COVID-19 is the most significant global health emergency in recent memory, with hundreds of thousands dead and widespread economic disruption.
The goal of this project is to understand the distribution, treatments and effects of COVID-19, particularly on underserved populations, ultimately improving public health. I am a co-investigator of two COVID-19 projects with the following aims:
To develop and apply state-of-the-art statistical and machine learning techniques to better prevent and optimally treat the deadly disease. More info on the studies can be found here and here.
To develop deep convolutional neural network survival methods to fully exploit medical images and time-dependent features to predict the probability of experiencing death, ICU admission, ICU discharge and hospital discharge. In collaboration with Weill Cornell and Columbia University.
Manuscripts:
Santacatterina, M., Sanders JW, Weintraub WS, and the North Carolina Covid-19 Community Research Partnership (2021). Prevention of Covid-19 with the BNT162b2 and mRNA-1273 Vaccines, The New England Journal of Medicine, 2021
Shu, M., Bowen, R. S., Herrmann, C., Qi, G., Santacatterina, M., & Zabih, R. (2021). Deep survival analysis with longitudinal X-rays for COVID-19, ICCV21, 2021
The COVID-19 Community Research Partnership Study Group. Duration of SARS-CoV-2 sero-positivity in a large longitudinal sero-surveillance cohort: the COVID-19 Community Research Partnership, BMC Infectious Diseases, 2021
Williamson, J.C., Wierzba, T.F., Santacatterina, M., et al., Analysis of Accumulated SARS-CoV-2 Seroconversion in North Carolina: The COVID-19 Community Research Partnership, medRxiv, 2021