I investigate the epidemiology and population genetics of human infectious diseases with a focus on malaria and its mosquito vectors in particular: How strategies can be devised to slow the emergence and spread of resistance; How best to detect and monitor the spread of resistance to inform public health policy choice; How public health policy and health systems can be modified to mitigate the impact of resistance once it has arisen.
Areas of interest
The general epidemiology and population genetics of human infectious diseases. Drug resistance in malaria and how strategies can be devised to slow its emergence and spread. How best to detect and monitor the spread of resistance to inform public health policy choice.
Ian Hastings graduated in Zoology from Edinburgh University in 1981. He then squandered three years attempting to understand immunology before returning to quantitative/population genetics and obtained his PhD from Edinburgh University in 1989. He worked on mouse genetics and general theoretical population genetics before taking up an MRC fellowship in 1994 to work on malaria population genetics. He moved to the Liverpool School of Tropical Medicine in 1999 and has continued to work on malaria population genetics and, more recently, the problem of drug resistance in HIV.
Experience has shown that treating infectious agents inevitably leads to the evolution of drug resistance; they key questions are how long will the process take, and how can we adopt strategies to delay it? Essentially we cannot answer the first question for two main reasons. Resistance is encoded by genetic mutations which in the case of resistance to some antimalarial drugs is exceedingly rare. This means that luck plays a huge role: we may be lucky and not get a mutation for a long time, or we may be unlucky and a mutation nullifies a drug within a short time (see figure).
The other reason is that it is obviously unethical to experiment on people with severe diseases and we remain profoundly ignorant about much of the basic biology underlying the infection dynamics; this means it is impossible to calibrate our calculation in several key areas. My work essentially consists of making population genetic models of the process, in which we can 'guesstimate' the unmeasured parameters and assess their impact. The models can then play a vital role in addressing the second question i.e. how can we devise strategies to slow the spread of resistance once it has arisen?