Department of Systems and Computing Engineering, Universidad de Los Andes, Bogotá, Colombia.
School of Management, Universidad de los Andes, Bogotá, Colombia.
Popul Health Metr. 2023 Oct 21;21(1):16. doi: 10.1186/s12963-023-00316-8.
The COVID-19 pandemic has disrupted the healthcare system, leading to delays in detection of other non-COVID-19 diseases. This paper presents ANE Framework (Analytics for Non-COVID-19 Events), a reliable and user-friendly analytical forecasting framework designed to predict the number of patients with non-COVID-19 diseases. Prior to 2020, there were analytical models focused on specific illnesses and contexts. Then, most models have focused on understanding COVID-19 behavior. There is a lack of analytical frameworks that enable disease forecasting for non-COVID-19 diseases.
The ANE Framework utilizes time series analysis to generate forecasting models. The framework leverages daily data from official government sources and employs SARIMA models to forecast the number of non-COVID-19 cases, such as tuberculosis and suicide attempts.
The framework was tested on five different non-COVID-19 events. The framework performs well across all events, including tuberculosis and suicide attempts, with a Mean Absolute Percentage Error (MAPE) of up to 20% and the consistency remains independent of the behavior of each event. Moreover, a pairwise comparison of averages can lead to over or underestimation of the impact. The disruption caused by the pandemic resulted in a 17% gap (2383 cases) between expected and reported tuberculosis cases, and a 19% gap (2464 cases) for suicide attempts. These gaps varied between 20 and 64% across different cities and regions. The ANE Framework has proven to be reliable for analyzing several diseases and exhibits the flexibility to incorporate new data from various sources. Regular updates and the inclusion of new associated data enhance the framework's effectiveness.
Current pandemic shows the necessity of developing flexible models to be adapted to different illness data. The framework developed proved to be reliable for the different diseases analyzed, presenting enough flexibility to update with new data or even include new data from different databases. To keep updated on the result of the project allows the inclusion of new data associated with it. Similarly, the proposed strategy in the ANE framework allows for improving the quality of the obtained results with news events.
COVID-19 大流行扰乱了医疗体系，导致其他非 COVID-19 疾病的检测出现延误。本文提出了 ANE 框架（非 COVID-19 事件分析），这是一个可靠且用户友好的分析预测框架，旨在预测非 COVID-19 疾病患者的数量。在 2020 年之前，有一些分析模型专注于特定的疾病和背景。然后，大多数模型都专注于了解 COVID-19 的行为。缺乏能够预测非 COVID-19 疾病的分析框架。
ANE 框架利用时间序列分析生成预测模型。该框架利用来自官方政府来源的每日数据，并采用 SARIMA 模型预测非 COVID-19 病例数量，例如结核病和自杀企图。
该框架在五个不同的非 COVID-19 事件上进行了测试。该框架在所有事件上表现良好，包括结核病和自杀企图，平均绝对百分比误差（MAPE）高达 20%，并且一致性独立于每个事件的行为。此外，平均值的成对比较可能导致对影响的高估或低估。大流行造成的破坏导致结核病病例的预期和报告之间出现 17%的差距（2383 例），自杀企图的差距为 19%（2464 例）。这些差距在不同的城市和地区之间在 20%到 64%之间变化。ANE 框架已被证明可用于分析多种疾病，并具有灵活性，可以从各种来源纳入新数据。定期更新和纳入新的相关数据可提高框架的有效性。
当前的大流行表明需要开发灵活的模型来适应不同的疾病数据。所开发的框架已被证明可用于分析不同的疾病，具有足够的灵活性，可以使用新数据进行更新，甚至可以纳入来自不同数据库的数据。保持对项目结果的更新可以纳入与之相关的新数据。同样，ANE 框架中提出的策略允许通过新闻事件提高获得结果的质量。
