Forecasting the Number of Human Immunodeficiency Virus Infections in the Korean Population Using the Autoregressive Integrated Moving Average Model

Abstract

ObjectivesFrom the introduction of HIV into the Republic of Korea in 1985 through 2012, 9,410 HIV-infected Koreans have been identified. Since 2000, there has been a sharp increase in newly diagnosed HIV-infected Koreans. It is necessary to estimate the changes in HIV infection to plan budgets and to modify HIV/AIDS prevention policy. We constructed autoregressive integrated moving average (ARIMA) models to forecast the number of HIV infections from 2013 to 2017.MethodsHIV infection data from 1985 to 2012 were used to fit ARIMA models. Akaike Information Criterion and Schwartz Bayesian Criterion statistics were used to evaluate the constructed models. Estimation was via the maximum likelihood method. To assess the validity of the proposed models, the mean absolute percentage error (MAPE) between the number of observed and fitted HIV infections from 1985 to 2012 was calculated. Finally, the fitted ARIMA models were used to forecast the number of HIV infections from 2013 to 2017.ResultsThe fitted number of HIV infections was calculated by optimum ARIMA (2,2,1) model from 1985–2012. The fitted number was similar to the observed number of HIV infections, with a MAPE of 13.7%. The forecasted number of new HIV infections in 2013 was 962 (95% confidence interval (CI): 889–1,036) and in 2017 was 1,111 (95% CI: 805–1,418). The forecasted cumulative number of HIV infections in 2013 was 10,372 (95% CI: 10,308–10,437) and in 2017 was14,724 (95% CI: 13,893–15,555) by ARIMA (1,2,3).ConclusionBased on the forecast of the number of newly diagnosed HIV infections and the current cumulative number of HIV infections, the cumulative number of HIV-infected Koreans in 2017 would reach about 15,000.

Using an Autoregressive Integrated Moving Average Model to Predict the Incidence of Hemorrhagic Fever with Renal Syndrome in Zibo, China, 2004–2014

“…Methods: ARIMA models are the most commonly used time series prediction models (16). We constructed ARIMA models for monthly HFRS incidence in Zibo from 1983 to 2012.…”

“…Autoregressive integrated moving average (ARIMA) models, which take into account changing trends, periodic changes, and random disturbances in time series, are very useful in modeling the temporal dependence structure of a time series (15). In epidemiology, ARIMA models have been successfully applied to predict the incidence of infectious diseases, such as HIV (16), influenza (17,18), malaria incidence (19), and others (20)(21)(22).…”

Prediction of Poor Inhabitant Number Using Least Square and Moving Average Method

“…As one of the choices to describe a future trend, time series analysis can be applied to reflect dynamic variable from one time to another [3]. From the previous studies by using Least Square [4][5][6][7][8] and Moving Average [3] [9][10][11][12][13][14] method, the data and analysis showed the future prediction. It was defined as a management process in making decision.…”

Hybrid Decomposition Time-Series Forecasting by DirRec Strategy: Electric Load Forecasting Using Machine-Learning

“…Time-series forecasting techniques have been influenced, from the 1960s on, by linear statistical methods such as ARIMA [5] models. ARIMA have been used in many different fields, including energy [5], economics [6], health [7] and tourism [8]. Since the time-series data in real world usually have non-linear characteristics [9], using ARIMA is not optimal for most of the real-world problems [10].…”