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Within the geo-simulation research domain, micro-simulation and agent-based modeling often require the creation of synthetic populations. Creating such data is a time-consuming task and often lacks social networks, which are crucial for studying human interactions (e.g., disease spread, disaster response) while at the same time impacting decision-making. We address these challenges by introducing a Python based method that uses the open data including that from 2020 U.S. Census data to generate a large-scale realistic geographically explicit synthetic population for America’s 50 states and Washington D.C. along with the stylized social networks (e.g., home, work and schools). The resulting synthetic population can be utilized within various geo-simulation approaches (e.g., agent-based modeling), exploring the emergence of complex phenomena through human interactions and further fostering the study of urban digital twins. 

Cyclic ketene acetals (CKAs) are among the most well-studied monomers for radical ring-opening polymerization (rROP). However, ring-retaining side reactions and low reactivities in homopolymerization and copolymerization remain significant challenges for existing CKAs. Here, we report that a class of monosaccharide CKAs can be facilely prepared from a short and scalable synthetic route and can undergo quantitative, regiospecific, and stereoselective rROP. NMR analyses and degradation experiments revealed a reaction mechanism involving a propagating radical at the C2 position of pyranose, with different monosaccharides exhibiting distinct stereoselectivity in the radical addition of the monomer. Furthermore, adding maleimide was found to improve the incorporation efficiency of the monosaccharide CKA in the copolymerization with vinyl monomers, producing unique degradable terpolymers with carbohydrate motifs in the polymer backbone. 

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.