Infection Waves in Pandemics and Risk Prediction: Physical Diffusion Theory and Data Comparisons
- https://doi.org/10.2991/jracr.k.210609.001How to use a DOI?
- COVID-19, infection waves, community spreading, diffusion theory, data, predictions, policies
We predict the magnitude and estimate the uncertainties of the spread, growth and maximum expected long-term infection rates that affect emergency policies and plans. For the COVID-19 and 1918 viral pandemics, large second or successive peaks, waves or plateaux of increased infections occur long after the initial rapid onset. The key question is what physical model can explain and predict their occurrence trends and timing? We establish the principal that the timing and magnitude of such increases can be based on the well-known physics of classical diffusion theory, so is fundamentally different from the commonly used multi-parameter epidemiological methods. This physical model illuminates our understanding of the societal viral progress, providing quantitative predictions, estimates and uncertainties supporting risk decision-making and resilient medical planning. We obtain an approximate relation for predicting the risk of the observed magnitudes, timing and uncertainties of second and more waves, as needed for proactive emergency pandemic planning, bed count and decision-making purposes. The dynamic results and characteristics are compared and fitted to data using just two physical parameters for a number of countries and regions, and the concept shown to apply for both entire national and local regional populations. The present analysis quantitatively shows how much the timing and magnitude are reduced by more learning and effective countermeasures. The medical system and health policy must recognize and pro-actively plan for such inexorable diffusive spread and large residual infection waves.
- © 2021 The Author. Published by Atlantis Press B.V.
- Open Access
- This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - JOUR AU - Romney B. Duffey PY - 2021 DA - 2021/06/16 TI - Infection Waves in Pandemics and Risk Prediction: Physical Diffusion Theory and Data Comparisons JO - Journal of Risk Analysis and Crisis Response SP - 67 EP - 74 VL - 11 IS - 2 SN - 2210-8505 UR - https://doi.org/10.2991/jracr.k.210609.001 DO - https://doi.org/10.2991/jracr.k.210609.001 ID - Duffey2021 ER -