Climate, disease and archaeology

Vector borne disease (VBD) are some of the most debilitating, accounting for massive loss in terms of human lives, overall social well-being, political stability and economic growth. Malaria is perhaps the most debilitating disease of all time. The relationship between humans, mosquitoes and the malaria parasite has impacted us on en evolutionary level; yet more recently emerging disease such as Rift Valley Fever Virus (RVF) pose entirely new threats to humans.

The research undertaken in our lab takes a novel approach to assessing, predicting, and potentially providing solutions to tackle VBDs. We mine evidence from the archaeo-historic record, alongside hyper-local climate, land-use and settlement patterns, calibrated and aligned with major weather systems such as El Niño using satellite data. By consolidating evidence from a large number of natural experiments our research investigates the complex relationships between disease, climate, infrastructure and human behaviour. The massive datasets generated are assessed using neural networks built on Artificial Intelligence (AI) algorithms, producing models that could in the future help to guide 21st century public health interventions. These models allow offer the possibility to disentangle the constellation of factors that lead to epidemics and identify interventions for specific regions, to either target human behavior, provide an ecological management strategy, or combination of multiple interventions. Seetah serves as the Faculty Lead for Stanford’s Malaria Working Group, a recent body aiming to harness a multidisciplinary approach to tackling malaria under the aegis of the Center for Innovation in Global Health.

Lab members and collaborators work primarily on models to study malaria, utilizing data gathered from Mauritius. The framework so far developed is also being applied to RVF, with data from Kenya. RVF can decimate livestock populations. Human casualties are strongly correlated with animal slaughter. The RVF project bridges the gap between clinical / epidemiological research, and the behavioral factors that influence risk.