Friday, September 27, 2024

Genomic profiling and spatial SEIR modeling of COVID-19 transmission

Lineage distribution of SARS-CoV-2 across
geographic regions of Ontario, Canada,
Western New York, and New York City over time
In the past we have posted on using agent-based models for explore the spread of diseases. We have been keeping up with this work especially in light of COVID-19. To this end we are excited to introduce our new paper entitled "Genomic Profiling and Spatial SEIR Modeling of COVID-19 Transmission in Western New York" published in Frontiers in Microbiology In this paper have been collaborating with other researchers at the University at Buffalo who focus  on the genomic sequencing of various lineages distribution of SARS-CoV-2. What is special about this  new paper is that we explore how such linages change over space and time and how this relates to movement patterns. If this sounds of interest, below you can read the abstract of the paper, see some the lineages in different regions which change over space and time, and our agent-based model which explores how different lineages might spread though peoples movement patterns. At the bottom of the post, you can see the full reference and the link to the paper itself.  

Abstract: 

The COVID-19 pandemic has prompted an unprecedented global effort to understand and mitigate the spread of the SARS-CoV-2 virus. In this study, we present a comprehensive analysis of COVID-19 in Western New York (WNY), integrating individual patient-level genomic sequencing data with a spatially informed agent-based disease Susceptible-Exposed-Infectious-Recovered (SEIR) computational model. The integration of genomic and spatial data enables a multi-faceted exploration of the factors influencing the transmission patterns of COVID-19, including genetic variations in the viral genomes, population density, and movement dynamics in New York State (NYS). Our genomic analyses provide insights into the genetic heterogeneity of SARS-CoV-2 within a single lineage, at region-specific resolutions, while our population analyses provide models for SARS-CoV-2 lineage transmission. Together, our findings shed light on localized dynamics of the pandemic, revealing potential cross-county transmission networks. This interdisciplinary approach, bridging genomics and spatial modeling, contributes to a more comprehensive understanding of COVID-19 dynamics. The results of this study have implications for future public health strategies, including guiding targeted interventions and resource allocations to control the spread of similar viruses.
Phylogenetic and spatial–temporal distribution of omicron BA.2.12.1. (A) Geographic introduction and organization of BA.2.12.1 lineage from February 2022 to November 2022, by percentage of SARS-CoV-2 circulating in each county per month. N/A represents counties with no BA.2.12.1 cases sequenced. (B) Phylogenetic clustering of jukes-cantor distance estimations between consensus sequences of 2,737 samples. Lineages on the phylogenetic tree are color-coded by county; Erie County (pink), Monroe County (green), Onondaga County (blue), and Westchester County (chartreuse). (C) Hierarchical clustering of sample-to-sample distance estimation of 2,737 BA.2.12.1 lineages in four counties across NYS, with k-means clustering k = 4.
SEIR model schematic and dynamics. (A) Schematics of SEIR model including general parameter and synthetic population parameter sets, and model initialization and function (B) R0 = 3 Susceptibility, Exposed, Infectious, and Recovered curves based on the introduction of two infected agents, monitored over time. (C) R0 = 5, (D) R0 = 8.
Commuter behavior dynamics in WNY. Estimated commuter populations originating in a specific county. (A) Commuter behavior with Erie County origins. (B) Commuter behavior from Niagara County origin. (C) Commuter behavior from Monroe County origin. (D) Composite Commuter behavior network.

Full Reference: 

Bard, J.E., Jiang, N., Emerson, J., Bartz, M., Lamb, N.A., Marzullo, B.J., Pohlman, A., Boccolucci, A., Nowak, N.J., Yergeau, D.A., Crooks, A.T. and Surtees, J. (2024), Genomic Profiling and Spatial SEIR Modeling of COVID-19 Transmission in Western New York, Frontiers in Microbiology, 15. Available at  https://doi.org/10.3389/fmicb.2024.1416580  (pdf)

Monday, September 23, 2024

Social Simulation Conference (SSC 2024)

Last week I had the honor to give a keynote talk entitled "Exploring the World from the Bottom Up with GIS and Agent-based Models: Past, Present and Future" at the 19th annual Social Simulation Conference which is the European Social Simulation Association (ESSA) annual conference. Attending the conference was a great experience being exposed to various applications of social simulation, catching up with old friends and meeting many new people. For anyone interested below I have pasted the abstract from my talk and the slides from the talk can be found here

Abstract

 We have seen explosion in the availability of data along with utilizing such data in agent-based models. At the same time, we have seen a huge growth in computational power and the associating agent-based models to real world locations through the use of geographical information systems (GIS). This talk will explore how geographically explicit agent-based models have grown and evolved over the last 20 years taking advantage of the explosion of data and computational power. It will showcase a selection of applications of agent-based models and how they can be used to explore the world from the bottom up and with a specific emphasis on cities and regions. Through examples, I will demonstrate how GIS can be used to build agent-based models ranging from using spatial data to create the artificial worlds that the agents inhabit to utilizing demographic data to build synthetic populations. However, it is not just data that is important when building agent-based models but also how do we incorporate human behavior and theory into such models along with considerations of connecting agents through various types of social and spatial networks. While this might appear simple, there are many challenges associated with this which will be discussed using representative examples ranging from basic patterns of life to vaccination uptake. The talk will conclude with what opportunities are emerging in light of the recent growth in artificial intelligence (AI) with respect to building agent-based models. 

 Keywords: Agent-based modeling, AI, GIS, Social Networks, Cities.

Types of Problems Agent-Based Models have Explored
Growth of Geographical Agent-based models.

Referece: 
Crooks, A.T. (2024), Exploring the World from the Bottom Up with GIS and Agent-based Models: Past, Present and Future. The 19th Annual Social Simulation Conference, 16th –20th September, Cracow, Poland.