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Risk analysis for dengue suitability in Africa using the ArcGIS predictive analysis tools (PA tools)
Accepted Manuscript Title: Risk analysis for dengue suitability in Africa using the ArcGIS Predictive Analysis Tools (PA Tools) Author: David F. Attaway Kathryn H. Jacobsen Allan Falconer Germana Manca Nigel M. Waters PII: DOI: Reference:
S0001-706X(16)30080-8 http://dx.doi.org/doi:10.1016/j.actatropica.2016.02.018 ACTROP 3862
To appear in:
Acta Tropica
Received date: Revised date: Accepted date:
14-12-2015 20-2-2016 27-2-2016
Please cite this article as: Attaway, David F., Jacobsen, Kathryn H., Falconer, Allan, Manca, Germana, Waters, Nigel M., Risk analysis for dengue suitability in Africa using the ArcGIS Predictive Analysis Tools (PA Tools).Acta Tropica http://dx.doi.org/10.1016/j.actatropica.2016.02.018 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Risk analysis for dengue suitability in Africa using the ArcGIS Predictive Analysis Tools (PA Tools)
David F. Attaway1,2, Kathryn H. Jacobsen3, Allan Falconer1, Germana Manca4, and Nigel M. Waters1,5
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Department of Geography and GeoInformation Science, George Mason University, 4400 University Drive, MS 6C3, Fairfax VA, 22030-4444, USA 2 Esri, 8615 Westwood Center Drive, Vienna, VA, 22182, USA 3 Department of Global and Community Health, George Mason University, 4400 University Drive, MS 5B7, Fairfax, VA, 22030-4444, USA 4 Laboratory for Geoinformatics and Earth Observations, Department of Geography and Institute for Cyberscience, The Pennsylvania State University, University Park, PA, USA 5 Institute of Public Health, Faculty of Medicine, University of Calgary, 3280 Hospital Drive NW, Calgary, AB, T2N 4Z6, Canada
Corresponding Author: David Frost Attaway Esri 8615 Westwood Center Drive Vienna, Virginia 22182, USA Tel: (703) 506-9515 ext. 5127 Cell: 214-681-6162 Email: [email protected]
Graphical abstract
Highlights:
Provides localized predictions necessary to make informed decisions about where to focus disease prevention and control efforts. A baseline model of dengue range that can be updated as new environmental, population, and health data become available. Adaptable methodologies for mapping emerging disease risks will be vital as global climate changes occur
Abstract Background: Risk maps identifying suitable locations for infection transmission are important 1
for public health planning. Data on dengue infection rates are not readily available in most places where the disease is known to occur. Methods: A newly available add-in to Esri’s ArcGIS software package, the ArcGIS Predictive Analysis Toolset (PA Tools), was used to identify locations within Africa with environmental characteristics likely to be suitable for transmission of dengue virus. Results: A more accurate, robust, and localized (1 km x 1 km) dengue risk map for Africa was created based on bioclimatic layers, elevation data, high-resolution population data, and other environmental factors that a search of the peer-reviewed literature showed to be associated with dengue risk. Variables related to temperature, precipitation, elevation, and population density were identified as good predictors of dengue suitability. Areas of high dengue suitability occur primarily within West Africa and parts of Central Africa and East Africa, but even in these regions the suitability is not homogenous. Conclusion: This risk mapping technique for an infection transmitted by Aedes mosquitoes draws on entomological, epidemiological, and geographic data. The method could be applied to other infectious diseases (such as Zika) in order to provide new insights for public health officials and others making decisions about where to increase disease surveillance activities and implement infection prevention and control efforts. The ability to map threats to human and animal health is important for tracking vectorborne and other emerging infectious diseases and modeling the likely impacts of climate change.
Keywords dengue; geographic information systems; risk mapping; developing countries; medical geography; Africa
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Background With global average temperatures projected to increase between 0.5°F [0.28°C] to 8.6°F [4.78°C] by 2100, with a likely increase of at least 2.7°F [1.5°C] for all scenarios except the one representing the most aggressive mitigation of greenhouse gas emissions (Intergovernmental Panel on Climate Change 2013), an efficient method for modeling how these changes will affect human disease suitability is needed (Stocker et al., 2013). In this paper, we examine the climate factors that predict the presence of dengue fever in Africa. The incidence of dengue is known to be underestimated in African countries, where viral testing capacity is limited and data reporting and surveillance systems are sparse (Amarasinghe et al., 2011; Bhatt et al., 2013a,b; Brady et al., 2012; Franco et al., 2010; and Sessions et al., 2013), so there is a need for improved maps of dengue risk in this region. Dengue fever is thought to be the most rapidly spreading mosquito-borne viral disease in the world (World Health Organization 2012). In the last 50 years, the incidence of dengue has increased 30-fold (World Health Organization 2012). In the past decade, the range of the disease has expanded to new countries and been found across a range of urban and rural settings. Although outbreaks of dengue in Africa have been reported, data on baseline incidence and prevalence rates are not readily available for the region (Amarasinghe et al., 2011). The specific goal of this analysis was to create a more accurate, robust, and localized analysis (1 km x 1 km) of dengue risk mapping in Africa based on bioclimatic layers, elevation data, high-resolution population data, and other environmental factors that our search of the peerreviewed literature showed to be associated with dengue risk (Table 1). This level of spatial resolution was used in a recent map of dengue risk that was developed for the continent of Europe, but is has not previously been used in Africa (Rogers et al., 2014). The key analytic 3
method used in this paper is the ArcGIS Predictive Analysis Tools (PA Tools) function, which incorporates adaptable analysis within the geographic user interface and therefore allows immediate visualizations of how various climate and other inputs affect the suitability model (Esri 2014a). The PA Tools are built to accommodate an iterative detective-like workflow which can be based on historical evidence, doctrine, or pattern of life. This repeatable and shareable tradecraft does not require GIS expertise to use, and the raster-based dynamic approach provides for speed in modeling. The simple interface provides for building queries, and it saves time and disk space compared to similar out-of-the-box GIS tools (Esri 2014a). Improved visualizations of the risk areas for dengue will enable public health officials and policymakers to identify highrisk areas and allow for the most efficient use of dengue prevention and control resources. PA Tools could be applied to a variety of other infectious diseases and other world regions when the transmission of the agent is influenced by environmental variables that show geographical variation. In this paper, we use dengue virus in Africa as a way to highlight the tool’s utility and potential.
Data Sources After reviewing the dengue literature (Table 1), we identified a need to acquire a variety of spatial data, population density data, environmental data (including bioclimatic layers, land cover data, and water bodies data), and disease data. The sources of the data used in our model are specified below. Table 2 summarizes the various layers included in the model.
2.1 Spatial data.
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The Global Administrative Areas (GADM) database contained the current administrative boundary data for Africa (Global Administrative Areas 2012). Data were projected via a projected coordinate system (WGS_1984_Web_Mercator_Auxiliary_Sphere) (Battersby et al., 2014).
2.2 Dengue data. Dengue occurrence data were acquired from a previously published dataset (Bhatt et al., 2013a). These 360 cases from Africa had been identified by the authors of the dataset via searches of online databases, including PubMed, ISI Web of Science, and PROMED, and HealthMap (Bhatt et al., 2013b). The data were current as of 2012 and were vetted through a rigorous quality control assessment (Bhatt et al., 2013b). In order to make our maps comparable to the earlier visualizations created with the existing dataset, we restricted our case data to only those points from the previous analyses.
2.3 Population density data. The accuracy of human infection suitability estimates is dependent on accurate human population density data (Bhatt et al., 2013a,b; Hassan et al., 2012; Moffett et al., 2007; Rogers et al., 2006). The LandScan 2011 Global Population Database contains high-resolution (1 km x 1 km) population density data developed by Oak Ridge National Laboratory (ORNL) for the United States Department of Defense (DoD) (Bright et al., 2011).
2.4 Elevation data.
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The WorldClim Elevation database contains high-resolution (1km x 1 km) environmental information (Hijmans et al., 2005). Previous studies have suggested that dengue transmission is limited above 1700 meters (Lozano-Fuentes et al., 2012).
