Examining the Change of Human Mobility Adherent to Social Restriction Policies and Its Effect on COVID-19 Cases in Australia.
Identifieur interne : 000F18 ( Main/Corpus ); précédent : 000F17; suivant : 000F19Examining the Change of Human Mobility Adherent to Social Restriction Policies and Its Effect on COVID-19 Cases in Australia.
Auteurs : Siqin Wang ; Yan Liu ; Tao HuSource :
- International journal of environmental research and public health [ 1660-4601 ] ; 2020.
English descriptors
- KwdEn :
- Australia (epidemiology), Betacoronavirus (MeSH), COVID-19 (MeSH), Coronavirus Infections (epidemiology), Coronavirus Infections (prevention & control), Guideline Adherence (statistics & numerical data), Humans (MeSH), Pandemics (prevention & control), Pneumonia, Viral (epidemiology), Pneumonia, Viral (prevention & control), Public Policy (MeSH), Quarantine (MeSH), SARS-CoV-2 (MeSH), Travel (MeSH).
- MESH :
- geographic , epidemiology : Australia.
- epidemiology : Coronavirus Infections, Pneumonia, Viral.
- prevention & control : Coronavirus Infections, Pandemics, Pneumonia, Viral.
- statistics & numerical data : Guideline Adherence.
- Betacoronavirus, COVID-19, Humans, Public Policy, Quarantine, SARS-CoV-2, Travel.
Abstract
The policy induced decline of human mobility has been recognised as effective in controlling the spread of COVID-19, especially in the initial stage of the outbreak, although the relationship among mobility, policy implementation, and virus spread remains contentious. Coupling the data of confirmed COVID-19 cases with the Google mobility data in Australia, we present a state-level empirical study to: (1) inspect the temporal variation of the COVID-19 spread and the change of human mobility adherent to social restriction policies; (2) examine the extent to which different types of mobility are associated with the COVID-19 spread in eight Australian states/territories; and (3) analyse the time lag effect of mobility restriction on the COVID-19 spread. We find that social restriction policies implemented in the early stage of the pandemic controlled the COVID-19 spread effectively; the restriction of human mobility has a time lag effect on the growth rates of COVID-19, and the strength of the mobility-spread correlation increases up to seven days after policy implementation but decreases afterwards. The association between human mobility and COVID-19 spread varies across space and time and is subject to the types of mobility. Thus, it is important for government to consider the degree to which lockdown conditions can be eased by accounting for this dynamic mobility-spread relationship.
DOI: 10.3390/ijerph17217930
PubMed: 33137958
PubMed Central: PMC7662641
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pubmed:33137958Le document en format XML
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Coupling the data of confirmed COVID-19 cases with the Google mobility data in Australia, we present a state-level empirical study to: (1) inspect the temporal variation of the COVID-19 spread and the change of human mobility adherent to social restriction policies; (2) examine the extent to which different types of mobility are associated with the COVID-19 spread in eight Australian states/territories; and (3) analyse the time lag effect of mobility restriction on the COVID-19 spread. We find that social restriction policies implemented in the early stage of the pandemic controlled the COVID-19 spread effectively; the restriction of human mobility has a time lag effect on the growth rates of COVID-19, and the strength of the mobility-spread correlation increases up to seven days after policy implementation but decreases afterwards. The association between human mobility and COVID-19 spread varies across space and time and is subject to the types of mobility. Thus, it is important for government to consider the degree to which lockdown conditions can be eased by accounting for this dynamic mobility-spread relationship.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" IndexingMethod="Curated" Owner="NLM"><PMID Version="1">33137958</PMID><DateCompleted><Year>2020</Year><Month>11</Month><Day>12</Day></DateCompleted><DateRevised><Year>2020</Year><Month>12</Month><Day>18</Day></DateRevised><Article PubModel="Electronic"><Journal><ISSN IssnType="Electronic">1660-4601</ISSN><JournalIssue CitedMedium="Internet"><Volume>17</Volume><Issue>21</Issue><PubDate><Year>2020</Year><Month>10</Month><Day>29</Day></PubDate></JournalIssue><Title>International journal of environmental research and public health</Title><ISOAbbreviation>Int J Environ Res Public Health</ISOAbbreviation></Journal><ArticleTitle>Examining the Change of Human Mobility Adherent to Social Restriction Policies and Its Effect on COVID-19 Cases in Australia.</ArticleTitle><ELocationID EIdType="pii" ValidYN="Y">E7930</ELocationID><ELocationID EIdType="doi" ValidYN="Y">10.3390/ijerph17217930</ELocationID><Abstract><AbstractText>The policy induced decline of human mobility has been recognised as effective in controlling the spread of COVID-19, especially in the initial stage of the outbreak, although the relationship among mobility, policy implementation, and virus spread remains contentious. Coupling the data of confirmed COVID-19 cases with the Google mobility data in Australia, we present a state-level empirical study to: (1) inspect the temporal variation of the COVID-19 spread and the change of human mobility adherent to social restriction policies; (2) examine the extent to which different types of mobility are associated with the COVID-19 spread in eight Australian states/territories; and (3) analyse the time lag effect of mobility restriction on the COVID-19 spread. We find that social restriction policies implemented in the early stage of the pandemic controlled the COVID-19 spread effectively; the restriction of human mobility has a time lag effect on the growth rates of COVID-19, and the strength of the mobility-spread correlation increases up to seven days after policy implementation but decreases afterwards. The association between human mobility and COVID-19 spread varies across space and time and is subject to the types of mobility. Thus, it is important for government to consider the degree to which lockdown conditions can be eased by accounting for this dynamic mobility-spread relationship.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Wang</LastName><ForeName>Siqin</ForeName><Initials>S</Initials><Identifier Source="ORCID">0000-0002-1809-7088</Identifier><AffiliationInfo><Affiliation>School of Earth and Environmental Sciences, The University of Queensland, St Lucia QLD 4067, Australia.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Liu</LastName><ForeName>Yan</ForeName><Initials>Y</Initials><Identifier Source="ORCID">0000-0002-1612-779X</Identifier><AffiliationInfo><Affiliation>School of Earth and Environmental Sciences, The University of Queensland, St Lucia QLD 4067, Australia.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Hu</LastName><ForeName>Tao</ForeName><Initials>T</Initials><Identifier 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MajorTopicYN="Y">social restriction policy</Keyword></KeywordList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2020</Year><Month>09</Month><Day>07</Day></PubMedPubDate><PubMedPubDate PubStatus="revised"><Year>2020</Year><Month>10</Month><Day>26</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2020</Year><Month>10</Month><Day>27</Day></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2020</Year><Month>11</Month><Day>3</Day><Hour>1</Hour><Minute>2</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2020</Year><Month>11</Month><Day>4</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2020</Year><Month>11</Month><Day>13</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>epublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">33137958</ArticleId><ArticleId IdType="pii">ijerph17217930</ArticleId><ArticleId 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