Fine-Scale Mapping by Spatial Risk Distribution Modeling for Regional Malaria Endemicity and Its Implications under the Low-to-Moderate Transmission Setting in Western Cambodia

Abstract

The disease burden of malaria has decreased as malaria elimination efforts progress. The mapping approach that uses spatial risk distribution modeling needs some adjustment and reinvestigation in accordance with situational changes. Here we applied a mathematical modeling approach for standardized morbidity ratio (SMR) calculated by annual parasite incidence using routinely aggregated surveillance reports, environmental data such as remote sensing data, and non-environmental anthropogenic data to create fine-scale spatial risk distribution maps of western Cambodia. Furthermore, we incorporated a combination of containment status indicators into the model to demonstrate spatial heterogeneities of the relationship between containment status and risks. The explanatory model was fitted to estimate the SMR of each area (adjusted Pearson correlation coefficient R2 = 0.774; Akaike information criterion AIC = 149.423). A Bayesian modeling framework was applied to estimate the uncertainty of the model and cross-scale predictions. Fine-scale maps were created by the spatial interpolation of estimated SMRs at each village. Compared with geocoded case data, corresponding predicted values showed conformity [Spearman’s rank correlation r = 0.662 in the inverse distance weighed interpolation and 0.645 in ordinal kriging (95% confidence intervals of 0.414–0.827 and 0.368–0.813, respectively), Welch’s t-test; Not significant]. The proposed approach successfully explained regional malaria risks and fine-scale risk maps were created under low-to-moderate malaria transmission settings where reinvestigations of existing risk modeling approaches were needed. Moreover, different representations of simulated outcomes of containment status indicators for respective areas provided useful insights for tailored interventional planning, considering regional malaria endemicity.

Spatiotemporal Modeling for Fine-Scale Maps of Regional Malaria Endemicity and Its Implications for Transitional Complexities in a Routine Surveillance Network in Western Cambodia

“…Whereas the place of malaria hotspots did not change dramatically during the study period, the magnitude of risk at these places differed at each interval of time. The visual representations of hotspots in the fine-scale map created here were well aligned with actual areas at high risk, already identified through other sources ( 48 , 55 ) as well as in our previous work which was validated by an examination of alignment between the estimated risk and the risk calculated by geocoded case data ( 36 ). These results indicate that the maps created by the present approach do not provide misguiding presentation.…”

“…The data collected to build the malaria risk model are described elsewhere ( 36 ). Briefly, malaria case data were collected from Cambodia Malaria Bulletin reports from 2010 to 2013 ( 38 , 39 ).…”

“…A digital elevation model at 30-m resolution was extracted from the ASTER GDEM database 2 ( 46 ) and used to calculate the topographic wetness index (TWI). Based on findings from our previous study ( 36 ), we extracted data from surrounding circular buffers within a 5-km radius diameter for the NDVI and the TWI, and within a 1-km radius diameter from villages for the NDWI, where the strongest correlation between each variable and the EBSMR was observed within the studied range of distance. Extracted data were aggregated and averaged by the number of villages at the district level to reflect the overall condition of target districts.…”

“…In low-to-moderate malaria transmission settings, measuring the annual parasite incidence (API) can be an alternative reliable method for reporting new malaria infections when supported by rigorous surveillance systems ( 35 ). We previously reported the application of a mathematical modeling approach for obtaining a standardized morbidity ratio (SMR), calculated using the API, through routinely aggregated surveillance reports and environmental covariates captured from space satellites, as well as through using non-environmental anthropogenic covariates, such as the status of bed net distribution and reported artemisinin resistance, to create fine-scale spatial risk distribution maps in western Cambodia ( 36 ). The model successfully explained regional malaria risks.…”

Tools to accelerate falciparum malaria elimination in Cambodia: a meeting report

“…In several areas of Cambodia, provincial malaria officers supported by WHO consultants are analysing granular spatial surveillance data and are mapping hotspots. In addition, several research groups are currently working on mapping transmission foci [28][29][30][31][32]. The major challenge remains to decide what effective actions can be implemented based on these surveillance data.…”