Despite notable advances in medicine over the last century, recent pandemics such as COVID-19 remind us that the threat of infectious diseases to human populations is very real. While continuing advances in medicine are essential, information technologies can greatly improve our ability to detect and contain the devastating effects of infectious diseases. In this direction, public health agencies collect, periodically update, and publicly report field data containing geolocated information about the tested, infected, recovered, hospitalized, and deceased individuals in those areas affected by the disease. However, this data is unreliable, incomplete, and coarse-grained; therefore, health agencies can greatly benefit from information technologies to filter and analyze field data in order to make reliable predictions about the future spread of the disease. Moreover, the final objective of a health agency is to use this information to design efficient strategies to contain the spread of infectious diseases. To achieve this objective, health agencies have at their disposal epidemic-control resources, such as social distancing, traffic restrictions, and the distribution of pharmaceutical resources (whenever available). Due to the heterogeneity and high cost of these resources, finding the cost-optimal allocation of each type of resource throughout the population is a very challenging problem of utmost societal impact. In this project, we propose to develop an integrated framework for modeling, prediction, and cost-optimal control of epidemic outbreaks using finite resources and unreliable data.
In order to implement practical epidemic-control tools, it is necessary to first develop mathematical models able to replicate salient geo-temporal features of disease transmission. These patterns are strongly influenced by the geography of the area over which the disease is spreading, as well as the mobility patterns of the population. In this direction, we will use complex contact graphs to model both realistic geographical constraints and mobility patterns. In particular, the vertices of this graph correspond to towns/districts and its links represent interactions between them. On top of this contact graph, we will build a dynamical model aiming to replicate the complex geo-temporal spread of the disease. In this direction, we will consider a system of stochastic processes, coupled through the edges of the contact graph, to model the evolution of the disease. Once the model of the spread is tuned, we will then proceed to the design of a coordinated strategy to contain the spread of the infection by distributing resources throughout the population. In this direction, we will design and implement an optimization program to find the cost-optimal allocation of heterogeneous resources given a finite budget. In this research task, we must deal with the inherent uncertainty of field data, as well as the presence of sampling biases that can have a dramatic impact on the fairness of the cost-optimal allocation of resources. The success of the proposed research program would greatly improve our ability to efficiently detect and appropriately react to epidemic outbreaks, whereupon a rapid control response can be deployed.
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.
