Time series prediction for the epidemic trends of monkeypox using the ARIMA, exponential smoothing, GM (1, 1) and LSTM deep learning methods

Wei, Wudi; Wang, Gang; Tao, Xing; Luo, Qiang; Chen, Lixiang; Bao, Xiuli; Liu, Yuxuan; Jiang, Junjun; Liang, Hao; Li, Ye

doi:10.1099/jgv.0.001839

Cited by 13 publications

(3 citation statements)

References 46 publications

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“…Predictive data-mining tools are designed to help us understand what the useful information looks like and what has happened during past procedures. Time series forecasting models have advantages for public health policy applications [ 59 ]. ARIMA-based modeling become a standard tool for time series and simple enough to be widely understood and thus, it could be integrated into microbial growth-survival fields [ 60 ].…”

Section: Discussionmentioning

confidence: 99%

The ARIMA model approach for the biofilm-forming capacity prediction of Listeria monocytogenes recovered from carcasses

Dishan,

Barel,

Hizlisoy

et al. 2024

BMC Vet Res

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The present study aimed to predict the biofilm-formation ability of L. monocytogenes isolates obtained from cattle carcasses via the ARIMA model at different temperature parameters. The identification of L. monocytogenes obtained from carcass samples collected from slaughterhouses was determined by PCR. The biofilm-forming abilities of isolates were phenotypically determined by calculating the OD value and categorizing the ability via the microplate test. The presence of some virulence genes related to biofilm was revealed by QPCR to support the biofilm profile genotypically. Biofilm-formation of the isolates was evaluated at different temperature parameters (37 °C, 22 °C, 4 °C and − 20 °C). Estimated OD values were obtained with the ARIMA model by dividing them into eight different estimation groups. The prediction performance was determined by performance measurement metrics (ME, MAE, MSE, RMSE, MPE and MAPE). One week of incubation showed all isolates strongly formed biofilm at all controlled temperatures except − 20 °C. In terms of the metrics examined, the 3 days to 7 days forecast group has a reasonable prediction accuracy based on OD values occurring at 37 °C, 22 °C, and 4 °C. It was concluded that measurements at 22 °C had lower prediction accuracy compared to predictions from other temperatures. Overall, the best OD prediction accuracy belonged to the data obtained from biofilm formation at -20 °C. For all temperatures studied, especially after the 3 days to 7 days forecast group, there was a significant decrease in the error metrics and the forecast accuracy increased. When evaluating the best prediction group, the lowest RMSE at 37 °C (0.055), 22 °C (0.027) and 4 °C (0.024) belonged to the 15 days to 21 days group. For the OD predictions obtained at -20 °C, the 15 days to 21 days prediction group had also good performance (0.011) and the lowest RMSE belongs to the 7 days to 15 days group (0.007). In conclusion, this study will guide in using indicator parameters to evaluate biofilm forming ability to predict optimum temperature-time. The ARIMA models integrated with this study can be useful tools for industrial application and risk assessment studies using different parameters such as pH, NaCl concentration, and especially temperature applied during food processing and storage on the biofilm-formation ability of L. monocytogenes.

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Section: Discussionmentioning

confidence: 99%

The ARIMA model approach for the biofilm-forming capacity prediction of Listeria monocytogenes recovered from carcasses

Dishan,

Barel,

Hizlisoy

et al. 2024

BMC Vet Res

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“…Among the models tested, NeuralProphet emerged as the most efficient, achieving a low RMSE and high accuracy in predicting future cases. The work of Wei et al [131] addressed the increasing prevalence of MPox cases in non-endemic countries, particularly in North America and Europe since May 2022. The researchers employed various forecasting models, such as ARIMA, exponential smoothing, LSTM, and GM(1,1), to predict daily cumulative confirmed MPox cases in different regions.…”

Section: Review Of Work Related To Time-series Forecasting In the Con...mentioning

confidence: 99%

Marburg Virus Outbreak and a New Conspiracy Theory: Findings from a Comprehensive Analysis and Forecasting of Web Behavior

