Modeling Temperature Effects on Population Density of the Dengue Mosquito Aedes aegypti

A systematic review of Aedes aegypti population dynamics models based on differential equations

The global spread of Aedes aegypti and the associated public health risk have stimulated the development of several mathematical models to predict population dynamics in response to biological or environmental changes in real, future, or simulated scenarios. The aim of this study is to identify published articles on differential equation-based population dynamics models of Aedes aegypti, highlight their differences and commonalities, and examine their application in surveillance and control programs. Following the PRISMA guidelines, a systematic review was conducted in seven electronic databases (Scopus, PUBMED, IEEE Xplore, Science Direct, DOAJ, Scielo, and Google Scholar), with the last update on 8 February 2023. The initial search yielded 513 studies, of which 31 were finally selected. The articles analyzed showed great variability in the equations, processes, and variables included, with temperature being the most common environmental factor. Only a few models incorporated spatial heterogeneity or validation methods. Our findings suggest that improving the generation of temporal and spatially explicit forecasts through interdisciplinary collaboration, the use of new technologies, and validation with field data is essential for these models to effectively support public health efforts. Differential equation-based population dynamics models offer valuable insights and could greatly benefit mosquito surveillance programs if standardized and tailored to relevant scales.

Both consumptive and non-consumptive effects of predators impact mosquito populations and have implications for disease transmission

Predator-prey interactions influence prey traits through both consumptive and non-consumptive effects, and variation in these traits can shape vector-borne disease dynamics. Meta-analysis methods were employed to generate predation effect sizes by different categories of predators and mosquito prey. This analysis showed that multiple families of aquatic predators are effective in consumptively reducing mosquito survival, and that the survival of Aedes, Anopheles, and Culex mosquitoes is negatively impacted by consumptive effects of predators. Mosquito larval size was found to play a more important role in explaining the heterogeneity of consumptive effects from predators than mosquito genus. Mosquito survival and body size were reduced by non-consumptive effects of predators, but development time was not significantly impacted. In addition, Culex vectors demonstrated predator avoidance behavior during oviposition. The results of this meta-analysis suggest that predators limit disease transmission by reducing both vector survival and vector size, and that associations between drought and human West Nile virus cases could be driven by the vector behavior of predator avoidance during oviposition. These findings are likely to be useful to infectious disease modelers who rely on vector traits as predictors of transmission.

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.

The Role of Temperature in Shaping Mosquito-Borne Viruses Transmission

Mosquito-borne diseases having the greatest impact on human health are typically prevalent in the tropical belt of the world. However, these diseases are conquering temperate regions, raising the question of the role of temperature on their dynamics and expansion. Temperature is one of the most significant abiotic factors affecting, in many ways, insect vectors and the pathogens they transmit. Here, we debate the veracity of this claim by synthesizing current knowledge on the effects of temperature on arboviruses and their vectors, as well as the outcome of their interactions.

The Role of Vector Trait Variation in Vector-Borne Disease Dynamics

Many important endemic and emerging diseases are transmitted by vectors that are biting arthropods. The functional traits of vectors can affect pathogen transmission rates directly and also through their effect on vector population dynamics. Increasing empirical evidence shows that vector traits vary significantly across individuals, populations, and environmental conditions, and at time scales relevant to disease transmission dynamics. Here, we review empirical evidence for variation in vector traits and how this trait variation is currently incorporated into mathematical models of vector-borne disease transmission. We argue that mechanistically incorporating trait variation into these models, by explicitly capturing its effects on vector fitness and abundance, can improve the reliability of their predictions in a changing world. We provide a conceptual framework for incorporating trait variation into vector-borne disease transmission models, and highlight key empirical and theoretical challenges. This framework provides a means to conceptualize how traits can be incorporated in vector borne disease systems, and identifies key areas in which trait variation can be explored. Determining when and to what extent it is important to incorporate trait variation into vector borne disease models remains an important, outstanding question.