Literature DB >> 21986608

Predicting the number of cases of dengue infection in Ribeirão Preto, São Paulo State, Brazil, using a SARIMA model.

Edson Zangiacomi Martinez1, Elisângela Aparecida Soares da Silva.   

Abstract

This study aimed to develop a forecasting model for the incidence of dengue in Ribeirão Preto, São Paulo State, Brazil, using time series analysis. The model was performed using the Seasonal Autoregressive Integrated Moving Average (SARIMA). Firstly, we fitted a model considering monthly notifications of cases of dengue recorded from 2000 to 2008 in Ribeirão Preto. We then extracted predicted values for 2009 from the adjusted model and compared them with the number of cases observed for that year. The SARIMA (2,1,3)(1,1,1)12 model offered best fit for the dengue incidence data. The results showed that the seasonal ARIMA model predicts the number of dengue cases very effectively and reliably, and is a useful tool for disease control and prevention.

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Year:  2011        PMID: 21986608     DOI: 10.1590/s0102-311x2011000900014

Source DB:  PubMed          Journal:  Cad Saude Publica        ISSN: 0102-311X            Impact factor:   1.632


  13 in total

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4.  Infectious disease prediction with kernel conditional density estimation.

Authors:  Evan L Ray; Krzysztof Sakrejda; Stephen A Lauer; Michael A Johansson; Nicholas G Reich
Journal:  Stat Med       Date:  2017-09-14       Impact factor: 2.373

5.  Modeling Occurrence of Dengue Cases in Malaysia.

Authors:  Mohammad Nurul Azam; Mahbuba Yeasmin; Nasar U Ahmed; Hrishikesh Chakraborty
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6.  Dengue outbreaks: unpredictable incidence time series.

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7.  Dynamics of dengue outbreaks in gangetic West Bengal: A trend and time series analysis.

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8.  Forecasting the morbidity and mortality of dengue fever in KSA: A time series analysis (2006-2016).

Authors:  Wajd A Abualamah; Naeema A Akbar; Hussain S Banni; Mohammed A Bafail
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Review 9.  Climate change and dengue: a critical and systematic review of quantitative modelling approaches.

Authors:  Suchithra Naish; Pat Dale; John S Mackenzie; John McBride; Kerrie Mengersen; Shilu Tong
Journal:  BMC Infect Dis       Date:  2014-03-26       Impact factor: 3.090

10.  Application of a new hybrid model with seasonal auto-regressive integrated moving average (ARIMA) and nonlinear auto-regressive neural network (NARNN) in forecasting incidence cases of HFMD in Shenzhen, China.

Authors:  Lijing Yu; Lingling Zhou; Li Tan; Hongbo Jiang; Ying Wang; Sheng Wei; Shaofa Nie
Journal:  PLoS One       Date:  2014-06-03       Impact factor: 3.240

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