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Machado-Machado E.A.,Clark University | Machado-Machado E.A.,Clark Labs
Applied Geography | Year: 2012

Dengue is considered the most important vector borne virus disease worldwide placing some 2.5 billion people at risk globally. Despite the public health concern about dengue fever, spatially explicit suitability assessments for this disease are limited due to data restrictions and the challenges posed by the complexity of the interactions among its risk factors, which involve social, economic, and ecological processes. This paper demonstrates an empirical approach to identify suitable areas for dengue fever using species distribution modeling and evaluates the relative contribution of climatic and socio-economic factors as dengue fever suitability determinants. Several models showing the potential distribution of dengue fever within all the Mexican municipalities are produced using different sets of predictor variables. The results suggest that at the scale of this study the climatic variables were more important determinants of suitability for dengue fever than the socio-economic variables considered in this study. All the models perform well (average testing AUC about 0.8) and show similar patterns. The model with the least number of variables and best performance includes the variables minimum temperature of the coldest month, mean temperature of the wettest quarter, and annual precipitation. However, there is not a high variability of AUC scores among the models generated. © 2011 Elsevier Ltd. Source


Anyamba A.,NASA | Linthicum K.J.,U.S. Department of Agriculture | Small J.L.,NASA | Collins K.M.,NASA | And 6 more authors.
PLoS Neglected Tropical Diseases | Year: 2012

Background: Recent clusters of outbreaks of mosquito-borne diseases (Rift Valley fever and chikungunya) in Africa and parts of the Indian Ocean islands illustrate how interannual climate variability influences the changing risk patterns of disease outbreaks. Although Rift Valley fever outbreaks have been known to follow periods of above-normal rainfall, the timing of the outbreak events has largely been unknown. Similarly, there is inadequate knowledge on climate drivers of chikungunya outbreaks. We analyze a variety of climate and satellite-derived vegetation measurements to explain the coupling between patterns of climate variability and disease outbreaks of Rift Valley fever and chikungunya. Methods and Findings: We derived a teleconnections map by correlating long-term monthly global precipitation data with the NINO3.4 sea surface temperature (SST) anomaly index. This map identifies regional hot-spots where rainfall variability may have an influence on the ecology of vector borne disease. Among the regions are Eastern and Southern Africa where outbreaks of chikungunya and Rift Valley fever occurred 2004-2009. Chikungunya and Rift Valley fever case locations were mapped to corresponding climate data anomalies to understand associations between specific anomaly patterns in ecological and climate variables and disease outbreak patterns through space and time. From these maps we explored associations among Rift Valley fever disease occurrence locations and cumulative rainfall and vegetation index anomalies. We illustrated the time lag between the driving climate conditions and the timing of the first case of Rift Valley fever. Results showed that reported outbreaks of Rift Valley fever occurred after ~3-4 months of sustained above-normal rainfall and associated green-up in vegetation, conditions ideal for Rift Valley fever mosquito vectors. For chikungunya we explored associations among surface air temperature, precipitation anomalies, and chikungunya outbreak locations. We found that chikungunya outbreaks occurred under conditions of anomalously high temperatures and drought over Eastern Africa. However, in Southeast Asia, chikungunya outbreaks were negatively correlated (p<0.05) with drought conditions, but positively correlated with warmer-than-normal temperatures and rainfall. Conclusions/Significance: Extremes in climate conditions forced by the El Niño/Southern Oscillation (ENSO) lead to severe droughts or floods, ideal ecological conditions for disease vectors to emerge, and may result in epizootics and epidemics of Rift Valley fever and chikungunya. However, the immune status of livestock (Rift Valley fever) and human (chikungunya) populations is a factor that is largely unknown but very likely plays a role in the spatial-temporal patterns of these disease outbreaks. As the frequency and severity of extremes in climate increase, the potential for globalization of vectors and disease is likely to accelerate. Understanding the underlying patterns of global and regional climate variability and their impacts on ecological drivers of vector-borne diseases is critical in long-range planning of appropriate disease and disease-vector response, control, and mitigation strategies. Source


