ArticleMalaria journal2025
Comparison of ARIMA and FB-Prophet time series models for the prediction of national and regional malaria incidence in Uganda.
Article in Malaria journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Hotspot Mapping, Spatiotemporal Clustering and Forecasting of Malaria Incidence in Uganda, 2014-2023: Strategies to Inform Targeted Public Health Interventions.Journal of epidemiology and global health · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
backgroundWithin sub-Saharan Africa, Uganda carries the third largest burden of malaria with 5% of global cases. Due to the stochastic nature of malaria incidence, resource allocation of preventive measures, rapid diagnostic tests, and chemotherapeutics is a significant challenge. To better identify areas at risk and address the challenge of resource allocation, this study aimed to: (1) characterize national and regional malaria incidence in Uganda, and (2) compare the performance of time series models in predicting malaria incidence at national and regional levels.
methodsAggregated data from District Health Information Software 2 (DHIS2), was used to assess national and regional malaria incidence in Uganda from 2020 through 2023. Auto-regressive moving average (ARIMA) models of national and regional malaria incidence were then created. The same data was applied to FB-Prophet, an open source generalized additive time series model. Training and validation datasets were created for each model, which ran for 41 and 6 months, respectively. Model performance was then evaluated via key performance indicators including mean average error (MAE), root mean square error (RMSE), and mean average percentage error (MAPE).
resultsThe incidence of malaria within Uganda increased from 200.5 cases per 1000 persons annually in 2021 to 265.4 cases per 1000 persons annually in 2022. The northern regions of West Nile and Acholi, along with Busoga region in the east, experienced the highest burden and incidence of malaria. The mean regional MAE, MAPE, and RMSE was 0.007, 31.2, and 0.01, respectively for ARIMA, and 0.01, 47.8, and 0.01, respectively for FB-Prophet. The ARIMA model outperformed the FB-Prophet model at the national level and in 14 of 15 regions.
conclusionsTime series models accurately predicted malaria incidence on a national and regional scale in Uganda. Both the ARIMA and FB-Prophet models have the potential to guide malaria resource allocation and response efforts among other malaria control interventions deployed in Uganda and possibly in other malaria endemic settings.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.