A systems biology approach to COVID-19 progression in population
Advances in Protein Chemistry and Structural Biology, ISSN: 1876-1623, Vol: 127, Page: 291-314
2021
- 7Citations
- 33Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Citations7
- Citation Indexes7
- CrossRef4
- Captures33
- Readers33
- 33
Article Description
A number of models in mathematical epidemiology have been developed to account for control measures such as vaccination or quarantine. However, COVID-19 has brought unprecedented social distancing measures, with a challenge on how to include these in a manner that can explain the data but avoid overfitting in parameter inference. We here develop a simple time-dependent model, where social distancing effects are introduced analogous to coarse-grained models of gene expression control in systems biology. We apply our approach to understand drastic differences in COVID-19 infection and fatality counts, observed between Hubei (Wuhan) and other Mainland China provinces. We find that these unintuitive data may be explained through an interplay of differences in transmissibility, effective protection, and detection efficiencies between Hubei and other provinces. More generally, our results demonstrate that regional differences may drastically shape infection outbursts. The obtained results demonstrate the applicability of our developed method to extract key infection parameters directly from publically available data so that it can be globally applied to outbreaks of COVID-19 in a number of countries. Overall, we show that applications of uncommon strategies, such as methods and approaches from molecular systems biology research to mathematical epidemiology, may significantly advance our understanding of COVID-19 and other infectious diseases.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S1876162321000377; http://dx.doi.org/10.1016/bs.apcsb.2021.03.003; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85105359022&origin=inward; http://www.ncbi.nlm.nih.gov/pubmed/34340771; https://linkinghub.elsevier.com/retrieve/pii/S1876162321000377; https://dx.doi.org/10.1016/bs.apcsb.2021.03.003
Elsevier BV
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