New mathematical Model to track epidemics


New mathematical Model to track epidemics


Leaders, around the world, are relying on mathematics to take crucial economic and public health decisions, as COVID-19 infests the earth. Now, a fresh model has been developed that will take into account mutations in diseases to enhance the tracking of epidemics. Before deploying them, however, investigators are looking to apply the methodology to analyze the effects of the countermeasures taken to alleviate the outbreak.

Princeton and Carnegie Mellon researchers have developed this model that considers mutations in diseases. And before deploying it they would like to take into account interventions such as isolation and quarantines and then evaluate the effect of an epidemic’s spread as the pathogen mutates to expand itself.

The problem

Presently, data is collected from doctors and health workers to predict the progression of a disease. Poor, the professor of Electrical Engineering at the Henry Strater University said that the current model used is unable to account for the changes in the disease even as it spreads. Therefore, countering the disease is a dilemma for world leaders. If decision-makers could know how a mutation affects transmission or virulence, then it will help them better issue isolation orders or when required, dispatch resources into an affected area.

Physical readings are converted into abstract parameters to easily understand the protocols of mutations


Taking a cue from an article published on the 17th of March in the Proceedings of the National Academy of Sciences researchers felt that if they could accurately consider the measures to fight the disease, it will provide critical insights into the best steps to be taken in the event of a pandemic. The article itself illustrates a model that tracks variations in an epidemic due to mutations of the disease organism. Researches are also looking to adopt a similar model to record public health measures taken to arrest the epidemic.


Having examined information flow through social media networks, it was inferred that even slight changes in information will have effects on the spread of information. According to Poor, the expansion of the virus is very similar to a rumor that is placed on social media. The rate of transmission depends upon the type of information that his being transmitted.

The model

While staying agnostic about the physical network and connectivity between individuals, information is isolated into connected nodes that might contain information about the potential sources of infection. During an ongoing pandemic, extracting accurate data can be rather difficult because circumstances tend to shift daily. Information is spreading like wildfire and there is no room for waiting and collecting data to get insights and then arrive at a decision. The model aims to fill this gap.

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