Forecast model analysis for the morbidity of tuberculosis in Xinjiang, China

Predicting the incidence of infectious diarrhea with symptom surveillance data using a stacking-based ensembled model

BACKGROUND

Infectious diarrhea remains a major public health problem worldwide. This study used stacking ensemble to developed a predictive model for the incidence of infectious diarrhea, aiming to achieve better prediction performance.

METHODS

Based on the surveillance data of infectious diarrhea cases, relevant symptoms and meteorological factors of Guangzhou from 2016 to 2021, we developed four base prediction models using artificial neural networks (ANN), Long Short-Term Memory networks (LSTM), support vector regression (SVR) and extreme gradient boosting regression trees (XGBoost), which were then ensembled using stacking to obtain the final prediction model. All the models were evaluated with three metrics: mean absolute percentage error (MAPE), root mean square error (RMSE), and mean absolute error (MAE).

RESULTS

Base models that incorporated symptom surveillance data and weekly number of infectious diarrhea cases were able to achieve lower RMSEs, MAEs, and MAPEs than models that added meteorological data and weekly number of infectious diarrhea cases. The LSTM had the best prediction performance among the four base models, and its RMSE, MAE, and MAPE were: 84.85, 57.50 and 15.92%, respectively. The stacking ensembled model outperformed the four base models, whose RMSE, MAE, and MAPE were 75.82, 55.93, and 15.70%, respectively.

CONCLUSIONS

The incorporation of symptom surveillance data could improve the predictive accuracy of infectious diarrhea prediction models, and symptom surveillance data was more effective than meteorological data in enhancing model performance. Using stacking to combine multiple prediction models were able to alleviate the difficulty in selecting the optimal model, and could obtain a model with better performance than base models.

Time series analysis and short-term forecasting of monkeypox outbreak trends in the 10 major affected countries

BACKGROUND

Considering the rapidly spreading monkeypox outbreak, WHO has declared a global health emergency. Still in the category of being endemic, the monkeypox disease shares numerous clinical characters with smallpox. This study focuses on determining the most effective combination of autoregressive integrated moving average model to encapsulate time dependent flow behaviour of the virus with short run prediction.

METHODS

This study includes the data of confirmed reported cases and cumulative cases from eight most burdened countries across the globe, over the span of May 18, 2022, to December 31, 2022. The data was assembled from the website of Our World in Data and it involves countries such as United States, Brazil, Spain, France, Colombia, Mexico, Peru, United Kingdom, Germany and Canada. The job of modelling and short-term forecasting is facilitated by the employment of autoregressive integrated moving average. The legitimacy of the estimated models is argued by offering numerous model performance indices such as, root mean square error, mean absolute error and mean absolute prediction error.

RESULTS

The best fit models were deduced for each country by using the data of confirmed reported cases of monkeypox infections. Based on diverse set of performance evaluation criteria, the best fit models were then employed to provide forecasting of next twenty days. Our results indicate that the USA is expected to be the hardest-hit country, with an average of 58 cases per day with 95% confidence interval of (00-400). The second most burdened country remained Brazil with expected average cases of 23 (00-130). The outlook is not much better for Spain and France, with average forecasts of 52 (00-241) and 24 (00-121), respectively.

CONCLUSION

This research provides profile of ten most severely hit countries by monkeypox transmission around the world and thus assists in epidemiological management. The prediction trends indicate that the confirmed cases in the USA may exceed than other contemporaries. Based on the findings of this study, it remains plausible to recommend that more robust health surveillance strategy is required to control the transmission flow of the virus especially in USA.

Modeling Global Monkeypox Infection Spread Data: A Comparative Study of Time Series Regression and Machine Learning Models

The global impact of COVID-19 has heightened concerns about emerging viral infections, among which monkeypox (MPOX) has become a significant public health threat. To address this, our study employs a comprehensive approach using three statistical techniques: Distribution fitting, ARIMA modeling, and Random Forest machine learning to analyze and predict the spread of MPOX in the top ten countries with high infection rates. We aim to provide a detailed understanding of the disease dynamics and model theoretical distributions using country-specific datasets to accurately assess and forecast the disease's transmission. The data from the considered countries are fitted into ARIMA models to determine the best time series regression model. Additionally, we employ the random forest machine learning approach to predict the future behavior of the disease. Evaluating the Root Mean Square Errors (RMSE) for both models, we find that the random forest outperforms ARIMA in six countries, while ARIMA performs better in the remaining four countries. Based on these findings, robust policy-making should consider the best fitted model for each country to effectively manage and respond to the ongoing public health threat posed by monkeypox. The integration of multiple modeling techniques enhances our understanding of the disease dynamics and aids in devising more informed strategies for containment and control.

Impact of Previous Pulmonary Tuberculosis on Chronic Obstructive Pulmonary Disease: Baseline Results from a Prospective Cohort Study

OBJECTIVE

Pulmonary tuberculosis (PTB) is a significant risk factor for COPD, and Xinjiang, China, has a high incidence of pulmonary tuberculosis. The effects of tuberculosis history on airflow restriction, clinical symptoms, and acute episodes in COPD patients have not been reported in the local population. Besides, the exact relationship between lung function changes in people with a history of tuberculosis and COPD risk is not clear.

METHODS

This study is based on the Xinjiang baseline survey data included in the Natural Population Cohort Study in Northwest China from June to December, 2018. Subjects' questionnaires, physical examination, and lung function tests were performed through a face-to-face field survey to analyze the impact of previous pulmonary tuberculosis on local COPD. Furthermore, we clarified the specific relationship between pulmonary function decline and the probability of developing COPD in people with a history of tuberculosis.

RESULTS

A total of 3249 subjects were eventually enrolled in this study, including 87 with a history of tuberculosis and 3162 non-TB. The prevalence of COPD in the prior TB group was significantly higher than that in the control group (p-value = 0.005). First, previous pulmonary tuberculosis is an essential contributor to airflow limitation in the general population and patients with COPD. In all subjects included, pulmonary function, FEV1% predicted (p-value < 0.001), and FEV1/FVC (%) (p-value < 0.001) were significantly lower in the prior TB group than in the control group. Compared to non-TB group, FEV1% prediction (p-value = 0.019) and FEV1/FVC (%) (p-value = 0.016) were found to be significantly reduced, and airflow restriction (p-value = 0.004) was more severe in prior TB group among COPD patients. Second, COPD patients in the prior TB group had more severe clinical symptoms. Compared with no history of tuberculosis, mMRC (p-value = 0.001) and CAT (p-value = 0.002) scores were higher in the group with a history of tuberculosis among COPD patients. Third, compared with the non-TB group, the number of acute exacerbations per year (p-values=0.008), the duration of each acute exacerbation (p-values=0.004), and hospitalization/ patient/year (p-values<0.001) were higher in the group with a history of tuberculosis among COPD patients. Finally, a dose-response relationship between FEV1/FVC (%) and the probability of developing COPD in people with previous pulmonary TB was observed; when FEV1/FVC (%) was < 80.8, the risk of COPD increased by 13.5% per unit decrease in lung function [0.865(0.805, 0.930)].

CONCLUSION

COPD patients with previous pulmonary tuberculosis have more severe airflow limitations and clinical symptoms and are at higher risk for acute exacerbations. Furthermore, lung function changes in people with a history of tuberculosis were associated with a dose-response relationship with the probability of developing COPD.

Estimating the tuberculosis incidence using a SARIMAX-NNARX hybrid model by integrating meteorological factors in Qinghai Province, China

Tuberculosis (TB) is recognized as being a major public health concern owing to its increase in Qinghai, China. In this study, we aimed to estimate the long-term effects of meteorological variables on TB incidence and construct an advanced hybrid model with seasonal autoregressive integrated moving average (SARIMA) and a neural network nonlinear autoregression (SARIMAX-NNARX) by integrating meteorological factors and evaluating the model fitting and prediction effect. During 2005-2017, TB experienced an upward trend with obvious periodic and seasonal characteristics, peaking in spring and winter. The results showed that TB incidence was positively correlated with average relative humidity (ARH) with a 2-month lag (β = 1.889, p = 0.003), but negatively correlated with average atmospheric pressure (AAP) with a 1-month lag (β =  - 1.633, p = 0.012), average temperature (AT) with a 2-month lag (β =  - 0.093, p = 0.027), and average wind speed (AWS) with a 0-month lag (β =  - 13.221, p = 0.033), respectively. The SARIMA (3,1,0)(1,1,1)_12, SARIMAX(3,1,0)(1,1,1)₁₂, and SARIMAX(3,1,0)(1,1,1)₁₂-NNARX(15,3) were considered preferred models based on the evaluation criteria. Of them, the SARIMAX-NNARX technique had smaller error values than the SARIMA and SARIMAX models in both fitting and forecasting aspects. The sensitivity analysis also revealed the robustness of the mixture forecasting model. Therefore, the SARIMAX-NNARX model by integrating meteorological variables can be used as an accurate method for forecasting the epidemic trends which would be great importance for TB prevention and control in the coming periods in Qinghai.

