Ewan Cameron (Oxford, Dept of Zoology)

Progress and (Statistical) Challenges in Malariology

Abstract: In this talk I will describe some key statistical challenges faced by researchers aiming to quantify the burden of disease arising from Plasmodium falciparum malaria at the population level. These include covariate selection in the 'big data' setting, handling spatially-correlated residuals at scale, calibration of individual simulation models of disease transmission, and the embedding of continuous-time, discrete-state Markov Chain solutions within hierarchical Bayesian models. In each case I will describe the pragmatic solutions we've implemented to-date within the Malaria Atlas Project, and highlight more sophisticated solutions we'd like to have in the near-future if the right statistical methodology and computational tools can be identified and/or developed to this end.

References:

http://www.nature.com/nature/journal/v526/n7572/abs/nature15535.html

http://www.nature.com/ncomms/2015/150907/ncomms9170/full/ncomms9170.html

http://www.ncbi.nlm.nih.gov/pubmed/25890035

http://link.springer.com/article/10.1186/s12936-015-0984-9