Query: data cities covid

title: Basic epidemiological parameter values from data of real world in mega cities: the characteristics of COVID 19 in Beijing, China
abstract: With the spread of SARS CoV 2 worldwide, understanding the basic epidemiological parameter values of COVID 19 from real world data in mega cities is essential for disease prevention and control.  To investigate the epidemiological parameters in SARS CoV 2 infected cases in Beijing, we studied all confirmed cases and close contacts in Beijing from Jan 1st to Apr 3rd 2020. The epidemiological and virological characteristics of SARS CoV 2 were analyzed.  A total of 602 cases were positive for SARS CoV 2, including 585 confirmed patients and 17 asymptomatic infections. The imported cases were mainly from Wuhan initially and then from abroad. Among 585 confirmed case patients, the median age was 39 years old. The mean incubation period was 6.3 days. The secondary attack rate among households was higher than social contacts (15.6 vs 4.6% The secondary attack rate of healthcare workers (HCWs) was higher than non HCWs' (7.3 vs 4.2% The basic reproduction number was 2.0, and the average serial interval was 7.6 days. No significant genetic variant was identified.  The transmissibility of SARS CoV 2 was relatively high, especially among households and from HCWs, which draws specific public health attention. So far, no evidence of widespread circulation of SARS CoV 2 in communities in Beijing was found.

title: The ambient ozone and COVID 19 transmissibility in China: A data driven ecological study of 154 cities
abstract: Highlights We quantified the COVID 19 transmissibility by the basic reproductive number. The COVID 19 transmissibility could be negatively associated with ambient ozone. The daily 1 h maximum ozone might cover 7.6% of the transmissibility variability. It echoes a previous study of negative effects of ozone on the flu transmission. The ground level ozone may be a "double edged sword" to public health.

title: Temperature significantly changes COVID 19 transmission in (sub)tropical cities of Brazil
abstract: The coronavirus disease 2019 (COVID 19) outbreak has become a severe public health issue. The novelty of the virus prompts a search for understanding of how ecological factors affect the transmission and survival of the virus. Several studies have robustly identified a relationship between temperature and the number of cases. However, there is no specific study for a tropical climate such as Brazil. This work aims to determine the relationship of temperature to COVID 19 infection for the state capital cities of Brazil. Cumulative data with the daily number of confirmed cases was collected from February 27 to April 1, 2020, for all 27 state capital cities of Brazil affected by COVID 19. A generalized additive model (GAM) was applied to explore the linear and nonlinear relationship between annual average temperature compensation and confirmed cases. Also, a polynomial linear regression model was proposed to represent the behavior of the growth curve of COVID 19 in the capital cities of Brazil. The GAM dose response curve suggested a negative linear relationship between temperatures and daily cumulative confirmed cases of COVID 19 in the range from 16.8 degC to 27.4 degC. Each 1 degC rise of temperature was associated with a 4.8951% (t 2.29, p 0.0226) decrease in the number of daily cumulative confirmed cases of COVID 19. A sensitivity analysis assessed the robustness of the results of the model. The predicted R squared of the polynomial linear regression model was 0.81053. In this study, which features the tropical temperatures of Brazil, the variation in annual average temperatures ranged from 16.8 degC to 27.4 degC. Results indicated that temperatures had a negative linear relationship with the number of confirmed cases. The curve flattened at a threshold of 25.8 degC. There is no evidence supporting that the curve declined for temperatures above 25.8 degC. The study had the goal of supporting governance for healthcare policymakers.

title: Estimating and Simulating a Sird Model of Covid 19 for Many Countries, States, and Cities
abstract: We use data on deaths in New York City, Madrid, Stockholm, and other world cities as well as in various U.S. states and various countries and regions to estimate a standard epidemiological model of COVID 19. We allow for a time varying contact rate in order to capture behavioral and policy induced changes associated with social distancing. We simulate the model forward to consider possible futures for various countries, states, and cities, including the potential impact of herd immunity on re opening. Our current baselinemortality rate (IFR) is assumed to be 1.0% but we recognize there is substantial uncertainty about this number. Our model fits the death data equally well with alternative mortality rates of 0.5% or 1.2% so this parameter is unidentified in our data. However, its value matters enormously for the extent to which various places can relax social distancing without spurring a resurgence of deaths.

title: Smart cities and a data driven response to COVID 19
abstract: This commentary describes the rapid development of a COVID 19 data dashboard utilising existing Urban Observatory Internet of Things (IoT) data and analytics infrastructure. Existing data capture systems were rapidly repurposed to provide real time insights into the impacts of lockdown policy on urban governance.