2.5 Land cover data. A raster version of the GlobCover (GLC) land cover map produced for the year 2009 was obtained from the European Space Agency (ESA) and the Université Catholique de Louvain (UCL) (Bontemps et al., 2010). The dataset, a 300 m global land cover map produced from an automated classification of the medium resolution imaging spectrometer and full resolution time series (MERIS FR), includes 22 global classes within the raster dataset (Bontemps et al., 2010; Bontemps et al., 2011).
2.6 Bioclimatic layers data. The WorldClim Bioclimatic variables database provided the temperature and rainfall parameters for the model. These were selected from previous scientific studies of mosquito distribution and dengue transmission (Bhatt et al., 2013a,b; Brady et al., 2012; Hijmans et al., 2005; Rogers 2006; Rogers et al., 2006; Rogers et al., 2014; Simmons et al., 2012).
2.7 Water body data. The United Nations’ Food and Agriculture Organization (FAO) database contained water body vector (WBD) information at a scale of 1:1000000 and current as of 2000. This provided a reference scale compatible with the 1km raster data sets (Gassert et al., 2013; Jenness et al.,
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2007; Hoogeveen 2000). This dataset was cross-referenced with the GlobCover 2009 (GLC) dataset for consistency with classifications.
Methods As a first step toward identifying areas suitable to dengue, we evaluated the variables known to be associated with dengue risk, and used a search of the literature to identify parameters for a dengue model (Table 1). For example, temperature parameters were set to the minimum and maximum thresholds established by published literature, 10 °C and 30 °C (Carrington et al., 2013; Halstead 2008; Tun-Lin et al., 2000; Turell and Lundstrom 1990). Similar restrictions were set for elevation, population density, and precipitation. The Query Expression Editor within PA Tools allowed us to find locations where zero to four of the four key environmental risks for dengue were true for a group of single-band rasters (that is, single variable coverages).
𝑄𝑢𝑒𝑟𝑦 𝐸𝑥𝑝𝑟𝑒𝑠𝑠𝑖𝑜𝑛 𝐸𝑞𝑢𝑎𝑡𝑖𝑜𝑛 = ((𝑌1 (𝐻𝑅𝑃1 ) + 𝑌2 (𝐻𝑅𝑃2 ) + 𝑌3 (𝐻𝑅𝑃3 ) + 𝑌4 (𝐻𝑅𝑃4 ))
Y = raster datasets for four high risk parameters HRP = Four High Risk Parameters from Table 1
Highly suitable = All 4 parameters
Somewhat suitable = 3 parameters
Limited suitability = 2 parameters
Highly unsuitable = 1 parameter
Not suitable = 0 parameters
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To confirm the relevance of these parameters to dengue risk, we calculated the proportion of the 360 dengue case data points (Bhatt et al., 2013a,b) that were observed to fall within each layer of the map (Figure 1) and within combinations of map layers (Figure 2). The results of these calculations are presented in Table 3. These analyses were made using tools within ArcGIS PA Tools. The Query Factor Input Table (QFit) generates a predictive analysis query using a set of input data points (the 360 dengue occurrences) and a set of raster datasets (the predictor environmental variables). The QFit tool allows the user to take a set of observations, such as dengue occurrence points, and then compare these observations to the values for a set of predictor variables, that are represented as a group of raster coverages, in order to determine what values are characteristic of the occurrence points (Equation 2) (Esri 2014a,b).
𝑍 = (𝑋(𝑛 = 360) ∶ 𝑌1 – 𝑌17 )) Z = Final results from running model with all 17 variables listed in Table 2 X = Dengue case data (n = 360 dengue data points) Yn = Layers included in model
Since we already knew the values for the various environmental factors that were most important in determining the locations with high risk of dengue (Table 1), we manually constructed the query for the current dengue fever risk and then applied it to our rasters to see which locations satisfied those conditions. For this model, the 17 variables were grouped into 4 classes – explained in Table 1 – along with the values that were used to classify the suitability. Having all 4 parameters meant the location was deemed to be Highly Suitable. The use of an
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adaptable raster format allows for greater efficiency in computer processing of the models as well as for easy dissemination of results. Model specifications are saved in xml format, which limits the file size and enables easier loading of queries and sharing of analytic workflows (Rezayat 2000). One of the key advantages of using PA Tools is the greater efficiency in processing which occurs from only one raster layer being adaptively processed at a time. PA Tools does not change the pixels of the original raster datasets (that is, elevation, temperature, population density, and precipitation); it only modifies a single function raster dataset, which can be saved as an .afr file. (A function raster dataset, when saved to a file (.afr), defines the processing to be performed on a dataset (Esri 2014a; Hogland and Anderson 2014).) This increases the flexibility of the model when new case data are added as inputs, as might occur when mapping an outbreak, and when a model is run numerous times with modified parameters. More importantly, this method limits the amount of memory required to complete the analysis. Before the PA Tools procedure was created, this analysis required the researcher to process each and every individual layer for each specification separately and then to use time, computer resources, and data space to combine all layers into one single analysis layer. The researcher would then have to export each iteration that occurred using different variables. For example, if the researcher wanted to calculate the difference between elevation and temperature datasets, the analyst would have to perform the calculations and then export the results, run statistics on the data, and recalculate the suitability. PA Tools speeds up this processing, even on older computers, by using QFit and the Query Expression Editor to quickly and efficiently visualize the effects of various model inputs. The dengue suitability analysis in this paper uses PA Tools to demonstrate how different variables contribute to predictions of dengue risk. This analytic approach limits the spatial bias
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that results from only using presence points as the basis for suitability, but also uses previous research data to validate the model outputs. Emerging infections like dengue highlight the need for more efficient, quicker, and cheaper processing of health risks. Suitability mapping provides entomological, epidemiological, and geographical researchers with an opportunity to increase their analytic efficiency and the rapid dissemination of their investigations.
Results and Discussion We used the risk factors identified in published literature to assign risk values to various parameters, as per Table 1. For example, we assumed that places with low temperatures below 10°C (BIO6) and places with high temperatures above 30°C (BIO1, BIO5, BIO9, and BIO10) were considered to have very low risk of dengue. We confirmed that these environmentally highrisk areas were conducive to dengue transmission by evaluating how many of the 360 case points were captured within high risk areas (Table 2). Figure 3 shows the map that was created by marking all areas where one or more of the risk factors from Table 1—elevation, population density, temperature, or precipitation—were present. Dark red areas mark 1 km x 1 km squares where all four parameters were present, indicating high suitability for dengue outbreaks. Light red shows places where three of the parameters were present: elevation, population density, and temperature. Yellow identifies locations where elevation and temperature criteria were met. Blue areas met the elevation criteria. Dark blue shows locations where none of the four high risk parameters exist. Figure 4 shows a map that compares the high risk (dark red) areas from Figure 3, which were identified by PA Tools using environmental data only, with a layer created from the dengue case data (shown in blue in Figure 4). The blue areas in Figure 4 were mapped by using the QFit
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Modeler to identify areas with elevation, population density, temperature, and precipitation data similar to the values found at the locations of the 360 dengue cases. In short, the QFit Modeler captures not only the dengue points but also those points that have the same environmental characteristics as the places where dengue has been observed. The QFit model captured 86% of the dengue case data. (The model did not capture 100% of the points because locations within Chad, Mali, Saudi Arabia, Sudan, Northern Somalia, and Yemen represented anomalies where locations did not fulfill the high risk parameters in Table 1.) The PA Tools suitability risk analysis based on environmental parameters (in red) captured 83% of the cases from the original dataset when using a 3km search distance and 67% of the cases with no search radius. Human population density at a localized scale is a commonly identified variable for disease suitability modeling (Bhatt et al., 2013a,b; Hassan et al., 2012; Moffett et al., 2007; Rogers et al., 2006). We found that, after considering the environmental parameters for the infection, higher population density is associated with a higher suitability for dengue. Our model incorporated a more localized population density analysis than previous studies (Bhatt et al., 2013a,b; Brady et al., 2012; Rogers et al., 2014; Rogers 2006; Simmons et al., 2012). We found that cases are more likely to be identified in high-density population centers than those located far from health services. This finding may be due simply to the fact that urban cases of dengue are more likely to be diagnosed and reported than rural cases. However, this finding is consistent with other studies of dengue around the world, as noted in our literature search it is incumbent on dengue fever researchers to extend their research into locations that are remote from existing health services. The PA Tools risk map also found additional locations, such as Luanda, Angola, which are suitable environments for dengue outbreaks. Dengue cases from Angola were not included in
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the 360 cases that were compiled through the end of 2012 (Bhatt et al., 2013a,b), so no Angolan cases were included in our QFit risk model (Bhatt et al., 2013a). However, the PA Tools risk map accurately predicted that Luanda was a suitable dengue habitat. Indeed, an outbreak in Angola occurred from April to June 2013 (Sessions et al., 2013). Incorporating additional analysis variables has the potential to create more high suitability areas which could be located using PA Tools even without nearby occurrence points (Sessions et al., 2013). We also identified other isolated locations that could be high risk areas for dengue, such as:
Central African Republic near Bangui and near the western border adjacent to Cameroon
Democratic Republic of Congo (DRC) along the border between South Sudan and the south west border neighboring Angola
the northwestern portion of Ethiopia along the border between Sudan and South Sudan
Kenya along one isolated location adjacent to the northern border between Ethiopia and near the coastal areas of Mombasa, Malindi, and Kilifi
portions of the countries Liberia and Sierra Leone
South Sudan near Juba and along the border between Uganda and the DRC
Tanzania along the coast near Dar es Salaam and continuing sporadically in isolated locations along the coast to Mozambique The identification of potential high risk areas allows for proactive disease prevention and
control measures to be implemented before outbreaks occur. As environmental conditions change, re-running the PA Tools model with revised inputs will provide critical predictions of additional locations that have become suitable environments for dengue fever. It will also be important to update the high-risk values in Table 1 to refine the mapping parameters as new risk factor data become available.
