Preparing Cities for Future Pandemics: Unraveling the Influence of Urban and Housing Variables on COVID-19 Incidence in Santiago de Chile

Kuschel, Katherina; Carrasco, Raúl; Idrovo-Aguirre, Byron J.; Durán, Claudia; Contreras-Reyes, Javier E.

doi:10.3390/healthcare11162259

Cited by 3 publications

(2 citation statements)

References 60 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Places where people gather in large numbers, such as commercial and service facilities, are more likely to promote the spread of COVID-19, often becoming the first outbreak site, and leading to pandemics in other areas of the city due to population density ( 51 , 52 ). Regions with high building density and floor area ratio also tend to create population density, and the higher the aggregation degree of urban space, the more likely it is to be an enclosed space with poor ventilation, which promotes the spread of viruses ( 53 , 54 ).…”

Section: Discussionmentioning

confidence: 99%

The impact of urban spatial environment on COVID-19: a case study in Beijing

Yang,

Li,

et al. 2024

Front. Public Health

View full text Add to dashboard Cite

Epidemics are dangerous and difficult to prevent and control, especially in urban areas. Clarifying the correlation between the COVID-19 Outbreak Frequency and the urban spatial environment may help improve cities’ ability to respond to such public health emergencies. In this study, we firstly analyzed the spatial distribution characteristics of COVID-19 Outbreak Frequency by correlating the geographic locations of COVID-19 epidemic-affected neighborhoods in the city of Beijing with the time point of onset. Secondly, we created a geographically weighted regression model combining the COVID-19 Outbreak Frequency with the external spatial environmental elements of the city. Thirdly, different grades of epidemic-affected neighborhoods in the study area were classified according to the clustering analysis results. Finally, the correlation between the COVID-19 Outbreak Frequency and the internal spatial environmental elements of different grades of neighborhoods was investigated using a binomial logistic regression model. The study yielded the following results. (i) Epidemic outbreak frequency was evidently correlated with the urban external spatial environment, among building density, volume ratio, density of commercial facilities, density of service facilities, and density of transportation facilities were positively correlated with COVID-19 Outbreak Frequency, while water and greenery coverage was negatively correlated with it. (ii) The correlation between COVID-19 Outbreak Frequency and the internal spatial environmental elements of neighborhoods of different grades differed. House price and the number of households were positively correlated with the COVID-19 Outbreak Frequency in low-end neighborhoods, while the number of households was positively correlated with the COVID-19 Outbreak Frequency in mid-end neighborhoods. In order to achieve spatial justice, society should strive to address the inequality phenomena of income gaps and residential differentiation, and promote fair distribution of spatial environments.

show abstract

Section: Discussionmentioning

confidence: 99%

The impact of urban spatial environment on COVID-19: a case study in Beijing

Yang,

Li,

et al. 2024

Front. Public Health

View full text Add to dashboard Cite

show abstract

“…It is critical that such pattern discovery is based on the best available statistical modeling approaches to minimize possible prediction errors. The functional time series analysis can reveal the temporal variability, rate of change, and specific age-group modelling for mortality series, as well as the effect of age differences, providing additional insight for forecasting age-related policies, future population age structure, and hospital resource management (Kuschel et al [39]). Wider use of functional time series analysis (FTSA) to obtain more accurate estimates in public health, demography, and insurance policy studies should be considered.…”

Section: Discussionmentioning

confidence: 99%

Forecasting Canadian Age-Specific Mortality Rates: Application of Functional Time Series Analysis

Rahman,

Jiang

2023

Mathematics

View full text Add to dashboard Cite

In the insurance and pension industries, as well as in designing social security systems, forecasted mortality rates are of major interest. The current research provides statistical methods based on functional time series analysis to improve mortality rate prediction for the Canadian population. The proposed functional time series-based model was applied to the three-mortality series: total, male and female age-specific Canadian mortality rate over the year 1991 to 2019. Descriptive measures were used to estimate the overall temporal patterns and the functional principal component regression model (fPCA) was used to predict the next ten years mortality rate for each series. Functional autoregressive model (fAR (1)) was used to measure the impact of one year age differences on mortality series. For total series, the mortality rates for children have dropped over the whole data period, while the difference between young adults and those over 40 has only been falling since about 2003 and has leveled off in the last decade of the data. A moderate to strong impact of age differences on Canadian age-specific mortality series was observed over the years. Wider application of fPCA to provide more accurate estimates in public health, demography, and age-related policy studies should be considered.

show abstract

SARS-CoV-2 Pandemic in a Small-Sized Municipality in Ceará State, Brazil: Temporal and Spatial Evolution

Nascimento dos Santos,

Alencar,

Heukelbach

2024

TropicalMed

View full text Add to dashboard Cite

Data on the temporal and spatial evolution of SARS-CoV-2 and local control measures and their effects on morbidity and mortality patterns in rural Brazil are scarce. We analyzed the data from case notification systems, epidemiological investigation reports, and municipal decrees in Itapajé, a small municipality in Ceará State in northeast Brazil. For spatial and spatio-temporal analyses, cases and deaths were mapped. There were a total of 3020 cases of COVID-19, recorded between April 2020 and December 2021; 135 (4.5%) died. The cumulative incidence and mortality rates were 5650.3 cases and 252.6 deaths per 100,000 people, respectively. The index case of SARS-CoV-2 in Itapajé was diagnosed in March 2020. The first peak of cases and deaths occurred in May 2020. The second wave peaked in May 2021, with the highest number of deaths in March 2021. According to the spatial analysis, the highest density of cases and deaths occurred in the central urban areas. In these areas, there were also the clusters of highest risk according to the spatio-temporal analyses. The municipal government issued 69 decrees on restriction measures, surveillance, and the maintenance of social isolation as a response to the pandemic. The spread of the SARS-CoV-2 pandemic in Itapajé mirrored the dynamics in large metropolitan regions, going from central neighborhoods of low socio-economic status to the wealthier peripheries.

show abstract

Preparing Cities for Future Pandemics: Unraveling the Influence of Urban and Housing Variables on COVID-19 Incidence in Santiago de Chile

Cited by 3 publications

References 60 publications

The impact of urban spatial environment on COVID-19: a case study in Beijing

The impact of urban spatial environment on COVID-19: a case study in Beijing

Forecasting Canadian Age-Specific Mortality Rates: Application of Functional Time Series Analysis

SARS-CoV-2 Pandemic in a Small-Sized Municipality in Ceará State, Brazil: Temporal and Spatial Evolution

Contact Info

Product

Resources

About