title: Meta regression of COVID 19 prevalence/fatality on socioeconomic characteristics of data from top 50 U.S. large cities
abstract: To screen potential risk and protective socioeconomic factors for Coronavirus disease 2019 (COVID 19) prevalence and fatality, meta regression of data from top 50 U.S. large population cities was performed. The population estimate (in 2019) of each country to which the city belongs was abstracted from the "County Population  From the "Johns Hopkins Coronavirus Resource Center," the cumulative number of confirmed cases and deaths of COVID 19 in each country was obtained on May 22, 2020. Socioeconomic characteristics of each country were extracted from the "2014 2018 American Community Survey (ACS) 5 Year Data Profile" and "Small Area Income and Poverty Estimates (SAIPE) Program (for 2018)" Radom effects meta regression was performed using OpenMetaAnalyst (http:/www.cebm.brown.edu/openmeta/index.html) A coefficient (slope of the meta regression line) for COVID 19 prevalence was significantly negative for male sex, education attainment, computer and Internet use, and private health insurance. Whereas, the coefficient was significantly positive for black race, never matrimony, unemployment, and poverty. In the multivariable model, the coefficient was significantly negative for male sex (P 0.036) and computer use (P 0.024) and significantly positive for never matrimony (P 0.001) A coefficient for COVID 19 fatality was significantly negative for no health insurance, and significantly positive for elderly, unemployment, and public coverage. In the multivariable model, the coefficient was significantly positive for only elderly (P 0.002) In conclusion, a number of socioeconomic factors, e.g. male sex (negatively for prevalence) elderly (positively for fatality) never matrimony (positively for prevalence) and computer use (negatively for prevalence) may be associated with COVID 19.

title: Early estimation of the case fatality rate of COVID 19 in mainland China: a data driven analysis.
abstract: An ongoing outbreak of pneumonia caused by a novel coronavirus [severe acute respiratory syndrome coronavirus (SARS CoV) 2] named COVID 19, hit a major city of China, Wuhan in December 2019 and subsequently spread to other provinces/regions of China and overseas. Several studies have been done to estimate the basic reproduction number in the early phase of this outbreak, yet there are no reliable estimates of case fatality rate (CFR) for COVID 19 to date.  In this study, we used a purely data driven statistical method to estimate the CFR in the early phase of the COVID 19 outbreak. Daily numbers of laboratory confirmed COVID 19 cases and deaths were collected from January 10 to February 3, 2020 and divided into three clusters: Wuhan city, other cities of Hubei province, and other provinces of mainland China. Simple linear regression model was applied to estimate the CFR from each cluster.  We estimated that CFR during the first weeks of the epidemic ranges from 0.15% (95% CI: 0.12 0.18% in mainland China excluding Hubei through 1.41% (95% CI: 1.38 1.45% in Hubei province excluding the city of Wuhan to 5.25% (95% CI: 4.98 5.51% in Wuhan.  Our early estimates suggest that the CFR of COVID 19 is lower than the previous coronavirus epidemics caused by SARS CoV and Middle East respiratory syndrome coronavirus (MERS CoV)

title: Evidence that high temperatures and intermediate relative humidity might favor the spread of COVID 19 in tropical climate: A case study for the most affected Brazilian cities
abstract: This study aimed to analyze how meteorological conditions such as temperature, humidity and rainfall can affect the spread of COVID 19 in five Brazilian (Sao Paulo, Rio de Janeiro, Brasilia, Manaus and Fortaleza) cities. The cities selected were those with the largest number of confirmed cases considering data of April 18. Variables such as number of cumulative cases, new daily cases and contamination rate were employed for this study. Our results showed that higher mean temperatures and average relative humidity favored the COVID 19 transmission, differently from reports from coldest countries or periods of time under cool temperatures. Thus, considering the results obtained, intersectoral policies and actions are necessary, mainly in cities where the contamination rate is increasing rapidly. Thus, prevention and protection measures should be adopted in these cities aiming to reduce transmission and the possible collapse of the health system.

title: On the Coronavirus (COVID 19) Outbreak and the Smart City Network: Universal Data Sharing Standards Coupled with Artificial Intelligence (AI) to Benefit Urban Health Monitoring and Management
abstract: As the Coronavirus (COVID 19) expands its impact from China, expanding its catchment into surrounding regions and other countries, increased national and international measures are being taken to contain the outbreak. The placing of entire cities in 'lockdown' directly affects urban economies on a multi lateral level, including from social and economic standpoints. This is being emphasised as the outbreak gains ground in other countries, leading towards a global health emergency, and as global collaboration is sought in numerous quarters. However, while effective protocols in regard to the sharing of health data is emphasised, urban data, on the other hand, specifically relating to urban health and safe city concepts, is still viewed from a nationalist perspective as solely benefiting a nation's economy and its economic and political influence. This perspective paper, written one month after detection and during the outbreak, surveys the virus outbreak from an urban standpoint and advances how smart city networks should work towards enhancing standardization protocols for increased data sharing in the event of outbreaks or disasters, leading to better global understanding and management of the same.

title: The Impact of the COVID 19 Pandemic on Consumption: Learning from High Frequency Transaction Data
abstract: We use daily transaction data in 214 cities to study the impact of COVID 19 on consumption after China's outbreak in late January 2020. Based on difference in differences estimation, daily offline consumption via UnionPay card and QR scanner transactions fell by 32% or 18.57 million RMB per city, during the twelve week period. Spending on goods and services were both significantly affected, with a decline of 33% and 34% respectively; within finer categories, dining entertainment and travel saw the greatest dip of 64% and 59% The consumption decrease is prevalent across cities with the largest drop occurring in the epicenter Wuhan (by 70% Consumption responded negatively to the day to day changes in epidemic severity while distancing measures remained stable. Consumption had rebounded back to the baseline level by the end of March but dropped to 20% in early April due to the elevated risk of a second wave of infections. We infer that China's offline consumption decreased by over 1.22 trillion RMB in the three month post outbreak period, or 1.2% of China's 2019 GDP. Our estimates suggest a significant economic benefit of containing the virus through a lessened consumption decrease and a faster consumption recovery.