Modeling and Forecasting Monkeypox Cases Using Stochastic Models

BACKGROUND

Monkeypox virus is gaining attention due to its severity and spread among people. This study sheds light on the modeling and forecasting of new monkeypox cases. Knowledge about the future situation of the virus using a more accurate time series and stochastic models is required for future actions and plans to cope with the challenge.

METHODS

We conduct a side-by-side comparison of the machine learning approach with the traditional time series model. The multilayer perceptron model (MLP), a machine learning technique, and the Box-Jenkins methodology, also known as the ARIMA model, are used for classical modeling. Both methods are applied to the Monkeypox cumulative data set and compared using different model selection criteria such as root mean square error, mean square error, mean absolute error, and mean absolute percentage error.

RESULTS

With a root mean square error of 150.78, the monkeypox series follows the ARIMA (7,1,7) model among the other potential models. Comparatively, we use the multilayer perceptron (MLP) model, which employs the sigmoid activation function and has a different number of hidden neurons in a single hidden layer. The root mean square error of the MLP model, which uses a single input and ten hidden neurons, is 54.40, significantly lower than that of the ARIMA model. The actual confirmed cases versus estimated or fitted plots also demonstrate that the multilayer perceptron model has a better fit for the monkeypox data than the ARIMA model.

CONCLUSIONS AND RECOMMENDATION

When it comes to predicting monkeypox, the machine learning method outperforms the traditional time series. A better match can be achieved in future studies by applying the extreme learning machine model (ELM), support vector machine (SVM), and some other methods with various activation functions. It is thus concluded that the selected data provide a real picture of the virus. If the situations remain the same, governments and other stockholders should ensure the follow-up of Standard Operating Procedures (SOPs) among the masses, as the trends will continue rising in the upcoming 10 days. However, governments should take some serious interventions to cope with the virus.

LIMITATION

In the ARIMA models selected for forecasting, we did not incorporate the effect of covariates such as the effect of net migration of monkeypox virus patients, government interventions, etc.

Development and comparison of predictive models for sexually transmitted diseases-AIDS, gonorrhea, and syphilis in China, 2011-2021

Background

Accurate incidence prediction of sexually transmitted diseases (STDs) is critical for early prevention and better government strategic planning. In this paper, four different forecasting models were presented to predict the incidence of AIDS, gonorrhea, and syphilis.

Methods

The annual percentage changes in the incidence of AIDS, gonorrhea, and syphilis were estimated by using joinpoint regression. The performance of four methods, namely, the autoregressive integrated moving average (ARIMA) model, Elman neural network (ERNN) model, ARIMA-ERNN hybrid model and long short-term memory (LSTM) model, were assessed and compared. For 1-year prediction, the collected data from 2011 to 2020 were used for modeling to predict the incidence in 2021. For 5-year prediction, the collected data from 2011 to 2016 were used for modeling to predict the incidence from 2017 to 2021. The performance was evaluated based on four indices: mean square error (MSE), mean absolute error (MAE), and mean absolute percentage error (MAPE).

Results

The morbidities of AIDS and syphilis are on the rise, and the morbidity of gonorrhea has declined in recent years. The optimal ARIMA models were determined: ARIMA(2,1,2)(0,1,1)12, ARIMA(1,1,2)(0,1,2)12, and ARIMA(3,1,2)(1,1,2)12 for AIDS, gonorrhea, and syphilis 1-year prediction, respectively; ARIMA (2,1,2)(0,1,1)12, ARIMA(1,1,2)(0,1,2)12, and ARIMA(2,1,1)(0,1,0)12 for AIDS, gonorrhea and syphilis 5-year prediction, respectively. For 1-year prediction, the MAPEs of ARIMA, ERNN, ARIMA-ERNN, and LSTM for AIDS are 23.26, 20.24, 18.34, and 18.63, respectively; For gonorrhea, the MAPEs are 19.44, 18.03, 17.77, and 5.09, respectively; For syphilis, the MAPEs are 9.80, 9.55, 8.67, and 5.79, respectively. For 5-year prediction, the MAPEs of ARIMA, ERNN, ARIMA-ERNN, and LSTM for AIDS are 12.86, 23.54, 14.74, and 25.43, respectively; For gonorrhea, the MAPEs are 17.07, 17.95, 16.46, and 15.13, respectively; For syphilis, the MAPEs are 21.88, 24.00, 20.18 and 11.20, respectively. In general, the performance ranking of the four models from high to low is LSTM, ARIMA-ERNN, ERNN, and ARIMA.

Conclusion

The time series predictive models show their powerful performance in forecasting STDs incidence and can be applied by relevant authorities in the prevention and control of STDs.

Comparison of ARIMA, ETS, NNAR, TBATS and hybrid models to forecast the second wave of COVID-19 hospitalizations in Italy

The coronavirus disease (COVID-19) is a severe, ongoing, novel pandemic that emerged in Wuhan, China, in December 2019. As of January 21, 2021, the virus had infected approximately 100 million people, causing over 2 million deaths. This article analyzed several time series forecasting methods to predict the spread of COVID-19 during the pandemic's second wave in Italy (the period after October 13, 2020). The autoregressive moving average (ARIMA) model, innovations state space models for exponential smoothing (ETS), the neural network autoregression (NNAR) model, the trigonometric exponential smoothing state space model with Box-Cox transformation, ARMA errors, and trend and seasonal components (TBATS), and all of their feasible hybrid combinations were employed to forecast the number of patients hospitalized with mild symptoms and the number of patients hospitalized in the intensive care units (ICU). The data for the period February 21, 2020-October 13, 2020 were extracted from the website of the Italian Ministry of Health ( www.salute.gov.it ). The results showed that (i) hybrid models were better at capturing the linear, nonlinear, and seasonal pandemic patterns, significantly outperforming the respective single models for both time series, and (ii) the numbers of COVID-19-related hospitalizations of patients with mild symptoms and in the ICU were projected to increase rapidly from October 2020 to mid-November 2020. According to the estimations, the necessary ordinary and intensive care beds were expected to double in 10 days and to triple in approximately 20 days. These predictions were consistent with the observed trend, demonstrating that hybrid models may facilitate public health authorities' decision-making, especially in the short-term.

Modelling the persistence of Covid-19 positivity rate in Italy

The current Covid-19 pandemic is severely affecting public health and global economies. In this context, accurately predicting its evolution is essential for planning and providing resources effectively. This paper aims at capturing the dynamics of the positivity rate (PPR) of the novel coronavirus using the Heterogeneous Autoregressive (HAR) model. The use of this model is motivated by two main empirical features arising from the analysis of PPR time series: the changing long-run level and the persistent autocorrelation structure. Compared to the most frequently used Autoregressive Integrated Moving Average (ARIMA) models, the HAR is able to reproduce the strong persistence of the data by using components aggregated at different interval sizes, remaining parsimonious and easy to estimate. The relative merits of the proposed approach are assessed by performing a forecasting study on the Italian dataset. As a robustness check, the analysis of the positivity rate is also conducted by considering the case of the United States. The ability of the HAR-type models to predict the PPR at different horizons is evaluated through several loss functions, comparing the results with those generated by ARIMA models. The Model Confidence Set is used to test the significance of differences in the predictive performances of the models under analysis. Our findings suggest that HAR-type models significantly outperform ARIMA specifications in terms of forecasting accuracy. We also find that the PPR could represent an important metric for monitoring the evolution of hospitalizations, as the peak of patients in intensive care units occurs within 12-16 days after the peak in the positivity rate. This can help governments in planning socio-economic and health policies in advance.

