ArticlePopulation health metrics2023
Analytical reference framework to analyze non-COVID-19 events.
Article in Population health metrics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundThe 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.
methodsThe 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.
resultsThe 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.
conclusionsCurrent 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.
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