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UAH-developed tool predicts effects of pandemic and mitigation efforts

HUNTSVILLE — Epidemiologists and public health officials have a new predictive tool to analyze the course of pandemics, thanks to a University of Alabama in Huntsville professor and a UAH alumnus.

In work a journal reviewer referred to as seminal, they provide a mathematical solution to a model which describes chemical autocatalysis. Based on an analogy between autocatalysis and epidemiology, their formula predicts the spread of a pandemic.

The formula can be used to judge the effects of measures designed to stem the epidemic. As such, they say it can aid public health authorities in deploying resources to mitigate a pandemic’s effects.

Created by Dr. James Baird, professor of chemistry at UAH, and alumnus Dr. Douglas A. Barlow of Alderman Barlow Laboratories in Trenton, Fla., the formula calculates the spread of diseases of either viral or bacterial origin. It also takes into account the effects of  mitigation efforts such as masking, social distancing, quarantine, vaccination rates and the efficacy of medical treatment.

According to Baird, the formula is a highly accurate approximate solution to the mathematical theory of epidemics developed by British scientists in 1927.

No one since has been able to find a solution to that equation that doesn’t require several pages to write down.

“… Doug Barlow and I fared no differently from our predecessors in the search for an exact solution,” said Baird. “We did, however, find the next best thing, which was an approximate analytic solution that can be written down on one line.”

Baird presented the model in May at the Southeastern Theoretical Chemistry Association meeting in Atlanta.

“The World Health Organization could program our equation into a hand-held computer,” Dr. Baird said. “Our formula is able to predict the time required for the number of infected individuals to achieve its maximum.”

The formula predicts the number of hospitalizations, death rates, community exposure rates and related variables. It also calculates the populations of susceptible, infectious and recovered individuals, and predicts a clean separation between the period of onset of the disease and the period of subsidence

Baird said his curiosity was sparked in 2020 by news reports describing the rapid increase in numbers of people infected by COVID-19.

“The rate of infection initially accelerates until it reaches a point where the infection rate is balanced by the recovery rate of infected individuals, at which point the number of infected people peaks and then starts to decay,” he said.

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