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

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Nicolò Gozzi ◽  
Michele Tizzoni ◽  
Matteo Chinazzi ◽  
Leo Ferres ◽  
Alessandro Vespignani ◽  
...  
Keyword(s):  
Social Inequalities ◽  
Mobile Phone Data ◽  
Government Interventions ◽  
Epidemic Dynamics ◽  
Santiago De Chile ◽  
Spatio Temporal ◽  
Systemic Health ◽  
The Impact ◽  
The City ◽  
Governmental Response

AbstractWe 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.

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

2020 ◽  
Author(s):  
Nicolò Gozzi ◽  
Michele Tizzoni ◽  
Matteo Chinazzi ◽  
Leo Ferres ◽  
Alessandro Vespignani ◽  
...  
Keyword(s):  
Social Inequalities ◽  
Mobile Phone Data ◽  
Government Interventions ◽  
Epidemic Dynamics ◽  
Santiago De Chile ◽  
Spatio Temporal ◽  
Systemic Health ◽  
The Impact ◽  
The City ◽  
Governmental Response

AbstractWe 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 1st 2020, we estimate a detection rate of 102 cases per 1,000 infections (90% CI: [95 - 112 per 1,000]). We show that the introduction of a full lockdown on May 15th, 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 hetero-geneity 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.

scholarly journals Determinants of SARS-CoV-2 transmission to guide vaccination strategy in a city

2020 ◽  
Author(s):  
Sarah C. Brüningk ◽  
Juliane Klatt ◽  
Madlen Stange ◽  
Alfredo Mari ◽  
Myrta Brunner ◽  
...  
Keyword(s):  
Vaccination Strategy ◽  
Population Level ◽  
Data Sets ◽  
Epidemic Dynamics ◽  
Modelling Analysis ◽  
City Population ◽  
Senior Population ◽  
Spatio Temporal ◽  
Socioeconomic Data ◽  
The City

Transmission chains within cities provide an important contribution to case burden and economic impact during the ongoing COVID-19 pandemic, and should be a major focus for preventive measures to achieve containment. Here, at very high spatio-temporal resolution, we analysed determinants of SARS-CoV-2 transmission in a medium-sized European city. We combined detailed epidemiological, mobility, and socioeconomic data-sets with whole genome sequencing during the first SARS-CoV-2 wave. Both phylogenetic clustering and compartmental modelling analysis were performed based on the dominating viral variant (B.1-C15324T; 60% of all cases). Here we show that transmissions on the city population level are driven by the socioeconomically weaker and highly mobile groups. Simulated vaccination scenarios showed that vaccination of a third of the population at 90% efficacy prioritising the latter groups would induce a stronger preventive effect compared to vaccinating exclusively senior population groups first. Our analysis accounts for both social interaction and mobility on the basis of molecularly related cases, thereby providing high confidence estimates of the underlying epidemic dynamics that may readily be translatable to other municipal areas.

scholarly journals The Displacement of Santiago de Chile’s Downtown during 1990–2015: Travel Time Effects on Eradicated Population

2019 ◽  
Vol 12 (1) ◽  
pp. 289 ◽  
Author(s):  
Gonzalo Suazo-Vecino ◽  
Juan Carlos Muñoz ◽  
Luis Fuentes Arce
Keyword(s):  
Travel Time ◽  
Surface Area ◽  
Low Income ◽  
Central Area ◽  
Time Effects ◽  
Socioeconomic Groups ◽  
Santiago De Chile ◽  
Average Travel Time ◽  
The Impact ◽  
The City

The center of activities of Santiago de Chile has been continuously evolving towards the eastern part of the city, where the most affluent residents live. This paper characterizes the direction and magnitude of this evolution through an indicator stating how much the built surface area for service purposes grows in different areas in the city. To identify the impact of this evolution, we compare residents’ travel-time distributions from different sectors in the city to the central area. This travel-time comparison is focused on the sectors where informal settlements were massively eradicated between 1978–1985 and those areas where the settlements were relocated. This analysis show that this policy and the consequent evolution of the city were detrimental to the affected families, significantly increasing average travel time to the extended center of the city and inequality among different socioeconomic groups in the city. Although the phenomenon is quite visible to everyone, it has not received any policy reaction from the authority. These findings suggest that middle and low-income sectors would benefit if policies driving the evolution of the center of activities towards them were implemented.

scholarly journals The spatio-temporal epidemic dynamics of COVID-19 outbreak in Africa