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Our new maps extend the analyses of dengue risk previously published for Africa (Bhatt et al., 2013a,b), and incorporate additional parameters for predicting the locations where dengue suitability is highest. This localized high-resolution analysis for Africa provides a more detailed map of dengue risk than previous studies that focused on global scale analyses (Bhatt et al., 2013a,b; Brady et al., 2012; Rogers et al., 2006; Rogers et al., 2014; Rogers 2006; Simmons et al., 2012). Similar methods can be used to integrate spatial modeling techniques for other diseases such as malaria (Kienberger and Hagenlocher 2014). Our approach provides an efficient way to analyze the environmental variables and tailor the analysis based on both predictor variables (raster datasets) and case location data (point data). The places shown in red in Figures 3 and 4 are the locations currently at highest risk of dengue, and are therefore the sites where dengue prevention activities such as vector control and public health education will be most costeffective, especially with the first dengue fever vaccine approved by Mexico (Pollack 2015). The maps will need to be updated as new climate data become available and as more dengue case reports are compiled (Kienberger and Hagenlocher 2014; Collenberg et al., 2006). Finally, the predictive mapping described in the present paper at the macro level and across continental Africa complements and extends other predictive mapping studies that have been conducted at the micro level. For example, a predictive map of dengue fever in three Colombian cities (Bello, Medellín and Itagüí) found that the variables with strong predictive power included high humidity and temperature, but not elevation (Arboleda and Peterson 2009). That study also noted inter-urban differences in variable importance and suggested “human social dimensions” to disease transmission including presumably the ability of individuals to protect themselves from infection (Arboleda and Peterson 2009). Incorporating additional socioeconomic and behavioral variables into a PA Tools analysis will improve detailed localized
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maps. Local and regional intervention strategies for diseases transmitted by the Aedes aegypti mosquito need to be prioritized (Conn et al., 2015). This need has become even more critical as the Zika virus has spread across the Americas (Bogoch et al., 2016). Our approach and those of previous geographers (Rogers et al., 2014; Collenberg et al., 2006) point toward strategies for public health interventions.
Conclusions The adaptable analysis incorporated within this paper demonstrates how more localized and useful disease risk maps can be efficiently created with PA Tools and the appropriate predictor variables. The dengue suitability maps produced using PA Tools and presented in this paper used a variety of spatial data and risk factor information from the peer-reviewed literature to provide a more detailed assessment of the places in Africa that are likely to have the highest risk for dengue fever. PA Tools allowed us to efficiently, quickly, and inexpensively create new maps of dengue risk that can be used for planning public health interventions. This type of dengue risk analysis provides the localized predictions necessary for various governmental entities (both local and national) to make informed decisions about where to focus their disease prevention and control efforts and where to step up health surveillance activities. The PA Tools methodology demonstrated here also provides a baseline model of dengue range that can be updated with PA Tools as new environmental, population, and health data become available. Adaptable methodologies for mapping emerging disease risks will be vital as global climate changes occur.
Competing interests
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The authors declare that they have no competing interests.
Authors’ contributions DFA, KHJ, and NMW outlined the paper. DFA drafted the manuscript. All authors were involved in study conception and analysis, provided critical revisions, and approved the submission of the final manuscript.
Acknowledgments Many thanks to Professor Simon Iain Hay and colleagues for making their data published in Nature available for use in testing this methodology. Thanks also to the staff from Esri, especially Ian Campbell, Keith Ailshie, Dr. Lauren Scott, and Lauren Rosenshein Bennett, for their assistance and cooperation in making these new spatial tools available for testing prior to their public release. We would also like to thank Oak Ridge Laboratory for making their LandScan 2011 data available for use in the model.
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31. Machado-Machado, E.A., 2012. Empirical mapping of suitability to dengue fever in Mexico using species distribution modeling. Appl. Geogr. 33, 82-93. 32. McLean, D.M., Clarke, A.M., Coleman, J.C., Montalbetti, C.A., Skidmore, A.G., Walters, T.E., Wise, R., 1974. Vector capability of Aedes aegypti mosquitoes for California encephalitis and dengue viruses at various temperatures. Can. J. Microbiol. 20, 255-262. 33. McLean, D.M., Miller, M.A., Grass, P.N., 1975. Dengue virus transmission by mosquitoes incubated at low temperatures. Mosq. News. 35, 322-327. 34. Moffett, A., Shackelford, N., Sarkar, S., 2007. Malaria in Africa: vector species’ niche models and relative risk maps. PLoS One 2, e824. 35. Pinto, E., Coelho, M., Oliver, L., Massad E., 2011. The influence of climate variables on dengue in Singapore. Int. J. Environ. Health Res. 21, 415-426. 36. Pollack, A., 2015. First Dengue Fever Vaccine Approved by Mexico. http://www.nytimes.com/2015/12/10/business/first-dengue-fever-vaccine-approved-bymexico.html?_r=2 (accessed 16.02.19). 37. Raheel, U., Faheem, M., Riaz, M., Kanwal, N., Javed, F., Zaidi, N., Qadri, I., 2011. Dengue fever in the Indian subcontinent: an overview. J. Infect. Dev. Countries. 5, 239247. 38. Rezayat, M., 2000. Knowledge-based product development using XML and KCs. Comput. Aided. Design. 32, 299-309. 39. Rohani, A., Wong, Y.C., Zamre, I., Lee, H.L., Zurainee, M.N., 2009. The effect of extrinsic incubation temperature on development of dengue serotype 2 and 4 viruses in Aedes aegypti (L.). Southeast Asian J. Trop. Med. 40, 942-950.
