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Abstract Building realistically complex models of infectious disease transmission that are relevant for informing public health is conceptually challenging and requires knowledge of coding architecture that can implement key modeling conventions. For example, many of the models built to understand COVID-19 dynamics have included stochasticity, transmission dynamics that change throughout the epidemic due to changes in host behavior or public health interventions, and spatial structures that account for important spatio-temporal heterogeneities. Here we introduce an R package, SPARSEMODr, that allows users to simulate disease models that are stochastic and spatially explicit, including a model for COVID-19 that was useful in the early phases of the epidemic. SPARSEMOD stands for SPAtial Resolution-SEnsitive Models of Outbreak Dynamics, and our goal is to demonstrate particular conventions for rapidly simulating the dynamics of more complex, spatial models of infectious disease. In this report, we outline the features and workflows of our software package that allow for user-customized simulations. We believe the example models provided in our package will be useful in educational settings, as the coding conventions are adaptable, and will help new modelers to better understand important assumptions that were built into sophisticated COVID-19 models. 

Background: In the wake of the COVID-19 pandemic, scientists have scrambled to collect and analyze SARS-CoV-2 genomic data to inform public health responses to COVID-19 in real-time. Open-source phylogenetic and data visualization platforms for monitoring SARS-CoV-2 genomic epidemiology have rapidly gained popularity for their ability to illuminate spatial-temporal transmission patterns worldwide. However, the utility of such tools to inform public health decision-making for COVID-19 in real-time remains to be explored. Objective: The objective of this study was to convene experts in public health, infectious diseases, virology, and bioinformatics – many of whom were actively engaged in the COVID-19 response at the time of their participation – to discuss the application of phylodynamic tools to inform pandemic responses. Methods: A series of four virtual focus group discussions were hosted between June 2020 and June 2021, covering the pre- and post-variant and vaccination eras of the COVID-19 crisis. Audio recordings were transcribed verbatim, and an iterative, thematic qualitative framework was used for analysis. Results: Of the 41 individuals invited, 23 total participants (56.1%) agreed to participate. Across the four focus group sessions, 15 (65%) of the participants were female, 17 (74%) were white, and 5 (22%) were black. Participants were described as molecular epidemiologists (ME, n=9), clinician-researchers (n=3), infectious disease experts (ID, n=4), and public health professionals (PH) at the local (n=4), state (n=2), and federal (n=1) levels. Collectively, participants felt that successful uptake of phylodynamic tools relies on the strength of academic-public health partnerships. They called for interoperability standards in sequence data sharing and cited many resource issues that must be addressed, including timeliness and cost, in addition to improving issues related to sampling bias and the translation of phylodynamic findings into public health action. Conclusions: This was the first qualitative study to characterize the perspectives of key experts regarding the utility of phylodynamic tools for the public health response to COVID-19. The focus group participants identified key areas for improvement of existing and future phylogenetic and data visualization platforms for monitoring SARS-CoV-2 genomic epidemiology. This information is critical to both policymakers and developers as they consider how to handle existing and emerging SARS-CoV-2 variants during the ongoing crisis.