A statistical analysis of the seasonality in pulmonary tuberculosis

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.

Evaluating Strategies For Tuberculosis to Achieve the Goals of WHO in China: A Seasonal Age-Structured Model Study

Although great progress has been made in the prevention and mitigation of TB in the past 20 years, China is still the third largest contributor to the global burden of new TB cases, accounting for 833,000 new cases in 2019. Improved mitigation strategies, such as vaccines, diagnostics, and treatment, are needed to meet goals of WHO. Given the huge variability in the prevalence of TB across age-groups in China, the vaccination, diagnostic techniques, and treatment for different age-groups may have different effects. Moreover, the statistics data of TB cases show significant seasonal fluctuations in China. In view of the above facts, we propose a non-autonomous differential equation model with age structure and seasonal transmission rate. We derive the basic reproduction number, [Formula: see text], and prove that the unique disease-free periodic solution, [Formula: see text] is globally asymptotically stable when [Formula: see text], while the disease is uniformly persistent and at least one positive periodic solution exists when [Formula: see text]. We estimate that the basic reproduction number [Formula: see text] ([Formula: see text]), which means that TB is uniformly persistent. Our results demonstrate that vaccinating susceptible individuals whose ages are over 65 and between 20 and 24 is much more effective in reducing the prevalence of TB, and each of the improved vaccination strategy, diagnostic strategy, and treatment strategy leads to substantial reductions in the prevalence of TB per 100,000 individuals compared with current approaches, and the combination of the three strategies is more effective. Scenario A (i.e., coverage rate [Formula: see text], diagnosis rate [Formula: see text], relapse rate [Formula: see text]) is the best and can reduce the prevalence of TB per 100,000 individuals by [Formula: see text] and [Formula: see text] in 2035 and 2050, respectively. Although the improved strategies will significantly reduce the incidence rate of TB, it is challenging to achieve the goal of WHO in 2050. Our findings can provide guidance for public health authorities in projecting effective mitigation strategies of TB.

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.

The possible impact of socioeconomic, income, and educational status on adverse effects of drug and their therapeutic episodes in patients targeted with a combination of tuberculosis interventions

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Socio-economic status is very important in patient receiving DOT therapy.

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Income influences disease occurrence as well as adverse events.

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Occupation increases the risk of diseases and results increase risk of adverse events.

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Low income results mal nutrition and increased risk of drug intolerance or adverse events.

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DOTS is an effective treatment strategy for MTB patients.

First-line antituberculosis (anti-TB) compounds have been considered as proven components of the Directly Observed Treatment-Short course (DOTS). Drug therapy against tuberculosis has been categorized as I, II, or III following the Revised National Tuberculosis Control Program guidelines. Anti-TB are drugs are quite common and show limited adverse effects. However, first-line anti-TB compounds mediated DOTS therapy and were found with several complications. Thus, those drugs have been discontinued. Therefore, the present study was designed to find out the possible impact of socioeconomic, income, and educational status on the adverse effects of drugs and their therapeutic episodes in patients targeted with a combination of tuberculosis intervention. This study found that an increased incidence of tuberculosis was found in patients who have finished high school, contributing to a high percentage of adverse effects. Notably, adverse events were shown maximally in poor patients compared with rich- or high-income patients. On the contrary, a high prevalence of adverse events was shown to be increased in partially skilled workers compared with full-skilled workers. Consequently, adversely considerable events were implicated to be raised in patients associated with minimal socioeconomic class. Such interesting factors would help in monitoring such events in experimental patients.

The relationship between seasonality, latitude and tuberculosis notifications in Pakistan

Background

Pakistan ranks amongst the top 20 highest burden tuberculosis (TB) countries in the world. Approximately 369,548 cases of TB (all forms) were notified in 2018, with an estimated incidence of 265 per 100,000 people per year. In other settings, TB has been shown to demonstrate seasonal variation, with higher incidence in the spring/summer months and lower incidence in the autumn/winter; the amplitude of seasonal variation has also been reported to be higher with increasing distance from the equator.

Methods

Notifications of newly-diagnosed pulmonary and extrapulmonary TB cases were obtained for 139 districts in Pakistan from 2011 to 2017. Data were provided by the Pakistan National TB Control Programme, Islamabad, Pakistan. Statistical analyses were performed to determine whether there was seasonal variation in TB notifications in Pakistan; whether the amplitude of seasonal variation in TB notifications varied according to latitude; whether the amplitude of seasonal variation of TB in Pakistan differed between extrapulmonary TB vs. pulmonary TB. To assess the quarterly seasonality of TB, we used the X-13-ARIMA-SEATS seasonal adjustment programme from the United States Census Bureau. The mean difference and corresponding 95% confidence intervals of seasonal amplitudes between different latitudes and clinical phenotype of TB were estimated using linear regression.

Results

TB notifications were highest in quarter 2, and lowest in quarter 4. The mean amplitude of seasonal variation was 25.5% (95% CI 25.0 to 25.9%). The mean seasonal amplitude of TB notifications from latitude 24.5°N- < 26.5°N was 29.5% (95% CI 29.3 to 29.7%) whilst the mean seasonal amplitude of TB notifications from latitude 34.5°N - < 36.5°N was 21.7% (95% CI 19.6 to 23.9%). The mean seasonal amplitude of TB notifications across Pakistan between latitudes 24.5°N to 36.5°N reached statistically significant difference (p < 0.001). The amplitude of seasonal variation was greater for extrapulmonary TB (mean seasonal amplitude: 32.6, 95% CI 21.4 to 21.8%) vs. smear positive pulmonary TB mean seasonal amplitude: 21.6, 95% CI 32.1 to 33.1%), p < 0.001.

Conclusion

TB notifications in Pakistan exhibit seasonal variation with a peak in quarter 2 (April–June) and trough in quarter 4 (October–December). The amplitude of seasonality decreases with increasing latitude, and is more pronounced for extrapulmonary than for pulmonary TB.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12879-021-05899-x.

Epidemiological and time series analysis on the incidence and death of AIDS and HIV in China

Background

To investigate the regional and age-specific distribution of AIDS/HIV in China from 2004 to 2017 and to conduct time series analysis of the epidemiological trends.

Method

Using official surveillance data from publicly accessible database of the national infectious disease reporting system, we described long-term patterns of incidence and death in AIDS/HIV, analyzed age group and regional epidemic characteristics, and established Autoregressive Integrated Moving Average (ARIMA) models for time series analysis.

Result

The incidence and death of AIDS/HIV have increased rapidly from 2004 to 2017, with significant difference regarding age groups and provincial regions (a few provinces appear as hot spots). With goodness-of-fit criteria and using data from 2004 to 2015, ARIMA (0,1,3) × (2,0,0), ARIMA (3,1,0) × (1,0,1), and ARIMA (0,1,2) × (2,0,0) were chosen as the optimal model for the incidence of AIDS, HIV, and combined; ARIMA (0,1,3) × (1,0,0) was chosen as the optimal model for the death of AIDS, HIV, and combined. ARIMA models robustly predicted the incidence and death of AIDS/HIV in 2016 and 2017.

Conclusion

A focused intervention strategy targeting specific regions and age groups is essential for the prevention and control of AIDS/HIV. ARIMA models function as data-driven and evidence-based methods to forecast the trends of infectious diseases and formulate public health policies.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-020-09977-8.

Analysis and predication of tuberculosis registration rates in Henan Province, China: an exponential smoothing model study

Background

The World Health Organization End TB Strategy meant that compared with 2015 baseline, the reduction in pulmonary tuberculosis (PTB) incidence should be 20 and 50% in 2020 and 2025, respectively. The case number of PTB in China accounted for 9% of the global total in 2018, which ranked the second high in the world. From 2007 to 2019, 854 672 active PTB cases were registered and treated in Henan Province, China. This study was to assess whether the WHO milestones could be achieved in Henan Province.