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40. Rogers, D.J., 2006. Predicted probability occurrence for dengue: models for vectors and vector-borne diseases. Adv. Parasitol. 62, 1-35. 41. Rogers, D.J., Wilson, A.J., Hay, S.I., Graham, A.J., 2006. The global distribution of yellow fever and dengue. Adv. Parasitol. 62, 181-220. 42. Rogers, D.J., Suk, J.E., Semenza, J.C., 2014. Using global maps to predict the risk of dengue in Europe. Acta tropica 129, 1-14. 43. Sessions, O.M., Khan, K., Hou, Y., Meltzer, E., Quan, M., Schwartz, E., Gubler, D.J., Wilder-Smith, A., 2013. Exploring the origin and potential for spread of the 2013 dengue outbreak in Luanda, Angola. Glob. Health. Action. 6, 21822. 44. Simmons, C.P., Farrar, J.J., van Vinh Chau, N., Wills, B., 2012. Dengue. N. Engl. J. Med. 366, 1423-1432. 45. Stocker, T.F., Qin, D., Plattner, G.K., Tignor, M., Allen, S.K., Boschung, J., Nauels, A., Xia, Y., Bex, V., Midgley, P.M., 2013. Climate Change 2013: The Physical Science Basis, United Kingdom and New York: Cambridge University Press. 46. Tun-Lin, W., Burkot, T.R., Kay, B.H., 2000. Effects of temperature and larval diet on development rates and survival of the dengue vector Aedes aegypti in north Queensland, Australia. Med. Vet. Entomol. 14, 31-37. 47. Turell, M.J., Lundstrom, J.O., 1990. Effect of environmental temperature on the vector competence of Aedes aegypti and Ae. taeniorhynchus for Ockelbo virus. Am. J. Trop. Med. Hyg. 43, 543-550. 48. Watts, D.M., Burke, D.S., Harrison, B.A., Whitmire, R.E., Nisalak, A., 1987. Effect of temperature on the vector efficiency of Aedes aegypti for Dengue 2 virus. Am. J. Trop. Med. Hyg. 36, 143-152.
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49. Wiwanitkit, V., 2006. An observation on correlation between rainfall and the prevalence of clinical cases of dengue in Thailand. J. Vector. Borne. Dis. 43, 73. 50. World Health Organization, 2012. Global strategy for dengue prevention and control 2012-2020, WHO Press, France. http://apps.who.int/iris/bitstream/10665/75303/1/9789241504034_eng.pdf (accessed 16.02.19).
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Figure 1 - Suitability models run for various univariate variables.
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Figure 2 - Suitability models run for various bivariate variables.
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Figure 3 – Africa ArcGIS Predictive Analysis Tools (PA Tools) Analysis.
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Figure 4 - Comparison of two PA Tools models run for dengue suitability in Africa.
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Temperature
Precipitation
Population Density
Elevation & Land cover
Table 1: Peer reviewed literature documenting environmental constraints for dengue. Dengue suitability were grouped into 4 classes based off the high risk parameters. Having a highly suitable = All 4 parameters, Somewhat suitable = 3 parameters, Limited suitability = 2 parameters, Highly unsuitable = 1 parameter, and Not suitable = 0 parameters. Catego Locatio High Risk Evidence Source ry n Parameters Brazil, Anything above 1500 meters limits Hagenlocher et al., ALT ≤ 1800 Colombi dengue 2013 AND a (11 ≤ GLC ≤ Ae. aegypti mosquitoes were 190 abundant at elevations up to 1300 m, OR Lozano-Fuentes et moderately abundant from 1300 to Mexico WBD ≤ al., 2012 1700 m, and still present but rare 200km) from 1700 to 2150 m Dengue fever is characterized by high human population densities more or Global Rogers et al. 2014 less wherever it occurs POP > 0 Population density in itself is a good Malaysi Hassan et al. 2012 measure of risk. a Thailand BIO12 ≥ Greater amounts of precipitation are , Wiwanitkit 2006, 55mm, BIO13 associated with higher dengue Singapo Heng et al., 1998 > 0, BIO14 > infection risk re .2mm, BIO16> 0, High volume of rainfall increases BIO17 > 0, Malaysi Aedes survival by providing more Hassan et al., 2012 AND BIO19 a breeding sites. >0 Laborat Turell & Lundstrom Infertile eggs at 10 degrees Celsius ory Test 1990 Ae. aegypti needs a winter isotherm Global Halstead 2008 higher than 10°C for its survival Ae. aegypti are capable of transmitting virus under laboratory Laborat McLean et al., 1975; conditions after incubation at ory Test Watts et al., 1987 temperatures as low as 13°C MinTemp ≥ 10 °C AND Temperature around an intermediate MaxTemp ≤ mean (26°C) can alter life-history 30 °C traits, population dynamics, and the Laborat Watts et al., 1987; ability of a mosquito to become ory Test McLean et al., 1974 infected with and transmit dengue virus (DENV) Turell & Lundstrom Lower temperatures generally extend Laborat 1990; McLean et al., the duration of Extrinsic incubation ory Test 1975; Rohani et al., period (EIP) 2009
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Ae. aegypti vector competence for DENV has been detected up to a maximum of 35°C Adult reproductive function is impaired in the high 30°s with adult Ae. aegypti survival declining as temperature continues to rise The variables ‘minimum temperature of the coldest month’ and ‘mean temperature of the coldest quarter’ were the most important variables for modeling dengue. Extrinsic incubation period (EIP) of the dengue virus decreases at temperatures between 30 and 35°C in the mosquito Higher temperatures are associated with higher dengue incidence in humans Occurrence of dengue in Aedes at temperatures above 18-20°C Biological model for suitability of dengue virus transmission across intraannual temperature cycles.
Thailand
McLean et al. 1975
Australi a, Laborat ory Test
Carrington et al., 2013; Tun-Lin et al., 2000
Mexico
Machado-Machado 2012
Laborat ory Test
Watts et al., 1987; McLean et al., 1974
Peru, Singapo re, India Peru, U.S.A.
Chowell et al., 2011; Pinto et al., 2011; Rahel et al., 2011 Chowell et al., 2011; McLean et al., 1974
Global
Bhatt et al. 2013a,b; Gething et al. 2011
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Table 2: Layers included in the model. A variety of spatial data, population density data, and environmental data (including bioclimatic layers, land cover data, and water bodies data) were used in dengue suitability analysis. Category Abbreviation Description Source Elevation data (ALT), 2009 WorldClim, European Space Elevation ALT, GLC, & Global land cover data (GLC), Agency (ESA) and the & Land WBD & 2000 Africa water bodies Université Catholique de cover data (WBD) Louvain (UCL) & UNFAO Population 2011 Human population density POP ORNL - Landscan Density data (POP) BIO12 Annual precipitation MaxPrecip BIO13 Precipitation of wettest month MaxPrecip BIO14 Precipitation of driest month Precipitati MinPrecip on BIO16 Precipitation of wettest quarter MaxPrecip BIO17 Precipitation of driest quarter MinPrecip BIO19 Precipitation of coldest quarter MinPrecip BIO1 Annual mean temperature MaxTemp WorldClim Mean diurnal range (mean of BIO2 monthly (max temp - min MinTemp temp)) BIO4 Temperature seasonality MinTemp (standard deviation *100) Temperatu BIO5 Max temperature of warmest re MaxTemp month BIO6 Min temperature of coldest MinTemp month BIO9 Mean temperature of driest MaxTemp quarter BIO10 Mean temperature of warmest MaxTemp quarter
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Table 3: Given dengue occurrence points, the table compares the observations to the values for a set of predictor variables. The proportion and percentage of the 360 dengue case data points captured by various layers in the GIS are documented. Percenta Cou Tota Factor Parameters ge nt l Included UNIVARIATE MODELS Elevation ALT ≤ 1800 (meters) 346 360 96% Minimum MinTemp ≥ 100 (10° C) 341 360 95% Temperature Human Population POP > 0 308 337 91% Density* GLC ≥ 11 (Irrigated Crops) AND GLC ≤ Land Cover (2009) 306 360 85% 190 (Artificial areas) Maximum MaxTemp ≤ 300 (30° C) 297 360 83% Temperature Max Precipitation BIO13 > 0, BIO16> 0, AND BIO12 ≥ 208 360 58% Average 550 (55mm) Distance from WBD ≤ 200km 147 301 49% Water** Min Precipitation BIO14 > 20 (.2mm) AND BIO17 > 0 78 360 22% Average BIVARIATE MODELS Elevation AND Min ALT ≤ 1800 (meters) AND MinTemp ≥ 341 360 95% Temp 10° C Min Temp AND MinTemp ≥ 10° C AND POP > 0 290 337 86% Population* Elevation AND Max ALT ≤ 1800 (meters) AND MaxTemp ≤ 297 360 83% Temp 30° C Land Cover AND GLC ≥ 11 (Irrigated Crops) AND GLC ≤ 268 337 80% Population* 190 (Artificial areas) AND POP > 0 Max Temp AND Human Population MaxTemp ≤ 30° C AND POP > 0 258 337 77% Density* GLC ≥ 11 (Irrigated Crops) AND GLC ≤ Land Cover AND 190 (Artificial areas) AND MinTemp ≥ 265 360 74% Min Temp 10° C Elevation AND ALT ≤ 1800 (meters) AND POP > 0 233 337 69% Population* GLC ≥ 11 (Irrigated Crops) AND GLC ≤ Land Cover AND 190 (Artificial areas) AND Max Temp ≤ 233 360 65% Max Temp 300 (30° C) Elevation AND Max ALT ≤ 1800 (meters) AND MaxPrecip 213 360 59% Precipitation Avg Parameters Min Temp AND Max MinTemp ≥ 10° C AND MaxPrecip Avg 207 360 58% Precip Parameters 30
