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1.
Sci Rep ; 11(1): 24491, 2021 12 29.
Article in English | MEDLINE | ID: covidwho-1591547

ABSTRACT

There is an ongoing need for scientific analysis to help governments and public health authorities make decisions regarding the COVID-19 pandemic. This article presents a methodology based on data mining that can offer support for coping with epidemic diseases. The methodological approach was applied in São Paulo, Rio de Janeiro and Manaus, the cities in Brazil with the most COVID-19 deaths until the first half of 2021. We aimed to predict the evolution of COVID-19 in metropolises and identify air quality and meteorological variables correlated with confirmed cases and deaths. The statistical analyses indicated the most important explanatory environmental variables, while the cluster analyses showed the potential best input variables for the forecasting models. The forecast models were built by two different algorithms and their results have been compared. The relationship between epidemiological and environmental variables was particular to each of the three cities studied. Low solar radiation periods predicted in Manaus can guide managers to likely increase deaths due to COVID-19. In São Paulo, an increase in the mortality rate can be indicated by drought periods. The developed models can predict new cases and deaths by COVID-19 in studied cities. Furthermore, the methodological approach can be applied in other cities and for other epidemic diseases.


Subject(s)
COVID-19/epidemiology , COVID-19/mortality , Data Mining/methods , Brazil/epidemiology , COVID-19/pathology , Cities/epidemiology , Humans , Models, Theoretical , Morbidity , Pandemics/prevention & control , SARS-CoV-2/pathogenicity
2.
PLoS One ; 16(3): e0247794, 2021.
Article in English | MEDLINE | ID: covidwho-1575402

ABSTRACT

BACKGROUND: Identified in December 2019 in the city of Wuhan, China, the outbreak of COVID-19 spread throughout the world and its impacts affect different populations differently, where countries with high levels of social and economic inequality such as Brazil gain prominence, for understanding of the vulnerability factors associated with the disease. Given this scenario, in the absence of a vaccine or safe and effective antiviral treatment for COVID-19, nonpharmacological measures are essential for prevention and control of the disease. However, many of these measures are not feasible for millions of individuals who live in territories with increased social vulnerability. The study aims to analyze the spatial distribution of COVID-19 incidence in Brazil's municipalities (counties) and investigate its association with sociodemographic determinants to better understand the social context and the epidemic's spread in the country. METHODS: This is an analytical ecological study using data from various sources. The study period was February 25 to September 26, 2020. Data analysis used global regression models: ordinary least squares (OLS), spatial autoregressive model (SAR), and conditional autoregressive model (CAR) and the local regression model called multiscale geographically weighted regression (MGWR). FINDINGS: The higher the GINI index, the higher the incidence of the disease at the municipal level. Likewise, the higher the nurse ratio per 1,000 inhabitants in the municipalities, the higher the COVID-19 incidence. Meanwhile, the proportional mortality ratio was inversely associated with incidence of the disease. DISCUSSION: Social inequality increased the risk of COVID-19 in the municipalities. Better social development of the municipalities was associated with lower risk of the disease. Greater access to health services improved the diagnosis and notification of the disease and was associated with more cases in the municipalities. Despite universal susceptibility to COVID-19, populations with increased social vulnerability were more exposed to risk of the illness.


Subject(s)
COVID-19/epidemiology , Nurses/statistics & numerical data , Brazil/epidemiology , COVID-19/diagnosis , COVID-19/mortality , Cities/epidemiology , Demography , Female , Humans , Incidence , Male , Risk Factors , Socioeconomic Factors , Spatial Analysis , Spatial Regression
3.
Sci Rep ; 11(1): 20121, 2021 10 11.
Article in English | MEDLINE | ID: covidwho-1532138

ABSTRACT

The Brazilian strategy to overcome the spread of COVID-19 has been particularly criticized due to the lack of a national coordinating effort and an appropriate testing program. Here, a successful approach to control the spread of COVID-19 transmission is described by the engagement of public (university and governance) and private sectors (hospitals and oil companies) in Macaé, state of Rio de Janeiro, Brazil, a city known as the National Oil Capital. In 2020 between the 17th and 38th epidemiological week, over two percent of the 206,728 citizens were subjected to symptom analysis and RT-qPCR testing by the Federal University of Rio de Janeiro, with positive individuals being notified up to 48 h after swab collection. Geocodification and spatial cluster analysis were used to limit COVID-19 spreading in Macaé. Within the first semester after the outbreak of COVID-19 in Brazil, Macaé recorded 1.8% of fatalities associated with COVID-19 up to the 38th epidemiological week, which was at least five times lower than the state capital (10.6%). Overall, considering the successful experience of this joint effort of private and public engagement in Macaé, our data suggest that the development of a similar strategy countrywise could have contributed to a better control of the COVID-19 spread in Brazil. Quarantine decree by the local administration, comprehensive molecular testing coupled to scientific analysis of COVID-19 spreading, prevented the catastrophic consequences of the pandemic as seen in other populous cities within the state of Rio de Janeiro and elsewhere in Brazil.


