Urban structure and the risk of influenza A (H1N1) outbreaks in municipal districts

How do density, employment and transit affect the prevalence of COVID-19 pandemic? A study of 3,141 counties across the United States

Previous research has explored the effect of the built environment on the spread of the coronavirus disease (COVID-19) pandemic. This study extends the existing literature by examining the relationship between pandemic prevalence and density, employment, and transit factors at the county level. Using multilinear spatial-lag regressions and time series clustering analyses on the Smart Location Database encompassing 3141 counties in the United States, our findings reveal the following: (1) Density, employment, and transit variables yield heterogeneous effects to infection rate, death rate, and mortality rate. (2) Pedestrian-oriented road density is positively correlated to the prevalence of COVID-19, every 0.011 miles/acre increase is associated with 1% increase in the infection rate. (3) A consistent negative correlation is observed between jobs per household and infection rate, while a decrease in unemployment rate leads to an increase in the death rate. (4) The results from time series analysis suggest that areas characterized by low auto-oriented intersection density but high pedestrian-oriented road density are more susceptible to the impacts of pandemics. This highlights the need to prioritize pandemic prevention efforts in the suburban and rural areas with low population density, as emphasized in existing literature emphasized.

Spatial genetic structure of 2009 H1N1 pandemic influenza established as a result of interaction with human populations in mainland China

Identifying the spatial patterns of genetic structure of influenza A viruses is a key factor for understanding their spread and evolutionary dynamics. In this study, we used phylogenetic and Bayesian clustering analyses of genetic sequences of the A/H1N1pdm09 virus with district-level locations in mainland China to investigate the spatial genetic structure of the A/H1N1pdm09 virus across human population landscapes. Positive correlation between geographic and genetic distances indicates high degrees of genetic similarity among viruses within small geographic regions but broad-scale genetic differentiation, implying that local viral circulation was a more important driver in the formation of the spatial genetic structure of the A/H1N1pdm09 virus than even, countrywide viral mixing and gene flow. Geographic heterogeneity in the distribution of genetic subpopulations of A/H1N1pdm09 virus in mainland China indicates both local to local transmission as well as broad-range viral migration. This combination of both local and global structure suggests that both small-scale and large-scale population circulation in China is responsible for viral genetic structure. Our study provides implications for understanding the evolution and spread of A/H1N1pdm09 virus across the population landscape of mainland China, which can inform disease control strategies for future pandemics.

Relationships between building attributes and COVID-19 infection in London

In the UK, all domestic COVID-19 restrictions have been removed since they were introduced in March 2020. After illustrating the spatial-temporal variations in COVID-19 infection rates across London, this study then particularly aimed to examine the relationships of COVID-19 infection rates with building attributes, including building density, type, age, and use, since previous studies have shown that the built environment plays an important role in public health. Multisource data from national health services and the London Geomni map were processed with GIS techniques and statistically analysed. From March 2020 to April 2022, the infection rate of COVID-19 in London was 3,159.28 cases per 10,000 people. The spatial distribution across London was uneven, with a range from 1,837.88 to 4,391.79 per 10,000 people. During this period, it was revealed that building attributes played a significant role in COVID-19 infection. It was noted that higher building density areas had lower COVID-19 infection rates in London. Moreover, a higher percentage of historic or flat buildings tended to lead to a decrease in infection rates. In terms of building use, the rate of COVID-19 infection tended to be lower in public buildings and higher in residential buildings. Variations in the infection rate were more sensitive to building type; in particular, the percentage of residents living in flats contributed the most to variations in COVID-19 infection rates, with a value of 2.3%. This study is expected to provide support for policy and practice towards pandemic-resilient architectural design.

Compact cities and the Covid-19 pandemic: Systematic review of the associations between transmission of Covid-19 or other respiratory viruses and population density or other features of neighbourhood design

Living in compact neighbourhoods that are walkable, well connected, with accessible green space can benefit physical and mental health. However, the pandemic raises concern up to what extent features of compact neighbourhood design affect transmission of viral respiratory infections. We conducted a systematic review to identify, appraise and synthesise evidence reporting associations between transmission of respiratory viruses, including Covid-19, and dwelling or population density or other features of neighbourhood design. Twenty-one studies met our inclusion criteria. These studies used different measures of neighbourhood design, contributing to inconsistent findings. Whereas no convincing conclusion can be drawn here, the outcome of this review indicates that robust, global evidence is warranted to inform future policies and legislation concerned with compact neighbourhood design and transmission of respiratory and viral infection.

