Using a stochastic continuous-time Markov chain model to examine alternative timing and duration of the COVID-19 lockdown in Kuwait: what can be done now?

Background Kuwait had its first COVID-19 in late February, and until October 6, 2020 it recorded 108,268 cases and 632 deaths. Despite implementing one of the strictest control measures-including a three-week complete lockdown, there was no sign of a declining epidemic curve. The objective of the current analyses is to determine, hypothetically, the optimal timing and duration of a full lockdown in Kuwait that would result in controlling new infections and lead to a substantial reduction in case hospitalizations. Methods The analysis was conducted using a stochastic Continuous-Time Markov Chain (CTMC), eight state model that depicts the disease transmission and spread of SARS-CoV 2. Transmission of infection occurs between individuals through social contacts at home, in schools, at work, and during other communal activities. Results The model shows that a lockdown 10 days before the epidemic peak for 90 days is optimal but a more realistic duration of 45 days can achieve about a 45% reduction in both new infections and case hospitalizations. Conclusions In the view of the forthcoming waves of the COVID19 pandemic anticipated in Kuwait using a correctly-timed and sufficiently long lockdown represents a workable management strategy that encompasses the most stringent form of social distancing with the ability to significantly reduce transmissions and hospitalizations.

Queueing-Inventory with One Essential and m Optional Items with Environment Change Process Forming Correlated Renewal Process (MEP)

We consider a queueing inventory with one essential and m optional items for sale. The system evolves in environments that change randomly. There are n environments that appear in a random fashion governed by a Marked Markovian Environment change process. Customers demand the main item plus none, one, or more of the optional items, but were restricted to at most one unit of each optional item. Service time of the main item is phase type distributed and that of optional items have exponential distributions with parameters that depend on the type of the item, as well as the environment under consideration. If the essential item is not available, service will not be provided. The lead times of optional and main items have exponential distributions having parameters that depend on the type of the item. The condition for stability of the system is analyzed by considering a multi-dimensional continuous time Markov chain that represent the evolution of the system. Under this condition, various performance characteristics of the system are derived. In terms of these, a cost function is constructed and optimal control policies of the different types of commodities are investigated. Numerical results are provided to give a glimpse of the system performance.

Economic study of maintenances and outage resolution in an electricity distribution network using the continuous-time Markov chain

PurposeThe purpose of this paper is to describe a novel method to compromise between planned (regulated) maintenance and outage initiation and unplanned (unregulated) maintenance and to find an economic model using which one can perform maintenance adequately and in the most optimal state.Design/methodology/approachIn this paper, a system consisting of similar components is considered, and the role of each component in the system is explained. Then, the cost pertaining to failure in each asset is determined. Costs such as energy not supplied, penalties, human resources to resolve the defect and replacing assets are taken into account. Finally, a new comprehensive objective is proposed, and optimization is performed for a sample system.FindingsIn this paper, some graphs have been plotted from which plenty of information may be extracted. This is mentioned in the Conclusion.Originality/valueIn this paper, some graphs have been plotted from which plenty of information may be extracted. This is mentioned in the Conclusion.

ANALISIS MODEL EPIDEMIK SVEIR DENGAN CONTINUOUS TIME MARKOV CHAIN (CTMC) PADA PENYAKIT RUBELLA

Penyakit rubella yang dikenal dengan sebutan campak Jerman adalah penyakit menular yang disebabkan oleh virus rubella. Penelitian ini mempelajari model dinamika penyebaran penyakit rubella menggunakan model SVEIR yang merupakan modifikasi dari model yang dikembangkan oleh Beraud dan Saito, dengan pendekatan stokastik CTMC. Simulasi dilakukan untuk mengamati pengaruh perubahan: nilai awal, laju infeksi ( ), tingkat efektivitas vaksin ( ), dan laju vaksinasi ( ). Berdasarkan hasil simulasi diperoleh bahwa, perubahan nilai awal mempengaruhi peluang terjadinya wabah. Semakin tinggi laju sembuh dapat menurunkan peluang wabah. Sedangkan semakin tinggi tingkat efektivitas vaksin dan laju vaksinasi menyebabkan nilai semakin rendah serta nilai peluang wabah yang cukup kecil; artinya peluang bebas penyakit semakin besar dan menghilangnya penyakit rubella dari sistem