Dynamic Patterns and Modeling of Early COVID-19 Transmission by Dynamic Mode Decomposition

Details

• Journal Article:
  Preventing Chronic Disease (PCD)
• Personal Author:
  Fang, Dehong ; Guo, Lei ; Hughes, M. Courtney ; Tan, Jifu
• Description:
  Introduction ; Understanding the transmission patterns and dynamics of COVID-19 is critical to effective monitoring, intervention, and control for future pandemics. The aim of this study was to investigate the spatial and temporal characteristics of COVID-19 transmission during the early stage of the outbreak in the US, with the goal of informing future responses to similar outbreaks. ; Methods ; We used dynamic mode decomposition (DMD) and national data on COVID-19 cases (April 6, 2020–October 9, 2020) to model the spread of COVID-19 in the US as a dynamic system. DMD can decompose the complex evolution of disease cases into linear combinations of simple spatial patterns or structures (modes) with time-dependent mode amplitudes (coefficients). The modes reveal the hidden dynamic behaviors of the data. We identified geographic patterns of COVID-19 spread and quantified time-dependent changes in COVID-19 cases during the study period. ; Results ; The magnitude analysis from the dominant mode in DMD showed that California, Louisiana, Kansas, Georgia, and Texas had higher numbers of COVID-19 cases than other areas during the study period. States such as Arizona, Florida, Georgia, Massachusetts, New York, and Texas showed simultaneous increases in the number of COVID-19 cases, consistent with data from the Centers for Disease Control and Prevention. ; Conclusion ; Results from DMD analysis indicate that certain areas in the US shared similar trends and similar spatiotemporal transmission patterns of COVID-19. These results provide valuable insights into the spread of COVID-19 and can inform policy makers and public health authorities in designing and implementing mitigation interventions.
• Subjects:
  • Humans
  • COVID-19
• Source:
  Prev Chronic Dis. 2023; 20
• DOI:
  http://dx.doi.org/10.5888/pcd20.230089
• ISSN:
  1545-1151
• Pubmed ID:
  37884317
• Pubmed Central ID:
  PMC10625432
• Document Type:
  Journal Article
• Place as Subject:
  United States
• Volume:
  20
• Download URL:
  https://stacks.cdc.gov/view/cdc/151124/cdc_151124_DS1.pdf
• File Type:
  [PDF - 648.97 KB]
• Collection(s):
  Preventing Chronic Disease
• Main Document Checksum:
  urn:sha-512:644a4dd8a605b6955a498abe5edd00d20c41de957496bd29847e368fa0a16554d66162c979a8fd30ca682c25d8523425a8adc3f7d484450d710537f63bde922e