Biostatistics COVID-19 Research
This page highlights the professional and community service work done by members of Michigan Biostats with the goal of fostering collaboration and shared service work both within and outside of the department.
I have been inspired by the outpouring of research and service from so many people: from within our department to across the globe. Together we are working ceaselessly to predict and mitigate the viruses' spread, influence governmental policies, find cures, protect medical workers, design testing strategies, characterizing disparities in outcomes and perform contact tracing. With our scientific training we can make a difference!
- Bhramar Mukherjee, Department Chair
Tom Braun (Faculty)
The current clinical trial will examine how endothelial dysfunction, a hypothesized cause of SARS-CoV2 mortality, can be addressed with the endothelial stabilizing agent defibrotide, which is FDA approved for use in patients with hepatic sinusoidal obstruction syndrome following hematopoietic cell transplantation.
Tom Braun (Faculty)
AAT is a naturally occurring serine protease inhibitor and has shown pre-clinical and clinical evidence of preserved organ function without increased the risk of infection in highly inflammatory conditions with dysregulated T cell responses. This clinical trial will test the hypothesis that infusion of exogenous AAT in hospitalized patients with moderate to severe COVID-19 will prevent clinically significant progression of disease.
Lu Wang (Faculty)
This is a randomized clinical trial designed to evaluate the efficacy of ICQ-950AN to prevent COVID-19 clinical infection in high risk individuals (HRIs) such as health care workers, first responders, essential workers, and their families. We will also evaluate SARS-CoV-2 seroconversion in participants taking ICQ-950AN, as well as the safety and tolerability of ICQ-950AN.
Community and Health Workers Service
Our efforts in this "Fight COVID-19" campaign focus on securing adequate personal protective equipment (PPE) to protect our frontline colleagues at Michigan Medicine and healthcare workers at local hospitals so that they can perform their heroic work more safely.
Based on incomplete statistics, we have donated 164,271 pieces of PPEs to Michigan Medicine, IHA, St Josheph Mercy Hospital, Detroit Medical Center, DMC Sinai Grace Hospital, Pittsfiled Police, VA in Detroit, Dearborn Beaumont, Beaumont Wayne, Henry Ford Hospital, Glacier Hills Care & Rehabilitation Center, Detroit Rescue Mission Ministries, Convenant House Michigan, Homeless Youth Program in Ypsilanti, SOS Community Services in Ypsilanti, Food Gatherers in Ann Arbor, DMC Harper Hutzel Hospital, Hope Clinic, Essential Workers Mother's Pantry, St Josheph Mercy Hospital Canton, Hurley Medical Center Flint, Washtenaw County Community Health Service, Doyle Ryder Education Center, Packard Health, Homewatch Care Givers, U-M GICT Internal Medicine CMR, Green Rd Post Office, and PACE.
UM-ACP deeply appreciates the great efforts of our ACP members, collaborators and U-M alumni all over the world. The huge support from our postdocs, students and parents from China, the local Chinese community, U-M staff, and all those who have donated and participated will be remembered forever. We are proud of everyone who is in this drive. Any achievement in this campaign is impossible without you! Thank you!!
We were engaged in COVID-19 related activism work for India, specifically the state of West Bengal, from where we come. We raised our public health voice about undercounts and misreporting/false reporting of COVID data in West Bengal, assembling a group of public health scientists from the US (mostly biostatisticians of Bengal origin) and writing an open letter to the Chief Minister of West Bengal. The letter created a lot of political and public uproar, eventually leading to revised data reporting and COVID statistics for the state. Faculty outside UM involved in the initiative: Sudipto Banerjee, Nilanjan Chatterjee, Malay Ghosh, Sanjib Basu, Debashree Ray.
