Abstract:The New York City Department of Health and Mental Hygiene has operated an emergency department syndromic surveillance system since 2001, using temporal and spatial scan statistics run on a daily basis for cluster detection. Since the system was originally implemented, a number of new methods have been proposed for use in cluster detection. We evaluated six temporal and four spatial/spatio-temporal detection methods using syndromic surveillance data spiked with simulated injections. The algorithms were compared… Show more
“…According to our results, the performance of understudy methods in different approaches to outbreaks simulation was absolutely different. It's worth mentioning that these results were consistent with other studies conducted in this field (42,43). The performance of outbreak detection methods was affected by many factors including the type of outbreaks, duration, and magnitude.…”
Background Early detection of outbreaks is very important for surveillance systems. Due to the importance of the subject and lack of similar studies in Iran, the aim of this study was to determine the performance of the Wavelet-Based Outbreak detection method)WOD(in detecting outbreaks and to compare its performance with Poisson regression-based model and Exponential weighted moving average (EWMA) using data of simulated pertussis outbreaks in Iran. Methods The data on suspected cases of pertussis from 25th February 2012 to 23rd March 2018 in Iran was used. The performance of the WOD (Daubechies 10 and Haar wavelets), Poisson regression-based method, and EWMA Compared in terms of timeliness and detection of outbreak days using the simulation of different outbreaks (literature-based and researcher-made outbreaks). The sensitivity, specificity, false alarm and false negative rate, positive and negative likelihood ratios, under ROC areas and median timeliness were used to assess the performance of the methods. Results In a literature-based outbreak simulation, the highest and lowest sensitivity, false negative in the detection of injected outbreaks were seen in Daubechies 10 (db10), with sensitivity 0.59 (0.56-0.62), and Haar wavelets with 0.57 (0.54-0.60). In the researcher-made outbreaks, the EWMA (K=0.5) with sensitivity 0.92 (0.90-0.94) had the best performance. About timeliness, the WOD methods showed the best performance in the early warning of the outbreak in both simulation approaches. Conclusions Performance of the WOD in the early alarming outbreaks was appropriate. However, it's better as the method was used along with other methods in public health surveillance systems.
“…According to our results, the performance of understudy methods in different approaches to outbreaks simulation was absolutely different. It's worth mentioning that these results were consistent with other studies conducted in this field (42,43). The performance of outbreak detection methods was affected by many factors including the type of outbreaks, duration, and magnitude.…”
Background Early detection of outbreaks is very important for surveillance systems. Due to the importance of the subject and lack of similar studies in Iran, the aim of this study was to determine the performance of the Wavelet-Based Outbreak detection method)WOD(in detecting outbreaks and to compare its performance with Poisson regression-based model and Exponential weighted moving average (EWMA) using data of simulated pertussis outbreaks in Iran. Methods The data on suspected cases of pertussis from 25th February 2012 to 23rd March 2018 in Iran was used. The performance of the WOD (Daubechies 10 and Haar wavelets), Poisson regression-based method, and EWMA Compared in terms of timeliness and detection of outbreak days using the simulation of different outbreaks (literature-based and researcher-made outbreaks). The sensitivity, specificity, false alarm and false negative rate, positive and negative likelihood ratios, under ROC areas and median timeliness were used to assess the performance of the methods. Results In a literature-based outbreak simulation, the highest and lowest sensitivity, false negative in the detection of injected outbreaks were seen in Daubechies 10 (db10), with sensitivity 0.59 (0.56-0.62), and Haar wavelets with 0.57 (0.54-0.60). In the researcher-made outbreaks, the EWMA (K=0.5) with sensitivity 0.92 (0.90-0.94) had the best performance. About timeliness, the WOD methods showed the best performance in the early warning of the outbreak in both simulation approaches. Conclusions Performance of the WOD in the early alarming outbreaks was appropriate. However, it's better as the method was used along with other methods in public health surveillance systems.
