition probabilities (TPs) between different population levels. A MC model is based on the assumption that the future state of the variable is only dependent on the present state and is suitable in cases of short and noisy data characterized by a complex and random behavior. The aim is to introduce and generalize a formulation of conditional Markov chain models (CMSs) for predicting probability transition estimates of arthropod vector populations. In this context, first we present the basic principles and assumptions behind Markov chain modeling approach, with an intuitive interpretation of the integration of conditional Markov chains (CMCs) and then demonstrate the usefulness of the approach in predicting the abundance of Culex sp. We conclude that the conditional Markov chain technique is recommended as viable for modeling populations that explicit random dynamics and predict their future evolution. Although, the Markov models generated in this work provide an accurate abstraction of the vector disease progress observed within the dataset used for their generation, we envision the current approach as an entry point into the medical entomology literature and methods for predicting arthropod vector diseases dynamics. Abstract Understanding and predicting mosquito population dynamics is crucial for gaining insight into the abundance of arthropod disease vectors and for the design of effective vector control strategies. In this work, a climate-conditioned Markov chain (CMC) model was developed and applied for the first time to predict the dynamics of vectors of important medical diseases. Temporal changes in mosquito population profiles were generated to simulate the probabilities of a high population impact. The simulated transition probabilities of the mosquito populations achieved from the trained model are very near to the observed data transitions that have been used to parameterize and validate the model. Thus, the CMC model satisfactorily describes the temporal evolution of the mosquito population process. In general, our numerical results, when temperature is considered as the driver of change, indicate that it is more likely for the population system to move into a state of high population level when the former is a state of a lower population level than the opposite. Field data on frequencies of successive mosquito population levels, which were not used for the data inferred MC modeling, were assembled to obtain an empirical intensity transition matrix and the frequencies observed. Our findings match to a certain degree the empirical results in which the probabilities follow analogous patterns while no significant differences were observed between the transition matrices of the CMC model and the validation data (ChiSq = 14.58013, df = 24, p = 0.9324451). The proposed modeling approach is a valuable eco-epidemiological study. Moreover, compared to traditional Markov chains, the benefit of the current CMC model is that it takes into account the stochastic conditional properties of ecological-related climate variables. The current modeling approach could save costs and time in establishing vector eradication programs and mosquito surveillance programs.

Simple Summary Arthropod vectors are responsible for transmitting a large number of diseases, and for most, there are still not available effective vaccines. Vector disease control is mostly achieved by a sustained prediction of vector populations to maintain support for surveillance and control activities. Mathematical models may assist in predicting arthropod population dynamics. However, arthropod dynamics, and mosquitoes particularly, due their complex life cycle, often exhibit an abrupt and non-linear occurrence. Therefore, there is a growing interest in describing mosquito population dynamics using new methodologies. In this work, we made an effort to gain insights into the non-linear population dynamics of Culex sp. adults, aiming to introduce straightforward soft-computing techniques based on artificial neural networks (ANNs). We propose two kind of models, one autoregressive, handling temperature as an exogenous driver and population as an endogenous one, and a second based only on the exogenous factor. To the best of our knowledge, this is the first study using recurrent neural networks and the most influential environmental variable for prediction of the WNv vector Culex sp. population dynamics, providing a new framework to be used in arthropod decision-support systems. A central issue of public health strategies is the availability of decision tools to be used in the preventive management of the transmission cycle of vector-borne diseases. In this work, we present, for the first time, a soft system computing modeling approach using two dynamic artificial neural network (ANNs) models to describe and predict the non-linear incidence and time evolution of a medically important mosquito species, Culex sp., in Northern Greece. The first model is an exogenous non-linear autoregressive recurrent neural network (NARX), which is designed to take as inputs the temperature as an exogenous variable and mosquito abundance as endogenous variable. The second model is a focused time-delay neural network (FTD), which takes into account only the temperature variable as input to provide forecasts of the mosquito abundance as the target variable. Both models behaved well considering the non-linear nature of the adult mosquito abundance data. Although, the NARX model predicted slightly better (R = 0.623) compared to the FTD model (R = 0.534), the advantage of the FTD over the NARX neural network model is that it can be applied in the case where past values of the population system, here mosquito abundance, are not available for their forecasting.

