Modeling and Biological Control of Mosquitoes

Abstract

Models can be useful at many different levels when considering complex issues such as biological control of mosquitoes. At an early stage, exploratory models are valuable in exploring the characteristics of an ideal biological control agent and for guidance in data collection. When more data are available, models can be used to explore alternative control strategies and the likelihood of success. There are few modeling studies that explicitly consider biological control in mosquitoes; however, there have been many theoretical studies of biological control in other insect systems and of mosquitoes and mosquito-borne diseases in general. Examples are used here to illustrate important aspects of designing, using and interpreting models. The stability properties of a model are valuable in assessing the potential of a biological control agent, but may not be relevant to a mosquito population with frequent environmental perturbations. The time scale and goal of proposed control strategies are important considerations when analyzing a model. The underlying biology of the mosquito host and the biological control agent must be carefully considered when deciding what to include in a model. Factors such as density dependent population growth in the host, the searching efficiency and aggregation of a natural enemy, and the resource base of both have been shown to influence the stability and dynamics of the interaction. Including existing mosquito control practices into a model is useful if biological control is proposed for locations with current insecticidal control. The development of Integrated Pest Management (IPM) strategies can be enhanced using modeling techniques, as a wide variety of options can be simulated and examined. Models can also be valuable in comparing alternate routes of disease transmission and to investigate the level of control needed to reduce transmission.

Can Horton Hear the Whos? The Importance of Scale in Mosquito-Borne Disease

“…Can we identify the key elements so models will be able to predict outbreaks or appropriately test control programs? Incorporation of different scales into models and validation studies will present challenges which will require a balance between the inclusion of adequate details and application of appropriate sensitivity analyses (Lord 2007). …”

Complementarity of empirical and process-based approaches to modelling mosquito population dynamics with Aedes albopictus as an example—Application to the development of an operational mapping tool of vector populations

“…Two main approaches are used to understand and predict mosquito population dynamics: i) process-based (or mechanistic) models describing biological knowledge within a mathematical or computational framework, and ii) empirical (or statistical) models, which try to find, from the observed data, a predictive function of the response variable (mosquito populations) based on a set of predictors within a statistical or a machine learning framework. Both approaches have been successfully applied to different mosquito species and geographical contexts [5][6][7][8][9][10][11][12][13][14][15][16][17], resulting in a better understanding of their distribution [5-8, 11, 12, 16] and dynamics [9,10,13,17,18] and the assessment of different mosquito control strategies [19,20]. However, most case studies only develop one of the two approaches (either empirical [5-8, 11, 12, 14, 16] or process-based [9,10,13,15,17] depending on the availability of data and knowledge), and do not compare the capacity of the two approaches to predict mosquito population dynamics.…”

Importance of Remotely-Sensed Vegetation Variables for Predicting the Spatial Distribution of African Citrus Triozid (Trioza erytreae) in Kenya

“…One of the most frequently used approaches for producing vector distribution maps is the ecological niche (EN) modeling approach [22]. EN models statistically link spatial variabilities in a set of predictor variables to the distribution of species of interest that can be a plant disease vector like ACT [23,24]. The dependence of plant disease propagation on spatio-temporal environmental niche factors of the disease vector has recently received considerable attention [25].…”