Seasonal autoregressive moving average model-based prediction of bacteriophage dysentery incidence trends in Xinjiang / 公共卫生与预防医学

Objective To analyze the epidemiological characteristics of bacillary dysentery in Xinjiang from 2005-2018, to explore the feasibility and applicability of seasonal autoregressive moving average model to predict the incidence pattern of bacillary dysentery in Xinjiang, and to provide a scientific basis for decision-making in the prevention and control of bacillary dysentery. Methods Descriptive analysis was used to analyze the epidemiological characteristics of bacillary dysentery, and Python software was used to construct a SARIMA model and predict the incidence trend. Results The average annual reported incidence rate of bacillary dysentery in Xinjiang from 2005-2018 was 35.71/100 000, with peak incidence concentrated in June-October. The difference in the incidence rate of bacillary dysentery among the age groups was statistically significant (χ2=145605.90, P60 years age groups. The resulting model was SARIMA (0,1,2)(0,1,1)12 with all parameters statistically significant (P12 model has good accuracy in predicting the incidence of bacillary dysentery in Xinjiang and can be used for medium-term prediction of the disease.

Comparison of the effects of combined model and single model in HFRS incidence fitting and prediction / 公共卫生与预防医学

Objective To compare the prediction effect of combined model and single model in HFRS incidence fitting and prediction, and to provide a reference for optimizing HFRS prediction model. Methods The province with the highest incidence in China (Heilongjiang Province) in recent years was selected as the research site. The monthly incidence data of HFRS in Heilongjiang Province from 2004 to 2017 were collected. The data from 2004 to 2016 was used as training data, and the data from January to December 2017 was used as test data. The training data was used to train SARIMA , ETS and NNAR models, respectively. The reciprocal variance method and particle swarm optimization algorithm (PSO) were used to calculate the model coefficients of SARIMA, ETS and NNAR, respectively, to construct combined model A and combined model B. The established models were used to predict the incidence of HFRS from January to December 2017. The fitted and predicted values of the five models were compared with the training data and test data, respectively. Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), Root Mean Standard Deviation (RMSE), and Mean Error Rate (MER) were used to evaluate the model fitting and prediction effects. Results The optimal SARIMA model was SARIMA(1,0,2)(2,1,1)12. The optimal ETS model was ETS(M, N, M), and the smoothing parameter =0.738，=1*10. The optimal NNAR model was NNAR(13,1,7)12. The residuals of the three single models were white noise (P>0.05). The expression of combined model A was ŷ=0.134*ySARIMA+0.162*yETS+0.704*yNNAR; the expression of combined model B was ŷ=0.246*ySARIMA+0.435*yETS+0.319*yNNAR. The MAPE, MAE, RMSE, and MER fitted by SARIMA, ETS, NNAR, combined model A and combined model B were 24.10%, 0.11, 0.17, 23.29%; 17.14%, 0.08, 0.14, 17.96%; 6.33%, 0.02, 0.03, 4.25%; 9.03%, 0.03, 0.05, 7.51%; 13.16%, 0.06, 0.09, 12.33%, respectively. The MAPE, MAE, RMSE, and MER predicted by the five models were 18.70%, 0.05, 0.06, 19.62%; 23.83%, 0.06, 0.07, 24.49%; 28.30%, 0.07, 0.10, 29.21%; 21.69%, 0.06, 0.08, 22.63%; 17.39%, 0.05, 0.07, 18.76%, respectively. Conclusion The fitting and prediction effects of the combined models are better than the single models. The combined model based on PSO to calculate the weight of the single model is the optimal model.

Comparison of random forests and SARIMA in Predicting Brucellosis Incidence / 公共卫生与预防医学

Objective To compare the effects of random forest and SARIMA (Seasonal Autoregressive Integrated Moving Average) on predicting incidence rate of brucellosis. Methods Using Brucellosis cases reported in the China Disease Prevention and Control Information System from 2005 to 2017, two models, random forest and SARIMA, were established for training and forecasting, and the forecasting results of the two models were compared. Results The R2 (R Squared) and RMSE (Root Mean Squared Error) of SARIMA model and random forest model are 0.904, 0.034351, 0.927 and 0.03345 respectively. Conclusion Both models have high prediction accuracy and can predict the incidence of brucellosis. Random forest prediction is a little bit better than SARIMA model and has more practical value.

