Mosquito Ecology

Author
Affiliation

University of Washington

Overview

Mosquito behavior and ecology plays a core role in malaria epidemiology, but it has played only a minor role in malaria transmission dynamics and control.

In George Macdonald’s model, mosquito infection dynamics were summarized by a few core parameters: the ecology part was never spelled out [1,2]. The classic parameter set was codified in formulas for \(R_0\) and vectorial capacity (VC) [24]. While the original formala for the VC was derived from a very specific set of assumptions, the formula for VC has taken on a life of its own [57].

In developing ramp.xds, we recognized several major types of models describing adult mosquito ecology. The MY component modules fall into two categories:

  • In basic ecology models, the compartments for adult mosquito populations represent infection states. Macdonald’s mosquito models were not extensible, so the base model for ramp.xds development was published by Joan Aron and Bob May [8].

  • In behavioral state models, the compartments subdivide a population by behavioral states as well as infection states [9].

A complete framework for mosquito ecology needs to explicitly define egg laying and then couple it to the dynamics of immature mosquito populations in aquatic habitats, and emergence [10].

  • To model

In mosquito ecology, there has been a long-standing interest in using parity to measure the reproductive age of a mosquito.

Basic Ecology

Adult Mosquitoes

In 1982, Joan Aron and Bob May published a model that made that implicit model explicit and then extended it. In that model \(M\) is a variable describing adult mosquito density, \(\Lambda(t)\) the emergence rate of adult mosquitoes, and \(g\) the death rate [8]. In this model, an equation described mosquito population dynamics: \[ \frac{dM}{dt} = \Lambda(t) - g M \]

Basic Infection Dynamics

To model infection dynamics, we let \(f\) be the overall feeding rate, \(q\) the human fraction, and \(\kappa\) the net infectiousness (NI). Letting \(Y\) denote the density of infected mosquitoes, we get: \[\frac{dY}{dt}= fq\kappa(M-Y)-g Y\] To get the density of infecious mosquitoes, \(Z,\) Aron & May use a fixed delay to model the latent period, commonly known as the extrinsic incubation period (EIP). If mosquitoes become infected \(\tau\) days after they became infected, then they survive the EIP with probability \(e^{-g\tau}\), and we introduce the subscript \(\tau\) to denote the lagged value of a term of variable: \[\frac{dZ}{dt}= fq\kappa_\tau(M_\tau-Y_\tau) e^{-g\tau} -g Z\] The net infectious biting rate is \(fqZ\)

Aquatic Ecology

A model for adult mosquito population dynamics can be coupled to a model for aquatic mosquito population dynamics if we model egg laying by adult mosquitoes. Software developed around a basic model by Smith et al. (2013) [10]: adults lay eggs in aquatic habitats, where larvae compete for resources.

Spatial Dynamics

In ramp.xds, we established a convention for modeling mosquito spatial ecology using a demographic matrix for both survival and dipsersal, \(\Omega.\) In a spatial model with \(N_p\) patches, \(\Lambda(t)\) is a vector of length \(N_p\), \(M\) a vector of state variables of length \(N_p\), \(\Omega\) a \(N_p \times N_p\) matrix, and \[ \frac{dM}{dt} = \Lambda(t) - \Omega \cdot M \] If \(\Lambda\) is a constant, then mosquito population density will come to a steady state \[ \bar M = \Omega^{-1} \cdot \Lambda.\] and we can expand the definition of vectorial capacity for spatial models. Vectorial capacity is the number of infectious bites arising from all the mosquitoes blood feeding on a single available human in a single patch on a single day.

The classic formula is problematic, for various reasons, and software development was based on two formulas (using either \(\Lambda\) or \(\bar M\)) that are equivalent (under Macdonald’s assumptions): \[V = \frac {\bar M} H \frac{f^2 q^2}{g} e^{-g\tau} = \frac \Lambda H \frac{f^2 q^2}{g^2} e^{-g\tau}\]

Aquatic Ecology

Non-Autonomous Dynamics

To get a model that is equivalent to the one used by George Macdonald, we set \(N_p=1\), and if we let \(m=\bar M/H,\) \(a=fq\), \(y = Y/M,\) and \(z=Z/M.\)

Behavioral States

Behavioral state models for mosquito ecology consider the physiological status of a mosquito and its associated behaviors [9,11]. Gravid mosquitoes will tend to be seeking water to lay eggs, but after laying eggs, the mosquito will seek blood or perhaps sugar. Compartmental models consider both behavioral states and infection states, so they are more complex than basic models. In ramp.library

References

1.
Macdonald G. The analysis of the sporozoite rate. Tropical Diseases Bulletin. 1952;49: 569–586. Available: https://www.ncbi.nlm.nih.gov/pubmed/14958825
2.
Smith DL, McKenzie FE. Statics and dynamics of malaria infection in Anopheles mosquitoes. Malaria Journal. 2004;3: 13. doi:10.1186/1475-2875-3-13
3.
Macdonald G. The analysis of equilibrium in malaria. Tropical Diseases Bulletin. 1952;49: 813–829. Available: https://www.ncbi.nlm.nih.gov/pubmed/12995455
4.
Garrett-Jones C. Prognosis for interruption of malaria transmission through assessment of the mosquito’s vectorial capacity. Nature. 1964;204: 1173–1175. doi:10.1038/2041173a0
5.
Dye C. Vectorial capacity: Must we measure all its components? Parasitology Today. 1986;2: 203–209. doi:10.1016/0169-4758(86)90082-7
6.
Kiszewski A, Mellinger A, Spielman A, Malaney P, Sachs SE, Sachs J. A global index representing the stability of malaria transmission. Am J Trop Med Hyg. 2004;70: 486–498. Available: http://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=15155980&retmode=ref&cmd=prlinks
7.
Mordecai EA, Caldwell JM, Grossman MK, Lippi CA, Johnson LR, Neira M, et al. Thermal biology of mosquito-borne disease. Ecol Lett. 2019;22: 1690–1708. doi:10.1111/ele.13335
8.
Aron JL, May RM. The population dynamics of malaria. In: Anderson RM, editor. The Population Dynamics of Infectious Diseases: Theory and Applications. Boston, MA: Springer US; 1982. pp. 139–179. Available: https://doi.org/10.1007/978-1-4899-2901-3_5
9.
Le Menach A, McKenzie FE, Flahault A, Smith DL. The unexpected importance of mosquito oviposition behaviour for malaria: Non-productive larval habitats can be sources for malaria transmission. Malar J. 2005;4: 23. doi:10.1186/1475-2875-4-23
10.
Smith DL, Perkins TA, Tusting LS, Scott TW, Lindsay SW. Mosquito Population Regulation and Larval Source Management in Heterogeneous Environments. PLOS ONE. 2013;8: e71247. doi:10.1371/journal.pone.0071247
11.
Wu SL, Sánchez C HM, Henry JM, Citron DT, Zhang Q, Compton K, et al. Vector bionomics and vectorial capacity as emergent properties of mosquito behaviors and ecology. PLoS Comput Biol. 2020;16: e1007446. doi:10.1371/journal.pcbi.1007446