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1.
Cad Saude Publica ; 36(9): e00184820, 2020.
Article in English | MEDLINE | ID: covidwho-835998

ABSTRACT

The inter-cities mobility network is of great importance in understanding outbreaks, especially in Brazil, a continental-dimension country. We adopt the data from the Brazilian Ministry of Health and the terrestrial flow of people between cities from the Brazilian Institute of Geography and Statistics database in two scales: cities from Brazil, without the North region, and from the São Paulo State. Grounded on the complex networks approach, and considering that the mobility network serves as a proxy for the SARS-CoV-2 spreading, the nodes and edges represent cities and flows, respectively. Network centrality measures such as strength and degree are ranked and compared to the list of cities, ordered according to the day that they confirmed the first case of COVID-19. The strength measure captures the cities with a higher vulnerability of receiving new cases. Besides, it follows the interiorization process of SARS-CoV-2 in the São Paulo State when the network flows are above specific thresholds. Some countryside cities such as Feira de Santana (Bahia State), Ribeirão Preto (São Paulo State), and Caruaru (Pernambuco State) have strength comparable to states' capitals. Our analysis offers additional tools for understanding and decision support to inter-cities mobility interventions regarding the SARS-CoV-2 and other epidemics.


Subject(s)
Coronavirus Infections/epidemiology , Pneumonia, Viral/epidemiology , Travel , Betacoronavirus , Brazil/epidemiology , Cities , Humans , Pandemics
2.
Sci Rep ; 10(1): 16213, 2020 10 01.
Article in English | MEDLINE | ID: covidwho-811544

ABSTRACT

Italy was the first, among all the European countries, to be strongly hit by the COVID-19 pandemic outbreak caused by the severe acute respiratory syndrome coronavirus 2 (Sars-CoV-2). The virus, proven to be very contagious, infected more than 9 million people worldwide (in June 2020). Nevertheless, it is not clear the role of air pollution and meteorological conditions on virus transmission. In this study, we quantitatively assessed how the meteorological and air quality parameters are correlated to the COVID-19 transmission in two large metropolitan areas in Northern Italy as Milan and Florence and in the autonomous province of Trento. Milan, capital of Lombardy region, it is considered the epicenter of the virus outbreak in Italy. Our main findings highlight that temperature and humidity related variables are negatively correlated to the virus transmission, whereas air pollution (PM2.5) shows a positive correlation (at lesser degree). In other words, COVID-19 pandemic transmission prefers dry and cool environmental conditions, as well as polluted air. For those reasons, the virus might easier spread in unfiltered air-conditioned indoor environments. Those results will be supporting decision makers to contain new possible outbreaks.


Subject(s)
Air Pollution/statistics & numerical data , Coronavirus Infections/epidemiology , Humidity , Pneumonia, Viral/epidemiology , Temperature , Cities/statistics & numerical data , Coronavirus Infections/transmission , Humans , Italy , Pandemics , Pneumonia, Viral/transmission , Urban Population/statistics & numerical data
3.
J Acoust Soc Am ; 148(3): 1748, 2020 09.
Article in English | MEDLINE | ID: covidwho-811488

ABSTRACT

The lockdown that Madrid has suffered during the months of March to June 2020 to try to control and minimize the spread of COVID-19 has significantly altered the acoustic environment of the city. The absence of vehicles and people on the streets has led to a noise reduction captured by the monitoring network of the City of Madrid. In this article, an analysis has been carried out to describe the reduction in noise pollution that has occurred and to analyze the changes in the temporal patterns of noise, which are strongly correlated with the adaptation of the population's activity and behavior to the new circumstances. The reduction in the sound level ranged from 4 to 6 dBA for the indicators Ld, Le, and Ln, and this is connected to a significant variation in the daily time patterns, especially during weekends, when the activity started earlier in the morning and lasted longer at midday, decreasing significantly in the afternoon.