Application of TBATS in the prediction of mumps incidence / 公共卫生与预防医学

Objective To explore the applicability of the TBATS in predicting the incidence of mumps. Methods The incidence of mumps of Jiangxi Province from 2004 to 2017 was used as the demonstration data. The incidence of mumps in Jiangxi Province from July to December 2017 was used as test data. The training data from January 2004 to June 2017 were used to train the TBATS and the SARIMA, and predict the value from July to December 2017. The fitted and predicted values were compared with the test data. The MAPE, RMSE, MAE and MER were used to evaluate model fitting and prediction effects. Results SARIMA (1,0,0)(1,1,0)12 with drift was the optimal SARIMA. The MAPE, MAE, RMSE and MER fitted by the TBATS and the SARIMA were 15.06%, 0.21, 0.29, 13.57% and 21.93%, 0.29, 0.41, 18.73%, respectively. The MAPE, MAE, RMSE and MER predicted by the TBATS and the SARIMA were 7.95%, 0.08, 0.11, 7.12% and 15.33%, 0.17, 0.18, 14.93%. Conclusion The TBATS has high accuracy in predicting the incidence of mumps and is worthy of popularization and application.

Seasonal characteristics and time series analysis of hepatitis E in Shanghai from 2005 to 2019 / 上海预防医学

Objective:To analyze the seasonal characteristics and incidence trend of hepatitis E from 2005 to 2019 in Shanghai， and provide references for the prevention and treatment of hepatitis E. Methods:The seasonal characteristics of hepatitis E in Shanghai from 2005 to 2019 were analyzed by circular distribution method. The incidence trend of hepatitis E was analyzed by ARIMA （autoregressive moving average model）. Results:The peak period of hepatitis E in Shanghai from 2005 to 2019 was from November 17 to June 9， and the peak day was on February 27. The time series shows that the optimal model is SARIMA（0，1，1）×（0，1，1）12， Akaike information criterion（AIC） and Schwartz Bayesian information criterion （SBC） are 1 243.799 and 1 250.035 respectively， and the residual is white noise sequence. The mean absolute percentage error（MAPE）between the predicted value， and the actual value of this model is 20.253%. The forecast shows a slight decrease in the number of cases of hepatitis E in 2020-2021 compared with 2019， but it is still at a high level. Conclusion:The incidence of hepatitis E in Shanghai shows a solid seasonal characteristic. Health education and prevention/control measures should be conducted well before the epidemic peak. Based on the short-term prediction， the incidence of hepatitis E would still be high. Effective prevention and control strategies should be developed， and active measures should be taken.

Prediction of malaria cases in the southeastern Iran using climatic variables: An 18-year SARIMA time series analysis

Objective: To predict future trends in the incidence of malaria cases in the southeast of Iran as the most important area of malaria using Seasonal Autoregressive Integrated Moving Average (SARIMA) model, and to check the effect of meteorological variables on the disease incidence. Methods: SARIMA method was applied to fit a model on malaria incidence from April 2001 to March 2018 in Sistan and Baluchistan province in southeastern Iran. Climatic variables such as temperature, rainfall, rainy days, humidity, sunny hours and wind speed were also included in the multivariable model as covariates. Then, the best fitted model was adopted to predict the number of malaria cases for the next 12 months. Results: The best-fitted univariate model for the prediction of malaria in the southeast of Iran was SARIMA (1,0,0)(1,1,1)12 [Akaike Information Criterion (AIC)=307.4, validation root mean square error (RMSE)=0.43]. The occurrence of malaria in a given month was mostly related to the number of cases occurring in the previous 1 (p=1) and 12 (P=1) months. The inverse number of rainy days with 8-month lag ( =0.329 2) and temperature with 3-month lag ( =-0.002 6) were the best predictors that could improve the predictive performance of the univariate model. Finally, SARIMA (1,0,0)(1,1,1)12 including mean temperature with a 3-month lag (validation RMSE=0.414) was selected as the final multivariable model. Conclusions: The number of malaria cases in a given month can be predicted by the number of cases in the prior 1 and 12 months. The number of rainy days with an 8-month lag and temperature with a 3-month lag can improve the predictive power of the model.

