Secular Seasonality and Trend Forecasting of Tuberculosis Incidence Rate in China Using the Advanced Error-Trend-Seasonal Framework

Epidemiological changes of scarlet fever before, during and after the COVID-19 pandemic in Chongqing, China: a 19-year surveillance and prediction study

BACKGROUND

This study aimed to investigate the epidemiological changes in scarlet fever before, during and after the COVID-19 pandemic (2005-2023) and predict the incidence of the disease in 2024 and 2025 in Chongqing Municipality, Southwest China.

METHODS

Descriptive analysis was used to summarize the characteristics of the scarlet fever epidemic. Spatial autocorrelation analysis was utilized to explore the distribution pattern of the disease, and the seasonal autoregressive integrated moving average (SARIMA) model was constructed to predict its incidence in 2024 and 2025.

RESULTS

Between 2005 and 2023, 9,593 scarlet fever cases were reported in Chongqing, which resulted in an annual average incidence of 1.6694 per 100,000 people. Children aged 3-7 were the primary victims of this disease, with the highest average incidence found among children aged 6 (5.0002 per 100,000 people). Kindergarten children were the dominant infected population, accounting for as much as 54.32% of cases, followed by students (34.09%). The incidence for the male was 1.51 times greater than that for the female. The monthly distribution of the incidence showed a bimodal pattern, with one peak occurring between April and June and another in November or December. The spatial autocorrelation analysis revealed that scarlet fever cases were markedly clustered; the areas with higher incidence were mainly concentrated in Chongqing's urban areas and its adjacent districts, and gradually spreading to remote areas after 2020. The incidence of scarlet fever increased by 106.54% and 39.33% in the post-upsurge period (2015-2019) and the dynamic zero-COVID period (2020-2022), respectively, compared to the pre-upsurge period (2005-2014) (P < 0.001). During the dynamic zero-COVID period, the incidence of scarlet fever decreased by 68.61%, 25.66%, and 10.59% (P < 0.001) in 2020, 2021, and 2022, respectively, compared to the predicted incidence. In 2023, after the dynamic zero-COVID period, the reported cases decreased to 1.5168 per 100,000 people unexpectedly instead of increasing. The cases of scarlet fever are predicted to increase in 2024 (675 cases) and 2025 (705 cases).

CONCLUSIONS

Children aged 3-7 years are the most affected population, particularly males, and kindergartens and primary schools serving as transmission hotspots. It is predicted that the high incidence of scarlet fever in Chongqing will persist in 2024 and 2025, and the outer districts (counties) beyond urban zone would bear the brunt of the impact. Therefore, imminent public health planning and resource allocation should be focused within those areas.

Changes in incidence and clinical features of tuberculosis with regard to the COVID-19 outbreak in Southern Iran

INTRODUCTION

Tuberculosis (TB), caused by Mycobacterium tuberculosis, remains a significant global health threat. It results in substantial mortality and may be underrecognized due to insufficient screening and diagnostic challenges. Furthermore, TB's impact is closely linked to complex socioeconomic and healthcare factors. The COVID-19 pandemic has exacerbated these challenges due to similarities in clinical presentation and transmission dynamics with TB. Socioeconomic factors such as limited access to healthcare services, resource constraints, and social stigma further complicate TB management. Historically, TB faced increased burdens during natural disasters, wars, and pandemics. This study analyzes TB incidence changes, emphasizing the crucial need for timely diagnosis within the context of COVID-19 measures.

METHOD

This cross-sectional study, conducted at Shiraz's TB referral center in Southern Iran, covered the period from January 1, 2018, to December 31, 2022. We analyzed patient data, including epidemiological and demographic factors, clinical and radiological features, and treatment outcomes. Data were compared between the pre-COVID-19 pandemic era and the COVID-19 pandemic era (from March 2020), using standard and regression analyses. A P-value of less than 0.05 was considered statistically significant.

