scholarly journals Towards real-time Scan-versus-BIM :methods applications and challenges

2021 ◽  
Author(s):  
Marcus Wallbaum ◽  
Ranjith K. Soman
Keyword(s):  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Nicolás Rosillo ◽  
Javier Del-Águila-Mejía ◽  
Ayelén Rojas-Benedicto ◽  
María Guerrero-Vadillo ◽  
Marina Peñuelas ◽  
...  

Abstract Background On June 21st de-escalation measures and state-of-alarm ended in Spain after the COVID-19 first wave. New surveillance and control strategy was set up to detect emerging outbreaks. Aim To detect and describe the evolution of COVID-19 clusters and cases during the 2020 summer in Spain. Methods A near-real time surveillance system to detect active clusters of COVID-19 was developed based on Kulldorf’s prospective space-time scan statistic (STSS) to detect daily emerging active clusters. Results Analyses were performed daily during the summer 2020 (June 21st – August 31st) in Spain, showing an increase of active clusters and municipalities affected. Spread happened in the study period from a few, low-cases, regional-located clusters in June to a nationwide distribution of bigger clusters encompassing a higher average number of municipalities and total cases by end-August. Conclusion STSS-based surveillance of COVID-19 can be of utility in a low-incidence scenario to help tackle emerging outbreaks that could potentially drive a widespread transmission. If that happens, spatial trends and disease distribution can be followed with this method. Finally, cluster aggregation in space and time, as observed in our results, could suggest the occurrence of community transmission.


Author(s):  
S. R. Snare ◽  
H. Torp ◽  
F. Orderud ◽  
B. O. Haugen
Keyword(s):  

2022 ◽  
Author(s):  
Ahmad Alsayed ◽  
Mostafa R. Nabawy ◽  
Akilu Yunusa-Kaltungo ◽  
Mark K. Quinn ◽  
Farshad Arvin
Keyword(s):  

Micron ◽  
2018 ◽  
Vol 106 ◽  
pp. 1-6 ◽  
Author(s):  
Yingxu Zhang ◽  
Yingzi Li ◽  
Guanqiao Shan ◽  
Yifu Chen ◽  
Zhenyu Wang ◽  
...  

2020 ◽  
Author(s):  
Stefanos Tyrovolas ◽  
Iago Giné-Vázquez ◽  
Daniel Fernández ◽  
Mariathi Morena ◽  
Ai Koyanagi ◽  
...  

BACKGROUND On January 30, 2020, World Health Organization (WHO) declared the current novel coronavirus disease 2019 (COVID-19) as a public health emergency of international concern and later characterized it as a pandemic. Since then the virus has also rapidly spread among Latin American, Caribbean and African countries. OBJECTIVE The first aim of this study was to identify new emerging COVID-19 clusters over time and in space in Latin American, Caribbean, and African regions [mostly low and middle-income countries (LMICs)], using a prospective space-time scan measurement approach. The second aim was to assess the impact of real-time population mobility patterns between January 21st to May 18th, under the implemented government interventions, measurements and policy restrictions, on COVID-19 spread, among those regions and globally. METHODS We created a global COVID-19 database merging WHO daily case reports (of 218 countries, regions and territories) with other measures such as population density, country income levels for January 21st to May 15th, 2020. A score of government policy interventions was created ranging from “light”, “intermediate”, and “high”, to “very high” interventions. Prospective space-time scan statistic methods were applied in five time periods between January to May 2020 and a stepped-wedged regression mixed model analysis was used. RESULTS We found that COVID-19 emerging clusters within these five periods of time grew from 7 emerging clusters to 28 by mid-May. We also detected various increasing and decreasing relative risk estimates of COVID-19 spread among Latin American, Caribbean and African countries within the period of analysis. Globally, as well as regionally (Latin American, Caribbean and Africa), population mobility to parks and similar leisure areas during all the implemented control policies were related with accelerated COVID-19 spread. For countries in Africa, population mobility for work reasons during high and very high levels of implemented control policies were related with increased virus spread. CONCLUSIONS Prospective space-time scan is a measurement approach that LMICs countries could easily use to detect emerging clusters in a timely manner and implement specific control policies and interventions to slow down COVID-19 transmission. In addition, real time population mobility obtained from crowdsourced digital data could be useful for current and future targeted public health and mitigation policies among Latin American, Caribbean and African countries as well as globally.


2010 ◽  
Vol 139 (1) ◽  
pp. 19-26 ◽  
Author(s):  
C. C. VAN DEN WIJNGAARD ◽  
F. DIJKSTRA ◽  
W. VAN PELT ◽  
L. VAN ASTEN ◽  
M. KRETZSCHMAR ◽  
...  

SUMMARYLarge Q-fever outbreaks were reported in The Netherlands from May 2007 to 2009, with dairy-goat farms as the putative source. Since Q-fever outbreaks at such farms were first reported in 2005, we explored whether there was evidence of human outbreaks before May 2007. Space–time scan statistics were used to look for clusters of lower-respiratory infections (LRIs), hepatitis, and/or endocarditis in hospitalizations, 2005–2007. We assessed whether these were plausibly caused by Q fever, using patients' age, discharge diagnoses, indications for other causes, and overlap with reported Q fever in goats/humans. For seven detected LRI clusters and one hepatitis cluster, we considered Q fever a plausible cause. One of these clusters reflected the recognized May 2007 outbreak. Real-time syndromic surveillance would have detected four of the other clusters in 2007, one in 2006 and two in 2005, which might have resulted in detection of Q-fever outbreaks up to 2 years earlier.


2021 ◽  
Author(s):  
Nicolás Rosillo ◽  
Javier del-Águila-Mejía ◽  
Ayelén Rojas-Benedicto ◽  
María Guerrero-Vadillo ◽  
Marina Peñuelas ◽  
...  

Abstract Background:On June 21st de-escalation measures and state-of-alarm ended in Spain after the COVID-19 first wave. New surveillance and control strategy was set up to detect emerging outbreaks.Aim:To detect and describe the evolution of COVID-19 clusters and cases during the 2020 summer in Spain.Methods:A near-real time surveillance system to detect active clusters of COVID-19 was developed based on Kulldorf´s prospective space-time scan statistic (STSS) to detect daily emerging active clusters. Results:Analysis were performed daily during the summer 2020 (June 21st – August 31st) in Spain, showing an increase of active clusters and municipalities affected. Spread happened in the study period from a few, low-cases, regional-located clusters in June to a nationwide distribution of bigger clusters encompassing a higher average number of municipalities and total cases by end-August.Conclusion:STSS-based surveillance of COVID-19 can be of utility in a low-incidence scenario to help tackle emerging outbreaks that could potentially drive a widespread transmission. If that happens, spatial trends and disease distribution can be followed with this method. Finally, cluster aggregation in space and time, as observed in our results, could suggest the occurrence of community transmission.


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