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

ABSTRACT: 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.

SUBMITTER: Ullah S

PROVIDER: S-EPMC6007829 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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

Ullah Sami S Daud Hanita H Dass Sarat C SC Fanaee-T Hadi H Khalil Alamgir A

PloS one 20180619 6

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 ca ...[more]

PMID: 29920540

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Project description:BackgroundHepatitis C virus (HCV) infection is a debilitating chronic health problem and can be fatal if left untreated. Illness perceptions are self-manifested beliefs that influence the ability of individuals to cope with their disease and perceive it as manageable or threatening condition. Limited evidence is available from low resource settings regarding patient perception about HCV. In this study, we aimed to assess the perception of individuals with HCV, the impact of their sociodemographic and clinical characteristics on their HCV perception, and its link to patient-oriented treatment outcomes.MethodsA cross-sectional survey was undertaken enrolling individuals with HCV who attended Hepatitis C clinics at two hospitals of Khyber Pakhtunkhwa, Pakistan. Illness perception was measured using Brief Illness Perception Questionnaire (BIPQ). Descriptive statistics, Kruskal Wallis tests and Mann Whitney U tests were performed to study patient sociodemographic and clinical characteristics and to analyze the questionnaire results. Multivariable linear regression was used to assess determinants associated with perception scores.ResultsParticipants represented poor HCV perception and their overall mean BIPQ score was 43.35, SD = 13.15. Participants had a low degree of understanding about their illness (mean coherence score = 2.92, SD = 1.85). Individuals with more than four years, compared to less than one year, of estimated HCV infection were more likely to view that their illness would continue (mean timeline score = 6.27, SD = 2.50 versus 5.36, SD = 2.53; respectively, p < 0.01). Similarly, individuals with hepatic cirrhosis, compared to without, were more likely to attribute symptoms to their disease (mean identity score = 5.48, SD = 2.14 versus 4.89, SD = 2.38; respectively, p = 0.04). Female participants reported higher degrees at which the illness affected them emotionally (i.e., emotional representation) and lower coherence about HCV than males (p = 0.04 and 0.006, respectively). Individuals who did not achieve sustained virological response 24 weeks after treatment with interferon-based therapy, compared to treatment naïve individuals, reported lower trust in being successfully treated with newer anti-HCV agents (i.e., direct acting antivirals) (p = 0.029). However, multivariable linear regression revealed that no sociodemographic or clinical determinants were associated with a higher BIPQ score (i.e., more threatening, or negative perceptions).ConclusionIndividuals with HCV in Pakistan generally report threatening or negative views about HCV infection. Lack of trust in treatment efficacy was also apparent, especially in those who experienced failed anti-HCV treatments in the past. Healthcare professionals should consider these perceptions when treating individuals with HCV to optimize their compliance by aligning their perception with the high effectiveness of current anti-HCV therapies.

Dataset Information

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

Publications

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

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