Use my Search Websuite to scan PubMed, PMCentral, Journal Hosts and Journal Archives, FullText. Kick-your-searchterm to multiple Engines kick-your-query now !>

A dictionary by aggregated review articles of nephrology, medicine and the life sciences Your one-stop-run pathway from word to the immediate pdf of peer-reviewed on-topic knowledge.

10.2196/18936 http://scihub22266oqcxt.onion/10.2196/18936 32240973!7144575!32240973     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       JMIR+Mhealth+Uhealth 2020 ; 8 (4): e18936 Nephropedia Template TP {{tp|p=32240973|t=2020. Peer-to-Peer Contact Tracing: Development of a Privacy-Preserving Smartphone App |pdf=|usr=}}{{32240973}} gab.com Text Peer-to-Peer Contact Tracing: Development of a Privacy-Preserving Smartphone App JO: JMIR Mhealth Uhealth http://www.kidney.de/pget.php?&tw=1&pmid=32240973 #FOAvip BACKGROUND: The novel coronavirus disease 2019 (COVID-19) pandemic is an urgent public health crisis, with epidemiologic models predicting severe consequences, including high death rates, if the virus is permitted to run its course without any intervention or response. Contact tracing using smartphone technology is a powerful tool that may be employed to limit disease transmission during an epidemic or pandemic; yet, contact tracing apps present significant privacy concerns regarding the collection of personal data such as location. OBJECTIVE: The aim of this study is to develop an effective contact tracing smartphone app that respects user privacy by not collecting location information or other personal data. METHODS: We propose the use of an anonymized graph of interpersonal interactions to conduct a novel form of contact tracing and have developed a proof-of-concept smartphone app that implements this approach. Additionally, we developed a computer simulation model that demonstrates the impact of our proposal on epidemic or pandemic outbreak trajectories across multiple rates of adoption. RESULTS: Our proof-of-concept smartphone app allows users to create "checkpoints" for contact tracing, check their risk level based on their past interactions, and anonymously self-report a positive status to their peer network. Our simulation results suggest that higher adoption rates of such an app may result in a better controlled epidemic or pandemic outbreak. CONCLUSIONS: Our proposed smartphone-based contact tracing method presents a novel solution that preserves privacy while demonstrating the potential to suppress an epidemic or pandemic outbreak. This app could potentially be applied to the current COVID-19 pandemic as well as other epidemics or pandemics in the future to achieve a middle ground between drastic isolation measures and unmitigated disease spread. Twit Text FOAVip Peer-to-Peer Contact Tracing: Development of a Privacy-Preserving Smartphone App ????JMIR Mhealth Uhealth http://www.kidney.de/pget.php?&tw=1&pmid=32240973 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32240973 #FOAvip English Wikipedia <ref>{{Cite pmid|32240973}}</ref> Peer-to-Peer Contact Tracing: Development of a Privacy-Preserving Smartphone App #MMPMID32240973 Yasaka TM; Lehrich BM; Sahyouni R JMIR Mhealth Uhealth 2020[Apr]; 8 (4): e18936 PMID32240973show ga BACKGROUND: The novel coronavirus disease 2019 (COVID-19) pandemic is an urgent public health crisis, with epidemiologic models predicting severe consequences, including high death rates, if the virus is permitted to run its course without any intervention or response. Contact tracing using smartphone technology is a powerful tool that may be employed to limit disease transmission during an epidemic or pandemic; yet, contact tracing apps present significant privacy concerns regarding the collection of personal data such as location. OBJECTIVE: The aim of this study is to develop an effective contact tracing smartphone app that respects user privacy by not collecting location information or other personal data. METHODS: We propose the use of an anonymized graph of interpersonal interactions to conduct a novel form of contact tracing and have developed a proof-of-concept smartphone app that implements this approach. Additionally, we developed a computer simulation model that demonstrates the impact of our proposal on epidemic or pandemic outbreak trajectories across multiple rates of adoption. RESULTS: Our proof-of-concept smartphone app allows users to create "checkpoints" for contact tracing, check their risk level based on their past interactions, and anonymously self-report a positive status to their peer network. Our simulation results suggest that higher adoption rates of such an app may result in a better controlled epidemic or pandemic outbreak. CONCLUSIONS: Our proposed smartphone-based contact tracing method presents a novel solution that preserves privacy while demonstrating the potential to suppress an epidemic or pandemic outbreak. This app could potentially be applied to the current COVID-19 pandemic as well as other epidemics or pandemics in the future to achieve a middle ground between drastic isolation measures and unmitigated disease spread. |*Betacoronavirus[MESH] |*Mobile Applications[MESH] |*Self Report[MESH] |*Smartphone[MESH] |COVID-19[MESH] |COVID-19 Testing[MESH] |Clinical Laboratory Techniques[MESH] |Computer Simulation[MESH] |Contact Tracing/*methods[MESH] |Coronavirus Infections/diagnosis/epidemiology/*transmission[MESH] |Humans[MESH] |Interpersonal Relations[MESH] |Pandemics[MESH] |Peer Group[MESH] |Pneumonia, Viral/diagnosis/epidemiology/*transmission[MESH] |Privacy[MESH] |Public Health[MESH] |Risk Assessment[MESH]Peer-to-Peer Contact Tracing: Development of a Privacy-Preserving Smar(epmc).pdf DeepDyve Pubget Overpricing