Identifying and Visualizing Space-Time Clusters of Vector-Borne Diseases Michael Desjardins 1,2 , Alexander Hohl 3, , Eric Delmelle 1,2 , Irene Casas 4 1 Center for Applied Geographic Information Science, University of North Carolina at Charlotte, Charlotte, NC 28223, U.S.A. 2 Department of Geography and Earth Sciences, University of North Carolina at Charlotte, Charlotte, NC 28223, U.S.A. 3 Department Geography, The University of Utah, Salt Lake City, UT, 84112, U.S.A. 4 Department of Social Sciences, Louisiana Tech University, Ruston, LA 71272, U.S.A. Abstract Annually, vector-borne diseases (VBDs) such as malaria, dengue fever, chikungunya, and Zika are responsible for over one billion infections and one million deaths around the world. Many VBDs are showing an expanded range due to climate change, overpopulation, globalization, urbanization, and other factors. As a result, novel outbreaks are occurring in many regions of the 1
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Identifying and Visualizing Space-Time Clusters of Vector-Borne Diseases
Michael Desjardins1,2, Alexander Hohl3,, Eric Delmelle1,2, Irene Casas4
1 Center for Applied Geographic Information Science, University of North Carolina at Charlotte,
Charlotte, NC 28223, U.S.A.
2 Department of Geography and Earth Sciences, University of North Carolina at Charlotte,
Charlotte, NC 28223, U.S.A.
3 Department Geography, The University of Utah, Salt Lake City, UT, 84112, U.S.A.
4 Department of Social Sciences, Louisiana Tech University, Ruston, LA 71272, U.S.A.
Abstract
Annually, vector-borne diseases (VBDs) such as malaria, dengue fever, chikungunya, and Zika
are responsible for over one billion infections and one million deaths around the world. Many
VBDs are showing an expanded range due to climate change, overpopulation, globalization,
urbanization, and other factors. As a result, novel outbreaks are occurring in many regions of the
world, such as the Americas and the Caribbean. Due to the increased presence of VBDs, it is
critical to identify significant disease clusters to facilitate surveillance and intervention strategies
with the goal of mitigating future outbreaks. A variety of space-time methods exist to identify
significant space-time clusters of VBDs, such as the space-time Kernel Density Estimation and
space-time scan statistics (SaTScan). However, the vast majority of the literature solely
visualizes the results in two-dimensions (2D), simply showing the spatial dimensions of the
disease clusters. Visualizing space-time clusters in a three-dimensional (3D) environment can
improve the understanding of the spatiotemporal dynamics of a disease outbreak, especially the
size, duration, and movement of the clusters during the study period. Furthermore, 2D and 3D
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visualization methods can complement each other, further improving our understanding of VBD
outbreaks. This chapter will discuss some popular space-time methods to identify significant
space-time clusters of VBDs, and present 2D and 3D visualization techniques that can improve
the understanding of space-time cluster dynamics. We provide a case study of VBDs in
Colombia during outbreaks in 2015 and 2016 using a SaTScan approach with aggregated case
and population data. The strengths and limitations of each method will also be discussed to shed
light on the appropriateness of using each approach.
Department (cluster 1) had a combined relative risk of 2.49, with 675 observed cases of CHIK
and 20,990 observed cases of DENF.
4.6 Visualizing CHIK and DENF Clusters in 3D
Figures 3-5 visualize the space-time clusters of CHIK, DENF, and co-occurrence of
CHIK and DENF in a 3D-enviornment, respectively. The design of the 3D visualizations
include the following elements: (1) cylinders representing the size, location, and the duration of
the cluster; (2) black rings around each cluster represent a particular week during the study
period; (3) a 2D layer of the municipalities belonging to a cluster, which is superimposed on
Colombia; (4) a 2D layer of the radii of the clusters superimposed on Colombia; (5) labels that
denote a cluster’s ID; and (6) two temporal axis with labels to denote the start and end dates of
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each cluster. The 3D visualizations improve the conceptualization of the space-time dynamics of
the reported clusters.
For example, figure 3 shows that the five space-time clusters of CHIK began and ended
during the first half of 2015. Clusters 1-3 lasted the longest, while affecting the south-central
portions of Colombia. Clusters 4 and 5 occurred in the north-central portions of the country,
while they had very short durations between January and March of 2015, respectively. Figure 4
shows that two DENF clusters occurred in 2015 (2 and 3), while four (1, 4-6) occurred in 2016.
Cluster 2 in the central region of Colombia began in January 2015 and lasted until late June
2015. The next cluster (3) appeared in August 2015, which lasted until January 2016. The four
clusters of DENF in 2016 all began in January and lasted until June and July; while they affected
the central and western portions of the country. Figure 5 clearly indicates that four out of the six
multivariate clusters occurred during 2015, with two occurring in 2016. Again, clusters 3 and 4
only include significant clustering of DENF, not significant co-occurrence of both DENF and
CHIK (Table 3). Therefore, 2015 was a more severe epidemic year regarding the co-occurrence
of DENF and CHIK, since clusters 1, 2, and 6 occurred in the first half of 2015; while cluster 5
was the only cluster displaying significant co-occurrence in 2016.
