Cargando…

Malaria Risk Stratification and Modeling the Effect of Rainfall on Malaria Incidence in Eritrea

BACKGROUND: Malaria risk stratification is essential to differentiate areas with distinct malaria intensity and seasonality patterns. The development of a simple prediction model to forecast malaria incidence by rainfall offers an opportunity for early detection of malaria epidemics. OBJECTIVES: To...

Descripción completa

Detalles Bibliográficos
Autores principales: Kifle, Meron Mehari, Teklemariam, Tsega Tekeste, Teweldeberhan, Adam Mengesteab, Tesfamariam, Eyasu Habte, Andegiorgish, Amanuel Kidane, Azaria Kidane, Eyob
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6466923/
https://www.ncbi.nlm.nih.gov/pubmed/31061663
http://dx.doi.org/10.1155/2019/7314129
_version_ 1783411202277244928
author Kifle, Meron Mehari
Teklemariam, Tsega Tekeste
Teweldeberhan, Adam Mengesteab
Tesfamariam, Eyasu Habte
Andegiorgish, Amanuel Kidane
Azaria Kidane, Eyob
author_facet Kifle, Meron Mehari
Teklemariam, Tsega Tekeste
Teweldeberhan, Adam Mengesteab
Tesfamariam, Eyasu Habte
Andegiorgish, Amanuel Kidane
Azaria Kidane, Eyob
author_sort Kifle, Meron Mehari
collection PubMed
description BACKGROUND: Malaria risk stratification is essential to differentiate areas with distinct malaria intensity and seasonality patterns. The development of a simple prediction model to forecast malaria incidence by rainfall offers an opportunity for early detection of malaria epidemics. OBJECTIVES: To construct a national malaria stratification map, develop prediction models and forecast monthly malaria incidences based on rainfall data. METHODS: Using monthly malaria incidence data from 2012 to 2016, the district level malaria stratification was constructed by nonhierarchical clustering. Cluster validity was examined by the maximum absolute coordinate change and analysis of variance (ANOVA) with a conservative post hoc test (Bonferroni) as the multiple comparison test. Autocorrelation and cross-correlation analyses were performed to detect the autocorrelation of malaria incidence and the lagged effect of rainfall on malaria incidence. The effect of rainfall on malaria incidence was assessed using seasonal autoregressive integrated moving average (SARIMA) models. Ljung–Box statistics for model diagnosis and stationary R-squared and Normalized Bayesian Information Criteria for model fit were used. Model validity was assessed by analyzing the observed and predicted incidences using the spearman correlation coefficient and paired samples t-test. RESULTS: A four cluster map (high risk, moderate risk, low risk, and very low risk) was the most valid stratification system for the reported malaria incidence in Eritrea. Monthly incidences were influenced by incidence rates in the previous months. Monthly incidence of malaria in the constructed clusters was associated with 1, 2, 3, and 4 lagged months of rainfall. The constructed models had acceptable accuracy as 73.1%, 46.3%, 53.4%, and 50.7% of the variance in malaria transmission were explained by rainfall in the high-risk, moderate-risk, low-risk, and very low-risk clusters, respectively. CONCLUSION: Change in rainfall patterns affect malaria incidence in Eritrea. Using routine malaria case reports and rainfall data, malaria incidences can be forecasted with acceptable accuracy. Further research should consider a village or health facility level modeling of malaria incidence by including other climatic factors like temperature and relative humidity.
format Online
Article
Text
id pubmed-6466923
institution National Center for Biotechnology Information
language English
publishDate 2019
publisher Hindawi
record_format MEDLINE/PubMed