Chen H.,Clark Labs | Pontius Jr. R.G.,Clark University
Landscape Ecology | Year: 2010

This paper proposes a method to quantify the goodness-of-fit of a land change projection along a gradient of an explanatory variable, by classifying pixels as one of four types: null successes, false alarms, hits, and misses. The method shows: (1) how the correctness and error of a land change projection are distributed along the gradient of an explanatory variable, (2) how the gradient of the explanatory variable relates to the stationarity of the land transition processes, and (3) how to use the insights from the previous two points to search for additional explanatory variables. The paper illustrates the method through a case study that applies the model Geomod in Central Massachusetts, USA. Results reveal that the model predicts more than the observed amount of change on flat slopes and less than the observed amount of change on steep slopes. One reason for these types of errors is that the land change process during the calibration interval is different than the process during the prediction interval with respect to slope. The method allows modelers to use the validation step as a diagnostic tool to search for potentially influential missing variables and to gain insight into land transition processes. The technique is designed to be applicable to a variety of types of land change models. © 2010 Springer Science+Business Media B.V. Source


Chen H.,Clark Labs | Pontius Jr. R.G.,Clark University
Environmental Modeling and Assessment | Year: 2011

It is important to know how the results from a land change model vary based on both the pixel resolution of the maps and the precision of the independent variables because subjective decisions or default values frequently determine these two factors. This paper presents an approach to measure the variation in model accuracy that is triggered by alteration of the pixel resolution and the precision of the independent variable, which are bins of distance to previously built area for our case study. We illustrate the principles with an application of the Geomod land change model contained in the Idrisi GIS, applied to simulate the gain of built land in central Massachusetts, USA. Results reveal four general principles: (1) change in pixel resolution using the majority-takes-all rule can influence quantity error, (2) change in bin width of an independent variable does not influence the quantity error, (3) resolution and bin width interact so that bin width does not have an effect on error when bin widths are smaller than the pixel resolution, and (4) researchers are wise to examine the implications of their subjective decisions by plotting clearly how the resolution and bin size influence the mathematical relationships that the model uses. We have found no universal, hard, and fast rules that dictate how to decide on an appropriate pixel resolution and bin width, but our method demonstrates how these decisions can be influential. These insights can offer scientists guidance in how to prepare data in an appropriate manner. © 2010 Springer Science+Business Media B.V. Source


Delmelle E.M.,University of North Carolina at Charlotte | Zhu H.,Clark Labs | Zhu H.,University of Connecticut | Tang W.,University of North Carolina at Charlotte | Casas I.,Louisiana Tech University
Applied Geography | Year: 2014

The rapid propagation of vector-borne diseases, such as dengue fever, poses a threat to vulnerable populations, especially those in tropical regions. Prompt space-time analyses are critical elements for accurate outbreak detection and mitigation purposes. Open access web-based geospatial tools are particularly critical in developing countries lacking GIS software and expertise. Currently, online geospatial tools for the monitoring of surveillance data are confined to the mapping of aggregated data. In this paper, we present a web-based geospatial toolkit with a user-friendly interactive interface for the monitoring of dengue fever outbreaks, in space and time. Our geospatial toolkit is designed around the integration of (1) a spatial data management module in which epidemiologists upload spatio-temporal explicit data, (2) an analytical module running an accelerated Kernel Density Estimation (KDE) to map the outbreaks of dengue fever, (3) a spatial database module to extract pairs of disease events close in space and time and (4) a GIS mapping module to visualize space-time linkages of pairs of disease events. We illustrate our approach on a set of dengue fever cases which occurred in Cali (659 geocoded cases), an urban environment in Colombia. Results indicate that dengue fever cases are significantly clustered, but the degree of intensity varies across the city. The design and implementation of the on-line toolkit underscores the benefits of the approach to monitor vector-borne disease outbreaks in a timely manner and at different scales, facilitating the appropriate allocation of resources. The toolkit is designed collaboratively with health epidemiologists and is portable for other surveillance data at the individual level such as crime or traffic accidents. © 2014 . Source

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