Subject(s)
Coronavirus Infections , Noise , Pandemics , Pneumonia, Viral , Betacoronavirus , Cities , Environmental Monitoring , Humans , Spain
4.
Int J Environ Res Public Health ; 17(19)2020 09 30.
Article in English | MEDLINE | ID: covidwho-809550

ABSTRACT

During the first outbreak of the SARS-CoV-2 pandemic the population, focusing primarily on the risk of infection, was generally inattentive to the quality of indoor air. Spain, and the city of Madrid in particular, were among the world's coronavirus hotspots. The country's entire population was subject to a 24/7 lockdown for 45 days. This paper describes a comparative longitudinal survey of air quality in four types of housing in the city of Madrid before and during lockdown. The paper analysed indoor temperatures and variations in CO2, 2.5 µm particulate matter (PM2.5) and total volatile organic compound (TVOC) concentrations before and during lockdown. The mean daily outdoor PM2.5 concentration declined from 11.04 µg/m3 before to 7.10 µg/m3 during lockdown. Before lockdown the NO2 concentration values scored as 'very good' 46% of the time, compared to 90.9% during that period. Although the city's outdoor air quality improved, during lockdown the population's exposure to indoor pollutants was generally more acute and prolonged. Due primarily to concern over domestic energy savings, the lack of suitable ventilation and more intensive use of cleaning products and disinfectants during the covid-19 crisis, indoor pollutant levels were typically higher than compatible with healthy environments. Mean daily PM2.5 concentration rose by approximately 12% and mean TVOC concentration by 37% to 559%. The paper also puts forward a series of recommendations to improve indoor domestic environments in future pandemics and spells out urgent action to be taken around indoor air quality (IAQ) in the event of total or partial quarantining to protect residents from respiratory ailments and concomitantly enhanced susceptibility to SARS-CoV-2, as identified by international medical research.


Subject(s)
Air Pollution, Indoor/analysis , Coronavirus Infections , Pandemics , Pneumonia, Viral , Betacoronavirus , Carbon Dioxide , Cities , Housing/classification , Humans , Nitric Oxide , Particulate Matter , Spain , Volatile Organic Compounds
5.
BMC Public Health ; 20(1): 1486, 2020 Oct 01.
Article in English | MEDLINE | ID: covidwho-807197

ABSTRACT

BACKGROUND: The state of Ceará (Northeast Brazil) has shown a high incidence of coronavirus disease (COVID-19), and most of the cases that were diagnosed during the epidemic originated from the capital Fortaleza. Monitoring the dynamics of the COVID-19 epidemic is of strategic importance and requires the use of sensitive tools for epidemiological surveillance, including consistent analyses that allow the recognition of areas with a greater propensity for increased severity throughout the cycle of the epidemic. This study aims to classify neighborhoods in the city of Fortaleza according to their propensity for a severe epidemic of COVID-19 in 2020. METHODS: We conducted an ecological study within the geographical area of the 119 neighborhoods located in the city of Fortaleza. To define the main transmission networks (infection chains), we assumed that the spatial diffusion of the COVID-19 epidemic was influenced by population mobility. To measure the propensity for a severe epidemic, we calculated the infectivity burden (ItyB), infection burden (IonB), and population epidemic vulnerability index (PEVI). The propensity score for a severe epidemic in the neighborhoods of the city of Fortaleza was estimated by combining the IonB and PEVI. RESULTS: The neighborhoods with the highest propensity for a severe COVID-19 epidemic were Aldeota, Cais do Porto, Centro, Edson Queiroz, Vicente Pinzon, Jose de Alencar, Presidente Kennedy, Papicu, Vila Velha, Antonio Bezerra, and Cambeba. Importantly, we found that the propensity for a COVID-19 epidemic was high in areas with differing socioeconomic profiles. These areas include a very poor neighborhood situated on the western border of the city (Vila Velha), neighborhoods characterized by a large number of subnormal agglomerates in the Cais do Porto region (Vicente Pinzon), and those located in the oldest central area of the city, where despite the wealth, low-income groups have remained (Aldeota and the adjacent Edson Queiroz). CONCLUSION: Although measures against COVID-19 should be applied to the entire municipality of Fortaleza, the classification of neighborhoods generated through this study can help improve the specificity and efficiency of these measures.


Subject(s)
Coronavirus Infections/epidemiology , Epidemics , Pneumonia, Viral/epidemiology , Residence Characteristics/statistics & numerical data , Brazil/epidemiology , Cities/epidemiology , Humans , Incidence , Pandemics
6.
Global Health ; 16(1): 85, 2020 09 23.
Article in English | MEDLINE | ID: covidwho-797442

