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10.1007/s41324-020-00375-1 http://scihub22266oqcxt.onion/10.1007/s41324-020-00375-1 C7779164SpatialpredictionandmappingoftheCOVID-19hotspotinIndiausinggeostatisticaltechnique.!7779164!C7779164     free     free     free Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       ä-/-ä 2021 ; 29 (4): 479-94 Nephropedia Template TP {{tp|p=C7779164|t=2021. Spatial prediction and mapping of the COVID-19 hotspot in India using geostatistical technique |pdf=|usr=}}{{C7779164}} gab.com Text Spatial prediction and mapping of the COVID-19 hotspot in India using geostatistical technique JO: ä-/-ä http://www.kidney.de/pget.php?&tw=1&pmid=C7779164 #FOAvip The world has now facing a health crisis due to outbreak of novel coronavirus 2019 (COVID-19). The numbers of infection and death have been rapidly increasing which result in a serious threat to the social and economic crisis. India as the second most populous nation of the world has also running with a serious health crisis, where more than 8,300,500 people have been infected and 123,500 deaths due to this deadly pandemic. Therefore, it is urgent to highlight the spatial vulnerability to identify the area under risk. Taking India as a study area, a geospatial analysis was conducted to identify the hotspot areas of the COVID-19. In the present study, four factors naming total population, population density, foreign tourist arrivals to India and reported confirmed cases of the COVID-19 were taken as responsible factors for detecting hotspot of the novel coronavirus. The result of spatial autocorrelation showed that all four factors considered for hotspot analysis were clustered and the results were statistically significant (p value <?0.01). The result of Getis-Ord Gi* statistics revealed that the total population and reported COVID-19 cases have got high priority for considering hotspot with greater z-score (>?3 and >?0.7295 respectively). The present analysis reveals that the reported cases of COVID-19 are higher in Maharashtra, followed by Tamil Nadu, Gujarat, Delhi, Uttar Pradesh, and West Bengal. The spatial result and geospatial methodology adopted for detecting COVID-19 hotspot in the Indian subcontinent can help implement strategies both at the macro and micro level. In this regard, social distancing, avoiding social meet, staying at home, avoiding public transport, self-quarantine and isolation are suggested in hotspot zones; together with, the international support is also required in the country to work jointly for mitigating the spread of COVID-19.Electronic supplementary material: The online version of this article (10.1007/s41324-020-00375-1) contains supplementary material, which is available to authorized users. Twit Text FOAVip Spatial prediction and mapping of the COVID-19 hotspot in India using geostatistical technique ????ä-/-ä http://www.kidney.de/pget.php?&tw=1&pmid=C7779164 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=C7779164 #FOAvip English Wikipedia <ref>{{Cite pmid|C7779164}}</ref> Spatial prediction and mapping of the COVID-19 hotspot in India using geostatistical technique #MMPMIDC7779164 Parvin F; Ali SA; Hashmi SNI; Ahmad A ä-/-ä 2021[]; 29 (4): 479-94 PMIDC7779164show ga The world has now facing a health crisis due to outbreak of novel coronavirus 2019 (COVID-19). The numbers of infection and death have been rapidly increasing which result in a serious threat to the social and economic crisis. India as the second most populous nation of the world has also running with a serious health crisis, where more than 8,300,500 people have been infected and 123,500 deaths due to this deadly pandemic. Therefore, it is urgent to highlight the spatial vulnerability to identify the area under risk. Taking India as a study area, a geospatial analysis was conducted to identify the hotspot areas of the COVID-19. In the present study, four factors naming total population, population density, foreign tourist arrivals to India and reported confirmed cases of the COVID-19 were taken as responsible factors for detecting hotspot of the novel coronavirus. The result of spatial autocorrelation showed that all four factors considered for hotspot analysis were clustered and the results were statistically significant (p value ?3 and >?0.7295 respectively). The present analysis reveals that the reported cases of COVID-19 are higher in Maharashtra, followed by Tamil Nadu, Gujarat, Delhi, Uttar Pradesh, and West Bengal. The spatial result and geospatial methodology adopted for detecting COVID-19 hotspot in the Indian subcontinent can help implement strategies both at the macro and micro level. In this regard, social distancing, avoiding social meet, staying at home, avoiding public transport, self-quarantine and isolation are suggested in hotspot zones; together with, the international support is also required in the country to work jointly for mitigating the spread of COVID-19.Electronic supplementary material: The online version of this article (10.1007/s41324-020-00375-1) contains supplementary material, which is available to authorized users. äSpatial prediction and mapping of the COVID-19 hotspot in India using (epmc).pdf DeepDyve Pubget Overpricing