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Figure 4. 3D visualization of the DENF space-time clusters in Colombia (2015-2016)
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Figure 3. 3D visualization of the CHIK space-time clusters in Colombia (2015-2016)
Figure 5. 3D visualization of the multivariate space-time clusters in Colombia (2015-2016)
4.8 Discussion
The results of the case study highlights statistically significant space-time clusters of
DENF, CHIK, and regions of simultaneous excess incidence of both diseases (see multivariate
results). The reported space-time clusters of the univariate and multivariate cases correspond to
regions of suitable habitat ranges of Ae. aegypti and Ae. albopictus. However, due to the
cylindrical scanning window of the statistic, there are municipalities found in a cluster that are
above 1.7 kilometers (Aedes rarely found above this threshold). To circumvent this issue of
selecting municipalities where transmission is rare, relative risk was reported for each
municipality belonging to a cluster. Many of the municipalities with a relative risk of 0 are
found in regions with an elevation greater than 1.7 km. Reporting and visualizing the relative
risk for each municipality also facilities targeted interventions by identifying the municipalities
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that have statistically significant excess cases of each disease (i.e. RR > 1), reducing the
uncertainty of solely reporting the space-time clusters.
The multivariate STSS reported four clusters of space-time co-occurrence of both DENF
and CHIK. Since CHIK just recently appeared in Colombia, it is important to identify areas of
co-circulation with DENF, which is hyperendemic in many regions of the country. Since the
clinical manifestations of DENF and CHIK (also Zika) are similar, identifying the correct disease
via clinical diagnosis is challenging in regions of co-circulation (Silva Jr. et al. 2018). Unlike
DENF, chronic complications following a CHIK infection are common (de Andrade et al. 2010),
which may last for weeks, months, and even years. Therefore, it is critical to implement timely
and effective diagnostic methods (e.g. laboratory testing) to confirm the viral etiology between
DENF, CHIK, and Zika. Reducing misdiagnosis is especially important in areas of co-
circulation, and identifying areas that experience simultaneous outbreaks of DENF, CHIK, or
Zika (e.g. via multivariate STSS) can facilitate targeted interventions. Co-infection of DENF
and CHIK is also possible, however, there has not been any observable clinical significance,
such as exacerbated symptoms (Furuya-Kanamori et al. 2016).
The 3D visualizations (figures 3-5) can improve the understanding of the size, duration,
and movement of space-time clusters of disease (Desjardins et al. 2018a). 3D visualizations
should supplement traditional 2D approaches (Desjardins et al. 2018b), especially for space-time
analyses that include a large number of temporal observations. Otherwise, key space-time
patterns can be masked by solely using 2D techniques. However, the 3D visualizations provided
are static and crowding and occlusion could have been an issue if there were a larger number of
reported space-time clusters. Integrating the 3D visualizations in an interactive environment
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(e.g. web-GIS platform) can improve their effectiveness by allowing the user to move around the
image, for example.
The univariate and multivariate STSS approaches coupled with the 2D and 3D
visualizations are an example of exploratory VBD surveillance. The results can be used to
improve targeted interventions by identifying statistically significant space-time clusters, while
shedding light on which regions experienced the greatest burden of DENF and CHIK (i.e.
reporting relative risk per municipality). Further research can examine the risk factors that
influence VBD incidence and risk in the reported space-time clusters, while analysis at fine
geographic scales (e.g. neighborhoods) is necessary for local prevention and mitigation of DENF
and CHIK. Furthermore, we used aggregated data to detect the space-time clusters; which does
not reveal the exact locations where the infected individuals reside. If available, disaggregated
(individual-level, case data) should be utilized to minimize locational uncertainty of high-risk
locations. However, many privacy laws (e.g. HIPAA) require that data is aggregated to protect
the privacy of individuals.
5. Conclusions
Disease surveillance has become a vibrant field of research at the intersection of
statistics, computing, and health geography. The space-time clustering methods and visualization
approaches described in this chapter are not an exhaustive list (e.g. space-time Moran’s I – see
Lee and Li 2017), rather an example of some of the most commonly used exploratory techniques
in geospatial health. Overall, exploratory space-time cluster approaches should be used to shed
light on the space-time dynamics of epidemics and outbreaks and highlight the areas that
experienced the greatest burden of disease. Subsequent research is necessary to understand the
factors that influence disease transmission, while fine-level analysis (e.g. neighborhoods) can
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uncover local variations of disease incidence within at-risk areas. More research efforts should
focus on evaluating the effectiveness of 3D visualization approaches for space-time clusters,
such as user studies. 3D visualizations can also benefit from interactive environments that allow
the user to navigate freely, rather that static images (such as figures 3-5). Software that
specializes in space-time clustering techniques, such as SaTScan, may not allow visualization of
the results, which requires familiarity and training with a GIS and other visualization software.
Future developments in software should integrate visualization functionality to streamline
subsequent analysis. As novel technologies emerge and data becomes available, new
epidemiological questions will arise requiring to investigate additional facets of space-time
analytics. For instance, population data become increasingly detailed with respect to their spatial
and temporal resolutions, which will enable us to adjust clustering methods for spatially and
temporally inhomogeneous background populations. In addition, as techniques for tracking or
inferring individual people’s location are already available at large scales, research about space-
time disease clustering may shift focus from the point- and polygon-based paradigms to
trajectory-based methods.
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