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Khalifa University of Science and Technology has announced today that a team of its researchers has developed a mathematical model of the highly infectious COVID-19’s impact on a population, thereby providing a stepping stone for non-experts and policymakers to understand what to expect as the disease spreads. Since the model is open source, it can be used by anyone who wants to plug in the parameters.
Predicting the contagion and the number of fatalities remains crucial for societal and healthcare planning, as well as for forecasting resource needs, and for evaluating the impact any intervention may have. Each country responds to the COVID-19 pandemic with varying intensity, depending on its access to technology, as well as the availability of resources and manpower. This could range from universal social isolation, to selective isolation of the elderly and other measures.
The interdisciplinary Khalifa University team, comprising Dr. Jorge Rodríguez, Associate Professor, Chemical Engineering, Dr. Juan Acuña, Chair-Department of Epidemiology and Public Health, College of Medicine and Health Sciences, Dr. Mauricio Paton, Postdoc, Department of Chemical Engineering, and Research Engineer Dr. Joao Uratnai, applied such interventions to their model to determine the most effective way to slow the spread of the disease. They have also evaluated the use of personal protective equipment, including face masks, and the increase in availability of critical care beds.
The model developed by Khalifa University researchers describes individuals in a population (close, well-mixed community with no migrations), by infection stage and age group. It is based on individuals transitioning between infection stages and segregated by age group.
In addition to their age group, each individual belongs only to one of the possible stages of infection – healthy, asymptomatic, symptomatic, hospitalized or recovered, among others. The researchers consider that the model best describes a big city with ample use of public transportation. They then applied a number of static and dynamic interventions to the model’s parameters to simulate what would happen to the number of people in each disease stage.
Preliminary results have indicated that universal social isolation measures may be effective in reducing total fatalities, but only if they are strict and the average number of daily social interactions is reduced to very low numbers.
Dr. Rodríguez said: “Interestingly, selective isolation of only the age groups most vulnerable to the disease appears almost as effective in reducing total fatalities, but at a much lower economic impact. Most importantly, our results indicate that ending isolation measures too soon appears to render the previous isolation measures useless as the fatality rate eventually reaches nearly the same result as when nothing is done.”
Strategies for data sharing, generating and disseminating timely information are some of the steps the researchers have recommended for academia, policy makers, and the public.
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