Data Apps and Maps
Bhramar Mukherjee, Maxwell Salvatore, Alexander Rix, Michael Kleinsasser, Daniel Barker, Lili Wang, Rupam Bhattacharyya, Soumik Purkayastha, Debashree Ray, Shariq Mohammed, Aritra Halder, Debraj Bose, Peter Song, Mousumi Banerjee, Veera Baladandayuthapani, and Parikshit Ghosh.
A resource to describe the COVID-19 outbreak in India to date as well as prediction models under various hypothetical scenarios. The figure and forecasting models update as new data becomes available (i.e., at least daily). You may download PNG files of each figure by clicking on the camera icon when you are hovering within each plot. Please cite our medium article and this website in any publication that you use this resource for.
Bhramar Mukherjee (Faculty), Lauren Beesley (Staff), Jiacong Du (Student)
R01. The goal is to systematically study coronavirus diseases (with a focus on COVID-2019) using integrative ontological and bioinformatics approaches.
Grant submitted to Lifearc foundation .
This is a randomized multicenter study in patients with COVID 19 within 48 hours of intubation and initiation of mechanical ventilation. We hypothesize that single dose of surfactant will improve oxygenation, reduce ventilator free days and ICU length of stay in these critically ill patients.
In the News
AgenciaBrasil: Part of India is banned.
Asia Insurance Post: India faces spike in coronavirus cases, says study, in test for health system
BLOGGVALLEY.CO: Coronavirus lockdown.
Bmj.com: Covid-19: India imposes lockdown for 21 days and cases rise.
Brookings: COVID-19: Is India’s health infrastructure equipped to handle an epidemic?
Busines Insider India: Coronavirus cases in India can rise 230,000 in a month, and 1.3 million by May: Report.
Business Standard: India coronavirus dispatch: Long-term strategy needed against Covid-19
Currency Live: GBP/INR: Rupee Turns Bearish After Modi’s Lockdown Measures
Daily Mirror Online: India declares 21-day ’total lockdown’ as coronavirus cases rise.
El.Nacional.Cat: India confines 1.3 billion people with coronavirus
Evening Standard UK: India announces country-wide lockdown.
Hindustantimes: Coronavirus Crisis: 100,000 dead in 101 days.
Internetnewscast: India announces countrywide lockdown of 1.3 billion people.
Lankaeverything: Sri Lanka’s neighbour to go on total lockdown.
News.umich.edu: Coronavirus modeling, impact on India’s pandemic response.
Pakistan Defense: Overcome by anxiety: Indians in lockdown many can ill afford.
radar Amazonico: India is banned after spread of coronavirus.
Rediff.com: 'There will be a drastic drop with the lockdown'
Techinfeed: India Goes for 21 Days “Complete Lockdown”.
The Daily Observer: India under lockdown for 21 days.
The English Print.Com: India faces spike in Coronavirus cases.
The Express Tribune: Most of India under lockdown as coronavirus appears in small towns
The News Minute: Why is the lockdown 21 days long? Experts break it down for you.
The new paper: Indian cops enforce lockdown as cases expected to spike.
The Spinoff: Covid 19 Live Updates
The Telegraph India: Target Set for India to Reduce Figures
The Telegraph India: Calcutta Girls in UK Coronavirus Fight
The Telegraph India: Shine on: From a home of liberal arts to medicine & public health.
The Times of India: ‘Epidemiologic models show we need aggressive measures in the early phase
The Top 10 News: India extends lockdown as Coronavirus appears in small towns.
Travel Wire News: India extends lockdown as coronavirus appears in small towns.
World Socialist Web Site: Modi places India’s 1.3 billion people under lockdown
We developed an extended SIR epidemiological model that can handle time-dependent transmission rate and quarantine effects. The methods have been established in an R package available on Github.