Time series forecasting for tuberculosis incidence employing neural network models

Every effort aimed at stopping the expansion of Tuberculosis is important to national programs' struggle to combat this disease. Different computational tools have been proposed in order to design new strategies that allow managing potential patients and thus providing the correct treatment. In this work, artificial neural networks were used for time series forecasting, which were trained with information on reported cases obtained from the national vigilance institution in Colombia. Three neural models were proposed in order to determine the best one according to their forecasting performance. The first approach employed a nonlinear autoregressive model, the second proposal used a recurrent neural network, and the third proposal was based on radial basis functions. The results are presented in terms of the mean average percentage error, which indicates that the models based on traditional methods show better performance compared to connectionist ones. These models contribute to obtaining dynamic information about incidence, thus providing extra-help for health authorities to propose more strategies to control the disease's spread.

The research of SARIMA model for prediction of hepatitis B in mainland China

Hepatitis B virus infection is a major global public health concern. This study explored the epidemic characteristics and tendency of hepatitis B in 31 provinces of mainland China, constructed a SARIMA model for prediction, and provided corresponding preventive measures.Monthly hepatitis B case data from mainland China from 2013 to 2020 were obtained from the website of the National Health Commission of the People's Republic of China. Monthly data from 2013 to 2020 were used to build the SARIMA model and data from 2021 were used to test the model.Between 2013 and 2020, 9,177,313 hepatitis B cases were reported in mainland China. SARIMA(1,0,0)(0,1,1)12 was the optimal model and its residual was white noise. It was used to predict the number of hepatitis B cases from January to December 2021, and the predicted values for 2021 were within the 95% confidence interval.This study suggests that the SARIMA model simulated well based on epidemiological trends of hepatitis B in mainland China. The SARIMA model is a feasible tool for monitoring hepatitis B virus infections in mainland China.

Prediction and analysis of COVID-19 daily new cases and cumulative cases: times series forecasting and machine learning models

BACKGROUND

COVID-19 poses a severe threat to global human health, especially the USA, Brazil, and India cases continue to increase dynamically, which has a far-reaching impact on people's health, social activities, and the local economic situation.

METHODS

The study proposed the ARIMA, SARIMA and Prophet models to predict daily new cases and cumulative confirmed cases in the USA, Brazil and India over the next 30 days based on the COVID-19 new confirmed cases and cumulative confirmed cases data set(May 1, 2020, and November 30, 2021) published by the official WHO, Three models were implemented in the R 4.1.1 software with forecast and prophet package. The performance of different models was evaluated by using root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE).

RESULTS

Through the fitting and prediction of daily new case data, we reveal that the Prophet model has more advantages in the prediction of the COVID-19 of the USA, which could compose data components and capture periodic characteristics when the data changes significantly, while SARIMA is more likely to appear over-fitting in the USA. And the SARIMA model captured a seven-day period hidden in daily COVID-19 new cases from 3 countries. While in the prediction of new cumulative cases, the ARIMA model has a better ability to fit and predict the data with a positive growth trend in different countries(Brazil and India).

CONCLUSIONS

This study can shed light on understanding the outbreak trends and give an insight into the epidemiological control of these regions. Further, the prediction of the Prophet model showed sufficient accuracy in the daily COVID-19 new cases of the USA. The ARIMA model is suitable for predicting Brazil and India, which can help take precautions and policy formulation for this epidemic in other countries.

Using the SARIMA Model to Forecast the Fourth Global Wave of Cumulative Deaths from COVID-19: Evidence from 12 Hard-Hit Big Countries

The COVID-19 pandemic is a serious threat to all of us. It has caused an unprecedented shock to the world’s economy, and it has interrupted the lives and livelihood of millions of people. In the last two years, a large body of literature has attempted to forecast the main dimensions of the COVID-19 outbreak using a wide set of models. In this paper, I forecast the short- to mid-term cumulative deaths from COVID-19 in 12 hard-hit big countries around the world as of 20 August 2021. The data used in the analysis were extracted from the Our World in Data COVID-19 dataset. Both non-seasonal and seasonal autoregressive integrated moving averages (ARIMA and SARIMA) were estimated. The analysis showed that: (i) ARIMA/SARIMA forecasts were sufficiently accurate in both the training and test set by always outperforming the simple alternative forecasting techniques chosen as benchmarks (Mean, Naïve, and Seasonal Naïve); (ii) SARIMA models outperformed ARIMA models in 47 out 48 metrics (in forecasting future values), i.e., on 97.9% of all the considered forecast accuracy measures (mean absolute error [MAE], mean absolute percentage error [MAPE], mean absolute scaled error [MASE], and the root mean squared error [RMSE]), suggesting a clear seasonal pattern in the data; and (iii) the forecasted values from SARIMA models fitted very well the observed (real-time) data for the period 21 August 2021–19 September 2021 for almost all the countries analyzed. This article shows that SARIMA can be safely used for both the short- and medium-term predictions of COVID-19 deaths. Thus, this approach can help government authorities to monitor and manage the huge pressure that COVID-19 is exerting on national healthcare systems.

The research of ARIMA, GM(1,1), and LSTM models for prediction of TB cases in China

Background and objective

Tuberculosis (Tuberculosis, TB) is a public health problem in China, which not only endangers the population’s health but also affects economic and social development. It requires an accurate prediction analysis to help to make policymakers with early warning and provide effective precautionary measures. In this study, ARIMA, GM(1,1), and LSTM models were constructed and compared, respectively. The results showed that the LSTM was the optimal model, which can be achieved satisfactory performance for TB cases predictions in mainland China.

Methods

The data of tuberculosis cases in mainland China were extracted from the National Health Commission of the People’s Republic of China website. According to the TB data characteristics and the sample requirements, we created the ARIMA, GM(1,1), and LSTM models, which can make predictions for the prevalence trend of TB. The mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE) were applied to evaluate the effects of model fitting predicting accuracy.

Results

There were 3,021,995 tuberculosis cases in mainland China from January 2018 to December 2020. And the overall TB cases in mainland China take on a downtrend trend. We established ARIMA, GM(1,1), and LSTM models, respectively. The optimal ARIMA model is the ARIMA (0,1,0) × (0,1,0)12. The equation for GM(1,1) model was X(k+1) = -10057053.55e^(-0.01k) + 10153178.55 the Mean square deviation ratio C value was 0.49, and the Small probability of error P was 0.94. LSTM model consists of an input layer, a hidden layer and an output layer, the parameters of epochs, learning rating are 60, 0.01, respectively. The MAE, RMSE, and MAPE values of LSTM model were smaller than that of GM(1,1) and ARIMA models.

Conclusions

Our findings showed that the LSTM model was the optimal model, which has a higher accuracy performance than that of ARIMA and GM (1,1) models. Its prediction results can act as a predictive tool for TB prevention measures in mainland China.

Comparison of ARIMA and LSTM for prediction of hemorrhagic fever at different time scales in China

Objectives

This study intends to build and compare two kinds of forecasting models at different time scales for hemorrhagic fever incidence in China.

Methods

Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory Neural Network (LSTM) were adopted to fit monthly, weekly and daily incidence of hemorrhagic fever in China from 2013 to 2018. The two models, combined and uncombined with rolling forecasts, were used to predict the incidence in 2019 to examine their stability and applicability.

Results

ARIMA (2, 1, 1) (0, 1, 1)₁₂, ARIMA (1, 1, 3) (1, 1, 1)₅₂ and ARIMA (5, 0, 1) were selected as the best fitting ARIMA model for monthly, weekly and daily incidence series, respectively. The LSTM model with 64 neurons and Stochastic Gradient Descent (SGDM) for monthly incidence, 8 neurons and Adaptive Moment Estimation (Adam) for weekly incidence, and 64 neurons and Root Mean Square Prop (RMSprop) for daily incidence were selected as the best fitting LSTM models. The values of root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) of the models combined with rolling forecasts in 2019 were lower than those of the direct forecasting models for both ARIMA and LSTM. It was shown from the forecasting performance in 2019 that ARIMA was better than LSTM for monthly and weekly forecasting while the LSTM was better than ARIMA for daily forecasting in rolling forecasting models.

