Statistical Models for Outbreak Detection of Measles in North Cotabato, Philippines
菲律宾北哥打巴托省麻疹暴发检测的统计模型AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要明确使用 INAR、ARIMA 和周度病例预测开展暴发预警，对应时间序列与预测方法，应用领域为公共卫生。
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菲律宾北哥打巴托省麻疹暴发检测的统计模型
- 研究问题
- 如何利用菲律宾北哥打巴托省每周麻疹病例计数建立监测模型，兼顾模型拟合、暴发检测准确性和预警及时性？
- 方法与思路
- 作者用 2016—2021 年周度报告病例比较若干整数值自回归模型与 ARIMA，并从拟合、检测准确性和及时性评估表现。
- 材料陈述的贡献
- 摘要强调整数值模型与病例计数的适配性，并在所比较模型中推荐 ZINGINAR(1)，称其拟合与暴发检测表现较好。
- 阅读关注点
- 应核对暴发标签和阈值的定义、训练与评估划分、零膨胀处理，以及准确性和及时性指标是否反映实际监测需求。
- 阅读边界
- 这是 arXiv 预印本，不能据此认定已同行评审或期刊接收；摘要未报告指标数值或外部验证，推荐限于所述数据比较。
原文摘要
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.