Name
Abstract | ||
|---|---|---|
Influenza pandemics caused millions of deaths and massive economic losses worldwide in the last century. The impact of any future pandemic is likely to be greatest in developing countries as a result of their limited surveillance and healthcare resources. eHealth facilitates the detection and reporting of potential pandemic strains by using digital data transmitted, sorted and retrieved electronically both at the local site and at a distance. The implementation of eHealth is resource costly but developing countries have limited financial and technical resources. This adversely affects access to eHealth applications. Mobile communication technologies hold great promise in improving access to and affordability of eHealth services even to the poorest areas. This paper illustrates how a mobile phone SMS-based application can be applied to mHealth, potentially facilitating influenza pandemic surveillance in developing countries. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/HICSS.2010.274 | HICSS |
Keywords | Field | DocType |
medical information systems,health care,diseases,developing countries,limited surveillance,ehealth services,mobile phone,digital data transmission,potential pandemic strains,future pandemic,mhealth,potential pandemic strain,healthcare resources,mobile communication technology,sms-based application,surveillance,influenza pandemic,mobile communication technologies,electronic data sorting,ehealth facilitates,influenza pandemic surveillance,financial resources,electronic data retrieval,technical resources,ehealth service,mobile computing,pandemic,strain,mobile communication,developing country,countries | Health care,Mobile computing,Internet privacy,Computer science,Developing country,eHealth,mHealth,Mobile phone,Pandemic,Mobile telephony | Conference |
ISSN | ISBN | Citations |
1530-1605 E-ISBN : 978-1-4244-5510-2 | 978-1-4244-5510-2 | 10 |
PageRank | References | Authors |
0.63 | 1 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| JunHua Li | 1 | 27 | 3.12 |
| Nathan Moore | 2 | 10 | 0.63 |
| Shahriar Akter | 3 | 142 | 9.34 |
| Steven Bleisten | 4 | 10 | 0.63 |
| Pradeep Ray | 5 | 51 | 6.42 |