Conclusions

Both ARIMA and LSTM could be used to build a prediction model for the incidence of hemorrhagic fever. Different models might be more suitable for the incidence prediction at different time scales. The findings can provide a good reference for future selection of prediction models and establishments of early warning systems for hemorrhagic fever.

Temporal trends and prediction of bovine tuberculosis: a time series analysis in the North-East of Iran

BACKGROUND

Bovine tuberculosis (BTB) is a disease with high economic relevance.

AIMS

This study aimed to determine a fast alert surveillance system for bTB before the outbreak in the epidemic region of Iran.

METHODS

This cross-sectional study was conducted using the Auto-Regressive Integrated Moving Average (ARIMA) model for monthly bTB detections (reactors). These reactor cases result from the positive Tuberculin Purified Protein Derivative (PPD) test on cattle farms for the period between April 2007 and March 2019 in Razavi Khorasan province. Autocorrelation functions (ACF) and partial autocorrelation functions (PACF) plots were used to determine model parameters. The Akaike Information Criteria (AIC) were employed to select the best-fitted model. The root mean square error (RMSE) was applied for the evaluation of the models. Then, the best-fitted model was hired to predict the cases for 12 oncoming months. The data were analysed by STATA (ver. 14) software with a significant level at P≤0.05.

RESULTS

ARIMA (3, 0, 3) 12 was introduced as a recommended fitted model according to white noise residual test (Q=22.87 and P=0.98), lower AIC (541.85), and more precise model RMSE (1.50). However, the forecast values were more than the observed values.

CONCLUSION

The application and interpretation of ARIMA models are straightforward, and may be used as immediate tools for monitoring systems. However, we proposed an Auto-Regressive Integrated Moving Average with Exogenous Input (ARIMAX) model with some measurable exotic factors such as economic fluctuations, climate changes, and pulmonary tuberculosis to introduce a more precise and accurate model for the fast alert surveillance system.

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.

Using Autoregressive Integrated Moving Average (ARIMA) Modelling to Forecast Symptom Complexity in an Ambulatory Oncology Clinic: Harnessing Predictive Analytics and Patient-Reported Outcomes

An increasing incidence of cancer has led to high patient volumes and time challenges in ambulatory oncology clinics. By knowing how many patients are experiencing complex care needs in advance, clinic scheduling and staff allocation adjustments could be made to provide patients with longer or shorter timeslots to address symptom complexity. In this study, we used predictive analytics to forecast the percentage of patients with high symptom complexity in one clinic population in a given time period. Autoregressive integrated moving average (ARIMA) modelling was utilized with patient-reported outcome (PRO) data and patient demographic information collected over 24 weeks. Eight additional weeks of symptom complexity data were collected and compared to assess the accuracy of the forecasting model. The predicted symptom complexity levels were compared with observation data and a mean absolute predicting error of 5.9% was determined, indicating the model’s satisfactory accuracy for forecasting symptom complexity levels among patients in this clinic population. By using a larger sample and additional predictors, this model could be applied to other clinics to allow for tailored scheduling and staff allocation based on symptom complexity forecasting and inform system level models of care to improve outcomes and provide higher quality patient care.

The impact of COVID-19 pandemic on HIV care continuum in Jiangsu, China

BACKGROUND

The COVID-19 pandemic seriously threatens general public health services globally. This study aimed to evaluate the impact of the COVID-19 pandemic on the HIV care continuum in Jiangsu province, China.

METHODS

Data on newly diagnosed HIV persons for analysis were retrieved from Chinas' web-based Comprehensive Response Information Management System (CRIMS) for HIV/AIDS from 2016 to 2020. We recorded data for the first 3 months (January to March, 2020) of strictly implementing COVID-19 measures from publicly available disease databases of the Jiangsu provincial Health Committee. We used seasonal autoregressive integrated moving average (SARIMA) and exponential smoothing in forecasting the parameters. Subgroup differences were accessed using Chi-square tests.

RESULTS

Compared to the estimated proportions, the HIV testing rates decreased by 49.0% (919,938) in the first three months of implementing COVID-19 measures. Of an estimated 1555 new HIV diagnosis expected in the same period, only 63.0% (980) new diagnoses were recorded. According to actual data recorded during the said period, 980 positively tested persons received confirmatory tests, of which 71.4% (700) were reportedly linked to care. And only 49.5% (235) out of the expected 475 newly diagnosed HIV persons received CD4 cell count testing. Meanwhile 91.6% (208) of newly diagnosed HIV persons who received CD4 count tests reportedly initiated antiretroviral therapy (ART) compared to the 227 expected. Compared to the same period from 2016 to 2019, PLWH less than 30 years old and migrants were more likely to be affected by the COVID-19 policies.

CONCLUSIONS

The COVID-19 pandemic negatively impacted HIV healthcare systems in Jiangsu, China. Further measures that can counter the impact of the pandemic are needed to maintain the HIV care continuum.

Forecasting outbreak of COVID-19 in Turkey; Comparison of Box-Jenkins, Brown's exponential smoothing and long short-term memory models

The new coronavirus disease (COVID-19), which first appeared in China in December 2019, has pervaded throughout the world. Because the epidemic started later in Turkey than other European countries, it has the least number of deaths according to the current data. Outbreak management in COVID-19 is of great importance for public safety and public health. For this reason, prediction models can decide the precautionary warning to control the spread of the disease. Therefore, this study aims to develop a forecasting model, considering statistical data for Turkey. Box-Jenkins Methods (ARIMA), Brown's Exponential Smoothing model and RNN-LSTM are employed. ARIMA was selected with the lowest AIC values (12.0342, -2.51411, 12.0253, 3.67729, -4.24405, and 3.66077) as the best fit for the number of total case, the growth rate of total cases, the number of new cases, the number of total death, the growth rate of total deaths and the number of new deaths, respectively. The forecast values of the number of each indicator are stable over time. In the near future, it will not show an increasing trend in the number of cases for Turkey. In addition, the pandemic will become a steady state and an increase in mortality rates will not be expected between 17-31 May. ARIMA models can be used in fresh outbreak situations to ensure health and safety. It is vital to make quick and accurate decisions on the precautions for epidemic preparedness and management, so corrective and preventive actions can be updated considering obtained values.

The transmissibility of hepatitis C virus: a modelling study in Xiamen City, China

This study aimed at estimating the transmissibility of hepatitis C. The data for hepatitis C cases were collected in six districts in Xiamen City, China from 2004 to 2018. A population-mixed susceptible-infectious-chronic-recovered (SICR) model was used to fit the data and the parameters of the model were calculated. The basic reproduction number (R0) and the number of newly transmitted cases by a primary case per month (MNI) were adopted to quantitatively assess the transmissibility of hepatitis C virus (HCV). Eleven curve estimation models were employed to predict the trends of R0 and MNI in the city. The SICR model fits the reported HCV data well (P < 0.01). The median R0 of each district in Xiamen is 0.4059. R0 follows the cubic model curve, the compound curve and the power function curve. The median MNI of each district in Xiamen is 0.0020. MNI follows the cubic model curve, the compound curve and the power function curve. The transmissibility of HCV follows a decreasing trend, which reveals that under the current policy for prevention and control, there would be a high feasibility to eliminate the transmission of HCV in the city.

Testing the Accuracy of the ARIMA Models in Forecasting the Spreading of COVID-19 and the Associated Mortality Rate

Background and objectives: The current pandemic of SARS-CoV-2 has not only changed, but also affected the lives of tens of millions of people around the world in these last nine to ten months. Although the situation is stable to some extent within the developed countries, approximately one million have already died as a consequence of the unique symptomatology that these people displayed. Thus, the need to develop an effective strategy for monitoring, restricting, but especially for predicting the evolution of COVID-19 is urgent, especially in middle-class countries such as Romania. Material and Methods: Therefore, autoregressive integrated moving average (ARIMA) models have been created, aiming to predict the epidemiological course of COVID-19 in Romania by using two statistical software (STATGRAPHICS Centurion (v.18.1.13) and IBM SPSS (v.20.0.0)). To increase the accuracy, we collected data between the established interval (1 March, 31 August) from the official website of the Romanian Government and the World Health Organization. Results: Several ARIMA models were generated from which ARIMA (1,2,1), ARIMA (3,2,2), ARIMA (3,1,3), ARIMA (3,2,2), ARIMA (3,1,3), ARIMA (2,2,2) and ARIMA (1,2,1) were considered the best models. For this, we took into account the lowest value of mean absolute percentage error (MAPE) for March, April, May, June, July, and August (MAPE_March = 9.3225, MAPE_April = 0.975287, MAPE_May = 0.227675, MAPE_June = 0.161412, MAPE_July = 0.243285, MAPE_August = 0.163873, MAPE_{March – August} = 2.29175 for STATGRAPHICS Centurion (v.18.1.13) and MAPE_March = 57.505, MAPE_April = 1.152, MAPE_May = 0.259, MAPE_June = 0.185, MAPE_July = 0.307, MAPE_August = 0.194, and MAPE_{March – August} = 6.013 for IBM SPSS (v.20.0.0) respectively. Conclusions: This study demonstrates that ARIMA is a useful statistical model for making predictions and provides an idea of the epidemiological status of the country of interest.