Classical insect-specific flaviviruses (cISFs) have been widely detected in different countries in the last decades. Here, we characterize the near full-length genomes of two cISFs detected in mosquitoes collected in the city of Macap&aacute, state of Amap&aacute, Amazon region of Brazil. A total of 105 pools of female mosquitos were analyzed by next-generation sequencing (NGS). Comparative genomics and phylogenetic analysis identified three strains of cell fusing agent virus (CFAV) and two of Culex flavivirus (CxFV). All sequences were obtained from pools of Culex sp., except for one sequence of CFAV detected in a pool of Aedes aegypti. Both CxFV strains are phylogenetically related to a strain isolated in 2012 in the Southeast region of Brazil. The CFAV strains are the first of this species to be identified in Brazil and one of them is highly divergent from other strains of CFAV that have been detected worldwide. In conclusion, CFAV and CxFV, circulate in mosquitoes in Brazil. One strain of CFAV is highly divergent from others previously described, suggesting that a novel strain of CFAV is present in this region.

Background : There are various vector mosquitoes that cause filariasis such as Culex sp. Controlling the number of vectors with larvacide can reduce the spread of this disease. The extracts of Tithonia diversifolia (Hemsley) A. Gray leaves are known to have larvicidal effects on Aedes aegypti larvae. The purpose of this study is to determine the larvicidal effects of Tithonia diversifolia (Hemsley) A. Gray leaf water extract against Culex larvae. Methods : This was an experimental study conducted during the period of October to November 2012 at the Laboratory of Biochemistry, Faculty of Medicine, Universitas Padjadjaran. First trials were conducted to find the most effective larvicidal concentration of Tithonia diversifolia (Hemsley) A. Gray leaf water extract in killing 25 Culex larvae. A concentration of 100 ml was decided as the testing concentration which would be compared to a positive control containing Abate® and a negative control containing distilled water. The number of dead larvae was calculated every hour for 48 hours. Results : Effective larvicidal concentration of Tithonia diversifolia (Hemsley) A. Gray leaf water extract was 60% (p=0.000 compared to negative control), which was lower than Abate®. Result also showed LC50 and LC90 of 24% and 42%, respectively. Conclusions : Tithonia diversifolia (Hemsley) A. Gray leaf water extract has a larvicidal effect on Culex sp. larvae, although it is not as effective as Abate®, Hence, this leaf may be used as an alternative larvacide as these plants are easily available in the community and inexpensive. Keywords : Culex sp., filariasis, larvacide, Tithonia diversifolia (Hemsley) A. Gray DOI : 10.15850/amj.v2n1.441

Lymphatic filariasis is a mosquito borne disease, transmitted by mosquitoes such as Culex , Anopheles , Aedes , Mansonia . Filariasis infection resulting in disability. The important method to avoid being bitten from mosquitoes is by using a repellent. The purpose of this study were to compare repellency duration of citronella oil lotion against Culex sp. to Aedes sp. and to compare its repellency duration to N,N-diethyl-m-toluamide (DEET). Laboratory experimental design was conducted at each of five men as subjects using five randomly treatments: 20%, 40% citronella oil lotion (COL), lotion base (negative control), 12.5% DEET lotion with one-day time lag. Repellency duration, since the forearm was inserted into the cage until first mosquito land, was assessed using female Culex sp. and Aedes sp, according to modification of Fradin and Day method. Repellency duration was analyzed using one-way ANOVA, Tukey HSD followed by paired t-test level of significance with a=0.05 level of significance at p

A study on amplification of Cry1 gene and genome analysis of local isolate of Bacillus thuringiensis TU1 has been done. Amplification was done for cryI gene by PCR-technique. Genome analysis was performed using pulsed-field gel electrophoresis. Insecticidal specificity assay of the isolate to larvae was done in larvae of Heliothis armigera, Plutella xylostella, Aedes aegypti, and Culex sp. Isolate of commercial strain, B. thuringiensis var kurstaki strain HD-7, was used as control for amplification of cryI gene, genome analysis, and insecticidal specificity assay. The result showed that insecticidal specificity assay of the isolates of both TU1 and commercial have similar spectrum of toxicity to the larvae. Amplification of cry1 gene resulted to a fragment of approximately 550 bp in both TU1 and commercial. Genome profiles of TU1 and commercial strain were similar.

The dytiscid beetle Rhantus sikkimensis , Regimbart, 1899 (Coleoptera: Dytiscidae), a member of the freshwater insect communities of the Darjeeling Himalayas, were noted to predate on the coexisting larvae of Chironomus sp. Meigen. Evaluation of predation by R. sikkimensis on Chironomus sp. larvae, in the laboratory, revealed that a single adult morph of R. sikkimensis could kill and consume on an average 10–90 and 10–78 numbers of small and large Chironomus sp. larvae, respectively, per day, depending on the prey density. The attack rate ranged between 520 and 537, and the handling time ranged between 4.3 and 8.6 depending on the size of the preys. The predation varied with respect to predator density also, with a maximum of 151 larvae killed by three predators per day. Two indices of predation, ingestion rate (IR) ranging between 13.33 and 74.15 larvae/day/predator and clearance rate (CR) ranging between 19.67 and 39.99 L prey/day/predator, varied with the prey size and predator density, significantly, when the predation was observed for 9 consecutive days, at two predator densities. It was also noted that R. sikkimensis predated on an average 9.8 larvae of Chironomus sp. and 1 larva of Culex sp., when the larvae of both the species are present together as preys, showing a preference for the Chironomus sp. larvae.