ALT ≤ 1800 (meters) AND MaxPrecip Avg Parameters OR MinPrecip Avg 206 360 57% Parameters GLC ≥ 11 (Irrigated Crops) AND GLC ≤ Land Cover AND 190 (Artificial areas) AND MaxPrecip 201 360 56% Max Precipitation Avg Parameters Max Temp AND Max MaxTemp ≤ 30° C AND MaxPrecip Avg 183 360 51% Precip Parameters MinTemp AND MinTemp ≥ 10° C AND MinPrecip Avg 80 360 22% MinPrecip Parameters GLC ≥ 11 (Irrigated Crops) AND GLC ≤ Land Cover AND 190 (Artificial areas) AND MinPrecip 80 360 22% Min Precipitation Avg Parameters Elevation AND Min ALT ≤ 1800 (meters) AND MinPrecip 70 360 19% Precipitation Avg Parameters Max Temp AND Min MaxTemp ≤ 30° C AND MinPrecip Avg 49 360 14% Precip Parameters *Points falling in Jeddah, Saudi Arabia, fell outside the Area of Interest (AOI) for Population Density Data for Africa (23 points were removed) **Only water bodies for Africa were calculated with this analysis (59 points falling outside Africa were removed) *** Points within the Bhatt dataset were not completely the same as points collected in the all variables and parameters w/ 3km buffer. Model was calculated utilizing the Query Factor Input Table (QFit) which generates a predictive analysis query using a set of input data points and a set of raster datasets. Temperature parameters were set to the min and max thresholds established by published literature (10° C and 30° C) Elevation AND Precip (Max OR Min)
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S0001-706X(16)30080-8 http://dx.doi.org/doi:10.1016/j.actatropica.2016.02.018 ACTROP 3862
To appear in:
Acta Tropica
Received date: Revised date: Accepted date:
14-12-2015 20-2-2016 27-2-2016
Please cite this article as: Attaway, David F., Jacobsen, Kathryn H., Falconer, Allan, Manca, Germana, Waters, Nigel M., Risk analysis for dengue suitability in Africa using the ArcGIS Predictive Analysis Tools (PA Tools).Acta Tropica http://dx.doi.org/10.1016/j.actatropica.2016.02.018 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Risk analysis for dengue suitability in Africa using the ArcGIS Predictive Analysis Tools (PA Tools)
David F. Attaway1,2, Kathryn H. Jacobsen3, Allan Falconer1, Germana Manca4, and Nigel M. Waters1,5
1
Department of Geography and GeoInformation Science, George Mason University, 4400 University Drive, MS 6C3, Fairfax VA, 22030-4444, USA 2 Esri, 8615 Westwood Center Drive, Vienna, VA, 22182, USA 3 Department of Global and Community Health, George Mason University, 4400 University Drive, MS 5B7, Fairfax, VA, 22030-4444, USA 4 Laboratory for Geoinformatics and Earth Observations, Department of Geography and Institute for Cyberscience, The Pennsylvania State University, University Park, PA, USA 5 Institute of Public Health, Faculty of Medicine, University of Calgary, 3280 Hospital Drive NW, Calgary, AB, T2N 4Z6, Canada
Corresponding Author: David Frost Attaway Esri 8615 Westwood Center Drive Vienna, Virginia 22182, USA Tel: (703) 506-9515 ext. 5127 Cell: 214-681-6162 Email: [email protected]
Graphical abstract
Highlights:
Provides localized predictions necessary to make informed decisions about where to focus disease prevention and control efforts. A baseline model of dengue range that can be updated as new environmental, population, and health data become available. Adaptable methodologies for mapping emerging disease risks will be vital as global climate changes occur
Abstract Background: Risk maps identifying suitable locations for infection transmission are important 1
for public health planning. Data on dengue infection rates are not readily available in most places where the disease is known to occur. Methods: A newly available add-in to Esri’s ArcGIS software package, the ArcGIS Predictive Analysis Toolset (PA Tools), was used to identify locations within Africa with environmental characteristics likely to be suitable for transmission of dengue virus. Results: A more accurate, robust, and localized (1 km x 1 km) dengue risk map for Africa was created based on bioclimatic layers, elevation data, high-resolution population data, and other environmental factors that a search of the peer-reviewed literature showed to be associated with dengue risk. Variables related to temperature, precipitation, elevation, and population density were identified as good predictors of dengue suitability. Areas of high dengue suitability occur primarily within West Africa and parts of Central Africa and East Africa, but even in these regions the suitability is not homogenous. Conclusion: This risk mapping technique for an infection transmitted by Aedes mosquitoes draws on entomological, epidemiological, and geographic data. The method could be applied to other infectious diseases (such as Zika) in order to provide new insights for public health officials and others making decisions about where to increase disease surveillance activities and implement infection prevention and control efforts. The ability to map threats to human and animal health is important for tracking vectorborne and other emerging infectious diseases and modeling the likely impacts of climate change.
Keywords dengue; geographic information systems; risk mapping; developing countries; medical geography; Africa
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Background With global average temperatures projected to increase between 0.5°F [0.28°C] to 8.6°F [4.78°C] by 2100, with a likely increase of at least 2.7°F [1.5°C] for all scenarios except the one representing the most aggressive mitigation of greenhouse gas emissions (Intergovernmental Panel on Climate Change 2013), an efficient method for modeling how these changes will affect human disease suitability is needed (Stocker et al., 2013). In this paper, we examine the climate factors that predict the presence of dengue fever in Africa. The incidence of dengue is known to be underestimated in African countries, where viral testing capacity is limited and data reporting and surveillance systems are sparse (Amarasinghe et al., 2011; Bhatt et al., 2013a,b; Brady et al., 2012; Franco et al., 2010; and Sessions et al., 2013), so there is a need for improved maps of dengue risk in this region. Dengue fever is thought to be the most rapidly spreading mosquito-borne viral disease in the world (World Health Organization 2012). In the last 50 years, the incidence of dengue has increased 30-fold (World Health Organization 2012). In the past decade, the range of the disease has expanded to new countries and been found across a range of urban and rural settings. Although outbreaks of dengue in Africa have been reported, data on baseline incidence and prevalence rates are not readily available for the region (Amarasinghe et al., 2011). The specific goal of this analysis was to create a more accurate, robust, and localized analysis (1 km x 1 km) of dengue risk mapping in Africa based on bioclimatic layers, elevation data, high-resolution population data, and other environmental factors that our search of the peerreviewed literature showed to be associated with dengue risk (Table 1). This level of spatial resolution was used in a recent map of dengue risk that was developed for the continent of Europe, but is has not previously been used in Africa (Rogers et al., 2014). The key analytic 3
method used in this paper is the ArcGIS Predictive Analysis Tools (PA Tools) function, which incorporates adaptable analysis within the geographic user interface and therefore allows immediate visualizations of how various climate and other inputs affect the suitability model (Esri 2014a). The PA Tools are built to accommodate an iterative detective-like workflow which can be based on historical evidence, doctrine, or pattern of life. This repeatable and shareable tradecraft does not require GIS expertise to use, and the raster-based dynamic approach provides for speed in modeling. The simple interface provides for building queries, and it saves time and disk space compared to similar out-of-the-box GIS tools (Esri 2014a). Improved visualizations of the risk areas for dengue will enable public health officials and policymakers to identify highrisk areas and allow for the most efficient use of dengue prevention and control resources. PA Tools could be applied to a variety of other infectious diseases and other world regions when the transmission of the agent is influenced by environmental variables that show geographical variation. In this paper, we use dengue virus in Africa as a way to highlight the tool’s utility and potential.