COVID-19 prevalence estimation: Four most affected African countries

The world at large has been confronted with several disease outbreak which has posed and still posing a serious menace to public health globally. Recently, COVID-19 a new kind of coronavirus emerge from Wuhan city in China and was declared a pandemic by the World Health Organization. There has been a reported case of about 8622985 with global death of 457,355 as of 15.05 GMT, June 19, 2020. South-Africa, Egypt, Nigeria and Ghana are the most affected African countries with this outbreak. Thus, there is a need to monitor and predict COVID-19 prevalence in this region for effective control and management. Different statistical tools and time series model such as the linear regression model and autoregressive integrated moving average (ARIMA) models have been applied for disease prevalence/incidence prediction in different diseases outbreak. However, in this study, we adopted the ARIMA model to forecast the trend of COVID-19 prevalence in the aforementioned African countries. The datasets examined in this analysis spanned from February 21, 2020, to June 16, 2020, and was extracted from the World Health Organization website. ARIMA models with minimum Akaike information criterion correction (AICc) and statistically significant parameters were selected as the best models. Accordingly, the ARIMA (0,2,3), ARIMA (0,1,1), ARIMA (3,1,0) and ARIMA (0,1,2) models were chosen as the best models for SA, Nigeria, and Ghana and Egypt, respectively. Forecasting was made based on the best models. It is noteworthy to claim that the ARIMA models are appropriate for predicting the prevalence of COVID-19. We noticed a form of exponential growth in the trend of this virus in Africa in the days to come. Thus, the government and health authorities should pay attention to the pattern of COVID-19 in Africa. Necessary plans and precautions should be put in place to curb this pandemic in Africa.

Estimation of COVID-19 prevalence in Italy, Spain, and France

At the end of December 2019, coronavirus disease 2019 (COVID-19) appeared in Wuhan city, China. As of April 15, 2020, >1.9 million COVID-19 cases were confirmed worldwide, including >120,000 deaths. There is an urgent need to monitor and predict COVID-19 prevalence to control this spread more effectively. Time series models are significant in predicting the impact of the COVID-19 outbreak and taking the necessary measures to respond to this crisis. In this study, Auto-Regressive Integrated Moving Average (ARIMA) models were developed to predict the epidemiological trend of COVID-19 prevalence of Italy, Spain, and France, the most affected countries of Europe. The prevalence data of COVID-19 from 21 February 2020 to 15 April 2020 were collected from the World Health Organization website. Several ARIMA models were formulated with different ARIMA parameters. ARIMA (0,2,1), ARIMA (1,2,0), and ARIMA (0,2,1) models with the lowest MAPE values (4.7520, 5.8486, and 5.6335) were selected as the best models for Italy, Spain, and France, respectively. This study shows that ARIMA models are suitable for predicting the prevalence of COVID-19 in the future. The results of the analysis can shed light on understanding the trends of the outbreak and give an idea of the epidemiological stage of these regions. Besides, the prediction of COVID-19 prevalence trends of Italy, Spain, and France can help take precautions and policy formulation for this epidemic in other countries.

Exploring the Dynamics of Hemorrhagic Fever with Renal Syndrome Incidence in East China Through Seasonal Autoregressive Integrated Moving Average Models

Objective

The purpose of this study was to explore the dynamics of incidence of hemorrhagic fever with renal syndrome (HFRS) from 2000 to 2017 in Anqiu City, a city located in East China, and find the potential factors leading to the incidence of HFRS.

Methods

Monthly reported cases of HFRS and climatic data from 2000 to 2017 in the city were obtained. Seasonal autoregressive integrated moving average (SARIMA) models were used to fit the HFRS incidence and predict the epidemic trend in Anqiu City. Univariate and multivariate generalized additive models were fit to identify and characterize the association between the HFRS incidence and meteorological factors during the study period.

Results

Statistical analysis results indicate that the annualized average incidence at the town level ranged from 1.68 to 6.31 per 100,000 population among 14 towns in the city, and the western towns exhibit high endemic levels during the study periods. With high validity, the optimal SARIMA(0,1,1,)(0,1,1)₁₂ model may be used to predict the HFRS incidence. Multivariate generalized additive model (GAM) results show that the HFRS incidence increases as sunshine time and humidity increases and decreases as precipitation increases. In addition, the HFRS incidence is associated with temperature, precipitation, atmospheric pressure, and wind speed. Those are identified as the key climatic factors contributing to the transmission of HFRS.

Conclusion

This study provides evidence that the SARIMA models can be used to characterize the fluctuations in HFRS incidence. Our findings add to the knowledge of the role played by climate factors in HFRS transmission and can assist local health authorities in the development and refinement of a better strategy to prevent HFRS transmission.

Multifocal tuberculosis simulating a cancer-a case report

Background

Tuberculosis is a disease that may affect any organ of the body. Multifocal tuberculosis involving multiple systems with associated symptoms are rare, which makes the diagnosis challenging. Distinguishing multifocal tuberculosis from lesions metastatic from system malignancy is difficult. Single detection method is difficult to make a diagnosis. A combination of multiple methods is essential.

Case presentation

A 17-year-old male presented with a 20 days weakness of lower limbs, which aggravated for 6 days. The PET/CT showed increased metabolism of ileocecal intestinal and terminal ileum wall, multiple enlarged lymph node (LNs), multiple osteolytic bone lesions, and soft tissue intensity belong T7 and T8 vertebrae. To confirm the diagnosis of the disease, a biopsy of the mediastinum lymph nodes was carried out. Polymerase chain reaction (PCR) test of the specimen was positive for the Mycobacterium tuberculosis, the T-SPOT and Xpert MTB/RIF test were also positive, which suggested the presence of Mycobacterium tuberculosis. The final diagnosis was multifocal tuberculosis, the patients received the resection of the mass in the spine. Anti-tuberculosis drugs were given. The myodynamia and muscle tension of the patients recovered following the therapy.

Conclusions

Our results indicated that Multifocal tuberculosis should also be taken into consideration when lesions metastatic from system malignancy were suspected from images results even without the clinical symptoms of tuberculosis, and combination of multiple diagnosis methods were essential for the diagnosis of multifocal disease.

Time series analysis of temporal trends in hemorrhagic fever with renal syndrome morbidity rate in China from 2005 to 2019

Hemorrhagic fever with renal syndrome (HFRS) is seriously endemic in China with 70%~90% of the notified cases worldwide and showing an epidemic tendency of upturn in recent years. Early detection for its future epidemic trends plays a pivotal role in combating this threat. In this scenario, our study investigates the suitability for application in analyzing and forecasting the epidemic tendencies based on the monthly HFRS morbidity data from 2005 through 2019 using the nonlinear model-based self-exciting threshold autoregressive (SETAR) and logistic smooth transition autoregressive (LSTAR) methods. The experimental results manifested that the SETAR and LSTAR approaches presented smaller values among the performance measures in both two forecasting subsamples, when compared with the most extensively used seasonal autoregressive integrated moving average (SARIMA) method, and the former slightly outperformed the latter. Descriptive statistics showed an epidemic tendency of downturn with average annual percent change (AAPC) of −5.640% in overall HFRS, however, an upward trend with an AAPC = 1.213% was observed since 2016 and according to the forecasts using the SETAR, it would seemingly experience an outbreak of HFRS in China in December 2019. Remarkably, there were dual-peak patterns in HFRS incidence with a strong one occurring in November until January of the following year, additionally, a weak one in May and June annually. Therefore, the SETAR and LSTAR approaches may be a potential useful tool in analyzing the temporal behaviors of HFRS in China.