RESULTS

We analyzed 388 TB patients with a mean age of 48.38 ± 20.53 years, including 264 pulmonary cases (68.0%). The highest incidence of TB was recorded in 2019, representing 27.6% of the cases. During the COVID-19 era, logistic regression analysis identified significant associations with higher education levels (P = 0.032; OR = 1.380; 95% CI: 1.028-1.851), a decrease in symptoms such as sputum production (P = 0.004; OR = 0.342; 95% CI: 0.166-0.705) and chills (P = 0.036; OR = 0.282; 95% CI: 0.087-0.919), and an increase in symptoms of fatigue (P = 0.006; OR = 2.856; 95% CI: 1.358-6.005).

CONCLUSION

The COVID-19 pandemic has had a prolonged impact on TB cases in our country, resulting in a reduction in reported cases due to challenges in quarantine and screening. However, it has also led to a shift in TB patterns and a potential increase in latent TB cases and future mortality rates. Addressing the repercussions requires enhanced control strategies, prioritized service delivery, and secured funding for intensified case finding, expanded contact-tracing, community engagement, digital health tools, and uninterrupted access to medications.

Predicting the epidemiological trend of acute hemorrhagic conjunctivitis in China using Bayesian structural time-series model

This study aims to explore the application value of the Bayesian Time Structure Sequence (BSTS) model in estimating the acute hemorrhagic conjunctivitis (AHC) epidemics. The reported AHC cases spanning from January 2011 to October 2022 in China were collated. Utilizing R software, the BSTS and Autoregressive Integrated Moving Average (ARIMA) models were constructed using the data from January 2011 to December 2021. The prediction effect of both models was compared using the data from January to October 2022, and finally the AHC incidence from November 2022 to December 2023 was predicted. The results indicated that forecast errors under the BSTS model were lower than those under the ARIMA model. The actual AHC incidence in July 2022 from the ARIMA model deviated from the 95% confidence interval (CI) of the predicted value. However, the observed AHC incidence from the BSTS model fell within the 95% CI of the predicted value. Notably, the BSTS model predicted 26,474 new AHC cases in China from November 2022 to December 2023, exhibiting better prediction performance compared to the ARIMA model. This indicates that the BSTS model possesses a high application value for forecasting the epidemic trends of AHC, making it a valuable tool for disease surveillance and prevention strategies.

Forecasting tuberculosis incidence: a review of time series and machine learning models for prediction and eradication strategies

Despite efforts by the World Health Organization (WHO), tuberculosis (TB) remains a leading cause of fatalities globally. This study reviews time series and machine learning models for TB incidence prediction, identifies popular algorithms, and highlights the need for further research to improve accuracy and global scope. SCOPUS, PUBMED, IEEE, Web of Science, and PRISMA were used for search and article selection from 2012 to 2023. The results revealed that ARIMA, SARIMA, ETS, GRNN, BPNN, NARNN, NNAR, and RNN are popular time series and ML algorithms adopted for TB incidence rate predictions. The inaccurate TB incidence prediction and limited global scope of prior studies suggest a need for further research. This review serves as a roadmap for the WHO to focus on regions that require more attention for TB prevention and the need for more sophisticated models for TB incidence predictions.

Epidemiological trend in scarlet fever incidence in China during the COVID-19 pandemic: A time series analysis

Objective

Over the past decade, scarlet fever has caused a relatively high economic burden in various regions of China. Non-pharmaceutical interventions (NPIs) are necessary because of the absence of vaccines and specific drugs. This study aimed to characterize the demographics of patients with scarlet fever, describe its spatiotemporal distribution, and explore the impact of NPIs on the disease in the era of coronavirus disease 2019 (COVID-19) in China.

Methods

Using monthly scarlet fever data from January 2011 to December 2019, seasonal autoregressive integrated moving average (SARIMA), advanced innovation state-space modeling framework that combines Box-Cox transformations, Fourier series with time-varying coefficients, and autoregressive moving average error correction method (TBATS) models were developed to select the best model for comparing between the expected and actual incidence of scarlet fever in 2020. Interrupted time series analysis (ITSA) was used to explore whether NPIs have an effect on scarlet fever incidence, while the intervention effects of specific NPIs were explored using correlation analysis and ridge regression methods.

