No abstract

Progress in malaria control has stalled over the recent years. Knowledge on main drivers of transmission explaining small-scale variation in prevalence can inform targeted control measures. We collected finger-prick blood samples from 3061 individuals irrespective of clinical symptoms in 20 clusters in Busia in western Kenya and screened for Plasmodium falciparum parasites using qPCR and microscopy. Clusters spanned an altitude range of 207 meters (1077–1284 m). We mapped potential mosquito larval habitats and determined their number within 250 m of a household and distances to households using ArcMap. Across all clusters, P. falciparum parasites were detected in 49.8% (1524/3061) of individuals by qPCR and 19.5% (596/3061) by microscopy. Across the clusters, prevalence ranged from 26% to 70% by qPCR. Three to 34 larval habitats per cluster and 0–17 habitats within a 250m radius around households were observed. Using a generalized linear mixed effect model (GLMM), a 5% decrease in the odds of getting infected per each 10m increase in altitude was observed, while the number of larval habitats and their proximity to households were not statistically significant predictors for prevalence. Kitchen located indoors, open eaves, a lower level of education of the household head, older age, and being male were significantly associated with higher prevalence. Pronounced variation in prevalence at small scales was observed and needs to be taken into account for malaria surveillance and control. Potential larval habitat frequency had no direct impact on prevalence.

No abstract

Background. Progress in malaria control has stalled over the recent years. Knowledge on main drivers of transmission explaining small-scale variation in prevalence can inform targeted control measures.  Methods. We collected finger-prick blood samples from 3061 individuals irrespective of clinical symptoms in 20 clusters in Busia in western Kenya and screened for Plasmodium falciparum parasites using qPCR and microscopy. Clusters spanned an altitude range of 207 meters (1077-1284 m). We mapped potential mosquito larval habitats and determined their number within 250 m of a household and distances to households using ArcMap.  Results. Across all clusters, P. falciparum parasites were detected in 49.8% (1524/3061) of individuals by qPCR and 19.5% (596/3061) by microscopy. Across the clusters, prevalence ranged from 26% to 70% by qPCR. Three to 34 larval habitats per cluster and 0-17 habitats within a 250m radius around households were observed. Using a generalized linear mixed effect model (GLMM), a 5% decrease in the odds of getting infected per each 10m increase in altitude was observed, while the number of larval habitats and their proximity to households were not statistically significant predictors for prevalence. Kitchen located indoors, open eaves, a lower level of education of the household head, older age, and being male were significantly associated with higher prevalence.  Conclusion. Pronounced variation in prevalence at small scales was observed and needs to be taken into account for malaria surveillance and control. Potential larval habitat frequency had no direct impact on prevalence.

Homogeneity attack allows adversaries to obtain the exact values on the sensitive attributes for his targets without having to re-identify them from released data. Differential privacy (DP) is a mathematical concept that provides robust privacy guarantee against a wide range of privacy attacks. We propose a measure for disclosure risk from homogeneity attack; and derive closed-form relationships between the privacy loss parameters from DP and the disclosure risk from homogeneity attack when released data are multi-dimensional frequency distributions. The availability of the close-form relationships not only saves time and computational resources spent on calculating the relationships numerically, but also assists understanding of DP and privacy loss parameters by putting the abstract concepts in the context of a concrete privacy attack, and offers a different perspective when it comes to choosing privacy loss parameters and implementing differentially private mechanisms for data sanitization and release in practice. We apply the closed-form mathematical relationships in real-life data sets and demonstrate their consistency with the empirical assessment of the disclosure risk due to homogeneity attack on sanitized data.