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The recent world crisis caused by the rapid spreading of the COVID-19 variants elicited how urgent it is to develop a robust and reliable epidemiological model for effective policy decision-making. The aim of this paper is to investigate the impact of con-trol measures, e.g. face mask use, on the transmission dynamics of infectious diseases pandemic using two different approaches of mathematical modeling: deterministic (which is suited for homogeneous population) and agent-based (that can capture het-erogeneous attributes across individuals and social network) modeling. We discuss and compare the ability of agent-based and deterministic modeling in describing the transmission dynamics of infectious diseases using numerical simulation of the models with face mask use in order to study its impact on the epidemic curve. For the deterministic model, we employ an optimal control method where the objective function is the minimization of the number of infected individuals and the associated cost of appli-cation of the control measures. On the other hand, the control measures of the developed agent-based model are optimized using a derivative-free optimization approach. We present the numerical resolution of the study for both the deterministic and agent-based models. The aim is to reduce the transmission of infectious diseases to keep the healthcare services below full capacity and reduce the total number of infected individuals. The numerical results of the optimal control problem show the effectiveness of agent-based and deterministic modeling in flattening the infection curve with rather different strategies of face mask use. Solutions using the agent-based models tend to be more realistic, i.e. based on cycles of stricter and less strict control measures, whereas the optimal control ones follow a full policy-enforcing with subsequent alleviation style. [ABSTRACT FROM AUTHOR]

During pandemic circumstance world, the size of data is exploded like rocket speed. At the same time, there is a need to process it and investigate the enormous volumes of information. The company and research tool is Hadoop and is most broadly utilized in information serious applications. The Hadoop default processing nature is homogeneous. The limit of enormous measure of data in Hadoop is finished utilizing Hadoop Distributed File System. The performance is degraded when it applied to heterogeneous environment. Hadoop mainly uses 2 components viz HDFS used for storage purpose and MapReduce used for programming. In this paper we introduce Optimal Data Placement Algorithm (ODPA). [ABSTRACT FROM AUTHOR]

As of March 2022, there have been 3,116,564 infections and 32,827 coronavirus-related deaths reported in the country since the pandemic began on 1st March 2022. This drastic increase in numbers has been alarming everyone. Malaysia has successfully handled the first and second waves of the COVID-19 cases. However, the recent wave of omicron variants in Malaysia sees a massive increase in COVID-19 infections. Therefore, forecasting plays a vital role in determining preparedness for the current pandemic. This study aims to understand the behaviour of the current infection trend and provide valuable information to the authority to prevent future spikes of active cases. In this paper, the SIR model (Susceptible - Infected Recovered) was used to estimate the active cases in Malaysia. This forecast is essential to prepare for the current wave outbreak. Besides that, the number of susceptible can be reduced via vaccinations to reduce future outbreaks. Therefore, with proper planning and execution, the inoculation rates in Malaysia proved to be successful, and the impact of mass vaccination would be seen in future. [ABSTRACT FROM AUTHOR]