Predicting CCHF incidence and its related factors using time-series analysis in the southeast of Iran: comparison of SARIMA and Markov switching models

Ansari, Hossein; Mansournia, Mohammad Ali; Izadi, Shahrokh; Zeinali, M; Mahmoodi, Mahmood; Holakouie-Naieni, Kourosh

doi:10.1017/s0950268814001113

Cited by 16 publications

(13 citation statements)

References 31 publications

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“…SARIMA model construction Several statistical models have been used in the forecasting of infectious diseases [8][9][10][11][18][19][20]. SARIMA model is a traditional method to study the time-series dataset, and is powerful in applying reference data to study the control, prevention and forecast of seasonal infectious diseases [20,21]. In China, the outbreaks of HFRS on record have strong seasonality trends; therefore, we aimed to construct a SARIMA ( p, d, q) × (P, D, Q) S model to predict the HFRS incidence accurately in future.…”

Section: Epidemiology and Infection 1681mentioning

confidence: 99%

Spatiotemporal analysis and forecasting model of hemorrhagic fever with renal syndrome in mainland China

Sun

Zou

2018

Epidemiol. Infect.

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Hemorrhagic fever with renal syndrome (HFRS) caused by hantaviruses is a serious public health problem in China, accounting for 90% of HFRS cases reported globally. In this study, we applied geographical information system (GIS), spatial autocorrelation analyses and a seasonal autoregressive-integrated moving average (SARIMA) model to describe and predict HFRS epidemic with the objective of monitoring and forecasting HFRS in mainland China. Chinese HFRS data from 2004 to 2016 were obtained from National Infectious Diseases Reporting System (NIDRS) database and Chinese Centre for Disease Control and Prevention (CDC). GIS maps were produced to detect the spatial distribution of HFRS cases. The Moran's I was adopted in spatial global autocorrelation analysis to identify the integral spatiotemporal pattern of HFRS outbreaks, while the local Moran's Ii was performed to identify ‘hotspot’ regions of HFRS at province level. A fittest SARIMA model was developed to forecast HFRS incidence in the year 2016, which was selected by Akaike information criterion and Ljung–Box test. During 2004–2015, a total of 165 710 HFRS cases were reported with the average annual incidence at province level ranged from 0 to 13.05 per 100 000 persons. Global Moran's I analysis showed that the HFRS outbreaks presented spatially clustered distribution, with the degree of cluster gradually decreasing from 2004 to 2009, then turned out to be randomly distributed and reached lowest point in 2012. Local Moran's Ii identified that four provinces in northeast China contributed to a ‘high–high’ cluster as a traditional epidemic centre, and Shaanxi became another HFRS ‘hotspot’ region since 2011. The monthly incidence of HFRS decreased sharply from 2004 to 2009 in mainland China, then increased markedly from 2010 to 2012, and decreased again since 2013, with obvious seasonal fluctuations. The SARIMA ((0,1,3) × (1,0,1)12) model was the most fittest forecasting model for the dataset of HFRS in mainland China. The spatiotemporal distribution of HFRS in mainland China varied in recent years; together with the SARIMA forecasting model, this study provided several potential decision supportive tools for the control and risk-management plan of HFRS in China.

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Section: Epidemiology and Infection 1681mentioning

confidence: 99%

Spatiotemporal analysis and forecasting model of hemorrhagic fever with renal syndrome in mainland China

Sun

Zou

2018

Epidemiol. Infect.

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“…In two-state models, if we define state 2 as the disease outbreak period and 1 state as the non-outbreak period, the probability of an outbreak in period t+1 can be as follows (14):…”

Section: Methodsmentioning

confidence: 99%

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

Sadeghifar

Cheraghi

Hosseinkhani

2020

Preprint

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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%.

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“…In two-state models, if we de ne state 1 as the disease outbreak period and 2 state as the non-outbreak period, the probability of an outbreak in period t+1 can be as follows (14):…”

Section: Methodsmentioning

confidence: 99%

“…The second purpose of this study is to use MSM for prediction. A number of researchers have used the MSM in disease prediction (14) but so far the performance of this method has not been evaluated in brucellosis data.…”

Section: Introductionmentioning

confidence: 99%

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

Sadeghifar

Cheraghi

Hosseinkhani

2020

Preprint

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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%.

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Predicting CCHF incidence and its related factors using time-series analysis in the southeast of Iran: comparison of SARIMA and Markov switching models

Cited by 16 publications

References 31 publications

Spatiotemporal analysis and forecasting model of hemorrhagic fever with renal syndrome in mainland China

Spatiotemporal analysis and forecasting model of hemorrhagic fever with renal syndrome in mainland China

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

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

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