ABSTRACT

OBJECTIVES: Restricting mobility is a central aim for lowering contact rates and preventing COVID-19 transmission. Yet the impact on mobility of different non-pharmaceutical countermeasures in the earlier stages of the pandemic is not well-understood. DESIGN: Trends were evaluated using Citymapper's mobility index covering 2nd to 26th March 2020, expressed as percentages of typical usage periods from 0% as the lowest and 100% as normal. China and India were not covered. Multivariate fixed effects models were used to estimate the association of policies restricting movement on mobility before and after their introduction. Policy restrictions were assessed using the Oxford COVID-19 Government Response Stringency Index as well as measures coding the timing and degree of school and workplace closures, transport restrictions, and cancellation of mass gatherings. SETTING: 41 cities worldwide. MAIN OUTCOME MEASURES: Citymapper's mobility index. RESULTS: Mobility declined in all major cities throughout March. Larger declines were seen in European than Asian cities. The COVID-19 Government Response Stringency Index was strongly associated with declines in mobility (r = - 0.75, p < 0.001). After adjusting for time-trends, we observed that implementing non-pharmaceutical countermeasures was associated with a decline of mobility of 10.0% for school closures (95% CI: 4.36 to 15.7%), 15.0% for workplace closures (95% CI: 10.2 to 19.8%), 7.09% for cancelling public events (95% CI: 1.98 to 12.2%), 18.0% for closing public transport (95% CI: 6.74 to 29.2%), 13.3% for restricting internal movements (95% CI: 8.85 to 17.8%) and 5.30% for international travel controls (95% CI: 1.69 to 8.90). In contrast, as expected, there was no association between population mobility changes and fiscal or monetary measures or emergency healthcare investment. CONCLUSIONS: Understanding the effect of public policy on mobility in the early stages is crucial to slowing and reducing COVID-19 transmission. By using Citymapper's mobility index, this work provides the first evidence about trends in mobility and the impacts of different policy interventions, suggesting that closure of public transport, workplaces and schools are particularly impactful.


Subject(s)
Coronavirus Infections/prevention & control , Global Health , Pandemics/prevention & control , Pneumonia, Viral/prevention & control , Travel/statistics & numerical data , Cities/epidemiology , Coronavirus Infections/epidemiology , Geographic Information Systems , Humans , Pneumonia, Viral/epidemiology , Public Policy , Time Factors , Travel/legislation & jurisprudence , Volunteers
7.
Rev Esp Salud Publica ; 942020 Sep 23.
Article in Spanish | MEDLINE | ID: covidwho-797046

ABSTRACT

In December 2019, an acute respiratory disease outbreak from zoonotic origin was detected in the city of Wuhan, China. The outbreak's infectious agent was a type of coronavirus never seen. Thenceforth, the Covid-19 disease has rapidly spread to more than 200 countries around the world. To minimize the devastating effects of the virus, the States have adopted epidemiological measures of various kinds that involved enormous economic expenses and the massive use of the media to explain the measures to the entire population. For the prediction and mitigation of infectious events, various epidemiological models, such as SIR, SEIR, MSIR and MSEIR, are used. Among them, the most widely used is the SIR model, which is based on the analysis of the transition of individuals susceptible to infection (S) to the state of infected individuals that infect (I) and, finally, to that of recovered (cured or deceased) (R), by using differential equations. The objective of this article was the mathematical development of the SIR model and its application to predict the course of the Covid-19 pandemic in the city of Santa Marta (Colombia), in order to understand the reason behind several of the measures of containment adopted by the States of the world in the fight against the pandemic.


Subject(s)
Communicable Disease Control , Coronavirus Infections/epidemiology , Models, Statistical , Pneumonia, Viral/epidemiology , Betacoronavirus , Cities , Colombia/epidemiology , Coronavirus Infections/prevention & control , Disease Outbreaks , Humans , Pandemics/prevention & control , Pneumonia, Viral/prevention & control
8.
Med Sci Monit ; 26: e925974, 2020 Sep 25.
Article in English | MEDLINE | ID: covidwho-796027

