@article{10.11648/j.eeb.20251004.12,
  author = {Okello George Robert and Robert Wamala and Hellen Namawejje and Martin Kayitale Mbonye and John Rek and Sendege Susan Hebert},
  title = {Temporal Patterns of Climate Variability and Malaria Incidences Among Children (0-5) Years in Uganda: A Time Series Analysis},
  journal = {Ecology and Evolutionary Biology},
  volume = {10},
  number = {4},
  pages = {139-155},
  doi = {10.11648/j.eeb.20251004.12},
  url = {https://doi.org/10.11648/j.eeb.20251004.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.eeb.20251004.12},
  abstract = {Background: Malaria remains a major public health challenge in Uganda, particularly among children under five years of age. Between 2019 and 2023, the prevalence increased with age, from 3% in infants under six months to 12% in children aged 48–59 months, and was markedly higher in rural areas (11%) than in urban areas (3%). However, analysis of the data on malaria has been focused on a single variable, while the impact of climate variation on malaria is over several factors and over time. This study assesses the temporal patterns of climate variability and malaria incidence among children aged 0–5 years in Uganda using a time series analysis. Methods: The study analysed 150 monthly time series records from 2015 to 2022. It used the Vector Error Correction Model (VECM), which allows examination of both short-term changes and long-term relationships among variables. The variables included confirmed malaria cases in children under five years, rainfall, minimum and maximum temperatures, and vegetation cover. Data were obtained from the Ministry of Health/DHIS2, NASA Earth Data, CHIRPS, and NASA EOSDIS. Results: The results revealed significant long-term relationships and short-term feedback mechanisms between malaria incidence and climatic factors. The error correction term (ECT) for malaria was -0.006, indicating a slow adjustment to equilibrium. In contrast, rainfall, minimum temperature, and the Normalized Difference Vegetation Index (NDVI) showed correction behaviours, adjusting upward following deviations. Short-term changing aspects revealed that previous values of malaria cases among children under five years (coefficient = 0.091) and rainfall (coefficient = 0.061) positively influenced current malaria trends. The minimum temperature displayed strong autocorrelation (coefficient = 0.810), whereas the NDVI showed a large short-term response (coefficient = 140.100), highlighting its sensitivity to environmental shifts. Maximum temperature had a negative short-term association with malaria incidences (coefficient = -0.259), suggesting inverse seasonal effects. Conclusions: The study reveals significant short-term and long-term interactions among malaria cases among children under five years, rainfall, temperature, and NDVI. The presence of statistically significant error correction terms indicates that the system adjusts to restore equilibrium following deviations, with malaria cases among children under five years exhibiting consistent correction. Lagged coefficients show that past changes, particularly in minimum temperature and NDVI, exert a strong influence on current conditions.},
 year = {2025}
}

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TY  - JOUR
T1  - Temporal Patterns of Climate Variability and Malaria Incidences Among Children (0-5) Years in Uganda: A Time Series Analysis
AU  - Okello George Robert
AU  - Robert Wamala
AU  - Hellen Namawejje
AU  - Martin Kayitale Mbonye
AU  - John Rek
AU  - Sendege Susan Hebert
Y1  - 2025/12/09
PY  - 2025
N1  - https://doi.org/10.11648/j.eeb.20251004.12
DO  - 10.11648/j.eeb.20251004.12
T2  - Ecology and Evolutionary Biology
JF  - Ecology and Evolutionary Biology
JO  - Ecology and Evolutionary Biology
SP  - 139
EP  - 155
PB  - Science Publishing Group
SN  - 2575-3762
UR  - https://doi.org/10.11648/j.eeb.20251004.12
AB  - Background: Malaria remains a major public health challenge in Uganda, particularly among children under five years of age. Between 2019 and 2023, the prevalence increased with age, from 3% in infants under six months to 12% in children aged 48–59 months, and was markedly higher in rural areas (11%) than in urban areas (3%). However, analysis of the data on malaria has been focused on a single variable, while the impact of climate variation on malaria is over several factors and over time. This study assesses the temporal patterns of climate variability and malaria incidence among children aged 0–5 years in Uganda using a time series analysis. Methods: The study analysed 150 monthly time series records from 2015 to 2022. It used the Vector Error Correction Model (VECM), which allows examination of both short-term changes and long-term relationships among variables. The variables included confirmed malaria cases in children under five years, rainfall, minimum and maximum temperatures, and vegetation cover. Data were obtained from the Ministry of Health/DHIS2, NASA Earth Data, CHIRPS, and NASA EOSDIS. Results: The results revealed significant long-term relationships and short-term feedback mechanisms between malaria incidence and climatic factors. The error correction term (ECT) for malaria was -0.006, indicating a slow adjustment to equilibrium. In contrast, rainfall, minimum temperature, and the Normalized Difference Vegetation Index (NDVI) showed correction behaviours, adjusting upward following deviations. Short-term changing aspects revealed that previous values of malaria cases among children under five years (coefficient = 0.091) and rainfall (coefficient = 0.061) positively influenced current malaria trends. The minimum temperature displayed strong autocorrelation (coefficient = 0.810), whereas the NDVI showed a large short-term response (coefficient = 140.100), highlighting its sensitivity to environmental shifts. Maximum temperature had a negative short-term association with malaria incidences (coefficient = -0.259), suggesting inverse seasonal effects. Conclusions: The study reveals significant short-term and long-term interactions among malaria cases among children under five years, rainfall, temperature, and NDVI. The presence of statistically significant error correction terms indicates that the system adjusts to restore equilibrium following deviations, with malaria cases among children under five years exhibiting consistent correction. Lagged coefficients show that past changes, particularly in minimum temperature and NDVI, exert a strong influence on current conditions.
VL  - 10
IS  - 4
ER  - 

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