IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v18y2021i11p5692-d562452.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/18/11/5692/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/18/11/5692/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Abiodun Adeola & Katlego Ncongwane & Gbenga Abiodun & Thabo Makgoale & Hannes Rautenbach & Joel Botai & Omolola Adisa & Christina Botai, 2019. "Rainfall Trends and Malaria Occurrences in Limpopo Province, South Africa," IJERPH, MDPI, vol. 16(24), pages 1-15, December.
    2. Jiangtao Liu & Yueling Ma & Yuhong Wang & Sheng Li & Shuyu Liu & Xiaotao He & Lanyu Li & Lei Guo & Jingping Niu & Bin Luo & Kai Zhang, 2019. "The Impact of Cold and Heat on Years of Life Lost in a Northwestern Chinese City with Temperate Continental Climate," IJERPH, MDPI, vol. 16(19), pages 1-13, September.
    3. Matthew F. Chersich & Caradee Y. Wright & Francois Venter & Helen Rees & Fiona Scorgie & Barend Erasmus, 2018. "Impacts of Climate Change on Health and Wellbeing in South Africa," IJERPH, MDPI, vol. 15(9), pages 1-14, August.
    4. Makwelantle Asnath Sehlabana & Daniel Maposa & Alexander Boateng, 2020. "Modelling Malaria Incidence in the Limpopo Province, South Africa: Comparison of Classical and Bayesian Methods of Estimation," IJERPH, MDPI, vol. 17(14), pages 1-15, July.
    5. Samuel Kwasi Opoku & Walter Leal Filho & Fudjumdjum Hubert & Oluwabunmi Adejumo, 2021. "Climate Change and Health Preparedness in Africa: Analysing Trends in Six African Countries," IJERPH, MDPI, vol. 18(9), pages 1-29, April.
    6. Muhammad Farooq Umer & Shumaila Zofeen & Abdul Majeed & Wenbiao Hu & Xin Qi & Guihua Zhuang, 2019. "Effects of Socio-Environmental Factors on Malaria Infection in Pakistan: A Bayesian Spatial Analysis," IJERPH, MDPI, vol. 16(8), pages 1-12, April.
    7. Eric Kalunda Panzi & Ngianga II Kandala & Emery Luzolo Kafinga & Bertin Mbenga Tampwo & Ngianga-Bakwin Kandala, 2022. "Forecasting Malaria Morbidity to 2036 Based on Geo-Climatic Factors in the Democratic Republic of Congo," IJERPH, MDPI, vol. 19(19), pages 1-28, September.
    8. Eric Kalunda Panzi & Léon Ngongo Okenge & Eugénie Hamuli Kabali & Félicien Tshimungu & Angèle Keti Dilu & Felix Mulangu & Ngianga-Bakwin Kandala, 2022. "Geo-Climatic Factors of Malaria Morbidity in the Democratic Republic of Congo from 2001 to 2019," IJERPH, MDPI, vol. 19(7), pages 1-16, March.
    9. Nicholas Ngepah & Regina Conselho Mwiinga, 2022. "The Impact of Climate Change on Gender Inequality in the Labour Market: A Case Study of South Africa," Sustainability, MDPI, vol. 14(20), pages 1-28, October.
    10. Gbenga J. Abiodun & Olusola S. Makinde & Abiodun M. Adeola & Kevin Y. Njabo & Peter J. Witbooi & Ramses Djidjou-Demasse & Joel O. Botai, 2019. "A Dynamical and Zero-Inflated Negative Binomial Regression Modelling of Malaria Incidence in Limpopo Province, South Africa," IJERPH, MDPI, vol. 16(11), pages 1-19, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:18:y:2021:i:11:p:5692-:d:562452. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.