Yi Li (Faculty)
Estimation of time-varying transmission and removal rates underlying epidemiological processes: a new statistical tool for the COVID-19 pandemic
The coronavirus pandemic has rapidly evolved into an unprecedented crisis. The susceptible-infectious-removed (SIR) model and its variants have been used for modeling the pandemic. However, time-independent parameters in the classical models may not capture the dynamic transmission and removal processes, governed by virus containment strategies taken at various phases of the epidemic. Moreover, very few models account for possible inaccuracies of the reported cases. We propose a Poisson model with time-dependent transmission and removal rates to account for possible random errors in reporting and estimate a time-dependent disease reproduction number, which may be used to assess the effectiveness of virus control strategies. We apply our method to study the pandemic in several severely impacted countries, and analyze and forecast the evolving spread of the coronavirus. We have developed an interactive web application to facilitate readers' use of our method.
The coronavirus disease 2019 (COVID-19) has evolved into a global crisis. As of 5/5/2020, the US has become the most severely hit country with more than 1 million confirmed cases and 70,000 deaths. The spread of the virus has manifested diverse patterns across the 50 states, with states installing various quarantine polices at different time points. Moreover, how the epidemic in each state impacts those in other states remain unclear. The widely used susceptible-infected-recovered (SIR) model may not account for the noisy nature of the collected data, and cannot capture infection and recovery processes influenced by virus control strategies taken by the states. More importantly, it cannot disclose the interactive relationships among these processes across different states.
We propose a stochastic multi-community varying rate SIR (SMCVR-SIR) model to construct a covariate-dependent infection transmission network, and identify the important factors, such as time and virus control strategies, that may influence the change of the infection and removal rates as well as their interactions between communities or states. Our proposed model provides a systematic means to assess the effectiveness of COVID-19 containment strategies, and enables us to decipher the disease spread patterns across the 50 states and how they interact with each other.
Qinmengge Li (Student)
Fitted the designed compartment model, SECSDR model, with Japanese confirmed cases data. And investigated some basic properties of the COVID-19 spreading patterns.
An epidemiological forecast model and software assessing interventions on COVID-19 epidemic in China
We develop a health informatics toolbox that enables timely analysis and evaluation of the time-course dynamics of a range of infectious disease epidemics. As a case study, we examine the novel coronavirus (COVID-19) epidemic using the publicly available data from the China CDC. We extend the SIR model to incorporate various types of time-varying quarantine protocols. An R software package is made available for the public, and examples on the use of this software are illustrated.
The R software is available here.
We develop a spatiotemporal epidemiological forecast model that combines a spatial cellular automata (CA) with a temporal Susceptible-Antibody-Infectious-Removed (SAIR) model. This new toolbox provides a projection for the county-level prevalence of COVID-19 in each US county. Such localized risk projection is useful for decision-making on business reopening.
The novel viral strain, SARS-CoV-2, is highly contagious and hence easily spreads via human to human contact - which motivates a closer look at the inherent dynamics of the spread at a micro-scale and assess its multi-fold ramifications on the cultural, economic and health infrastructures. Using rapidly evolving COVID-19 data and using a data-driven contact network framework, we attempt to answer these questions and inform a phased approach to dealing with the pandemic by appropriate resource prioritization and allocation.
We take India as a case study to exemplify this. Nuanced modeling can aid more rapid containment and mitigation with minimal disruption to the overall economy and sensitivity to humanitarian concerns.
Zhenke Wu (Faculty)
The work use a unique data from Shenzhen China and provides insights into clinical progression of cases starting early in the course of infection. Patient characteristics near symptom onset have tremendous potential for informing strategic response and resource allocation.
We have contacted 30,000 participants from whom we have collected genetic data in Genes for Good to take a new survey on COVID-19 we have recently added to our Facebook application. We ask them questions about whether they have the disease, how it was diagnosed, severity, their overall health and activities that may influence disease risk. The plan is to probe this data set for genetic associations and contribute to other meta-analyses that are currently underway.