Results

From 2011 to 2017, the total number of scarlet fever cases was 400,691, with children aged 0-9 years being the main group affected. There were two annual incidence peaks (May to June and November to December). According to the best prediction model TBATS (0.002, {0, 0}, 0.801, {<12, 5>}), the number of scarlet fever cases was 72,148 and dual seasonality was no longer prominent. ITSA showed a significant effect of NPIs of a reduction in the number of scarlet fever episodes (β2 = -61526, P < 0.005), and the effect of canceling public events (c3) was the most significant (P = 0.0447).

Conclusions

The incidence of scarlet fever during COVID-19 was lower than expected, and the total incidence decreased by 80.74% in 2020. The results of this study indicate that strict NPIs may be of potential benefit in preventing scarlet fever occurrence, especially that related to public event cancellation. However, it is still important that vaccines and drugs are available in the future.

Forecasting of Milk Production in Northern Thailand Using Seasonal Autoregressive Integrated Moving Average, Error Trend Seasonality, and Hybrid Models

Milk production in Thailand has increased rapidly, though excess milk supply is one of the major concerns. Forecasting can reveal the important information that can support authorities and stakeholders to establish a plan to compromise the oversupply of milk. The aim of this study was to forecast milk production in the northern region of Thailand using time-series forecast methods. A single-technique model, including seasonal autoregressive integrated moving average (SARIMA) and error trend seasonality (ETS), and a hybrid model of SARIMA-ETS were applied to milk production data to develop forecast models. The performance of the models developed was compared using several error matrices. Results showed that milk production was forecasted to raise by 3.2 to 3.6% annually. The SARIMA-ETS hybrid model had the highest forecast performances compared with other models, and the ETS outperformed the SARIMA in predictive ability. Furthermore, the forecast models highlighted a continuously increasing trend with evidence of a seasonal fluctuation for future milk production. The results from this study emphasizes the need for an effective plan and strategy to manage milk production to alleviate a possible oversupply. Policymakers and stakeholders can use our forecasts to develop short- and long-term strategies for managing milk production.

Estimating the Effects of the COVID-19 Outbreak on the Reductions in Tuberculosis Cases and the Epidemiological Trends in China: A Causal Impact Analysis

Objective

COVID-19 may have a demonstrable influence on disease patterns. However, it remained unknown how tuberculosis (TB) epidemics are impacted by the COVID-19 outbreak. The purposes of this study are to evaluate the impacts of the COVID-19 outbreak on the decreases in the TB case notifications and to forecast the epidemiological trends in China.

Methods

The monthly TB incidents from January 2005 to December 2020 were taken. Then, we investigated the causal impacts of the COVID-19 pandemic on the TB case reductions using intervention analysis under the Bayesian structural time series (BSTS) method. Next, we split the observed values into different training and testing horizons to validate the forecasting performance of the BSTS method.

Results

The TB incidence was falling during 2005–2020, with an average annual percentage change of −3.186 (95% confidence interval [CI] −4.083 to −2.281), and showed a peak in March–April and a trough in January–February per year. The BSTS method assessed a monthly average reduction of 14% (95% CI 3.8% to 24%) in the TB case notifications from January–December 2020 owing to COVID-19 (probability of causal effect=99.684%, P=0.003), and this method generated a highly accurate forecast for all the testing horizons considering the small forecasting error rates and estimated a continued downward trend from 2021 to 2035 (annual percentage change =−2.869, 95% CI −3.056 to −2.681).

Conclusion

COVID-19 can cause medium- and longer-term consequences for the TB epidemics and the BSTS model has the potential to forecast the epidemiological trends of the TB incidence, which can be recommended as an automated application for public health policymaking in China. Considering the slow downward trend in the TB incidence, additional measures are required to accelerate the progress of the End TB Strategy.

