A Susceptible-Infected-Removed (SIR) model of COVID-19 epidemic trend in Malaysia under Movement Control Order (MCO) using a data fitting approach

Arifin, Wan Nor; Chan, Weng Howe; Amaran, Safiya; Musa, Kamarul Imran

doi:10.1101/2020.05.01.20084384

Cited by 9 publications

(6 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It reduces the infected population drastically which means social distancing is a major key to face this epidemic in this scenario. A similar flattened curve pattern was also observed in Cameroon, Egypt, and Malaysia (Arifin et al . 2020, Mahmud and Lim 2020).…”

Section: Discussionsupporting

confidence: 83%

Flattening the COVID 19 curve in susceptible forest indigenous tribes using SIR model

Adwibowo

2020

Preprint

View full text Add to dashboard Cite

COVID 19 is a global threat and globally spreading. The international cooperation involving indigenous peoples and local communities is urgently required in joint prevention to control the epidemic. Currently, many indigenous populations are continuing to face COVID 19. This study is concerned about the dynamic of COVID 19 pandemic among indigenous populations living in the remote Amazon rainforest enclaves. Using the Susceptible Infectious Recovered (SIR) model, the spread of the COVID 19 under 3 intervention scenarios (low, moderate, high) is simulated and predicted in indigenous tribe populations. The SIR model forecasts that without intervention, the epidemic peak may reach within 10 20 days. Nonetheless the peak can be reduced with strict interventions. Under low intervention, the COVID 19 cases are reduced to 73% and 56% of the total populations. While, in the scenario of high intervention, the COVID 19 peaks can be reduced to values ranging from 53% to 15% .To conclude, the simulated interventions tested by SIR model have reduced the pandemic peak and flattened the COVID 19 curve in indigenous populations. Nonetheless, it is mandatory to strengthen all mitigation efforts, reduce exposures, and decrease transmission rate as possible for COVID 19 containment.

show abstract

Section: Discussionsupporting

confidence: 83%

Flattening the COVID 19 curve in susceptible forest indigenous tribes using SIR model

Adwibowo

2020

Preprint

View full text Add to dashboard Cite

show abstract

“…The current situation has led the economy to collapse, causing severity on employment, wages, and livelihoods, undermining supply chains and upsetting industries, as well as provoking inequality, suffering and difficulties, especially among the poor Lin (2020). In Malaysia, the gross domestic product (GDP) exhibited reduction by 17.1% in the second quarter of 2020 (Arifin et al, 2020). The limited economic activity was due to restriction imposed during the MCO periods, which affected the industrial productivity in Malaysia.…”

Section: Journal Of Statistical Modeling and Analyticsmentioning

confidence: 99%

“…Department of Statistics Malaysia (DOSM) had reported that all sectors, excluding agricultural sectors, shows negative growth resulting from implementation of MCO (Arifin et al, 2020). The most affected sector was tourism, factoring from lack of international and local tourism activities.…”

Section: Journal Of Statistical Modeling and Analyticsmentioning

confidence: 99%

COVID-19 Pandemic and Its Effects on Household Income in Malaysia during Lockdown

Nidzam¹,

Hamid²,

Razali³

et al. 2022

JOSMA

View full text Add to dashboard Cite

The goal of this study is to identify the most vulnerable socioeconomic group of people to the effects of the pandemic as well as to examine the socioeconomic group that is more likely to be affected, in terms of income loss, by the pandemic. The data were collected via online questionnaire to the 395 respondents in Malaysia. The dependent variable involved in this study was income loss while the predicted variables included were age, gender, gross household income, employment sector, residential environment, education level and status of ‘Bantuan Sara Hidup’ (BSH) one-off relief. By employing Multinomial Logit Regression (MLoR) analysis via IBM SPSS Statistics 24, the most significant predictor is employment sector (P = 0.000), where those who are business owner, self-employed and unemployed are the most susceptible to the income loss due to COVID-19. The finding indicated those who own business or self-employed were 4.098 times more likely to have their income decrease (P = 0.002) while the unemployed respondents were 6.725 times more likely to have their income decrease (P = 0.014) rather than no changes in income during the COVID-19 outbreak.

show abstract

“…Our modelling results demonstrate that compliances of homogeneous populations show a higher overall infection, except for full compliance. This shows the importance of considering more realistic social networks like scale-free in simulations aimed at policymaking [16]. Different local and global connectivities affect the spread in different manners.…”

Section: Discussionmentioning

confidence: 99%

Computational modelling of COVID-19: A study of compliance and superspreaders

Lee

Pérez-Ortiz

Shawe‐Taylor

2021

Preprint

View full text Add to dashboard Cite

Background: The success of social distancing implementations of severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2) depends heavily on population compliance. Mathematical modelling has been used extensively to assess the rate of viral transmission from behavioural responses. Previous epidemics of SARS-Cov-2 have been characterised by superspreaders, a small number of individuals who transmit a disease to a large group of individuals, who contribute to the stochasticity (or randomness) of transmission compared to other pathogens such as Influenza. This growing evidence proves an urgent matter to understand transmission routes in order to target and combat outbreaks. Objective: To investigate the role of superspreaders in the rate of viral transmission with various levels of compliance. Method: A SEIRS inspired social network model is adapted and calibrated to observe the infected links of a general population with and without superspreaders on four compliance levels. Local and global connection parameters are adjusted to simulate close contact networks and travel restrictions respectively and each performance assessed. The mean and standard deviation of infections with superspreaders and non-superspreaders were calculated for each compliance level. Results: Increased levels of compliance of superspreaders proves a significant reduction in infections. Assuming long-lasting immunity, superspreaders could potentially slow down the spread due to their high connectivity. Discussion: The main advantage of applying the network model is to capture the heterogeneity and locality of social networks, including the role of superspreaders in epidemic dynamics. The main challenge is the immediate attention on social settings with targeted interventions to tackle superspreaders in future empirical work. Conclusion: Superspreaders play a central role in slowing down infection spread following compliance guidelines. It is crucial to adjust social distancing measures to prevent future outbreaks accompanied by population-wide testing and effective tracing.

show abstract

A Susceptible-Infected-Removed (SIR) model of COVID-19 epidemic trend in Malaysia under Movement Control Order (MCO) using a data fitting approach

Cited by 9 publications

References 11 publications

Flattening the COVID 19 curve in susceptible forest indigenous tribes using SIR model

Flattening the COVID 19 curve in susceptible forest indigenous tribes using SIR model

COVID-19 Pandemic and Its Effects on Household Income in Malaysia during Lockdown

Computational modelling of COVID-19: A study of compliance and superspreaders

Contact Info

Product

Resources

About