2018/08/31 by Steven G. Krantz, Peter L. Polyakov, Peter Polyakov +1
Biochemistry, Genetics and Molecular Biology · Mathematics · Physics and Astronomy · Psychology · #Algorithm #Applied mathematics #Artificial intelligence #COVID-19 epidemiological studies #Combinatorics #Complex Network Analysis Techniques #Computer science #Graph #Graph theory #Machine learning #Mathematical optimization #Mathematics #Mental Health Research Topics #Phase (matter) #Theoretical computer science #Wavelet #msc:05C90 #msc:42C40 #msc:92D30 #q-bio.OT
paper · pdf · doi:10.1016/j.jtbi.2020.110243
published as Journal of Theoretical Biology Volume 494, 7 June 2020, 110243 · 16 pages, new Figure on Meyer wavelets added
arxiv created 2018/09/04 · openalex publication_date 2020/03/12 · arxiv updated 2021/06/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
In this paper, we have proposed a two phase procedure (combining discrete graphs and wavelets) for constructing a true epidemic growth. In the first phase graph theory based approach was developed to update partial data available and in the second phase we used this partial data to generate a plausible complete data through wavelets. This procedure although novel and implementable, still leave some questions unanswered.