Statistical Models for Outbreak Detection of Measles in North Cotabato, Philippines

TL;DR

This study evaluates statistical models, particularly INAR models, for early detection of measles outbreaks in North Cotabato, Philippines, demonstrating that INAR models outperform classical ARIMA in accuracy and timeliness for count data.

Contribution

The paper introduces the application of INAR models, especially ZINGINAR (1), for outbreak detection, highlighting their advantages over traditional ARIMA models for count data.

Findings

INAR models provide more realistic forecasts for count data.

ZINGINAR (1) model offers the best fit and detection accuracy.

INAR models enable timely outbreak detection.

Abstract

A measles outbreak occurs when the number of cases of measles in the population exceeds the typical level. Outbreaks that are not detected and managed early can increase mortality and morbidity and incur costs from activities responding to these events. The number of measles cases in the Province of North Cotabato, Philippines, was used in this study. Weekly reported cases of measles from January 2016 to December 2021 were provided by the Epidemiology and Surveillance Unit of the North Cotabato Provincial Health Office. Several integer-valued autoregressive (INAR) time series models were used to explore the possibility of detecting and identifying measles outbreaks in the province along with the classical ARIMA model. These models were evaluated based on goodness of fit, measles outbreak detection accuracy, and timeliness. The results of this study confirmed that INAR models have the conceptual advantage over ARIMA since the latter produces non-integer forecasts, which are not realistic for count data such as measles cases. Among the INAR models, the ZINGINAR (1) model was recommended for having a good model fit and timely and accurate detection of outbreaks. Furthermore, policymakers and decision-makers from relevant government agencies can use the ZINGINAR (1) model to improve disease surveillance and implement preventive measures against contagious diseases beforehand.