COVID-19 epidemic in Malaysia: Impact of lock-down on infection dynamics

Naomie Salim; Weng Howe Chan; Shuhaimi Mansor; Nor Erne Nazira Bazin; Safiya Amaran; Ahmad Athif Mohd Faudzi; Anazida Zainal; Sharin Hazlin Huspi; Eric Jiun Hooi Khoo; Shaekh Mohammad Shithil

The SIR model is one of the simplest models to predict properties of how a disease spreads, for example total number of infected or the duration of an epidemic. The population is first divided into compartments, with the assumption that every individual in the same compartment has the same characteristics [15] . The model consists of three compartments: S for the number of susceptible, I for the number of infectious, and R for the number of recovered or deceased (or immune) individuals. This model is reasonably predictive for infectious diseases which are transmitted from human to human. During an epidemic, the number of susceptible individual falls rapidly as more of them are infected and thus enter the infectious and recovered compartments. Each member of the population typically progresses from susceptible to infectious to recovered. JP Morgan used a model based on SIR to estimate the COVID-19 epidemic curve in Malaysia [16] . The estimation is based on the potential size of the group that initially interacts with the infected group (i.e., group needs to get the virus test) to be around ~0.2% of the total population based on the total size of the test group in China's Hubei and South Korea, which is about 0.1 and 0.7% of the total population, respectively. The secondary infection rate (R0) was adopted based on infection parameters used in China (2) and South Korea (1.9) where a country would face a doubling of infection process in every 5-7 days in the early acceleration stage. Due to several containment measures and lower population density of Malaysia (96 people per sq. km of land area vs. Japan/ Korea: 347/ 212 according to World Bank), they set 1.7 as the initial setting of R0. Imperial College COVID-19 Response Team modified an individual-based simulation model for pandemic influenza planning by [17] to explore scenarios for COVID-19 in Great Britain. For their model, they assumed an incubation period of 5.1 days, infectiousness to occur from 12 hours prior to the onset of symptoms for those that are symptomatic and from 4.6 days after infection in those that are asymptomatic with an infectiousness profile over time that results in a 6.5-day mean generation time. They used R0=2.4 based on fits to the early growth-rate of the epidemic in Wuhan and symptomatic individuals as 50% more infectious than asymptomatic individuals. On recovery from infection, individuals are assumed to be immune to re-infection in the short term. In addition, Pueyo [18] uses the epidemic calculator provided by Goh [19] to predict the effect of control measures on spread of infection in the United States and how it will affect their healthcare services. Chen et al. proposed a time-delay dynamic system based on five compartments: external suspected people, infected people, confirmed people, isolated people, and cured people [20] . They also added external sources such as, spread rate, latent period, delay period, exposed people, and cured rate to describe the trend of COVID-19 outbreak.