Model adjusted r² values and coefficient estimates for the two consensus best-fit models when run using the meteorological ensemble dataset and each of the individual precipitation datasets.

<p>Coefficients estimates not significant at the α = 0.05 level are listed as NS. Model mean values represent the mean adjusted r² and coefficient estimates averaged across model results from the individual and weighted ensemble meteorological datasets (results from the ERA-Interim and NCEP/DOE datasets were not included in the model means due to their low accuracy in the study region). All model mean coefficients were significant at the α = 0.05 level.</p

Improvement of Disease Prediction and Modeling through the Use of Meteorological Ensembles: Human Plague in Uganda

<div><p>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 <em>in situ</em> 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 (<em>Yersinia pestis</em> 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.</p> </div

Map of meteorological stations within 500 km of Arua, Uganda.

<p>Meteorological stations containing rainfall data for at least 25% of the days between 1998 and 2010 (red circles) within 500 km of the Arua airport (yellow circle).</p

Association between rainfall and plague occurrence.

<p>Relationship between square-root transformed number of annual suspect plague cases and (A) the standardized number of days of >10 mm in the dry season (December-February) prior to the start of the plague year in August (zero-year lag) and (B) the standardized number of days of 0.2–10 mm rainfall in June and July prior to the start of the plague year (zero-year lag). The rainfall data is the weighted ensemble of all seven rainfall datasets included in our analyses (TRMM, CMORPH, FEWS-NET, NCEP/DOE, ERA-Interim, GPCP, and Observational). Dotted lines are the regression coefficients estimates from the best two-variable model using the ensemble rainfall dataset.</p

Variability among the different temperature and rainfall data sources.

<p>(A) Standardized monthly mean temperatures (1998- present) from the Arua airport (Observational) and ERA-Interim datasets. (B) Standardized monthly days of rainfall >0.2 mm (1998-present) from Arua airport (Observational), National Centers for Environmental Prediction re-analysis II project (NCEP/DOE), USAID Famine Early Warning System Network (FEWS-Net), and Climate Prediction Center morphing technique (CMORPH) rainfall datasets. The other rainfall datasets were not included in the figure to maintain clarity.</p

Spatial distributions of plague, temperature, and rainfall in West Nile region of Uganda.

<p>(A) Reported cumulative plague incidence per 1,000 population from 1999–2007 in Vurra and Okoro counties of Uganda. (B) Average August rainfall (mm) and (C) average February maximum temperatures (°C) in northwestern Uganda. Temperature and rainfall averages were based on data from 1999–2009 generated using a 2 km Weather Research Forecasting (WRF) model <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0044431#pone.0044431-Monaghan1" target="_blank">[68]</a>.</p

Annual number of observed versus predicted suspect plague cases.

<p>(A) Plot of annual number of observed vs. predicted number of suspect human plague cases from the best-fit regression model using the meteorological ensemble dataset that uses a weighted average of all of the rainfall and temperature datasets included in this study (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0044431#pone-0044431-t002" target="_blank">Table 2</a> for model details). (B) Predicted number of suspect human plague cases from the best-fit regression model using each of the individual rainfall datasets.</p

Meteorological datasets used in analysis of the interannual variation of suspect plague cases.

<p>Time span is the period of the analysis for which each dataset is available. Spatial resolution is represented by decimal degrees of latitude and longitude. Ensemble weight is the weight each individual dataset was given in the ensemble rainfall dataset based on ground-truthing. The last column provides a reference for additional information on each dataset.</p>1<p>Initial ensemble weight of 0.143 for Arua airport rainfall dataset was set at 0.143 (1/7) because dataset could not be ground-truthed.</p

Two Distinct <i>Yersinia pestis</i> Populations Causing Plague among Humans in the West Nile Region of Uganda

<div><p>Background</p><p>Plague is a life-threatening disease caused by the bacterium, <i>Yersinia pestis</i>. Since the 1990s, Africa has accounted for the majority of reported human cases. In Uganda, plague cases occur in the West Nile region, near the border with Democratic Republic of Congo. Despite the ongoing risk of contracting plague in this region, little is known about <i>Y</i>. <i>pestis</i> genotypes causing human disease.</p><p>Methodology/Principal Findings</p><p>During January 2004–December 2012, 1,092 suspect human plague cases were recorded in the West Nile region of Uganda. Sixty-one cases were culture-confirmed. Recovered <i>Y</i>. <i>pestis</i> isolates were analyzed using three typing methods, single nucleotide polymorphisms (SNPs), pulsed field gel electrophoresis (PFGE), and multiple variable number of tandem repeat analysis (MLVA) and subpopulations analyzed in the context of associated geographic, temporal, and clinical data for source patients. All three methods separated the 61 isolates into two distinct 1.ANT lineages, which persisted throughout the 9 year period and were associated with differences in elevation and geographic distribution.</p><p>Conclusions/Significance</p><p>We demonstrate that human cases of plague in the West Nile region of Uganda are caused by two distinct 1.ANT genetic subpopulations. Notably, all three typing methods used, SNPs, PFGE, and MLVA, identified the two genetic subpopulations, despite recognizing different mutation types in the <i>Y</i>. <i>pestis</i> genome. The geographic and elevation differences between the two subpopulations is suggestive of their maintenance in highly localized enzootic cycles, potentially with differing vector-host community composition. This improved understanding of <i>Y</i>. <i>pestis</i> subpopulations in the West Nile region will be useful for identifying ecologic and environmental factors associated with elevated plague risk.</p></div

AscI PFGE patterns for <i>Y</i>. <i>pestis</i> isolates from the West Nile region of Uganda.

<p>Lane (1) <i>Salmonella braenderup</i> H9812 molecular marker in kilobases. Lane (2) PFGE Group A pattern. Lane (3) PFGE Group B pattern.</p