Estimating the COVID-19 prevalence and mortality using a novel data-driven hybrid model based on ensemble empirical mode decomposition

In this study, we proposed a new data-driven hybrid technique by integrating an ensemble empirical mode decomposition (EEMD), an autoregressive integrated moving average (ARIMA), with a nonlinear autoregressive artificial neural network (NARANN), called the EEMD-ARIMA-NARANN model, to perform time series modeling and forecasting based on the COVID-19 prevalence and mortality data from 28 February 2020 to 27 June 2020 in South Africa and Nigeria. By comparing the accuracy level of forecasting measurements with the basic ARIMA and NARANN models, it was shown that this novel data-driven hybrid model did a better job of capturing the dynamic changing trends of the target data than the others used in this work. Our proposed mixture technique can be deemed as a helpful policy-supportive tool to plan and provide medical supplies effectively. The overall confirmed cases and deaths were estimated to reach around 176,570 [95% uncertainty level (UL) 173,607 to 178,476] and 3454 (95% UL 3384 to 3487), respectively, in South Africa, along with 32,136 (95% UL 31,568 to 32,641) and 788 (95% UL 775 to 804) in Nigeria on 12 July 2020 using this data-driven EEMD-ARIMA-NARANN hybrid technique. The contributions of this study include three aspects. First, the proposed hybrid model can better capture the dynamic dependency characteristics compared with the individual models. Second, this new data-driven hybrid model is constructed in a more reasonable way relative to the traditional mixture model. Third, this proposed model may be generalized to estimate the epidemic patterns of COVID-19 in other regions.

Estimating the Long-Term Epidemiological Trends and Seasonality of Hemorrhagic Fever with Renal Syndrome in China

OBJECTIVE

We aim to examine the adequacy of an innovation state-space modeling framework (called TBATS) in forecasting the long-term epidemic seasonality and trends of hemorrhagic fever with renal syndrome (HFRS).

METHODS

The HFRS morbidity data from January 1995 to December 2020 were taken, and subsequently, the data were split into six different training and testing segments (including 12, 24, 36, 60, 84, and 108 holdout monthly data) to investigate its predictive ability of the TBATS method, and its forecasting performance was compared with the seasonal autoregressive integrated moving average (SARIMA).

RESULTS

The TBATS (0.27, {0,0}, -, {<12,4>}) and SARIMA (0,1,(1,3))(0,1,1)12 were selected as the best TBATS and SARIMA methods, respectively, for the 12-step ahead prediction. The mean absolute deviation, root mean square error, mean absolute percentage error, mean error rate, and root mean square percentage error were 91.799, 14.772, 123.653, 0.129, and 0.193, respectively, for the preferred TBATS method and were 144.734, 25.049, 161.671, 0.203, and 0.296, respectively, for the preferred SARIMA method. Likewise, for the 24-, 36-, 60-, 84-, and 108-step ahead predictions, the preferred TBATS methods produced smaller forecasting errors over the best SARIMA methods. Further validations also suggested that the TBATS model outperformed the Error-Trend-Seasonal framework, with little exception. HFRS had dual seasonal behaviors, peaking in May-June and November-December. Overall a notable decrease in the HFRS morbidity was seen during the study period (average annual percentage change=-6.767, 95% confidence intervals: -10.592 to -2.778), and yet different stages had different variation trends. Besides, the TBATS model predicted a plateau in the HFRS morbidity in the next ten years.

CONCLUSION

The TBATS approach outperforms the SARIMA approach in estimating the long-term epidemic seasonality and trends of HFRS, which is capable of being deemed as a promising alternative to help stakeholders to inform future preventive policy or practical solutions to tackle the evolving scenarios.

Time Series Analysis and Forecasting of the Hand-Foot-Mouth Disease Morbidity in China Using An Advanced Exponential Smoothing State Space TBATS Model

Objective

The high morbidity, complex seasonality, and recurring risk of hand-foot-and-mouth disease (HFMD) exert a major burden in China. Forecasting its epidemic trends is greatly instrumental in informing vaccine and targeted interventions. This study sets out to investigate the usefulness of an advanced exponential smoothing state space framework by combining Box-Cox transformations, Fourier representations with time-varying coefficients and autoregressive moving average (ARMA) error correction (TBATS) method to assess the temporal trends of HFMD in China.

