Even though first studies focused of malaria in Africa, here is a cluster analysis of malaria data in Sri Lanka.

The previous cluster analysis of Sri Lanka malaria data showed how cautious one has to be when studying gappy data. Here are the main issues and therefore the basis of a reflection towards safe gappy data analysis

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.

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

Cluster analysis of the seasonal cycle and SVD of the year-to-year anomalies variability.

Overview the variability (seasonal cycle and year-to-year) of NDVI, rainfall and temperature in the districts of Madagascar where malaria is epidemical

Overview of the connections between variabilities (seasonal cycle and inter-annual) of the climate/environment data and the malaria data in Madagascar

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

Description of malaria in Sri Lanka