A utilização de uma rede neural artificial para previsão da incidência da malária no município de Cantá, estado de Roraima

Cunha, Guilherme Bernardino da; Luitgards-Moura, José Francisco; Naves, Eduardo Lázaro Martins; Andrade, Adriano O.; Pereira, Adriano Alves; Milagre, Selma Terezinha

doi:10.1590/s0037-86822010000500019

Cited by 14 publications

(9 citation statements)

References 8 publications

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“…Another study conducted in the municipality of Cantá, which tested an artificial neural network to predict the incidence of malaria in this municipality, confirmed the results of this analysis (da Cunha et al 2010). Between 2011 and 2013, malaria cases began to fall again.…”

Section: Discussionsupporting

confidence: 74%

Time trends and changes in the distribution of malaria cases in the Brazilian Amazon Region, 2004-2013

Lima

Lapouble

Duarte

2016

Mem. Inst. Oswaldo Cruz

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Recent efforts to reduce malaria incidence have had some successes. Nevertheless, malaria persists as a significant public health problem in the Brazilian Amazon. The objective of this study was to describe changes in malaria case characteristics and to identify trends in malaria incidence in the Brazilian Amazon. This study used data from the Malaria Epidemiological Surveillance and Case Notification Information System from 2004 to 2013. The annual parasite incidence (API) was calculated and joinpoint regression was used to assess the trends in API over time. There was a sharp increase in API in the state of Acre, followed by two periods of decrease. Pará also presented inconsistent decreases over the study period. Amapá, Amazonas, Rondônia, and Roraima showed statistically significant decreases over the period. The sharpest decrease occurred in Rondônia, with a reduction of 21.7% in the average annual percent change (AAPC) (AAPC: -21.7%; 95% confidence interval: -25.4%, -17.8%; p < 0.05). This panorama of malaria incidence highlights the importance of integrating evidence-based malaria surveillance and control. Malaria is highly preventable, and eliminating its transmission should be a goal in coming decades.

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Section: Discussionsupporting

confidence: 74%

Time trends and changes in the distribution of malaria cases in the Brazilian Amazon Region, 2004-2013

Lima

Lapouble

Duarte

2016

Mem. Inst. Oswaldo Cruz

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“…There are many models which can be used in infectious disease forecasting, such as Markov chain models [8], Grey models, general regression models, autoregressive integrated moving average class models (ARIMA) [11] and artificial neural network [12]. For better forecasting performance, hybrid models which combined two or more single models for communicable disease forecasting have also been explored, and previous findings indicate that hybrid models outperformed single models [13,14].…”

Section: Introductionmentioning

confidence: 99%

A hybrid seasonal prediction model for tuberculosis incidence in China

Cao

Wang

Tam

et al. 2013

BMC Med Inform Decis Mak

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BackgroundTuberculosis (TB) is a serious public health issue in developing countries. Early prediction of TB epidemic is very important for its control and intervention. We aimed to develop an appropriate model for predicting TB epidemics and analyze its seasonality in China.MethodsData of monthly TB incidence cases from January 2005 to December 2011 were obtained from the Ministry of Health, China. A seasonal autoregressive integrated moving average (SARIMA) model and a hybrid model which combined the SARIMA model and a generalized regression neural network model were used to fit the data from 2005 to 2010. Simulation performance parameters of mean square error (MSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) were used to compare the goodness-of-fit between these two models. Data from 2011 TB incidence data was used to validate the chosen model.ResultsAlthough both two models could reasonably forecast the incidence of TB, the hybrid model demonstrated better goodness-of-fit than the SARIMA model. For the hybrid model, the MSE, MAE and MAPE were 38969150, 3406.593 and 0.030, respectively. For the SARIMA model, the corresponding figures were 161835310, 8781.971 and 0.076, respectively. The seasonal trend of TB incidence is predicted to have lower monthly incidence in January and February and higher incidence from March to June.ConclusionsThe hybrid model showed better TB incidence forecasting than the SARIMA model. There is an obvious seasonal trend of TB incidence in China that differed from other countries.

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“…[4] One approach to developing a malaria predictive model is to use historical malaria case data and employ analytical predictive models such as mathematical modelling, a machine-learning approach (artificial neural networks) and statistical methods (generalised linear models and Seasonal Autoregressive Intergrated Moving Average (SARIMA) models). An understanding of the assumptions underlying a predictive model and its advantage(s) and disadvantage(s) is vital when developing a forecast model.…”

Section: Researchmentioning

confidence: 99%

A Seasonal Autoregressive Integrated Moving Average (SARIMA) forecasting model to predict monthly malaria cases in KwaZulu-Natal, South Africa

2018

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Background. South Africa (SA) in general, and KwaZulu-Natal (KZN) Province in particular, have stepped up efforts to eliminate malaria. To strengthen malaria control in KZN, a relevant malaria forecasting model is important. Objectives. To develop a forecasting model to predict malaria cases in KZN using the Seasonal Autoregressive Integrated Moving Average (SARIMA) time series approach. Methods. The study was carried out retrospectively using a clinically confirmed monthly malaria case dataset that was split into two. The first dataset (January 2005 -December 2013) was used to construct a SARIMA model by adopting the Box-Jenkins approach, while the second dataset (January -December 2014) was used to validate the forecast generated from the best-fit model. Results. Three plausible models were identified, and the SARIMA (0,1,1)(0,1,1) 12 model was selected as the best-fit model. This model was used to forecast malaria cases during 2014, and it was observed to fit closely with malaria cases reported in 2014. Conclusions. The SARIMA (0,1,1)(0,1,1) 12 model could serve as a useful tool for modelling and forecasting monthly malaria cases in KZN. It could therefore play a key role in shaping malaria control and elimination efforts in the province.

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A utilização de uma rede neural artificial para previsão da incidência da malária no município de Cantá, estado de Roraima

Cited by 14 publications

References 8 publications

Time trends and changes in the distribution of malaria cases in the Brazilian Amazon Region, 2004-2013

Time trends and changes in the distribution of malaria cases in the Brazilian Amazon Region, 2004-2013

A hybrid seasonal prediction model for tuberculosis incidence in China

A Seasonal Autoregressive Integrated Moving Average (SARIMA) forecasting model to predict monthly malaria cases in KwaZulu-Natal, South Africa

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