BackgroundRepeat national household surveys suggest highly variable malaria transmission and increasing coverage of high-impact malaria interventions throughout Zambia. Many areas of very low malaria transmission, especially across southern and central regions, are driving efforts towards sub-national elimination.Case descriptionReactive case detection (RCD) is conducted in Southern Province and urban areas of Lusaka in connection with confirmed incident malaria cases presenting to a community health worker (CHW) or clinic and suspected of being the result of local transmission. CHWs travel to the household of the incident malaria case and screen individuals living in adjacent houses in urban Lusaka and within 140 m in Southern Province for malaria infection using a rapid diagnostic test, treating those testing positive with artemether–lumefantrine.DiscussionReactive case detection improves access to health care and increases the capacity for the health system to identify malaria infections. The system is useful for targeting malaria interventions, and was instrumental for guiding focal indoor residual spraying in Lusaka during the 2014/2015 spray season. Variations to maximize impact of the current RCD protocol are being considered, including the use of anti-malarials with a longer lasting, post-treatment prophylaxis.ConclusionThe RCD system in Zambia is one example of a malaria elimination surveillance system which has increased access to health care within rural communities while leveraging community members to build malaria surveillance capacity.

Abstract. Plague, a life-threatening flea-borne zoonosis caused by Yersinia pestis , has most commonly been reported from eastern Africa and Madagascar in recent decades. In these regions and elsewhere, prevention and control efforts are typically targeted at fine spatial scales, yet risk maps for the disease are often presented at coarse spatial resolutions that are of limited value in allocating scarce prevention and control resources. In our study, we sought to identify sub-village level remotely sensed correlates of elevated risk of human exposure to plague bacteria and to project the model across the plague-endemic West Nile region of Uganda and into neighboring regions of the Democratic Republic of Congo. Our model yielded an overall accuracy of 81%, with sensitivities and specificities of 89% and 71%, respectively. Risk was higher above 1,300 meters than below, and the remotely sensed covariates that were included in the model implied that localities that are wetter, with less vegetative growth and more bare soil during the dry month of January (when agricultural plots are typically fallow) pose an increased risk of plague case occurrence. Our results suggest that environmental and landscape features play a large part in classifying an area as ecologically conducive to plague activity. However, it is clear that future studies aimed at identifying behavioral and fine-scale ecological risk factors in the West Nile region are required to fully assess the risk of human exposure to Y. pestis .

The West Nile region of Uganda represents an epidemiologic focus for human plague in east Africa. However, limited capacity for diagnostic laboratory testing means few clinically diagnosed cases are confirmed and the true burden of disease is undetermined. The aims of the study were 1) describe the spatial distribution of clinical plague cases in the region, 2) identify ecologic correlates of incidence, and 3) incorporate these variables into predictive models that define areas of plague risk. The model explained 74% of the incidence variation and revealed that cases were more common above 1,300 m than below. Remotely-sensed variables associated with differences in soil or vegetation were also identified as incidence predictors. The study demonstrated that plague incidence can be modeled at parish-level scale based on environmental variables and identified parishes where cases may be under-reported and enhanced surveillance and preventative measures may be implemented to decrease the burden of plague.

BackgroundDefining the number and location of sprayable structures (houses) is foundational to plan and monitor indoor residual spray (IRS) implementation, a primary intervention used to control the transmission of malaria. Only by mapping the location and type of all sprayable structures can IRS operations be planned, estimates of spray coverage determined, and targeted delivery of IRS to specific locations be achieved. Previously, field-based enumeration has been used to guide IRS campaigns, however, this approach is costly, time-consuming and difficult to scale. As a result, field-based enumeration typically fails to map all structures in a given area, making estimations less reliable and reducing the enumerated coverage.MethodsUsing open source satellite imagery and Geographic Information System software, satellite enumeration was conducted to guide IRS operations in 15 districts (91,302 km2) in northern Zambia during the 2014 spray season. Cost of satellite enumeration was compared to standard enumeration. Enumerated households were sampled to estimate sprayable surface area and wall type from the satellite enumeration using linear and logistic regression, respectively.ResultsIn comparison to the traditional field-based enumeration procedure, satellite-based enumeration was 22 times faster, and 10 times less costly. An estimated 98 % of the satellite enumerated buildings correctly classified roof type. Predicted surface area of each household correlated at a value of 0.91 with measured surface area of each household.ConclusionFor IRS campaigns, high quality and high coverage enumeration data aid in planning, through informed insecticide procurement. Through the identification of geographical areas and populations to target, enumeration data guide operations and assist monitoring and evaluation of IRS through the unbiased estimation of coverage achieved. Satellite enumeration represents a quick, cheap and accurate system to provide these data, and has potential applications beyond IRS for delivery of other targeted or non-targeted interventions (e.g. net distributions, mass drug administration, immunization campaigns, or even sampling frames for field studies).