How does COVID-19 pandemic affect the country? How many people can be hit in a state and how many of them will succumb to the disease? When is it going to peak? How long should the government continue with the lockdown? What is the damage to the economy and what is its impact on each sector? These are some of the questions that haunt not only the decision makers but every sensible citizen in the country now.
A new mathematical model developed by Princeton and Carnegie Mellon researchers has come to assist decision makers in evaluating the effects of countermeasures to an epidemic before they actually deploy them. The model could give leaders critical insights into the best steps they could take to counter the spread of disease in the face of pandemics.
Mathematicians use modelling to represent, analyse and make predictions or otherwise provide insight into real world phenomena. Real world scenarios can be designed into a mathematical model to bring clarity to big messy questions amid fast changing variables. These models aim to make simplifying assumptions in order to arrive at tractable equations.
Dealing with the novel coronavirus is an unprecedented situation which the world could not have foreseen. In order to track the COVID-19 pandemic, make predictions about the disease's progression and take decisions, as of now, the government is solely dependent on data from doctors and health workers.
The Princeton and Carnegie Mellon model improves tracking of epidemics by accounting for mutations in diseases. Here, the new math modelling can accommodate important variables to figure out an exact solution.
“We want to be able to consider interventions like quarantines, isolating people etc, and then see how they affect an epidemic’s spread when the pathogen is mutating as it spreads,” said H. Vincent Poor, one of the researchers on this study and Princeton's interim dean of engineering.
The existing model is unable to deal with fast changing and unstable variable. The inability to account for changes in the disease can make it more difficult for leaders to counter a disease’s spread. Knowing how a mutation could affect transmission or virulence could help leaders decide when to institute isolation orders or dispatch additional resources to an area.
Poor, the Michael Henry Strater University Professor of Electrical Engineering, said the model most widely used today is not designed to account for changes in the disease being tracked. “In reality, these are physical things, but in this model, they are abstracted into parameters that can help us more easily understand the effects of policies and of mutations.” he said.
Obtaining accurate information is extremely difficult during an ongoing pandemic when circumstances shift daily, as we have seen with the COVID-19 virus. “It’s like a wildfire. You can’t always wait until you collect data to make decisions—having a model can help fill this void,” Poor said. “Hopefully, this model could give leaders another tool to better understand the reasons why, for example, the COVID-19 virus is spreading so much more rapidly than predicted, and thereby help them deploy more effective and timely countermeasures.
The study published in the Proceedings of the National Academy of Sciences describes how their model is able to track changes in epidemic spread caused by mutation of a disease organism. The team is now engaged in improving the model to account for public health measures taken to stem an epidemic as well.