To support the broad public health community in responding to infectious disease outbreak, we design, test, evaluate, and implement innovative respiratory disease modeling techniques, forecasting methods, and analytical tools to aid timely decision making.
A Selection of Publications and Open-source Tools:
PLANES
We have developed a novel approach for assessing the plausibility of infectious disease forecasts and surveillance data, PLANES (PLausibility ANalysis of Epidemiological Signals). Here we describe PLANES, provide a demonstration analysis, and discuss how to use the open-source rplanes R package. PLANES aims to enable modelers and public health end-users to evaluate forecast plausibility and surveillance data integrity, ultimately improving early warning systems and informing evidence-based decision-making. The rplanes package is available on GitHub here.
Forecasting Influenza-Like Illness (ILI) during the COVID-19 Pandemic
Near-term probabilistic forecasts for infectious diseases such as COVID-19 and influenza play an important role in public health communication and policymaking. From 2013-2019, the FluSight challenge run by the Centers for Disease Control and Prevention invited researchers to develop and submit forecasts using influenza-like illness (ILI) as a measure of influenza burden. Here we examine how several statistical models and an autoregressive neural network model perform for forecasting ILI during the COVID-19 pandemic, where historical patterns of ILI were highly disrupted.
VP Nagraj
Senior Data Scientist
For more information about Infectious Disease Modeling and Forecasting: