scholarly journals Predicting the Incidence of Brucellosis in Western Iran using Markov switching model

2020 ◽  
Author(s):  
Maryam Mohammadian-Khoshnoud ◽  
Majid Sadeghifar ◽  
Zahra Cheraghi ◽  
Zahra Hosseinkhani
Keyword(s):  
Bayesian Information Criterion ◽  
Climatic Factors ◽  
Markov Switching ◽  
Information Criterion ◽  
Suitable Model ◽  
Time Lags ◽  
Markov Switching Model ◽  
Average Wind Speed ◽  
Switching Model ◽  
Negative Effect

Abstract Objective: Brucellosis is a zoonosis almost chronic disease. Brucellosis bacteria can remain in the environment for a long time. Thus, climate irregularities could pave the way for the survival of the bacterium Brucellosis. The aim of this study is to investigate the effect of climatic factors as well as predicting the incidence of Brucellosis in Qazvin province using the Markov switching model. This study is a secondary study of data collected from 2010 to 2019 in Qazvin province. The data include Brucellosis cases and climatic parameters. Two state Markov switching model with time lags of zero, one and two was fitted to the data. The Bayesian information criterion was used to evaluate the models. Results: According to the Bayesian information criterion, the two-state Markov switching model with a one-month lag is a suitable model. The month, the average wind speed, the minimum temperature have a positive effect on the number of Brucellosis, the age and rainfall have a negative effect. The results show that the probability of an outbreak for the third month of 2019 is 0.30%.

scholarly journals Predicting the Incidence of Brucellosis in Western Iran using Markov switching model

2020 ◽  
Author(s):  
Maryam Mohammadian-Khoshnoud ◽  
Majid Sadeghifar ◽  
Zahra Cheraghi ◽  
Zahra Hosseinkhani
Keyword(s):  
Bayesian Information Criterion ◽  
Climatic Factors ◽  
Markov Switching ◽  
Information Criterion ◽  
Suitable Model ◽  
Time Lags ◽  
Markov Switching Model ◽  
Average Wind Speed ◽  
Switching Model ◽  
Negative Effect

Abstract Objective Brucellosis is a zoonosis almost chronic disease. Brucellosis bacteria can remain in the environment for a long time. Thus, climate irregularities could pave the way for the survival of the bacterium Brucellosis. The aim of this study is to investigate the effect of climatic factors as well as predicting the incidence of Brucellosis in Qazvin province using the Markov switching model. This study is a secondary study of data collected from 2010 to 2019 in Qazvin province. The data include Brucellosis cases and climatic parameters. Two state Markov switching model with time lags of zero, one and two was fitted to the data. The Bayesian information criterion was used to evaluate the models. Results According to the Bayesian information criterion, the two-state Markov switching model with a one-month lag is a suitable model. The month, the average wind speed, the minimum temperature have a positive effect on the number of Brucellosis, the age and rainfall have a negative effect. The results show that the probability of an outbreak for the third month of 2019 is 0.30%.

scholarly journals Predicting the Incidence of Brucellosis in Western Iran using Markov switching model

2020 ◽  
Author(s):  
Maryam Mohammadian-Khoshnoud ◽  
Majid Sadeghifar ◽  
Zahra Cheraghi ◽  
Zahra Hosseinkhani
Keyword(s):  
Climatic Factors ◽  
Markov Switching ◽  
Information Criterion ◽  
Suitable Model ◽  
Time Lags ◽  
Markov Switching Model ◽  
Average Wind Speed ◽  
Switching Model ◽  
Negative Effect ◽  
Secondary Study

Abstract Objective Brucellosis is a zoonosis almost chronic disease. Brucellosis bacteria can remain in the environment for a long time. Thus, climate irregularities could pave the way for the survival of the bacterium Brucellosis. Brucellosis is more common in men 25 to 29 years of age, in the western provinces, and in the spring months. The aim of this study is to investigate the effect of climatic factors as well as predicting the incidence of Brucellosis in Qazvin province using the Markov switching model (MSM). This study is a secondary study of data collected from 2010 to 2019 in Qazvin province. The data include Brucellosis cases and climatic parameters. Two state MSM with time lags of 0, 1 and 2 was fitted to the data. The Bayesian information criterion (BIC) was used to evaluate the models.Results According to the BIC, the two-state MSM with a one-month lag is a suitable model. The month, the average-wind-speed, the minimum-temperature have a positive effect on the number of brucellosis, the age and rainfall have a negative effect. The results show that the probability of an outbreak for the third month of 2019 is 0.30%.

scholarly journals Predicting the incidence of brucellosis in Western Iran using Markov switching model

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Maryam Mohammadian-Khoshnoud ◽  
Majid Sadeghifar ◽  
Zahra Cheraghi ◽  
Zahra Hosseinkhani
Keyword(s):  
Climatic Factors ◽  
Markov Switching ◽  
Information Criterion ◽  
Suitable Model ◽  
Time Lags ◽  
Markov Switching Model ◽  
Average Wind Speed ◽  
Switching Model ◽  
Negative Effect ◽  
Secondary Study

Abstract Objective Brucellosis is a zoonosis almost chronic disease. Brucellosis bacteria can remain in the environment for a long time. Thus, climate irregularities could pave the way for the survival of the bacterium brucellosis. Brucellosis is more common in men 25 to 29 years of age, in the western provinces, and in the spring months. The aim of this study is to investigate the effect of climatic factors as well as predicting the incidence of brucellosis in Qazvin province using the Markov switching model (MSM). This study is a secondary study of data collected from 2010 to 2019 in Qazvin province. The data include brucellosis cases and climatic parameters. Two state MSM with time lags of 0, 1 and 2 was fitted to the data. The Bayesian information criterion (BIC) was used to evaluate the models. Results According to the BIC, the two-state MSM with a 1-month lag is a suitable model. The month, the average-wind-speed, the minimum-temperature have a positive effect on the number of brucellosis, the age and rainfall have a negative effect. The results show that the probability of an outbreak for the third month of 2019 is 0.30%.

scholarly journals Predicting the Incidence of Brucellosis in Western Iran using Markov switching model

2020 ◽  
Author(s):  
Maryam Mohammadian-Khoshnoud ◽  
Majid Sadeghifar ◽  
Zahra Cheraghi ◽  
Zahra Hosseinkhani
Keyword(s):  
Climatic Factors ◽  
Markov Switching ◽  
Information Criterion ◽  
Suitable Model ◽  
Time Lags ◽  
Markov Switching Model ◽  
Average Wind Speed ◽  
Switching Model ◽  
Negative Effect ◽  
Secondary Study

Abstract ObjectiveBrucellosis is a zoonosis almost chronic disease. Brucellosis bacteria can remain in the environment for a long time. Thus, climate irregularities could pave the way for the survival of the bacterium Brucellosis. Brucellosis is more common in men 25 to 29 years of age, in the western provinces, and in the spring months. The aim of this study is to investigate the effect of climatic factors as well as predicting the incidence of Brucellosis in Qazvin province using the Markov switching model (MSM). This study is a secondary study of data collected from 2010 to 2019 in Qazvin province. The data include Brucellosis cases and climatic parameters. Two state MSM with time lags of 0, 1 and 2 was fitted to the data. The Bayesian information criterion (BIC) was used to evaluate the models.ResultsAccording to the BIC, the two-state MSM with a one-month lag is a suitable model. The month, the average-wind-speed, the minimum-temperature have a positive effect on the number of brucellosis, the age and rainfall have a negative effect. The results show that the probability of an outbreak for the third month of 2019 is 0.30%.

scholarly journals Predicting the Incidence of Brucellosis in Western Iran using Markov switching model

2021 ◽  
Author(s):  
Maryam Mohammadian-Khoshnoud ◽  
Majid Sadeghifar ◽  
Zahra Cheraghi ◽  
Zahra Hosseinkhani
Keyword(s):  
Climatic Factors ◽  
Markov Switching ◽  
Information Criterion ◽  
Suitable Model ◽  
Time Lags ◽  
Markov Switching Model ◽  
Average Wind Speed ◽  
Switching Model ◽  
Negative Effect ◽  
Secondary Study

Abstract ObjectiveBrucellosis is a zoonosis almost chronic disease. Brucellosis bacteria can remain in the environment for a long time. Thus, climate irregularities could pave the way for the survival of the bacterium Brucellosis. Brucellosis is more common in men 25 to 29 years of age, in the western provinces, and in the spring months. The aim of this study is to investigate the effect of climatic factors as well as predicting the incidence of Brucellosis in Qazvin province using the Markov switching model (MSM). This study is a secondary study of data collected from 2010 to 2019 in Qazvin province. The data include Brucellosis cases and climatic parameters. Two state MSM with time lags of 0, 1 and 2 was fitted to the data. The Bayesian information criterion (BIC) was used to evaluate the models.ResultsAccording to the BIC, the two-state MSM with a one-month lag is a suitable model. The month, the average-wind-speed, the minimum-temperature have a positive effect on the number of brucellosis, the age and rainfall have a negative effect. The results show that the probability of an outbreak for the third month of 2019 is 0.30%.

scholarly journals Stock Daily Price Regime Model Detection using Markov Switching Model

MATEMATIKA ◽  
2020 ◽  
Vol 36 (2) ◽  
pp. 127-140
Author(s):  
Wiwik Prihartani ◽  
Dwilaksana Abdullah Rasyid ◽  
Nur Iriawan
Keyword(s):  
Stock Prices ◽  
Stock Price ◽  
Transition Probability ◽  
Structural Break ◽  
Markov Switching ◽  
Information Criterion ◽  
Model Parameters ◽  
Markov Switching Model ◽  
Switching Model ◽  
Markov Transition

Changes in stock prices randomly occur due to market forces with reoccurrencepossibilities. This process, also known as the structural break model, is captured throughchanges in the linear model parameters among periods with the Markov Switching Model(MSwM) used for detection. Furthermore, using the smallest Akaike Information Criterion(AIC) value on all feasible MSwM alternatives formed for a daily stock price, the completeMSwM model with its Markov transition is determined. This method has been tested andapplied to daily stock price data in several sectors. The result showed that the number ofregime models coupled with its transition probability helped investors make investmentdecisions.

A Markov-Switching model for building occupant activity estimation

2019 ◽  
Vol 183 ◽  
pp. 672-683 ◽  
Author(s):  
Sebastian Wolf ◽  
Jan Kloppenborg Møller ◽  
Magnus Alexander Bitsch ◽  
John Krogstie ◽  
Henrik Madsen
Keyword(s):  
Markov Switching ◽  
Markov Switching Model ◽  
Switching Model ◽  
Activity Estimation
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