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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.
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Integrating genomics in population models to forecast translocation success
Whole‐genome sequencing is revolutionizing our understanding of organismal biology, including adaptations likely to influence demographic performance in different environments. Excitement over the potential of genomics to inform population dynamics has prompted multiple conservation applications, including genomics‐based decision‐making for translocation efforts. Despite interest in applying genomics to improve translocations, there is a critical research gap: we lack an understanding of how genomic differences translate into population dynamics in the real world. We review how genomics and genetics data could be used to inform organismal performance, including examples of how adaptive and neutral loci have been quantified in a translocation context, and future applications. Next, we discuss three main drivers of population dynamics: demographic structure, spatial barriers to movement, and introgression, and their consequences for translocations informed by genomic data. Finally, we provide a practical guide to different types of models, including size‐structured and spatial models, that could be modified to include genomics data. We then propose a framework to improve translocation success by repeatedly developing, selecting, and validating forecasting models. By integrating lab‐based and field‐collected data with model‐driven research, our iterative framework could address long‐standing challenges in restoration ecology, such as when selecting locally adapted genotypes will aid translocation of plants and animals.
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- Award ID(s):
- 1757324
- PAR ID:
- 10449062
- Publisher / Repository:
- Wiley-Blackwell
- Date Published:
- Journal Name:
- Restoration Ecology
- Volume:
- 29
- Issue:
- 4
- ISSN:
- 1061-2971
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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