Modelling Heterogeneity and Super Spreaders of the COVID-19 Spread through Malaysian Networks

Abstract

Malaysia is multi-ethnic and diverse country. Heterogeneity, in terms of population interactions, is ingrained in the foundation of the country. Malaysian policies and social distancing measures are based on daily infections and R0 (average number of infections per infected person), estimated from the data. Models of the Malaysian COVID-19 spread are mostly based on the established SIR compartmental model and its variants. These models usually assume homogeneity and symmetrical full mixing in the population; thus, they are unable to capture super-spreading events which naturally occur due to heterogeneity. Moreover, studies have shown that when heterogeneity is present, R0 may be very different and even possibly misleading. The underlying spreading network is a crucial element, as it introduces heterogeneity for a more representative and realistic model of the spread through specific populations. Heterogeneity introduces more complexities in the modelling due to its asymmetrical nature of infection compared to the relatively symmetrical SIR compartmental model. This leads to a different way of calculating R0 and defining super-spreaders. Quantifying a super-spreader individual is related to the idea of importance in a network. The definition of a super-spreading individual depends on how super-spreading is defined. Even when the spreading is defined, it may not be clear that a single centrality always correlates with super-spreading, since centralities are network dependent. We proposed using a measure of super-spreading directly related to R0 and that will give a measure of ‘spreading’ regardless of the underlying network. We captured the vulnerability for varying degrees of heterogeneity and initial conditions by defining a measure to quantify the chances of epidemic spread in the simulations. We simulated the SIR spread on a real Malaysian network to illustrate the effects of this measure and heterogeneity on the number of infections. We also simulated super-spreading events (based on our definition) within the bounds of heterogeneity to demonstrate the effectiveness of the newly defined measure. We found that heterogeneity serves as a natural curve-flattening mechanism; therefore, the number of infections and R0 may be lower than expected. This may lead to a false sense of security, especially since heterogeneity makes the population vulnerable to super-spreading events.

Comparing Friends and Peer Tutors Amidst COVID-19 Using Social Network Analysis

“…Refs. [33,34] proved that monitoring a person with high betweenness centrality has a huge impact in slowing the spread of COVID-19. Therefore, by applying the same concept to the dissemination of knowledge, nodes with high betweenness centrality may have a great impact and help accelerate the process of knowledge dissemination in the entire online learning community.…”

“…In addition, ref. [33] used a weighted student friendship network to prove that monitoring individuals with the highest betweenness centrality values who act as super-spreaders can be used in predicting the worst-case scenario and simulating the super-spreading dynamics of COVID-19. It brings to light the utility of network analysis in introducing a better monitoring approach by considering the role of individuals in the spread of epidemics in a community.…”

Effect of Airborne Pathogen Transmission Released by an Assailant in A Mosque Using CFD Simulation

“…𝑅 𝑜 = 𝛽/𝛾 (9) Where 𝛽 is the number of infected and 𝛾 is the mean infectious period 20 , and in this case it is 100 seconds. From Figure 9 we can obtain the number of infected prayers, 𝛽 thus R0 can be calculated.…”