Estimating the effect of social inequalities on the mitigation of COVID-19 across communities in Santiago de Chile

We study the spatio-temporal spread of SARS-CoV-2 in Santiago de Chile using anonymized mobile phone data from 1.4 million users, 22% of the whole population in the area, characterizing the effects of non-pharmaceutical interventions (NPIs) on the epidemic dynamics. We integrate these data into a me...

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Published in:Nature communications Vol. 12; no. 1; pp. 2429 - 9
Main Authors: Gozzi, Nicolò, Tizzoni, Michele, Chinazzi, Matteo, Ferres, Leo, Vespignani, Alessandro, Perra, Nicola
Format: Journal Article
Language:English
Published: London Nature Publishing Group UK 23.04.2021
Nature Publishing Group
Nature Portfolio
Subjects:
639/766/530/2801
692/308/174
692/699/255/2514
Algorithms
Cell phones
Cellular telephones
Chile - epidemiology
Communicable Disease Control - methods
Communicable Disease Control - statistics & numerical data
Coronaviruses
COVID-19
COVID-19 - epidemiology
COVID-19 - prevention & control
COVID-19 - virology
Disease control
Disease Transmission, Infectious - prevention & control
Disease Transmission, Infectious - statistics & numerical data
Economic impact
Epidemic models
Epidemics
Heterogeneity
Humanities and Social Sciences
Humans
Incidence
Mobility
Models, Theoretical
multidisciplinary
Pandemics
SARS-CoV-2 - isolation & purification
SARS-CoV-2 - physiology
Science
Science (multidisciplinary)
Severe acute respiratory syndrome coronavirus 2
Socioeconomic Factors
Time Factors
Viral diseases
Chile
ISSN:2041-1723, 2041-1723
Online Access:Get full text
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Abstract We study the spatio-temporal spread of SARS-CoV-2 in Santiago de Chile using anonymized mobile phone data from 1.4 million users, 22% of the whole population in the area, characterizing the effects of non-pharmaceutical interventions (NPIs) on the epidemic dynamics. We integrate these data into a mechanistic epidemic model calibrated on surveillance data. As of August 1, 2020, we estimate a detection rate of 102 cases per 1000 infections (90% CI: [95–112 per 1000]). We show that the introduction of a full lockdown on May 15, 2020, while causing a modest additional decrease in mobility and contacts with respect to previous NPIs, was decisive in bringing the epidemic under control, highlighting the importance of a timely governmental response to COVID-19 outbreaks. We find that the impact of NPIs on individuals’ mobility correlates with the Human Development Index of comunas in the city. Indeed, more developed and wealthier areas became more isolated after government interventions and experienced a significantly lower burden of the pandemic. The heterogeneity of COVID-19 impact raises important issues in the implementation of NPIs and highlights the challenges that communities affected by systemic health and social inequalities face adapting their behaviors during an epidemic. Fine-grained studies of epidemic spread and of the effect of nonpharmaceutical interventions are still needed to underpin demographic and socio-economic effects. Here, the authors study the spatial and temporal spread of SARS-CoV-2 in Santiago de Chile using anonymized mobile phone data.
AbstractList We study the spatio-temporal spread of SARS-CoV-2 in Santiago de Chile using anonymized mobile phone data from 1.4 million users, 22% of the whole population in the area, characterizing the effects of non-pharmaceutical interventions (NPIs) on the epidemic dynamics. We integrate these data into a mechanistic epidemic model calibrated on surveillance data. As of August 1, 2020, we estimate a detection rate of 102 cases per 1000 infections (90% CI: [95–112 per 1000]). We show that the introduction of a full lockdown on May 15, 2020, while causing a modest additional decrease in mobility and contacts with respect to previous NPIs, was decisive in bringing the epidemic under control, highlighting the importance of a timely governmental response to COVID-19 outbreaks. We find that the impact of NPIs on individuals’ mobility correlates with the Human Development Index of comunas in the city. Indeed, more developed and wealthier areas became more isolated after government interventions and experienced a significantly lower burden of the pandemic. The heterogeneity of COVID-19 impact raises important issues in the implementation of NPIs and highlights the challenges that communities affected by systemic health and social inequalities face adapting their behaviors during an epidemic.
We study the spatio-temporal spread of SARS-CoV-2 in Santiago de Chile using anonymized mobile phone data from 1.4 million users, 22% of the whole population in the area, characterizing the effects of non-pharmaceutical interventions (NPIs) on the epidemic dynamics. We integrate these data into a mechanistic epidemic model calibrated on surveillance data. As of August 1, 2020, we estimate a detection rate of 102 cases per 1000 infections (90% CI: [95-112 per 1000]). We show that the introduction of a full lockdown on May 15, 2020, while causing a modest additional decrease in mobility and contacts with respect to previous NPIs, was decisive in bringing the epidemic under control, highlighting the importance of a timely governmental response to COVID-19 outbreaks. We find that the impact of NPIs on individuals' mobility correlates with the Human Development Index of comunas in the city. Indeed, more developed and wealthier areas became more isolated after government interventions and experienced a significantly lower burden of the pandemic. The heterogeneity of COVID-19 impact raises important issues in the implementation of NPIs and highlights the challenges that communities affected by systemic health and social inequalities face adapting their behaviors during an epidemic.We study the spatio-temporal spread of SARS-CoV-2 in Santiago de Chile using anonymized mobile phone data from 1.4 million users, 22% of the whole population in the area, characterizing the effects of non-pharmaceutical interventions (NPIs) on the epidemic dynamics. We integrate these data into a mechanistic epidemic model calibrated on surveillance data. As of August 1, 2020, we estimate a detection rate of 102 cases per 1000 infections (90% CI: [95-112 per 1000]). We show that the introduction of a full lockdown on May 15, 2020, while causing a modest additional decrease in mobility and contacts with respect to previous NPIs, was decisive in bringing the epidemic under control, highlighting the importance of a timely governmental response to COVID-19 outbreaks. We find that the impact of NPIs on individuals' mobility correlates with the Human Development Index of comunas in the city. Indeed, more developed and wealthier areas became more isolated after government interventions and experienced a significantly lower burden of the pandemic. The heterogeneity of COVID-19 impact raises important issues in the implementation of NPIs and highlights the challenges that communities affected by systemic health and social inequalities face adapting their behaviors during an epidemic.