2020 ◽  
Author(s):  
Ezra Gayawan ◽  
Olawale Awe ◽  
Bamidele M Oseni ◽  
Ikemefuna C. Uzochukwu ◽  
Adeshina Adekunle ◽  
...  
Keyword(s):  
Temporal Dynamics ◽  
Poisson Model ◽  
The Novel ◽  
African Continent ◽  
Scarce Resources ◽  
Epidemic Dynamics ◽  
Disease Patterns ◽  
Spatio Temporal ◽  
Novel Coronavirus ◽  
The City

AbstractThe novel coronavirus (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), emerged in the city of Wuhan, China in December 2019. Although, the disease appears on the African continent late, it has spread to virtually all the countries. We provide early spatio-temporal dynamics of COVID-19 within the first 62 days of the disease’s appearance on the African continent. We used a two-parameter hurdle Poisson model to simultaneously analyze the zero counts and the frequency of occurrence. We investigate the effects of important healthcare capacities including hospital beds and number of medical doctors in the different countries. The results show that cases of the pandemic vary geographically across Africa with notable high incidence in neighboring countries particularly in West and North Africa. The burden of the disease (per 100,000) was most felt in Djibouti Tunisia, Morocco and Algeria. Temporally, during the first 4 weeks, the burden was highest in Senegal, Egypt and Mauritania, but by mid-April it shifted to Somalia, Chad, Guinea, Tanzania, Gabon, Sudan, and Zimbabwe. Currently, Namibia, Angola, South Sudan, Burundi and Uganda have the least burden. The findings could be useful in implementing epidemiological intervention and allocation of scarce resources based on heterogeneity of the disease patterns.

scholarly journals The impact of mobility network properties on predicted epidemic dynamics in Dhaka and Bangkok

2021 ◽  
Author(s):  
Tyler S. Brown ◽  
Kenth Engø-Monsen ◽  
Mathew V. Kiang ◽  
Ayesha S. Mahmud ◽  
Richard J. Maude ◽  
...  
Keyword(s):  
Geographic Distance ◽  
Large Cities ◽  
Epidemic Dynamics ◽  
Geographic Structure ◽  
Two Cities ◽  
User Data ◽  
Network Properties ◽  
Phone User ◽  
The Impact ◽  
The City

1AbstractProperties of city-level commuting networks are expected to influence epidemic potential of cities and modify the speed and spatial trajectory of epidemics when they occur. In this study, we use aggregated mobile phone user data to reconstruct commuter mobility networks for Bangkok (Thailand) and Dhaka (Bangladesh), two megacities in Asia with populations of 16 and 21 million people, respectively. We model the dynamics of directly-transmitted infections (such as SARS-CoV2) propagating on these commuting networks, and find that differences in network structure between the two cities drive divergent predicted epidemic trajectories: the commuting network in Bangkok is composed of geographically-contiguous modular communities and epidemic dispersal is correlated with geographic distance between locations, whereas the network in Dhaka has less distinct geographic structure and epidemic dispersal is less constrained by geographic distance. We also find that the predicted dynamics of epidemics vary depending on the local topology of the network around the origin of the outbreak. Measuring commuter mobility, and understanding how commuting networks shape epidemic dynamics at the city level, can support surveillance and preparedness efforts in large cities at risk for emerging or imported epidemics.

scholarly journals Interplay of global multi-scale human mobility, social distancing, government interventions, and COVID-19 dynamics

2020 ◽  
Author(s):  
Aniruddha Adiga ◽  
Lijing Wang ◽  
Adam Sadilek ◽  
Ashish Tendulkar ◽  
Srinivasan Venkatramanan ◽  
...  
Keyword(s):  
Human Mobility ◽  
The United States ◽  
Global Scale ◽  
International Travel ◽  
Population Mixing ◽  
Government Interventions ◽  
Social Distancing ◽  
Epidemic Dynamics ◽  
The Us ◽  
The Impact

