Abstract
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.
Highlights
Malaria risk stratification is essential to differentiate areas with distinct malaria intensity and seasonality patterns. e development of a simple prediction model to forecast malaria incidence by rainfall offers an opportunity for early detection of malaria epidemics
Validity of the stratified clusters was checked by examining the maximum absolute coordinate change and one-way analysis of variance (ANOVA) with a conservative post hoc test using Bonferroni as the multiple comparisons test to determine the difference of mean incidence rates between the constructed clusters
No malaria cases were reported in Areta and central Denkalia subzones during this period. e highest malaria incidences were mainly distributed in Gash Barka and Debub subzones, namely, May Ayni (5086/100, 000 population), May Mine (6271), Goluj (6417), Molqui (6744/100, 000), Barentu (7138/100, 000), Teseney (7861/100, 000), Ghindae (11302/100, 000), and Mogolo (14324/100, 000)
Summary
Malaria risk stratification is essential to differentiate areas with distinct malaria intensity and seasonality patterns. e development of a simple prediction model to forecast malaria incidence by rainfall offers an opportunity for early detection of malaria epidemics. To construct a national malaria stratification map, develop prediction models and forecast monthly malaria incidences based on rainfall data. Using monthly malaria incidence data from 2012 to 2016, the district level malaria stratification was constructed by nonhierarchical clustering. 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. Incidence of malaria in the constructed clusters was associated with 1, 2, 3, and 4 lagged months of rainfall. E 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. In the western lowlands (with a range of elevation between 700 and 1500 meters above sea level), malaria transmission is highly seasonal and the area is prone
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