Subject(s)
COVID-19 Nucleic Acid Testing/statistics & numerical data , COVID-19/epidemiology , Pandemics/statistics & numerical data , SARS-CoV-2/isolation & purification , Adolescent , Adult , Aged , Brazil/epidemiology , COVID-19/diagnosis , COVID-19/transmission , COVID-19/virology , Cities/epidemiology , Cities/statistics & numerical data , Female , Humans , Male , Middle Aged , RNA, Viral/isolation & purification , SARS-CoV-2/genetics , Young Adult
4.
Sci Rep ; 11(1): 22120, 2021 11 11.
Article in English | MEDLINE | ID: covidwho-1510614

ABSTRACT

The outbreak of the Coronavirus disease 2019 (COVID-19), and the drastic measures taken to mitigate its spread through imposed social distancing, have brought forward the need to better understand the underlying factors controlling spatial distribution of human activities promoting disease transmission. Focusing on results from 17,250 epidemiological investigations performed during early stages of the pandemic outbreak in Israel, we show that the distribution of carriers of the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), which causes COVID-19, is spatially correlated with two satellite-derived surface metrics: night light intensity and landscape patchiness, the latter being a measure to the urban landscape's scale-dependent spatial heterogeneity. We find that exposure to SARS-CoV-2 carriers was significantly more likely to occur in "patchy" parts of the city, where the urban landscape is characterized by high levels of spatial heterogeneity at relatively small, tens of meters scales. We suggest that this spatial association reflects a scale-dependent constraint imposed by the city's morphology on the cumulative behavior of the people inhabiting it. The presented results shed light on the complex interrelationships between humans and the urban landscape in which they live and interact, and open new avenues for implementation of multi-satellite data in large scale modeling of phenomena centered in urban environments.


Subject(s)
COVID-19/epidemiology , Cities/epidemiology , Human Activities , Humans , Israel/epidemiology , SARS-CoV-2/isolation & purification , Satellite Imagery , Urban Population
5.
Sci Rep ; 11(1): 20339, 2021 10 13.
Article in English | MEDLINE | ID: covidwho-1467132

ABSTRACT

This study investigated the environmental spatial heterogeneity of novel coronavirus (COVID-19) and spatial and temporal changes among the top-20 metropolitan cities of the Asia-Pacific. Remote sensing-based assessment is performed to analyze before and during the lockdown amid COVID-19 lockdown in the cities. Air pollution and mobility data of each city (Bangkok, Beijing, Busan, Dhaka, Delhi, Ho Chi Minh, Hong Kong, Karachi, Mumbai, Seoul, Shanghai, Singapore, Tokyo, Wuhan, and few others) have been collected and analyzed for 2019 and 2020. Results indicated that almost every city was impacted positively regarding environmental emissions and visible reduction were found in Aerosol Optical Depth (AOD), sulfur dioxide (SO2), carbon monoxide (CO), and nitrogen dioxide (NO2) concentrations before and during lockdown periods of 2020 as compared to those of 2019. The highest NO2 emission reduction (~ 50%) was recorded in Wuhan city during the lockdown of 2020. AOD was highest in Beijing and lowest in Colombo (< 10%). Overall, 90% movement was reduced till mid-April, 2020. A 98% reduction in mobility was recorded in Delhi, Seoul, and Wuhan. This analysis suggests that smart mobility and partial shutdown policies could be developed to reduce environmental pollutions in the region. Wuhan city is one of the benchmarks and can be replicated for the rest of the Asian cities wherever applicable.


Subject(s)
Air Pollution/prevention & control , COVID-19/epidemiology , Environmental Monitoring/methods , Aerosols/analysis , Air Pollutants/analysis , Air Pollution/analysis , Asia, Southeastern/epidemiology , Carbon Monoxide/analysis , Cities/epidemiology , Far East/epidemiology , Humans , Nitrogen Dioxide/analysis , Particulate Matter/analysis , Physical Distancing , SARS-CoV-2/pathogenicity , Sulfur Dioxide/analysis
6.
BMC Infect Dis ; 21(1): 816, 2021 Aug 14.
Article in English | MEDLINE | ID: covidwho-1440911

