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Semantic Scholar Open Access 2020 276 sitasi

Inferring high-resolution human mixing patterns for disease modeling

D. Mistry M. Litvinova A. P. Y. Piontti Matteo Chinazzi Laura Fumanelli +38 lainnya
Lihat Sumber DOI

Abstrak

Mathematical and computational modeling approaches are increasingly used as quantitative tools in the analysis and forecasting of infectious disease epidemics. The growing need for realism in addressing complex public health questions is, however, calling for accurate models of the human contact patterns that govern the disease transmission processes. Here we present a data-driven approach to generate effective population-level contact matrices by using highly detailed macro (census) and micro (survey) data on key socio-demographic features. We produce age-stratified contact matrices for 35 countries, including 277 sub-national administratvie regions of 8 of those countries, covering approximately 3.5 billion people and reflecting the high degree of cultural and societal diversity of the focus countries. We use the derived contact matrices to model the spread of airborne infectious diseases and show that sub-national heterogeneities in human mixing patterns have a marked impact on epidemic indicators such as the reproduction number and overall attack rate of epidemics of the same etiology. The contact patterns derived here are made publicly available as a modeling tool to study the impact of socio-economic differences and demographic heterogeneities across populations on the epidemiology of infectious diseases. The growing need for realism in addressing complex public health questions calls for accurate models of the human contact patterns that govern disease transmission. Here, the authors generate effective population-level contact matrices by using highly detailed macro (census) and micro (survey) data on key socio-demographic features.

Topik & Kata Kunci

Medicine Biology Physics Geography

Penulis (43)

D

D. Mistry

M

M. Litvinova

A

A. P. Y. Piontti

M

Matteo Chinazzi

L

Laura Fumanelli

M

M. Gomes

S

S. Haque

Q

Quan-Hui Liu

K

K. Mu

X

X. Xiong

M

M. Halloran

I

I. Longini

S

S. Merler

M

M. Ajelli

A

Alessandro Vespignani Institute for Disease Modeling

B

Bellevue

W

Wa

U

Usa

N

Northeastern University

B

Boston

M

Ma.

I

Institute for Scientific Interchange Foundation

T

Turin

I

Italy.

B

Bruno Kessler Foundation

T

Trento

F

Fundaccao Oswaldo Cruz

R

R. Janeiro

B

Brazil

C

College of Materials Science

S

Sichuan University

C

Chengdu

S

Sichuan

C

China

F

Fred Hutchinson Cancer Research Center

S

Seattle

D

D. Biostatistics

U

U. Washington

C

College of Public Health

H

Health Professions

U

U. Florida

G

Gainesville

F

Fl

Format Sitasi

APA MLA BibTeX
Mistry, D., Litvinova, M., Piontti, A.P.Y., Chinazzi, M., Fumanelli, L., Gomes, M. et al. (2020). Inferring high-resolution human mixing patterns for disease modeling. https://doi.org/10.1038/s41467-020-20544-y

Akses Cepat

Lihat di Sumber doi.org/10.1038/s41467-020-20544-y
Informasi Jurnal
Tahun Terbit
2020
Bahasa
en
Total Sitasi
276×
Sumber Database
Semantic Scholar
DOI
10.1038/s41467-020-20544-y
Akses
Open Access ✓