Measles outbreak detection in Metro Manila: Comparisons between ARIMA and INAR models

It is the goal of many developing countries to stop the spread of diseases. Part of this effort is to conduct ongoing surveillance of disease transmission to foresee future epidemics. However, in the Philippines, there is a lack of an automated method in determining their presence. This paper presen...

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Main Authors: Paman, Joshua Mari J., Santiago, Frank Niccolo M., Mojica, Vio Jianu C., Co, Frumencio F., Leong, Robert Neil F.
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Published: Animo Repository 2017
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Online Access:https://animorepository.dlsu.edu.ph/faculty_research/2320
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Institution: De La Salle University
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spelling oai:animorepository.dlsu.edu.ph:faculty_research-33192021-08-24T02:46:15Z Measles outbreak detection in Metro Manila: Comparisons between ARIMA and INAR models Paman, Joshua Mari J. Santiago, Frank Niccolo M. Mojica, Vio Jianu C. Co, Frumencio F. Leong, Robert Neil F. It is the goal of many developing countries to stop the spread of diseases. Part of this effort is to conduct ongoing surveillance of disease transmission to foresee future epidemics. However, in the Philippines, there is a lack of an automated method in determining their presence. This paper presents a comparison between an integer-valued autoregressive (INAR) model and the more commonly known autoregressive integrated moving average(ARIMA) models in detecting the presence of disease outbreaks. Daily measles reports spanning from January 1, 2010 to January 14, 2015 were obtained from the Department of Health and were used to motivate this study. Synthetic datasets were generated using a modified Serfling model. Similarity tests using a dynamic time warping algorithm were conducted to ensure that simulated datasets observe similar behavior with the original set. False positive rates, sensitivity rates, and delay in detection were then evaluated between the two models. The results gathered show that an INAR model performs favorably compared to an ARIMA model, posting higher sensitivity rates, similar lag times, and equivalent false positive rates for three-day signal events. © 2018, Philippine Statistical Association, Inc.. All rights reserved. 2017-01-01T08:00:00Z text https://animorepository.dlsu.edu.ph/faculty_research/2320 Faculty Research Work Animo Repository Measles--Philippines Box-Jenkins forecasting Statistics and Probability Virus Diseases
institution De La Salle University
building De La Salle University Library
continent Asia
country Philippines
Philippines
content_provider De La Salle University Library
collection DLSU Institutional Repository
topic Measles--Philippines
Box-Jenkins forecasting
Statistics and Probability
Virus Diseases
spellingShingle Measles--Philippines
Box-Jenkins forecasting
Statistics and Probability
Virus Diseases
Paman, Joshua Mari J.
Santiago, Frank Niccolo M.
Mojica, Vio Jianu C.
Co, Frumencio F.
Leong, Robert Neil F.
Measles outbreak detection in Metro Manila: Comparisons between ARIMA and INAR models
description It is the goal of many developing countries to stop the spread of diseases. Part of this effort is to conduct ongoing surveillance of disease transmission to foresee future epidemics. However, in the Philippines, there is a lack of an automated method in determining their presence. This paper presents a comparison between an integer-valued autoregressive (INAR) model and the more commonly known autoregressive integrated moving average(ARIMA) models in detecting the presence of disease outbreaks. Daily measles reports spanning from January 1, 2010 to January 14, 2015 were obtained from the Department of Health and were used to motivate this study. Synthetic datasets were generated using a modified Serfling model. Similarity tests using a dynamic time warping algorithm were conducted to ensure that simulated datasets observe similar behavior with the original set. False positive rates, sensitivity rates, and delay in detection were then evaluated between the two models. The results gathered show that an INAR model performs favorably compared to an ARIMA model, posting higher sensitivity rates, similar lag times, and equivalent false positive rates for three-day signal events. © 2018, Philippine Statistical Association, Inc.. All rights reserved.
format text
author Paman, Joshua Mari J.
Santiago, Frank Niccolo M.
Mojica, Vio Jianu C.
Co, Frumencio F.
Leong, Robert Neil F.
author_facet Paman, Joshua Mari J.
Santiago, Frank Niccolo M.
Mojica, Vio Jianu C.
Co, Frumencio F.
Leong, Robert Neil F.
author_sort Paman, Joshua Mari J.
title Measles outbreak detection in Metro Manila: Comparisons between ARIMA and INAR models
title_short Measles outbreak detection in Metro Manila: Comparisons between ARIMA and INAR models
title_full Measles outbreak detection in Metro Manila: Comparisons between ARIMA and INAR models
title_fullStr Measles outbreak detection in Metro Manila: Comparisons between ARIMA and INAR models
title_full_unstemmed Measles outbreak detection in Metro Manila: Comparisons between ARIMA and INAR models
title_sort measles outbreak detection in metro manila: comparisons between arima and inar models
publisher Animo Repository
publishDate 2017
url https://animorepository.dlsu.edu.ph/faculty_research/2320
_version_ 1709757494724657152