We study the spatio-temporal spread of SARS-CoV-2 in Santiago de Chile using anonymized mobile phone data from 1.4 million users, 22% of the whole population in the area, characterizing the effects of non-pharmaceutical interventions (NPIs) on the epidemic dynamics. We integrate these data into a mechanistic epidemic model calibrated on surveillance data. As of August 1, 2020, we estimate a detection rate of 102 cases per 1000 infections (90% CI: [95–112 per 1000]). We show that the introduction of a full lockdown on May 15, 2020, while causing a modest additional decrease in mobility and contacts with respect to previous NPIs, was decisive in bringing the epidemic under control, highlighting the importance of a timely governmental response to COVID-19 outbreaks. We find that the impact of NPIs on individuals’ mobility correlates with the Human Development Index of comunas in the city. Indeed, more developed and wealthier areas became more isolated after government interventions and experienced a significantly lower burden of the pandemic. The heterogeneity of COVID-19 impact raises important issues in the implementation of NPIs and highlights the challenges that communities affected by systemic health and social inequalities face adapting their behaviors during an epidemic. Fine-grained studies of epidemic spread and of the effect of nonpharmaceutical interventions are still needed to underpin demographic and socio-economic effects. Here, the authors study the spatial and temporal spread of SARS-CoV-2 in Santiago de Chile using anonymized mobile phone data.
We study the spatio-temporal spread of SARS-CoV-2 in Santiago de Chile using anonymized mobile phone data from 1.4 million users, 22% of the whole population in the area, characterizing the effects of non-pharmaceutical interventions (NPIs) on the epidemic dynamics. We integrate these data into a mechanistic epidemic model calibrated on surveillance data. As of August 1, 2020, we estimate a detection rate of 102 cases per 1000 infections (90% CI: [95–112 per 1000]). We show that the introduction of a full lockdown on May 15, 2020, while causing a modest additional decrease in mobility and contacts with respect to previous NPIs, was decisive in bringing the epidemic under control, highlighting the importance of a timely governmental response to COVID-19 outbreaks. We find that the impact of NPIs on individuals’ mobility correlates with the Human Development Index of comunas in the city. Indeed, more developed and wealthier areas became more isolated after government interventions and experienced a significantly lower burden of the pandemic. The heterogeneity of COVID-19 impact raises important issues in the implementation of NPIs and highlights the challenges that communities affected by systemic health and social inequalities face adapting their behaviors during an epidemic. Fine-grained studies of epidemic spread and of the effect of nonpharmaceutical interventions are still needed to underpin demographic and socio-economic effects. Here, the authors study the spatial and temporal spread of SARS-CoV-2 in Santiago de Chile using anonymized mobile phone data.
Fine-grained studies of epidemic spread and of the effect of nonpharmaceutical interventions are still needed to underpin demographic and socio-economic effects. Here, the authors study the spatial and temporal spread of SARS-CoV-2 in Santiago de Chile using anonymized mobile phone data.
ArticleNumber 2429
Author Perra, Nicola
Chinazzi, Matteo
Vespignani, Alessandro
Gozzi, Nicolò
Ferres, Leo
Tizzoni, Michele
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  givenname: Matteo
  surname: Chinazzi
  fullname: Chinazzi, Matteo
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  givenname: Leo
  orcidid: 0000-0002-5899-9051
  surname: Ferres
  fullname: Ferres, Leo
  organization: Data Science Institute, Universidad del Desarrollo, Telefónica R&D
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  fullname: Perra, Nicola
  email: n.perra@greenwich.ac.uk
  organization: Networks and Urban Systems Centre, University of Greenwich, Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University
BackLink https://www.ncbi.nlm.nih.gov/pubmed/33893279$$D View this record in MEDLINE/PubMed
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Snippet We study the spatio-temporal spread of SARS-CoV-2 in Santiago de Chile using anonymized mobile phone data from 1.4 million users, 22% of the whole population...
Fine-grained studies of epidemic spread and of the effect of nonpharmaceutical interventions are still needed to underpin demographic and socio-economic...
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SubjectTerms 639/766/530/2801
692/308/174
692/699/255/2514
Algorithms
Cell phones
Cellular telephones
Chile - epidemiology
Communicable Disease Control - methods
Communicable Disease Control - statistics & numerical data
Coronaviruses
COVID-19
COVID-19 - epidemiology
COVID-19 - prevention & control
COVID-19 - virology
Disease control
Disease Transmission, Infectious - prevention & control
Disease Transmission, Infectious - statistics & numerical data
Economic impact
Epidemic models
Epidemics
Heterogeneity
Humanities and Social Sciences
Humans
Incidence
Mobility
Models, Theoretical
multidisciplinary
Pandemics
SARS-CoV-2 - isolation & purification
SARS-CoV-2 - physiology
Science
Science (multidisciplinary)
Severe acute respiratory syndrome coronavirus 2
Socioeconomic Factors
Time Factors
Viral diseases
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Title Estimating the effect of social inequalities on the mitigation of COVID-19 across communities in Santiago de Chile
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