What are the underlying transmission patterns of COVID-19 outbreak? : (Record no. 1793)

000 -LEADER
fixed length control field 02272nam a22002537a 4500
003 - CONTROL NUMBER IDENTIFIER
control field DOH
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20201121165525.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 201121b ||||| |||| 00| 0 eng d
245 00 - TITLE STATEMENT
Title What are the underlying transmission patterns of COVID-19 outbreak? :
Remainder of title an age-specific social contact characterization /
Statement of responsibility, etc. Yang Liu [six others]
520 3# - SUMMARY, ETC.
Summary, etc. Background<br/>COVID-19 has spread to 6 continents. Now is opportune to gain a deeper understanding of what may have happened. The findings can help inform mitigation strategies in the disease-affected countries.<br/>Methods<br/>In this work, we examine an essential factor that characterizes the disease transmission patterns: the interactions among people. We develop a computational model to reveal the interactions in terms of the social contact patterns among the population of different age-groups. We divide a city's population into seven age-groups: 0–6 years old (children); 7–14 (primary and junior high school students); 15–17 (high school students); 18–22 (university students); 23–44 (young/middle-aged people); 45–64 years old (middle-aged/elderly people); and 65 or above (elderly people). We consider four representative settings of social contacts that may cause the disease spread: (1) individual households; (2) schools, including primary/high schools as well as colleges and universities; (3) various physical workplaces; and (4) public places and communities where people can gather, such as stadiums, markets, squares, and organized tours. A contact matrix is computed to describe the contact intensity between different age-groups in each of the four settings. By integrating the four contact matrices with the next-generation matrix, we quantitatively characterize the underlying transmission patterns of COVID-19 among different populations.<br/>
580 ## - LINKING ENTRY COMPLEXITY NOTE
Linking entry complexity note In: EClinicalMedicine 22 (2020) 100354
650 #2 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Coronavirus Infections
General subdivision transmission
650 #2 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Disease Outbreaks
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Liua, Yang
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Gua, Zhonglei
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Xiab, Shang
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Shib, Benyun
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Zhoub, Xiao-Nong
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Shig, Yong
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Liua, Jiming
856 ## - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier <a href="file:///C:/Users/User/Desktop/PIIS2589537020300985.pdf">file:///C:/Users/User/Desktop/PIIS2589537020300985.pdf</a>
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type COVID 19 Resource
999 ## -
-- 1793
-- 1793
Holdings
Withdrawn status Lost status Damaged status Not for loan Home library Current library Shelving location Date acquired Total Checkouts Full call number Barcode Date last seen Price effective from Koha item type
        DOH Central Library DOH Central Library Electronic Resource Section 11/21/2020   COVID-19-000060 D0001C000060 11/21/2020 11/21/2020 COVID 19 Resource