Spatial Analysis: COVID-19 Clusters and Factors In New York City

Place and Health
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Introduction
The threat from the emergence of epidemics, disasters, and public health emergencies poses a global health problem for the human race. When a pandemic spreads across many countries usually affects many people and causes several deaths. The impact of a pandemic varies from one place to another as some regions are likely to experience a more intense effect than others. The variation across different regions and territories is caused by factors such as demographic factors, physical environments, socio-economic factors, cultural factors, and behavioral characteristics of the population. Media coverage of spatial distribution of pandemic cases can be done through interactive maps from the onset of the illness. It's a common practice that even the World Health Organization(WHO) usually communicates the proliferation of pandemics by counting infections via territorial world maps. The pandemic outbreak of the new coronavirus-induced respiratory disease (COVID-19) has been covered since its onset using a published world map that is regularly updated by a team of researchers of the US-based Johns Hopkins University as the diseases continue to spread.
Spatial Analysis Of COVID-19 Clusters and Contextual Factors In New York City
A study was conducted by Cordes & Castro (2020) in New York to analyze COVID-19 clusters and their contextual factors. The first objective was to determine positivity rates, rates of testing, and the percentages of individuals infected for a better understanding of the case burden and access to testing. Another objective was for the evaluation of contextual factors that are related to the clusters across New York City. The two objectives sought to provide advanced knowledge on inequities existing in the city and current exposure of the city to the COVID-19 pandemic. The data would thus, be used during resource allocation to ensure equitable distribution.
There were three outcome variables created; testing rates, positivity rates, and proportion of positive tests. The study adopted the use of cluster analysis and contextual factor analysis. The cluster analysis deployed the use of choropleth maps for the three testing outcomes and covariate created using four quantile categories that enhance an equal number of zip codes in each category. To achieve a manageable size of clusters, the population in the clusters did not exceed 2 percent. The population at each zip code averaged 0.56 percent; therefore, a maximum cluster size created an approximation of one to six zip codes. The relationships between contextual factors and the testing outcomes at zip code levels were analyzed by the use of descriptive statistics and correlations.
The study focused on a place as a container that constitutes various social aspects of life. The description of the place as human territory resulting from economic, political, and social processes is clearly in line with the paper. The covariates that were used in New York included; proportions of whites, blacks, Hispanic, Asians, non-citizens, population using public transport, bachelors or graduates, rental income of above 50 percent, poverty, uninsured individuals, those with medium income, and those who require public assistance. The choropleth maps were created for spatial representation of the data.
The "place" was incorporated in the study by use of zip codes, clusters, and covariates. The total number of COVID-19 tests and the number of positive cases were separated into zip codes provided by the New York Department of Health. Zip code's total population data of the covariates were extracted from the American Community Survey (ACS) through the IPUMS National Historical Geographic Information System. In the four-quantile categorization, the highest category incorporated zip codes from 75th to 100th percentile while the lowest accounted for zip codes below 25th percentile of the distribution of a covariate. To measure spatial relation in each outcome, the global Moran's 1 test was used. The spread of each covariate was limited to zip codes found in each of the identified clusters. A theoretical framework was developed for an understanding of the inequality of case burden and test rates. The four frameworks included; clusters of high-test rates, low test rates, high positivity rates, and high positive test proportions. Cluster analysis was done by SaTScan v9.6, while Moran 1 done was done by GeoDa 1.14.0. the correlation analysis was done by R 3.6.2 and bivariate maps.
Places are more useful in health when viewed as nodes in networks rather than physical boundaries. Though the study done in New York was based on clusters and zip codes, much emphasis was given to the covariates, for instance, the whites and blacks. The relational perspective was taken into account by the use of variables like race, level of education, poverty income rates, and the modes of transportation rate. This improves the level of understanding of the spread of COVID-19. New York City was the epicenter of the virus, but many factors might have led to an increased number of cases. The use of a relational perspective was also useful as it eradicated the notion of inequalities in access to health services. From the findings, it is significant to note the positivity rates in individuals using public transport and also among the Asian communities. Further research can help find the reason behind the high positivity rates in the Asian people and those using public transport. The study should have also used the compositional approach. This approach could have identified other causes leading to a high number of positivity rates in New York. Such causes might be related behaviors of individuals, the number of people living in a Zipcode, and individual-based characteristics.
The study provides good spatial analysis. Data analyzed at the zip code level provided precise outcomes on which neighborhoods experienced higher case burden. The clustering helped identify areas with low testing rates and, therefore, more resources should be channeled to those areas. However, the study is limited, as it just describes the variables without providing numbers.
Infectious Diseases as Socio-Spatial Processes: The Covid-19 Outbreak in Germany
A study conducted by Andreas K. & Martin S. (2020) sought to understand infectious diseases as socio-spatial processes using a case study of a COVID-19 outbreak in Germany. The study aimed at understanding a broader perspective of the spatial diffusion of COVID-19 during the early weeks of the epidemic in Germany by analyzing how it shaped different dimensions of space. A multi-dimensional approach was used to integrate the relationship between infected people and the place. To implement the multi-dimensional perspective on socio-spatial processes, the TPSN framework was applied in order to investigate the spread of COVID-19 across Germany. This framework highlighted the interconnectedness of spatial categories of territory (T), place (P), scale (S), and network(N). The multi-dimensional perspective, combined with TPSN, provided a balanced understanding of how a pandemic can spread in a region.
The TPSN framework combined four spatial dimensions to provide insights into the concepts of disease transmission. From a contextual perspective, space constituted social actors and how they interact. This meant it was easier to get infected by COVID-19 because of the geographical proximity to individuals already infected by the virus. The study focused on a place constituting social relation factors. From the territorial perspective of spatial dimensions, the territory provides the general mobility of people, determined health systems and tracing methods, and provided strategies to curb th...