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1.
Appl Netw Sci ; 8(1): 16, 2023.
Article in English | MEDLINE | ID: covidwho-2288118

ABSTRACT

The COVID-19 pandemic has shed light on how the spread of infectious diseases worldwide are importantly shaped by both human mobility networks and socio-economic factors. However, few studies look at how both socio-economic conditions and the complex network properties of human mobility patterns interact, and how they influence outbreaks together. We introduce a novel methodology, called the Infection Delay Model, to calculate how the arrival time of an infection varies geographically, considering both effective distance-based metrics and differences in regions' capacity to isolate-a feature associated with socio-economic inequalities. To illustrate an application of the Infection Delay Model, this paper integrates household travel survey data with cell phone mobility data from the São Paulo metropolitan region to assess the effectiveness of lockdowns to slow the spread of COVID-19. Rather than operating under the assumption that the next pandemic will begin in the same region as the last, the model estimates infection delays under every possible outbreak scenario, allowing for generalizable insights into the effectiveness of interventions to delay a region's first case. The model sheds light on how the effectiveness of lockdowns to slow the spread of disease is influenced by the interaction of mobility networks and socio-economic levels. We find that a negative relationship emerges between network centrality and the infection delay after a lockdown, irrespective of income. Furthermore, for regions across all income and centrality levels, outbreaks starting in less central locations were more effectively slowed by a lockdown. Using the Infection Delay Model, this paper identifies and quantifies a new dimension of disease risk faced by those most central in a mobility network.

2.
The Brazilian Journal of Infectious Diseases ; 26:102591, 2022.
Article in English | ScienceDirect | ID: covidwho-2007540

ABSTRACT

Introdução O sequenciamento de genoma viral, projeções e visualizações por meio de modelos matemáticos, estatísticos e computacionais permitem acompanhar a disseminação de doenças infecciosas como a causada pela infecção pelo vírus SARS-CoV-2, a COVID-19. O monitoramento ativo e contínuo da evolução epidemiológica depende diretamente da vigilância atentando-se às variantes de preocupação, que podem ter maior transmissibilidade, virulência e letalidade que a linhagem original. Neste trabalho, apresentamos os resultados da genotipagem de amostras representativas distribuídas pelas Coordenadorias Regionais de Saúde do município de São Paulo. Os dados disponíveis para quase todo o ano de 2021 possuem informações como a data de coleta, data de primeiros sintomas, limiar Ct do exame de PCR, variante identificada e CEP do endereço de residência. Objetivo Na posse desses dados é possível analisar o padrão espaço-temporal da evolução da disseminação da COVID-19 no município de São Paulo por diferentes variantes, com o objetivo de determinar as regiões de surgimento de variantes de preocupação e estimar os padrões de mobilidade que permitam o espalhamento dessas variantes para diferentes locais. Método Os dados das amostras recebidas pela Secretaria de Saúde do Município de São Paulo são processados e completados com os resultados do sequenciamento genético por meio da técnica de PCR, determinando a variante identificada em cada uma dessas amostras. Depois, os dados passam por uma filtragem e correções de entradas, como as datas disponíveis e os CEPs. Em seguida, coordenadas geográficas dentro do município de São Paulo são obtidas, e mapas são construídos para mostrar o espalhamento da doença pelo município e a dominância de uma variante sobre a outra. Resultados O espalhamento da doença é visualizado por meio de mapas dinâmicos que permitem acompanhar o surgimento de variantes como a Gamma e a Delta em certas regiões do município, espalhando-se e dominando todo o território depois de um tempo. Com isso, foram identificadas as áreas mais suscetíveis e correlacionadas com os padrões de mobilidade urbana. Conclusão A vigilância da emergência e disseminação de variantes de preocupação permite a determinação de pontos chaves do comportamento viral e humano para determinar os locais mais suscetíveis a surtos e espalhamento de linhagens que são mais transmissíveis. Com isso, é possível estudar estratégias melhores para o combate não apenas da COVID-19, mas de outras doenças com padrões de transmissibilidade semelhantes. Ag. Financiadora FAPESP. Nr. Processo 2021/11953-5.

