Malaria temporal variation and modelling using time-series in Sussundenga district, Mozambique

João L. Ferrão, Dominique Earland, Anísio Novela, Roberto Mendes, Alberto Tungadza, Kelly M. Searle

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Article number5692
JournalInternational journal of environmental research and public health
Volume18
Issue number11
DOIs
StatePublished - Jun 1 2021

Bibliographical note

Funding Information:
Funding: This study was funded through a SEED grant from the University of Minnesota Center for Global Health and Social Responsibility (CGHSR).

Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.

Keywords

  • Malaria
  • Modelling
  • Sussundenga
  • Temporal

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