ABSTRACT

BACKGROUND: The coronavirus disease 2019 (COVID-19) has become a pandemic. Few studies have been conducted to investigate the spatio-temporal distribution of COVID-19 on nationwide city-level in China. OBJECTIVE: To analyze and visualize the spatiotemporal distribution characteristics and clustering pattern of COVID-19 cases from 362 cities of 31 provinces, municipalities and autonomous regions in mainland China. METHODS: A spatiotemporal statistical analysis of COVID-19 cases was carried out by collecting the confirmed COVID-19 cases in mainland China from January 10, 2020 to October 5, 2020. Methods including statistical charts, hotspot analysis, spatial autocorrelation, and Poisson space-time scan statistic were conducted. RESULTS: The high incidence stage of China's COVID-19 epidemic was from January 17 to February 9, 2020 with daily increase rate greater than 7.5%. The hot spot analysis suggested that the cities including Wuhan, Huangshi, Ezhou, Xiaogan, Jingzhou, Huanggang, Xianning, and Xiantao, were the hot spots with statistical significance. Spatial autocorrelation analysis indicated a moderately correlated pattern of spatial clustering of COVID-19 cases across China in the early phase, with Moran's I statistic reaching maximum value on January 31, at 0.235 (Z = 12.344, P = 0.001), but the spatial correlation gradually decreased later and showed a discrete trend to a random distribution. Considering both space and time, 19 statistically significant clusters were identified. 63.16% of the clusters occurred from January to February. Larger clusters were located in central and southern China. The most likely cluster (RR = 845.01, P < 0.01) included 6 cities in Hubei province with Wuhan as the centre. Overall, the clusters with larger coverage were in the early stage of the epidemic, while it changed to only gather in a specific city in the later period. The pattern and scope of clusters changed and reduced over time in China. CONCLUSIONS: Spatio-temporal cluster detection plays a vital role in the exploration of epidemic evolution and early warning of disease outbreaks and recurrences. This study can provide scientific reference for the allocation of medical resources and monitoring potential rebound of the COVID-19 epidemic in China.


Subject(s)
COVID-19 , China/epidemiology , Cities/epidemiology , Humans , Pandemics , SARS-CoV-2 , Spatio-Temporal Analysis
7.
PLoS One ; 16(9): e0257604, 2021.
Article in English | MEDLINE | ID: covidwho-1435617

ABSTRACT

BACKGROUND: Patients with COVID-19 are follow-up in primary care and long COVID is scarcely defined. The study aim was to describe SARS-CoV-2 pneumonia and cut-offs for defining long COVID in primary care follow-up patients. METHODS: A retrospective observational study in primary care in Madrid, Spain, was conducted. Data was collected during 6 months (April to September) in 2020, during COVID-19 first wave, from patients ≥ 18 years with SARS-CoV-2 pneumonia diagnosed. Variables: sociodemographic, comorbidities, COVID-19 symptoms and complications, laboratory test and chest X-ray. Descriptive statistics were used, mean (standard deviation (SD)) and medians (interquartile range (IQR)) respectively. Differences were detected applying X2 test, Student's T-test, ANOVA, Wilcoxon-Mann-Whitney or Kruskal-Wallis depending on variable characteristics. RESULTS: 155 patients presented pneumonia in day 7.8 from the onset (79.4% were hospitalized, median length of 7.0 days (IQR: 3.0, 13.0)). After discharge, the follow-up lasted 54.0 median days (IQR 42.0, 88.0) and 12.2 mean (SD 6.4) phone calls were registered per patient. The main symptoms and their duration were: cough (41.9%, 12 days), dyspnoea (31.0%, 15 days), asthenia (26.5%, 21 days). Different cut-off points were applied for long COVID and week 4 was considered the best milestone (28.3% of the sample still had symptoms after week 4) versus week 12 (8.3%). Patients who still had symptoms >4 weeks follow-up took place over 81.0 days (IQR: 50.5, 103.0), their symptoms were more prevalent and lasted longer than those ≤ 4 weeks: cough (63.6% 30 days), dyspnoea (54.6%, 46 days), and asthenia (56.8%, 29 days). Embolism was more frequent in patients who still had symptoms >4 weeks than those with symptoms ≤4 weeks (9.1% vs 1.8%, p value 0.034). CONCLUSION: Most patients with SARS-CoV-2 pneumonia recovered during the first 4 weeks from the beginning of the infection. The cut-off point to define long COVID, as persisting symptoms, should be between 4 to 12 weeks from the onset of the symptoms.


Subject(s)
COVID-19/complications , Primary Health Care/statistics & numerical data , Adult , COVID-19/epidemiology , Cities/epidemiology , Female , Humans , Male , Middle Aged , Retrospective Studies , Spain/epidemiology
8.
PLoS One ; 16(9): e0257347, 2021.
Article in English | MEDLINE | ID: covidwho-1416895

ABSTRACT

BACKGROUND: Brazil, as many other countries, have been heavily affected by COVID-19. This study aimed to analyze the impact of Primary health care and the family health strategy (FHS) coverage, the scores of the National Program for Improving Primary Care Access and Quality (PMAQ), and socioeconomic and social indicators in the number of COVID-19 cases in Brazilian largest cities. METHODS: This is an ecological study, carried out through the analysis of secondary data on the population of all Brazilian main cities, based on the analysis of a 26-week epidemiological epidemic week series by COVID-19. Statistical analysis was performed using Generalized Linear Models with an Autoregressive work correlation matrix. RESULTS: It was shown that greater PHC coverage and greater FHS coverage together with an above average PMAQ score are associated with slower dissemination and lower burden of COVID-19. CONCLUSION: It is evident that cities with less social inequality and restrictions of social protection combined with social development have a milder pandemic scenario. It is necessary to act quickly on these conditions for COVID-19 dissemination by timely actions with high capillarity. Expanding access to PHC and social support strategies for the vulnerable are essential.