Tendência do número de casos da tuberculose no estado de Minas Gerais / Trend in the number of cases of tuberculosis in the state of Minas Gerais

Objetivo: mostrar uma análise do comportamento e da previsão da série referente aos casos confirmados de tuberculose (TB), notificados no sistema de informação de agravos de notificação no estado de Minas Gerais (MG). Métodos: análise de séries temporais e estudo do efeito da sazonalidade, tendência e intervenção. Os dados são mensais relativos ao número de casos confirmados de tuberculose, concernente aos anos de 2001 a 2019, que foram retirados do site DATASUS. Resultados: ajustou-se um modelo SARIMA(1,1,0)(1,0,0)12, o qual o mais adequado foi o que considerou as possíveis intervenções na série. Conclusão: não deve haver um aumento nem queda brusca no número de casos de TB em MG no ano de 2020.

Objective: to show an analysis of the behavior and forecast of the series referring to confirmed cases of tuberculosis (TB), notified in the information system for notification of diseases in the state of Minas Gerais (MG). Methods: analysis of time series and study of the effect of seasonality, trend and intervention. The data are monthly regarding the number of confirmed cases of tuberculosis, concerning the years 2001 to 2019, which were removed from the DATASUS website. Results: a model SARIMA(1,1,0)(1,0,0)12 was adjusted, the most appropriate being the one that considered the possible interventions in the series. Conclusion: there should not be an increase in the sharp drop in the number of TB cases in MG in 2020.

Variação sazonal e temporal da qualidade das águas em um ponto do Córrego Gameleiras usando técnicas quimiométricas robustas / Seasonal and temporal variation in water quality from one point at Gameleiras' stream using robust chemometric techniques

RESUMO O córrego Gameleiras é afluente do reservatório de Volta Grande e tem sido afetado pela eutrofização e "bloom" de algas, devido ao aumento das taxas de fósforo e outros contaminadores, recebidos via seus tributários. Esta pesquisa teve como objetivo avaliar a variação sazonal e temporal da qualidade das águas em um ponto de coleta do córrego, próximo ao reservatório, no período de 1998 a 2014. Foram utilizadas técnicas estatísticas robustas, como análise de agrupamento e séries temporais.

ABSTRACT Gameleiras' stream is a tributary of the Volta Grande reservoir and has been affected by eutrophication and "bloom" of algae due to increased phosphorus rates and other contaminants received via its tributaries. This research aimed at evaluating the seasonal and temporal variation of water quality at a spot at the stream, near the reservoir, from 1998 to 2014. Robust statistical techniques, such as cluster analysis and time series, were employed.

Forecasting the number of zoonotic cutaneous leishmaniasis cases in south of Fars province, Iran using seasonal ARIMA time series method

Objective To predict the trend of cutaneous leishmaniasis and assess the relationship between the disease trend and weather variables in south of Fars province using Seasonal Autoregressive Integrated Moving Average (SARIMA) model. Methods The trend of cutaneous leishmaniasis was predicted using Mini tab software and SARIMA model. Besides, information about the disease and weather conditions was collected monthly based on time series design during January 2010 to March 2016. Moreover, various SARIMA models were assessed and the best one was selected. Then, the model's fitness was evaluated based on normality of the residuals’ distribution, correspondence between the fitted and real amounts, and calculation of Akaike Information Criteria (AIC) and Bayesian Information Criteria (BIC). Results The study results indicated that SARIMA model (4,1,4)(0,1,0)

Forecasting the number of zoonotic cutaneous leishmaniasis cases in south of Fars province, Iran using seasonal ARIMA time series method

OBJECTIVE@#To predict the trend of cutaneous leishmaniasis and assess the relationship between the disease trend and weather variables in south of Fars province using Seasonal Autoregressive Integrated Moving Average (SARIMA) model.@*METHODS@#The trend of cutaneous leishmaniasis was predicted using Mini tab software and SARIMA model. Besides, information about the disease and weather conditions was collected monthly based on time series design during January 2010 to March 2016. Moreover, various SARIMA models were assessed and the best one was selected. Then, the model's fitness was evaluated based on normality of the residuals' distribution, correspondence between the fitted and real amounts, and calculation of Akaike Information Criteria (AIC) and Bayesian Information Criteria (BIC).@*RESULTS@#The study results indicated that SARIMA model (4,1,4)(0,1,0) in general and SARIMA model (4,1,4)(0,1,1) in below and above 15 years age groups could appropriately predict the disease trend in the study area. Moreover, temperature with a three-month delay (lag3) increased the disease trend, rainfall with a four-month delay (lag4) decreased the disease trend, and rainfall with a nine-month delay (lag9) increased the disease trend.@*CONCLUSIONS@#Based on the results, leishmaniasis follows a descending trend in the study area in case drought condition continues, SARIMA models can suitably measure the disease trend, and the disease follows a seasonal trend.

