The SARS-CoV2 virus, first reported in China at the end of 2019, has generated a global pandemic, a disease called COVID-19, producing a large number of deaths, along with a great impact on the global economy and the daily life of millions of people. While a small proportion of countries are experiencing a reduction in the number of new daily cases, particularly due to the effect of vaccines, the disease continues to spread rapidly in many countries. Due to humanity’s lack of innate immunity, one of the main ways to prevent contagion is to reduce the chances of viral transmission by reducing person-to-person contact: a strategy generally called non-pharmaceutical interventions (NPI). In this sense, defining strategies to control the spread of the disease and, at the same time, preserve people’s well-being, prevent job losses and limit the impact on the economy, has become an urgent task.
Without the right tools to accurately forecast the outcome of possible countermeasures aimed at controlling the spread of the virus, reducing the impact on the healthcare system, designing and executing optimal strategies is a complex task. Mathematical tools to model the spread of different infectious diseases have been developed since the early 20th century and have already been applied to the study of Covid-19. However, current approaches lack the parameters to explicitly incorporate into the model those dynamics that are not directly related to the infection process, but that have a considerable impact on it, such as social behavior and the movement of people, while maintaining simplicity. and prediction. power of differential equations. To address this problem, we have developed a novel approach to modeling the spread of infectious diseases such as Covid-19, significantly expanding the traditional SEIR model. In doing so, we have incorporated a mobility matrix to model the movement of people between different geographic regions, as well as a set of appropriate parameters to modulate people’s behavior in ways that resemble real-world aspects such as changes in social behavior, quarantines, lockdowns and sanitary controls, among others. These new aspects of the model are extremely relevant to understanding propagation dynamics and developing large-scale mitigation strategies. That is, it is possible not only to identify where the contagion is most frequent, but also in which areas, despite the fact that there may not be as many infected people as other areas, can contribute more to the spread of the disease.
The objective of this project, funded by the US Air Force Office of Research (AFOSR), is to develop tools that allow us to understand the current situation of the pandemic (nowcasting) and its projected impact (forecasting), both in the population and in the health system, through a georeferenced model to study the spread of the SARS-CoV2 virus.