COVID-19 Modeling Projects
Impact of travel restrictions on introduction of SARS-CoV-2
Working with Nate Grubaugh (EMD) and other colleagues, we used a combination of genomic epidemiology and modeling to show that early SARS-CoV-2 transmission in Connecticut was likely driven by domestic (rather than international) introductions. Moreover, the risk of domestic importation to Connecticut exceeded that of international importation by mid-March regardless of the estimated impact of federal travel restrictions.
PHMU Personnel: Virginia Pitzer, Kayoko Shioda, Anne Wyllie, Nathan Grubaugh
Preprint Publication [submitted to Cell]
Influence of testing policies on estimates of epidemic growth rates. [Work in progress]
We are examining how changes in testing policies (e.g. an increase in test availability or more targeted testing) may impact estimates of the epidemic growth rate, basic reproductive number (R0), and effective reproductive number (Rt). This has important implications for model-based inferences about the (potential) impact of social distancing and other control measures.
PHMU Personnel: Virginia Pitzer
Adaptive decision models to inform when to tighten and when to relax social distancing interventions
A decision support tool that explicitly accounts for the tradeoffs between the health benefits and economic costs of social distancing interventions to assist policy makers in their determination of the optimal timing of these interventions based on the latest epidemiological data collected through surveillance systems.
Tracking the COVID-19 epidemic using high-volume non-traditional data
Due to the lack of consistent and widespread testing for coronavirus, there is a need to use non-traditional data sources to track the progression of the epidemic. We are using high-volume electronic data from emergency departments, hospitals, and doctors visits to track the rate of people seeking care for syndromes that would be consistent with COVID-19 (e.g., fever, cough). We use statistical models to separate out increases related to COVID-19 from seasonal variations and increases due to influenza.
PHMU Personnel: Dan Weinberger (in collaboration with Connecticut and Arizona DPH), Ted Cohen, Virginia Pitzer, Josh Warren, Marcus Russi, Alyssa Amick, Forrest Crawford, Kelsie Cassell, Ernest Asare, Yu-Han Kao.
COVID-19 Statistics, Policy and Epidemiology Collective (C-SPEC)
We have developed 1) a dynamic, population-level epidemic model to track and predict the trajectory of the virus in response to non-pharmacologic interventions; and 2) a queuing model to predict impact/congestion on hospital resources. We are working closely with numerous jurisdictions (including the six Bay Area counties, the NYC DPH, the State of Connecticut, and the City of New Orleans) to adapt our model inputs and advise.
PHMU personnel: This is a collaboration with investigators at Harvard, Stanford, and the University of Maryland. Faculty leaders: Gregg Gonsalves; David Paltiel, Reza Yaesoubi. Other PHMU investigators and trainees: Ruthie Birger, Melanie Chitwood, Tyler Copple, Forrest Crawford, Hanna Ehrlich, Margret Erlendsdottir, Soheil Eshghi, Suzan Iloglu, Yu-Han Kao, Stephanie Perniciaro, Maile Phillips, Kayoko Shioda, Thomas Thornhill, Elizabeth White, Anna York.
Design of clinical trials for COVID-19 therapies
PHMU personnel: David Paltiel in collaboration with colleagues at the Massachusetts General and Brigham & Womens Hospitals.
What happened in Florida?
The COVID-19 epidemic has grown rapidly in Florida, with the greatest concentration in Broward and Miami-Dade counties. Health officials have warned that hospitals may soon be overwhelmed with patients requiring intensive care and artificial respiration. Governor Ron DeSantis recently issued a stay-at-home order, roughly 2 weeks after similarly impacted states issued the same mandate. This project seeks to estimate the number of excess hospitalizations and deaths due to COVID-19 that resulted from this delay in implementation of public health intervention.
Effect of Stay-at-home mandates on human mobility patterns from mobile device data
Through a private sector partnership I am working on understanding the epidemiological consequences of reductions in mobility using a new metric designed to accurately measure “potential contacts” outside the home, with a view to influencing nationwide public health guidelines and policy directly within the Trump administration.
PHMU Personnel: Forrest Crawford