Infection Waves in Pandemics and Risk Prediction: Physical Diffusion Theory and Data Comparisons

DOI

10.2991/jracr.k.210609.001How to use a DOI?

Keywords

COVID-19; infection waves; community spreading; diffusion theory; data; predictions; policies

Abstract

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.

Copyright

© 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/).

Download article (PDF)
View full text (HTML)

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  - 10.2991/jracr.k.210609.001
ID  - Duffey2021
ER  -