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Early Warning and Prediction of Scarlet Fever in China Using the Baidu Search Index and Autoregressive Integrated Moving Average With Explanatory Variable (ARIMAX) Model: Time Series Analysis

Early Warning and Prediction of Scarlet Fever in China Using the Baidu Search Index and Autoregressive Integrated Moving Average With Explanatory Variable (ARIMAX) Model: Time Series Analysis

By incorporating a multitude of exogenous variables, the ARIMA with explanatory variables (ARIMAX) model has shown enhanced capability in forecasting and delineating the progression of disease incidence [10]. The applicability of the ARIMA model in scarlet fever forecasting and early warning has been substantiated in China [3].

Tingyan Luo, Jie Zhou, Jing Yang, Yulan Xie, Yiru Wei, Huanzhuo Mai, Dongjia Lu, Yuecong Yang, Ping Cui, Li Ye, Hao Liang, Jiegang Huang

J Med Internet Res 2023;25:e49400

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Syndromic Surveillance Models Using Web Data: The Case of Influenza in Greece and Italy Using Google Trends

Syndromic Surveillance Models Using Web Data: The Case of Influenza in Greece and Italy Using Google Trends

Finally, we compared the two cases, having as criteria the statistical correlation coefficient r and the results of the ARIMA models. We conducted experiments with the ARIMA model with small parameters (parameters from 0-3), as the data sample is relatively small, and the ARIMA (1, 0, 0) model was found the only one to be statistically significant at level of P After creating the flu score, we used the model ARIMA (1, 0, 0) [29], a model also known as the Box-Jenkins model [30].

Loukas Samaras, Elena García-Barriocanal, Miguel-Angel Sicilia

JMIR Public Health Surveill 2017;3(4):e90

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Utility of the Comprehensive Health and Stringency Indexes in Evaluating Government Responses for Containing the Spread of COVID-19 in India: Ecological Time-Series Study

Utility of the Comprehensive Health and Stringency Indexes in Evaluating Government Responses for Containing the Spread of COVID-19 in India: Ecological Time-Series Study

The twin advantage of simple structure and immediate applicability of autoregressive integrated moving average (ARIMA) and seasonal ARIMA (SARIMA) made them lucrative for analyzing the current study data set [32]. ARIMA considers only the past values for prediction, whereas SARIMA also considers the seasonality patterns, making it a more robust algorithm for prediction.

Kamal Kishore, Vidushi Jaswal, Anuj Kumar Pandey, Madhur Verma, Vipin Koushal

JMIR Public Health Surveill 2023;9:e38371

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