Detecting space-time disease clusters with arbitrary shapes and sizes using a co-clustering approach

Ullah, Sami; Daud, Hanita; Dass, Sarat C.; Khan, Habib Nawaz; Khalil, Alamgir

doi:10.4081/gh.2017.567

Cited by 16 publications

(12 citation statements)

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“…It has important applications in the spatiotemporal data analysis. For example, in [ 15 ], the heatmap was used for spatiotemporal pattern recognition in the average daily temperature data, and in [ 16 ], for visualization of the space-time clusters in the malaria case data. Heatmap visualization of the high-risk clusters can be helpful to conduct the real-time and online surveillance.…”

Section: Introductionmentioning

confidence: 99%

An Eigenspace approach for detecting multiple space-time disease clusters: Application to measles hotspots detection in Khyber-Pakhtunkhwa, Pakistan

et al. 2018

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Identifying the abnormally high-risk regions in a spatiotemporal space that contains an unexpected disease count is helpful to conduct surveillance and implement control strategies. The EigenSpot algorithm has been recently proposed for detecting space-time disease clusters of arbitrary shapes with no restriction on the distribution and quality of the data, and has shown some promising advantages over the state-of-the-art methods. However, the main problem with the EigenSpot method is that it cannot be adapted to detect more than one spatiotemporal hotspot. This is an important limitation, since, in reality, we may have multiple hotspots, sometimes at the same level of importance. We propose an extension of the EigenSpot algorithm, called Multi-EigenSpot that is able to handle multiple hotspots by iteratively removing previously detected hotspots and re-running the algorithm until no more hotspots are found. In addition, a visualization tool (heatmap) has been linked to the proposed algorithm to visualize multiple clusters with different colors. We evaluated the proposed method using the monthly data on measles cases in Khyber-Pakhtunkhwa, Pakistan (Jan 2016- Dec 2016), and the efficiency was compared with the state-of-the-art methods: EigenSpot and Space-time scan statistic (SaTScan). The results showed the effectiveness of the proposed method for detecting multiple clusters in a spatiotemporal space.

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Section: Introductionmentioning

confidence: 99%

An Eigenspace approach for detecting multiple space-time disease clusters: Application to measles hotspots detection in Khyber-Pakhtunkhwa, Pakistan

et al. 2018

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“…Since linear interpolation is often inaccurate for non-linear trends like population growth rate, we would like to see more frequent population assessments conducted in regions where dengue is an ongoing risk, and while we understand that resources may not easily allow for this, the role of national census efforts in public health is often under-appreciated. Second, the cylindrical shape of the clusters does not represent the true shape of the clusters, while it is possible to use irregular search windows [46–48]. Third, the STSS reports the relative risk for the entire study period, while relative risk will likely vary temporally.…”

Section: Discussionmentioning

confidence: 99%

Detecting space-time clusters of dengue fever in Panama after adjusting for vector surveillance data

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Eskildsen

et al. 2019

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206 words 26 Long term surveillance of vectors and arboviruses is an integral aspect of disease prevention and 27 control systems in countries affected by increasing risk. Yet, little effort has been made to adjust space-28 time risk estimation by integrating disease case counts with vector surveillance data, which may result in 29 inaccurate risk projection when several vector species are present, and little is known about their likely 30 role in local transmission. Here, we integrate 13 years of dengue case surveillance and associated Aedes 31 occurrence data across 462 localities in 63 districts to estimate the risk of infection in the Republic of 32 Panama. Our space-time modelling approach detected the presence of five clusters, which varied by 33 duration, relative risk, and spatial extent after incorporating vector species as covariates. Dengue 34 prevalence (n = 49,910) was predicted by the presence of resident Aedes aegypti alone, while all other 35 covariates exhibited insignificant statistical relationships with it, including the presence and absence of 36invasive Aedes albopictus. Furthermore, the Ae. aegypti model contained the highest number of districts 37 with more dengue cases than would be expected given baseline population levels. This implies that 38 arbovirus case surveillance coupled with entomological surveillance can affect cluster detection and risk 39 estimation, improving efforts to understand outbreak dynamics at national scales. 40 41 Author Summary 42 Dengue cases have increased in tropical regions worldwide owing to climate change, 43 urbanization, and globalization facilitating the spread of Aedes mosquito vectors. National surveillance 44 programs monitor trends in dengue fever and inform the public about epidemiological scenarios where 45 outbreak preventive actions are most needed. Yet, most estimations of dengue risk so far derive only 46from disease case data, ignoring Aedes occurrence as a key aspect of dengue transmission dynamic. 47Here we illustrate how incorporating vector presence and absence as a model covariate can considerably 48 alter the characteristics of space-time cluster estimations of dengue cases. We further show that Ae.3 49 aegypti has likely been a greater driver of dengue infection in high risk districts of Panama than Ae. 50 albopictus, and provide a discussion of possible public health implications of both spatial and non-51 spatial model outcomes. 52 Text Word Count: 3658 53 Introduction 54 Dengue fever, a disease transmitted to humans by Aedes mosquitoes, is endemic to 128 55 countries, with 3.9 billion people considered at-risk [1]. Dengue fever cases have increased dramatically 56 worldwide throughout the previous several decades [2], likely a result of climate change [3], 57 urbanization [4], globalization [5], and the spread of the invasive Aedes albopictus [6]. As a result of 58 both recent and historical risk, many countries employ national surveillance programs to monitor trends 59 in dengue fever and inform local health authorities to the pl...

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“…It must be noted that the cylindrical shape of the clusters does not represent the true shape of the clusters. While it is possible to use irregular search windows [34–36], cluster borders in the model outputs are not perfectly in line with the borders of the risk in reality.…”

Section: Methodsmentioning

confidence: 99%

Detecting space-time clusters of dengue fever in Panama after adjusting for vector surveillance data

et al. 2019

View full text Add to dashboard Cite

Long term surveillance of vectors and arboviruses is an integral aspect of disease prevention and control systems in countries affected by increasing risk. Yet, little effort has been made to adjust space-time risk estimation by integrating disease case counts with vector surveillance data, which may result in inaccurate risk projection when several vector species are present, and when little is known about their likely role in local transmission. Here, we integrate 13 years of dengue case surveillance and associated Aedes occurrence data across 462 localities in 63 districts to estimate the risk of infection in the Republic of Panama. Our exploratory space-time modelling approach detected the presence of five clusters, which varied by duration, relative risk, and spatial extent after incorporating vector species as covariates. The Ae. aegypti model contained the highest number of districts with more dengue cases than would be expected given baseline population levels, followed by the model accounting for both Ae. aegypti and Ae. albopictus. This implies that arbovirus case surveillance coupled with entomological surveillance can affect cluster detection and risk estimation, potentially improving efforts to understand outbreak dynamics at national scales.

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Detecting space-time disease clusters with arbitrary shapes and sizes using a co-clustering approach

Cited by 16 publications

References 16 publications

An Eigenspace approach for detecting multiple space-time disease clusters: Application to measles hotspots detection in Khyber-Pakhtunkhwa, Pakistan

An Eigenspace approach for detecting multiple space-time disease clusters: Application to measles hotspots detection in Khyber-Pakhtunkhwa, Pakistan

Detecting space-time clusters of dengue fever in Panama after adjusting for vector surveillance data

Detecting space-time clusters of dengue fever in Panama after adjusting for vector surveillance data

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