Consultas de urgencia general y por causa respiratoria en la Red de establecimientos del Sistema Nacional de Servicios de Salud (SNSS): un modelo predictivo en el Servicio de Salud de Chiloé / General emergency consultations for respiratory causes at hospitals from the National Network of Health Services System (SNSS): a predictive model in the Health Service, Chiloé, Chile

Objective: The objective of this study was to evaluate a forecasting system, based on SARIMA models for demand Questions Total and Respiratory Emergency, measured in terms of the number of recorded weekly for the consolidated five visits Hospitals Health Service Chiloé network, Chiloé, Chile. Method: Identification, setting, prognosis and SARIMA (Seasonal Autoregressive Integrated the Moving Averages) Model validation for time series queries demand and total respiratory hospital emergency Health Service Chiloé, Chile. Results: applying the protocol identification Box-Jenkins ARIMA models seasonal component, we observed that the number of total views and consolidated emergency presents a seasonal 26-week stats and follows a family of Autoregressive Models-Seasonal SARIMA (p, 0.0) x (P, D, 0), which, for the case study SARIMA a particular structure (1,0,0) x (1,1,0) 26,for both cases, only distinct constant inclusion therein, which, when used to predict, showed failure prognosis according to the statistical models MAPE urgency and respiratory total 5.8% and 4.27% respectively. Conclusions: The predictive performance of the proposed system shows that the methodology set is valid to be used as a tool of demand management of emergency visits in Chiloé Health Service, whereas the projection of this model predicts increasing certainty their forecast periods to the extent that it is incorporating new demand periods which will enhance its use as a tool for planning and management.

Objetivo: El objetivo de esta publicación es evaluar un sistema de pronóstico, a partir de modelos SARIMA para la demanda de Consultas de Urgencia Total y Respiratorias, medida en términos del número de visitas registradas semanalmente para el consolidado de los cinco Hospitales de la Red del Servicio de Salud Chiloé, Chile. Método: Identificación, ajuste, pronóstico y validación de modelo SARIMA para la serie temporal de demanda por consultas de urgencia total y respiratoria en los hospitales del Servicio de Salud Chiloé. Resultados: al aplicar el protocolo de identificación Box-Jenkins de Modelos ARIMA con componente estacional, se observó que las serie de consultas totales y de urgencia consolidadas presenta una estacionalidad de 26 semanas estadísticas y sigue una familia de modelos Autorregresivo - Estacional SARIMA (p,0,0)x (P,D,0), el cual, para el caso en estudio presenta una estructura particular SARIMA (1,0,0)x(1,1,0)26, para ambos casos, diferenciados solamente en la inclusión de la constante en el mismo, los cuales, al ser usados para pronosticar, mostraron un error de pronóstico según el estadístico MAPE para los modelos de urgencia total y respiratoria de 5,8% y 4,27% respectivamente. Conclusiones: el rendimiento predictivo del sistema propuesto evidencia que la metodología expuesta es válida para ser usada como una herramienta de gestión de la demanda de consultas de urgencia en el Servicio de Salud Chiloé, considerando que la proyección de este modelo augura cada vez mayor certeza en sus períodos de pronóstico en la medida que se vaya incorporando la demanda de nuevos períodos lo que potenciará su uso como herramienta para la planificación y gestión.

Forecasting incidence of dengue in Rajasthan, using time series analyses.

Aim: To develop a prediction model for dengue fever/dengue haemorrhagic fever (DF/DHF) using time series data over the past decade in Rajasthan and to forecast monthly DF/DHF incidence for 2011. Materials and Methods: Seasonal autoregressive integrated moving average (SARIMA) model was used for statistical modeling. Results: During January 2001 to December 2010, the reported DF/DHF cases showed a cyclical pattern with seasonal variation. SARIMA (0,0,1) (0,1,1) 12 model had the lowest normalized Bayesian information criteria (BIC) of 9.426 and mean absolute percentage error (MAPE) of 263.361 and appeared to be the best model. The proportion of variance explained by the model was 54.3%. Adequacy of the model was established through Ljung-Box test (Q statistic 4.910 and P-value 0.996), which showed no significant correlation between residuals at different lag times. The forecast for the year 2011 showed a seasonal peak in the month of October with an estimated 546 cases. Conclusion: Application of SARIMA model may be useful for forecast of cases and impending outbreaks of DF/DHF and other infectious diseases, which exhibit seasonal pattern.