3.
BMJ Glob Health ; 6(4)2021 04.
Article in English | MEDLINE | ID: covidwho-1476465

ABSTRACT

INTRODUCTION: Little evidence exists on the differential health effects of COVID-19 on disadvantaged population groups. Here we characterise the differential risk of hospitalisation and death in São Paulo state, Brazil, and show how vulnerability to COVID-19 is shaped by socioeconomic inequalities. METHODS: We conducted a cross-sectional study using hospitalised severe acute respiratory infections notified from March to August 2020 in the Sistema de Monitoramento Inteligente de São Paulo database. We examined the risk of hospitalisation and death by race and socioeconomic status using multiple data sets for individual-level and spatiotemporal analyses. We explained these inequalities according to differences in daily mobility from mobile phone data, teleworking behaviour and comorbidities. RESULTS: Throughout the study period, patients living in the 40% poorest areas were more likely to die when compared with patients living in the 5% wealthiest areas (OR: 1.60, 95% CI 1.48 to 1.74) and were more likely to be hospitalised between April and July 2020 (OR: 1.08, 95% CI 1.04 to 1.12). Black and Pardo individuals were more likely to be hospitalised when compared with White individuals (OR: 1.41, 95% CI 1.37 to 1.46; OR: 1.26, 95% CI 1.23 to 1.28, respectively), and were more likely to die (OR: 1.13, 95% CI 1.07 to 1.19; 1.07, 95% CI 1.04 to 1.10, respectively) between April and July 2020. Once hospitalised, patients treated in public hospitals were more likely to die than patients in private hospitals (OR: 1.40%, 95% CI 1.34% to 1.46%). Black individuals and those with low education attainment were more likely to have one or more comorbidities, respectively (OR: 1.29, 95% CI 1.19 to 1.39; 1.36, 95% CI 1.27 to 1.45). CONCLUSIONS: Low-income and Black and Pardo communities are more likely to die with COVID-19. This is associated with differential access to quality healthcare, ability to self-isolate and the higher prevalence of comorbidities.


Subject(s)
COVID-19/ethnology , COVID-19/mortality , Ethnicity/statistics & numerical data , Hospital Mortality/ethnology , Pneumonia, Viral , Poverty Areas , Residence Characteristics/statistics & numerical data , Adult , Aged , Aged, 80 and over , Brazil/epidemiology , Cross-Sectional Studies , Female , Health Status Disparities , Humans , Male , Middle Aged , Pandemics , Pneumonia, Viral/epidemiology , SARS-CoV-2 , Seroepidemiologic Studies , Socioeconomic Factors
4.
Patterns (N Y) ; 2(10): 100349, 2021 Oct 08.
Article in English | MEDLINE | ID: covidwho-1428309

ABSTRACT

In response to the coronavirus pandemic, governments implemented social distancing, attempting to block the virus spread within territories. While it is well accepted that social isolation plays a role in epidemic control, the precise connections between mobility data indicators and epidemic dynamics are still a challenge. In this work, we investigate the dependency between a social isolation index and epidemiological metrics for several Brazilian cities. Classic statistical methods are employed to support the findings. As a first, initially surprising, result, we illustrate how there seems to be no apparent functional relationship between social isolation data and later effects on disease incidence. However, further investigations identified two regimes of successful employment of social isolation: as a preventive measure or as a remedy, albeit remedy measures require greater social isolation and bring higher burden to health systems. Additionally, we exhibit cases of successful strategies involving lockdowns and an indicator-based mobility restriction plan.

