Characterizing the dynamics underlying global spread of epidemics

Lin Wang; Joseph T. Wu

doi:10.1038/s41467-017-02344-z

Characterizing the dynamics underlying global spread of epidemics

Lin Wang, Joseph T. Wu^*

^*Corresponding author for this work

Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review

118 Citations (Scopus)

39 Downloads (CityUHK Scholars)

Abstract

Over the past few decades, global metapopulation epidemic simulations built with worldwide air-transportation data have been the main tool for studying how epidemics spread from the origin to other parts of the world (e.g., for pandemic influenza, SARS, and Ebola). However, it remains unclear how disease epidemiology and the air-transportation network structure determine epidemic arrivals for different populations around the globe. Here, we fill this knowledge gap by developing and validating an analytical framework that requires only basic analytics from stochastic processes. We apply this framework retrospectively to the 2009 influenza pandemic and 2014 Ebola epidemic to show that key epidemic parameters could be robustly estimated in real-time from public data on local and global spread at very low computational cost. Our framework not only elucidates the dynamics underlying global spread of epidemics but also advances our capability in nowcasting and forecasting epidemics.

Original language	English
Article number	218
Journal	Nature Communications
Volume	9
Online published	15 Jan 2018
DOIs	https://doi.org/10.1038/s41467-017-02344-z
Publication status	Published - 2018
Externally published	Yes

Funding

We thank M. Lipsitch, B. J. Cowling, J. M. Read, K. Leung, H. Choi and Y. Zhang for helpful discussions. We thank C. K. Lam for assistance in data processing and technical support. We thank the Official Airline Guide, Center for International Earth Science Information Network at Columbia University, and World Health Organization (WHO) for providing their databases. This research was conducted in part using the research computing facilities and advisory services offered by Information Technology Services, The University of Hong Kong; and was done in part on the Olympus High Performance Compute Cluster at the Pittsburgh Supercomputing Center at Carnegie Mellon University, which is supported by National Institute of General Medical Sciences Models of Infectious Disease Agent Study (MIDAS) Informatics Services Group Grant 1U24GM110707. This research was supported by Harvard Center for Communicable Disease Dynamics from the National Institute of General Medical Sciences MIDAS Initiative (Grant No. U54GM088558), Area of Excellence Scheme of the Hong Kong University Grants Committee (Grant No. AoE/M-12/06), Research Grants Council Collaborative Research Fund (Grant No. CityU8/CRF/12G), and a commissioned grant from the Health and Medical Research Fund from the Government of the Hong Kong Special Administrative Region (Grant Nos. HKS-15-E03, HKS-17-E13). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute of General Medical Sciences, the National Institutes of Health. The funding bodies had no role in study design, data collection and analysis, preparation of the manuscript, or the decision to publish.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Publisher's Copyright Statement

This full text is made available under CC-BY 4.0. https://creativecommons.org/licenses/by/4.0/

RGC Funding Information

RGC-funded

Access Output

10.1038/s41467-017-02344-z

108934973.pdfFinal Published version, 926 KBLicence: CC BY

Other Access Options

Check CityUHK Library

Permanent Link

Right click to copy link

Cite this

@article{818a1c79ed8d41ce8873bf5a7958ad54,

title = "Characterizing the dynamics underlying global spread of epidemics",

abstract = "Over the past few decades, global metapopulation epidemic simulations built with worldwide air-transportation data have been the main tool for studying how epidemics spread from the origin to other parts of the world (e.g., for pandemic influenza, SARS, and Ebola). However, it remains unclear how disease epidemiology and the air-transportation network structure determine epidemic arrivals for different populations around the globe. Here, we fill this knowledge gap by developing and validating an analytical framework that requires only basic analytics from stochastic processes. We apply this framework retrospectively to the 2009 influenza pandemic and 2014 Ebola epidemic to show that key epidemic parameters could be robustly estimated in real-time from public data on local and global spread at very low computational cost. Our framework not only elucidates the dynamics underlying global spread of epidemics but also advances our capability in nowcasting and forecasting epidemics.",

author = "Lin Wang and Wu, {Joseph T.}",

note = "Publisher Copyright: {\textcopyright} 2018 The Author(s).",

year = "2018",

doi = "10.1038/s41467-017-02344-z",

language = "English",

volume = "9",

journal = "Nature Communications",

issn = "2041-1723",

publisher = "Springer Nature",

}

TY - JOUR

T1 - Characterizing the dynamics underlying global spread of epidemics

AU - Wang, Lin

AU - Wu, Joseph T.

PY - 2018

Y1 - 2018

N2 - Over the past few decades, global metapopulation epidemic simulations built with worldwide air-transportation data have been the main tool for studying how epidemics spread from the origin to other parts of the world (e.g., for pandemic influenza, SARS, and Ebola). However, it remains unclear how disease epidemiology and the air-transportation network structure determine epidemic arrivals for different populations around the globe. Here, we fill this knowledge gap by developing and validating an analytical framework that requires only basic analytics from stochastic processes. We apply this framework retrospectively to the 2009 influenza pandemic and 2014 Ebola epidemic to show that key epidemic parameters could be robustly estimated in real-time from public data on local and global spread at very low computational cost. Our framework not only elucidates the dynamics underlying global spread of epidemics but also advances our capability in nowcasting and forecasting epidemics.

AB - Over the past few decades, global metapopulation epidemic simulations built with worldwide air-transportation data have been the main tool for studying how epidemics spread from the origin to other parts of the world (e.g., for pandemic influenza, SARS, and Ebola). However, it remains unclear how disease epidemiology and the air-transportation network structure determine epidemic arrivals for different populations around the globe. Here, we fill this knowledge gap by developing and validating an analytical framework that requires only basic analytics from stochastic processes. We apply this framework retrospectively to the 2009 influenza pandemic and 2014 Ebola epidemic to show that key epidemic parameters could be robustly estimated in real-time from public data on local and global spread at very low computational cost. Our framework not only elucidates the dynamics underlying global spread of epidemics but also advances our capability in nowcasting and forecasting epidemics.

UR - http://www.scopus.com/inward/record.url?scp=85040790397&partnerID=8YFLogxK

UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85040790397&origin=recordpage

U2 - 10.1038/s41467-017-02344-z

DO - 10.1038/s41467-017-02344-z

M3 - RGC 21 - Publication in refereed journal

C2 - 29335536

AN - SCOPUS:85040790397

SN - 2041-1723

VL - 9

JO - Nature Communications

JF - Nature Communications

M1 - 218

ER -

Characterizing the dynamics underlying global spread of epidemics

Abstract

Funding

UN SDGs

Publisher's Copyright Statement

RGC Funding Information

Access Output

Other Access Options

Permanent Link

Other Files and Links

Fingerprint

CRF: Syndromic Surveillance and Modeling for Infectious Diseases

Cite this

Characterizing the dynamics underlying global spread of epidemics

Abstract

Funding

UN SDGs

Publisher's Copyright Statement

RGC Funding Information

Access Output

Other Access Options

Permanent Link

Other Files and Links

Fingerprint

Projects

CRF: Syndromic Surveillance and Modeling for Infectious Diseases

Cite this