Statistical methods for predicting tuberculosis incidence based on data from Guangxi, China

Background

Tuberculosis (TB) remains a serious public health problem with substantial financial burden in China. The incidence of TB in Guangxi province is much higher than that in the national level, however, there is no predictive study of TB in recent years in Guangxi, therefore, it is urgent to construct a model to predict the incidence of TB, which could provide help for the prevention and control of TB.

Methods

Box-Jenkins model methods have been successfully applied to predict the incidence of infectious disease. In this study, based on the analysis of TB incidence in Guangxi from January 2012 to June 2019, we constructed TB prediction model by Box-Jenkins methods, and used root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) to test the performance and prediction accuracy of model.

Results

From January 2012 to June 2019, a total of 587,344 cases of TB were reported and 879 cases died in Guangxi. Based on TB incidence from January 2012 to December 2018, the SARIMA((2),0,(2))(0,1,0)₁₂ model was established, the AIC and SC of this model were 2.87 and 2.98, the fitting accuracy indexes, such as RMSE, MAE and MAPE were 0.98, 0.77 and 5.8 respectively; the prediction accuracy indexes, such as RMSE, MAE and MAPE were 0.62, 0.45 and 3.77, respectively. Based on the SARIMA((2),0,(2))(0,1,0)₁₂ model, we predicted the TB incidence in Guangxi from July 2019 to December 2020.

Conclusions

This study filled the gap in the prediction of TB incidence in Guangxi in recent years. The established SARIMA((2),0,(2))(0,1,0)₁₂ model has high prediction accuracy and good prediction performance. The results suggested the change trend of TB incidence predicted by SARIMA((2),0,(2))(0,1,0)₁₂ model from July 2019 to December 2020 was similar to that in the previous two years, and TB incidence will experience slight decrease, the predicted results can provide scientific reference for the prevention and control of TB in Guangxi, China.

Application of a long short-term memory neural network: a burgeoning method of deep learning in forecasting HIV incidence in Guangxi, China

Guangxi, a province in southwestern China, has the second highest reported number of HIV/AIDS cases in China. This study aimed to develop an accurate and effective model to describe the tendency of HIV and to predict its incidence in Guangxi. HIV incidence data of Guangxi from 2005 to 2016 were obtained from the database of the Chinese Center for Disease Control and Prevention. Long short-term memory (LSTM) neural network models, autoregressive integrated moving average (ARIMA) models, generalised regression neural network (GRNN) models and exponential smoothing (ES) were used to fit the incidence data. Data from 2015 and 2016 were used to validate the most suitable models. The model performances were evaluated by evaluating metrics, including mean square error (MSE), root mean square error, mean absolute error and mean absolute percentage error. The LSTM model had the lowest MSE when the N value (time step) was 12. The most appropriate ARIMA models for incidence in 2015 and 2016 were ARIMA (1, 1, 2) (0, 1, 2)₁₂ and ARIMA (2, 1, 0) (1, 1, 2)₁₂, respectively. The accuracy of GRNN and ES models in forecasting HIV incidence in Guangxi was relatively poor. Four performance metrics of the LSTM model were all lower than the ARIMA, GRNN and ES models. The LSTM model was more effective than other time-series models and is important for the monitoring and control of local HIV epidemics.

An Advanced Data-Driven Hybrid Model of SARIMA-NNNAR for Tuberculosis Incidence Time Series Forecasting in Qinghai Province, China

Purpose

Qinghai province has invariably been under an ongoing threat of tuberculosis (TB), which has not only been an obstacle to local development but also hampers the prevention and control process for ending the TB epidemic. Forecasting for future epidemics will serve as the base for early detection and planning resource requirements. Here, we aim to develop an advanced detection technique driven by the recent TB incidence series, by fusing a seasonal autoregressive integrated moving average (SARIMA) with a neural network nonlinear autoregression (NNNAR).

Methods

We collected the TB incidence data between January 2004 and December 2016. Subsequently, the subsamples from January 2004 to December 2015 were employed to measure the efficiency of the single SARIMA, NNNAR, and hybrid SARIMA-NNNAR approaches, whereas the hold-out subsamples were used to test their predictive performances. We finally selected the best-performing technique by considering minimum metrics including the mean absolute error, root-mean-squared error, mean absolute percentage error and mean error rate .

Results

During 2004–2016, the reported TB cases totaled 71,080 resulting in the morbidity of 97.624 per 100,000 persons annually in Qinghai province and showed notable peak activities in late winter and early spring. Moreover, the TB incidence rate was surging by 5% per year. According to the above-mentioned criteria, the best-fitting basic and hybrid techniques consisted of SARIMA(2,0,2)(1,1,0)₁₂, NNNAR(7,1,4)₁₂ and SARIMA(2,0,2)(1,1,0)₁₂-NNNAR(3,1,7)₁₂, respectively. Amongst them, the hybrid technique showed superiority in both mimic and predictive parts, with the lowest values of the measured metrics in both the parts. The sensitivity analysis indicated the same results.

Conclusion

The best-mimicking SARIMA-NNNAR hybrid model outperforms the best-simulating basic SARIMA and NNNAR models, and has a potential application in forecasting and assessing the TB epidemic trends in Qinghai. Furthermore, faced with the major challenge of the ongoing upsurge in TB incidence in Qinghai, there is an urgent need for formulating specific preventive and control measures.

Time series analysis and forecasting of chlamydia trachomatis incidence using surveillance data from 2008 to 2019 in Shenzhen, China

Chlamydia trachomatis (CT) infection has been a major public health threat globally. Monitoring and prediction of CT epidemic status and trends are important for programme planning, allocating resources and assessing impact; however, such activities are limited in China. In this study, we aimed to apply a seasonal autoregressive integrated moving average (SARIMA) model to predict the incidence of CT infection in Shenzhen city, China. The monthly incidence of CT between January 2008 and June 2019 in Shenzhen was used to fit and validate the SARIMA model. A seasonal fluctuation and a slightly increasing pattern of a long-term trend were revealed in the time series of CT incidence. The monthly CT incidence ranged from 4.80/100 000 to 21.56/100 000. The mean absolute percentage error value of the optimal model was 8.08%. The SARIMA model could be applied to effectively predict the short-term CT incidence in Shenzhen and provide support for the development of interventions for disease control and prevention.

Development and validation of a predictive ecological model for TB prevalence

Background

Nationally representative tuberculosis (TB) prevalence surveys provide invaluable empirical measurements of TB burden but are a massive and complex undertaking. Therefore, methods that capitalize on data from these surveys are both attractive and imperative. The aim of this study was to use existing TB prevalence estimates to develop and validate an ecological predictive statistical model to indirectly estimate TB prevalence in low- and middle-income countries without survey data.

Methods

We included national and subnational estimates from 30 nationally representative surveys and 2 district-level surveys in India, resulting in 50 data points for model development (training set). Ecological predictors included TB notification and programmatic data, co-morbidities and socio-environmental factors extracted from online data repositories. A random-effects multivariable binomial regression model was developed using the training set and was used to predict bacteriologically confirmed TB prevalence in 63 low- and middle-income countries across Africa and Asia in 2015.

Results

Out of the 111 ecological predictors considered, 14 were retained for model building (due to incompleteness or collinearity). The final model retained for predictions included five predictors: continent, percentage retreated cases out of all notified, all forms TB notification rates per 100 000 population, population density and proportion of the population under the age of 15. Cross-fold validations in the training set showed very good average fit (R-sq = 0.92).

Conclusion

Predictive ecological modelling is a useful complementary approach to indirectly estimating TB burden and can be considered alongside other methods in countries with limited robust empirical measurements of TB among the general population.