Methods

The active PTB numbers in Henan Province from 2007 to 2019, registered in Chinese Tuberculosis Information Management System were analyzed to predict the active PTB registration rates in 2020 and 2025, which is conductive to early response measures to ensure the achievement of the WHO milestones. The time series model was created by monthly active PTB registration rates from 2007 to 2016, and the optimal model was verified by data from 2017 to 2019. The Ljung-Box Q statistic was used to evaluate the model. The statistically significant level is α = 0.05. Monthly active PTB registration rates and 95% confidence interval (CI) from 2020 to 2025 were predicted.

Results

High active PTB registration rates in March, April, May and June showed the seasonal variations. The exponential smoothing winter’s multiplication model was selected as the best-fitting model. The predicted values were approximately consistent with the observed ones from 2017 to 2019. The annual active PTB registration rates were predicted as 49.1 (95% CI: 36.2–62.0) per 100 000 population and 34.4 (95% CI: 18.6–50.2) per 100 000 population in 2020 and 2025, respectively. Compared with the active PTB registration rate in 2015, the reduction will reach 23.7% (95% CI, 3.2–44.1%) and 46.8% (95% CI, 21.4–72.1%) in 2020 and 2025, respectively.

Conclusions

The high active PTB registration rates in spring and early summer indicate that high risk of tuberculosis infection in late autumn and winter in Henan Province. Without regard to the CI, the first milestone of WHO End TB Strategy in 2020 will be achieved. However, the second milestone in 2025 will not be easily achieved unless there are early response measures in Henan Province, China.

Stochastic modelling for predicting COVID-19 prevalence in East Africa Countries

Coronavirus (COVID-19) has continued to be a global threat to public health. As the matter of fact, it needs unreserved effort to monitor the prevalence of the virus. However, applying an effective prediction of the prevalence is thought to be the fundamental requirement to effectively control the spreading rate. Time series models have extensively been considered as the convenient methods to predict the prevalence or spreading rate of the disease. This study, therefore, aimed to apply the Autoregressive Integrated Moving Average (ARIMA) modeling approach for projecting coronavirus (COVID-19) prevalence patterns in East Africa Countries, mainly Ethiopia, Djibouti, Sudan and Somalia. The data for the study were obtained from the reports of confirmed COVID-19 cases by the official website of Johns Hopkins University from 13th March, 2020 to 30th June, 2020.The results of the study, then, showed that in the coming four month, the number of COVID-19 positive people in Ethiopia may reach up to 56,610 from 5,846 on June 30, 2020 in average-rate scenario. However, in worst case scenario forecast, the model showed that the cases will be around 84,497. The analysis further depicted that with average interventions and control scenario, cumulative number of infected persons in Djibouti, Somalia and Sudan will increase from 4,656, 2,904 and 9,258 respectively at the end of June to 8,336, 3,961 and 21,388, which is by the end of October, 2020, after four-months. But, with insufficient intervention, the number of infected persons may grow quickly and reach up to 14,072, 10,037 and 38,174 in Djibouti, Somalia and Sudan respectively. Generally, the extent of the coronavirus spreading was increased from time to time in the past four month, until 30 th June, 2020, and it is expected to continue quicker than before for the coming 4-month, until the end of October, 2020, in Ethiopia, Djibouti, Somalia, and Sudan and more rapidly than before in Sudan and Ethiopia, while the peak will remain unknown yet. Therefore, an effective implementation of the preventive measures and a rigorous compliance by avoiding negligence with the rules such as prohibiting public gatherings, travel restrictions, personal protection measures, and social distancing may alleviate the spreading rates of the virus, particularly, Sudan and Ethiopia. Moreover, more efforts should be exerted on Ethiopian side to control the population movement across all the border areas and to strengthen border quarantining. Further, through updating more new data with continuous reconsideration of predictive model, provide useful and more precise prediction. Applying, ARIMAX-Transfer Function model in region-wise by take in to consideration of climatic data like temperature and humidity in each countries looking spatial pattern for reliable measure of COVID-19 prevalence.

Epidemiological characteristics of pulmonary tuberculosis in Anhui Province, Eastern China from 2013 to 2018

OBJECTIVE

Pulmonary tuberculosis (TB) is a severe infectious respiratory disease, the burden of which remains high in China. To provide scientific evidence for developing more targeted prevention and control strategies, this study aimed to determine the incidence trends and explore the epidemiological characteristics of pulmonary TB in Anhui Province, Eastern China between 2013 and 2018.

METHODS

The retrospective study analyzed information regarding pulmonary TB cases reported by the National Infectious Disease Reporting System and census data collected from the Anhui Provincial Bureau of Statistics.

RESULTS

Overall, 211,892 cases of TB patients were reported in Anhui Province, China between 2013 and 2018, with an average annual reported incidence rate of 57.7 per 100,000 persons. A significant decrease in the incidence rate of pulmonary TB (p < 0.001) was observed during the study period. Men had a higher incidence rate of pulmonary TB than women (p < 0.001). The highest annual average reported incidence rate was 204.2 per 100,000 persons in those aged 70-74 years. The number of farmers with pulmonary TB, i.e., 155,415, accounted for 73.4% of all cases. Moreover, the peak period of reported cases was from January to March. Four cities along the Yangtze River-Anqing, Tongling, Chizhou, and Wuhu-reported significantly higher incidence rates of pulmonary TB than other cities (p < 0.001).

CONCLUSIONS

From 2013 to 2018, there was a significant decline in the incidence rate of pulmonary TB in Anhui Province, with peaks occurring from January to March. Prevention and control strategies targeting men, people aged 70-74 years, farmers, and the four cities along the Yangtze River should be strengthened.

A 5-year experience characterizing the demographic and clinical profile and directly observed treatment short-course treatment outcome in National Tuberculosis Center of Duhok province, Iraqi Kurdistan

BACKGROUND

The objectives were to describe the demographic and clinical profile and treatment outcomes in the National Tuberculosis Program Center of Duhok governorate.

METHODS

This was a descriptive retrospective study of all forms of drug-susceptible tuberculosis cases in the National Tuberculosis Program Center of Duhok. The Electronic Nominal Recording Reporting System data of tuberculosis cases were reviewed during 2014-2018. Information on the demographic, clinical, and laboratory characteristics of the patients were analyzed. Tuberculosis trends and treatment outcomes were determined.

RESULTS

A total of 1063 tuberculosis patients visited the National Tuberculosis Program Center, of whom 905 were from Duhok. The estimated tuberculosis notification rate per 100,000 people in Duhok governorate was 14.06, 16.16, 10.43, 11.05, and 10.34 for the years 2014, 2015, 2016, 2017, and 2018, respectively. The most affected age group was 15-24 years. The male cases were predominant. Most patients (97.3%) were native Iraqi. There were 718 (67.5%) pulmonary tuberculosis cases and 345 (32.5%) extra-pulmonary tuberculosis cases; tuberculosis lymphadenitis was the most common presentation. The majority of extra-pulmonary tuberculosis patients were females aged 15-24 years (p = 0.019), and patients aged ⩾65 years were associated with pulmonary tuberculosis and extra-pulmonary tuberculosis in males (p ⩽ 0.001). The highest tuberculosis incidence occurred in winter (288 patients). The patient treatment outcomes were as follows: 90.7% successful treatment, 1.6% lost to follow-up, 6.7% death, 0.3 transferred out, and 0.8 treatment failure.

CONCLUSION

There was a high frequency of extra-pulmonary tuberculosis, which may reflect overestimation in its diagnosis. Therefore, meticulous evaluations should be provided. The treatment outcome was satisfactory in the center. Hence, we should maintain the favorable work to attain tuberculosis control objectives. Performing GeneXpert for all tuberculosis cases and introducing culture and drug susceptibility testing should be an urgent plan to strengthen the diagnosis of susceptible and drug-resistant tuberculosis cases.