Subject(s)
COVID-19/epidemiology , Pandemics , Quality of Health Care , Social Determinants of Health , Brazil/epidemiology , Cities/epidemiology , Humans
9.
Dig Dis Sci ; 66(11): 3635-3658, 2021 11.
Article in English | MEDLINE | ID: covidwho-1406167

ABSTRACT

AIM: To report revolutionary reorganization of academic gastroenterology division from COVID-19 pandemic surge at metropolitan Detroit epicenter from 0 infected patients on March 9, 2020, to > 300 infected patients in hospital census in April 2020 and > 200 infected patients in April 2021. SETTING: GI Division, William Beaumont Hospital, Royal Oak, has 36 GI clinical faculty; performs > 23,000 endoscopies annually; fully accredited GI fellowship since 1973; employs > 400 house staff annually since 1995; tertiary academic hospital; predominantly voluntary attendings; and primary teaching hospital, Oakland-University-Medical-School. METHODS: This was a prospective study. Expert opinion. Personal experience includes Hospital GI chief > 14 years until 2020; GI fellowship program director, several hospitals > 20 years; author of > 300 publications in peer-reviewed GI journals; committee-member, Food-and-Drug-Administration-GI-Advisory Committee > 5 years; and key hospital/medical school committee memberships. Computerized PubMed literature review was performed on hospital changes and pandemic. Study was exempted/approved by Hospital IRB, April 14, 2020. RESULTS: Division reorganized patient care to add clinical capacity and minimize risks to staff of contracting COVID-19 infection. Affiliated medical school changes included: changing "live" to virtual lectures; canceling medical student GI electives; exempting medical students from treating COVID-19-infected patients; and graduating medical students on time despite partly missing clinical electives. Division was reorganized by changing "live" GI lectures to virtual lectures; four GI fellows temporarily reassigned as medical attendings supervising COVID-19-infected patients; temporarily mandated intubation of COVID-19-infected patients for esophagogastroduodenoscopy; postponing elective GI endoscopies; and reducing average number of endoscopies from 100 to 4 per weekday during pandemic peak! GI clinic visits reduced by half (postponing non-urgent visits), and physical visits replaced by virtual visits. Economic pandemic impact included temporary, hospital deficit subsequently relieved by federal grants; hospital employee terminations/furloughs; and severe temporary decline in GI practitioner's income during surge. Hospital temporarily enhanced security and gradually ameliorated facemask shortage. GI program director contacted GI fellows twice weekly to ameliorate pandemic-induced stress. Divisional parties held virtually. GI fellowship applicants interviewed virtually. Graduate medical education changes included weekly committee meetings to monitor pandemic-induced changes; program managers working from home; canceling ACGME annual fellowship survey, changing ACGME physical to virtual site visits; and changing national conventions from physical to virtual. CONCLUSION: Reports profound and pervasive GI divisional changes to maximize clinical resources devoted to COVID-19-infected patients and minimize risks of transmitting infection.


Subject(s)
COVID-19/economics , COVID-19/epidemiology , Economics, Hospital/organization & administration , Gastroenterology/education , Hospital Administration/methods , SARS-CoV-2 , Cities/economics , Cities/epidemiology , Education, Medical, Graduate/organization & administration , Gastroenterology/economics , Hospital Administration/economics , Humans , Internship and Residency , Michigan/epidemiology , Organizational Affiliation/economics , Organizational Affiliation/organization & administration , Prospective Studies , Schools, Medical/organization & administration
10.
Biomed Res Int ; 2021: 5546790, 2021.
Article in English | MEDLINE | ID: covidwho-1405239

ABSTRACT

The spread of COVID-19 worldwide continues despite multidimensional efforts to curtail its spread and provide treatment. Efforts to contain the COVID-19 pandemic have triggered partial or full lockdowns across the globe. This paper presents a novel framework that intelligently combines machine learning models and the Internet of Things (IoT) technology specifically to combat COVID-19 in smart cities. The purpose of the study is to promote the interoperability of machine learning algorithms with IoT technology by interacting with a population and its environment to curtail the COVID-19 pandemic. Furthermore, the study also investigates and discusses some solution frameworks, which can generate, capture, store, and analyze data using machine learning algorithms. These algorithms can detect, prevent, and trace the spread of COVID-19 and provide a better understanding of the disease in smart cities. Similarly, the study outlined case studies on the application of machine learning to help fight against COVID-19 in hospitals worldwide. The framework proposed in the study is a comprehensive presentation on the major components needed to integrate the machine learning approach with other AI-based solutions. Finally, the machine learning framework presented in this study has the potential to help national healthcare systems in curtailing the COVID-19 pandemic in smart cities. In addition, the proposed framework is poised as a pointer for generating research interests that would yield outcomes capable of been integrated to form an improved framework.


