Modelling input-output flows of severe acute respiratory syndrome in mainland China
Identifieur interne : 000F79 ( Main/Exploration ); précédent : 000F78; suivant : 000F80Modelling input-output flows of severe acute respiratory syndrome in mainland China
Auteurs : Li Wang [République populaire de Chine] ; Jinfeng Wang [République populaire de Chine] ; Chengdong Xu [République populaire de Chine] ; Tiejun Liu [République populaire de Chine]Source :
- BMC Public Health [ 1471-2458 ] ; 2016.
Abstract
Severe acute respiratory syndrome (SARS) originated in China in 2002, and it spread to 26 provinces in mainland China and 32 countries across five continents in a matter of months. This outbreak resulted in 774 deaths. However, the spatial features and potential determinants of SARS input-output flows remain unclear.
We used an adjusted spatial interaction model to examine the spatial effects and potential factors associated with SARS input-output flows.
The presence of origin-based spatial dependence positively affected SARS input-output flows from the neighbours of the origin regions. Two components of the input-output flows, migrant and hospitalization flows, exhibited distinctive features. The origin-based and destination-based spatial dependence positively affected migrant flows (i.e., due to those seeking jobs) from the neighbours of origin and destination locations. Similarly, the destination-based spatial dependence also positively affected hospitalization flows (i.e., due to those seeking treatment) from the neighbours of destination regions. However, the origin-to-destination based spatial dependence negatively affected hospitalisation flows from the neighbours of origin-to-destination regions. The direct effects accounted for 78 % of the SARS input-output flows, which was 3.56-fold greater than the indirect effects. Differences in regional income drove the SARS input-output flows. Therefore, urban income had a positive effect, whereas rural income had a negative effect. Total interregional flows increased by 3.54 % with a 1 % increase in urban income, and intraregional flows increased by 8.35 %. In contrast, the total interregional flows decreased by 3.38 % with a 1 % increase in rural income, and intraregional flows declined by 2.29 %. Railway capacity, per person gross domestic product (PGDP), urban rate and the law of distance decay also affected the input-output flows.
Our results confirm that the SARS input-output flows presented significant geographic spatial heterogeneity and spatial effects. Income differences were the major cause of the flows between pairs of regions. Railway capacity, PGDP, and urban rate also played important roles. These findings provide valuable information for the Chinese government to control the future spread of nationwide epidemics.
The online version of this article (doi:10.1186/s12889-016-2867-6) contains supplementary material, which is available to authorized users.
Url:
DOI: 10.1186/s12889-016-2867-6
PubMed: 26924026
PubMed Central: 4770707
Affiliations:
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<front><div type="abstract" xml:lang="en"><sec><title>Background</title>
<p id="Par1">Severe acute respiratory syndrome (SARS) originated in China in 2002, and it spread to 26 provinces in mainland China and 32 countries across five continents in a matter of months. This outbreak resulted in 774 deaths. However, the spatial features and potential determinants of SARS input-output flows remain unclear.</p>
</sec>
<sec><title>Methods</title>
<p id="Par2">We used an adjusted spatial interaction model to examine the spatial effects and potential factors associated with SARS input-output flows.</p>
</sec>
<sec><title>Results</title>
<p id="Par3">The presence of origin-based spatial dependence positively affected SARS input-output flows from the neighbours of the origin regions. Two components of the input-output flows, migrant and hospitalization flows, exhibited distinctive features. The origin-based and destination-based spatial dependence positively affected migrant flows (i.e., due to those seeking jobs) from the neighbours of origin and destination locations. Similarly, the destination-based spatial dependence also positively affected hospitalization flows (i.e., due to those seeking treatment) from the neighbours of destination regions. However, the origin-to-destination based spatial dependence negatively affected hospitalisation flows from the neighbours of origin-to-destination regions. The direct effects accounted for 78 % of the SARS input-output flows, which was 3.56-fold greater than the indirect effects. Differences in regional income drove the SARS input-output flows. Therefore, urban income had a positive effect, whereas rural income had a negative effect. Total interregional flows increased by 3.54 % with a 1 % increase in urban income, and intraregional flows increased by 8.35 %. In contrast, the total interregional flows decreased by 3.38 % with a 1 % increase in rural income, and intraregional flows declined by 2.29 %. Railway capacity, per person gross domestic product (PGDP), urban rate and the law of distance decay also affected the input-output flows.</p>
</sec>
<sec><title>Conclusions</title>
<p id="Par4">Our results confirm that the SARS input-output flows presented significant geographic spatial heterogeneity and spatial effects. Income differences were the major cause of the flows between pairs of regions. Railway capacity, PGDP, and urban rate also played important roles. These findings provide valuable information for the Chinese government to control the future spread of nationwide epidemics.</p>
</sec>
<sec><title>Electronic supplementary material</title>
<p>The online version of this article (doi:10.1186/s12889-016-2867-6) contains supplementary material, which is available to authorized users.</p>
</sec>
</div>
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<affiliations><list><country><li>République populaire de Chine</li>
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<name sortKey="Liu, Tiejun" sort="Liu, Tiejun" uniqKey="Liu T" first="Tiejun" last="Liu">Tiejun Liu</name>
<name sortKey="Liu, Tiejun" sort="Liu, Tiejun" uniqKey="Liu T" first="Tiejun" last="Liu">Tiejun Liu</name>
<name sortKey="Wang, Jinfeng" sort="Wang, Jinfeng" uniqKey="Wang J" first="Jinfeng" last="Wang">Jinfeng Wang</name>
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