A SARIMA forecasting model to predict the number of cases of dengue in Campinas, State of São Paulo, Brazil / Um modelo SARIMA para predição do número de casos de dengue em Campinas, Estado de São Paulo

INTRODUCTION: Forecasting dengue cases in a population by using time-series models can provide useful information that can be used to facilitate the planning of public health interventions. The objective of this article was to develop a forecasting model for dengue incidence in Campinas, southeast Brazil, considering the Box-Jenkins modeling approach. METHODS: The forecasting model for dengue incidence was performed with R software using the seasonal autoregressive integrated moving average (SARIMA) model. We fitted a model based on the reported monthly incidence of dengue from 1998 to 2008, and we validated the model using the data collected between January and December of 2009. RESULTS: SARIMA (2,1,2) (1,1,1)12 was the model with the best fit for data. This model indicated that the number of dengue cases in a given month can be estimated by the number of dengue cases occurring one, two and twelve months prior. The predicted values for 2009 are relatively close to the observed values. CONCLUSIONS: The results of this article indicate that SARIMA models are useful tools for monitoring dengue incidence. We also observe that the SARIMA model is capable of representing with relative precision the number of cases in a next year.

INTRODUÇÃO: A predição do número de casos de dengue em uma população utilizando modelos de series temporais pode trazer informações úteis para um melhor planejamento de intervenções públicas de saúde. O objetivo deste artigo é desenvolver um modelo capaz de descrever e predizer a incidência de dengue em Campinas, sudeste do Brasil, considerando a metodologia de Box e Jenkins. MÉTODOS: O modelo seasonal autoregressive integrated moving average (SARIMA) para os dados de incidência de dengue em Campinas, foi implementado no programa R. Ajustamos um modelo baseado na incidência mensal notificada da doença de 1998 a 2008 e validado pelos dados de janeiro a dezembro de 2009. RESULTADOS: O modelo SARIMA (2,1,2) (1,1,1)12 foi o mais adequado aos dados. Este modelo indicou que o número de casos de dengue em um dado mês pode ser estimado pelo número de casos ocorridos há um, dois e doze meses. Os valores preditos para 2009 são relativamente próximos aos valores observados. CONCLUSÕES: Os resultados deste artigo indicam que os modelos SARIMA são ferramentas úteis para o monitoramento da incidência da dengue. Observamos ainda que o modelo SARIMA é capaz de representar com relativa precisão o número de casos de dengue em um ano consecutivo à série de dados usada no ajuste do modelo.

Influence of relative humidity in Vibrio cholerae infection: A time series model.

Background & objectives : Spread of cholera in West Bengal is known to be related to its ecosystem which favours Vibrio cholerae. Incidence of cholera has not been correlated with temperature, relative humidity and rainfall, which may act as favourable factors. The aim of this study was to investigate the relational impact of climate changes on cholera. Methods : Monthly V. cholerae infection data for of the past 13 years (1996-2008), average relative humidity (RH), temperature and rainfall in Kolkata were considered for the time series analysis of Seasonal Auto-Regressive Integrated Moving Average (SARIMA) model to investigate relational impact of climatic association of V. cholerae infection and General Linear Model (GLM) for point estimation. Results : The SARIMA (1,0,0)(0,1,1) model revealed that monthly average RH was consistently linear related to V. cholerae infection during monsoon season as well as temperature and rainfall were non-stationary, AR(1), SMA(1) and SI(1) (P<0.001) were highly significant with seasonal difference. The GLM has identified that consistent (<10%) range of RH (86.78 ± 4.13, CV=5.0, P <0.001) with moderate to highest (>7 cm) rainfall (10.1 ± 5.1, CV=50.1, P <0.001) and wide (>5-10°C) range of temperature (29.00 ± 1.64, CV=5.6, P <0.001) collectively acted as an ideal climatic condition for V. cholerae infection. Increase of RH to 21 per cent influenced an unusual V. cholerae infection in December 2008 compared to previous years. Interpretation & conclusions : V. cholerae infection was associated higher RH (>80%) with 29°C temperature with intermittent average (10 cm) rainfall. This model also identified periodicity and seasonal patterns of cholera in Kolkata. Heavy rainfall indirectly influenced the V. cholerae infection, whereas no correlation was found with high temperature.