Data Sources After reviewing the dengue literature (Table 1), we identified a need to acquire a variety of spatial data, population density data, environmental data (including bioclimatic layers, land cover data, and water bodies data), and disease data. The sources of the data used in our model are specified below. Table 2 summarizes the various layers included in the model.
2.1 Spatial data.
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The Global Administrative Areas (GADM) database contained the current administrative boundary data for Africa (Global Administrative Areas 2012). Data were projected via a projected coordinate system (WGS_1984_Web_Mercator_Auxiliary_Sphere) (Battersby et al., 2014).
2.2 Dengue data. Dengue occurrence data were acquired from a previously published dataset (Bhatt et al., 2013a). These 360 cases from Africa had been identified by the authors of the dataset via searches of online databases, including PubMed, ISI Web of Science, and PROMED, and HealthMap (Bhatt et al., 2013b). The data were current as of 2012 and were vetted through a rigorous quality control assessment (Bhatt et al., 2013b). In order to make our maps comparable to the earlier visualizations created with the existing dataset, we restricted our case data to only those points from the previous analyses.
2.3 Population density data. The accuracy of human infection suitability estimates is dependent on accurate human population density data (Bhatt et al., 2013a,b; Hassan et al., 2012; Moffett et al., 2007; Rogers et al., 2006). The LandScan 2011 Global Population Database contains high-resolution (1 km x 1 km) population density data developed by Oak Ridge National Laboratory (ORNL) for the United States Department of Defense (DoD) (Bright et al., 2011).
2.4 Elevation data.
5
The WorldClim Elevation database contains high-resolution (1km x 1 km) environmental information (Hijmans et al., 2005). Previous studies have suggested that dengue transmission is limited above 1700 meters (Lozano-Fuentes et al., 2012).
2.5 Land cover data. A raster version of the GlobCover (GLC) land cover map produced for the year 2009 was obtained from the European Space Agency (ESA) and the Université Catholique de Louvain (UCL) (Bontemps et al., 2010). The dataset, a 300 m global land cover map produced from an automated classification of the medium resolution imaging spectrometer and full resolution time series (MERIS FR), includes 22 global classes within the raster dataset (Bontemps et al., 2010; Bontemps et al., 2011).
2.6 Bioclimatic layers data. The WorldClim Bioclimatic variables database provided the temperature and rainfall parameters for the model. These were selected from previous scientific studies of mosquito distribution and dengue transmission (Bhatt et al., 2013a,b; Brady et al., 2012; Hijmans et al., 2005; Rogers 2006; Rogers et al., 2006; Rogers et al., 2014; Simmons et al., 2012).
2.7 Water body data. The United Nations’ Food and Agriculture Organization (FAO) database contained water body vector (WBD) information at a scale of 1:1000000 and current as of 2000. This provided a reference scale compatible with the 1km raster data sets (Gassert et al., 2013; Jenness et al.,
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2007; Hoogeveen 2000). This dataset was cross-referenced with the GlobCover 2009 (GLC) dataset for consistency with classifications.
Methods As a first step toward identifying areas suitable to dengue, we evaluated the variables known to be associated with dengue risk, and used a search of the literature to identify parameters for a dengue model (Table 1). For example, temperature parameters were set to the minimum and maximum thresholds established by published literature, 10 °C and 30 °C (Carrington et al., 2013; Halstead 2008; Tun-Lin et al., 2000; Turell and Lundstrom 1990). Similar restrictions were set for elevation, population density, and precipitation. The Query Expression Editor within PA Tools allowed us to find locations where zero to four of the four key environmental risks for dengue were true for a group of single-band rasters (that is, single variable coverages).
𝑄𝑢𝑒𝑟𝑦 𝐸𝑥𝑝𝑟𝑒𝑠𝑠𝑖𝑜𝑛 𝐸𝑞𝑢𝑎𝑡𝑖𝑜𝑛 = ((𝑌1 (𝐻𝑅𝑃1 ) + 𝑌2 (𝐻𝑅𝑃2 ) + 𝑌3 (𝐻𝑅𝑃3 ) + 𝑌4 (𝐻𝑅𝑃4 ))
Y = raster datasets for four high risk parameters HRP = Four High Risk Parameters from Table 1
Highly suitable = All 4 parameters
Somewhat suitable = 3 parameters
Limited suitability = 2 parameters
Highly unsuitable = 1 parameter
Not suitable = 0 parameters
7
To confirm the relevance of these parameters to dengue risk, we calculated the proportion of the 360 dengue case data points (Bhatt et al., 2013a,b) that were observed to fall within each layer of the map (Figure 1) and within combinations of map layers (Figure 2). The results of these calculations are presented in Table 3. These analyses were made using tools within ArcGIS PA Tools. The Query Factor Input Table (QFit) generates a predictive analysis query using a set of input data points (the 360 dengue occurrences) and a set of raster datasets (the predictor environmental variables). The QFit tool allows the user to take a set of observations, such as dengue occurrence points, and then compare these observations to the values for a set of predictor variables, that are represented as a group of raster coverages, in order to determine what values are characteristic of the occurrence points (Equation 2) (Esri 2014a,b).
𝑍 = (𝑋(𝑛 = 360) ∶ 𝑌1 – 𝑌17 )) Z = Final results from running model with all 17 variables listed in Table 2 X = Dengue case data (n = 360 dengue data points) Yn = Layers included in model
Since we already knew the values for the various environmental factors that were most important in determining the locations with high risk of dengue (Table 1), we manually constructed the query for the current dengue fever risk and then applied it to our rasters to see which locations satisfied those conditions. For this model, the 17 variables were grouped into 4 classes – explained in Table 1 – along with the values that were used to classify the suitability. Having all 4 parameters meant the location was deemed to be Highly Suitable. The use of an
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adaptable raster format allows for greater efficiency in computer processing of the models as well as for easy dissemination of results. Model specifications are saved in xml format, which limits the file size and enables easier loading of queries and sharing of analytic workflows (Rezayat 2000). One of the key advantages of using PA Tools is the greater efficiency in processing which occurs from only one raster layer being adaptively processed at a time. PA Tools does not change the pixels of the original raster datasets (that is, elevation, temperature, population density, and precipitation); it only modifies a single function raster dataset, which can be saved as an .afr file. (A function raster dataset, when saved to a file (.afr), defines the processing to be performed on a dataset (Esri 2014a; Hogland and Anderson 2014).) This increases the flexibility of the model when new case data are added as inputs, as might occur when mapping an outbreak, and when a model is run numerous times with modified parameters. More importantly, this method limits the amount of memory required to complete the analysis. Before the PA Tools procedure was created, this analysis required the researcher to process each and every individual layer for each specification separately and then to use time, computer resources, and data space to combine all layers into one single analysis layer. The researcher would then have to export each iteration that occurred using different variables. For example, if the researcher wanted to calculate the difference between elevation and temperature datasets, the analyst would have to perform the calculations and then export the results, run statistics on the data, and recalculate the suitability. PA Tools speeds up this processing, even on older computers, by using QFit and the Query Expression Editor to quickly and efficiently visualize the effects of various model inputs. The dengue suitability analysis in this paper uses PA Tools to demonstrate how different variables contribute to predictions of dengue risk. This analytic approach limits the spatial bias
9
that results from only using presence points as the basis for suitability, but also uses previous research data to validate the model outputs. Emerging infections like dengue highlight the need for more efficient, quicker, and cheaper processing of health risks. Suitability mapping provides entomological, epidemiological, and geographical researchers with an opportunity to increase their analytic efficiency and the rapid dissemination of their investigations.
