Spatial-temporal modeling of COVID-19 areas in Italy and the role of urban settings

COVID-19 severely affected Italy from the beginning of the pandemic. The number of cases can be analysed within statistical methods, to understand its spread over time, in conjunction with factors as meteorological and environmental conditions, socio-economic conditions and urban settings [1]. The role of spatial aggregation of data is seldom investigated in detail, as the information available to the public is often limited to administrative boundaries.

We investigated the number of infections stratified by spatial location and time, analyzing each of the 107 provinces of Italy during the two infection waves in 2020. Infections were greater in urban areas such as Rome, Milan and Naples. We further investigated the role of urban areas by considering specific spatial aggregations that explicitly included indicators of human presence [2].

We used the hhh4 endemic-epidemic model to study the spatial-temporal pattern of COVID-19 [3]. The model includes three components, representing autoregressive effects (transmission of disease within a single province), neighborhood effects (transmission between provinces) and endemic effects (sporadic events by unobserved sources of infection).  Covariates included daily temperature, humidity, employment rate and number of high-care hospital beds for each province. In addition, we considered specific covariates to account for urban indicators: population, population density, the proportion of urban area, average area of cities, and number of cities. Covariates were considered on both the autoregressive and neighbourhood components to determine the effect of transmission within and between provinces. To simulate the spread between provinces on the neighbourhood component, we considered the spatial adjacency between provinces, and considered decreasing importance with increasing distance.

Outputs from the model included the risk ratios (RRs) of the covariates, with resulting RR of 0.89 on the autoregressive component and RR of 0.83 on the neighbourhood component. An existing study found that higher temperatures were related to a decline in daily confirmed COVID-19 case counts with a corresponding RR of 0.80 [4].

We specifically looked at covariates related to urban settings, as an existing study showed positive correlation between population density and COVID-19 transmission rate [5]. Our results showed a RR of 1.23 (autoregressive component) and RR of 1.48 (neighbourhood component), suggesting that larger population density leads to more infections, and that movement of people across provinces could lead to a higher risk of COVID-19 cases.  Province area, average city area and number of cities were not statistically significant.

Eventually, we explicitly considered the role of urban settings by aggregating spatial-temporal data within individual urban areas [2], instead of administrative boundaries. As COVID-19 data itself were available at the province level, we distributed them to urban areas proportionally to the area occupied within each province; other data was actually aggregated within urban polygons. We argue that study of the spatial-temporal transmission of infection using urban areas may provide reliable results and help selecting characteristics in urban settings that may favour or prevent the spread of diseases.

[1] M. Agnoletti et al. DOI: 10.1016/j.landurbplan.2020

[2] M. Alvioli. DOI: 10.1016/j.landurbplan.2020.103906

[3] S. Meyer et al. DOI: 10.1214/14-AOAS743

[4] J. Liu et al. DOI: 10.1016/j.scitotenv.2020.138513

[5] K.T.L. Sy et al. DOI: 10.1371/journal.pone.024927