Comparison of Malaria Simulations Driven by Meteorological Observations and Reanalysis Products in Senegal.

The analysis of the spatial and temporal variability of climate parameters is crucial to study the impact of climate-sensitive vector-borne diseases such as malaria. The use of malaria models is an alternative way of producing potential malaria historical data for Senegal due to the lack of reliable observations for malaria outbreaks over a long time period. Consequently, here we use the Liverpool Malaria Model (LMM), driven by different climatic datasets, in order to study and validate simulated malaria parameters over Senegal.

Mosquito breeding site water temperature observations and simulations towards improved vector-borne disease models for Africa.

An energy budget model is developed to predict water temperature of typical mosquito larval developmental habitats. It assumes a homogeneous mixed water column driven by empirically derived fluxes. The model shows good agreement at both hourly and daily time scales with 10-min temporal resolution observed water temperatures, monitored between June and November 2013 within a peri-urban area of Kumasi, Ghana.

A breeding site model for regional, dynamical malaria simulations evaluated using in situ temporary ponds observations.

Daily observations of potential mosquito developmental habitats in a suburb of Kumasi in central Ghana reveal a strong variability in their water persistence times, which ranged between 11 and 81 days. The persistence of the ponds was strongly tied with rainfall, location and size of the puddles. A simple power-law relationship is found to fit the relationship between the average pond depth and area well.

A regional-scale, high resolution dynamical malaria model that accounts for population density, climate and surface hydrology.

Background: The relative roles of climate variability and population related effects in malaria transmission could be better understood if regional-scale dynamical malaria models could account for these factors.

Methods: A new dynamical community malaria model is introduced that accounts for the temperature and rainfall influences on the parasite and vector life cycles which are finely resolved in order to correctly represent the delay between the rains and the malaria season. The rainfall drives a simple but physically based representation of the surface hydrology.

The impact of regional climate change on malaria risk due to greenhouse forcing and land-use changes in tropical Africa.

Background: Climate change will probably alter the spread and transmission intensity of malaria in Africa.

Objectives: In this study, we assessed potential changes in the malaria transmission via an integrated weather-disease model.

Methods: We simulated mosquito biting rates using the Liverpool Malaria Model (LMM).

Development of a new version of the Liverpool Malaria Model. II. Calibration and validation for West Africa.

Background: In the first part of this study, an extensive literature survey led to the construction of a new version of the Liverpool Malaria Model (LMM). A new set of parameter settings was provided and a new development of the mathematical formulation of important processes related to the vector population was performed within the LMM. In this part of the study, so far undetermined model parameters are calibrated through the use of data from field studies.

Development of a new version of the Liverpool Malaria Model. I. Refining the parameter settings and mathematical formulation of basic processes based on a literature review.

Background: A warm and humid climate triggers several water-associated diseases such as malaria. Climate- or weather-driven malaria models, therefore, allow for a better understanding of malaria transmission dynamics. The Liverpool Malaria Model (LMM) is a mathematical-biological model of malaria parasite dynamics using daily temperature and precipitation data.