| Abstract: |
| The ability of an infected host to self-recover to health is termed resilience. In this talk, we will explore how resilience manifests in malarial disease at the scales of: (i) epidemiology, (ii) vector dispersal, (iii) physiology, (iv) immunity, (v) multi-omic, and (vi) computational drug design. I will demonstrate that, more than possible, it is desirable to study infectious disease across scales, as we can gain a global understanding that no studies at any individual scale can provide. The glue for this intellectual effort was the vertical integration of diverse quantitative methods such as compartmentalized epidemiological models, reaction-diffusion partial differential equations for organism dispersal in a landscape, machine learning to detect infection before the onset of symptoms, gene regulatory networks via ordinary differential equations, and computational drug design. The backbone of this effort was a robust data management architecture capable of managing efficiently hundreds of thousands of files, terabytes of data, and secure access for dozens of researchers. I will comment on the intriguing avenues for research and the tremendous challenges in science training that this line of inquiry open. I will also comment on opportunities for future research. |
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