Forecasting incidence of tuberculosis cases in Brazil based on various univariate time-series models

Tuberculosis (TB) remains the world\u27s deadliest infectious disease and is a serious public health problem. Control for this disease still presents several difficulties, requiring strategies for the execution of immediate combat and intervention actions. Given that changes through the decision-making process are guided by current information and future prognoses, it is critical that a country\u27s public health managers rely on accurate predictions that can detect the evolving incidence phenomena. of TB. Thus, this study aims to analyze the accuracy of predictions of three univariate models based on time series of diagnosed TB cases in Brazil, from January 2001 to June 2018, in order to establish which model presents better performance. For the second half of 2018. From this, data were collected from the Department of Informatics of the Unified Health System (DATASUS), which were submitted to the methods of Simple Exponential Smoothing (SES), Holt-Winters Exponential Smoothing (HWES) and the Integrated Autoregressive Moving Average (ARIMA) model. In the performance analysis and model selection, six criteria based on precision errors were established: Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percent Error (MAPE) and Theil\u27s U statistic (U1 and U2). According to the results obtained, the HWES (0.2, 0.1, 0.1) presented a high performance in relation to the error metrics, consisting of the best model compared to the other two methodologies compared here

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

Pemodelan Runtun Waktu pada Pola Kemunculan Penyakit dengan SARIMA

Penyakit adalah salah satu masalah kesehatan manusia. Dalam mengatasi masalah kesehatan yang ada, maka diperlukan analisis prediksi untuk membantu mengatasinya lebih awal dan merencanakan pencegahan serta pengendalian terhadap penyakit tersebut. Penelitian ini bertujuan untuk mengetahui Prediksi pola runtun waktu penyakit pada pemodelan data kesehatan di RSUD Argamakmur dengan diketahuinya pola-pola penyakit yang ada. Prediksi Pola Runtun waktu yang memiliki pola musiman, yang mengambil seluruh kemungkinan pola-pola data yang ada, sehingga akan memprediksi dan menganalisis runtun waktu dalam mendapatkan model prediksi. Penelitian ini menggunakan analisis runtun waktu untuk pemodelan SARIMA. Hasil yang diperoleh adalah Prediksi pada 6 bulan ke depan dari Model Terbaik yang didapatkan, yaitu : data penyakit Demam Thypoid ARIMA(1,1,1) mengalami kenaikan 3,08%, data penyakit Gastroeteritis ARIMA(1,0,1) mengalami kenaikan 0,51%, data penyakit Dispepsia ARIMA(0,1,2) mengalami kenaikan 0,55%, data penyakit Anemia Akut&nbsp; ARIMA(1,0,2) mengalami penurunan 0,4%, data penyakit Bronkopneumonia ARIMA(1,0,1) mengalami penurunan 0,58%, data penyakit Diare Akut ARIMA(1,0,1) mengalami kenaikan 0,2%, data penyakit Vertigo&nbsp; ARIMA(1,0,2) mengalami penurunan 0,64%, data penyakit Stroke ARIMA(1,1,1) mengalami penurunan 0,28%, data penyakit Tumor ARIMA(1,0,1) mengalami penurunan 1%, data penyakit Asma ARIMA(1,0,1) mengalami penurunan&nbsp; 0,21%, data penyakit DM ARIMA(1,0,1) mengalami penurunan 0,47%, dan data penyakit TB Paru ARIMA(1,0,1) mengalami penurunan 0,14%

Seasonality and trend prediction of scarlet fever incidence in mainland China from 2004 to 2018 using a hybrid SARIMA-NARX model

Background Scarlet fever is recognized as being a major public health issue owing to its increase in notifications in mainland China, and an advanced response based on forecasting techniques is being adopted to tackle this. Here, we construct a new hybrid method incorporating seasonal autoregressive integrated moving average (SARIMA) with a nonlinear autoregressive with external input(NARX) to analyze its seasonality and trend in order to efficiently prevent and control this re-emerging disease. Methods Four statistical models, including a basic SARIMA, basic nonlinear autoregressive (NAR) method, traditional SARIMA-NAR and new SARIMA-NARX hybrid approaches, were developed based on scarlet fever incidence data between January 2004 and July 2018 to evaluate its temporal patterns, and their mimic and predictive capacities were compared to discover the optimal using the mean absolute percentage error, root mean square error, mean error rate, and root mean square percentage error. Results The four preferred models identified were comprised of the SARIMA(0,1,0)(0,1,1)12, NAR with 14 hidden neurons and five delays, SARIMA-NAR with 33 hidden neurons and five delays, and SARIMA-NARX with 16 hidden neurons and 4 delays. Among which presenting the lowest values of the aforementioned indices in both simulation and prediction horizons is the SARIMA-NARX method. Analyses from the data suggested that scarlet fever was a seasonal disease with predominant peaks of summer and winter and a substantial rising trend in the scarlet fever notifications was observed with an acceleration of 9.641% annually, particularly since 2011 with 12.869%, and moreover such a trend will be projected to continue in the coming year. Conclusions The SARIMA-NARX technique has the promising ability to better consider both linearity and non-linearity behind scarlet fever data than the others, which significantly facilitates its prevention and intervention of scarlet fever. Besides, under current trend of ongoing resurgence, specific strategies and countermeasures should be formulated to target scarlet fever