Association between short-term exposure to ambient air pollutants and the risk of tuberculosis outpatient visits: A time-series study in Hefei, China

BACKGROUND

The current evidence has presented mixed results between air pollutants exposure and the progression of tuberculosis (TB). The purpose of this study was to explore the association between short-term exposure to air pollutants and the risk of TB outpatient visits in Hefei, China.

METHODS

Time-series analysis was used to assess the effect of short-term exposure to ambient air pollutants on the risk of TB outpatient visits. A Poisson generalized linear regression model combined with a distributed lag non-linear model (DLNM) was applied to explore the association. The effects of different gender (male, female), age (≤65 years old, >65 years old) and season (cold season, warm season) on the risk of TB were investigated by stratified analysis. Sensitivity analyses were conducted to test the robustness of our findings.

RESULTS

A total of 22,749 active TB cases were identified from November 1, 2013 to December 31, 2018 in Hefei. The overall exposure-response curve showed that the concentration of particulate matter with aerodynamic diameter less than 2.5 μm (PM_2.5) and nitrogen dioxide (NO₂) exposure were positively correlated with the risk of TB outpatient visits, while ozone (O₃) and sulfur dioxide (SO₂) exposure were negatively correlated with the risk of TB outpatient visits. The maximum lag-specific and cumulative relative risk (RR) of TB outpatient visits were 1.057 [95%CI: 1.002-1.115, lag 3 day] and 1.559 (95%CI: 1.057-2.300, lag 13 days) for each 10 μg/m³ increase in PM_2.5; 1.026 (95% CI: 1.008-1.044, lag 0 day) and 1.559 (95%CI: 1.057-2.300, lag 07 days) for each 10 μg/m³ increase in NO₂; 0.866 (95% CI: 0.801-0.935, lag 5 day) and 0.852 (95%CI: 1.01-1.11, lag 0-14 days) for each 10 μg/m³ increase in SO₂ in the single-pollutant model. There was only a negative association between O₃ exposure and the cumulative risk of TB outpatient visits (RR = 0.960, 95%CI: 0.936-0.984, lag 07 days). Stratified analyses showed that the effects of SO₂ and O₃ exposure were different between warm and cold seasons. The effect of NO₂ exposure remained statistically significant in male, younger, and cold season subgroups. Besides, elderly people are more susceptible to PM_2.5 exposure.

CONCLUSION

This study suggests that exposure to PM_2.5, NO₂, SO₂, and O₃ are associated with the risk of TB outpatient visits. Seasonal variation may have a greater impact on the risk of TB outpatient visits compared with gender and age.

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.

Seasonality of newly notified pulmonary tuberculosis in Japan, 2007-2015

BACKGROUND

The seasonality of pulmonary tuberculosis (TB) incidence may indicate season-specific risk factors that could be controlled if they were better understood. The aims of this study were to elucidate how the incidence of TB changes seasonally and to determine the factors influencing TB incidence, to reduce the TB burden in Japan.

METHODS

We assessed the seasonality of newly notified TB cases in Japan using national surveillance data collected between 2007 and 2015. To investigate age and sex differences, seasonal variation was analyzed according to sex for all cases and then by stratified age groups (0-4, 5-14, 15-24, 25-44, 45-64, 65-74, and ≥ 75 years). We used Roger's test to analyze the cyclic monthly trends in seasonal variation of TB incidence.

RESULTS

A total of 199,856 newly notified TB cases (male, 62.2%) were reported over the past 9-year period. Among them, 60.6% involved patients aged ≥65 years. Overall, the peak months of TB incidence occurred from April to October, excluding September. In the analysis stratified by age group, a significant seasonal variation in TB cases was observed for age groups ≥15 years, whereas no seasonal variation was observed for age groups ≤14 years. For female patients aged ≥25 years, the peak TB epidemic period was seen from June to December, excluding November. Male patients in the same age groups exhibited declining TB incidence from September to March.

CONCLUSIONS

TB incidence exhibits seasonality in Japan for people aged > 15 years and peaks in summer to fall. Monthly trends differ according to age and sex. For age groups ≥25 years, cases in women showed longer peaks from June to December whereas cases in men declined from September to December. These results suggest that the seasonality of TB incidence in Japan might be influenced by health checkups in young adults, reactivation of latent TB infection with aging, and lifestyle habits in older adults. These findings can contribute to establishing the potential determinants of TB seasonality in Japan.

Application of a hybrid model in predicting the incidence of tuberculosis in a Chinese population

Objective: To investigate suitable forecasting models for tuberculosis (TB) in a Chinese population by comparing the predictive value of the autoregressive integrated moving average (ARIMA) model and the ARIMA-generalized regression neural network (GRNN) hybrid model. Methods: We used the monthly incidence rate of TB in Lianyungang city from January 2007 through June 2016 to construct a fitting model, and we used the incidence rate from July 2016 to December 2016 to evaluate the forecasting accuracy. The root mean square error (RMSE), mean absolute percentage error (MAPE), mean absolute error (MAE) and mean error rate (MER) were used to assess the performance of these models in fitting and forecasting the incidence of TB. Results: The ARIMA (10, 1, 0) (0, 1, 1)₁₂ model was selected from plausible ARIMA models, and the optimal spread value of the ARIMA-GRNN hybrid model was 0.23. For the fitting dataset, the RMSE, MAPE, MAE and MER were 0.5594, 11.5000, 0.4202 and 0.1132, respectively, for the ARIMA (10, 1, 0) (0, 1, 1)₁₂ model, and 0.5259, 11.2181, 0.3992 and 0.1075, respectively, for the ARIMA-GRNN hybrid model. For the forecasting dataset, the RMSE, MAPE, MAE and MER were 0.2805, 8.8797, 0.2261 and 0.0851, respectively, for the ARIMA (10, 1, 0) (0, 1, 1)₁₂ model, and 0.2553, 5.7222, 0.1519 and 0.0571, respectively, for the ARIMA-GRNN hybrid model. Conclusions: The ARIMA-GRNN hybrid model was shown to be superior to the single ARIMA model in predicting the short-term TB incidence in the Chinese population, especially in fitting and forecasting the peak and trough incidence.

Period of Measurement in Time-Series Predictions of Disease Counts from 2007 to 2017 in Northern Nevada: Analytics Experiment

BACKGROUND

The literature in statistics presents methods by which autocorrelation can identify the best period of measurement to improve the performance of a time-series prediction. The period of measurement plays an important role in improving the performance of disease-count predictions. However, from the operational perspective in public health surveillance, there is a limitation to the length of the measurement period that can offer meaningful and valuable predictions.

OBJECTIVE

This study aimed to establish a method that identifies the shortest period of measurement without significantly decreasing the prediction performance for time-series analysis of disease counts.

METHODS

The data used in this evaluation include disease counts from 2007 to 2017 in northern Nevada. The disease counts for chlamydia, salmonella, respiratory syncytial virus, gonorrhea, viral meningitis, and influenza A were predicted.

RESULTS

Our results showed that autocorrelation could not guarantee the best performance for prediction of disease counts. However, the proposed method with the change-point analysis suggests a period of measurement that is operationally acceptable and performance that is not significantly different from the best prediction.

CONCLUSIONS

The use of change-point analysis with autocorrelation provides the best and most practical period of measurement.

Estimating the incidence of tuberculosis cases reported at a tertiary hospital in Ghana: a time series model approach

Background

The incidence of Tuberculosis (TB) differs among countries and contributes to morbidity and mortality especially in the developing countries. Trends and seasonal changes in the number of patients presenting with TB have been studied worldwide including sub-Saharan Africa. However, these changes are unknown at the Korle-Bu Teaching Hospital (KBTH). The aim of this study was to obtain a time series model to estimate the incidence of TB cases at the chest clinic of the Korle-Bu Teaching hospital.

Methods

A time series analysis using a Box-Jenkins approach propounded as an autoregressive moving average (ARIMA) was conducted on the monthly TB cases reported at the KBTH from 2008 to 2017. Various models were stated and compared and the best was found to be based on the Akaike Information Criterion and Bayesian Information Criterion.