ABSTRACT

BACKGROUND Coronavirus disease 2019 (COVID-19) is a new infectious disease, and acute respiratory syndrome (ARDS) plays an important role in the process of disease aggravation. The detailed clinical course and risk factors of ARDS have not been well described. MATERIAL AND METHODS We retrospectively investigated the demographic, clinical, and laboratory data of adult confirmed cases of COVID-19 in Beijing Ditan Hospital from Jan 20 to Feb 29, 2020 and compared the differences between ARDS cases and non-ARDS cases. Univariate and multivariate logistic regression methods were employed to explore the risk factors associated with ARDS. RESULTS Of the 130 adult patients enrolled in this study, the median age was 46.5 (34-62) years and 76 (58.5%) were male. ARDS developed in 26 (20.0%) and 1 (0.8%) death occurred. Fever occurred in 114 patients, with a median highest temperature of 38.5 (38-39)°C and median fever duration of 8 (3-11) days. The median time from illness onset to ARDS was 10 (6-13) days, the median time to chest CT improvement was 17 (14-21) days, and median time to negative nucleic acid test result was 27 (17-33) days. Multivariate regression analysis showed increasing odds of ARDS associated with age older than 65 years (OR=4.75, 95% CL1.26-17.89, P=0.021), lymphocyte counts [0.5-1×109/L (OR=8.80, 95% CL 2.22-34.99, P=0.002); <0.5×109/L(OR=36.23, 95% CL 4.63-2083.48, P=0.001)], and temperature peak ≥39.1°C (OR=5.35, 95% CL 1.38-20.76, P=0.015). CONCLUSIONS ARDS tended to occur in the second week of the disease course. Potential risk factors for ARDS were older age (>65 years), lymphopenia (≤1.0×109/L), and temperature peak (≥39.1°C). These findings could help clinicians to predict which patients will have a poor prognosis at an early stage.


Subject(s)
Betacoronavirus , Coronavirus Infections/complications , Pandemics , Pneumonia, Viral/complications , Respiratory Distress Syndrome, Adult/etiology , Adult , Aged , Aged, 80 and over , Bacterial Infections/etiology , China , Cities/epidemiology , Comorbidity , Coronavirus Infections/epidemiology , Female , Fever/etiology , Humans , Logistic Models , Lymphopenia/etiology , Male , Middle Aged , Pneumonia, Viral/epidemiology , Retrospective Studies , Risk Factors
9.
An Acad Bras Cienc ; 92(4): e20201139, 2020.
Article in English | MEDLINE | ID: covidwho-788923

ABSTRACT

The spread of SARS-CoV-2 and the distribution of cases worldwide followed no clear biogeographic, climatic, or cultural trend. Conversely, the internationally busiest cities in all countries tended to be the hardest hit, suggesting a basic, mathematically neutral pattern of the new coronavirus early dissemination. We tested whether the number of flight passengers per time and the number of international frontiers could explain the number of cases of COVID-19 worldwide by a stepwise regression. Analysis were taken by 22 May 2020, a period when one would claim that early patterns of the pandemic establishment were still detectable, despite of community transmission in various places. The number of passengers arriving in a country and the number of international borders explained significantly 49% of the variance in the distribution of the number of cases of COVID-19, and number of passengers explained significantly 14.2% of data variance for cases per million inhabitants. Ecological neutral theory may explain a considerable part of the early distribution of SARS-CoV-2 and should be taken into consideration to define preventive international actions before a next pandemic.


Subject(s)
Coronavirus Infections/epidemiology , Coronavirus Infections/transmission , Pneumonia, Viral/epidemiology , Pneumonia, Viral/transmission , Travel , Aircraft , Betacoronavirus , Cities , Humans , Models, Theoretical , Pandemics
10.
PLoS One ; 15(9): e0239699, 2020.
Article in English | MEDLINE | ID: covidwho-788894

ABSTRACT

The current outbreak of the coronavirus disease 2019 (COVID-19) is an unprecedented example of how fast an infectious disease can spread around the globe (especially in urban areas) and the enormous impact it causes on public health and socio-economic activities. Despite the recent surge of investigations about different aspects of the COVID-19 pandemic, we still know little about the effects of city size on the propagation of this disease in urban areas. Here we investigate how the number of cases and deaths by COVID-19 scale with the population of Brazilian cities. Our results indicate small towns are proportionally more affected by COVID-19 during the initial spread of the disease, such that the cumulative numbers of cases and deaths per capita initially decrease with population size. However, during the long-term course of the pandemic, this urban advantage vanishes and large cities start to exhibit higher incidence of cases and deaths, such that every 1% rise in population is associated with a 0.14% increase in the number of fatalities per capita after about four months since the first two daily deaths. We argue that these patterns may be related to the existence of proportionally more health infrastructure in the largest cities and a lower proportion of older adults in large urban areas. We also find the initial growth rate of cases and deaths to be higher in large cities; however, these growth rates tend to decrease in large cities and to increase in small ones over time.


