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BackgroundA major health burden in Cameroon is malaria, a disease that is sensitive to climate, environment and socio-economic conditions, but whose precise relationship with these drivers is still uncertain. An improved understanding of the relationship between the disease and its drivers, and the ability to represent these relationships in dynamic disease models, would allow such models to contribute to health mitigation and adaptation planning. This work collects surveys of malaria parasite ratio and entomological inoculation rate and examines their relationship with temperature, rainfall, population density in Cameroon and uses this analysis to evaluate a climate sensitive mathematical model of malaria transmission.MethodsCo-located, climate and population data is compared to the results of 103 surveys of parasite ratio (PR) covering 18,011 people in Cameroon. A limited set of campaigns which collected year-long field-surveys of the entomological inoculation rate (EIR) are examined to determine the seasonality of disease transmission, three of the study locations are close to the Sanaga and Mefou rivers while others are not close to any permanent water feature. Climate-driven simulations of the VECTRI malaria model are evaluated with this analysis.ResultsThe analysis of the model results shows the PR peaking at temperatures of approximately 22 °C to 26 °C, in line with recent work that has suggested a cooler peak temperature relative to the established literature, and at precipitation rates at 7 mm day−1, somewhat higher than earlier estimates. The malaria model is able to reproduce this broad behaviour, although the peak occurs at slightly higher temperatures than observed, while the PR peaks at a much lower rainfall rate of 2 mm day−1. Transmission tends to be high in rural and peri-urban relative to urban centres in both model and observations, although the model is oversensitive to population which could be due to the neglect of population movements, and differences in hydrological conditions, housing quality and access to healthcare. The EIR follows the seasonal rainfall with a lag of 1 to 2 months, and is well reproduced by the model, while in three locations near permanent rivers the annual cycle of malaria transmission is out of phase with rainfall and the model fails.ConclusionMalaria prevalence is maximum at temperatures of 24 to 26 °C in Cameroon and rainfall rates of approximately 4 to 6 mm day−1. The broad relationships are reproduced in a malaria model although prevalence is highest at a lower rainfall maximum of 2 mm day−1. In locations far from water bodies malaria transmission seasonality closely follows that of rainfall with a lag of 1 to 2 months, also reproduced by the model, but in locations close to a seasonal river the seasonality of malaria transmission is reversed due to pooling in the transmission to the dry season, which the model fails to capture.

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In this study, an analysis of present day climate simulation (1998–2008) is presented for the Central African (CA) region with the COnsortium for Small‐scale MOdelling in CLimate Mode (CCLM) regional climate model, forced by the ERA‐Interim (ERAINT) reanalysis data. The ability of the CCLM to simulate the observed precipitation with particular focus on the mean spatial pattern, low‐level circulation, seasonal cycles, and daily characteristics is evaluated. Likewise, the added value of the regional model CCLM compared to the driving ERAINT reanalysis is also investigated. It is shown that ERAINT and CCLM exhibit quite different sign of bias, which is an indication of the importance of internal variability and fine scale processes representation for the simulation of surface climate. Despite the CCLM is constantly dry over southern CA, the model succeeds to reproduce reasonably the mean spatial patterns of precipitation and low‐level circulation features, along with the associated seasonal cycles over the whole CA and majority of the five selected analysis sub‐regions. Results also show that daily precipitation indices are well represented, although the better performance greatly depends on the considered seasons. Nevertheless, CCLM substantially outperforms the ERAINT daily precipitation characteristics, thus highlighting the added value of the downscaling exercise over the region. The analysis of daily precipitation indices also reveals that the dry character of the model could probably be connected to the underestimation of the simulated less intense events, which in turn result to an overestimation of the simulated dry spell duration.

This paper investigates the relationship between the Normalized Difference Vegetation Index (NDVI) and extracted rainfall in the Global Precipitation Climatology Project (GPCP) in Central Africa between latitudes 15• S and 20• N and longitudes 0 • E and 31• E. Monthly NDVI and GPCP datasets for the period 1982-2000 have been used.The Index of Segmentation of Fourier Components (ISFC) has been applied on the NDVI dataset to segment Central Africa into four bioclimatic ecoregions (BCERs). In order to compare the differential response of vegetation growth to rainfall, an analysis of the inter-annual, intra-annual and seasonal variability for each BCER has been carried out, and the correlations between NDVI and rainfall have been assessed. The plot of the annual cycles of both variables revealed a coherent onset, peak and decay, with a time lag of 1 month for almost all the zones, except the zones, semi-desert and steppe, where a season of short and intense rainfall was observed. The correlation coefficients computed between the two variables are relatively high, especially in brush-grass savannah, where they reach up to 0.90 at a time lag of 1 month. The phenological transition points and phases show that the rangeAtmosphere 2013, 4 412 between the +1 and −1 time lags corresponds to the duration of the maturity of vegetation.Overall, there is a strong similarity between temporal patterns of NDVI and rainfall, showing that the NDVI can be considered a sensitive indicator of the interannual variability of rainfall.