Results

There was no evidence of obvious increasing or decreasing trend in the TB data. The log-transformed of the data achieved stationarity with fairly stable variations around the mean of the series. ARIMA (1, 0, 1) or ARMA (1,1) was obtained as the best model. The monthly forecasted values of the best model ranged from 53 to 55 for the year 2018; however, the best model does not always produce the best results with respect to the mean absolute and mean square errors.

Conclusions

Irregular fluctuations were observed in the 10 -year data studied. The model equation to estimate the expected monthly TB cases at KBTH produced an AR coefficient of 0.971 plus an MA coefficient of − 0.826 with a constant value of 4.127. The result is important for developing a hypothesis to explain the dynamics of TB occurrence so as to outline prevention programmes, optimal use of resources and effective service delivery.

Electronic supplementary material

The online version of this article (10.1186/s12889-018-6221-z) contains supplementary material, which is available to authorized users.

A mathematical model for the seasonal transmission of schistosomiasis in the lake and marshland regions of China

Schistosomiasis, a parasitic disease caused by \textit{ Schistosoma Japonicum}, is still one of the most serious parasitic diseases in China and remains endemic in seven provinces, including Hubei, Anhui, Hunan, Jiangsu, Jiangxi, Sichuan, and Yunnan. The monthly data of human schistosomiasis cases in Hubei, Hunan, and Anhui provinces (lake and marshland regions) released by the Chinese Center for Disease Control and Prevention (China CDC) display a periodic pattern with more cases in late summer and early autumn. Based on this observation, we construct a deterministic model with periodic transmission rates to study the seasonal transmission dynamics of schistosomiasis in these lake and marshland regions in China. We calculate the basic reproduction number R0, discuss the dynamical behavior of solutions to the model, and use the model to fit the monthly data of human schistosomiasis cases in Hubei. We also perform some sensitivity analysis of the basic reproduction number R0 in terms of model parameters. Our results indicate that treatment of at-risk population groups, improving sanitation, hygiene education, and snail control are effective measures in controlling human schistosomiasis in these lakes and marshland regions.

Mixing in regional-structure model about the influence of floating population and optimal control about TB in Guangdong province of China

In the last 60 years, great progress has been made in controlling and preventing tuberculosis in China. However, the number of tuberculosis cases has increased dramatically in the last 25 years, mainly due to the lack of effective control measures of immigrating populations with tuberculosis. In order to explore the effective control and prevention measures we propose a deterministic model to study the transmission dynamics of tuberculosis in Guangdong province of China in this paper. The model consists of susceptible, exposed and infectious recovered subpopulations of immigrating populations from other provinces and the local population of Guangdong. We obtain the effective reproduction number. Based on the analysis, we also establish an optimal immune programming model, and get the optimal proportion of vaccine coverage with control of the effective reproduction number. Simulation is used to determine the validation and reliability. Our study demonstrates that the immigrating population from different provinces needs to be vaccinated according to the incidence rate of TB in their original provinces, and it is an effective way to prevent the outbreak of tuberculosis in Guangdong.

Environmental Factors Related to Pulmonary Tuberculosis in HIV-Infected Patients in the Combined Antiretroviral Therapy (cART) Era

The aim of our study was to evaluate the seasonal variations and whether short-term exposure to environmental risk factors, such as climate and air pollution, is associated with PTB-related hospital admissions in human immunodeficiency virus (HIV)-infected patients in Spain during the era of combined antiretroviral therapy (cART). A retrospective study was carried out using data from the Minimum Basic Data Set (MBDS) and the State Meteorological Agency (AEMET) of Spain. The primary outcome variable was hospital admissions with PTB diagnosis. The environmental risk factors evaluated were season, temperature, humidity, NO2, SO2, O3, PM10, and CO. Overall, HIV-infected patients had a lower frequency of PTB-related hospital admissions in summer (22.8%) and autumn (22.4%), but higher values in winter (26.6%) and spring (28.2%). Using a Bayesian temporal model, PTB-related hospital admissions were less frequent in summer-autumn and more abundant in winter-spring during the first years of follow-up. During the later years of follow-up, the seasonal trends continued resulting in the lowest values in autumn and the highest in spring. When considering short-term exposure to environmental risk factors, lower temperatures at 1 week (odds ratio (OR) = 1.03; p = 0.008), 1.5 weeks (OR = 1.03; p<0.001), 2 weeks (OR = 1.04; p<0.001), and 3 weeks (OR = 1.03; p<0.001) prior to PTB admission. In addition, higher concentration of NO2 at the time of admission were significantly associated with higher likelihoods of PTB-related hospital admission in HIV-infected patients when 1.5 weeks (OR = 1.1; p = 0.044) and 2 weeks (OR = 1.21; p<0.001) were used as controls. Finally, higher concentration of SO2 at 1.5 weeks prior to PTB admission was significantly associated with a higher likelihood of PTB-related hospital admissions (OR = 0.92; p = 0.029). In conclusion, our data suggest an apparent seasonal variation in hospital admissions of HIV-infected patients with a PTB diagnosis (summer/autumn vs. winter/spring), as well as a link to short-term exposure to environmental risk factors, such as temperature and ambient NO2 and SO2.

On the association between outdoor PM2.5 concentration and the seasonality of tuberculosis for Beijing and Hong Kong

Tuberculosis (TB) is still a serious public health problem in various countries. One of the long-elusive but critical questions about TB is what the risk factors are and how they contribute for its seasonality. An ecologic study was conducted to examine the association between the variation of outdoor PM_2.5 concentration and the TB seasonality based on the monthly TB notification and PM_2.5 concentration data of Hong Kong and Beijing. Both descriptive analysis and Poisson regression analysis suggested that the outdoor PM_2.5 concentration could be a potential risk factor for the seasonality of TB disease. The significant relationship between the number of TB cases and PM_2.5 concentration was not changed when regression models were adjusted by sunshine duration, a potential confounder. The regression analysis showed that a 10 μg/m³ increase in PM_2.5 concentrations during winter is significantly associated with a 3% (i.e. 18 and 14 cases for Beijing and Hong Kong, respectively) increase in the number of TB cases notified during the coming spring or summer for both Beijing and Hong Kong. Three potential mechanisms were proposed to explain the significant relationship: (1) increased PM_2.5 exposure increases host's susceptibility to TB disease by impairing or modifying the immunology of the human respiratory system; (2) increased indoor activities during high outdoor PM_2.5 episodes leads to an increase in human contact and thus the risk of TB transmission; (3) the seasonal change of PM_2.5 concentration is correlated with the variation of other potential risk factors of TB seasonality. Preliminary evidence from the analysis of this work favors the first mechanism about the PM_2.5 exposure-induced immunity impairment. This work adds new horizons to the explanation of the TB seasonality and improves our understanding of the potential mechanisms affecting TB incidence, which benefits the prevention and control of TB disease.

Temporal and long-term trend analysis of class C notifiable diseases in China from 2009 to 2014

OBJECTIVES

Time series models are effective tools for disease forecasting. This study aims to explore the time series behaviour of 11 notifiable diseases in China and to predict their incidence through effective models.

SETTINGS AND PARTICIPANTS

The Chinese Ministry of Health started to publish class C notifiable diseases in 2009. The monthly reported case time series of 11 infectious diseases from the surveillance system between 2009 and 2014 was collected.

METHODS

We performed a descriptive and a time series study using the surveillance data. Decomposition methods were used to explore (1) their seasonality expressed in the form of seasonal indices and (2) their long-term trend in the form of a linear regression model. Autoregressive integrated moving average (ARIMA) models have been established for each disease.

RESULTS

The number of cases and deaths caused by hand, foot and mouth disease ranks number 1 among the detected diseases. It occurred most often in May and July and increased, on average, by 0.14126/100 000 per month. The remaining incidence models show good fit except the influenza and hydatid disease models. Both the hydatid disease and influenza series become white noise after differencing, so no available ARIMA model can be fitted for these two diseases.