AbstractThis work quantifies the impact of interventions to curtail mobility and social interactions in order to control the COVID-19 pandemic. We analyze the change in world-wide mobility at multiple spatio-temporal resolutions – county, state, country – using an anonymized aggregate mobility map that captures population flows between geographic cells of size 5 km2. We show that human mobility underwent an abrupt and significant change, partly in response to the interventions, resulting in 87% reduction of international travel and up to 75% reduction of domestic travel. Taking two very different countries sampled from the global spectrum, we observe a maximum reduction of 42% in mobility across different states of the United States (US), and a 68% reduction across the states of India between late March and late April. Since then, there has been an uptick in flows, with the US seeing 53% increase and India up to 38% increase with respect to flows seen during the lockdown. As we overlay this global map with epidemic incidence curves and dates of government interventions, we observe that as case counts rose, mobility fell – often before stay-at-home orders were issued. Further, in order to understand mixing within a region, we propose a new metric to quantify the effect of social distancing on the basis of mobility. We find that population mixing has decreased considerably as the pandemic has progressed and are able to measure this effect across the world. Finally, we carry out a counterfactual analysis of delaying the lockdown and show that a one week delay would have doubled the reported number of cases in the US and India. To our knowledge, this work is the first to model in near real-time, the interplay of human mobility, epidemic dynamics and public policies across multiple spatial resolutions and at a global scale.

scholarly journals The impact of mobility restriction measures on the reproduction index of Covid-19 in the city of Queretaro, Mexico

2020 ◽  
Author(s):  
Oscar San Roman Orozco ◽  
Santiago Agraz Orozco ◽  
Isidro A Gutierrez Alvarez ◽  
Vasiliki Radaios
Keyword(s):  
Social Factors ◽  
Mitigation Strategy ◽  
Behavioral Changes ◽  
Government Interventions ◽  
Reproductive Rates ◽  
Key Events ◽  
The Impact ◽  
The City ◽  
Official Data ◽  
Sustained Increase

An observational study based on official data (CONACYT and Ministry of Health) was carried out in which the effective reproduction index R(e) and the reproduction index R0 are compared with the mobility presented by Google. Additionally, an overview of the development of the pandemic in Queretaro, Mexico. Highlights key events; such as the main government interventions and social factors that could affect the society behavior. A positive relationship is observed between Re, R0, and the levels of mobility presented by Google. This indicates that an increase in mobility is associated with the transmission of SARS-CoV-2. In February, a significant decrease in mobility is observed, which lasts until approximately May 1st. This period corresponds to an R0 and R(e) between 1.17 and 1.87. After May 1st, there is a sustained increase in mobility levels. And, as of May 16, the effective reproduction index R (e) and the reproduction index R0 begin to increase. This is expected as it reflects the delay between the infection and the diagnosis of COVID-19. The R0 and R (e) increase from 1.45 on May 16 to 3.59 on July 5. According to the baseline of normal mobility levels, an increase from -49.6% on May 1st, to -20.6% on July 5 was observed. Based on these data, we conclude that the relaxation of restrictive mobility measures should be reconsidered. Despite this, mobility restrictions must not be a unique mitigation strategy for controlling the Reproductive Index. A comprehensive approach is needed, which generates socio-behavioral changes that allow a further reduction in reproductive rates.

scholarly journals The spatio-temporal epidemic dynamics of COVID-19 outbreak in Africa

2020 ◽  
Vol 148 ◽  
Author(s):  
Ezra Gayawan ◽  
Olushina O. Awe ◽  
Bamidele M. Oseni ◽  
Ikemefuna C. Uzochukwu ◽  
Adeshina Adekunle ◽  
...  
Keyword(s):  
Temporal Dynamics ◽  
Virus Disease ◽  
Poisson Model ◽  
The Novel ◽  
Scarce Resources ◽  
Epidemic Dynamics ◽  
Disease Patterns ◽  
High Incidence ◽  
Spatio Temporal ◽  
The City

Abstract Corona virus disease 2019 (COVID-19), caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was first detected in the city of Wuhan, China in December 2019. Although, the disease appeared in Africa later than other regions, it has now spread to virtually all countries on the continent. We provide early spatio-temporal dynamics of COVID-19 within the first 62 days of the disease's appearance on the African continent. We used a two-parameter hurdle Poisson model to simultaneously analyse the zero counts and the frequency of occurrence. We investigate the effects of important healthcare capacities including hospital beds and number of medical doctors in different countries. The results show that cases of the pandemic vary geographically across Africa with notably high incidence in neighbouring countries particularly in West and North Africa. The burden of the disease (per 100 000) mostly impacted Djibouti, Tunisia, Morocco and Algeria. Temporally, during the first 4 weeks, the burden was highest in Senegal, Egypt and Mauritania, but by mid-April it shifted to Somalia, Chad, Guinea, Tanzania, Gabon, Sudan and Zimbabwe. Currently, Namibia, Angola, South Sudan, Burundi and Uganda have the least burden. These findings could be useful in guiding epidemiological interventions and the allocation of scarce resources based on heterogeneity of the disease patterns.

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