tetano
Editor, Senior Moderator
Biosens Bioelectron
. 2020 Sep 16;169:112617.
doi: 10.1016/j.bios.2020.112617. Online ahead of print.
An integrated biosensor system with mobile health and wastewater-based epidemiology (iBMW) for COVID-19 pandemic
Kang Mao[SUP] 1 [/SUP], Hua Zhang[SUP] 2 [/SUP], Zhugen Yang[SUP] 3 [/SUP]
Affiliations
Abstract
The outbreak of coronavirus disease (COVID-19) has caused a significant public health challenge worldwide. A lack of effective methods for screening potential patients, rapidly diagnosing suspected cases, and accurately monitoring of the epidemic in real time to prevent the rapid spread of COVID-19 raises significant difficulties in mitigating the epidemic in many countries. As effective point-of-care diagnosis tools, simple, low-cost and rapid sensors have the potential to greatly accelerate the screening and diagnosis of suspected patients to improve their treatment and care. In particular, there is evidence that multiple pathogens have been detected in sewage, including SARS-CoV-2, providing significant opportunities for the development of advanced sensors for wastewater-based epidemiology that provide an early warning of the pandemic within the population. Sensors could be used to screen potential carriers, provide real-time monitoring and control of the epidemic, and even support targeted drug screening and delivery within the integration of emerging mobile health (mHealth) technology. In this communication, we discuss the feasibility of an integrated point-of-care biosensor system with mobile health for wastewater-based epidemiology (iBMW) for early warning of COVID-19, screening and diagnosis of potential infectors, and improving health care and public health. The iBMW will provide an effective approach to prevent, evaluate and intervene in a fast, affordable and reliable way, thus enabling real-time guidance for the government in providing effective intervention and evaluating the effectiveness of intervention.
Keywords: Early warning; Mobile health; Pandemic; Point-of-use sensors; Wastewater-based epidemiology.
. 2020 Sep 16;169:112617.
doi: 10.1016/j.bios.2020.112617. Online ahead of print.
An integrated biosensor system with mobile health and wastewater-based epidemiology (iBMW) for COVID-19 pandemic
Kang Mao[SUP] 1 [/SUP], Hua Zhang[SUP] 2 [/SUP], Zhugen Yang[SUP] 3 [/SUP]
Affiliations
- PMID: 32998066
- DOI: 10.1016/j.bios.2020.112617
Abstract
The outbreak of coronavirus disease (COVID-19) has caused a significant public health challenge worldwide. A lack of effective methods for screening potential patients, rapidly diagnosing suspected cases, and accurately monitoring of the epidemic in real time to prevent the rapid spread of COVID-19 raises significant difficulties in mitigating the epidemic in many countries. As effective point-of-care diagnosis tools, simple, low-cost and rapid sensors have the potential to greatly accelerate the screening and diagnosis of suspected patients to improve their treatment and care. In particular, there is evidence that multiple pathogens have been detected in sewage, including SARS-CoV-2, providing significant opportunities for the development of advanced sensors for wastewater-based epidemiology that provide an early warning of the pandemic within the population. Sensors could be used to screen potential carriers, provide real-time monitoring and control of the epidemic, and even support targeted drug screening and delivery within the integration of emerging mobile health (mHealth) technology. In this communication, we discuss the feasibility of an integrated point-of-care biosensor system with mobile health for wastewater-based epidemiology (iBMW) for early warning of COVID-19, screening and diagnosis of potential infectors, and improving health care and public health. The iBMW will provide an effective approach to prevent, evaluate and intervene in a fast, affordable and reliable way, thus enabling real-time guidance for the government in providing effective intervention and evaluating the effectiveness of intervention.
Keywords: Early warning; Mobile health; Pandemic; Point-of-use sensors; Wastewater-based epidemiology.