Malaria amidst the COVID-19 pandemic in Gabon: an application of autoregressive integrated moving average (ARIMA) models within an interrupted time series (ITS) framework to hospital-based data.

ABSTRACT:

Background

Coinciding with the SARS-CoV-2 pandemic, malaria cases and malaria-related deaths increased globally between 2020 and 2022. However, evidence linking the pandemic to increased malaria burden remains ambiguous. We assessed the extent to which an observed malaria resurgence in Lambaréné, Gabon, can be associated with pandemic-related disruptions in malaria control programmes.

Methods

Using observational data from two tertiary referral hospitals, spanning 2018 to early 2023, we applied autoregressive integrated moving average (ARIMA) models in an interrupted time series (ITS) framework to test for changes in trends and levels following the onset of the pandemic. The primary outcome is the monthly malaria diagnosis rate (per 1000 all-cause hospital diagnoses). As a su

SUBMITTER: Roeder F 

PROVIDER: S-EPMC12417088 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature