<span style="color: #222222;">Mathematical modelling can compute a more realistic picture of the infection rate of COVID-19, enabling better prevention and preparation, according to a team led by an Indian-origin researcher.<br />” <br />” Such models can include information reported about the coronavirus, including the clearly underreported numbers of cases, and factor in knowns like the density and age distribution of the population in an area, the researchers wrote in the journal Infection Control and Hospital Epidemiology.<br />” <br />” Actual pandemic preparedness depends on true cases in the population whether or not they have been identified, said Arni S.R. Srinivasa Rao from Augusta University in the US.<br />” <br />” With better numbers we can better assess how long the virus will persist and how bad it will get. Without these numbers, how can health care systems and workers prepare for what is needed? Rao said.<br />” <br />” Better numbers also are critical to better protecting the population and overall pandemic preparedness, according to Rao and his colleague Steven G. Krantz, professor at Washington University in the US.<br />” <br />” The researchers used their mathematical model, which takes COVID-19 numbers from sources like the World Health Organization.&nbsp; They then used factors like an area's population density, proportion of population living in urban areas where people tend to live in closer proximity, and populations in three age groups — zero to 14, 15 to 64, and over 65 — to grow more accurate numbers.<br />” <br />” Because this virus is so infectious, they also considered transmission probability, Rao said.<br />” <br />” The researchers also looked at the number of new cases daily above 10 and up to the first reported peak, and the date ranges for those peaks as an indicator of the trend in reported case numbers.<br />” <br />”Emerging information about how long the virus survives on a variety of surfaces and in the air will further refine their model, Rao said.</span><br />