Temporal analysis of visceral leishmaniasis between 2000 and 2019 in Ardabil Province, Iran: A time-series study using ARIMA model

ObjectivesHuman brucellosis is a public health problem endangering health and property in China. Predicting the trend and the seasonality of human brucellosis is of great significance for its prevention. In this study, a comparison between the autoregressive integrated moving average (ARIMA) model and the eXtreme Gradient Boosting (XGBoost) model was conducted to determine which was more suitable for predicting the occurrence of brucellosis in mainland China.DesignTime-series study.SettingMainland China.MethodsData on human brucellosis in mainland China were provided by the National Health and Family Planning Commission of China. The data were divided into a training set and a test set. The training set was composed of the monthly incidence of human brucellosis in mainland China from January 2008 to June 2018, and the test set was composed of the monthly incidence from July 2018 to June 2019. The mean absolute error (MAE), root mean square error (RMSE) and mean absolute percentage error (MAPE) were used to evaluate the effects of model fitting and prediction.ResultsThe number of human brucellosis patients in mainland China increased from 30 002 in 2008 to 40 328 in 2018. There was an increasing trend and obvious seasonal distribution in the original time series. For the training set, the MAE, RSME and MAPE of the ARIMA(0,1,1)×(0,1,1)12 model were 338.867, 450.223 and 10.323, respectively, and the MAE, RSME and MAPE of the XGBoost model were 189.332, 262.458 and 4.475, respectively. For the test set, the MAE, RSME and MAPE of the ARIMA(0,1,1)×(0,1,1)12 model were 529.406, 586.059 and 17.676, respectively, and the MAE, RSME and MAPE of the XGBoost model were 249.307, 280.645 and 7.643, respectively.ConclusionsThe performance of the XGBoost model was better than that of the ARIMA model. The XGBoost model is more suitable for prediction cases of human brucellosis in mainland China.

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Comparison of autoregressive integrated moving average model and generalised regression neural network model for prediction of haemorrhagic fever with renal syndrome in China: a time-series study

BMJ Open ◽

10.1136/bmjopen-2018-025773 ◽

2019 ◽

Vol 9 (6) ◽

pp. e025773 ◽

Cited By ~ 7

Author(s):

Ya-wen Wang ◽

Zhong-zhou Shen ◽

Yu Jiang

Keyword(s):

Time Series ◽

Hybrid Model ◽

Moving Average ◽

Arima Model ◽

Haemorrhagic Fever ◽

Autoregressive Integrated Moving Average ◽

Time Series Study ◽

Series Study ◽

Monthly Incidence ◽

Better Than

ObjectivesHaemorrhagic fever with renal syndrome (HFRS) is a serious threat to public health in China, accounting for almost 90% cases reported globally. Infectious disease prediction may help in disease prevention despite some uncontrollable influence factors. This study conducted a comparison between a hybrid model and two single models in forecasting the monthly incidence of HFRS in China.DesignTime-series study.SettingThe People’s Republic of China.MethodsAutoregressive integrated moving average (ARIMA) model, generalised regression neural network (GRNN) model and hybrid ARIMA-GRNN model were constructed by R V.3.4.3 software. The monthly reported incidence of HFRS from January 2011 to May 2018 were adopted to evaluate models’ performance. Root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) were adopted to evaluate these models’ effectiveness. Spatial stratified heterogeneity of the time series was tested by month and another GRNN model was built with a new series.ResultsThe monthly incidence of HFRS in the past several years showed a slight downtrend and obvious seasonal variation. A total of four plausible ARIMA models were built and ARIMA(2,1,1) (2,1,1)12model was selected as the optimal model in HFRS fitting. The smooth factors of the basic GRNN model and the hybrid model were 0.027 and 0.043, respectively. The single ARIMA model was the best in fitting part (MAPE=9.1154, MAE=89.0302, RMSE=138.8356) while the hybrid model was the best in prediction (MAPE=17.8335, MAE=152.3013, RMSE=196.4682). GRNN model was revised by building model with new series and the forecasting performance of revised model (MAPE=17.6095, MAE=163.8000, RMSE=169.4751) was better than original GRNN model (MAPE=19.2029, MAE=177.0356, RMSE=202.1684).ConclusionsThe hybrid ARIMA-GRNN model was better than single ARIMA and basic GRNN model in forecasting monthly incidence of HFRS in China. It could be considered as a decision-making tool in HFRS prevention and control.

