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10.17392/1215-20

http://scihub22266oqcxt.onion/10.17392/1215-20
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32602300!ä!32602300

suck abstract from ncbi


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pmid32602300      Med+Glas+(Zenica) 2020 ; 17 (2): 265-274
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  • Spatio-temporal data visualization for monitoring of control measures in the prevention of the spread of COVID-19 in Bosnia and Herzegovina #MMPMID32602300
  • Ponjavic M; Karabegovic A; Ferhatbegovic E; Tahirovic E; Uzunovic S; Travar M; Pilav A; Mulic M; Karakas S; Avdic N; Mulabdic Z; Pavic G; Bico M; Vasilj I; Mamic D; Hukic M
  • Med Glas (Zenica) 2020[Aug]; 17 (2): 265-274 PMID32602300show ga
  • Aim The damage caused by the COVID-19 pandemic has made the prevention of its further spread at the top of the list of priorities of many governments and state institutions responsible for health and civil protection around the world. This prevention implies an effective system of epidemiological surveillance and the application of timely and effective control measures. This research focuses on the application of techniques for modelling and geovisualization of epidemic data with the aim of simple and fast communication of analytical results via geoportal. Methods The paper describes the approach applied through the project of establishing the epidemiological location-intelligence system for monitoring the effectiveness of control measures in preventing the spread of COVID-19 in Bosnia and Herzegovina. Results Epidemic data were processed and the results related to spatio-temporal analysis of the infection spread were presented by compartmental epidemic model, reproduction number R, epi-curve diagrams as well as choropleth maps for different levels of administrative units. Geovisualization of epidemic data enabled the release of numerous information from described models and indicators, providing easier visual communication of the spread of the disease and better recognition of its trend. Conclusion The approach involves the simultaneous application of epidemic models and epidemic data geovisualization, which allows a simple and rapid evaluation of the epidemic situation and the effects of control measures. This contributes to more informative decision-making related to control measures by suggesting their selective application at the local level.
  • |*Communicable Disease Control[MESH]
  • |*Spatio-Temporal Analysis[MESH]
  • |Betacoronavirus[MESH]
  • |Bosnia and Herzegovina/epidemiology[MESH]
  • |COVID-19[MESH]
  • |Coronavirus Infections/epidemiology/*prevention & control/transmission[MESH]
  • |Data Visualization[MESH]
  • |Epidemics[MESH]
  • |Epidemiological Monitoring[MESH]
  • |Geographic Mapping[MESH]
  • |Health Information Systems[MESH]
  • |Humans[MESH]
  • |Models, Statistical[MESH]
  • |Pandemics/*prevention & control[MESH]
  • |Pneumonia, Viral/epidemiology/*prevention & control/transmission[MESH]


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  • suck abstract from ncbi

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