COVID-19 Epidemics Monitored Through the Logarithmic Growth Rate and SIR Model
Journal Title: Journal of Clinical Immunology & Microbiology - Year 2022, Vol 3, Issue 3
Abstract
The SIR model is often used to analyse and forecast an epidemic. In this model, the number of patients exponentially increases and decreases in the early and late phases; hence the logarithmic growth rate K is constant at the phases. However, in the case of COVID-19 epidemics, K never remains constant but increases and decreases linearly. Simulation showed that a situation in which smaller epidemics were repeated with short time intervals makes the changes in K; it also showed relationship between K to the mean infectious time τ and the basic reproduction number R0. Using this relationship, we analysed epidemic data from 279 countries and regions. The changes in K represented the state of the epidemics and were several weeks to a month ahead of the changes in the number of confirmed cases. If the negative peaks of K could not be reduced to 0.1, the number of patients remained high. To control the epidemic, it was important to observe K daily, not to allow K to remain positive continuously and to terminate a peak with a series of K-negative days. To accomplish this, it was necessary to shorten τ by finding and isolating a patient earlier.
Authors and Affiliations
Tomokazu Konishi
Keywords
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- EP ID EP709875
- DOI http://dx.doi.org/10.46889/JCIM.2022.3302
- Views 44
- Downloads 0
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