New model may allow authorities to track rate of spread of COVID 19-like diseases

 The COVID-19 pandemicencouraged the use of "modelling" to determine the rate of spread and help in effectively assessing predictive and preventive measures.

Similarly, a new model — combining two classic methodologies — utilised the COVID-19 pandemic data to improve disease prediction.

"SIR" — susceptible, infected and recovered — which explains the movement of an individual from one portion to another.

​A two-step framework was used by Paula Moraga and her team from KAUST to model data on infected areas over time for various age groups. On various places with specific age group contact patterns, they merged the widely used "SIR model and a point process model based."

The principal investigator, André Amaral, thinks that their model offers better predictions when compared to prior methods. He also notes that "it also accounts for various ages classes so we can treat these organisations separately, likely to result in finer regulation over the number of infectious cases."

So when method was used to make accurate predictions in the case study of COVID-19 infections in Cali, Colombia, it seemed to have a great deal of potential.

Amaral added that "the features of the model can assist decision-makers in remain at the forefront areas and vulnerable groups to build better disease control strategies."

If it fits their division model, this model can also be used to study and evaluate other infectious diseases like flu and avian flu.

Their system also describes various age groups and their habits of contact, which would be useful for decision-makers to focus on a particular age group or area to stop the spread of disease without wasting time and resources.

​Amaral proposed that they might broaden their strategy and replace other models for SIR.

"This would increase the number of situations the model can be used for and allow us to account for various epidemic dynamics, added Amaral.

Further, he said: "Last but not least, we could work on creating ensemble approaches that integrate predictions from many models and take into account potential data collection time delays in order to enhance the model's predictive abilities.

He also thinks that their clear presentation the value of accurate data pertaining to various age groups, historical periods, and populations, which enables them to understand the disease and its treatments in controlling or eradicate it from society.