Temporal trends analysis of human brucellosis incidence in mainland China from 2004 to 2018

With the re-emergence of brucellosis in mainland China since the mid-1990s, an increasing threat to public health tends to become even more violent, advanced warning plays a pivotal role in the control of brucellosis. However, a model integrating the autoregressive integrated moving average (ARIMA) with Error-Trend-Seasonal (ETS) methods remains unexplored in the epidemiological prediction. The hybrid ARIMA-ETS model based on discrete wavelet transform was hence constructed to assess the epidemics of human brucellosis from January 2004 to February 2018 in mainland China. The preferred hybrid model including the best-performing ARIMA method for approximation-forecasting and the best-fitting ETS approach for detail-forecasting is evidently superior to the standard ARIMA and ETS techniques in both three in-sample simulating and out-of-sample forecasting horizons in terms of the minimum performance indices of the root mean square error, mean absolute error, mean error rate and mean absolute percentage error. Whereafter, an ahead prediction from March to December in 2018 displays a dropping trend compared to the preceding years. But being still present, in various trends, in the present or future. This hybrid model can be highlighted in predicting the temporal trends of human brucellosis, which may act as the potential for far-reaching implications for prevention and control of this disease.

Comparison of ARIMA and GM(1,1) models for prediction of hepatitis B in China

Background

Hepatitis B virus (HBV) infection is a major public health threat in China for China has a hepatitis B prevalence of more than one million people in 2017 year. Disease incidence prediction may help hepatitis B prevention and control. This study intends to build and compare 2 forecasting models for hepatitis B incidence in China.

Methods

Autoregressive integrated moving average (ARIMA) model and grey model GM(1,1) were adopted to fit the monthly incidence of hepatitis B in China from March 2010 to October 2017. The fitting and forecasting performances of the 2 models were evaluated. The better one was adopted to predict the incidence from November 2017 to March 2018. Database was built by Excel 2016 and statistical analysis was completed using R 3.4.3 software.

Results

Descriptive analysis showed that the incidence of hepatitis B in China has seasonal variation and has shown a downward trend from 2010 to 2017. We selected the ARIMA (3,1,1) (0,1,2)₁₂ model among all the ARIMA models for it has the lowest AIC value. Model expression of GM (1,1) was X⁽¹⁾ (k + 1) = 3386876.7478e^0.0249k − 3289206.7428. The root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) of ARIMA(3,1,1)(0,1,2)₁₂ model were lower than GM(1,1) model on fitting part and forecasting part. According to the forecast results, the incidence may have a slight fluctuation during the following months.

Conclusions

The ARIMA model showed better hepatitis B fitting and forecasting performance than GM(1,1) model. It is a potential decision supportive tool for controlling hepatitis B in China before a predictive hepatitis B outbreak.

Predicting Infectious Disease Using Deep Learning and Big Data

Infectious disease occurs when a person is infected by a pathogen from another person or an animal. It is a problem that causes harm at both individual and macro scales. The Korea Center for Disease Control (KCDC) operates a surveillance system to minimize infectious disease contagions. However, in this system, it is difficult to immediately act against infectious disease because of missing and delayed reports. Moreover, infectious disease trends are not known, which means prediction is not easy. This study predicts infectious diseases by optimizing the parameters of deep learning algorithms while considering big data including social media data. The performance of the deep neural network (DNN) and long-short term memory (LSTM) learning models were compared with the autoregressive integrated moving average (ARIMA) when predicting three infectious diseases one week into the future. The results show that the DNN and LSTM models perform better than ARIMA. When predicting chickenpox, the top-10 DNN and LSTM models improved average performance by 24% and 19%, respectively. The DNN model performed stably and the LSTM model was more accurate when infectious disease was spreading. We believe that this study's models can help eliminate reporting delays in existing surveillance systems and, therefore, minimize costs to society.

Forecasting the incidence of tuberculosis in China using the seasonal auto-regressive integrated moving average (SARIMA) model

Objectives

The aims of this study were to develop a forecasting model for the incidence of tuberculosis (TB) and analyze the seasonality of infections in China; and to provide a useful tool for formulating intervention programs and allocating medical resources.

Methods

Data for the monthly incidence of TB from January 2004 to December 2015 were obtained from the National Scientific Data Sharing Platform for Population and Health (China). The Box–Jenkins method was applied to fit a seasonal auto-regressive integrated moving average (SARIMA) model to forecast the incidence of TB over the subsequent six months.

Results

During the study period of 144 months, 12,321,559 TB cases were reported in China, with an average monthly incidence of 6.4426 per 100,000 of the population. The monthly incidence of TB showed a clear 12-month cycle, and a seasonality with two peaks occurring in January and March and a trough in December. The best-fit model was SARIMA (1,0,0)(0,1,1)₁₂, which demonstrated adequate information extraction (white noise test, p > 0.05). Based on the analysis, the incidence of TB from January to June 2016 were 6.6335, 4.7208, 5.8193, 5.5474, 5.2202 and 4.9156 per 100,000 of the population, respectively.

Conclusions

According to the seasonal pattern of TB incidence in China, the SARIMA model was proposed as a useful tool for monitoring epidemics.

Pancreatic Cancer Mortality in China: Characteristics and Prediction

OBJECTIVES

During the last decade, the mortality rate of pancreatic cancer in China has significantly increased. We analyzed data for the period 1991-2014 to investigate the distribution of mortality rates and predict trends for the next 5 years.

METHODS

We obtained the pancreatic mortality data from the Chinese cancer annual report. Trend surface analysis was applied to study the geographical distribution. We used curve estimation, time series, grey box modeling, and joinpoint regression to predict the mortality rate.

RESULTS

Standardized pancreatic cancer mortality rate increased during 1991-2014 and might peak in the ensuing 5 years in China. The mortality rate was higher among elderly people and in urban and northeast/eastern areas than among young people and in rural and middle/western areas.

CONCLUSIONS

Pancreatic cancer mortality shows an increasing trend, which is related to the socioeconomic development of China and the ageing of the population. Prevention strategies should be aimed at urban men 45 years or older, especially those residing in higher-mortality rate areas.

Application of seasonal auto-regressive integrated moving average model in forecasting the incidence of hand-foot-mouth disease in Wuhan, China

Outbreaks of hand-foot-mouth disease (HFMD) have occurred many times and caused serious health burden in China since 2008. Application of modern information technology to prediction and early response can be helpful for efficient HFMD prevention and control. A seasonal auto-regressive integrated moving average (ARIMA) model for time series analysis was designed in this study. Eighty-four-month (from January 2009 to December 2015) retrospective data obtained from the Chinese Information System for Disease Prevention and Control were subjected to ARIMA modeling. The coefficient of determination (R ²), normalized Bayesian Information Criterion (BIC) and Q-test P value were used to evaluate the goodness-of-fit of constructed models. Subsequently, the best-fitted ARIMA model was applied to predict the expected incidence of HFMD from January 2016 to December 2016. The best-fitted seasonal ARIMA model was identified as (1,0,1)(0,1,1)₁₂, with the largest coefficient of determination (R ²=0.743) and lowest normalized BIC (BIC=3.645) value. The residuals of the model also showed non-significant autocorrelations (P _{Box-Ljung (Q)}=0.299). The predictions by the optimum ARIMA model adequately captured the pattern in the data and exhibited two peaks of activity over the forecast interval, including a major peak during April to June, and again a light peak for September to November. The ARIMA model proposed in this study can forecast HFMD incidence trend effectively, which could provide useful support for future HFMD prevention and control in the study area. Besides, further observations should be added continually into the modeling data set, and parameters of the models should be adjusted accordingly.

A New Hybrid Model Using an Autoregressive Integrated Moving Average and a Generalized Regression Neural Network for the Incidence of Tuberculosis in Heng County, China

It is a daunting task to eradicate tuberculosis completely in Heng County due to a large transient population, human immunodeficiency virus/tuberculosis coinfection, and latent infection. Thus, a high-precision forecasting model can be used for the prevention and control of tuberculosis. In this study, four models including a basic autoregressive integrated moving average (ARIMA) model, a traditional ARIMA-generalized regression neural network (GRNN) model, a basic GRNN model, and a new ARIMA-GRNN hybrid model were used to fit and predict the incidence of tuberculosis. Parameters including mean absolute error (MAE), mean absolute percentage error (MAPE), and mean square error (MSE) were used to evaluate and compare the performance of these models for fitting historical and prospective data. The new ARIMA-GRNN model had superior fit relative to both the traditional ARIMA-GRNN model and basic ARIMA model when applied to historical data and when used as a predictive model for forecasting incidence during the subsequent 6 months. Our results suggest that the new ARIMA-GRNN model may be more suitable for forecasting the tuberculosis incidence in Heng County than traditional models.

