As a physicist, I am trained to look at patterns in data. For example, the movement of tiny particles may seem random, but it contains patterns and symmetries.
The same can be said of human movement and cooperation. Most people also go between familiar places (home and work, for example), and they may meet the same people, like colleagues, for days. But, naturally, there is a random interaction in our complex modern world, and there is an excellent chance to meet strangers as we move from place to place. The human movement consists of both “normal patterns and random variations.”
Tracking and predicting human movements and interactions can help study the spread of infectious diseases. But how does one account for random events in nature? Some scientists have used cell phone data in Germany to track the effects of human exposure to the spread of COVID-19.
But can there be an easier way?
The March 2020 article in the Washington Post gave my colleagues and me an idea. This article sought to explain to readers how public broadcasting could delay the spread of the new coronavirus. The writers made the comparison using dots of different colors that moved randomly and constantly collided. The “infected” dots (representing humans) collide with those that are “virus-free” and transmit the virus.
The Washington Post model has studied the spread of disease as a communicative process – a theory that has been studied in great detail mathematically. The dots also remind us of the random (Brownian) movement of gas atoms and distribution – a well-studied physics, chemistry, and engineering problem.
Encouraged by that article, we look at how to find functional patterns in the random patterns of human movement to study the spread of a highly contagious disease such as COVID-19. The exercise began as a deviation from the mind during the closure. Since then, there has been a result of three peer-reviewed articles. Our models seemed more accurate compared to the look.
In the third article, Professor D.P. Mahapatra and I learned the most challenging part about predicting the frequency of multiple infections using our Monte Carlo model. The results were compared with reported data from four representative countries: India (approximately 1.4 billion people), the US (330 million), South Africa (60 million), and Serbia (7 million ). This has shown good coherence with the timing of COVID-19 waves encountered in these countries.
Opportunity game: To build our model, we used what is known as the Monte Carlo Simulation, which is commonly used in physics and various fields such as engineering and finance. Monte Carlo Road is named after a high-end casino in Monaco, where lucky games are commonplace like any other casino.
What makes Monte Carlo imitations so appealing is their ability to predict different outcomes as they prepare for the occurrence of random variables or elements. In gambling, for example, alternatives may include the player, the dealer, the card swing, and the number of players around the table.
In the case of the spread of disease by contact (or closely related), the random alternative is human movement. To address this in our simulation, we have used what is known as probability theory and mathematical physics as “random movement.” This process aims to determine the possible location of any moving theme. Each distinct effect is an abridged image, the bulk of which is combined to form a composition.
Our first paper, published in 2021, read about the impact of travel restrictions and interventions to prevent the spread of COVID-19. The simulation showed that the increase in the number of infections between the controlled and limited populations followed the law of gravity rather than the perceived growth rate used for modeling multiple diseases.
Energy law estimates have shown that the number of infections (or deaths) among the estimated population increased in proportion to the time (partial) force. Another interesting point taken from this paper is that the behavior of the epidemic growth law emerged naturally in our analogy. Such growth in power-law was reflected in early data from China and was interpreted using a modified epidemiological model that includes locking and other social segregation scenarios.
This paper was followed by a second publication in December 2021, which provided a model for evaluating and making accurate predictions of incomplete epidemics of growth that followed the measurement of energy legislation.