RAPID Project on COVID-19 Hotspot Detection

RAPID: Collaborative Research: Covid-19 Hotspot Network Size and Node Counting using Consensus Estimation

This project is funded by  NSF award  2032106 

Collaborative with Arizona State University

Co-PI Team:   Mahesh Banavar,  Cihan Tepedelenlioglu, Stephanie A. Schucker, Andreas Spanias

Student Research Associates: 

Gowtham Muniraju  (ASU) and Surya Raja Monalisa Achalla  (Clarkson)

In order to open up the economy in light of the reality of COVID-19, a suite of solutions is needed to minimize the spread of COVID-19 which includes providing tools for businesses to minimize the risk for their employees and customers. It is important to detect transmission hotspots where the contact between infected and uninfected persons is higher than average. This project will provide information to assess precisely the size, density, and locations of COVID-19 hotspots and enable issuing well-informed advisories based on data-driven continuous risk assessment. Every step will be taken to ensure privacy and network security and specific algorithms will be developed for secure access and information transfer. The project will access databases at CDC, Johns Hopkins, and the WHO, and create a comprehensive website to disseminate real-time localized COVID-19 hotspot data while maintaining privacy. The project will create new algorithms and embed them in iOS and Android apps that will continuously interact with databases. The software for mobile devices as well as central hubs will be made publicly available through APIs for use by the broader community.

The project will use advanced consensus-based methods for estimating network area/size, node locations and node counts in a network based on minimal transmit-receive data. The proposed methods will lead to significant improvements compared to existing algorithms. The project will design consensus-based algorithms to estimate (a) the center, radius, and consequently, the size of the network, and (b) the number of users in the network. Localization algorithms will be designed that work with noisy and incomplete data. The proposed work is different from the contact-tracing technology used by Google and Apple which is limited to newer devices. The proposed algorithms and software will advance the state of the art while retaining compatibility with emerging and existing mobile technology. The project will help reduce COVID-19 infections and save lives. The research will also have applicability to other fields such as the E911 system, indoor user tracking, infrastructure-free implementations applicable to robotics, autonomous systems, and vehicle fleets, and location-aware patient care and other mobile health applications. The developed algorithms can be used in other emergency situations, such as locating clusters of sheltering groups in the case of earthquakes and tsunamis, to assist first responders in finding survivors after an event, and for detecting transmission nodes in the case of future pandemics or future waves of COVID-19. Outreach activities will be integrated with the research and include the creation of software and web content for dissemination.

Press release: 

https://www.clarkson.edu/news/keeping-our-communities-safe-one-smartphone-app-time 

GUI for Mathematical Modeling of COVID-19 Using a Modified SEIRS model

Mathematical representations of infectious diseases include compartment-based SEIR and SEIRS models. These models are represented using coupled differential equations that capture the flow of populations from one compartment to another. While these models have been used for several infectious diseases such as HIV/AIDS, tuberculosis, Dengue fever, and COVID-19, the models do not generally incorporate compartments for vaccinated populations, asymptomatic infections, and the possibility of reinfection. 

Here, we demonstrate a modified Susceptible - Exposed - Infected - Recovered - Susceptible (SEIRS) compartment model for COVID-19. We incorporate the compartments for vaccinated and non-vaccinated populations and those with symptomatic and asymptomatic infections. 

In the GUI below, we show progression in each compartment and attractor plots, for different sets of parameter values that can be provided by the user.  

The theoretical basis for this work is described in:

S.A. Lavanya Shri, B. Patel, M. Malu, M.K. Banavar, C. Tepedelenlioglu, A. Spanias, S. Schuckers, "Analysis of a modified SEIRS compartmental model for infectious diseases," Asilomar Conference on Signals, Systems, and Computers, 2023 (accepted).  

Link: https://lavanyashrisa.github.io/SEIRS-COVID-19-UI/

Background Work in Consensus Estimation

[1] S. Zhang, C. Tepedelenlioglu, A. Spanias, M. Banavar, Distributed Network Structure Estimation Using Consensus Methods, Morgan & Claypool Publishers, 2018.

[2] S. Zhang, C. Tepedelenlioğlu, M.K. Banavar and A. Spanias, “Distributed Node Counting in Wireless Sensor Networks in the Presence of Communication Noise,” IEEE Sensors Journal, 2017.

[3] X. Zhang, C. Tepedelenlioglu, M. Banavar, A. Spanias, Node Localization in Wireless Sensor Networks, Morgan & Claypool Publishers, 2016.

[4] M. Banavar, K. Mack, Localization using wireless signals, US Patent US10117051B2, 2018.

[5] X. Zhang, C. Tepedelenlioglu, M.K. Banavar, A. Spanias, Maximum likelihood localization in the presence of channel uncertainties, US Patent US9507011B2, 2016.

[6] S. Zhang, C. Tepedelenlioglu, A. Spanias, Distributed Network Center and Area Estimation, US Patent

US10440553B2, 2019.