Mentor(s): Dr. Pej Rohani group
Abstract: Influenza viruses, such as H3N3 and H1N1, are a major cause of illness and death worldwide, accounting for over 3 million severe cases and approximately 500,000 deaths each year. These viruses also have a significant economic impact. The use of predictive modelling has been a useful in detecting new strains of the virus and developing the vaccines against the virus.
In this project, we will compare the predictive performance of density-based outlier detection algorithms, specifically the Empirical-Cumulative-distribution-based Outlier Detection (ECOD) algorithm, with isolation forest and one-class support vector machines (SVMs) in detecting unusual physicochemical properties in influenza A viruses. By identifying viruses with significantly different physicochemical properties, we can identify those that may be antigenically distinct, which has important implications for vaccine development and public health efforts.
We will use both simulated and benchmark data from Smith et al. for the experiments and fit the models using Python libraries such as Sciklearn, Pandas, Numpy, and PyoD. We will then use R packages, including ggplot2, ggdensity, dplyr, and tidyverse, for data visualization and analysis.
The ECOD algorithm has three desirable properties for this task: it can be fitted with limited hyperparameter tuning, it is computationally effective, and it improves interpretability. We will compare the predictive and inferential performance of all three algorithms. The results for isolation forest and one-class SVMs will be provided from previous experiments. We will end the project with a final report.
Is the project computational, empirical, or both? Computational.

Gowri Vadmal, a student at Stanford University, worked in the lab of Dr. Pej Rohani

Abstract Pertussis was considered one of the great diseases of childhood with most people experiencing a bout of the infection by the age of 15.  The initial roll out of vaccines in the 1950s led to a marked decline in pertussis incidence, with optimism over its potential elimination.  Over the past 20-30 years, however, a clear increasing trend in pertussis cases has emerged.  A number of putative Pertussis (whooping cough) is caused mainly by the bacterium Bordetella pertussis. Many countries use acellular pertussis vaccines containing the antigen pertactin (PRN), which plays an important role in pathogenesis. In recent years, we’ve observed an increasing number of B. pertussis isolates that are PRN-deficient and able to infect people even in highly vaccinated countries. We used SIRV models to look at the fitness cost of the bacterium losing PRN and the advantage of being able to infect already vaccinated people. Strains can invade the population only if their leakiness (ability to infect vaccinated people) and transmission are above the threshold for invasion, which depends on vaccination coverage and the cost of immune evasion for the strain. At low vaccination coverage, strains with high leakiness dominate the system when the fitness cost to evade immunity is low, but as cost increases for the strains to infect people, there is bistability between the wildtype and mutant strains. However, at higher vaccination coverage, the wild type completely fades out and the strains with the highest leakiness dominate the system. Thus, the conditions for B. pertussis mutant invasion can change depending on a population’s vaccine coverage, the cost of losing the pertactin gene, and the advantage of being able to evade vaccine immunity.