Seung-Jong Park
$158,592
Ajitesh Srivastava
University of Southern California
California
Computer and Information Science and Engineering (CISE)
The recent outbreak of COVID-19 and its world-wide impact calls for urgent measures to contain the epidemic. Predicting the speed and severity of infectious diseases like COVID-19 and allocating medical resources appropriately is central to dealing with epidemics. Epidemics like COVID-19 not only affect world-wide health, but also have profound economic and social impact. Containing the epidemic, providing informed predictions and preventing future epidemics is essential for the global population to resume their day-to-day work and travel without fear. Shortage of resources puts undue stress on healthcare system further risking health of the community. Preparedness and better management of available resources would require specific predictions at the level of cities and counties around the world rather than solely at the level of countries. The project will provide a predictive understanding of the spread of the virus by developing machine learning based computational models to study the transmission of the virus and evaluate the impact of various interventions on disease spread. The project will learn infection prediction models for COVID-19 considering the following. (i) Predicting at state/county/city-level rather than country-level as finer granularity is essential in planning and managing resources. (ii) How infectious a person is changes over time. Learning the model through observed data will help in understanding of the temporal nature of the virality. (iii) At such granularity travel is a significant reason for the spread and needs to be accounted for. (iv) Available data needs to be “corrected” by finding the number of underlying unreported cases that are not observed and yet influence the epidemic dynamics. The project will also solve the resource allocation problem based on the prediction – for instance if a certain number of masks will be available next week in a certain state, how should they be distributed across different hospitals in the state (which hospitals and how many in each state)?Proposed project ReCOVER will use a novel fine-grained, heterogeneous infection rate model to perform predictions at various granularities (hospital/airports, city, state, country) while accounting for human mobility. ReCOVER will integrate data from various sources to build highly accurate models for prediction of the epidemic across the world at various granularity. Due to the ability to capture temporal heterogeneity in infection rate, the approach has the potential to provide insights into infectious nature of COVID-19 which are not fully understood yet. The project will address the issue of unreported cases through temporal analysis of historical infections and correct the data. The right granularities of modeling will be automatically identified, e.g., when to model a state over its cities to trade-off precision for higher reliability in predictions. The proposed project also formulates and solves a resource allocation problem that can guide the response to contain the epidemic and prevent future outbreaks. This is provided by optimal solutions to resource allocation over a network where each node (representing a region) has a function that captures probabilistic response. While the project obtains data with COVID-19 in consideration, the model and algorithms developed under the project are applicable to a wide class of contagious diseases. The project will culminate into an interactive customizable tool that can be used to perform predictions and resource management by a qualified user such as a government entity tasked with managing the epidemic response. The data and code will also be shared with research community.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.