“…This method only requires the location and date of each attack and makes no assumptions about the fine-scale distribution of atrisk humans across the survey area (Kulldorff et al, 2005a), whereas methods such as log-Gaussian Cox processes (Diggle, Moraga, Rowlingson, & Taylor, 2013) assume the at-risk population distribution is either known or is uniform across the landscape (Kulldorff et al, 2005a) which is rarely the case. Not only do space-time scan methods require fewer assumptions, but they also generally outperform spatiotemporal methods and are easier to perform (Mathes et al, 2017), and the SaTScan software is freely available with a graphic user interface requiring minimal epidemiological training (https://www.SaTScan.org/). Spatiotemporal clusters were identified from a significant excess of cases occurring within a geographical area over a continuous period of time.…”
Section: Spatiotemporal Patterns In Attacksmentioning
1. Large carnivores of the genus Panthera can pose serious threats to public safety.Although the annual number of attacks on humans is rare compared to livestock depredation, such incidents undermine popular support for wildlife conservation and require immediate responses to protect human life.2. We used a space-time scan method to perform a novel spatiotemporal analysis of 908 attacks on humans by lions, leopards, and tigers to estimate the risks of further attacks in the same locales.3. We found that a substantial proportion of attacks were clustered in time and space, but the dimension of these outbreaks varied between species. Lion outbreaks included more human fatalities, persisted for longer periods of time, and extended over larger areas than tiger or leopard outbreaks. 4. Synthesis and applications. Our analysis reveals the typical spatiotemporal patterns of past lion, leopard, and tiger attacks on humans. In future, this technique could be used by relevant agencies to warn local people of risks from further attacks within a certain time and distance following an initial incident by each species. Furthermore, the approach can help identify areas requiring management interventions to address such threats. K E Y W O R D S anthropogenic landscape, attacks on humans, big cats, human-wildlife conflict, Panthera, space-time scan, spatiotemporal clustering 1 | INTRODUC TI ON Despite dramatic declines in carnivore populations over the past century (Ripple et al., 2014), lion Panthera leo, leopard Panthera pardus, and tiger Panthera tigris attacks on humans elicit highly negative responses that present a profound conservation challenge in many parts of Asia and Africa. Nearly, a thousand people were attacked by African lions in southern Tanzania between 1990 and
“…This has implications for using surveillance data to characterise and predict disease dynamics [57]. Using spatially-indexed surveillance data to characterise urban disease activity and to detect spatial disease clusters and other patterns is challenging [58]. For this reason, spatially-explicit models of infection at urban scales are of real value, despite the limitations of the available mobility data sets informing the models.…”
Background
Infectious diseases spread through inherently spatial processes. Road and air traffic data have been used to model these processes at national and global scales. At metropolitan scales, however, mobility patterns are fundamentally different and less directly observable. Estimating the spatial distribution of infection has public health utility, but few studies have investigated this at an urban scale. In this study we address the question of whether the use of urban-scale mobility data can improve the prediction of spatial patterns of influenza infection. We compare the use of different sources of urban-scale mobility data, and investigate the impact of other factors relevant to modelling mobility, including mixing within and between regions, and the influence of hub and spoke commuting patterns.
Methods
We used journey-to-work (JTW) data from the Australian 2011 Census, and GPS journey data from the Sygic GPS Navigation & Maps mobile app, to characterise population mixing patterns in a spatially-explicit SEIR (susceptible, exposed, infectious, recovered) meta-population model.
Results
Using the JTW data to train the model leads to an increase in the proportion of infections that arise in central Melbourne, which is indicative of the city’s spoke-and-hub road and public transport networks, and of the commuting patterns reflected in these data. Using the GPS data increased the infections in central Melbourne to a lesser extent than the JTW data, and produced a greater heterogeneity in the middle and outer regions. Despite the limitations of both mobility data sets, the model reproduced some of the characteristics observed in the spatial distribution of reported influenza cases.
Conclusions
Urban mobility data sets can be used to support models that capture spatial heterogeneity in the transmission of infectious diseases at a metropolitan scale. These data should be adjusted to account for relevant urban features, such as highly-connected hubs where the resident population is likely to experience a much lower force of infection that the transient population. In contrast to national and international scales, the relationship between mobility and infection at an urban level is much less apparent, and requires a richer characterisation of population mobility and contact.
Electronic supplementary material
The online version of this article (10.1186/s12889-019-6968-x) contains supplementary material, which is available to authorized users.
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