Predictive Modeling of Seasonal Mosquito Population Patterns with Neural Networks

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

A Temperature Conditioned Markov Chain Model for Predicting the Dynamics of Mosquito Vectors of Disease

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

Age, sex, adult and larval diet shape starvation resistance in the Mediterranean fruit fly: an ecological and gerontological perspective.

The ability of an animal to withstand periods of food deprivation is a key driver of invasion success (biodiversity), adaptation to new conditions, and a crucial determinant of senescence in populations. Starvation resistance (SR) is a highly plastic trait and varies in relation to environmental and genetic variables. However, beyond Drosophila, SR has been studied poorly. Exploiting an interesting model species in invasion and ageing studies-the Mediterranean fruit fly (Ceratitis capitata)- we investigated how age, food and gender, shape SR in this species. We measured SR in adults feeding in rich and poor dietary conditions, which had been reared either on natural hosts or artificial larval diet, for every single day across their lifespan. We defined which factor is the most significant determinant of SR and we explored potential links between SR and ageing. We found that SR declines with age, and that age-specific patterns are shaped in relation to adult and larval diet. Females exhibited higher SR than males. Age and adult diet were the most significant determinants of SR, followed by gender and the larval diet. Starvation resistance proved to be a weak predictor of functional ageing. Possible underlying mechanisms, ecological and gerontological significance and potential applied benefits are discussed

Prevención del virus del Nilo Occidental, la filariasis y la encefalitis. Métodos para predecir la abundancia de Culex sp en un entorno mediterráneo

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

An in vitro ULV olfactory bioassay method for testing the repellent activity of essential oils against moths

A prototype olfactory device was developed and used for first time to study the bioactivity of Ultra Low Volumes (ULV) of three essential oilsagainst the moth pest Anarsia lineatella (Lepidoptera: Gelechiidae). Particle sizes calibration and standard ULV time-doses range tests were performed prior the olfactory bioassays. Three essential oils were tested Cymbopogon citratus (Lemon Grass), Gaultheria procumbens (Winter Grass) and Rosmarinus officinalis (Rosmarin) according to the proposed method. The most active oil was that of R. officinalis and moths expressed approximately 3–5 fold faster moving behavior (50% repellence response times to ULV, RT50: 20–30 min) compared to G. procumbens (RT50:74–79 min) and C. citratus (RT50:82–96 min). Apart from direct observed repellence, moths sprayed with ULV show clearly signs of knock down symptoms and high fatality in a period 15–60 min after their treatment especial in the case of R. officinalis. Longevity of female moths was significantly affected by the initial ULV application. Furthermore, choice test showed that essential oils significantly deterred oviposition in most cases. Considering the urgent need for alternative to conventional pesticides the current work may provide a framework of testing the bioactivity of bio rational compounds in the form of ULV and under Lab conditions. Keywords: Ultra low volume, Biorational insecticides, Choice experiments, Knock down, Probit analysis, IP

Soft Computing of a Medically Important Arthropod Vector with Autoregressive Recurrent and Focused Time Delay Artificial Neural Networks

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

MOESM1 of Using multivariate cross correlations, Granger causality and graphical models to quantify spatiotemporal synchronization and causality between pest populations

Additional file 1. Actual ecological time series registered during successive growth seasons of the years 2003–2011 (3-day time intervals). W1: temperature (°C), W2: relative humidity (%), X1–X8: A. orana, Y1–Y3: A. lineatella and Z1–Z2: G. molesta moth populations (individuals)