Project 1: Mitigation of COVID-19 in the Era of Vaccination
Civil and Environmental Engineering
The successful development of various COVID-19 vaccines has brought hope to the world that the control of this virus is within reach. However, at least 70-80% of the population must be vaccinated in order to achieve the target levels of herd immunity and to safely contain the virus. In that respect, several challenges still exist on both the national and international levels. These include: (1) The anti-vaxxers groups who spread misinformation about the vaccine technology pushing some people away from taking part of the vaccination campaign, (2) The emergence of new COVID-19 strains which higher transmission and some of which may escape vaccination, (3) Logistical problems in producing, distributing and administering vaccines doses on a large scale. While it is expected that there will be enough doses for 300 million Americans by fall 2020, the situation worldwide is very different. For some countries, it is expected that they will need 2-4 years in order to vaccinate enough of their population to achieve herd immunity. In a highly connected world like the one we live in, this disparity in vaccine distribution may have damaging effects including giving the virus enough time to mutate and evolve into more resistant strains as well as other side effects on global economy.
The objective of this project is multifold: (1) To analyze COVID-19 vaccine misinformation on social media and explore its correlation with the vaccine intake rate in different states. (2) To build data-calibrated epidemiological models that assess the effect of vaccination on the virus spread and severity, and (3) To model the effect of vaccine disparity on the future of COVID-19 transmission worldwide, and to assess the need of continuation of various non-pharmaceutical interventions (e.g. mask wearing and social distancing).
Interested students should have a good mathematical background and are able to code in Matlab, Python, or C++.
Project 2: Resolving Racial Health Disparities by using Advanced Statistics and Machine Learning on Complex Multidimensional Datasets
Food Science & Human Nutrition
Advanced, big-data computational methods developed in this project will provide tools for a true systems approach to health disparities, that will allow a multi-scale, multi-dimensional analysis of all aspects of this problem. This will arm citizen scientists with necessary data to argue their case to legislators, and identify the right complex of factors to be targeted as part of the societal intervention strategy.
Health disparities, be it racial, economic, rural-urban, gender- or age-based, have come to the forefront across the world. Two kinds of research have emerged: (1) elucidating the biological, social, economical and psychological mechanisms of health disparities, and (2) developing interventions that engage community in targeting these mechanisms to reduce health disparities. The first category is based on analysis of complex multidimensional datasets containing molecular, genetic and biometric information from individuals, plus their socioeconomic status, local environment/safety, degree of segregation, access to medical care/education, and levels of pollution. We will develop novel statistical and machine learning approaches to harmonize these heterogeneous data and detect important contributors to health disparities. The second category targets practical solutions for health disparities, lead by the community members working with policy makers. Our idea is to arm community members with tools that document their situation in scientifically rigorous ways, empowering these citizen scientists to pinpoint actionable items, problems and solutions to health disparities, and work with policy-makers to address them. One approach is to crowdsource data collection and analysis by providing citizen scientists with mobile apps to collect, upload, share and analyze relevant data.
Students will (1) develop novel methods for multi-scale data integration and analytics, using advanced statistical and machine learning techniques; and (2) prototype the software and mobile apps based on those novel computational approaches, for the purposes of elucidating Racial Health Disparities.