Subject(s)
Coronavirus Infections/transmission , Pneumonia, Viral/transmission , Population Density , Age Distribution , Betacoronavirus , Brazil/epidemiology , Cities/epidemiology , Health Services/supply & distribution , Health Services/trends , Humans , Pandemics/statistics & numerical data , Time Factors
11.
Rev Esp Salud Publica ; 942020 Sep 23.
Article in Spanish | MEDLINE | ID: covidwho-784053

ABSTRACT

In December 2019, an acute respiratory disease outbreak from zoonotic origin was detected in the city of Wuhan, China. The outbreak's infectious agent was a type of coronavirus never seen. Thenceforth, the Covid-19 disease has rapidly spread to more than 200 countries around the world. To minimize the devastating effects of the virus, the States have adopted epidemiological measures of various kinds that involved enormous economic expenses and the massive use of the media to explain the measures to the entire population. For the prediction and mitigation of infectious events, various epidemiological models, such as SIR, SEIR, MSIR and MSEIR, are used. Among them, the most widely used is the SIR model, which is based on the analysis of the transition of individuals susceptible to infection (S) to the state of infected individuals that infect (I) and, finally, to that of recovered (cured or deceased) (R), by using differential equations. The objective of this article was the mathematical development of the SIR model and its application to predict the course of the Covid-19 pandemic in the city of Santa Marta (Colombia), in order to understand the reason behind several of the measures of containment adopted by the States of the world in the fight against the pandemic.


Subject(s)
Communicable Disease Control , Coronavirus Infections/epidemiology , Models, Statistical , Pneumonia, Viral/epidemiology , Betacoronavirus , Cities , Colombia/epidemiology , Coronavirus Infections/prevention & control , Disease Outbreaks , Humans , Pandemics/prevention & control , Pneumonia, Viral/prevention & control
12.
MMWR Morb Mortal Wkly Rep ; 69(37): 1319-1323, 2020 Sep 18.
Article in English | MEDLINE | ID: covidwho-782536

ABSTRACT

Reports suggest that children aged ≥10 years can efficiently transmit SARS-CoV-2, the virus that causes coronavirus disease 2019 (COVID-19) (1,2). However, limited data are available on SARS-CoV-2 transmission from young children, particularly in child care settings (3). To better understand transmission from young children, contact tracing data collected from three COVID-19 outbreaks in child care facilities in Salt Lake County, Utah, during April 1-July 10, 2020, were retrospectively reviewed to explore attack rates and transmission patterns. A total of 184 persons, including 110 (60%) children had a known epidemiologic link to one of these three facilities. Among these persons, 31 confirmed COVID-19 cases occurred; 13 (42%) in children. Among pediatric patients with facility-associated confirmed COVID-19, all had mild or no symptoms. Twelve children acquired COVID-19 in child care facilities. Transmission was documented from these children to at least 12 (26%) of 46 nonfacility contacts (confirmed or probable cases). One parent was hospitalized. Transmission was observed from two of three children with confirmed, asymptomatic COVID-19. Detailed contact tracing data show that children can play a role in transmission from child care settings to household contacts. Having SARS-CoV-2 testing available, timely results, and testing of contacts of persons with COVID-19 in child care settings regardless of symptoms can help prevent transmission. CDC guidance for child care programs recommends the use of face masks, particularly among staff members, especially when children are too young to wear masks, along with hand hygiene, frequent cleaning and disinfecting of high-touch surfaces, and staying home when ill to reduce SARS-CoV-2 transmission (4).


Subject(s)
Child Day Care Centers , Coronavirus Infections/epidemiology , Coronavirus Infections/transmission , Disease Outbreaks , Pneumonia, Viral/epidemiology , Pneumonia, Viral/transmission , Adolescent , Adult , Aged , Betacoronavirus/isolation & purification , Child , Child, Preschool , Cities/epidemiology , Clinical Laboratory Techniques , Contact Tracing , Coronavirus Infections/diagnosis , Female , Humans , Infant , Male , Middle Aged , Pandemics , Utah/epidemiology , Young Adult
13.
JMIR Public Health Surveill ; 6(3): e21152, 2020 09 18.
Article in English | MEDLINE | ID: covidwho-781805

ABSTRACT

BACKGROUND: Several countries adopted lockdown to slowdown the exponential transmission of the coronavirus disease (COVID-19) epidemic. Disease transmission models and the epidemic forecasts at the national level steer the policy to implement appropriate intervention strategies and budgeting. However, it is critical to design a data-driven reliable model for nowcasting for smaller populations, in particular metro cities. OBJECTIVE: The aim of this study is to analyze the transition of the epidemic from subexponential to exponential transmission in the Chennai metro zone and to analyze the probability of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) secondary infections while availing the public transport systems in the city. METHODS: A single geographical zone "Chennai-Metro-Merge" was constructed by combining Chennai District with three bordering districts. Subexponential and exponential models were developed to analyze and predict the progression of the COVID-19 epidemic. Probabilistic models were applied to assess the probability of secondary infections while availing public transport after the release of the lockdown. RESULTS: The model predicted that transition from subexponential to exponential transmission occurs around the eighth week after the reporting of a cluster of cases. The probability of secondary infections with a single index case in an enclosure of the city bus, the suburban train general coach, and the ladies coach was found to be 0.192, 0.074, and 0.114, respectively. CONCLUSIONS: Nowcasting at the early stage of the epidemic predicts the probable time point of the exponential transmission and alerts the public health system. After the lockdown release, public transportation will be the major source of SARS-CoV-2 transmission in metro cities, and appropriate strategies based on nowcasting are needed.