5.
Sci Rep ; 11(1): 13001, 2021 06 21.
Article in English | MEDLINE | ID: covidwho-1279897

ABSTRACT

Although international airports served as main entry points for SARS-CoV-2, the factors driving the uneven geographic spread of COVID-19 cases and deaths in Brazil remain mostly unknown. Here we show that three major factors influenced the early macro-geographical dynamics of COVID-19 in Brazil. Mathematical modeling revealed that the "super-spreading city" of São Paulo initially accounted for more than 85% of the case spread in the entire country. By adding only 16 other spreading cities, we accounted for 98-99% of the cases reported during the first 3 months of the pandemic in Brazil. Moreover, 26 federal highways accounted for about 30% of SARS-CoV-2's case spread. As cases increased in the Brazilian interior, the distribution of COVID-19 deaths began to correlate with the allocation of the country's intensive care units (ICUs), which is heavily weighted towards state capitals. Thus, severely ill patients living in the countryside had to be transported to state capitals to access ICU beds, creating a "boomerang effect" that contributed to skew the distribution of COVID-19 deaths. Therefore, if (i) a lockdown had been imposed earlier on in spreader-capitals, (ii) mandatory road traffic restrictions had been enforced, and (iii) a more equitable geographic distribution of ICU beds existed, the impact of COVID-19 in Brazil would be significantly lower.


Subject(s)
COVID-19/prevention & control , COVID-19/transmission , Carrier State/transmission , Critical Care/methods , Pandemics/prevention & control , Quarantine/methods , SARS-CoV-2 , Travel-Related Illness , Automobiles , Brazil/epidemiology , COVID-19/epidemiology , COVID-19/virology , Cities/epidemiology , Humans , Intensive Care Units , Models, Theoretical
6.
Science ; 372(6544): 815-821, 2021 05 21.
Article in English | MEDLINE | ID: covidwho-1186201

ABSTRACT

Cases of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in Manaus, Brazil, resurged in late 2020 despite previously high levels of infection. Genome sequencing of viruses sampled in Manaus between November 2020 and January 2021 revealed the emergence and circulation of a novel SARS-CoV-2 variant of concern. Lineage P.1 acquired 17 mutations, including a trio in the spike protein (K417T, E484K, and N501Y) associated with increased binding to the human ACE2 (angiotensin-converting enzyme 2) receptor. Molecular clock analysis shows that P.1 emergence occurred around mid-November 2020 and was preceded by a period of faster molecular evolution. Using a two-category dynamical model that integrates genomic and mortality data, we estimate that P.1 may be 1.7- to 2.4-fold more transmissible and that previous (non-P.1) infection provides 54 to 79% of the protection against infection with P.1 that it provides against non-P.1 lineages. Enhanced global genomic surveillance of variants of concern, which may exhibit increased transmissibility and/or immune evasion, is critical to accelerate pandemic responsiveness.


Subject(s)
COVID-19/epidemiology , COVID-19/virology , Communicable Diseases, Emerging/epidemiology , Communicable Diseases, Emerging/virology , SARS-CoV-2/classification , SARS-CoV-2/genetics , Spike Glycoprotein, Coronavirus/genetics , Angiotensin-Converting Enzyme 2/metabolism , Brazil/epidemiology , Epidemiological Monitoring , Genome, Viral , Genomics , Humans , Models, Theoretical , Molecular Epidemiology , Mutation , Protein Binding , SARS-CoV-2/isolation & purification , Spike Glycoprotein, Coronavirus/metabolism , Viral Load
7.
Geophysical Research Letters ; 47(16), 2020.
Article | Web of Science | ID: covidwho-779947

ABSTRACT

Decrease of seismic noise level, after reduction of traffic due to the COVID-19 pandemic, has been observed worldwide. The possibility of using seismic noise as another proxy to estimate social isolation was tested with a station within Rio de Janeiro city. We used the isolation index measured from smartphone movement to calibrate the seismic noise levels and estimated an Isolation Seismic Index,ISI(% of the population at home), using the seismic noise energy. Noise levels best correlate with isolation measures in the frequency range 4-8 Hz. Small differences between the smartphone and theISIindexes are interpreted as differences in social activities and noise sources. All mobility indexes are proxies to the actual isolation. AlthoughISIdoes not measure the number of people outside, it measures the number of noise sources (vehicles, trains, factories, etc.) and can be used as additional information to interpret anomalies in other proxies.