Tian Gu (Student)
Evaluating the risk of pre-existing comorbidities on COVID-19 mortality in China, a collaborative work with researchers from Shanghai Jiao Tong University and East China Normal University
In the earlier days of the pandemic, I was deeply inspired by data scientists (aka public heath warriors/data-based prophet) who utilized their specialty to make a difference to the community, especially my mentors. Collecting publicly confirmed cases in China, our team hoped to provide substantial evidence to identify the high risk patients in March when little was known.
Phil Boonstra (Faculty)
This project uses data from ELSO, a worldwide registry of ECMO runs, to study the outcomes and final dispositions of patients with Covid-19 receiving ECMO support.
The COVID-19 Data Project is collecting the number of cases and deaths daily for every county in the United States. The project also has a large group working on quality assurance, comparing our numbers to multiple sources and researching discrepancies, with the goal of creating the most accurate data possible. The project is also planning several research project utilizing the data including looking at the effect of stay-at-home and other policies and the relationship between cases, deaths and vulnerable populations.
Mousumi Banerjee (Faculty)
Survey study in Southeast Michigan on patient-reported clinical and economic outcomes of severe COVID. We are using a statewide COVID-19 registry to identify adult survivors of severe COVID-19—defined as requiring mechanical ventilation for >24 hours —from three large hospital systems in Southeast Michigan, and survey them to assess long-term health-related quality of life, functional independence, mental health, work loss, and financial toxicity. This is a collaboration with colleagues in Michigan Medicine and Institute for Healthcare Policy and Innovation. Sarah Hawley, Justin Dimick, John Scott.
Mousumi Banerjee (Faculty)
Survey work to study impacts of social distancing policies around the world (multiple countries in North and South America, Europe, Asia). The COVID-19 pandemic has triggered a wide variety of “social distancing” policies across and within countries around the developed and developing world. Some countries and local jurisdictions have mandated the closure of businesses, schools, and recreational areas, while others have issued “stay at home” orders (both voluntary and non-voluntary), travel bans, curfews, and other restrictions on movement. These policies were then enforced differently, with some jurisdictions relying upon moral persuasion, others proactively regulating behavior, and still others assessing fines or some other form of punishment for violations. The survey portion of this project explores how – and the extent to which – people are actually practicing and coping with social distancing. We plan to distribute two waves (May-June 2020, Aug-Sept 2020) of the survey in over a dozen languages and locations. The data science portion of the project will consist of scraping official government websites to determine what policies are being implemented to combat the spread of COVID-19 and at what level(s) of government, and how information about these policies is being disseminated to citizens. We will also scrap data from Open Street Maps to determine healthcare infrastructure by neighborhood. This is a collaboration with colleagues in social science, public health, Ford School and International Institute. Laura Rozek, Elizabeth King, Pauline Jones, Ann Lin, Twila Tardif.
Omics and Molecular Markers
Lili Zhao (Faculty)
The study will measure biomarkers and associate them with cardiovascular, kidney and nervous system complications outcomes, using the Michigan Medicine COVID-19 Cohort.
Laura Scott (Faculty), Anne Jackson (Staff), Li Guan (Student)
T2D and obesity are risk factors severe illness in individuals infected with COVID19. The receptor for COVID19, ACE2, is expressed in many tissues across the body including subcutaneous adipose tissue. Our work aims to identify genetic variation that influences ACE2 gene expression in subcutaneous adipose tissue and to identify associations between T2D-related phenotypes, such as obesity and fasting insulin, and ACE2 expression.
Work performed with investigators from other institutions: Kerrin Small, Karen Mohlke, Sarah Brotman and Julia El-Sayed Moustafa
Lana Garmire (Faculty)
Coronavirus disease (COVID-19) is an infectious disease discovered in 2019 and currently in outbreak across the world. Lung injury with severe respiratory failure is the leading cause of death in COVID-19, brought by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, there still lacks efficient treatment for COVID-19 induced lung injury and acute respiratory failure. Drugs can be developed to target proteins such as Angiotensin-converting enzyme 2 (ACE2), inhibited by spike protein of SARS-CoV-2. We have previously proposed two candidate drugs, COL-3 (a chemically modified tetracycline) and CGP-60474 (a cyclin-dependent kinase inhibitor), for treating lung injuries in COVID-19, based on their abilities to reverse the gene expression patterns in HCC515 cells treated with ACE2 inhibitor and in human COVID-19 patient lung tissues. We are continuing research in this area to expand the search of drugs to additional targets using bioinformatics approaches.