Subject(s)
Coronavirus Infections/transmission , Epidemics , Pneumonia, Viral/transmission , Public Health , Transportation , Betacoronavirus , Cities , Communicable Disease Control/methods , Coronavirus , Coronavirus Infections/epidemiology , Coronavirus Infections/virology , Humans , India/epidemiology , Models, Statistical , Motor Vehicles , Pandemics , Pneumonia, Viral/epidemiology , Pneumonia, Viral/virology , Railroads , Severe Acute Respiratory Syndrome
14.
MMWR Morb Mortal Wkly Rep ; 69(37): 1319-1323, 2020 Sep 18.
Article in English | MEDLINE | ID: covidwho-778764

ABSTRACT

Reports suggest that children aged ≥10 years can efficiently transmit SARS-CoV-2, the virus that causes coronavirus disease 2019 (COVID-19) (1,2). However, limited data are available on SARS-CoV-2 transmission from young children, particularly in child care settings (3). To better understand transmission from young children, contact tracing data collected from three COVID-19 outbreaks in child care facilities in Salt Lake County, Utah, during April 1-July 10, 2020, were retrospectively reviewed to explore attack rates and transmission patterns. A total of 184 persons, including 110 (60%) children had a known epidemiologic link to one of these three facilities. Among these persons, 31 confirmed COVID-19 cases occurred; 13 (42%) in children. Among pediatric patients with facility-associated confirmed COVID-19, all had mild or no symptoms. Twelve children acquired COVID-19 in child care facilities. Transmission was documented from these children to at least 12 (26%) of 46 nonfacility contacts (confirmed or probable cases). One parent was hospitalized. Transmission was observed from two of three children with confirmed, asymptomatic COVID-19. Detailed contact tracing data show that children can play a role in transmission from child care settings to household contacts. Having SARS-CoV-2 testing available, timely results, and testing of contacts of persons with COVID-19 in child care settings regardless of symptoms can help prevent transmission. CDC guidance for child care programs recommends the use of face masks, particularly among staff members, especially when children are too young to wear masks, along with hand hygiene, frequent cleaning and disinfecting of high-touch surfaces, and staying home when ill to reduce SARS-CoV-2 transmission (4).


Subject(s)
Child Day Care Centers , Coronavirus Infections/epidemiology , Coronavirus Infections/transmission , Disease Outbreaks , Pneumonia, Viral/epidemiology , Pneumonia, Viral/transmission , Adolescent , Adult , Aged , Betacoronavirus/isolation & purification , Child , Child, Preschool , Cities/epidemiology , Clinical Laboratory Techniques , Contact Tracing , Coronavirus Infections/diagnosis , Female , Humans , Infant , Male , Middle Aged , Pandemics , Utah/epidemiology , Young Adult
15.
F1000Res ; 9: 232, 2020.
Article in English | MEDLINE | ID: covidwho-769909

ABSTRACT

Since the first identified case of COVID-19 in Wuhan, China, the disease has developed into a pandemic, imposing a major challenge for health authorities and hospitals worldwide. Mathematical transmission models can help hospitals to anticipate and prepare for an upcoming wave of patients by forecasting the time and severity of infections. Taking the city of Heidelberg as an example, we predict the ongoing spread of the disease for the next months including hospital and ventilator capacity and consider the possible impact of currently imposed countermeasures.