Methods

Data from January 2009 to December 2019 were drawn, and then they were split into two segments comprising the in-sample training data and out-of-sample testing data to develop and validate the TBATS model, and its fitting and forecasting abilities were compared with the most frequently used seasonal autoregressive integrated moving average (SARIMA) method.

Results

Following the modelling procedures of the SARIMA and TBATS methods, the SARIMA (1,0,1)(0,1,1)₁₂ and TBATS (0.024, {1,1}, 0.855, {<12,4>}) specifications were recognized as being the optimal models, respectively, for the 12-step ahead forecasting, along with the SARIMA (1,0,1)(0,1,1)₁₂ and TBATS (0.062, {1,3}, 0.86, {<12,4>}) models as being the optimal models, respectively, for the 24-step ahead forecasting. Among them, the optimal TBATS models produced lower error rates in both 12-step and 24-step ahead forecasting aspects compared to the preferred SARIMA models. Descriptive analysis of the data showed a significantly high level and a marked dual seasonal pattern in the HFMD morbidity.

Conclusion

The TBATS model has the capacity to outperform the most frequently used SARIMA model in forecasting the HFMD incidence in China, and it can be recommended as a flexible and useful tool in the decision-making process of HFMD prevention and control in China.

Forecasting the Tuberculosis Incidence Using a Novel Ensemble Empirical Mode Decomposition-Based Data-Driven Hybrid Model in Tibet, China

Objective

The purpose of this study is to develop a novel data-driven hybrid model by fusing ensemble empirical mode decomposition (EEMD), seasonal autoregressive integrated moving average (SARIMA), with nonlinear autoregressive artificial neural network (NARNN), called EEMD-ARIMA-NARNN model, to assess and forecast the epidemic patterns of TB in Tibet.

Methods

The TB incidence from January 2006 to December 2017 was obtained, and then the time series was partitioned into training subsamples (from January 2006 to December 2016) and testing subsamples (from January to December 2017). Among them, the training set was used to develop the EEMD-SARIMA-NARNN combined model, whereas the testing set was used to validate the forecasting performance of the model. Whilst the forecasting accuracy level of this novel method was compared with the basic SARIMA model, basic NARNN model, error-trend-seasonal (ETS) model, and traditional SARIMA-NARNN mixture model.

Results

By comparing the accuracy level of the forecasting measurements including root-mean-square error, mean absolute deviation, mean error rate, mean absolute percentage error, and root-mean-square percentage error, it was shown that the EEMD-SARIMA-NARNN combined method produced lower error rates than the others. The descriptive statistics suggested that TB was a seasonal disease, peaking in late winter and early spring and a trough in autumn and early winter, and the TB epidemic indicated a drastic increase by a factor of 1.7 from 2006 to 2017 in Tibet, with average annual percentage change of 5.8 (95% confidence intervals: 3.5–8.1).

Conclusion

This novel data-driven hybrid method can better consider both linear and nonlinear components in the TB incidence than the others used in this study, which is of great help to estimate and forecast the future epidemic trends of TB in Tibet. Besides, under present trends, strict precautionary measures are required to reduce the spread of TB in Tibet.

Customer relationship management analysis of outpatients in a Chinese infectious disease hospital using drug-proportion recency-frequency-monetary model

BACKGROUND

Identifying the patient types with different economic values can be useful for hospital development.

OBJECTIVE

This work uses the theory of customer relationship management (CRM) to analyze the outpatients in the hospital for infectious diseases in Shanghai, China.

METHODS

A total of 2,271,020 data elements of outpatients in the research unit between August 2009 and December 2019 were extracted, analyzed and cleaned to obtain 171,107 valid data elements (1 element per person). The main diseases were viral hepatitis B (VHB) and acquired immunodeficiency syndrome (AIDS), and the average percentage of drug expenditure was 80.39 %. We innovatively expanded the classic RFM (R: recency, F: frequency, M: monetary) model in CRM to the dRFM (d: percentage of drug expenditure) model. We selected the best clustering algorithm from the K-means, Kohonen and two-step clustering methods to find the optimal model to distinguish the types of patients with different economic values and the best decision-making algorithm from the C5.0, CART classification regression tree, CHAID and QUEST algorithms to verify the model.

