Leveraging Computational Modeling to Understand Infectious Diseases
We present advances in modeling techniques that have led to fundamental disease discoveries and impacted clinical translation.Recent FindingsCombining mechanistic models and machine learning algorithms has led to improvements in the treatment ofShigella and tuberculosis through the development of novel compounds. Modeling of the epidemic dynamics of malaria at the within-host and between-host level has afforded the development of more effective vaccination and antimalarial therapies. Similarly, in-host and host-host models have supported the development of new HIV treatment modalities and an improved understanding of the immune involvement in influenza. In addition, large-scale transmission models of SARS-CoV-2 have furthered the understanding of coronavirus disease and allowed for rapid policy implementations on travel restrictions and contract tracing apps.SummaryComputational modeling is now more than ever at the forefront of infectious disease research due to the COVID-19 pandemic. This review highlights how infectious diseases can be better understood by connecting scientists from medicine and molecular biology with those in computer science and applied mathematics.
Publication date: Available online 10 October 2020Source: Journal of Genetics and GenomicsAuthor(s): Chengqi Wang, Justin Gibbons, Swamy R. Adapa, Jenna Oberstaller, Xiangyun Liao, Min Zhang, John H. Adams, Rays H.Y. Jiang
Publication date: Available online 10 October 2020Source: American Journal of Kidney DiseasesAuthor(s): Shreeram Akilesh, Cynthia C. Nast, Michifumi Yamashita, Kammi Henriksen, Vivek Charu, Megan L. Troxell, Neeraja Kambham, Erika Bracamonte, Donald Houghton, Naila I. Ahmed, Chyi Chyi Chong, Bijin Thajudeen, Shehzad Rehman, Firas Khoury, Jonathan E. Zuckerman, Jeremy Gitomer, Parthassarathy C. Raguram, Shanza Mujeeb, Ulrike Schwarze, M. Brendan Shannon
Publication date: Available online 9 October 2020Source: Reumatología Clínica (English Edition)Author(s): Lina María Saldarriaga Rivera, Daniel Fernández Ávila, Wilson Bautista Molano, Daniel Jaramillo Arroyave, Alain Jasaf Bautista Ramírez, Adriana Díaz Maldonado, Jorge Hernán Izquierdo, Edwin Jáuregui, María Constanza Latorre Muñoz, Juan Pablo Restrepo, Juan Sebastián Segura Charry
CONCLUSIONS: This single practice study showed total patient contact was similar over both sample periods, but most contact in 2020 was virtual. Further longitudinal multi-practice studies to confirm these findings and describe future consultation patterns are needed to inform general practice service delivery post-COVID-19. PMID: 33032304 [PubMed - in process]
Publication date: Available online 1 October 2020Source: Academic RadiologyAuthor(s): Neo Poyiadji, Chad Klochko, Jeff LaForce, Manuel L. Brown, Brent Griffith
Curious what people think with pandemic and lack of away rotations.
Publication date: 15 February 2021Source: Personality and Individual Differences, Volume 170Author(s): Brian W. Haas, Fumiko Hoeft, Kazufumi Omura
Publication date: Available online 10 October 2020Source: Academic PediatricsAuthor(s): Bonnie Crume
Authors: Hui KK PMID: 33034297 [PubMed - as supplied by publisher]
Authors: Lam PT PMID: 33034296 [PubMed - as supplied by publisher]
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