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1. Using Mobility Data to Understand and Forecast COVID19 Dynamics

   Wang, L; Ben, X; Adiga, A; Tendulkar, A; Venkatramanan, S; Vullikanti, A; Aggarwal, G; Talekar, A; Chen, J; Lewis, B; et al (December 2020, medRxiv)

   null (Ed.)

   Disease dynamics, human mobility, and public policies co-evolve during a pandemic such as COVID-19. Understanding dynamic human mobility changes and spatial interaction patterns are crucial for understanding and forecasting COVID- 19 dynamics. We introduce a novel graph-based neural network(GNN) to incorporate global aggregated mobility flows for a better understanding of the impact of human mobility on COVID-19 dynamics as well as better forecasting of disease dynamics. We propose a recurrent message passing graph neural network that embeds spatio-temporal disease dynamics and human mobility dynamics for daily state-level new confirmed cases forecasting. This work represents one of the early papers on the use of GNNs to forecast COVID-19 incidence dynamics and our methods are competitive to existing methods. We show that the spatial and temporal dynamic mobility graph leveraged by the graph neural network enables better long-term forecasting performance compared to baselines. 

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2. Application of phylodynamic tools to inform the public health response to COVID-19: a qualitative analysis of expert opinions. (Preprint)

   https://doi.org/10.2196/39409  

   Rich, Shannan Nicole; Richards, Veronica; Mavian, Carla; Rife Magalis, Brittany; Grubaugh, Nathan; Rasmussen, Sonja; Dellicour, Simon; Vrancken, Bram; Carrington, Christine; Fisk-Hoffman, Rebecca; et al (February 2023, JMIR Formative Research)

   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. 

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3. Immunogenetic Association Underlying Severe COVID-19

   https://doi.org/10.3390/vaccines8040700  

   McCoy, Kendall; Peterson, Autumn; Tian, Yun; Sang, Yongming (December 2020, Vaccines)

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   SARS-CoV2 has caused the current pandemic of new coronavirus disease 2019 (COVID-19) worldwide. Clinical outcomes of COVID-19 illness range broadly from asymptotic and mild to a life-threatening situation. This casts uncertainties for defining host determinants underlying the disease severity. Recent genetic analyses based on extensive clinical sample cohorts using genome-wide association studies (GWAS) and high throughput sequencing curation revealed genetic errors and gene loci associated with about 20% of life-threatening COVID-19 cases. Significantly, most of these critical genetic loci are enriched in two immune signaling pathways, i.e., interferon-mediated antiviral signaling and chemokine-mediated/inflammatory signaling. In line with these genetic profiling studies, the broad spectrum of COVID-19 illness could be explained by immuno-pathological regulation of these critical immunogenetic pathways through various epigenetic mechanisms, which further interconnect to other vital components such as those in the renin–angiotensin–aldosterone system (RAAS) because of its direct interaction with the virus causing COVID-19. Together, key genes unraveled by genetic profiling may provide targets for precisely early risk diagnosis and prophylactic design to relieve severe COVID-19. The confounding epigenetic mechanisms may be key to understanding the clinical broadness of COVID-19 illness. 

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4. Impact of public sentiments on the transmission of COVID-19 across a geographical gradient

   https://doi.org/10.7717/peerj.14736  

   Agusto, Folashade B.; Numfor, Eric; Srinivasan, Karthik; Iboi, Enahoro A.; Fulk, Alexander; Saint Onge, Jarron M.; Peterson, A. Townsend (January 2023, PeerJ)

   COVID-19 is a respiratory disease caused by a recently discovered, novel coronavirus, SARS-COV-2. The disease has led to over 81 million confirmed cases of COVID-19, with close to two million deaths. In the current social climate, the risk of COVID-19 infection is driven by individual and public perception of risk and sentiments. A number of factors influences public perception, including an individual’s belief system, prior knowledge about a disease and information about a disease. In this article, we develop a model for COVID-19 using a system of ordinary differential equations following the natural history of the infection. The model uniquely incorporates social behavioral aspects such as quarantine and quarantine violation. The model is further driven by people’s sentiments (positive and negative) which accounts for the influence of disinformation. People’s sentiments were obtained by parsing through and analyzing COVID-19 related tweets from Twitter, a social media platform across six countries. Our results show that our model incorporating public sentiments is able to capture the trend in the trajectory of the epidemic curve of the reported cases. Furthermore, our results show that positive public sentiments reduce disease burden in the community. Our results also show that quarantine violation and early discharge of the infected population amplifies the disease burden on the community. Hence, it is important to account for public sentiment and individual social behavior in epidemic models developed to study diseases like COVID-19. 

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5. Healthcare Sustainability: Hospitalization Rate Forecasting with Transfer Learning and Location-Aware News Analysis

   https://doi.org/10.3390/su152215840  

   Chen, Jing; Creamer, Germán G.; Ning, Yue; Ben-Zvi, Tal (November 2023, Sustainability)

   Monitoring and forecasting hospitalization rates are of essential significance to public health systems in understanding and managing overall healthcare deliveries and strategizing long-term sustainability. Early-stage prediction of hospitalization rates is crucial to meet the medical needs of numerous patients during emerging epidemic diseases such as COVID-19. Nevertheless, this is a challenging task due to insufficient data and experience. In addition, relevant existing work neglects or fails to exploit the extensive contribution of external factors such as news, policies, and geolocations. In this paper, we demonstrate the significant relationship between hospitalization rates and COVID-19 infection cases. We then adapt a transfer learning architecture with dynamic location-aware sentiment and semantic analysis (TLSS) to a new application scenario: hospitalization rate prediction during COVID-19. This architecture learns and transfers general transmission patterns of existing epidemic diseases to predict hospitalization rates during COVID-19. We combine the learned knowledge with time series features and news sentiment and semantic features in a dynamic propagation process. We conduct extensive experiments to compare the proposed approach with several state-of-the-art machine learning methods with different lead times of ground truth. Our results show that TLSS exhibits outstanding predictive performance for hospitalization rates. Thus, it provides advanced artificial intelligence (AI) techniques for supporting decision-making in healthcare sustainability. 

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