8.
Science ; 369(6508): 1255-1260, 2020 09 04.
Article in English | MEDLINE | ID: covidwho-675945

ABSTRACT

Brazil currently has one of the fastest-growing severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) epidemics in the world. Because of limited available data, assessments of the impact of nonpharmaceutical interventions (NPIs) on this virus spread remain challenging. Using a mobility-driven transmission model, we show that NPIs reduced the reproduction number from >3 to 1 to 1.6 in São Paulo and Rio de Janeiro. Sequencing of 427 new genomes and analysis of a geographically representative genomic dataset identified >100 international virus introductions in Brazil. We estimate that most (76%) of the Brazilian strains fell in three clades that were introduced from Europe between 22 February and 11 March 2020. During the early epidemic phase, we found that SARS-CoV-2 spread mostly locally and within state borders. After this period, despite sharp decreases in air travel, we estimated multiple exportations from large urban centers that coincided with a 25% increase in average traveled distances in national flights. This study sheds new light on the epidemic transmission and evolutionary trajectories of SARS-CoV-2 lineages in Brazil and provides evidence that current interventions remain insufficient to keep virus transmission under control in this country.


Subject(s)
Betacoronavirus/genetics , Coronavirus Infections/epidemiology , Coronavirus Infections/transmission , Pneumonia, Viral/epidemiology , Pneumonia, Viral/transmission , Basic Reproduction Number , Bayes Theorem , Betacoronavirus/classification , Brazil/epidemiology , COVID-19 , COVID-19 Testing , Cities/epidemiology , Clinical Laboratory Techniques , Coronavirus Infections/diagnosis , Coronavirus Infections/prevention & control , Coronavirus Infections/virology , Europe , Evolution, Molecular , Genome, Viral , Humans , Models, Genetic , Models, Statistical , Pandemics/prevention & control , Phylogeny , Phylogeography , Pneumonia, Viral/prevention & control , Pneumonia, Viral/virology , SARS-CoV-2 , Spatio-Temporal Analysis , Travel , Urban Population
9.
PLoS One ; 15(7): e0235732, 2020.
Article in English | MEDLINE | ID: covidwho-647345

ABSTRACT

Mobile geolocation data is a valuable asset in the assessment of movement patterns of a population. Once a highly contagious disease takes place in a location the movement patterns aid in predicting the potential spatial spreading of the disease, hence mobile data becomes a crucial tool to epidemic models. In this work, based on millions of anonymized mobile visits data in Brazil, we investigate the most probable spreading patterns of the COVID-19 within states of Brazil. The study is intended to help public administrators in action plans and resources allocation, whilst studying how mobile geolocation data may be employed as a measure of population mobility during an epidemic. This study focuses on the states of São Paulo and Rio de Janeiro during the period of March 2020, when the disease first started to spread in these states. Metapopulation models for the disease spread were simulated in order to evaluate the risk of infection of each city within the states, by ranking them according to the time the disease will take to infect each city. We observed that, although the high-risk regions are those closer to the capital cities, where the outbreak has started, there are also cities in the countryside with great risk. The mathematical framework developed in this paper is quite general and may be applied to locations around the world to evaluate the risk of infection by diseases, in special the COVID-19, when geolocation data is available.


Subject(s)
Coronavirus Infections/epidemiology , Mobile Applications , Models, Biological , Pneumonia, Viral/epidemiology , Brazil/epidemiology , COVID-19 , Cities/epidemiology , Computer Simulation , Disease Outbreaks , Health Status Indicators , Humans , Pandemics , Population Density , Travel
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