ANOSPEX: A Stochastic, Spatially Explicit Model for Studying Anopheles Metapopulation Dynamics

Abstract

Anopheles mosquitoes transmit malaria, a major public health problem among many African countries. One of the most effective methods to control malaria is by controlling the Anopheles mosquito vectors that transmit the parasites. Mathematical models have both predictive and explorative utility to investigate the pros and cons of different malaria control strategies. We have developed a C++ based, stochastic spatially explicit model (ANOSPEX; Ano phelesSpatially-Explicit) to simulate Anopheles metapopulation dynamics. The model is biologically rich, parameterized by field data, and driven by field-collected weather data from Macha, Zambia. To preliminarily validate ANOSPEX, simulation results were compared to field mosquito collection data from Macha; simulated and observed dynamics were similar. The ANOSPEX model will be useful in a predictive and exploratory manner to develop, evaluate and implement traditional and novel strategies to control malaria, and for understanding the environmental forces driving Anopheles population dynamics.

Hitting a Moving Target: A Model for Malaria Elimination in the Presence of Population Movement

“…Metapopulation modelling is one method to describe movement between geographical areas with several applications in malaria and other infectious diseases. The metapopulation concept has been used to examine the spread of chloroquine resistance [ 16 ], model malaria transmission assuming the migration of the mosquitoes only [ 17 – 19 ], and account for human migration also [ 20 – 23 ]. Mathematical modelling of malaria in Mpumalanga includes a climate-based fuzzy distribution model [ 24 ], an eco-hydrological model [ 25 ] and the use of the SaTScan methodology to detect local malaria clusters to guide the Mpumalanga Malaria Control Programme [ 26 ].…”

Mathematical assessment of the role of temperature and rainfall on mosquito population dynamics

“…For instance, such models allow for the determination of parameters, or variables, that influences the life-cycle of the mosquitoes (Ahumada et al 2004;Cailly et al 2012;Tran et al 2013). The spatio-temporal dynamics of mosquito populations (in urban areas) have been studied in Cummins et al (2012) and Oluwagbemi et al (2013). Furthermore, several models have been developed to predict the temporal dynamics of mosquito abundance, in the presence of climate change, using either statistical (Wang et al 2011), stochastic (Otero et al 2006) or deterministic formulations (Lutambi et al 2013).…”

“…Furthermore, several models have been developed to predict the temporal dynamics of mosquito abundance, in the presence of climate change, using either statistical (Wang et al 2011), stochastic (Otero et al 2006) or deterministic formulations (Lutambi et al 2013). However, most of the existing models of mosquito population dynamics were built for a specific mosquito species, within a specific geographic context (e.g., Anopheles gambiae in the Sahel (Yamana and Eltahir 2013); Anopheles arabiensis in Zambia (Oluwagbemi et al 2013) and culex in Canada (Wang et al 2011)) and may not be applied to other mosquito species or areas. A model for the dynamics of general species of mosquitoes is developed in Cailly et al (2012).…”

The effect of dispersal and preferential mating on the genetic control of mosquitoes

“…In our mathematical approach we chose to make simplifying assumptions and generalizations about the life cycle and behaviour of mosquitoes. This is common in the literature, and we do it here in order to examine specific questions within a simplified framework that does not rely on the detailed, difficult and error-prone parameterisations of more complex agent-based models [46,48,56,59]. Of course, it is vital that we understand the limitations of simplified models and that making common simplifying assumptions -while useful -should never obscure the importance of gaining a richer empirical understanding.…”

“…Many studies have investigated the effect of genetic mosquito control technologies within a single species from the perspective of spatial spread or containment. The most complex and richly parameterised of these used agent-based stochastic modelling approaches, capturing explicit spatial heterogeneity through the distribution of larval breeding sites [48,59], blood-feeding sites [56] and bodies of standing water [58]. The most simple and intuitive approaches consider the growth and decline in frequency of invad-ing alleles in population genetics frameworks [17,50].…”