Following my work on stability analysis of a class of dynamical systems called Montone Systems, I came up with results on mathematical epidemiology. In particular, I worked on epidemic networks in which the parameters of the network, i.e the way individuals in the population interact with each other is time-varying. Obviously, that is how people in a society interact with each other but mathematical analysis of such networks can be challenging, hence many researchers shy away from considering such issues in the simulations. That work was a heavily mathematical work, which led to the following publication.
At the outbreak of the SARS-CoV-2 pandemic, I got back into mathematical epidemiology and it happened spontaneously. The idea for my most recent work in this area came to my mind when I saw a list of the number of people who have tested positive in the early stages of the spread of the virus in Wuhan, divided into age groups. And given my earlier theoretical work and also the skills I obtained in my other projects as a data scientist, I came up with an idea to estimate the contact rates of an age-stratified compartmental model based on that data and implemented it. In other words, my model could predict the number of infected people in any population based on real-world data. I published my results initially in medRxiv. Later on, I added new results on how to optimally distribute a limited number of vaccine units in a population, in a way that would have the most impact in lowering the number of infected individuals. I publish all these results as a peer-review paper, which can be found here. Alongside the paper, I also published two open-source libraries, one I developed to estimate the values of the models based on real-world data using an optimisation scheme, that can be found here, and the other to simulate the trajectories of the model, including the results of the optimisations, that can be found here.