TY - JOUR
T1 - Consecutive Rate Model for Covid Infections and Deaths and Prediction of Level-Off Time
AU - Madiligama, Amila
AU - Vandervort, Zachary
AU - Khan, Arshad
N1 - Publisher Copyright:
© 2022 The Authors. Published by American Chemical Society.
PY - 2022/12/27
Y1 - 2022/12/27
N2 - Covid-19 infection and death rates are predicted based on a simple two-step consecutive reaction rate model. The infection rate is analogous to the first step of a consecutive reaction that results in an intermediate, and the death rate is analogous to the second step of the consecutive reaction in which a small fraction of the intermediate terminates in a product formation irreversibly. The model has been thoroughly tested, especially with infection data from different countries and two of the USA states California and New York, and predicts a linear infection-time relationship in the early stage of Covid infection. That is, the number of infections in 6 days is double the number of infections in 3 days, and infections in 9 days is 3 times the number of infections in 3 days, etc. In the later stage, the infection curve deviates from the linear relationship and follows a first-order constant "half-life"relationship. In the time interval of one half-life, the infection rises to 50% of the level-off value (maximum); during the second half-life, it rises by another 25% (50/2); and in the third half-life, it rises by another 12.5% (25/2), etc. That is, the infection curve reaches 50% (one "half-life"), 75% (two half-lives), 87.5% (three half-lives), etc. of the level off value after the time interval of one to three half-lives. Available data support our predictions.
AB - Covid-19 infection and death rates are predicted based on a simple two-step consecutive reaction rate model. The infection rate is analogous to the first step of a consecutive reaction that results in an intermediate, and the death rate is analogous to the second step of the consecutive reaction in which a small fraction of the intermediate terminates in a product formation irreversibly. The model has been thoroughly tested, especially with infection data from different countries and two of the USA states California and New York, and predicts a linear infection-time relationship in the early stage of Covid infection. That is, the number of infections in 6 days is double the number of infections in 3 days, and infections in 9 days is 3 times the number of infections in 3 days, etc. In the later stage, the infection curve deviates from the linear relationship and follows a first-order constant "half-life"relationship. In the time interval of one half-life, the infection rises to 50% of the level-off value (maximum); during the second half-life, it rises by another 25% (50/2); and in the third half-life, it rises by another 12.5% (25/2), etc. That is, the infection curve reaches 50% (one "half-life"), 75% (two half-lives), 87.5% (three half-lives), etc. of the level off value after the time interval of one to three half-lives. Available data support our predictions.
UR - https://www.scopus.com/pages/publications/85145574394
UR - https://www.scopus.com/pages/publications/85145574394#tab=citedBy
U2 - 10.1021/acsomega.2c06006
DO - 10.1021/acsomega.2c06006
M3 - Article
C2 - 36569213
AN - SCOPUS:85145574394
SN - 2470-1343
VL - 7
SP - 48059
EP - 48066
JO - ACS Omega
JF - ACS Omega
IS - 51
ER -