Forecasting TB notifications at Zengeza clinic in Chitungwiza, Zimbabwe

This study uses monthly time series data on TB notifications at Zengeza clinic in Chitungwiza from January 2013 to December 2018; to forecast TB notifications using the Box & Jenkins (1970) approach to univariate time series analysis. Diagnostic tests indicate that TBN is an I (0) variable. Based on the AIC, the study presents the SARMA (2, 0, 2)(1, 0, 1)12 model, the diagnostic tests further show that this model is quite stable and hence acceptable for forecasting the TB notifications at Zengeza clinic. The selected optimal model shows that the TB notifications will decline over the out-of-sample period. The main policy recommendation emanating from this study is that there should be continued intensification of TB surveillance and control programmes in order to reduce TB incidences not only at Zengeza clinic but also in Zimbabwe at large

Data-Centric Epidemic Forecasting: A Survey

The COVID-19 pandemic has brought forth the importance of epidemic forecasting for decision makers in multiple domains, ranging from public health to the economy as a whole. While forecasting epidemic progression is frequently conceptualized as being analogous to weather forecasting, however it has some key differences and remains a non-trivial task. The spread of diseases is subject to multiple confounding factors spanning human behavior, pathogen dynamics, weather and environmental conditions. Research interest has been fueled by the increased availability of rich data sources capturing previously unobservable facets and also due to initiatives from government public health and funding agencies. This has resulted, in particular, in a spate of work on 'data-centered' solutions which have shown potential in enhancing our forecasting capabilities by leveraging non-traditional data sources as well as recent innovations in AI and machine learning. This survey delves into various data-driven methodological and practical advancements and introduces a conceptual framework to navigate through them. First, we enumerate the large number of epidemiological datasets and novel data streams that are relevant to epidemic forecasting, capturing various factors like symptomatic online surveys, retail and commerce, mobility, genomics data and more. Next, we discuss methods and modeling paradigms focusing on the recent data-driven statistical and deep-learning based methods as well as on the novel class of hybrid models that combine domain knowledge of mechanistic models with the effectiveness and flexibility of statistical approaches. We also discuss experiences and challenges that arise in real-world deployment of these forecasting systems including decision-making informed by forecasts. Finally, we highlight some challenges and open problems found across the forecasting pipeline.Comment: 67 pages, 12 figure

Hybrid machine learning models for forecasting surgical case volumes at a hospital

Abstract Recent developments in machine learning and deep learning have led to the use of multiple algorithms to make better predictions. Surgical units in hospitals allocate their resources for day surgeries based on the number of elective patients, which is mostly disrupted by emergency surgeries. Sixteen different models were constructed for this comparative study, including four simple and twelve hybrid models for predicting the demand for endocrinology, gastroenterology, vascular, urology, and pediatric surgical units. The four simple models used were seasonal autoregressive integrated moving average (SARIMA), support vector regression (SVR), multilayer perceptron (MLP), and long short-term memory (LSTM). The twelve hybrid models used were a combination of any two of the above-mentioned simple models, namely, SARIMA–SVR, SVR–SARIMA, SARIMA–MLP, MLP–SARIMA, SARIMA–LSTM, LSTM–SARIMA, SVR–MLP, MLP–SVR, SVR–LSTM, LSTM–SVR, MLP–LSTM, and LSTM–MLP. Data from the period 2012–2018 were used to build and test the models for each surgical unit. The results indicated that, in some cases, the simple LSTM model outperformed the others while, in other cases, there was a need for hybrid models. This shows that surgical units are unique in nature and need separate models for predicting their corresponding surgical volumes. View Full-Text Keywords: time series, seasonal autoregressive integrated moving average, machine learning, hybrid model, demand, hospital, surgical unitpublishedVersio

Comparison of exponential smoothing and ARIMA time series models for forecasting COVID-19 cases: a secondary data analysis

Background: In order to manage outbreaks and plan resources, health systems must be capable of accurately projecting COVID-19 case patterns. Health systems can effectively predict future illness patterns by using mathematical and statistical modelling of infectious diseases. Different methods have been used with comparatively good accuracy for various prediction goals in medical sciences. Some illustrations are provided by statistical techniques intended to forecast epidemic cases. In order to increase healthcare systems readiness, this study aimed to identify the most accurate models for COVID-19 with a high global prevalence of positive cases. Methods: Exponential smoothing model and ARIMA were employed on time series datasets to forecast confirmed cases in upcoming months and hence the effectiveness of these predictive models were compared on the basis of performance measures. Results: It was seen that the ARIMA (0,0,2) model is best fitted with smaller values of performance measures (RMSE=4.46 and MAE=2.86) while employed on the recent dataset for short duration. Holt-Winters Exponential smoothing model was found to be more accurate to deal with a longer period of time series based data. Conclusions: The study revealed that working with recent dataset is more accurate to forecast the number of confirmed cases as compared to the data collected for longer period. The early-stage warnings through these predictive models would be beneficial for governments and health professionals to be prepared with the strategies at different levels for public health prevention