Space-time scan statistics of 2007-2013 dengue incidence in Cimahi city, Indonesia

Dhewantara, Pandji Wibawa; Ruliansyah, Andri; Fuadiyah, M. Ezza Azmi; Astuti, Endang Puji; Widawati, Mutiara

doi:10.4081/gh.2015.373

Cited by 21 publications

(22 citation statements)

References 33 publications

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“…Our research and other Southeast Asian studies carried out by Ling et al (2014) in Malaysia and Dhewantara et al (2015) in Indonesia confirm that dengue cases tend to cluster showing both a spatial and a temporal epidemiological component. The findings referred to highlight the importance of detailed geographical analysis of disease cases in heterogeneous environments with a focus on clustered populations at different spatial and temporal scales.…”

Section: Discussionsupporting

confidence: 83%

Space-time clustering characteristics of dengue based on ecological, socio-economic and demographic factors in northern Sri Lanka

et al. 2015

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The aim of the present study was to identify geographical areas and time periods of potential clusters of dengue cases based on ecological, socio-economic and demographic factors in northern Sri Lanka from January 2010 to December 2013. Remote sensing (RS) was used to develop an index comprising rainfall, humidity and temperature data. Remote sensing data gathered by the AVNIR-2 instrument onboard the ALOS satellite were used to detect urbanisation, and a digital land cover map was used to extract land cover information. Other data on relevant factors and dengue outbreaks were collected through institutions and extant databases. The analysed RS data and databases were integrated into a geographical information system (GIS) enabling space-time clustering analysis. Our results indicate that increases in the number of combinations of ecological, socio-economic and demographic factors that are present or above the average contribute to significantly high rates of space-time dengue clusters. The spatio-temporal association that consolidates the two kinds of associations into one can ensure a more stable model for forecasting. An integrated spatiotemporal prediction model at a smaller level using ecological, socioeconomic and demographic factors could lead to substantial improvements in dengue control and prevention by allocating the right resources to the appropriate places at the right time.

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Section: Discussionsupporting

confidence: 83%

Space-time clustering characteristics of dengue based on ecological, socio-economic and demographic factors in northern Sri Lanka

et al. 2015

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“…Geographic Information Systems (GIS) and cluster detection methods–both, purely spatial as well as in time and space have proven useful to locate possible outbreaks of infectious diseases [33–35], including pertussis [30], resulting in a timely and effective response in affected areas. Such an approach might ultimately help to minimize the spread of pertussis at an early stage when the risk of transmission is highest [36].…”

Section: Introductionmentioning

confidence: 99%

Is the current pertussis incidence only the results of testing? A spatial and space-time analysis of pertussis surveillance data using cluster detection methods and geographically weighted regression modelling

et al. 2017

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BackgroundDespite high vaccination coverage, pertussis incidence in the Netherlands is amongst the highest in Europe with a shifting tendency towards adults and elderly. Early detection of outbreaks and preventive actions are necessary to prevent severe complications in infants. Efficient pertussis control requires additional background knowledge about the determinants of testing and possible determinants of the current pertussis incidence. Therefore, the aim of our study is to examine the possibility of locating possible pertussis outbreaks using space-time cluster detection and to examine the determinants of pertussis testing and incidence using geographically weighted regression models.MethodsWe analysed laboratory registry data including all geocoded pertussis tests in the southern area of the Netherlands between 2007 and 2013. Socio-demographic and infrastructure-related population data were matched to the geo-coded laboratory data. The spatial scan statistic was applied to detect spatial and space-time clusters of testing, incidence and test-positivity. Geographically weighted Poisson regression (GWPR) models were then constructed to model the associations between the age-specific rates of testing and incidence and possible population-based determinants.ResultsSpace-time clusters for pertussis incidence overlapped with space-time clusters for testing, reflecting a strong relationship between testing and incidence, irrespective of the examined age group. Testing for pertussis itself was overall associated with lower socio-economic status, multi-person-households, proximity to primary school and availability of healthcare. The current incidence in contradiction is mainly determined by testing and is not associated with a lower socioeconomic status.DiscussionTesting for pertussis follows to an extent the general healthcare seeking behaviour for common respiratory infections, whereas the current pertussis incidence is largely the result of testing. More testing would thus not necessarily improve pertussis control. Detecting outbreaks using space-time cluster detection is feasible but needs to adjust for the strong impact of testing on the detection of pertussis cases.