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Tuberculosis forecasting and temporal trends by gender and age in a high endemic city of Northeast Brazil: Are we achieving the disease elimination stage?

10.21203/rs.3.rs-147043/v1 ◽

2021 ◽

Author(s):

Hamilton Leandro Andrade ◽

Luiz Arroyo ◽

Antônio Carlos Ramos ◽

Marcelino Neto ◽

Melina Yamamura ◽

...

Keyword(s):

Time Series ◽

Temporal Trend ◽

Moving Average ◽

Arima Model ◽

Temporal Trends ◽

Northeast Brazil ◽

Age Group ◽

Time Series Study ◽

Gender And Age ◽

Series Study

Abstract Objective: to describe the temporal trend of tuberculosis cases according to gender and age group and to make forecasts in an endemic municipality of northeast Brazil. Method: This was a Time Series study, carried out in a municipality in the northeast of Brazil. Population was composed of tuberculosis cases among residents of the municipality, reported between the years 2002 and 2018. An exploratory analysis of the monthly rates of tuberculosis detection, smoothed according to gender and age group, was performed. Subsequently, the progression of the trend and predictions of the disease were also characterized according to these aspects. For the trends forecast, the seasonal autoregressive linear integrated moving average – Seasonal ARIMA model and the usual Box-Jenkins method were used to choose the most appropriate models.Results: A total of 1,620 cases of tuberculosis were reported, with an incidence of 49.7 cases per 100,000 inhabitants in men and 34.0 per 100,000 in women. Regarding the incidence for both genders, there was a decreasing trend, which was similar for age. Evidence resulting from the application of the time series shows a decreasing trend between the years 2002–2018, however, it is unlikely that there will be a significant fall in the disease before 2022.

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Space-time risk cluster of visceral leishmaniasis in Brazilian endemic region with high social vulnerability: An ecological time series study

PLoS Neglected Tropical Diseases ◽

10.1371/journal.pntd.0009006 ◽

2021 ◽

Vol 15 (1) ◽

pp. e0009006

Author(s):

Caique J. N. Ribeiro ◽

Allan D. dos Santos ◽

Shirley V. M. A. Lima ◽

Eliete R. da Silva ◽

Bianca V. S. Ribeiro ◽

...

Keyword(s):

Time Series ◽

Visceral Leishmaniasis ◽

Social Vulnerability ◽

Space Time ◽

Scan Statistics ◽

Urban Residents ◽

Time Series Study ◽

The Social ◽

Brazilian Northeast ◽

Series Study

Background Despite visceral leishmaniasis (VL) being epidemic in most Brazilian regions, the Northeast region is responsible for the highest morbidity and mortality outcomes within the country. Objective To analyse the spatiotemporal dynamics of VL cases to identify the temporal trends and high-risk areas for VL transmission, as well as the association of the disease with social vulnerability in Brazilian Northeast. Methods We carried out an ecological time series study employing spatial analysis techniques using all VL confirmed cases of 1,794 municipalities of Brazilian Northeast between the years 2000 to 2017. The Social Vulnerability Index (SVI) was used to represent the social vulnerability. Incidence rates were standardized and smoothed by the Local Empirical Bayesian Method. Time trends were examined through segmented linear regression. Spatiotemporal analysis consisted of uni- and bivariate Global and Local Moran indexes and space-time scan statistics. Results Incidence rate remained stable and ranged from 4.84 to 3.52 cases/100,000 inhabitants. There was higher case prevalence between males (62.71%), children and adolescents (63.27%), non-white (69.75%) and urban residents (62.58%). Increasing trends of new cases were observed among adult male subjects (≥ 40 years old) and urban residents. Importantly, VL incidence showed a direct spatial dependence. Spatial and space-time clusters were identified in sertão and meio-norte sub-regions, overlapping with high social vulnerability areas. Conclusions VL is a persistent health issue in Brazilian Northeast and associated with social vulnerability. Space-time clustering of VL cases in socially vulnerable municipalities demands intersectoral public policies of surveillance and control, with focus on reducing inequalities and improving living conditions for regional inhabitants.

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