Abhi develops statistical and machine learning methods for large spatial datasets as well as Bayesian models for multi-source epidemiological datasets.

Aki uses Bayesian methods and statistical computing to tackle methodological challenges in healthcare analytics and large-scale biomedical applications.

Mathematical modeling of infectious diseases plays an important role in the development and evaluation of intervention plans. These plans, such as the development of vaccines, are usually pathogen-specific, but laboratory confirmation of all pathogen-specific infections is rarely available. If an epidemic is a consequence of co-circulation of several pathogens, it is desirable to jointly model these pathogens in order to study the transmissibility of the disease to help inform public health policy.

A major challenge in utilizing laboratory test data is that it is not available for every infected person. Appropriate imputation of the missing pathogen information often requires a prohibitive amount of computation. To address it, we extend our earlier hierarchical Bayesian multi-pathogen framework that uses a latent process to link the disease counts and the lab test data. Under the proposed model, imputation of the unknown pathogen-specific cases can be effectively avoided by exploiting the relationship between multinomial and Poisson distributions. A variable selection prior is used to identify the risk factors and their proper functional form respecting the linear-nonlinear hierarchy. The efficiency gains of the proposed model and the performance of the selection priors are examined through simulation studies and on a real data case study from hand, foot and mouth disease (HFMD) in China.