spelling pubmed-64669232019-05-06 Malaria Risk Stratification and Modeling the Effect of Rainfall on Malaria Incidence in Eritrea Kifle, Meron Mehari Teklemariam, Tsega Tekeste Teweldeberhan, Adam Mengesteab Tesfamariam, Eyasu Habte Andegiorgish, Amanuel Kidane Azaria Kidane, Eyob J Environ Public Health Research Article BACKGROUND: Malaria risk stratification is essential to differentiate areas with distinct malaria intensity and seasonality patterns. The development of a simple prediction model to forecast malaria incidence by rainfall offers an opportunity for early detection of malaria epidemics. OBJECTIVES: To construct a national malaria stratification map, develop prediction models and forecast monthly malaria incidences based on rainfall data. METHODS: Using monthly malaria incidence data from 2012 to 2016, the district level malaria stratification was constructed by nonhierarchical clustering. Cluster validity was examined by the maximum absolute coordinate change and analysis of variance (ANOVA) with a conservative post hoc test (Bonferroni) as the multiple comparison test. Autocorrelation and cross-correlation analyses were performed to detect the autocorrelation of malaria incidence and the lagged effect of rainfall on malaria incidence. The effect of rainfall on malaria incidence was assessed using seasonal autoregressive integrated moving average (SARIMA) models. Ljung–Box statistics for model diagnosis and stationary R-squared and Normalized Bayesian Information Criteria for model fit were used. Model validity was assessed by analyzing the observed and predicted incidences using the spearman correlation coefficient and paired samples t-test. RESULTS: A four cluster map (high risk, moderate risk, low risk, and very low risk) was the most valid stratification system for the reported malaria incidence in Eritrea. Monthly incidences were influenced by incidence rates in the previous months. Monthly incidence of malaria in the constructed clusters was associated with 1, 2, 3, and 4 lagged months of rainfall. The constructed models had acceptable accuracy as 73.1%, 46.3%, 53.4%, and 50.7% of the variance in malaria transmission were explained by rainfall in the high-risk, moderate-risk, low-risk, and very low-risk clusters, respectively. CONCLUSION: Change in rainfall patterns affect malaria incidence in Eritrea. Using routine malaria case reports and rainfall data, malaria incidences can be forecasted with acceptable accuracy. Further research should consider a village or health facility level modeling of malaria incidence by including other climatic factors like temperature and relative humidity. Hindawi 2019-04-01 /pmc/articles/PMC6466923/ /pubmed/31061663 http://dx.doi.org/10.1155/2019/7314129 Text en Copyright © 2019 Meron Mehari Kifle et al. http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Kifle, Meron Mehari
Teklemariam, Tsega Tekeste
Teweldeberhan, Adam Mengesteab
Tesfamariam, Eyasu Habte
Andegiorgish, Amanuel Kidane
Azaria Kidane, Eyob
Malaria Risk Stratification and Modeling the Effect of Rainfall on Malaria Incidence in Eritrea
title Malaria Risk Stratification and Modeling the Effect of Rainfall on Malaria Incidence in Eritrea
title_full Malaria Risk Stratification and Modeling the Effect of Rainfall on Malaria Incidence in Eritrea
title_fullStr Malaria Risk Stratification and Modeling the Effect of Rainfall on Malaria Incidence in Eritrea
title_full_unstemmed Malaria Risk Stratification and Modeling the Effect of Rainfall on Malaria Incidence in Eritrea
title_short Malaria Risk Stratification and Modeling the Effect of Rainfall on Malaria Incidence in Eritrea
title_sort malaria risk stratification and modeling the effect of rainfall on malaria incidence in eritrea
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6466923/
https://www.ncbi.nlm.nih.gov/pubmed/31061663
http://dx.doi.org/10.1155/2019/7314129
work_keys_str_mv AT kiflemeronmehari malariariskstratificationandmodelingtheeffectofrainfallonmalariaincidenceineritrea
AT teklemariamtsegatekeste malariariskstratificationandmodelingtheeffectofrainfallonmalariaincidenceineritrea
AT teweldeberhanadammengesteab malariariskstratificationandmodelingtheeffectofrainfallonmalariaincidenceineritrea
AT tesfamariameyasuhabte malariariskstratificationandmodelingtheeffectofrainfallonmalariaincidenceineritrea
AT andegiorgishamanuelkidane malariariskstratificationandmodelingtheeffectofrainfallonmalariaincidenceineritrea
AT azariakidaneeyob malariariskstratificationandmodelingtheeffectofrainfallonmalariaincidenceineritrea