CONCLUSION

Time series analysis of effective surveillance time series is useful for better understanding the occurrence of the 11 types of infectious disease.

The Use of an Autoregressive Integrated Moving Average Model for Prediction of the Incidence of Dysentery in Jiangsu, China

The aim of this study is to forecast the incidence of bacillary dysentery with a prediction model. We collected the annual and monthly laboratory data of confirmed cases from January 2004 to December 2014. In this study, we applied an autoregressive integrated moving average (ARIMA) model to forecast bacillary dysentery incidence in Jiangsu, China. The ARIMA (1, 1, 1) × (1, 1, 2)12 model fitted exactly with the number of cases during January 2004 to December 2014. The fitted model was then used to predict bacillary dysentery incidence during the period January to August 2015, and the number of cases fell within the model's CI for the predicted number of cases during January-August 2015. This study shows that the ARIMA model fits the fluctuations in bacillary dysentery frequency, and it can be used for future forecasting when applied to bacillary dysentery prevention and control.

Time Series Modelling of Syphilis Incidence in China from 2005 to 2012

BACKGROUND

The infection rate of syphilis in China has increased dramatically in recent decades, becoming a serious public health concern. Early prediction of syphilis is therefore of great importance for heath planning and management.

METHODS

In this paper, we analyzed surveillance time series data for primary, secondary, tertiary, congenital and latent syphilis in mainland China from 2005 to 2012. Seasonality and long-term trend were explored with decomposition methods. Autoregressive integrated moving average (ARIMA) was used to fit a univariate time series model of syphilis incidence. A separate multi-variable time series for each syphilis type was also tested using an autoregressive integrated moving average model with exogenous variables (ARIMAX).

RESULTS

The syphilis incidence rates have increased three-fold from 2005 to 2012. All syphilis time series showed strong seasonality and increasing long-term trend. Both ARIMA and ARIMAX models fitted and estimated syphilis incidence well. All univariate time series showed highest goodness-of-fit results with the ARIMA(0,0,1)×(0,1,1) model.

CONCLUSION

Time series analysis was an effective tool for modelling the historical and future incidence of syphilis in China. The ARIMAX model showed superior performance than the ARIMA model for the modelling of syphilis incidence. Time series correlations existed between the models for primary, secondary, tertiary, congenital and latent syphilis.

Vitamin D and respiratory tract infections in childhood

BACKGROUND

Respiratory tract infections (RTIs) remain among of the most important causes of morbidity and mortality among children. Several studies have associated vitamin D deficiency with an increased risk of RTIs, and vitamin D supplementation has been proposed as a possible preventive measure against RTIs in children. The main aim of this review is to summarize the current evidence from the literature about the link between vitamin D and RTIs in children.

DISCUSSION

Several recent studies have shown that vitamin D has different immunomodulatory properties associated with the risk of RTIs in childhood. In this regard, it is very important to understand the definition of deficiency and insufficiency of vitamin D and when and how to treat this condition. Unfortunately, there is no consensus, although a level of at least 10 ng/mL 25-hydroxycholecalciferol (25[OH]D) is thought to be necessary to promote bone mineralization and calcium homeostasis, and a concentration between 20 ng/mL and 50 ng/mL is considered adequate to provide an immunomodulatory effect. Available data support a role for vitamin D deficiency in the risk of pediatric tuberculosis, recurrent acute otitis media, and severe bronchiolitis, whereas further studies are needed to confirm an association in children with recurrent pharyngotonsillitis, acute rhinosinusitis and community-acquired pneumonia.

CONCLUSIONS

Maintenance of adequate vitamin D status may be an effective and inexpensive prophylactic method against some RTIs, but the supplementation regimen has not been clearly defined. Further clinical trials are needed to determine the 25(OH)D concentrations associated with an increased risk of RTIs and optimal vitamin D supplementation regimen according to the type of RTI while also taking into consideration vitamin D receptor polymorphisms.

The association of extreme temperatures and the incidence of tuberculosis in Japan

Seasonal variation in the incidence of tuberculosis (TB) has been widely assumed. However, few studies have investigated the association between extreme temperatures and the incidence of TB. We collected data on cases of TB and mean temperature in Fukuoka, Japan for 2008-2012 and used time-series analyses to assess the possible relationship of extreme temperatures with TB incident cases, adjusting for seasonal and interannual variation. Our analysis revealed that the occurrence of extreme heat temperature events resulted in a significant increase in the number of TB cases (relative risk (RR) 1.20, 95 % confidence interval (CI) 1.01-1.43). We also found that the occurrence of extreme cold temperature events resulted in a significant increase in the number of TB cases (RR 1.23, 95 % CI 1.05-1.45). Sex and age did not modify the effect of either heat or cold extremes. Our study provides quantitative evidence that the number of TB cases increased significantly with extreme heat and cold temperatures. The results may help public health officials predict extreme temperature-related TB incidence and prepare for the implementation of preventive public health interventions.

Analyzing seasonality of tuberculosis across Indian states and union territories

A significant seasonal variation in tuberculosis (TB) is observed in north India during 2006-2011, particularly in states like Himachal Pradesh, Haryana and Rajasthan. To quantify the seasonal variation, we measure average amplitude (peak to trough distance) across seasons in smear positive cases of TB and observe that it is maximum for Himachal Pradesh (40.01%) and minimum for Maharashtra (3.87%). In north India, smear positive cases peak in second quarter (April-June) and reach a trough in fourth quarter (October-December), however low seasonal variation is observed in southern region of the country. The significant correlations as 0.64 (p-value<0.001), 0.54 (p-value<0.01) and 0.42 (p-value<0.05) are observed between minimum temperature and seasonality of TB at lag-1 in north, central and northeast India respectively. However, in south India, this correlation is not significant.

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

Tuberculosis is a major global public health problem, which also affects economic and social development. China has the second largest burden of tuberculosis in the world. The tuberculosis morbidity in Xinjiang is much higher than the national situation; therefore, there is an urgent need for monitoring and predicting tuberculosis morbidity so as to make the control of tuberculosis more effective. Recently, the Box-Jenkins approach, specifically the autoregressive integrated moving average (ARIMA) model, is typically applied to predict the morbidity of infectious diseases; it can take into account changing trends, periodic changes, and random disturbances in time series. Autoregressive conditional heteroscedasticity (ARCH) models are the prevalent tools used to deal with time series heteroscedasticity. In this study, based on the data of the tuberculosis morbidity from January 2004 to June 2014 in Xinjiang, we establish the single ARIMA (1, 1, 2) (1, 1, 1)₁₂ model and the combined ARIMA (1, 1, 2) (1, 1, 1)₁₂-ARCH (1) model, which can be used to predict the tuberculosis morbidity successfully in Xinjiang. Comparative analyses show that the combined model is more effective. To the best of our knowledge, this is the first study to establish the ARIMA model and ARIMA-ARCH model for prediction and monitoring the monthly morbidity of tuberculosis in Xinjiang. Based on the results of this study, the ARIMA (1, 1, 2) (1, 1, 1)₁₂-ARCH (1) model is suggested to give tuberculosis surveillance by providing estimates on tuberculosis morbidity trends in Xinjiang, China.

Time series analysis of demographic and temporal trends of tuberculosis in Singapore

Background

Singapore is an intermediate tuberculosis (TB) incidence country, with a recent rise in TB incidence from 2008, after a fall in incidence since 1998. This study identified population characteristics that were associated with the recent increase in TB cases, and built a predictive model of TB risk in Singapore.

Methods

Retrospective time series analysis was used to study TB notification data collected from 1995 to 2011 from the Singapore Tuberculosis Elimination Program (STEP) registry. A predictive model was developed based on the data collected from 1995 to 2010 and validated using the data collected in 2011.

