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 .

Climate and weather influence the occurrence, distribution, and incidence of infectious diseases, particularly those caused by vector-borne or zoonotic pathogens. Thus, models based on meteorological data have helped predict when and where human cases are most likely to occur. Such knowledge aids in targeting limited prevention and control resources and may ultimately reduce the burden of diseases. Paradoxically, localities where such models could yield the greatest benefits, such as tropical regions where morbidity and mortality caused by vector-borne diseases is greatest, often lack high-quality in situ local meteorological data. Satellite- and model-based gridded climate datasets can be used to approximate local meteorological conditions in data-sparse regions, however their accuracy varies. Here we investigate how the selection of a particular dataset can influence the outcomes of disease forecasting models. Our model system focuses on plague (Yersinia pestis infection) in the West Nile region of Uganda. The majority of recent human cases have been reported from East Africa and Madagascar, where meteorological observations are sparse and topography yields complex weather patterns. Using an ensemble of meteorological datasets and model-averaging techniques we find that the number of suspected cases in the West Nile region was negatively associated with dry season rainfall (December-February) and positively with rainfall prior to the plague season. We demonstrate that ensembles of available meteorological datasets can be used to quantify climatic uncertainty and minimize its impacts on infectious disease models. These methods are particularly valuable in regions with sparse observational networks and high morbidity and mortality from vector-borne diseases.

Plague is a highly virulent fleaborne zoonosis that occurs throughout many parts of the world; most suspected human cases are reported from resource-poor settings in sub-Saharan Africa. During 2008–2016, a combination of active surveillance and laboratory testing in the plague-endemic West Nile region of Uganda yielded 255 suspected human plague cases; approximately one third were laboratory confirmed by bacterial culture or serology. Although the mortality rate was 7% among suspected cases, it was 26% among persons with laboratory-confirmed plague. Reports of an unusual number of dead rats in a patient’s village around the time of illness onset was significantly associated with laboratory confirmation of plague. This descriptive summary of human plague in Uganda highlights the episodic nature of the disease, as well as the potential that, even in endemic areas, illnesses of other etiologies might be being mistaken for plague.

Abstract. East Africa has been identified as a region where vector-borne and zoonotic diseases are most likely to emerge or re-emerge and where morbidity and mortality from these diseases is significant. Understanding when and where humans are most likely to be exposed to vector-borne and zoonotic disease agents in this region can aid in targeting limited prevention and control resources. Often, spatial and temporal distributions of vectors and vector-borne disease agents are predictable based on climatic variables. However, because of coarse meteorological observation networks, appropriately scaled and accurate climate data are often lacking for Africa. Here, we use a recently developed 10-year gridded meteorological dataset from the Advanced Weather Research and Forecasting Model to identify climatic variables predictive of the spatial distribution of human plague cases in the West Nile region of Uganda. Our logistic regression model revealed that within high elevation sites (above 1,300 m), plague risk was positively associated with rainfall during the months of February, October, and November and negatively associated with rainfall during the month of June. These findings suggest that areas that receive increased but not continuous rainfall provide ecologically conducive conditions for Yersinia pestis transmission in this region. This study serves as a foundation for similar modeling efforts of other vector-borne and zoonotic disease in regions with sparse observational meteorologic networks.

The US Food and Drug Administration recently approved ciprofloxacin for treatment of plague (Yersina pestis infection) based on animal studies. Published evidence of efficacy in humans is sparse. We report 5 cases of culture-confirmed human plague treated successfully with oral ciprofloxacin, including 1 case of pneumonic plague.