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<p>Malaria remains a significant public health concern in Ethiopia. Understanding the historical backgrounds and future trends of malaria incidence is crucial for customizing local specific intervention strategies. The spatiotemporal patterns and putative modeling of future trends of malaria in the Jimma Zone have not been thoroughly investigated. This study aimed to model the spatiotemporal patterns and prediction of future malaria trends in Jimma Zone, Oromia, Ethiopia, Ethiopia from 2022 to 2026. This study analyzed a decade of historical malaria data (2012–2021) from 15 districts of Jimma Zone. A District-level annual parasite incidence (API) and Standardized incidence ratio (SIR) were calculated, and malaria risk maps were developed. Moran’s I and Monte Carlo statistics were used for assessing spatial clustering of malaria incidence, while time series analysis with the Autoregressive Integrated Moving Average (ARIMA) model was used for analyzing the temporal patterns and predicted for the future trends. A total of 1,718,471 individuals were screened for malaria between 2012 and 2021, with 359,655 (20.93%) confirmed cases. <i>Plasmodium falciparum</i>,<i> P. vivax</i> and mixed infections accounted for 52.71%, 43.62% and 3.67%, respectively. Malaria incidence decreased dramatically by 96.13%, with the API falling from 87.97 per 1,000 population at risk in 2012 to 3.40 in 2021. It was revealed that there was random distribution of malaria transmission across districts. API and SIR maps identified potential risk areas and cold spots. The disease exhibited seasonal patterns, peaking from August to November (maximum in September) and from March to June. Despite a decline trend, malaria remains a potential threat in areas like Chora Botor and Shebe Sonbo. Forecasting with the ARIMA (0, 1, 3) x (1, 1, 0) model for 2022–2026 suggests stability in malaria incidence, with seasonal fluctuations but no significant trends. Malaria incidence in Jimma Zone was significantly decreased from 2012 to 2021, reaching notably low risk levels by 2021. However, the disease persists in areas such as Chora Botor and Shebe Sonbo, indicating ongoing risk of transmission. The seasonal pattern of malaria underscores the need for targeted interventions during peak periods. Spatial autocorrelation analysis showed no distinct patterns, suggesting the necessity for continued surveillance in historically high-risk areas. While forecasts suggest a stable malaria cases, maintaining vigilance and adapting interventions based on emerging trends is essential to sustain control progress towards elimination. Finally, future studies should incorporate key factors like climate variables (temperature, rainfall, humidity), topographic features, intervention coverage, and other environmental determinants, which were not included in our analysis.</p>

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Unraveling the spatiotemporal clustering of malaria incidence and modeling a decade of epidemiological data in Jimma Zone, Oromia, Ethiopia

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月17日

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  • Abdissa Biruksew,
  • Ashenafi Demeke,
  • Zewdie Birhanu,
  • Lemu Golassa,
  • Assefa Legesse Sisay,
  • Jihad Kemal,
  • Delenasaw Yewhalaw

摘要

Malaria remains a significant public health concern in Ethiopia. Understanding the historical backgrounds and future trends of malaria incidence is crucial for customizing local specific intervention strategies. The spatiotemporal patterns and putative modeling of future trends of malaria in the Jimma Zone have not been thoroughly investigated. This study aimed to model the spatiotemporal patterns and prediction of future malaria trends in Jimma Zone, Oromia, Ethiopia, Ethiopia from 2022 to 2026. This study analyzed a decade of historical malaria data (2012–2021) from 15 districts of Jimma Zone. A District-level annual parasite incidence (API) and Standardized incidence ratio (SIR) were calculated, and malaria risk maps were developed. Moran’s I and Monte Carlo statistics were used for assessing spatial clustering of malaria incidence, while time series analysis with the Autoregressive Integrated Moving Average (ARIMA) model was used for analyzing the temporal patterns and predicted for the future trends. A total of 1,718,471 individuals were screened for malaria between 2012 and 2021, with 359,655 (20.93%) confirmed cases. Plasmodium falciparum, P. vivax and mixed infections accounted for 52.71%, 43.62% and 3.67%, respectively. Malaria incidence decreased dramatically by 96.13%, with the API falling from 87.97 per 1,000 population at risk in 2012 to 3.40 in 2021. It was revealed that there was random distribution of malaria transmission across districts. API and SIR maps identified potential risk areas and cold spots. The disease exhibited seasonal patterns, peaking from August to November (maximum in September) and from March to June. Despite a decline trend, malaria remains a potential threat in areas like Chora Botor and Shebe Sonbo. Forecasting with the ARIMA (0, 1, 3) x (1, 1, 0) model for 2022–2026 suggests stability in malaria incidence, with seasonal fluctuations but no significant trends. Malaria incidence in Jimma Zone was significantly decreased from 2012 to 2021, reaching notably low risk levels by 2021. However, the disease persists in areas such as Chora Botor and Shebe Sonbo, indicating ongoing risk of transmission. The seasonal pattern of malaria underscores the need for targeted interventions during peak periods. Spatial autocorrelation analysis showed no distinct patterns, suggesting the necessity for continued surveillance in historically high-risk areas. While forecasts suggest a stable malaria cases, maintaining vigilance and adapting interventions based on emerging trends is essential to sustain control progress towards elimination. Finally, future studies should incorporate key factors like climate variables (temperature, rainfall, humidity), topographic features, intervention coverage, and other environmental determinants, which were not included in our analysis.