Alleviating Vulnerabilities of the Possible Outbreaks of Measles: A Data Trend Analysis and Prediction of Possible Cases
Hidear Talirongan, Markdy Y. Orong, Florence Jean B. Talirongan
Alleviating Vulnerabilities of the Possible Outbreaks of Measles: A Data Trend Analysis and Prediction of Possible Cases
Hidear Talirongan , Markdy Y. Orong & Florence Jean B. Talirongan Misamis University, Ozamiz City, Philippines. DOI: Under assignment
Copyright: ©2020 Hidear Talirongan et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Article Received: 26 August 2020 Article Accepted: 27 November 2020 Article Published: 28 December 2020
A B S T R A C T
Measles is considered as a highly contagious disease that leads to serious complications around the world. Thus, the paper determined the trend and the five-year forecasted data of the Measles in the Philippines. This study utilized the time series data for trend analysis and data forecasting using the ARIMA model to visualize the measles cases. Figures for the time-series and forecasted results are individually presented with the use of GRETL software. Results showed that there was an increasing pattern of the disease from 2016 to 2019. However, there was a decreasing pattern of its occurrence in the next five years based on the five-year forecast. Nevertheless, with the results of the study, there is still a need to improve the different intervention plans of the authority in alleviating the occurrence of the disease though it yielded a decreasing pattern in the future since it is evident that the figure of the forecasted data is still approximately 15,000 and above.
Keywords:
ARIMA, Measles, Predictions, Trends, Analysis.
1. Introduction
2. Theoretical Framework
Several literature and studies have started to explore data mining techniques in health and disease prediction [15]. Wang, Shen, Jiang, et al. applied ARIMA in predicting hemorrhagic fever with renal syndrome in China from 2011 until 2018 [16]. Mbau forecasted the number of lung diseases using ARIMA [17]. In the study conducted by He and Tao, the data from 2007 until 2015 was utilized to describe the influenza virus's epidemiology during the past nine influenza years [18]. Thus, the ARIMA model effectively predicts the positive rate of the influenza virus in a short time. Sato presented the process of using the ARIMA model on disease management, where the short period segments were encouraged to analyze each disease [19]. Several countries use the ARIMA model in forecasting the SARS-COV-2 diseases, including the Islamic Republic of Iran, Pakistan, Australia, [20]–[22]. Singh et al. studied the prediction of COVID-19 to 15 countries [23]. The model was used to forecast the estimated cases, deaths, and recoveries among the top fifteen (15) countries from April 24, 2020, until July 7, 2020. Some examples of the ARIMA model include predicting the beds that were occupied during the time where there was an epidemic of severe acute respiratory syndrome (SARS) at a hospital in Singapore [24]. Another study conducted in China recommended the need for an appropriate model to predict, using historical data like cases of hemorrhagic fever with kidney syndrome. Currently, China has 90% of cases of this disease reported globally, and the use of ARIMA models enables them to create better management and short-term predictions of the disease [20], [25], [26].
3. Materials and Methods
Materials:
The data to be used in the study are situational report (SitRep) on measles outbreak taken from the Republic of the Philippines National Disaster Risk Reduction and Management Council (NDRRMC), Department of Health, Epidemiology Bureau, Public Health Surveillance Division, and International Federation of Red Cross and Red Crescent Societies. There were seventeen regions (17) in the Philippines, where each region has several cases and reported deaths. The historical data of reported confirmed cases of measles from 2015 until 2019 in the Philippines was utilized.
Methods:
The current study utilized the Autoregressive Integrated Moving Average (ARIMA) model employed in many fields to construct models for forecasting time series [22], [27]. ARIMA(p,d,q) model is used to forecast the data pattern of diseases for the next fourteen years. Time-series predictions are basically based on the changes over time in historical data sets and can produce mathematical models by using statistical data that can be extrapolated [28]–[30]. The ARIMA (p,d,q) model is defined as follows: Xt = Φ 1 X t-1 + … + Φ p X t-p + a t –Θ a t-1 - … - Θ (1) q a t-q Fig.1.
Predicting Measles Trends
4. Results and Discussion
The time-series plot showing the trend of reported confirmed cases of measles in the Philippines is presented in Figure 2. The 5-year historical data used was from the year 2015-2019. It is evident in the figure that there was a decreased pattern of the disease from the year 2015 to 2016.
Fig.2.
The Time-Series Plot of Measles in the Philippines However, it is evident in the figure that there was a rapid increase in the disease based on the historical data from 2016 until 2019. The result is in line with the report of the WHO [6], [28] that there was an increase of cases, which is 30% from 2016-2017. Moreover, the result could be supported by the report of Mahase [10] that Measles cases
ARIMA ALGORITHM
TEST DATA
ESTIMATED RESULTS
Fig.3.
A Five-Year Forecast
5. Conclusions and Recommendations
Obtaining forecasted data on the occurrences of measles cases in very helpful to the administration in designing preventive measures against the spread of cases. In the study, the time-series tool successfully presented the trends of the disease in the country, and ARIMA (1,0,1) model helps in predicting the increasing and decreasing pattern of the measles cases. There was an increased pattern of the disease in the country from 2016 until 2019, based on historical data. However, in the next five years, there will be a decreasing number of its occurrence based on the results of the forecast. With the current situation and the future figure of measles disease in the country, there is still a need to strengthen the campaign by the Department of Health to the community in dealing with how to prevent oneself from the infection and at the same time to revisit previous intervention plans and programs and evaluate which part of the plans that need to be focused as a way to prevent that spread of the infections.
References