Influenza surveillance and forecast with smartphone sensors

Sang Hoon Lee, Yunmook Nah, Lynn Choi

Research output: Contribution to journalArticle

Abstract

In this paper we introduce an influenza surveillance and forecast system (ISFS) that can track the proliferation of influenza and predict potential infections by analyzing smartphone sensor readings. While previous studies investigate social connectivity to deduce proliferation paths, we focus on the physical contacts of each individual that are the dominant cause of influenza infections. To estimate the probability of an infection through each physical contact we measure the surrounding features of each contact including the staying time of a contact, the human density and the openness of the space, and the infection status of each individual. By using a smartphone equipped with various sensors we can estimate the infection status of its owner by analyzing both the envelope of incoming sound and the surrounding features of the contact. A surveillance server, which aggregates the information from multiple smartphones, monitors the infection status of influenza and ranks both high risk persons and influential persons that have to be vaccinated promptly. To evaluate the forecast accuracy of ISFS we have implemented a full ISFS including an Android ISFS client and compare the forecast accuracy of ISFS against that of the traditional forecast system based on social connectivity. Our evaluation results suggest that influenza surveillance and forecast should be performed based on human activity rather than social connectivity. This would not only improve the forecast accuracy but it can also improve the cost efficiency and the suppression effect of vaccinations by finding the most influential persons in the proliferation paths.

Original languageEnglish
Pages (from-to)237-259
Number of pages23
JournalComputing
Volume97
Issue number3
DOIs
Publication statusPublished - 2015

Fingerprint

Influenza
Smartphones
Surveillance
Forecast
Sensor
Sensors
Infection
Contact
Proliferation
Servers
Person
Connectivity
Acoustic waves
Costs
Cost Efficiency
Path
Vaccination
Estimate
Envelope
Deduce

Keywords

  • Influential people
  • Influenza forecast
  • Influenza surveillance
  • Social networks

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Software
  • Numerical Analysis
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

Cite this

Influenza surveillance and forecast with smartphone sensors. / Lee, Sang Hoon; Nah, Yunmook; Choi, Lynn.

In: Computing, Vol. 97, No. 3, 2015, p. 237-259.

Research output: Contribution to journalArticle

Lee, Sang Hoon ; Nah, Yunmook ; Choi, Lynn. / Influenza surveillance and forecast with smartphone sensors. In: Computing. 2015 ; Vol. 97, No. 3. pp. 237-259.
@article{fc5dcbdf17574a2a95604d24df9dec2d,
title = "Influenza surveillance and forecast with smartphone sensors",
abstract = "In this paper we introduce an influenza surveillance and forecast system (ISFS) that can track the proliferation of influenza and predict potential infections by analyzing smartphone sensor readings. While previous studies investigate social connectivity to deduce proliferation paths, we focus on the physical contacts of each individual that are the dominant cause of influenza infections. To estimate the probability of an infection through each physical contact we measure the surrounding features of each contact including the staying time of a contact, the human density and the openness of the space, and the infection status of each individual. By using a smartphone equipped with various sensors we can estimate the infection status of its owner by analyzing both the envelope of incoming sound and the surrounding features of the contact. A surveillance server, which aggregates the information from multiple smartphones, monitors the infection status of influenza and ranks both high risk persons and influential persons that have to be vaccinated promptly. To evaluate the forecast accuracy of ISFS we have implemented a full ISFS including an Android ISFS client and compare the forecast accuracy of ISFS against that of the traditional forecast system based on social connectivity. Our evaluation results suggest that influenza surveillance and forecast should be performed based on human activity rather than social connectivity. This would not only improve the forecast accuracy but it can also improve the cost efficiency and the suppression effect of vaccinations by finding the most influential persons in the proliferation paths.",
keywords = "Influential people, Influenza forecast, Influenza surveillance, Social networks",
author = "Lee, {Sang Hoon} and Yunmook Nah and Lynn Choi",
year = "2015",
doi = "10.1007/s00607-014-0415-8",
language = "English",
volume = "97",
pages = "237--259",
journal = "Computing",
issn = "0010-485X",
publisher = "Springer Wien",
number = "3",

}

TY - JOUR

T1 - Influenza surveillance and forecast with smartphone sensors

AU - Lee, Sang Hoon

AU - Nah, Yunmook

AU - Choi, Lynn

PY - 2015

Y1 - 2015

N2 - In this paper we introduce an influenza surveillance and forecast system (ISFS) that can track the proliferation of influenza and predict potential infections by analyzing smartphone sensor readings. While previous studies investigate social connectivity to deduce proliferation paths, we focus on the physical contacts of each individual that are the dominant cause of influenza infections. To estimate the probability of an infection through each physical contact we measure the surrounding features of each contact including the staying time of a contact, the human density and the openness of the space, and the infection status of each individual. By using a smartphone equipped with various sensors we can estimate the infection status of its owner by analyzing both the envelope of incoming sound and the surrounding features of the contact. A surveillance server, which aggregates the information from multiple smartphones, monitors the infection status of influenza and ranks both high risk persons and influential persons that have to be vaccinated promptly. To evaluate the forecast accuracy of ISFS we have implemented a full ISFS including an Android ISFS client and compare the forecast accuracy of ISFS against that of the traditional forecast system based on social connectivity. Our evaluation results suggest that influenza surveillance and forecast should be performed based on human activity rather than social connectivity. This would not only improve the forecast accuracy but it can also improve the cost efficiency and the suppression effect of vaccinations by finding the most influential persons in the proliferation paths.

AB - In this paper we introduce an influenza surveillance and forecast system (ISFS) that can track the proliferation of influenza and predict potential infections by analyzing smartphone sensor readings. While previous studies investigate social connectivity to deduce proliferation paths, we focus on the physical contacts of each individual that are the dominant cause of influenza infections. To estimate the probability of an infection through each physical contact we measure the surrounding features of each contact including the staying time of a contact, the human density and the openness of the space, and the infection status of each individual. By using a smartphone equipped with various sensors we can estimate the infection status of its owner by analyzing both the envelope of incoming sound and the surrounding features of the contact. A surveillance server, which aggregates the information from multiple smartphones, monitors the infection status of influenza and ranks both high risk persons and influential persons that have to be vaccinated promptly. To evaluate the forecast accuracy of ISFS we have implemented a full ISFS including an Android ISFS client and compare the forecast accuracy of ISFS against that of the traditional forecast system based on social connectivity. Our evaluation results suggest that influenza surveillance and forecast should be performed based on human activity rather than social connectivity. This would not only improve the forecast accuracy but it can also improve the cost efficiency and the suppression effect of vaccinations by finding the most influential persons in the proliferation paths.

KW - Influential people

KW - Influenza forecast

KW - Influenza surveillance

KW - Social networks

UR - http://www.scopus.com/inward/record.url?scp=85028166817&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85028166817&partnerID=8YFLogxK

U2 - 10.1007/s00607-014-0415-8

DO - 10.1007/s00607-014-0415-8

M3 - Article

AN - SCOPUS:85028166817

VL - 97

SP - 237

EP - 259

JO - Computing

JF - Computing

SN - 0010-485X

IS - 3

ER -