Subject(s)
Coronavirus Infections/epidemiology , Coronavirus Infections/transmission , Models, Theoretical , Pneumonia, Viral/epidemiology , Pneumonia, Viral/transmission , Betacoronavirus , Cities/epidemiology , Germany/epidemiology , Humans , Pandemics
16.
Bull World Health Organ ; 98(9): 632-637, 2020 Sep 01.
Article in English | MEDLINE | ID: covidwho-769112

ABSTRACT

Problem: On 21 January 2020, the city of Taizhou, China, reported its first imported coronavirus disease 2019 (COVID-19) case and subsequently the number of cases rapidly increased. Approach: To organize the emergency responses, the government of Taizhou established on 23 January 2020 novel headquarters for prevention and control of the COVID-19 outbreak, by coordinating different governmental agencies. People at high risk of acquiring COVID-19, as well as probable and confirmed cases, were identified and quarantined. The government closed public venues and limited gatherings. The Taizhou Health Commission shared information about identified COVID-19 patients and probable cases with affected agencies. To timely track and manage close contacts of confirmed cases, Taizhou Center for Disease Control and Prevention did epidemiological investigations. Medical institutions or local centers for disease control and prevention reported confirmed cases to the national Center for Disease Control and Prevention. Local setting: Taizhou, a city in Zhejiang province with about 6 million residents, reported 18 confirmed COVID-2019 cases by 23 January 2020, which ranked it third globally in number of cases after Wuhan and Xiaogan cities in the Hubei province. Relevant changes: In total, 146 confirmed cases (85 cases imported and 61 cases through community transmission) and no deaths due to COVID-19 had been reported in Taizhou by 1 June 2020. Between 16 February and 1 June 2020, no confirmed case had been reported. Lesson learnt: Identifying and managing imported cases and people at risk for infection, timely information sharing, limiting gatherings and ensuring collaborations between different agencies were important in controlling COVID-19.


Subject(s)
Communicable Disease Control/organization & administration , Coronavirus Infections/epidemiology , Coronavirus Infections/prevention & control , Pandemics/prevention & control , Pneumonia, Viral/epidemiology , Pneumonia, Viral/prevention & control , Betacoronavirus , Centers for Disease Control and Prevention, U.S. , China/epidemiology , Cities , Disease Outbreaks/prevention & control , Humans , United States
17.
Sci Total Environ ; 746: 141129, 2020 Dec 01.
Article in English | MEDLINE | ID: covidwho-676570

ABSTRACT

The current changes in vehicle movement due to 'lockdown' conditions (imposed in cities worldwide in response to the COVID-19 epidemic) provide opportunities to quantify the local impact of 'controlled interventions' on air quality and establish baseline pollution concentrations in cities. Here, we present a case study from Auckland, New Zealand, an isolated Southern Hemisphere city, which is largely unaffected by long-range pollution transport or industrial sources of air pollution. In this city, traffic flows reduced by 60-80% as a result of a government-led initiative to contain the virus by limiting all transport to only essential services. In this paper, ambient pollutant concentrations of NO2, O3, BC, PM2.5, and PM10 are compared between the lockdown period and comparable periods in the historical air pollution record, while taking into account changes in the local meteorology. We show that this 'natural experiment' in source emission reductions had significant but non-linear impacts on air quality. While emission inventories and receptor modelling approaches confirm the dominance of traffic sources for NOx (86%), and BC (72%) across the city, observations suggest a consequent reduction in NO2 of only 34-57% and a reduction in BC of 55-75%. The observed reductions in PM2.5 (still likely to be dominated by traffic emissions), and PM10 (dominated by sea salt, traffic emissions to a lesser extent, and affected by seasonality) were found to be significantly less (8-17% for PM2.5 and 7-20% for PM10). The impact of this unplanned controlled intervention shows the importance of establishing accurate, local-scale emission inventories, and the potential of the local atmospheric chemistry and meteorology in limiting their accuracy.


Subject(s)
Air Pollutants/analysis , Air Pollution/analysis , Air Pollution/prevention & control , Coronavirus Infections , Pandemics , Pneumonia, Viral , Severe Acute Respiratory Syndrome , Betacoronavirus , Cities , Environmental Monitoring , Humans , New Zealand/epidemiology , Particulate Matter/analysis
18.
Sci Total Environ ; 742: 140931, 2020 Nov 10.
Article in English | MEDLINE | ID: covidwho-641193