Results

There was a significant difference in demographic characteristics between resident and non-resident TB cases. TB risk was higher in non-residents than in residents throughout the period. We found no significant association between demographic and macro-economic factors and annual incidence of TB with or without adjusting for the population-at-risk. Despite growing non-resident population, there was a significant decrease in the non-resident TB risk (p < 0.0001). However, there was no evidence of trend in the resident TB risk over this time period, though differences between different demographic groups were apparent with ethnic minorities experiencing higher incidence rates.

Conclusion

The study found that despite an increasing size of non-resident population, TB risk among non-residents was decreasing at a rate of about 3% per year. There was an apparent seasonality in the TB reporting.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2458-14-1121) contains supplementary material, which is available to authorized users.

Seasonal variation of newly notified pulmonary tuberculosis cases from 2004 to 2013 in Wuhan, China

BACKGROUND

Although there was a report about the seasonal variation in Wuhan city, it only analyzed the prevalence data of pulmonary tuberculosis (TB) cases, and just studied the seasonality by subgroup of smear positive and negative from 2006 to 2010 by spectral analysis. In this study, we investigated the seasonality of the total newly notified pulmonary TB cases by subgroups such as time period, sex, age, occupation, district, and sputum smear result from 2004 to 2013 in Wuhan by a popular seasonal adjustment model (TRAMO-SEATS).

METHODS

Monthly pulmonary TB cases from 2004 to 2013 in Wuhan were analyzed by the TRAMO-SEATS seasonal adjustment program. Seasonal amplitude was calculated and compared within the subgroups.

RESULTS

From 2004 to 2013, there were 77.76 thousand newly notified pulmonary TB cases in Wuhan, China. There was a dominant peak spring peak (March) with seasonal amplitude of 56.81% and a second summer peak (September) of 43.40%, compared with the trough month (December). The spring seasonal amplitude in 2004-2008 was higher than that of 2009-2013(P<0.05). There were no statistical differences for spring seasonal amplitude within subgroups of gender, age, district, and sputum smear result (P>0.05). However, there were significant differences in spring seasonal amplitude by occupation, with amplitude ranging from 59.37% to 113.22% (P<0.05). The summer seasonal amplitude in 2004-2008 was higher than that of 2009-2013(P<0.05). There were no statistical differences in summer seasonal amplitude within subgroups of gender, district, sputum smear result(P>0.05). There were significant differences in summer seasonal amplitude by age, with amplitude ranging from 36.05% to 100.09% (P<0.05). Also, there were significant differences in summer seasonal amplitude by occupation, with amplitude ranging from 43.40% to 109.88% (P<0.05).

CONCLUSIONS

There was an apparent seasonal variation in pulmonary TB cases in Wuhan. We speculated that spring peak in our study was most likely caused by the increased reactivation of the latent TB due to vitamin D deficiency and high PM2.5 concentration, while the summer peak was mainly resulted from the enhanced winter transmission due to indoor crowding in winter, overcrowding of public transportation over the period of the Spring Festival and health care seeking delay in winter.

Forecasting tuberculosis incidence in iran using box-jenkins models

Background:

Predicting the incidence of tuberculosis (TB) plays an important role in planning health control strategies for the future, developing intervention programs and allocating resources.

Objectives:

The present longitudinal study estimated the incidence of tuberculosis in 2014 using Box-Jenkins methods.

Materials and Methods:

Monthly data of tuberculosis cases recorded in the surveillance system of Iran tuberculosis control program from 2005 till 2011 was used. Data was reviewed regarding normality, variance equality and stationary conditions. The parameters p, d and q and P, D and Q were determined, and different models were examined. Based on the lowest levels of AIC and BIC, the most suitable model was selected among the models whose overall adequacy was confirmed.

Results:

During 84 months, 63568 TB patients were recorded. The average was 756.8 (SD = 11.9) TB cases a month. SARIMA (0,1,1)(0,1,1)₁₂ with the lowest level of AIC (12.78) was selected as the most adequate model for prediction. It was predicted that the total nationwide TB cases for 2014 will be about 16.75 per 100,000 people.

Conclusions:

Regarding the cyclic pattern of TB recorded cases, Box-Jenkins and SARIMA models are suitable for predicting its prevalence in future. Moreover, prediction results show an increasing trend of TB cases in Iran.

Seasonality of tuberculosis in delhi, India: a time series analysis

Background. It is highly cost effective to detect a seasonal trend in tuberculosis in order to optimize disease control and intervention. Although seasonal variation of tuberculosis has been reported from different parts of the world, no definite and consistent pattern has been observed. Therefore, the study was designed to find the seasonal variation of tuberculosis in Delhi, India. Methods. Retrospective record based study was undertaken in a Directly Observed Treatment Short course (DOTS) centre located in the south district of Delhi. Six-year data from January 2007 to December 2012 was analyzed. Expert modeler of SPSS ver. 21 software was used to fit the best suitable model for the time series data. Results. Autocorrelation function (ACF) and partial autocorrelation function (PACF) at lag 12 show significant peak suggesting seasonal component of the TB series. Seasonal adjusted factor (SAF) showed peak seasonal variation from March to May. Univariate model by expert modeler in the SPSS showed that Winter's multiplicative model could best predict the time series data with 69.8% variability. The forecast shows declining trend with seasonality. Conclusion. A seasonal pattern and declining trend with variable amplitudes of fluctuation were observed in the incidence of tuberculosis.

Applications and comparisons of four time series models in epidemiological surveillance data

Public health surveillance systems provide valuable data for reliable predication of future epidemic events. This paper describes a study that used nine types of infectious disease data collected through a national public health surveillance system in mainland China to evaluate and compare the performances of four time series methods, namely, two decomposition methods (regression and exponential smoothing), autoregressive integrated moving average (ARIMA) and support vector machine (SVM). The data obtained from 2005 to 2011 and in 2012 were used as modeling and forecasting samples, respectively. The performances were evaluated based on three metrics: mean absolute error (MAE), mean absolute percentage error (MAPE), and mean square error (MSE). The accuracy of the statistical models in forecasting future epidemic disease proved their effectiveness in epidemiological surveillance. Although the comparisons found that no single method is completely superior to the others, the present study indeed highlighted that the SVMs outperforms the ARIMA model and decomposition methods in most cases.

Contribution of seasonality in transmission of Mycobacterium tuberculosis to seasonality in tuberculosis disease: a simulation study

A seasonal rise in tuberculosis (TB) notifications has been confirmed in several studies. Here, we examined one hypothesis for its cause: increased transmission of TB during wintertime due to crowding. Seasonality analysis was performed on actual and simulated notifications of clustered TB cases, which are considered to be representative of recent transmission, diagnosed from 1993 to 2004 in the Netherlands (n = 4,746). To test the hypothesis of winter crowding, notifications were simulated by adding patient delay and incubation period to an infection date randomly taken to be in winter in 80% of cases. The incubation periods were derived from frequency distributions for different TB disease localizations drawn from the literature. Seasonality analysis was performed using autocorrelation function plots and spectral analysis. Actual notifications showed strong seasonality in clustered TB and clustered extrapulmonary TB cases but not in clustered pulmonary TB cases. Analysis of simulated notifications revealed barely significant seasonality only in extrapulmonary TB cases. Our results suggest that increased transmission of TB during wintertime is unlikely to be the only cause of the seasonal peak in TB notifications. A factor closer to the notification date probably contributes to the seasonality observed in TB notifications.

Seasonal variation among tuberculosis suspects in four countries

The objective of the study was to analyze monthly trends across a calendar year in tuberculosis suspects and sputum smear-positive cases based on nationally representative samples of tuberculosis laboratory registers from Moldova, Mongolia, Uganda and Zimbabwe. Out of the 47 140 suspects registered in the tuberculosis laboratory registers, 13.4% (6312) were cases. The proportion varied from country to country, Moldova having the lowest (9%) and Uganda the highest (21%). From the monthly proportion of suspects and cases among total suspects and cases, seasonal variations were most marked in Mongolia which, among the four countries, also has the most pronounced seasonal variation in ambient temperature. Female suspects were consistently older than female cases in all four countries. Among males, male suspects were almost consistently older than male cases, most consistently in Moldova. Seasons seem to affect attendance to diagnostic laboratory services, evidenced by the contrasting findings of Mongolia (extreme continental northern climate) compared to Uganda (equatorial climate). A combination of external and possibly endogenous factors seems to determine whether tuberculosis suspects and cases present themselves to health care facilities.

