Cargando…

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

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....

Descripción completa

Detalles Bibliográficos
Autores principales: Ferrão, João L., Earland, Dominique, Novela, Anísio, Mendes, Roberto, Tungadza, Alberto, Searle, Kelly M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8198511/
https://www.ncbi.nlm.nih.gov/pubmed/34073319
http://dx.doi.org/10.3390/ijerph18115692
_version_ 1783707156397162496
author Ferrão, João L.
Earland, Dominique
Novela, Anísio
Mendes, Roberto
Tungadza, Alberto
Searle, Kelly M.
author_facet Ferrão, João L.
Earland, Dominique
Novela, Anísio
Mendes, Roberto
Tungadza, Alberto
Searle, Kelly M.
author_sort Ferrão, João L.
collection PubMed
description 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.
format Online
Article
Text
id pubmed-8198511
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-81985112021-06-14 Malaria Temporal Variation and Modelling Using Time-Series in Sussundenga District, Mozambique Ferrão, João L. Earland, Dominique Novela, Anísio Mendes, Roberto Tungadza, Alberto Searle, Kelly M. Int J Environ Res Public Health Article 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. MDPI 2021-05-26 /pmc/articles/PMC8198511/ /pubmed/34073319 http://dx.doi.org/10.3390/ijerph18115692 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Ferrão, João L.
Earland, Dominique
Novela, Anísio
Mendes, Roberto
Tungadza, Alberto
Searle, Kelly M.
Malaria Temporal Variation and Modelling Using Time-Series in Sussundenga District, Mozambique
title Malaria Temporal Variation and Modelling Using Time-Series in Sussundenga District, Mozambique
title_full Malaria Temporal Variation and Modelling Using Time-Series in Sussundenga District, Mozambique
title_fullStr Malaria Temporal Variation and Modelling Using Time-Series in Sussundenga District, Mozambique
title_full_unstemmed Malaria Temporal Variation and Modelling Using Time-Series in Sussundenga District, Mozambique
title_short Malaria Temporal Variation and Modelling Using Time-Series in Sussundenga District, Mozambique
title_sort malaria temporal variation and modelling using time-series in sussundenga district, mozambique
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8198511/
https://www.ncbi.nlm.nih.gov/pubmed/34073319
http://dx.doi.org/10.3390/ijerph18115692
work_keys_str_mv AT ferraojoaol malariatemporalvariationandmodellingusingtimeseriesinsussundengadistrictmozambique
AT earlanddominique malariatemporalvariationandmodellingusingtimeseriesinsussundengadistrictmozambique
AT novelaanisio malariatemporalvariationandmodellingusingtimeseriesinsussundengadistrictmozambique
AT mendesroberto malariatemporalvariationandmodellingusingtimeseriesinsussundengadistrictmozambique
AT tungadzaalberto malariatemporalvariationandmodellingusingtimeseriesinsussundengadistrictmozambique
AT searlekellym malariatemporalvariationandmodellingusingtimeseriesinsussundengadistrictmozambique