Abstract
BackgroundClimate is a major driving force behind malaria transmission and climate data are often used to account for the spatial, seasonal and interannual variation in malaria transmission.MethodsThis paper describes a mathematical-biological model of the parasite dynamics, comprising both the weather-dependent within-vector stages and the weather-independent within-host stages.ResultsNumerical evaluations of the model in both time and space show that it qualitatively reconstructs the prevalence of infection.ConclusionA process-based modelling structure has been developed that may be suitable for the simulation of malaria forecasts based on seasonal weather forecasts.
Highlights
Climate is a major driving force behind malaria transmission and climate data are often used to account for the spatial, seasonal and interannual variation in malaria transmission
The x-axis is the average temperature in Celsius and the y-axis is the fraction of the whole larval stage covered in a single day at the given temperature
This paper presents a first step in the preparation of a weather-driven dynamical model of malaria transmission, for use with both observed weather data and seasonal climate forecasts
Summary
Climate is a major driving force behind malaria transmission and climate data are often used to account for the spatial, seasonal and interannual variation in malaria transmission. The importance of climate as a driving force of malaria transmission has been known since the earliest days of research on this devastating parasitic disease. It is only with the advent of effective weather forecasting techniques that this knowledge may be implemented numerically. Because of the chaotic nature of the atmosphere, seasonal forecasts are necessarily probabilistic These probabilistic predictions are derived from multiple integrations of deterministic climate models. That event in East Africa was associated with devastating malaria epidemics[2] and, the health community has shown an increasing interest in the use of seasonal forecasts for predicting epidemics of climate related diseases[3]
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