Skip to content
Harvard T.H. Chan School of Public HealthHarvard T.H. Chan School of Public Health main site homepage
  • Quicklinks
    • About the School
    • Academics
    • Admissions
    • Research
    • Faculty
    • Student Life
    • News
    • Alumni
    • Frontiers
    • Make A Gift
Center for Communicable Disease Dynamics
Center for Communicable Disease Dynamics
  • Home
  • About
    • Director’s Welcome
    • About the Center
    • People
    • News & Events
      • Events Calendar
      • ID Epi Spring Seminar Series
      • 11th Annual Workshop to Increase Diversity in Mathematical Modeling and Public Health
    • Opportunities
    • Contact
  • Publications
  • COVID-19
  • Support CCDD

Publications

Filter Results

High-resolution Spatio-temporal Model for County-level COVID-19 Activity in the U.S

Author(s): Shixiang Zhu, Alexander Bukharin, Liyan Xie, Mauricio Santillana, Shihao Yang, Yao Xie|Journal: ArXiv| September 2020
We present an interpretable high-resolution spatio-temporal model to estimate COVID-19 deaths together with confirmed cases one-week ahead of the current…

Tracking disease outbreaks from sparse data with Bayesian inference

Author(s): Bryan Wilder, Michael J Mina, Milind Tambe|Journal: ArXiv| September 2020
The COVID-19 pandemic provides new motivation for a classic problem in epidemiology: estimating the empirical rate of transmission during an…

Pixelate to communicate: visualising uncertainty in maps of disease risk and other spatial continua

Author(s): Aimee R Taylor, James A Watson, Caroline O Buckee|Journal: ArXiv| May 2020
Maps have long been been used to visualise estimates of spatial variables, in particular disease burden and risk. Predictions made…

Fever and mobility data indicate social distancing has reduced incidence of communicable disease in the United States

Author(s): Parker Liautaud, Peter Huybers, Mauricio Santillana|Journal: ArXiv| April 2020
In March of 2020, many U.S. state governments encouraged or mandated restrictions on social interactions to slow the spread of…

A machine learning methodology for real-time forecasting of the 2019-2020 COVID-19 outbreak using Internet searches, news alerts, and estimates from mechanistic models.

Author(s): Liu D, Clemente L, Poirier C, Ding X, Chinazzi M, Vespignani A, Santillana M|Journal: ArXiv|PMID: 32550248| April 2020
We present a timely and novel methodology that combines disease estimates from mechanistic models with digital traces, via interpretable machine-learning…
Harvard T.H. Chan School of Public Health
677 Huntington Avenue Boston, MA 02115
+1 (617) 495‑1000
  • Harvard Chan Home
  • Contact Us
  • Harvard University Home
  • Make a Gift
  • Privacy Policy
  • Report Copyright Violation
  • Accessibility
Copyright © The President and Fellows of Harvard College