Subject(s)
COVID-19/epidemiology , Communicable Disease Control/methods , Machine Learning , Algorithms , Artificial Intelligence , COVID-19/prevention & control , COVID-19/transmission , Cities/epidemiology , Contact Tracing/methods , Delivery of Health Care , Humans , Internet of Things , Pandemics , SARS-CoV-2/pathogenicity
11.
Cad. Saúde Pública (Online) ; 37(6): e00039221, 2021. tab, graf
Article in Portuguese | LILACS (Americas) | ID: covidwho-1394632

ABSTRACT

O crescimento acentuado de casos e óbitos por COVID-19 tem levado à grande sobrecarga do sistema de saúde no Brasil, em especial em cidades como Manaus (Amazonas), Rio de Janeiro e São Paulo. A descrição do impacto da pandemia tem se baseado em números absolutos ou taxas de mortalidade brutas, não considerando o padrão de distribuição das faixas etárias nas diferentes regiões do país. Este estudo tem por objetivo comparar as taxas de mortalidade brutas por COVID-19 com as taxas padronizadas por idade nas capitais dos estados brasileiros e no Distrito Federal. As informações sobre óbito foram acessadas no Sistema de Informação de Vigilância da Gripe (SIVEP-Gripe), e os denominadores populacionais foram baseados nas estimativas disponibilizadas pelo Ministério da Saúde. Para o cálculo das taxas padronizadas por idade, utilizou-se a estrutura etária da população do Brasil estimada para 2020. Os resultados mostram que as maiores taxas brutas foram em Manaus (253,6/100 mil) e no Rio de Janeiro (253,2/100 mil). Após padronização por idade, houve aumento expressivo das taxas na Região Norte. A maior taxa ajustada foi vista em Manaus (412,5/100 mil) onde 33% de óbitos por COVID-19 ocorreram entre menores de 60 anos. A mortalidade em Manaus acima de 70 anos foi o dobro se comparada à do Rio de Janeiro e o triplo se comparada à de São Paulo. A utilização de taxas de mortalidade padronizadas por idade elimina vieses interpretativos, expondo, de forma marcante, o peso ainda maior da COVID-19 na Região Norte do país.


The sharp growth in COVID-19 cases and deaths has created a heavy overburden on Brazil's health system, especially in the cities of Manaus (Amazonas State), Rio de Janeiro, and São Paulo. The description of the pandemic's impact has been based on absolute numbers and crude mortality rates, failing to consider the age distribution patterns in different regions of the country. This study aims to compare the crude mortality rates from COVID-19 with age-standardized rates in the state capitals and Federal District. Information on deaths was accessed in the Information System on Influenza Surveillance (SIVEP-Gripe), and the population denominators were based on the estimate provided by the Brazilian Ministry of Health. Calculation of the age-standardized rates used the estimated age structure of the Brazilian population in 2020. The results show that the highest crude rates were in Manaus (253.6/100,000) and Rio de Janeiro (253.2/100,000). Age standardization led to a major increase in the North of Brazil. The highest age-adjusted rate was in Manaus (412.5/100,000), where 33% of COVID-19 deaths occurred in individuals under 60 years of age. Mortality in Manaus over 70 years of age was double that of Rio de Janeiro and triple that of São Paulo. The use of age-adjusted mortality rates eliminates interpretative biases, clearly exposing the even greater weight of COVID-19 in the North of Brazil.


El crecimiento acentuado de casos y óbitos por COVID-19 ha provocado una gran sobrecarga del sistema de salud en Brasil, en especial en ciudades como Manaus (Estado del Amazonas), Rio de Janeiro y São Paulo. La descripción del impacto de la pandemia se ha basado en números absolutos o tasas de mortalidad brutas, no considerando el patrón de distribución de las franjas etarias en las diferentes regiones del país. Este estudio tiene como objetivo comparar las tasas de mortalidad brutas por COVID-19, con las tasas estandarizadas por edad, en las capitales de los estados brasileños y en el Distrito Federal. Se accedió a la información sobre fallecimientos en el Sistema de Información de Vigilancia de la Gripe (SIVEP-Gripe), y los denominadores poblacionales se basaron en las estimaciones facilitadas por el Ministerio de Salud de Brasil. Para el cálculo de las tasas estandarizadas por edad, se utilizó la estructura etaria de la población de Brasil estimada para 2020. Los resultados muestran que las mayores tasas brutas se produjeron en Manaus (253,6/100.000) y en Rio de Janeiro (253,2/100.000). Tras la estandarización por edad, hubo un aumento expresivo de las tasas en la Región Norte. La mayor tasa ajustada fue vista en Manaus (412,5/100.000), donde un 33% de óbitos por COVID-19 se produjeron entre menores de 60 años. La mortalidad en Manaus por encima de 70 años fue el doble, si se compara con la de Rio de Janeiro, y el triple si se compara con la de São Paulo. La utilización de tasas de mortalidad estandarizadas por edad elimina sesgos interpretativos, exponiendo, de forma significativa, el peso todavía mayor de la COVID-19 en la Región Norte del país.