Results and Discussion We used the risk factors identified in published literature to assign risk values to various parameters, as per Table 1. For example, we assumed that places with low temperatures below 10°C (BIO6) and places with high temperatures above 30°C (BIO1, BIO5, BIO9, and BIO10) were considered to have very low risk of dengue. We confirmed that these environmentally highrisk areas were conducive to dengue transmission by evaluating how many of the 360 case points were captured within high risk areas (Table 2). Figure 3 shows the map that was created by marking all areas where one or more of the risk factors from Table 1—elevation, population density, temperature, or precipitation—were present. Dark red areas mark 1 km x 1 km squares where all four parameters were present, indicating high suitability for dengue outbreaks. Light red shows places where three of the parameters were present: elevation, population density, and temperature. Yellow identifies locations where elevation and temperature criteria were met. Blue areas met the elevation criteria. Dark blue shows locations where none of the four high risk parameters exist. Figure 4 shows a map that compares the high risk (dark red) areas from Figure 3, which were identified by PA Tools using environmental data only, with a layer created from the dengue case data (shown in blue in Figure 4). The blue areas in Figure 4 were mapped by using the QFit
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Modeler to identify areas with elevation, population density, temperature, and precipitation data similar to the values found at the locations of the 360 dengue cases. In short, the QFit Modeler captures not only the dengue points but also those points that have the same environmental characteristics as the places where dengue has been observed. The QFit model captured 86% of the dengue case data. (The model did not capture 100% of the points because locations within Chad, Mali, Saudi Arabia, Sudan, Northern Somalia, and Yemen represented anomalies where locations did not fulfill the high risk parameters in Table 1.) The PA Tools suitability risk analysis based on environmental parameters (in red) captured 83% of the cases from the original dataset when using a 3km search distance and 67% of the cases with no search radius. Human population density at a localized scale is a commonly identified variable for disease suitability modeling (Bhatt et al., 2013a,b; Hassan et al., 2012; Moffett et al., 2007; Rogers et al., 2006). We found that, after considering the environmental parameters for the infection, higher population density is associated with a higher suitability for dengue. Our model incorporated a more localized population density analysis than previous studies (Bhatt et al., 2013a,b; Brady et al., 2012; Rogers et al., 2014; Rogers 2006; Simmons et al., 2012). We found that cases are more likely to be identified in high-density population centers than those located far from health services. This finding may be due simply to the fact that urban cases of dengue are more likely to be diagnosed and reported than rural cases. However, this finding is consistent with other studies of dengue around the world, as noted in our literature search it is incumbent on dengue fever researchers to extend their research into locations that are remote from existing health services. The PA Tools risk map also found additional locations, such as Luanda, Angola, which are suitable environments for dengue outbreaks. Dengue cases from Angola were not included in
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the 360 cases that were compiled through the end of 2012 (Bhatt et al., 2013a,b), so no Angolan cases were included in our QFit risk model (Bhatt et al., 2013a). However, the PA Tools risk map accurately predicted that Luanda was a suitable dengue habitat. Indeed, an outbreak in Angola occurred from April to June 2013 (Sessions et al., 2013). Incorporating additional analysis variables has the potential to create more high suitability areas which could be located using PA Tools even without nearby occurrence points (Sessions et al., 2013). We also identified other isolated locations that could be high risk areas for dengue, such as:
Central African Republic near Bangui and near the western border adjacent to Cameroon
Democratic Republic of Congo (DRC) along the border between South Sudan and the south west border neighboring Angola
the northwestern portion of Ethiopia along the border between Sudan and South Sudan
Kenya along one isolated location adjacent to the northern border between Ethiopia and near the coastal areas of Mombasa, Malindi, and Kilifi
portions of the countries Liberia and Sierra Leone
South Sudan near Juba and along the border between Uganda and the DRC
Tanzania along the coast near Dar es Salaam and continuing sporadically in isolated locations along the coast to Mozambique The identification of potential high risk areas allows for proactive disease prevention and
control measures to be implemented before outbreaks occur. As environmental conditions change, re-running the PA Tools model with revised inputs will provide critical predictions of additional locations that have become suitable environments for dengue fever. It will also be important to update the high-risk values in Table 1 to refine the mapping parameters as new risk factor data become available.
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Our new maps extend the analyses of dengue risk previously published for Africa (Bhatt et al., 2013a,b), and incorporate additional parameters for predicting the locations where dengue suitability is highest. This localized high-resolution analysis for Africa provides a more detailed map of dengue risk than previous studies that focused on global scale analyses (Bhatt et al., 2013a,b; Brady et al., 2012; Rogers et al., 2006; Rogers et al., 2014; Rogers 2006; Simmons et al., 2012). Similar methods can be used to integrate spatial modeling techniques for other diseases such as malaria (Kienberger and Hagenlocher 2014). Our approach provides an efficient way to analyze the environmental variables and tailor the analysis based on both predictor variables (raster datasets) and case location data (point data). The places shown in red in Figures 3 and 4 are the locations currently at highest risk of dengue, and are therefore the sites where dengue prevention activities such as vector control and public health education will be most costeffective, especially with the first dengue fever vaccine approved by Mexico (Pollack 2015). The maps will need to be updated as new climate data become available and as more dengue case reports are compiled (Kienberger and Hagenlocher 2014; Collenberg et al., 2006). Finally, the predictive mapping described in the present paper at the macro level and across continental Africa complements and extends other predictive mapping studies that have been conducted at the micro level. For example, a predictive map of dengue fever in three Colombian cities (Bello, Medellín and Itagüí) found that the variables with strong predictive power included high humidity and temperature, but not elevation (Arboleda and Peterson 2009). That study also noted inter-urban differences in variable importance and suggested “human social dimensions” to disease transmission including presumably the ability of individuals to protect themselves from infection (Arboleda and Peterson 2009). Incorporating additional socioeconomic and behavioral variables into a PA Tools analysis will improve detailed localized
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maps. Local and regional intervention strategies for diseases transmitted by the Aedes aegypti mosquito need to be prioritized (Conn et al., 2015). This need has become even more critical as the Zika virus has spread across the Americas (Bogoch et al., 2016). Our approach and those of previous geographers (Rogers et al., 2014; Collenberg et al., 2006) point toward strategies for public health interventions.
Conclusions The adaptable analysis incorporated within this paper demonstrates how more localized and useful disease risk maps can be efficiently created with PA Tools and the appropriate predictor variables. The dengue suitability maps produced using PA Tools and presented in this paper used a variety of spatial data and risk factor information from the peer-reviewed literature to provide a more detailed assessment of the places in Africa that are likely to have the highest risk for dengue fever. PA Tools allowed us to efficiently, quickly, and inexpensively create new maps of dengue risk that can be used for planning public health interventions. This type of dengue risk analysis provides the localized predictions necessary for various governmental entities (both local and national) to make informed decisions about where to focus their disease prevention and control efforts and where to step up health surveillance activities. The PA Tools methodology demonstrated here also provides a baseline model of dengue range that can be updated with PA Tools as new environmental, population, and health data become available. Adaptable methodologies for mapping emerging disease risks will be vital as global climate changes occur.
Competing interests
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The authors declare that they have no competing interests.
Authors’ contributions DFA, KHJ, and NMW outlined the paper. DFA drafted the manuscript. All authors were involved in study conception and analysis, provided critical revisions, and approved the submission of the final manuscript.
Acknowledgments Many thanks to Professor Simon Iain Hay and colleagues for making their data published in Nature available for use in testing this methodology. Thanks also to the staff from Esri, especially Ian Campbell, Keith Ailshie, Dr. Lauren Scott, and Lauren Rosenshein Bennett, for their assistance and cooperation in making these new spatial tools available for testing prior to their public release. We would also like to thank Oak Ridge Laboratory for making their LandScan 2011 data available for use in the model.
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49. Wiwanitkit, V., 2006. An observation on correlation between rainfall and the prevalence of clinical cases of dengue in Thailand. J. Vector. Borne. Dis. 43, 73. 50. World Health Organization, 2012. Global strategy for dengue prevention and control 2012-2020, WHO Press, France. http://apps.who.int/iris/bitstream/10665/75303/1/9789241504034_eng.pdf (accessed 16.02.19).
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Figure 1 - Suitability models run for various univariate variables.
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Figure 2 - Suitability models run for various bivariate variables.
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Figure 3 – Africa ArcGIS Predictive Analysis Tools (PA Tools) Analysis.
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Figure 4 - Comparison of two PA Tools models run for dengue suitability in Africa.