Seasonal variations in notification of active tuberculosis cases in China, 2005-2012

Background

Although seasonal variation in tuberculosis (TB) incidence has been described in many countries, it remains unknown in China.

Methods

A time series decomposition analysis (X-12-ARIMA) was performed to examine the seasonal variation in active TB cases nationwide from 2005 through 2012 in China. Seasonal amplitude was calculated for the evaluation of TB seasonal variation.

Results

A total of 7.78 million active TB cases were reported over a period of 8 years. A spring peak (April) was observed with seasonal amplitude of 46.3%, compared with the winter trough (February). Most cases in provinces with subtropical and tropical monsoon climate showed lower amplitudes than those in temperate continental, plateau and mountain climate regions. The magnitude of seasonality varied inversely with annual average temperature, r (95% CI) = -0.71 (-0.79, -0.61). The seasonal amplitudes were 56.7, 60.5, 40.6, 46.4 and 50.9% for patients aged ≤14, 15–24, 25–44, 45–64, and ≥65 years, respectively. Students demonstrated greater seasonal amplitude than peasants, migrant workers and workers (115.3% vs. 43.5, 41.6 and 48.1%). Patients with pulmonary TB had lower amplitude compared to patients with pleural and other extra-pulmonary TB (EPTB) (45.9% vs. 52.0 and 56.3%). Relapse cases with sputum smear positive TB (SS+ TB) had significantly higher seasonal amplitude compared to new cases with sputum smear positive TB (52.2% vs. 41.6%).

Conclusions

TB is a seasonal disease in China. The peak and trough of TB transmission actually are in winter and in autumn respectively after factors of delay are removed. Higher amplitudes of TB seasonality are more likely to happen in temperate continental, plateau and mountain climate regions and regions with lower annual average temperature, and young person, students, patients with EPTB and relapse cases with SS+ TB are more likely to be affected by TB seasonality.

A hybrid seasonal prediction model for tuberculosis incidence in China

BACKGROUND

Tuberculosis (TB) is a serious public health issue in developing countries. Early prediction of TB epidemic is very important for its control and intervention. We aimed to develop an appropriate model for predicting TB epidemics and analyze its seasonality in China.

METHODS

Data of monthly TB incidence cases from January 2005 to December 2011 were obtained from the Ministry of Health, China. A seasonal autoregressive integrated moving average (SARIMA) model and a hybrid model which combined the SARIMA model and a generalized regression neural network model were used to fit the data from 2005 to 2010. Simulation performance parameters of mean square error (MSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) were used to compare the goodness-of-fit between these two models. Data from 2011 TB incidence data was used to validate the chosen model.

RESULTS

Although both two models could reasonably forecast the incidence of TB, the hybrid model demonstrated better goodness-of-fit than the SARIMA model. For the hybrid model, the MSE, MAE and MAPE were 38969150, 3406.593 and 0.030, respectively. For the SARIMA model, the corresponding figures were 161835310, 8781.971 and 0.076, respectively. The seasonal trend of TB incidence is predicted to have lower monthly incidence in January and February and higher incidence from March to June.

CONCLUSIONS

The hybrid model showed better TB incidence forecasting than the SARIMA model. There is an obvious seasonal trend of TB incidence in China that differed from other countries.

Comparative study of four time series methods in forecasting typhoid fever incidence in China

Accurate incidence forecasting of infectious disease is critical for early prevention and for better government strategic planning. In this paper, we present a comprehensive study of different forecasting methods based on the monthly incidence of typhoid fever. The seasonal autoregressive integrated moving average (SARIMA) model and three different models inspired by neural networks, namely, back propagation neural networks (BPNN), radial basis function neural networks (RBFNN), and Elman recurrent neural networks (ERNN) were compared. The differences as well as the advantages and disadvantages, among the SARIMA model and the neural networks were summarized and discussed. The data obtained for 2005 to 2009 and for 2010 from the Chinese Center for Disease Control and Prevention were used as modeling and forecasting samples, respectively. The performances were evaluated based on three metrics: mean absolute error (MAE), mean absolute percentage error (MAPE), and mean square error (MSE). The results showed that RBFNN obtained the smallest MAE, MAPE and MSE in both the modeling and forecasting processes. The performances of the four models ranked in descending order were: RBFNN, ERNN, BPNN and the SARIMA model.

Climate variability and outbreaks of infectious diseases in Europe

Several studies provide evidence of a link between vector-borne disease outbreaks and El Niño driven climate anomalies. Less investigated are the effects of the North Atlantic Oscillation (NAO). Here, we test its impact on outbreak occurrences of 13 infectious diseases over Europe during the last fifty years, controlling for potential bias due to increased surveillance and detection. NAO variation statistically influenced the outbreak occurrence of eleven of the infectious diseases. Seven diseases were associated with winter NAO positive phases in northern Europe, and therefore with above-average temperatures and precipitation. Two diseases were associated with the summer or spring NAO negative phases in northern Europe, and therefore with below-average temperatures and precipitation. Two diseases were associated with summer positive or negative NAO phases in southern Mediterranean countries. These findings suggest that there is potential for developing early warning systems, based on climatic variation information, for improved outbreak control and management.

Vitamin D in early childhood and the effect on immunity to Mycobacterium tuberculosis

A potential role for vitamin D as a therapeutic immunomodulator in tuberculosis (TB) has been recognised for over 150 years, but has only recently returned to the centre of the research arena due to the increasing awareness of the global vitamin D deficiency epidemic. As early as birth a child is often deficient in vitamin D, which may not only affect their bone metabolism but also modulate their immune function, contributing to the increased susceptibility to many infections seen early in life. Recent studies have begun to explain the mechanisms by which vitamin D affects immunity. Antimicrobial peptides are induced in conjunction with stimulation of innate pattern recognition receptors enhancing immunity to particular infections. In contrast the role of vitamin D within the adaptive immune response appears to be more regulatory in function, perhaps as a mechanism to reduce unwanted inflammation. In this paper we focus on the effect of vitamin D on immunity to TB. Where much of the attention has been paid by past reviews to the role of vitamin D in adult TB patients, this paper, where possible, focuses on research in paediatric populations.

Seasonality of tuberculosis in the United States, 1993-2008

BACKGROUND

Although seasonal variation in tuberculosis incidence has been described in several recent studies, the mechanism underlying this seasonality remains unknown. Seasonality of tuberculosis disease may indicate the presence of season-specific risk factors that could potentially be controlled if they were better understood. We conducted this study to determine whether tuberculosis is seasonal in the United States and to describe patterns of seasonality in specific populations.

METHODS

We performed a time series decomposition analysis of tuberculosis cases reported to the Centers for Disease Control and Prevention from 1993 through 2008. Seasonal amplitude of tuberculosis disease (the difference between the months with the highest and lowest mean case counts), was calculated for the population as a whole and for populations with select demographic, clinical, and epidemiologic characteristics.

RESULTS

A total of 243 432 laboratory-confirmed tuberculosis cases were reported over a period of 16 years. A mean of 21.4% more cases were diagnosed in March, the peak month, compared with November, the trough month. The magnitude of seasonality did not vary with latitude. The greatest seasonal amplitude was found among children aged <5 years and in cases associated with disease clusters.

CONCLUSIONS

Tuberculosis is a seasonal disease in the United States, with a peak in spring and trough in late fall. The latitude independence of seasonality suggests that reduced winter sunlight exposure may not be a strong contributor to tuberculosis risk. Increased seasonality among young children and clustered cases suggests that disease that is the result of recent transmission is more influenced by season than disease resulting from activation of latent infection.