Tom Braun (Faculty)
This is a prospective study designed to study healthcare workers at Michigan Medicine who are involved in direct face-to-face (in-person) patient care during the COVID-19 pandemic or HCWs who are not involved in direct (in-person) patient care, but work in a unit where COVID-19 patient care does or may occur. Subjects will be on-study for 30 days during which they will continuously wear a non-invasive TempTraq axillary temperature sensor patch and wear a Fitbit device.
This is a prospective observational study among those hospitalized COVID-19 patients who were enrolled or transferred to Michigan Medicine. We applied propensity score based causal inference methods to assess the effect of Tocilizumab among the ventilated COVID-19 patients at Michigan Medicine.
Bhramar Mukherjee, Debashree Ray, Maxwell Salvatore (Student), Rupam Bhattacharyya (Student), Lili Wang (Student), Jiacong Du (Student), Shariq Mohammed (Post Doc), Soumik Purkayastha (Student), Aritra Halder, Alexander Rix (Staff), Daniel Barker (Staff), Michael Kleinsasser (Staff), Yiwang Zhou (Student), Debraj Bose (Student), Peter Song (Faculty), Mousumi Banerjee (Faculty), Veerabhadran Baladandayuthapani (Faculty), Parikshit Ghosh
Predictions, Role of Interventions and Effects of a Historic National Lockdown in India's Response to the the COVID-19 Pandemic: Data Science Call to Arms
With only 536 cases and 11 fatalities, India took the historic decision of a 21-day national lockdown on March 25. The lockdown was first extended to May 3 soon after the analysis of this paper was completed, and then to May 18 while this paper was being revised. In this paper, we use a Bayesian extension of the Susceptible-Infected-Removed (eSIR) model designed for intervention forecasting to study the short- and long-term impact of an initial 21-day lockdown on the total number of COVID-19 infections in India compared to other less severe non-pharmaceutical interventions. We compare effects of hypothetical durations of lockdown on reducing the number of active and new infections. We find that the lockdown, if implemented correctly, can reduce the total number of cases in the short term, and buy India invaluable time to prepare its healthcare and disease-monitoring system. Our analysis shows we need to have some measures of suppression in place after the lockdown for increased benefit (as measured by reduction in the number of cases). A longer lockdown between 42-56 days is preferable to substantially “flatten the curve” when compared to 21-28 days of lockdown. Our models focus solely on projecting the number of COVID-19 infections and thus, inform policymakers about one aspect of this multi-faceted decision-making problem. We conclude with a discussion on the pivotal role of increased testing, reliable and transparent data, proper uncertainty quantification, accurate interpretation of forecasting models, reproducible data science methods and tools that can enable data-driven policymaking during a pandemic. Our software products are available at covind19.org.
Tools for Clinicians
Holly Hartman (Student), Matthew Schipper (Faculty), Kelly Kidwell (Faculty), Yilun Sun (Faculty), Theresa Devasia (Student), Elizabeth Chase (Student), Emily Morris (Student), Pin Li (Student), Kim Hochstedler(Student), Madeline Abbott (Student)
Currently, oncologists are weighing the benefits and risks of both immediate cancer treatment and delaying cancer treatment during the SARS-COV 2 pandemic. We used published estimates of COVID-19 mortality and epidemiologic trends and integrated this with survival estimates for cancer to develop the OncCOVID web app which allows clinicians to make personalized treatment plans for cancer patients during the pandemic.
Watch news video here.