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Temperature
Precipitation
Population Density
Elevation & Land cover
Table 1: Peer reviewed literature documenting environmental constraints for dengue. Dengue suitability were grouped into 4 classes based off the high risk parameters. Having a highly suitable = All 4 parameters, Somewhat suitable = 3 parameters, Limited suitability = 2 parameters, Highly unsuitable = 1 parameter, and Not suitable = 0 parameters. Catego Locatio High Risk Evidence Source ry n Parameters Brazil, Anything above 1500 meters limits Hagenlocher et al., ALT ≤ 1800 Colombi dengue 2013 AND a (11 ≤ GLC ≤ Ae. aegypti mosquitoes were 190 abundant at elevations up to 1300 m, OR Lozano-Fuentes et moderately abundant from 1300 to Mexico WBD ≤ al., 2012 1700 m, and still present but rare 200km) from 1700 to 2150 m Dengue fever is characterized by high human population densities more or Global Rogers et al. 2014 less wherever it occurs POP > 0 Population density in itself is a good Malaysi Hassan et al. 2012 measure of risk. a Thailand BIO12 ≥ Greater amounts of precipitation are , Wiwanitkit 2006, 55mm, BIO13 associated with higher dengue Singapo Heng et al., 1998 > 0, BIO14 > infection risk re .2mm, BIO16> 0, High volume of rainfall increases BIO17 > 0, Malaysi Aedes survival by providing more Hassan et al., 2012 AND BIO19 a breeding sites. >0 Laborat Turell & Lundstrom Infertile eggs at 10 degrees Celsius ory Test 1990 Ae. aegypti needs a winter isotherm Global Halstead 2008 higher than 10°C for its survival Ae. aegypti are capable of transmitting virus under laboratory Laborat McLean et al., 1975; conditions after incubation at ory Test Watts et al., 1987 temperatures as low as 13°C MinTemp ≥ 10 °C AND Temperature around an intermediate MaxTemp ≤ mean (26°C) can alter life-history 30 °C traits, population dynamics, and the Laborat Watts et al., 1987; ability of a mosquito to become ory Test McLean et al., 1974 infected with and transmit dengue virus (DENV) Turell & Lundstrom Lower temperatures generally extend Laborat 1990; McLean et al., the duration of Extrinsic incubation ory Test 1975; Rohani et al., period (EIP) 2009
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Ae. aegypti vector competence for DENV has been detected up to a maximum of 35°C Adult reproductive function is impaired in the high 30°s with adult Ae. aegypti survival declining as temperature continues to rise The variables ‘minimum temperature of the coldest month’ and ‘mean temperature of the coldest quarter’ were the most important variables for modeling dengue. Extrinsic incubation period (EIP) of the dengue virus decreases at temperatures between 30 and 35°C in the mosquito Higher temperatures are associated with higher dengue incidence in humans Occurrence of dengue in Aedes at temperatures above 18-20°C Biological model for suitability of dengue virus transmission across intraannual temperature cycles.
Thailand
McLean et al. 1975
Australi a, Laborat ory Test
Carrington et al., 2013; Tun-Lin et al., 2000
Mexico
Machado-Machado 2012
Laborat ory Test
Watts et al., 1987; McLean et al., 1974
Peru, Singapo re, India Peru, U.S.A.
Chowell et al., 2011; Pinto et al., 2011; Rahel et al., 2011 Chowell et al., 2011; McLean et al., 1974
Global
Bhatt et al. 2013a,b; Gething et al. 2011
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Table 2: Layers included in the model. A variety of spatial data, population density data, and environmental data (including bioclimatic layers, land cover data, and water bodies data) were used in dengue suitability analysis. Category Abbreviation Description Source Elevation data (ALT), 2009 WorldClim, European Space Elevation ALT, GLC, & Global land cover data (GLC), Agency (ESA) and the & Land WBD & 2000 Africa water bodies Université Catholique de cover data (WBD) Louvain (UCL) & UNFAO Population 2011 Human population density POP ORNL - Landscan Density data (POP) BIO12 Annual precipitation MaxPrecip BIO13 Precipitation of wettest month MaxPrecip BIO14 Precipitation of driest month Precipitati MinPrecip on BIO16 Precipitation of wettest quarter MaxPrecip BIO17 Precipitation of driest quarter MinPrecip BIO19 Precipitation of coldest quarter MinPrecip BIO1 Annual mean temperature MaxTemp WorldClim Mean diurnal range (mean of BIO2 monthly (max temp - min MinTemp temp)) BIO4 Temperature seasonality MinTemp (standard deviation *100) Temperatu BIO5 Max temperature of warmest re MaxTemp month BIO6 Min temperature of coldest MinTemp month BIO9 Mean temperature of driest MaxTemp quarter BIO10 Mean temperature of warmest MaxTemp quarter
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Table 3: Given dengue occurrence points, the table compares the observations to the values for a set of predictor variables. The proportion and percentage of the 360 dengue case data points captured by various layers in the GIS are documented. Percenta Cou Tota Factor Parameters ge nt l Included UNIVARIATE MODELS Elevation ALT ≤ 1800 (meters) 346 360 96% Minimum MinTemp ≥ 100 (10° C) 341 360 95% Temperature Human Population POP > 0 308 337 91% Density* GLC ≥ 11 (Irrigated Crops) AND GLC ≤ Land Cover (2009) 306 360 85% 190 (Artificial areas) Maximum MaxTemp ≤ 300 (30° C) 297 360 83% Temperature Max Precipitation BIO13 > 0, BIO16> 0, AND BIO12 ≥ 208 360 58% Average 550 (55mm) Distance from WBD ≤ 200km 147 301 49% Water** Min Precipitation BIO14 > 20 (.2mm) AND BIO17 > 0 78 360 22% Average BIVARIATE MODELS Elevation AND Min ALT ≤ 1800 (meters) AND MinTemp ≥ 341 360 95% Temp 10° C Min Temp AND MinTemp ≥ 10° C AND POP > 0 290 337 86% Population* Elevation AND Max ALT ≤ 1800 (meters) AND MaxTemp ≤ 297 360 83% Temp 30° C Land Cover AND GLC ≥ 11 (Irrigated Crops) AND GLC ≤ 268 337 80% Population* 190 (Artificial areas) AND POP > 0 Max Temp AND Human Population MaxTemp ≤ 30° C AND POP > 0 258 337 77% Density* GLC ≥ 11 (Irrigated Crops) AND GLC ≤ Land Cover AND 190 (Artificial areas) AND MinTemp ≥ 265 360 74% Min Temp 10° C Elevation AND ALT ≤ 1800 (meters) AND POP > 0 233 337 69% Population* GLC ≥ 11 (Irrigated Crops) AND GLC ≤ Land Cover AND 190 (Artificial areas) AND Max Temp ≤ 233 360 65% Max Temp 300 (30° C) Elevation AND Max ALT ≤ 1800 (meters) AND MaxPrecip 213 360 59% Precipitation Avg Parameters Min Temp AND Max MinTemp ≥ 10° C AND MaxPrecip Avg 207 360 58% Precip Parameters 30
ALT ≤ 1800 (meters) AND MaxPrecip Avg Parameters OR MinPrecip Avg 206 360 57% Parameters GLC ≥ 11 (Irrigated Crops) AND GLC ≤ Land Cover AND 190 (Artificial areas) AND MaxPrecip 201 360 56% Max Precipitation Avg Parameters Max Temp AND Max MaxTemp ≤ 30° C AND MaxPrecip Avg 183 360 51% Precip Parameters MinTemp AND MinTemp ≥ 10° C AND MinPrecip Avg 80 360 22% MinPrecip Parameters GLC ≥ 11 (Irrigated Crops) AND GLC ≤ Land Cover AND 190 (Artificial areas) AND MinPrecip 80 360 22% Min Precipitation Avg Parameters Elevation AND Min ALT ≤ 1800 (meters) AND MinPrecip 70 360 19% Precipitation Avg Parameters Max Temp AND Min MaxTemp ≤ 30° C AND MinPrecip Avg 49 360 14% Precip Parameters *Points falling in Jeddah, Saudi Arabia, fell outside the Area of Interest (AOI) for Population Density Data for Africa (23 points were removed) **Only water bodies for Africa were calculated with this analysis (59 points falling outside Africa were removed) *** Points within the Bhatt dataset were not completely the same as points collected in the all variables and parameters w/ 3km buffer. Model was calculated utilizing the Query Factor Input Table (QFit) which generates a predictive analysis query using a set of input data points and a set of raster datasets. Temperature parameters were set to the min and max thresholds established by published literature (10° C and 30° C) Elevation AND Precip (Max OR Min)
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