The Effect of the Built Environment on the COVID-19 Pandemic at the Initial Stage: A County-Level Study of the USA

The COVID-19 pandemic affected how people interact with the built environment and ways of human habitation are facing significant challenges. However, the existing literature has not adequately addressed how the built environment affected the early prevalence of the pandemic. This research aims to extend the existing literature by relating the initial stage pandemic conditions with more comprehensive measures of the built environment including density, diversity, road network, and accessibility at the county level across the United States and conducting bi-weekly comparisons. We collected infection, death, and mortality data in 3141 counties between 1 March to 8 June 2020 and collected seventeen built environment attributes. Our results show that: (1) Road density and street intersection density were significantly associated with the infection rate; (2) Population density only maintained a positive correlation to the prevalence of COVID-19 during the first two weeks, after which the relationship became negative; and (3) Transit accessibility also contributed significantly to the pandemic and the accessibility of transit-oriented jobs was highly correlated to the infection rate in the first two weeks. The study provides valuable insights for policymakers and stakeholders to adopt resource allocation strategies for context-specific conditions.

Impacts of Road Traffic Network and Socioeconomic Factors on the Diffusion of 2009 Pandemic Influenza A (H1N1) in Mainland China

The 2009 pandemic influenza virus caused the majority of the influenza A virus infections in China in 2009. It arrived in several Chinese cities from imported cases and then spread as people travelled domestically by all means of transportation, among which road traffic was the most commonly used for daily commuting. Spatial variation in socioeconomic status not only accelerates migration across regions but also partly induces the differences in epidemic processes and in responses to epidemics across regions. However, the roles of both road travel and socioeconomic factors have not received the attention they deserve. Here, we constructed a national highway network for and between 333 cities in mainland China and extracted epidemiological variables and socioeconomic factors for each city. We calculated classic centrality measures for each city in the network and proposed two new measures (SumRatio and Multicenter Distance). We evaluated the correlation between the centrality measures and epidemiological features and conducted a spatial autoregression to quantify the impacts of road network and socioeconomic factors during the outbreak. The results showed that epidemics had more significant relationships with both our new measures than the classic ones. Higher population density, higher per person income, larger SumRatio and Multicenter Distance, more hospitals and college students, and lower per person GDP were associated with higher cumulative incidence. Higher population density and number of slaughtered pigs were found to advance epidemic arrival time. Higher population density, more colleges and slaughtered pigs, and lower Multicenter Distance were associated with longer epidemic duration. In conclusion, road transport and socioeconomic status had significant impacts and should be considered for the prevention and control of future pandemics.

A spatial-temporal transmission model and early intervention policies of 2009 A/H1N1 influenza in South Korea

We developed a spatial-temporal model of the 2009 A/H1N1 influenza pandemic in the Seoul metropolitan area (SMA), which is located in the north-west of South Korea and is the second-most complex metropolitan area worldwide. This multi-patch influenza model consists of a SEIAR influenza transmission model and flow model between two districts. This model is based on the daily confirmed cases of A/H1N1 influenza collected by the Korea Center for Disease Control and Prevention from April 27 to September 15, 2009 and the daily commuting data from 33 districts of SMA reported in the 2010 Population and Housing Census (PHC). We analyzed the spread patterns of 2009 influenza in the SMA by the reproductive numbers and geographic information systems. During the early period of novel influenza pandemics, when pharmaceutical interventions are lacking, non-pharmaceutical public health interventions will be the most critical strategies for impeding the spread of influenza and delaying an epidemic. Using the spatial-temporal model developed herein, we also investigated the impact of non-pharmaceutical public health interventions, isolation and/or commuting restrictions, on the incidence reduction in various scenarios. Our model provides scientific evidence for predicting the spread of disease and preparedness for a future pandemic.