ABSTRACT

We investigated changes in traffic-related air pollutant concentrations in an urban area during the COVID-19 pandemic. The study was conducted in a mixed commercial-residential neighborhood in Somerville (MA, USA), where traffic is the dominant source of air pollution. Measurements were made between March 27 and May 14, 2020, coinciding with a dramatic reduction in traffic (71% drop in car and 46% drop in truck traffic) due to business shutdowns and a statewide stay-at-home advisory. Indicators of fresh vehicular emissions (ultrafine particle number concentration [PNC] and black carbon [BC]) were measured with a mobile monitoring platform on an interstate highway and major and minor roadways. Our results show that depending on road class, median PNC and BC contributions from traffic were 60-68% and 22-46% lower, respectively, during the lockdown compared to pre-pandemic conditions, and corresponding reductions in total on-road concentrations were 45-69% and 22-56%, respectively. A higher BC: PNC concentration ratio was observed during the lockdown period likely indicative of the higher fraction of diesel vehicles in the fleet during the lockdown. Overall, the scale of reductions in ultrafine particle and BC concentrations was commensurate with the reductions in traffic. This natural experiment allowed us to quantify the direct impacts of reductions in traffic emissions on neighborhood-scale air quality, which are not captured by the regional regulatory-monitoring network. These results underscore the importance of measurements of appropriate proxies for traffic emissions at relevant spatial scales. Our results are useful for exposure analysis as well as city and regional planners evaluating mitigation strategies for traffic-related air pollution.


Subject(s)
Air Pollutants/analysis , Air Pollution/analysis , Coronavirus Infections , Pandemics , Pneumonia, Viral , Betacoronavirus , Carbon , Cities , Environmental Monitoring , Humans , Particulate Matter/analysis , Vehicle Emissions/analysis
19.
Proc Natl Acad Sci U S A ; 117(39): 24180-24187, 2020 09 29.
Article in English | MEDLINE | ID: covidwho-759658

ABSTRACT

Standard epidemiological models for COVID-19 employ variants of compartment (SIR or susceptible-infectious-recovered) models at local scales, implicitly assuming spatially uniform local mixing. Here, we examine the effect of employing more geographically detailed diffusion models based on known spatial features of interpersonal networks, most particularly the presence of a long-tailed but monotone decline in the probability of interaction with distance, on disease diffusion. Based on simulations of unrestricted COVID-19 diffusion in 19 US cities, we conclude that heterogeneity in population distribution can have large impacts on local pandemic timing and severity, even when aggregate behavior at larger scales mirrors a classic SIR-like pattern. Impacts observed include severe local outbreaks with long lag time relative to the aggregate infection curve, and the presence of numerous areas whose disease trajectories correlate poorly with those of neighboring areas. A simple catchment model for hospital demand illustrates potential implications for health care utilization, with substantial disparities in the timing and extremity of impacts even without distancing interventions. Likewise, analysis of social exposure to others who are morbid or deceased shows considerable variation in how the epidemic can appear to individuals on the ground, potentially affecting risk assessment and compliance with mitigation measures. These results demonstrate the potential for spatial network structure to generate highly nonuniform diffusion behavior even at the scale of cities, and suggest the importance of incorporating such structure when designing models to inform health care planning, predict community outcomes, or identify potential disparities.


Subject(s)
Coronavirus Infections/epidemiology , Coronavirus Infections/transmission , Pneumonia, Viral/epidemiology , Pneumonia, Viral/transmission , Betacoronavirus , Cities/epidemiology , Coronavirus Infections/prevention & control , Delivery of Health Care , Demography , Health Status Disparities , Humans , Models, Statistical , Pandemics/prevention & control , Pneumonia, Viral/prevention & control , Social Networking , United States/epidemiology
20.
Cien Saude Colet ; 25(9): 3377-3384, 2020 Sep.
Article in English | MEDLINE | ID: covidwho-750934

ABSTRACT

At the end of 2019, the outbreak of COVID-19 was reported in Wuhan, China. The outbreak spread quickly to several countries, becoming a public health emergency of international interest. Without a vaccine or antiviral drugs, control measures are necessary to understand the evolution of cases. Here, we report through spatial analysis the spatial pattern of the COVID-19 outbreak. The study site was the State of São Paulo, Brazil, where the first case of the disease was confirmed. We applied the Kernel Density to generate surfaces that indicate where there is higher density of cases and, consequently, greater risk of confirming new cases. The spatial pattern of COVID-19 pandemic could be observed in São Paulo State, in which its metropolitan region standed out with the greatest cases, being classified as a hotspot. In addition, the main highways and airports that connect the capital to the cities with the highest population density were classified as medium density areas by the Kernel Density method.It indicates a gradual expansion from the capital to the interior. Therefore, spatial analyses are fundamental to understand the spread of the virus and its association with other spatial data can be essential to guide control measures.


Subject(s)
Coronavirus Infections/epidemiology , Disease Outbreaks , Pneumonia, Viral/epidemiology , Brazil/epidemiology , Cities , Humans , Pandemics , Public Health , Spatial Analysis
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