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“…Spatial and temporal windows are two major parameters when specifying space–time scan statistics for DF cluster detection [7, 13–15]. The calibration of parameters depends on the complicated interaction between vectors and human beings and therefore requires a process of trial and error.…”

Section: Discussionmentioning

confidence: 99%

“…Scan statistics have proven to be useful in detecting spatiotemporal clusters of diseases [5–7]. Although prospective scan statistics have been applied in detecting ongoing transmission of malaria [8], shigellosis [9, 10], and Campylobacter [5], what health workers might be interested in is the ability to relate existing hot spots to future infection [8, 11].…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, calibrating proper parameters such as spatial and temporal windows in scan statistics is difficult for non-expert users, and requires a process of model tuning. And few studies have rationalized the parameter specifications [7, 12]. Moreover, with the advance in online techniques and utilization of mobile phones, an online scan statistics tool might help public health workers detect ongoing transmission promptly, compared to the current desktop software.…”

Section: Introductionmentioning

confidence: 99%

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Online platform for applying space–time scan statistics for prospectively detecting emerging hot spots of dengue fever

et al. 2016

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BackgroundCases of dengue fever have increased in areas of Southeast Asia in recent years. Taiwan hit a record-high 42,856 cases in 2015, with the majority in southern Tainan and Kaohsiung Cities. Leveraging spatial statistics and geo-visualization techniques, we aim to design an online analytical tool for local public health workers to prospectively identify ongoing hot spots of dengue fever weekly at the village level.MethodsA total of 57,516 confirmed cases of dengue fever in 2014 and 2015 were obtained from the Taiwan Centers for Disease Control (TCDC). Incorporating demographic information as covariates with cumulative cases (365 days) in a discrete Poisson model, we iteratively applied space–time scan statistics by SaTScan software to detect the currently active cluster of dengue fever (reported as relative risk) in each village of Tainan and Kaohsiung every week. A village with a relative risk >1 and p value <0.05 was identified as a dengue-epidemic area. Assuming an ongoing transmission might continuously spread for two consecutive weeks, we estimated the sensitivity and specificity for detecting outbreaks by comparing the scan-based classification (dengue-epidemic vs. dengue-free village) with the true cumulative case numbers from the TCDC’s surveillance statistics.ResultsAmong the 1648 villages in Tainan and Kaohsiung, the overall sensitivity for detecting outbreaks increases as case numbers grow in a total of 92 weekly simulations. The specificity for detecting outbreaks behaves inversely, compared to the sensitivity. On average, the mean sensitivity and specificity of 2-week hot spot detection were 0.615 and 0.891 respectively (p value <0.001) for the covariate adjustment model, as the maximum spatial and temporal windows were specified as 50% of the total population at risk and 28 days. Dengue-epidemic villages were visualized and explored in an interactive map.ConclusionsWe designed an online analytical tool for front-line public health workers to prospectively detect ongoing dengue fever transmission on a weekly basis at the village level by using the routine surveillance data.

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Space-time scan statistics of 2007-2013 dengue incidence in Cimahi city, Indonesia

Cited by 21 publications

References 33 publications

Space-time clustering characteristics of dengue based on ecological, socio-economic and demographic factors in northern Sri Lanka

Space-time clustering characteristics of dengue based on ecological, socio-economic and demographic factors in northern Sri Lanka

Is the current pertussis incidence only the results of testing? A spatial and space-time analysis of pertussis surveillance data using cluster detection methods and geographically weighted regression modelling

Online platform for applying space–time scan statistics for prospectively detecting emerging hot spots of dengue fever

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