Analytical reference framework to analyze non-COVID-19 events

Abstract

Background: 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.

Methods: 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.

Results: 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.

Conclusions: 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.

Keywords: Forecasting models; No COVID-19 events; SARIMA; Suicide attempt; Tuberculosis.

Fig. 1

Framework overview

Fig. 2

File structure of event data about the national, department, and capital behavior for tuberculosis

Fig. 3

Reported new cases of tuberculosis per 100,000 habitants

Fig. 4

Nationwide cumulative impact on tuberculosis cases per period

Fig. 5

Nationwide indirect impact on tuberculosis cases per period

Fig. 6

Annual impact on tuberculosis for each capital and department, and its participation in the total number of forecasted cases

Fig. 7

Reported new cases of suicide attempts per 100,000 habitants

Fig. 8

Nationwide cumulative impact on suicide attempts per epidemiological period during 2020

Fig. 9

Nationwide indirect impact on suicide attempts per epidemiological period during 2020

Fig. 10

Annual impact on suicide attempts for each capital and department and its participation in the total number of forecasted cases

Fig. 11

Interactive visualization tool developed—Screen shot of Descriptive analysis page—Tuberculosis case

Fig. 12

Interactive visualization tool developed—Screen shot of Predictive analysis page—Tuberculosis case