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Malaria Temporal Variation and Modelling Using Time-Series in Sussundenga District, Mozambique

Author

Listed:
  • João L. Ferrão

    (Instiuto Superior de Ciências de Educação, Beira 2102, Mozambique)

  • Dominique Earland

    (School of Public Health, University of Minnesota, Minneapolis, MN 55455, USA)

  • Anísio Novela

    (Direcção Distrital de Saúde de Sussundenga, Sussundenga 2207, Mozambique)

  • Roberto Mendes

    (Centro de Informação Geográfica-Faculdade de Economia da UCM, Beira 2102, Mozambique)

  • Alberto Tungadza

    (Faculdade de Ciência de Saúde da UCM, Beira 2102, Mozambique)

  • Kelly M. Searle

    (School of Public Health, University of Minnesota, Minneapolis, MN 55455, USA)

Abstract

Malaria is one of the leading causes of morbidity and mortality in Mozambique, which has the fifth highest prevalence in the world. Sussundenga District in Manica Province has documented high P. falciparum incidence at the local rural health center (RHC). This study’s objective was to analyze the P. falciparum temporal variation and model its pattern in Sussundenga District, Mozambique. Data from weekly epidemiological bulletins (BES) was collected from 2015 to 2019 and a time-series analysis was applied. For temporal modeling, a Box-Jenkins method was used with an autoregressive integrated moving average (ARIMA). Over the study period, 372,498 cases of P. falciparum were recorded in Sussundenga. There were weekly and yearly variations in incidence overall ( p < 0.001). Children under five years had decreased malaria tendency, while patients over five years had an increased tendency. The ARIMA (2,2,1) (1,1,1) 52 model presented the least Root Mean Square being the most appropriate for forecasting. The goodness of fit was 68.15% for malaria patients less than five years old and 73.2% for malaria patients over five years old. The findings indicate that cases are decreasing among individuals less than five years and are increasing slightly in those older than five years. The P. falciparum case occurrence has a weekly temporal pattern peaking during the wet season. Based on the spatial and temporal distribution using ARIMA modelling, more efficient strategies that target this seasonality can be implemented to reduce the overall malaria burden in both Sussundenga District and regionally.

Suggested Citation

  • João L. Ferrão & Dominique Earland & Anísio Novela & Roberto Mendes & Alberto Tungadza & Kelly M. Searle, 2021. "Malaria Temporal Variation and Modelling Using Time-Series in Sussundenga District, Mozambique," IJERPH, MDPI, vol. 18(11), pages 1-16, May.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:11:p:5692-:d:562452
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    References listed on IDEAS

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    1. Abiodun M. Adeola & Joel O. Botai & Hannes Rautenbach & Omolola M. Adisa & Katlego P. Ncongwane & Christina M. Botai & Temitope C. Adebayo-Ojo, 2017. "Climatic Variables and Malaria Morbidity in Mutale Local Municipality, South Africa: A 19-Year Data Analysis," IJERPH, MDPI, vol. 14(11), pages 1-15, November.
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