RESULTS

After performing two rounds of K-means clustering analysis on three models: RFM, RFM + dRFM and dRFM, and 97,855 data elements were retained. The RFM + dRFM model was the optimal model, clustering the patients into 3 types: potential patients (24.2 %) to be retained, with a high drug expenditure and the last visit in more than 19.06 months, high-value patients (24.5 %) to be attracted, with the last visit in about 6.66 months; basal patients (51.3 %) to be kept, with the last visit in about 3.7 months. The model was then verified using the C5.0 decision tree algorithm with an accuracy rate of 99.97 %.

CONCLUSION

This objective CRM analysis of the patients in the hospital for infectious diseases using the dRFM model accurately identified different types of patients, providing an objective and effective basis for hospital management.

Forecasting the epidemiological trends of COVID-19 prevalence and mortality using the advanced α-Sutte Indicator

Forecasting the epidemics of the diseases is very valuable in planning and supplying resources effectively. This study aims to estimate the epidemiological trends of the coronavirus disease 2019 (COVID-19) prevalence and mortality using the advanced α-Sutte Indicator, and its prediction accuracy level was compared with the most frequently adopted autoregressive integrated moving average (ARIMA) method. Time-series analysis was performed based on the total confirmed cases and deaths of COVID-19 in the world, Brazil, Peru, Canada and Chile between 27 February 2020 and 30 June 2020. By comparing the prediction reliability indices, including the root mean square error, mean absolute error, mean error rate, mean absolute percentage error and root mean square percentage error, the α-Sutte Indicator was found to produce lower forecasting error rates than the ARIMA model in all data apart from the prevalence testing set globally. The α-Sutte Indicator can be recommended as a useful tool to nowcast and forecast the COVID-19 prevalence and mortality of these regions except for the prevalence around the globe in the near future, which will help policymakers to plan and prepare health resources effectively. Also, the findings of our study may have managerial implications for the outbreak in other countries.

Estimating the Prevalence and Mortality of Coronavirus Disease 2019 (COVID-19) in the USA, the UK, Russia, and India

Objective

The aim of this study is to apply the advanced error-trend-seasonal (ETS) framework to forecast the prevalence and mortality series of COVID-19 in the USA, the UK, Russia, and India, and the predictive performance of the ETS framework was compared with the most frequently used autoregressive integrated moving average (ARIMA) model.

Materials and Methods

The prevalence and mortality data of COVID-19 in the USA, the UK, Russia, and India between 20 February 2020 and 15 May 2020 were extracted from the WHO website. Then, the data subsamples between 20 February 2020 and 3 May 2020 were treated as the training horizon, and the others were used as the testing horizon to construct the ARIMA models and the ETS models.

Results

Based on the model evaluation criteria, the ARIMA (0,2,1) and ETS (M,MD,N), sparse coefficient ARIMA (0,2,(1,6)) and ETS (A,AD,M), ARIMA (1,1,1) and ETS (A,MD,A), together with ARIMA (2,2,1) and ETS (A,M,A) specifications were identified as the preferred ARIMA and ETS models for the prevalence data in the USA, the UK, Russia, and India, respectively; the ARIMA (0,2,1) and ETS (M,A,M), ARIMA (0,2,1) and ETS (M,A,N), ARIMA (0,2,1) and ETS (A,A,N), coupled with ARIMA (0,2,2) and ETS (M,M,N) specifications were selected as the optimal ARIMA and ETS models for the mortality data in these four countries, respectively. Among these best-fitting models, the ETS models produced smaller forecasting error rates than the ARIMA models in all the datasets.

Conclusion

The ETS framework can be used to nowcast and forecast the long-term temporal trends of the COVID-19 prevalence and mortality in the USA, the UK, Russia, and India, and which provides a notable performance improvement over the most frequently used ARIMA model. Our findings can aid governments as a reference to prepare for and respond to the COVID-19 pandemic both in restricting the transmission of the disease and in lowering the disease-related deaths in the upcoming days.