Subject(s)
Humans , Aged , Aged, 80 and over , COVID-19 , Brazil/epidemiology , Mortality , Cities/epidemiology , Age Distribution , SARS-CoV-2 , Middle Aged
12.
Sci Rep ; 11(1): 17649, 2021 09 03.
Article in English | MEDLINE | ID: covidwho-1392886

ABSTRACT

The ubiquitous activity of humans is a fundamental feature of urban environments affecting local wildlife in several ways. Testing the influence of human disturbance would ideally need experimental approach, however, in cities, this is challenging at relevant spatial and temporal scales. Thus, to better understand the ecological effects of human activity, we exploited the opportunity that the city-wide lockdowns due to the COVID-19 pandemic provided during the spring of 2020. We assessed changes in reproductive success of great tits (Parus major) at two urban habitats affected strikingly differently by the 'anthropause', and at an unaffected forest site. Our results do not support that urban great tits benefited from reduced human mobility during the lockdown. First, at one of our urban sites, the strongly (- 44%) reduced human disturbance in 2020 (compared to a long-term reference period) did not increase birds' reproductive output relative to the forest habitat where human disturbance was low in all years. Second, in the other urban habitat, recreational human activity considerably increased (+ 40%) during the lockdown and this was associated with strongly reduced nestling body size compared to the pre-COVID reference year. Analyses of other environmental factors (meteorological conditions, lockdown-induced changes in air pollution) suggest that these are not likely to explain our results. Our study supports that intensified human disturbance can have adverse fitness consequences in urban populations. It also highlights that a few months of 'anthropause' is not enough to counterweight the detrimental impacts of urbanization on local wildlife populations.


Subject(s)
COVID-19 , Ecosystem , Quarantine , Reproduction/physiology , SARS-CoV-2 , Songbirds/physiology , Animals , COVID-19/epidemiology , COVID-19/prevention & control , Cities/epidemiology , Female , Humans , Male
13.
J Toxicol Environ Health A ; 85(1): 14-28, 2022 01 02.
Article in English | MEDLINE | ID: covidwho-1390330

ABSTRACT

Meteorological parameters modulate transmission of the SARS-Cov-2 virus, the causative agent related to coronavirus disease-2019 (COVID-19) development. However, findings across the globe have been inconsistent attributed to several confounding factors. The aim of the present study was to investigate the relationship between reported meteorological parameters from July 1 to October 31, 2020, and the number of confirmed COVID-19 cases in 4 Brazilian cities: São Paulo, the largest city with the highest number of cases in Brazil, and the cities with greater number of cases in the state of Parana during the study period (Curitiba, Londrina and Maringa). The assessment of meteorological factors with confirmed COVID-19 cases included atmospheric pressure, temperature, relative humidity, wind speed, solar irradiation, sunlight, dew point temperature, and total precipitation. The 7- and 15-day moving averages of confirmed COVID-19 cases were obtained for each city. Pearson's correlation coefficients showed significant correlations between COVID-19 cases and all meteorological parameters, except for total precipitation, with the strongest correlation with maximum wind speed (0.717, <0.001) in São Paulo. Regression tree analysis demonstrated that the largest number of confirmed COVID-19 cases was associated with wind speed (between ≥0.3381 and <1.173 m/s), atmospheric pressure (<930.5mb), and solar radiation (<17.98e+3). Lower number of cases was observed for wind speed <0.3381 m/s and temperature <23.86°C. Our results encourage the use of meteorological information as a critical component in future risk assessment models.


Subject(s)
COVID-19/epidemiology , Brazil/epidemiology , Cities/epidemiology , Humans , Incidence , Meteorological Concepts , Risk Assessment , SARS-CoV-2
14.
Sci Rep ; 11(1): 12213, 2021 06 09.
Article in English | MEDLINE | ID: covidwho-1387476

ABSTRACT

As we enter a chronic phase of the SARS-CoV-2 pandemic, with uncontrolled infection rates in many places, relative regional susceptibilities are a critical unknown for policy planning. Tests for SARS-CoV-2 infection or antibodies are indicative but unreliable measures of exposure. Here instead, for four highly-affected countries, we determine population susceptibilities by directly comparing country-wide observed epidemic dynamics data with that of their main metropolitan regions. We find significant susceptibility reductions in the metropolitan regions as a result of earlier seeding, with a relatively longer phase of exponential growth before the introduction of public health interventions. During the post-growth phase, the lower susceptibility of these regions contributed to the decline in cases, independent of intervention effects. Forward projections indicate that non-metropolitan regions will be more affected during recurrent epidemic waves compared with the initially heavier-hit metropolitan regions. Our findings have consequences for disease forecasts and resource utilisation.


