markov chain model
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2022 ◽  
Vol 80 (1) ◽  
Mustafa Al-Zoughool ◽  
Tamer Oraby ◽  
Harri Vainio ◽  
Janvier Gasana ◽  
Joseph Longenecker ◽  

Abstract 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.

2022 ◽  
Vol 46 (4) ◽  
pp. 383-388

 The occurrences and non-occurrences of the rainfall can be described by a two-state Markov chain. A dry date is denoted by state 0 and wet date is denoted by state 1. We have taken the sample which follows a Poisson process with known parameter. Using this Poisson sample we have given a new approach to affect statistical inference for the law of the Markov chain and state estimation concerning un-observed past values or not yet observed future values. The paper aims at comparing the earlier fit of the data with the new approach.      

2022 ◽  
Vol 45 (1) ◽  
pp. 75-78

A statistical study of the annual rainfall data at Cocch Behar district during the periodt90 1 to t9S ~ has been undertaken by using. Markov chain model. One step 3 X3 Ma rcov chain model has beenused in (his study. The outcomes of the model reveal normal, bad and good year of rainfall a t the two stationsof thi s district. The hypoth esis of independence has been tested on the use of entropy and it has been ver ifiedusing likelih ood ra tio criterion. The results of the two methods are the same-tha t the yearly rainfall occurrencemay be regarded as independent at the two places o f the district.

Annisa Martina

Estimation of the number of demands for a product must be done correctly, so that the company can get maximum profit. Therefore, this study discusses how to estimate the amount of sales demand in a company correctly. The model that will be used to estimate sales demand is the Multivariate Markov Chain Model. This model can estimate the future state by observing the present state. The model requires parameter estimation values ​​first, namely the transition probability matrix and the weighted Markov chain, where in previous studies an estimation of the transition probability matrix has been carried out, so that in this study we will continue to estimate the weighted Markov chain parameters. This model is compatible with 5 data sequences (product types) defined as product 1, product 2, product 3, product 4, and product 5, with 6 conditions (no sales volume, very slow-moving, slow-moving, standard, fast moving, and very fast moving). As the result, the state probability for product 1, product 2 and product 3 in company 1 are stationary at state 6 (very fast moving), product 4 and product 5 are stationary at state 2 (very slow moving).

2021 ◽  
Vol 2/2021 (35) ◽  
pp. 76-92
Arkadiusz Manikowski ◽  

This paper presents a way of using the Markov chain model for the analysis of migration based on the example of banknote migration between regions in Poland. We have presented the application of the methodology for estimating one-step transition probabilities for the Markov chain based on macro-data gathered during the project conducted in the National Bank of Poland (NBP) in the period of December 2015–2018. We have shown the usefulness of state-aggregated Markov chain not only as a model of banknote migration but as migration in general. The banknotes are considered here as goods, so their migration is strictly related to, inter alia, the movement of people (commuting to work, business trips, etc.).Thus, the gravity-like properties of cash migration pointed to the gravity model as one of the most pervasive empirical models in regional science. Transition probability of the Markov chain expressing the attractive force between regions allows for estimating the gravity model for the identification of relevant reasons of note and, consequently, people migration.

2021 ◽  
Vol 42 (4) ◽  
pp. 393-400

For agricultural planning, it is important to know the sequence of dry, wet periods. For this purpose a week period may be taken as the optimum length of time. The success or failure of crops particularly under rainfed conditions is closely linked with the rainfall patterns. In this study the Markov chain model method has been applied to know the probability of having a dry or a wet week and consecutive dry or wet periods of 2 or 3 weeks during monsoon period over Andhra Pradesh.    

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