Construction and evaluation of two computational models for predicting the incidence of influenza in Nagasaki Prefecture, Japan

It remains challenging to forecast local, seasonal outbreaks of influenza. The goal of this study was to construct a computational model for predicting influenza incidence. We built two computational models including an Autoregressive Distributed Lag (ARDL) model and a hybrid model integrating ARDL with a Generalized Regression Neural Network (GRNN), to assess meteorological factors associated with temporal trends in influenza incidence. The modelling and forecasting performance of these two models were compared using observations collected between 2006 and 2015 in Nagasaki Prefecture, Japan. In both the training and forecasting stages, the hybrid model showed lower error rates, including a lower residual mean square error (RMSE) and mean absolute error (MAE) than the ARDL model. The lag of log-incidence, weekly average barometric pressure, and weekly average of air temperature were 4, 1, and 3, respectively in the ARDL model. The ARDL-GRNN hybrid model can serve as a tool to better understand the characteristics of influenza epidemic, and facilitate their prevention and control.

Case report: multiple systemic disseminated tuberculosis mimicking lymphoma on 18F-FDG PET/CT

RATIONALE

F-fluorodeoxyglucose positron emission tomography/computed tomography (F-18 FDG PET/CT) has an important role in the diagnosis of various malignancies. However, F-18 FDG can also exhibit intense accumulation in tissues in inflammatory conditions such as active tuberculosis (TB) and sarcoidosis.

PATIENT CONCERNS

We report a case of a 52-year-old female with irritable cough. CT showed a lung mass with multiple bilateral lung nodules, and sarcoidosis was suspected. F-18 FDG PET/CT was undertaken for the diagnosis and showed intense uptake of FDG in the mass in the lower lobe of the right lung, multiple lymph nodes, liver, and spleen. The maximum standardized uptake value of F-18 FDG was 43.58. This pattern of involvement most likely represents lymphomatous involvement.

DIAGNOSES

Histopathology suggested tubercular involvement.

INTERVENTION AND OUTCOMES

The patient received anti-TB treatment and recovered.

LESSONS

Abovementioned extent and distribution of F-18 FDG in tubercular lesion is relatively rare, thus, one must be observant and aware with regards to TB being a strong mimic of lymphoma in endemic regions.

Impact of weather factors on hand, foot and mouth disease, and its role in short-term incidence trend forecast in Huainan City, Anhui Province

Hand, foot, and mouth disease (HFMD) is one of the most common communicable diseases in China, and current climate change had been recognized as a significant contributor. Nevertheless, no reliable models have been put forward to predict the dynamics of HFMD cases based on short-term weather variations. The present study aimed to examine the association between weather factors and HFMD, and to explore the accuracy of seasonal auto-regressive integrated moving average (SARIMA) model with local weather conditions in forecasting HFMD. Weather and HFMD data from 2009 to 2014 in Huainan, China, were used. Poisson regression model combined with a distributed lag non-linear model (DLNM) was applied to examine the relationship between weather factors and HFMD. The forecasting model for HFMD was performed by using the SARIMA model. The results showed that temperature rise was significantly associated with an elevated risk of HFMD. Yet, no correlations between relative humidity, barometric pressure and rainfall, and HFMD were observed. SARIMA models with temperature variable fitted HFMD data better than the model without it (sR ² increased, while the BIC decreased), and the SARIMA (0, 1, 1)(0, 1, 0)₅₂ offered the best fit for HFMD data. In addition, compared with females and nursery children, males and scattered children may be more suitable for using SARIMA model to predict the number of HFMD cases and it has high precision. In conclusion, high temperature could increase the risk of contracting HFMD. SARIMA model with temperature variable can effectively improve its forecast accuracy, which can provide valuable information for the policy makers and public health to construct a best-fitting model and optimize HFMD prevention.

Forecast analysis of any opportunistic infection among HIV positive individuals on antiretroviral therapy in Uganda

Background

Predicting future prevalence of any opportunistic infection (OI) among persons infected with the human immunodeficiency virus (HIV) on highly active antiretroviral therapy (HAART) in resource poor settings is important for proper planning, advocacy and resource allocation. We conducted a study to forecast 5-years prevalence of any OI among HIV-infected individuals on HAART in Uganda.

Methods

Monthly observational data collected over a 10-years period (2004–2013) by the AIDS support organization (TASO) in Uganda were used to forecast 5-years annual prevalence of any OI covering the period 2014–2018. The OIs considered include 14 AIDS-defining OIs, two non-AIDS defining OIs (malaria & geohelminths) and HIV-associated Kaposi’s sarcoma. Box-Jenkins autoregressive integrated moving average (ARIMA) forecasting methodology was used.

Results

Between 2004 and 2013, a total of 36,133 HIV patients were enrolled on HAART of which two thirds (66 %) were female. Mean annual prevalence for any OI in 2004 was 57.6 % and in 2013 was 27.5 % (X²_trend = 122, b = −0.0283, p <0.0001). ARIMA (1, 1, 1) model was the most parsimonious and best fit for the data. The forecasted mean annual prevalence of any OI was 26.1 % (95 % CI 21.1–31.0 %) in 2014 and 15.3 % (95 % CI 10.4–20.3 %) in 2018.

Conclusions

While the prevalence of any OI among HIV positive individuals on HAART in Uganda is expected to decrease overall, it’s unlikely that OIs will be completely eliminated in the foreseeable future. There is therefore need for continued efforts in prevention and control of opportunistic infections in all HIV/AIDS care programmes in these settings.

Electronic supplementary material

The online version of this article (doi:10.1186/s12889-016-3455-5) contains supplementary material, which is available to authorized users.

Application of a Combined Model with Autoregressive Integrated Moving Average (ARIMA) and Generalized Regression Neural Network (GRNN) in Forecasting Hepatitis Incidence in Heng County, China

BACKGROUND

Hepatitis is a serious public health problem with increasing cases and property damage in Heng County. It is necessary to develop a model to predict the hepatitis epidemic that could be useful for preventing this disease.

METHODS

The autoregressive integrated moving average (ARIMA) model and the generalized regression neural network (GRNN) model were used to fit the incidence data from the Heng County CDC (Center for Disease Control and Prevention) from January 2005 to December 2012. Then, the ARIMA-GRNN hybrid model was developed. The incidence data from January 2013 to December 2013 were used to validate the models. Several parameters, including mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE) and mean square error (MSE), were used to compare the performance among the three models.

RESULTS

The morbidity of hepatitis from Jan 2005 to Dec 2012 has seasonal variation and slightly rising trend. The ARIMA(0,1,2)(1,1,1)12 model was the most appropriate one with the residual test showing a white noise sequence. The smoothing factor of the basic GRNN model and the combined model was 1.8 and 0.07, respectively. The four parameters of the hybrid model were lower than those of the two single models in the validation. The parameters values of the GRNN model were the lowest in the fitting of the three models.

CONCLUSIONS

The hybrid ARIMA-GRNN model showed better hepatitis incidence forecasting in Heng County than the single ARIMA model and the basic GRNN model. It is a potential decision-supportive tool for controlling hepatitis in Heng County.

A Case Report: Systemic Lymph Node Tuberculosis Mimicking Lymphoma on 18F-FDG PET/CT

F-fluorodeoxyglucose positron emission tomography--an established modality for evaluating malignancies--exhibits increased uptake under inflammatory conditions. A 21-year-old man came to our hospital with persistent pain in his right lower quadrant of abdomen for more than 1 month, but had no diarrhea, fever, chills, weight loss, or other constitutional symptoms. Colonoscopy analysis showed no organic diseases in his colorectum. Ultrasound results revealed multiple enlarged lymph nodes in the bilateral neck, axilla, and groin. Positron emission tomography analysis was performed and showed intense ¹⁸F-fluorodeoxyglucose accumulation in the bilateral neck, supraclavicular, pulmonary hilar, mediastinum, gastric paracardial, and mesenterium lymph node. These findings were considered typical for lymphoma. To confirm the diagnosis, we obtained a diagnostic biopsy in the left supraclavicular lymph node. The diagnosis of tuberculosis was confirmed in the final pathology. This uncommon case underscores the necessity of considering lymph node tuberculosis as a possible differential diagnosis in lymphoma.