Subject(s)
COVID-19/epidemiology , Pandemics/statistics & numerical data , COVID-19/mortality , COVID-19/prevention & control , Cities/epidemiology , Disease Susceptibility , Humans , Models, Statistical , Pandemics/prevention & control
15.
JMIR Public Health Surveill ; 7(8): e26604, 2021 08 26.
Article in English | MEDLINE | ID: covidwho-1374196

ABSTRACT

BACKGROUND: Although it is well-known that older individuals with certain comorbidities are at the highest risk for complications related to COVID-19 including hospitalization and death, we lack tools to identify communities at the highest risk with fine-grained spatial resolution. Information collected at a county level obscures local risk and complex interactions between clinical comorbidities, the built environment, population factors, and other social determinants of health. OBJECTIVE: This study aims to develop a COVID-19 community risk score that summarizes complex disease prevalence together with age and sex, and compares the score to different social determinants of health indicators and built environment measures derived from satellite images using deep learning. METHODS: We developed a robust COVID-19 community risk score (COVID-19 risk score) that summarizes the complex disease co-occurrences (using data for 2019) for individual census tracts with unsupervised learning, selected on the basis of their association with risk for COVID-19 complications such as death. We mapped the COVID-19 risk score to corresponding zip codes in New York City and associated the score with COVID-19-related death. We further modeled the variance of the COVID-19 risk score using satellite imagery and social determinants of health. RESULTS: Using 2019 chronic disease data, the COVID-19 risk score described 85% of the variation in the co-occurrence of 15 diseases and health behaviors that are risk factors for COVID-19 complications among ~28,000 census tract neighborhoods (median population size of tracts 4091). The COVID-19 risk score was associated with a 40% greater risk for COVID-19-related death across New York City (April and September 2020) for a 1 SD change in the score (risk ratio for 1 SD change in COVID-19 risk score 1.4; P<.001) at the zip code level. Satellite imagery coupled with social determinants of health explain nearly 90% of the variance in the COVID-19 risk score in the United States in census tracts (r2=0.87). CONCLUSIONS: The COVID-19 risk score localizes risk at the census tract level and was able to predict COVID-19-related mortality in New York City. The built environment explained significant variations in the score, suggesting risk models could be enhanced with satellite imagery.


Subject(s)
COVID-19/epidemiology , Cost of Illness , Residence Characteristics/statistics & numerical data , COVID-19/mortality , Cities/epidemiology , Health Status Indicators , Humans , New York City/epidemiology , Risk Assessment/methods , Risk Factors , Social Determinants of Health , United States/epidemiology , Unsupervised Machine Learning
16.
Braz. j. med. biol. res ; 54(11): e11191, 2021. tab, graf
Article in English | LILACS (Americas) | ID: covidwho-1372029

ABSTRACT

The present study focused on the scenario of confirmed cases of SARS-CoV-2 infection in the state of Minas Gerais (MG), Brazil, from March 2020 to March 2021. We evaluated the evolution of COVID-19 prevalence and death in one municipality from each of the 14 health macro-regions of MG state. Socio-demographic characteristics and variables related to the municipalities were analyzed. The raw dataset used in this study was freely sourced from the website Brasil.io. From the raw dataset, two time series were extracted: the cumulative confirmed cases of COVID-19 and cumulative death counts, and they were compared to the state data using a nowcasting approach. In order to make time series comparisons possible, all data was normalized per 100,000 inhabitants. When analyzing in light of colored wave code interventions initiated in August 2020 in MG, for the majority of the municipalities, there was an absence of clear influence on prevalence and deaths. The national holidays in the first semester of 2020 had a small impact on the COVID-19 prevalence of the municipalities, but the holidays in the second semester of 2020 and beginning of 2021 caused important impacts on COVID-19 prevalence. The low number of ICU beds in some municipalities contributed to the higher number of deaths. The analysis showed here is expected to contribute to the improvement of decision making of the MG government, as it opened a huge possibility to have the total macro-regions and state data analyzed.


Subject(s)
Humans , COVID-19 , Brazil/epidemiology , Cities/epidemiology , Culture Media , SARS-CoV-2
17.
Glob Public Health ; 16(8-9): 1396-1410, 2021.
Article in English | MEDLINE | ID: covidwho-1364688

ABSTRACT

The COVID-19 pandemic has overwhelmed health systems around the globe, and intensified the lethality of social and political inequality. In the United States, where public health departments have been severely defunded, Black, Native, Latinx communities and those experiencing poverty in the country's largest cities are disproportionately infected and disproportionately dying. Based on our collective ethnographic work in three global cities in the U.S. (San Francisco, Los Angeles, and Detroit), we identify how the political geography of racialisation potentiated the COVID-19 crisis, exacerbating the social and economic toll of the pandemic for non-white communities, and undercut the public health response. Our analysis is specific to the current COVID19 crisis in the U.S, however the lessons from these cases are important for understanding and responding to the corrosive political processes that have entrenched inequality in pandemics around the world.


