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. 2013 Aug 7:6:226.
doi: 10.1186/1756-3305-6-226.

Linking environmental variability to village-scale malaria transmission using a simple immunity model

Teresa K Yamana  1 , Arne Bomblies, Ibrahim M Laminou, Jean-Bernard Duchemin, Elfatih A B Eltahir
Affiliations

Linking environmental variability to village-scale malaria transmission using a simple immunity model

Teresa K Yamana et al. Parasit Vectors. .

Abstract

Background: Individuals continuously exposed to malaria gradually acquire immunity that protects from severe disease and high levels of parasitization. Acquired immunity has been incorporated into numerous models of malaria transmission of varying levels of complexity (e.g. Bull World Health Organ 50:347, 1974; Am J Trop Med Hyg 75:19, 2006; Math Biosci 90:385-396, 1988). Most such models require prescribing inputs of mosquito biting rates or other entomological or epidemiological information. Here, we present a model with a novel structure that uses environmental controls of mosquito population dynamics to simulate the mosquito biting rates, malaria prevalence as well as variability in protective immunity of the population.

Methods: A simple model of acquired immunity to malaria is presented and tested within the framework of the Hydrology, Entomology and Malaria Transmission Simulator (HYDREMATS), a coupled hydrology and agent-based entomology model. The combined model uses environmental data including rainfall, temperature, and topography to simulate malaria prevalence and level of acquired immunity in the human population. The model is used to demonstrate the effect of acquired immunity on malaria prevalence in two Niger villages that are hydrologically and entomologically very different. Simulations are conducted for the year 2006 and compared to malaria prevalence observations collected from the two villages.

Results: Blood smear samples from children show no clear difference in malaria prevalence between the two villages despite pronounced differences in observed mosquito abundance. The similarity in prevalence is attributed to the moderating effect of acquired immunity, which depends on prior exposure to the parasite through infectious bites - and thus the hydrologically determined mosquito abundance. Modelling the level of acquired immunity can affect village vulnerability to climatic anomalies.

Conclusions: The model presented has a novel structure constituting a mechanistic link between spatial and temporal environmental variability and village-scale malaria transmission. Incorporating acquired immunity into the model has allowed simulation of prevalence in the two villages, and isolation of the effects of acquired immunity in dampening the difference in prevalence between the two villages. Without these effects, the difference in prevalence between the two villages would have been significantly larger in response to the large differences in mosquito populations and the associated biting rates.

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Figures

Figure 1
Figure 1
(From Bomblies et al., [30]). Shows the location of the studied villages Banizoumbou ,Zindarou and Niger. The right panel depicts topography within the HAPEX-Sahel square degree, the subject of an intensive international hydrology and climatology research project that took place from 1991 until 1993. The Niger River is seen in the bottom left of the domain, and the “Dallol Bosso” relict river basin is seen on the right. Zindarou’s location within the Dallol Bosso results in the village’s unique hydrology, whereas Banizoumbou has a more arid hydrology that is typical of the Sahel.
Figure 2
Figure 2
Temperature and rainfall in Banizoumbou (red) and Zindarou (blue) in 2006. The figure on the left shows mean temperature, and the figure on the right shows monthly rainfall. This demonstrates that the two villages have very similar climates.
Figure 3
Figure 3
(From Bomblies et al., [30]) Modeled and observed Anopheles gambiae mosquito abundance in Banizoumbou and Zindarou. Mosquito abundance is very different in the two similarly sized villages, because of local hydrological differences. This is evident in the light trap captures (markers with dashed lines) and the simulation results (solid lines).
Figure 4
Figure 4
Schematic of HYDREMATS. This schematic diagram lists the major processes and key parameters represented by the Hydrology, Entomology and Immunology components of HYDREMATS. The arrows represent information that is passed from one component to the next. Model outputs from each component are spatially and temporally explicit.
Figure 5
Figure 5
Schematic of the immunology component of HYDREMATS. HYDREMATS models individual mosquito human and mosquito agents. The solid arrows represent processes as individual agents become infected, dashed lines indicate the movement of malaria parasite through mosquito bites. Each human agent has an immunity value imm, which is a function of the past infectious bites received by that individual. When a human is bitten by an infected mosquito, his probability of infection is b, which is a function of imm. After a latent period, the exposed human becomes infectious. The human then recovers at a mean rate of r, which is also a function of imm. A mosquito biting an infectious individual becomes infected with probability c. If infected, he goes through a temperature-dependent latent period and then become infectious to subsequent humans.
Figure 6
Figure 6
Observed prevalence in Banizoumbou (red) and Zindarou (blue), for the period December 2005 – February 2007. Error bars indicate 95% confidence intervals for each estimate.
Figure 7
Figure 7
Simulated malaria prevalence using static immunity model. Banizoumbou is shown in red, and Zindarou is shown in blue. The left panel shows overall prevalence for all age groups, and the right panel shows prevalence for children under 5. In this simulation, 2006 climate forcing was repeated twenty times. The time step is years, and the cycle is annual. The peaks of each cycle correspond to late August.
Figure 8
Figure 8
Simulated malaria prevalence using dynamic static immunity model. Banizoumbou is shown in red, and Zindarou is shown in blue. The left panel shows overall prevalence for all age groups, and the right panel shows prevalence for children under 5. In this simulation, 2006 climate forcing was repeated twenty times. The time step is years, and the cycle is annual. The peaks of each cycle correspond to late August.
Figure 9
Figure 9
Simulated mean immunity level using the dynamic immunity model in Banizoumbou (red) and Zindarou (blue). In the simulations using static immunity, the immunity in both villages remained at 0.2 for the duration of the simulation.
Figure 10
Figure 10
Sensitivity of model results to parameter values. Each parameter was decreased by 10%. Prevalence in Banizoumbou after 10 years of simulation under original parameterization (blue) and perturbed parameter (green) are shown.
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