Assessing trends and predictors of tuberculosis in Taiwan

BACKGROUND

Variety of environmental and individual factors can cause tuberculosis (TB) incidence change. The purpose of this study was to assess the characteristics of TB trends in the period 2004 - 2008 in Taiwan by month, year, gender, age, temperature, seasonality, and aborigines.

METHODS

The generalized regression models were used to examine the potential predictors for the monthly TB incidence in regional and national scales.

RESULTS

We found that (i) in Taiwan the average TB incidence was 68 per 100,000 population with mortality rate of 0.036 person-1 yr-1, (ii) the highest TB incidence rate was found in eastern Taiwan (116 per 100,000 population) with the largest proportion of TB relapse cases (8.17%), (iii) seasonality, aborigines, gender, and age had a consistent and dominant role in constructing TB incidence patterns in Taiwan, and (iv) gender, time trend, and 2-month lag maximum temperature showed strong association with TB trends in aboriginal subpopulations.

CONCLUSIONS

The proposed Poisson regression model is capable of forecasting patterns of TB incidence at regional and national scales. This study suggested that assessment of TB trends in eastern Taiwan presents an important opportunity for understanding the time-series dynamics and control of TB infections, given that this is the typical host demography in regions where these infections remain major public health problems.

Latent tuberculosis infection, tuberculin skin test and vitamin D status in contacts of tuberculosis patients: a cross-sectional and case-control study

Background

Deficient serum vitamin D levels have been associated with incidence of tuberculosis (TB), and latent tuberculosis infection (LTBI). However, to our knowledge, no studies on vitamin D status and tuberculin skin test (TST) conversion have been published to date. The aim of this study was to estimate the associations of serum 25-hydroxyvitamin D₃ (25[OH]D) status with LTBI prevalence and TST conversion in contacts of active TB in Castellon (Spain).

Methods

The study was designed in two phases: cross-sectional and case-control. From November 2009 to October 2010, contacts of 42 TB patients (36 pulmonary, and 6 extra-pulmonary) were studied in order to screen for TB. LTBI and TST conversion cases were defined following TST, clinical, analytic and radiographic examinations. Serum 25(OH)D levels were measured by electrochemiluminescence immunoassay (ECLIA) on a COBAS^® 410 ROCHE^® analyzer. Logistic regression models were used in the statistical analysis.

Results

The study comprised 202 people with a participation rate of 60.1%. Only 20.3% of the participants had a sufficient serum 25(OH)D (≥ 30 ng/ml) level. In the cross-sectional phase, 50 participants had LTBI and no association between LTBI status and serum 25(OH)D was found. After 2 months, 11 out of 93 negative LTBI participants, without primary prophylaxis, presented TST conversion with initial serum 25(OH)D levels: a:19.4% (7/36): < 20 ng/ml, b:12.5% (4/32):20-29 ng/ml, and c:0%(0/25) ≥ 30 ng/ml. A sufficient serum 25(OH)D level was a protector against TST conversion a: Odds Ratio (OR) = 1.00; b: OR = 0.49 (95% confidence interval (CI) 0.07-2.66); and c: OR = 0.10 (95% CI 0.00-0.76), trends p = 0.019, adjusted for high exposure and sputum acid-fast bacilli positive index cases. The mean of serum level 25(OH)D in TST conversion cases was lower than controls,17.5 ± 5.6 ng/ml versus 25.9 ± 13.7 ng/ml (p = 0.041).

Conclusions

The results suggest that sufficient serum 25(OH)D levels protect against TST conversion.

Seasonality of tuberculosis

OBJECTIVES

This study was designed to review previous studies and analyse the current knowledge and controversies related to seasonal variability of tuberculosis (TB) to examine whether TB has an annual seasonal pattern.

STUDY DESIGN AND METHODS

Systematic review of peer reviewed studies identified through literature searches using online databases belonging to PubMed and the Cochrane library with key words "Tuberculosis, Seasonal influence" and "Tuberculosis, Seasonal variation". The search was restricted to articles published in English. The references of the identified papers for further relevant publications were also reviewed.

RESULTS

Twelve studies conducted between the period 1971 and 2006 from 11 countries/regions around the world (South Western Cameroon, South Africa, India, Hong Kong, Japan, Kuwait, Spain, UK, Ireland, Russia, and Mongolia) were reviewed. A seasonal pattern of tuberculosis with a mostly predominant peak is seen during the spring and summer seasons in all of the countries (except South Western Cameroon and Russia).

CONCLUSIONS

The observation of seasonality leads to assume that the risk of transmission of M. tuberculosis does appear to be the greatest during winter months. Vitamin D level variability, indoor activities, seasonal change in immune function, and delays in the diagnosis and treatment of tuberculosis are potential stimuli of seasonal tuberculosis disease. Additionally, seasonal variation in food availability and food intake, age, and sex are important factors which can play a role in the tuberculosis notification variability. Prospective studies regarding this topic and other related subjects are highly recommended.

The testing season affects red deer skinfold increase in response to phytohaemagglutinin

Red deer (Cervus elaphus) have a pronounced seasonality in their physiology. The aim of this study was to assess the effect of the season on red deer responsiveness to skin testing with the phytohaemagglutinin (PHA) mitogen. Study subjects included 270 farmed adult red deer (19 stags and 251 hinds). The skin testing was carried out between January 2009 and August 2010. The animals were injected intradermally with a 0.1 ml volume containing 250 μg of PHA diluted in phosphate buffered saline. The skinfold thickness was measured immediately prior to injection and 72 h after administration, always by the same person and with three repeats per measurement. Single effects of sex and time on skin test responsiveness were significant (p < 0.001) as well as their interaction (p < 0.001). In winter (January), and considering the average of two years, the skinfold increase in response to the intradermal injection of 250 μg PHA was 2.1 times larger in stags and 1.4 times in hinds than in summer (August). While stags had 1.3 times larger responses than hinds in winter, the inverse occurred in summer, with 1.1 times larger responses in hinds. We also evidenced a limited inter-annual variation of skinfold increase in response to PHA in red deer. These findings have important consequences regarding the interpretation of skin test results in the ante-mortem diagnosis of tuberculosis and paratuberculosis, at least in deer.

Modeling tuberculosis incidence in Nepal

Background: Tuberculosis (TB) constitutes a large burden of infectious disease in Nepal. Objective: Model incidence of TB between 2003 and 2008 in Nepal. Methods: A retrospective study was conducted in Nepal of tuberculosis incidence by gender and location over the six years period. Data were obtained for 198,719 tuberculosis cases from the Nepal Tuberculosis Center (NTC). A negative binomial model with two multiplicative components as predictors was used and provided a good fit. Results: The model extracted a decreasing trend during the first five years followed by a drop in 2008.The overall incidence of TB was 1.31 cases per 1,000 population with a male to female incidence rate ratio of 1.86. There were pronounced spatial variations with higher rates occurring in the Terai region, followed by the Hill, and Mountain regions. Conclusion: Tuberculosis incidence showed a steady decreasing trend, but the number of cases was still very high. Gender differences existed in TB incidence in Nepal. Higher rates were observed in the Terai Region and urban areas. These findings highlight the need for the tuberculosis control measures to remain on a sustained and long-term basis for the high TB burden rate of Nepal.

A tuberculosis model with seasonality

The statistical data of tuberculosis (TB) cases show seasonal fluctuations in many countries. A TB model incorporating seasonality is developed and the basic reproduction ratio R(0) is defined. It is shown that the disease-free equilibrium is globally asymptotically stable and the disease eventually disappears if R(0)<1, and there exists at least one positive periodic solution and the disease is uniformly persistent if R(0)>1. Numerical simulations indicate that there may be a unique positive periodic solution which is globally asymptotically stable if R(0)>1. Parameter values of the model are estimated according to demographic and epidemiological data in China. The simulation results are in good accordance with the seasonal variation of the reported cases of active TB in China.