Putting black on white what happens to the variance of a decomposed signal using our time scale method more

Trying to make sense out of all the previous workmore

Here we check the consistency between the 25 malaria Districts data with the 115 sub-districts added up by District.more

An overview of the seasonal cycle and the year-to-year variability of malaria incidence in central districts of Madagascar.more

CCA is a commonly used tool in climate sciences to measure the linear relationship between two multidimensional variables. It also allows model building for forecasting. Here are presented three case studies illustrating how CCA analysis as well as model building and forecasting can be performed in Ingrid, prior to the set up of an Ingrid CCA function of its own.more

Climatological temperature, precipitation and malaria SVD analysis are put together to investigate the relationships between climate and malaria in Sri Lanka.more

Previous gap-filling techniques were not very satisfying in order to produce a synthetized analysis of the available dataset, whether because the dataset had to be split, or the choice of some time series to be discarded didn't seem fair, or because more than half of them had to be discarded. Here, a regularized EM algorithm is applied, discarding just a few time series, and filling all the remaining gaps. This page shows the results of such analysis with the default values provided.more

The previous cluster analysis on Sri Lanka malaria data has shown how cautious one should be when studying gappy data. Applying some selections and other filters on the data, as well as gaps filling techniques proved to impact the results of the final cluster analysis. This section highlights the difficulties encountered when selecting data, reconstructing signals and filling gaps. It asks the questions to be investigated to ensure a safe analysis.more

A cluster analysis on the total number of malaria cases reported may help detect structures in the spatial and time variability of the cases. It is a first step to try to relate the disease variability to known or yet to be known other climate phenomenons variability.more