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

Yamana TK, Bomblies A, Laminou IM, Duchemin JB, Eltahir EA - Parasit Vectors (2013)

Bottom Line: 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.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Massachusetts Institute of Technology, 15 Vassar Street, Cambridge, MA 02139, USA. tkcy@mit.edu

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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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.
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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.

Mentions: Figure 5 shows a schematic of the malaria transmission model within HYDREMATS. Solid arrows represent the progress of individual human and mosquito individuals through infectious states, and dashed arrows indicate the transmission of malaria parasites through mosquito bites. The parameters for the immunity model are listed in Table 1. The sensitivity of disease prevalence to parameter values was assessed by perturbing each parameter by 10% and observing the effect on mean annual prevalence after 10 years of simulation in Banizoumbou.


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

Yamana TK, Bomblies A, Laminou IM, Duchemin JB, Eltahir EA - Parasit Vectors (2013)

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.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3750354&req=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.
Mentions: Figure 5 shows a schematic of the malaria transmission model within HYDREMATS. Solid arrows represent the progress of individual human and mosquito individuals through infectious states, and dashed arrows indicate the transmission of malaria parasites through mosquito bites. The parameters for the immunity model are listed in Table 1. The sensitivity of disease prevalence to parameter values was assessed by perturbing each parameter by 10% and observing the effect on mean annual prevalence after 10 years of simulation in Banizoumbou.

Bottom Line: 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.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Massachusetts Institute of Technology, 15 Vassar Street, Cambridge, MA 02139, USA. tkcy@mit.edu

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.

Show MeSH
Related in: MedlinePlus