Subject(s)
COVID-19 , Pandemics , Politics , Anthropology, Cultural , COVID-19/epidemiology , Cities/epidemiology , Health Status Disparities , Humans , Los Angeles/epidemiology , Michigan/epidemiology , San Francisco/epidemiology
18.
JMIR Public Health Surveill ; 7(9): e30406, 2021 09 01.
Article in English | MEDLINE | ID: covidwho-1357488

ABSTRACT

BACKGROUND: Data on how SARS-CoV-2 enters and spreads in a population are essential for guiding public policies. OBJECTIVE: This study seeks to understand the transmission dynamics of SARS-CoV-2 in small Brazilian towns during the early phase of the epidemic and to identify core groups that can serve as the initial source of infection as well as factors associated with a higher risk of COVID-19. METHODS: Two population-based seroprevalence studies, one household survey, and a case-control study were conducted in two small towns in southeastern Brazil between May and June 2020. In the population-based studies, 400 people were evaluated in each town; there were 40 homes in the household survey, and 95 cases and 393 controls in the case-control study. SARS-CoV-2 serology testing was performed on participants, and a questionnaire was applied. Prevalence, household secondary infection rate, and factors associated with infection were assessed. Odds ratios (ORs) were calculated by logistic regression. Logistics worker was defined as an individual with an occupation focused on the transportation of people or goods and whose job involves traveling outside the town of residence at least once a week. RESULTS: Higher seroprevalence of SARS-CoV-2 was observed in the town with a greater proportion of logistics workers. The secondary household infection rate was 49.1% (55/112), and it was observed that in most households (28/40, 70%) the index case was a logistics worker. The case-control study revealed that being a logistics worker (OR 18.0, 95% CI 8.4-38.7) or living with one (OR 6.9, 95% CI 3.3-14.5) increases the risk of infection. In addition, having close contact with a confirmed case (OR 13.4, 95% CI 6.6-27.3) and living with more than four people (OR 2.7, 95% CI 1.1-7.1) were also risk factors. CONCLUSIONS: Our study shows a strong association between logistics workers and the risk of SARS-CoV-2 infection and highlights the key role of these workers in the viral spread in small towns. These findings indicate the need to focus on this population to determine COVID-19 prevention and control strategies, including vaccination and sentinel genomic surveillance.


Subject(s)
COVID-19/epidemiology , COVID-19/transmission , Communicable Diseases, Imported/epidemiology , Occupations/statistics & numerical data , Transportation/statistics & numerical data , Adolescent , Adult , Brazil/epidemiology , Case-Control Studies , Child , Child, Preschool , Cities/epidemiology , Family Characteristics , Female , Humans , Infant , Infant, Newborn , Male , Middle Aged , Risk Factors , Seroepidemiologic Studies , Young Adult
19.
Sci Rep ; 11(1): 16400, 2021 08 12.
Article in English | MEDLINE | ID: covidwho-1356583

ABSTRACT

We propose herein a mathematical model to predict the COVID-19 evolution and evaluate the impact of governmental decisions on this evolution, attempting to explain the long duration of the pandemic in the 26 Brazilian states and their capitals well as in the Federative Unit. The prediction was performed based on the growth rate of new cases in a stable period, and the graphics plotted with the significant governmental decisions to evaluate the impact on the epidemic curve in each Brazilian state and city. Analysis of the predicted new cases was correlated with the total number of hospitalizations and deaths related to COVID-19. Because Brazil is a vast country, with high heterogeneity and complexity of the regional/local characteristics and governmental authorities among Brazilian states and cities, we individually predicted the epidemic curve based on a specific stable period with reduced or minimal interference on the growth rate of new cases. We found good accuracy, mainly in a short period (weeks). The most critical governmental decisions had a significant temporal impact on pandemic curve growth. A good relationship was found between the predicted number of new cases and the total number of inpatients and deaths related to COVID-19. In summary, we demonstrated that interventional and preventive measures directly and significantly impact the COVID-19 pandemic using a simple mathematical model. This model can easily be applied, helping, and directing health and governmental authorities to make further decisions to combat the pandemic.


Subject(s)
COVID-19/epidemiology , Brazil/epidemiology , COVID-19/transmission , Cities/epidemiology , Humans , Models, Statistical , Pandemics , SARS-CoV-2/isolation & purification , Time Factors
20.
Int J Public Health ; 66: 1604215, 2021.
Article in English | MEDLINE | ID: covidwho-1348583

ABSTRACT

Objectives: To evaluate the long- and short-term effects of air pollution on COVID-19 transmission simultaneously, especially in high air pollution level countries. Methods: Quasi-Poisson regression was applied to estimate the association between exposure to air pollution and daily new confirmed cases of COVID-19, with mutual adjustment for long- and short-term air quality index (AQI). The independent effects were also estimated and compared. We further assessed the modification effect of within-city migration (WM) index to the associations. Results: We found a significant 1.61% (95%CI: 0.51%, 2.72%) and 0.35% (95%CI: 0.24%, 0.46%) increase in daily confirmed cases per 1 unit increase in long- and short-term AQI. Higher estimates were observed for long-term impact. The stratifying result showed that the association was significant when the within-city migration index was low. A 1.25% (95%CI: 0.0.04%, 2.47%) and 0.41% (95%CI: 0.30%, 0.52%) increase for long- and short-term effect respectively in low within-city migration index was observed. Conclusions: There existed positive associations between long- and short-term AQI and COVID-19 transmission, and within-city migration index modified the association. Our findings will be of strategic significance for long-run COVID-19 control.


Subject(s)
Air Pollution , COVID-19 , Air Pollution/adverse effects , Air Pollution/analysis , COVID-19/epidemiology , COVID-19/transmission , China/epidemiology , Cities/epidemiology , Humans
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