Desenvolveu-se neste estudo uma comparação entre larvicidas químicos e biológicos usados em programas de controle de mosquitos no Rio Grande do Sul. Em bioensaios de laboratório contra Culex quinquefasciatus constatou-se que as formulações biológicas líquidas Vectobac 12 AS e Teknar 3000 (Bacillus thuringiensis israelensis), ABG 6262 líquido e em pó (B. sphaericus 2362), foram altamente eficazes. Também as formulações experimentais de B. thuringiensis israelensis produzidas em laboratórios brasileiros foram consideradas adequadas. Entre as formulações químicas, os compostos piretróides Pirisa e K-Othrine produziram resultados melhores do que os organo-fosforados Lebaycid e Abate. Estes últimos produziram respostas dez vezes mais fracas do que o previsto em outros estudos. Em condições de campo, a dose de 1250 mg/m² para as formulações biológicas foi considerada adequada para a rotina das aplicações, porque permite superar as influências físicas do meio sobre os resultados. Somente as formulações de B. sphaericus produziram interrupções nas reinfestações dos focos de culicídeos observados. Períodos de até 39 semanas sem reinfestações foram observados em focos naturais e de um mês sem sobrevivência foi observado em tanques, onde procedia-se a reinfestação artificial. Este estudo sugere que as alternativas biológicas devem ser consideradas em programas de controle de mosquitos. Elas podem superar os problemas de resistência e eliminação, bem como da ausência de efeito residual nas aplicações de larvicidas. A comparison between chemical and biological larvicides in routine operations against mosquitoes in Rio Grande do Sul State was carried out in this study. In laboratory bioassays against Culex quinquefasciatus, biological formulations Vectobac 12 AS and Teknar 3000 (Bacillus thuringiensis israelensis) as well as ABG 6262 (B. sphaericus 2362), both in liquid and powder form, were highly effective. Locally produced B.thuringiensis israelensis, formulations also yielded good results. Among chemical larvicides, pyrethroid compounds Pirisa and K-Othrine yielded better results than the organophosphates Lebaycid and Abate. These last formulations yielded responses ten weaker than predicted in other studies. Under field conditions, a dose of 1250 mg/m² for biological formulations was considered adequate for routine application because at this level it is possible to overcome physical influences on results. Only B.sphaericus preparations caused important disruption of mosquito colonization in active breeding sites. Up to 39 weeks were tabulated without complete colonization in natural conditions and one month in artificially colonized tanks. This study suggests that biological alternatives should be considered in mosquito control programs. They may be a solution to such problems as resistance to larvicides, elimination of natural enemies, and short-lasting effects of applications.

Desenvolveu-se neste estudo uma comparação entre larvicidas químicos e biológicos usados em programas de controle de mosquitos no Rio Grande do Sul. Em bioensaios de laboratório contra Culex quinquefasciatus constatou-se que as formulações biológicas líquidas Vectobac 12 AS e Teknar 3000 (Bacillus thuringiensis israelensis), ABG 6262 líquido e em pó (B. sphaericus 2362), foram altamente eficazes. Também as formulações experimentais de B. thuringiensis israelensis produzidas em laboratórios brasileiros foram consideradas adequadas. Entre as formulações químicas, os compostos piretróides Pirisa e K-Othrine produziram resultados melhores do que os organo-fosforados Lebaycid e Abate. Estes últimos produziram respostas dez vezes mais fracas do que o previsto em outros estudos. Em condições de campo, a dose de 1250 mg/m² para as formulações biológicas foi considerada adequada para a rotina das aplicações, porque permite superar as influências físicas do meio sobre os resultados. Somente as formulações de B. sphaericus produziram interrupções nas reinfestações dos focos de culicídeos observados. Períodos de até 39 semanas sem reinfestações foram observados em focos naturais e de um mês sem sobrevivência foi observado em tanques, onde procedia-se a reinfestação artificial. Este estudo sugere que as alternativas biológicas devem ser consideradas em programas de controle de mosquitos. Elas podem superar os problemas de resistência e eliminação, bem como da ausência de efeito residual nas aplicações de larvicidas.