Thakur,

Cui,

Patel

et al. 2023

Computation

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During virus outbreaks in the recent past, web behavior mining, modeling, and analysis have served as means to examine, explore, interpret, assess, and forecast the worldwide perception, readiness, reactions, and response linked to these virus outbreaks. The recent outbreak of the Marburg Virus disease (MVD), the high fatality rate of MVD, and the conspiracy theory linking the FEMA alert signal in the United States on 4 October 2023 with MVD and a zombie outbreak, resulted in a diverse range of reactions in the general public which has transpired in a surge in web behavior in this context. This resulted in “Marburg Virus” featuring in the list of the top trending topics on Twitter on 3 October 2023, and “Emergency Alert System” and “Zombie” featuring in the list of top trending topics on Twitter on 4 October 2023. No prior work in this field has mined and analyzed the emerging trends in web behavior in this context. The work presented in this paper aims to address this research gap and makes multiple scientific contributions to this field. First, it presents the results of performing time-series forecasting of the search interests related to MVD emerging from 216 different regions on a global scale using ARIMA, LSTM, and Autocorrelation. The results of this analysis present the optimal model for forecasting web behavior related to MVD in each of these regions. Second, the correlation between search interests related to MVD and search interests related to zombies was investigated. The findings show that there were several regions where there was a statistically significant correlation between MVD-related searches and zombie-related searches on Google on 4 October 2023. Finally, the correlation between zombie-related searches in the United States and other regions was investigated. This analysis helped to identify those regions where this correlation was statistically significant.

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“…It consists of the grey system theory and grey prediction methods, aiming to reveal the inherent patterns of a system through the analysis and prediction of incomplete data. The GM(1,1), or grey model, which is a single-variable time series prediction model based on grey system theory, generates new data sequences by performing first-order accumulations on the original data, and then constructs differential equation models using these new data sequences to achieve the time series prediction [37]. The steps are as follows:…”

Section: Grey Model (11)mentioning

confidence: 99%

Response Characteristics and Water Inflow Prediction of Complex Groundwater Systems under High-Intensity Coal Seam Mining Conditions

Hua,

Zhang,

Meng

et al. 2023

Water

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With the gradual improvement in coal mining efficiency, the disturbance of groundwater systems caused by high-intensity mining also increases, leading to challenges in maintaining mine safety and protecting water resources in mining areas. How to accurately describe the dynamic changes in the groundwater system under mining and quantitatively predict mine water inflow are currently major problems to be addressed. Based on a full analysis of the response characteristics of a groundwater system to the extraction disturbance, this paper presents a new method to establish a mine hydrogeological conceptual model that can accurately represent the water inrush process. The unstructured-grid package of MODFLOW is used to accurately characterize the formation structure and finally make accurate water inflow predictions. Taking the Caojiatan coal mine in Shaanxi Province, China, as an example, a numerical model of unstructured water inflow is established, and the changes in the water inflow source and intensity are quantitatively evaluated. Compared with the traditional water inflow prediction method, the prediction accuracy of the new model is improved by 12–17%, which is achieved by detailing the response of the complex groundwater system under high-intensity mining conditions. The method presented in this paper has great significance and applicatory value for obtaining a comprehensive understanding of the disturbance characteristics of human underground engineering activities (e.g., coal mining) on groundwater systems, as well as accurately predicting water inflow.

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Time series prediction for the epidemic trends of monkeypox using the ARIMA, exponential smoothing, GM (1, 1) and LSTM deep learning methods

Cited by 13 publications

References 46 publications

The ARIMA model approach for the biofilm-forming capacity prediction of Listeria monocytogenes recovered from carcasses

The ARIMA model approach for the biofilm-forming capacity prediction of Listeria monocytogenes recovered from carcasses

Marburg Virus Outbreak and a New Conspiracy Theory: Findings from a Comprehensive Analysis and Forecasting of Web Behavior

Response Characteristics and Water Inflow Prediction of Complex Groundwater Systems under High-Intensity Coal Seam Mining Conditions

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