A Municipality-Based Approach Using Commuting Census Data to Characterize the Vulnerability to Influenza-Like Epidemic: The COVID-19 Application in Italy

Uncovering COVID-19 infection determinants in Portugal: towards an evidence-based spatial susceptibility index to support epidemiological containment policies

BACKGROUND

COVID-19 caused the largest pandemic of the twenty-first century forcing the adoption of containment policies all over the world. Many studies on COVID-19 health determinants have been conducted, mainly using multivariate methods and geographic information systems (GIS), but few attempted to demonstrate how knowing social, economic, mobility, behavioural, and other spatial determinants and their effects can help to contain the disease. For example, in mainland Portugal, non-pharmacological interventions (NPI) were primarily dependent on epidemiological indicators and ignored the spatial variation of susceptibility to infection.

METHODS

We present a data-driven GIS-multicriteria analysis to derive a spatial-based susceptibility index to COVID-19 infection in Portugal. The cumulative incidence over 14 days was used in a stepwise multiple linear regression as the target variable along potential determinants at the municipal scale. To infer the existence of thresholds in the relationships between determinants and incidence the most relevant factors were examined using a bivariate Bayesian change point analysis. The susceptibility index was mapped based on these thresholds using a weighted linear combination.

RESULTS

Regression results support that COVID-19 spread in mainland Portugal had strong associations with factors related to socio-territorial specificities, namely sociodemographic, economic and mobility. Change point analysis revealed evidence of nonlinearity, and the susceptibility classes reflect spatial dependency. The spatial index of susceptibility to infection explains with accuracy previous and posterior infections. Assessing the NPI levels in relation to the susceptibility map points towards a disagreement between the severity of restrictions and the actual propensity for transmission, highlighting the need for more tailored interventions.

CONCLUSIONS

This article argues that NPI to contain COVID-19 spread should consider the spatial variation of the susceptibility to infection. The findings highlight the importance of customising interventions to specific geographical contexts due to the uneven distribution of COVID-19 infection determinants. The methodology has the potential for replication at other geographical scales and regions to better understand the role of health determinants in explaining spatiotemporal patterns of diseases and promoting evidence-based public health policies.

Epidemiological and genomic findings of the first documented Italian outbreak of SARS-CoV-2 Alpha variant of concern

From 24 December 2020 to 8 February 2021, 163 cases of SARS-CoV-2 Alpha variant of concern (VOC) were identified in Chieti province, Abruzzo region. Epidemiological data allowed the identification of 14 epi-clusters. With one exception, all the epi-clusters were linked to the town of Guardiagrele: 149 contacts formed the network, two-thirds of which were referred to the family/friends context. Real data were then used to estimate transmission parameters. According to our method, the calculated Re(t) was higher than 2 before the 12 December 2020. Similar values were obtained from other studies considering Alpha VOC. Italian sequence data were combined with a random subset of sequences obtained from the GISAID database. Genomic analysis showed close identity between the sequences from Guardiagrele, forming one distinct clade. This would suggest one or limited unspecified viral introductions from outside to Abruzzo region in early December 2020, which led to the diffusion of Alpha VOC in Guardiagrele and in neighbouring municipalities, with very limited inter-regional mixing.

Understanding the Impact of Walkability, Population Density, and Population Size on COVID-19 Spread: A Pilot Study of the Early Contagion in the United States

The novel coronavirus disease 2019 (COVID-19) pandemic is an unprecedented global event that has been challenging governments, health systems, and communities worldwide. Available data from the first months indicated varying patterns of the spread of COVID-19 within American cities, when the spread was faster in high-density and walkable cities such as New York than in low-density and car-oriented cities such as Los Angeles. Subsequent containment efforts, underlying population characteristics, variants, and other factors likely affected the spread significantly. However, this work investigates the hypothesis that urban configuration and associated spatial use patterns directly impact how the disease spreads and infects a population. It follows work that has shown how the spatial configuration of urban spaces impacts the social behavior of people moving through those spaces. It addresses the first 60 days of contagion (before containment measures were widely adopted and had time to affect spread) in 93 urban counties in the United States, considering population size, population density, walkability, here evaluated through walkscore, an indicator that measures the density of amenities, and, therefore, opportunities for population mixing, and the number of confirmed cases and deaths. Our findings indicate correlations between walkability, population density, and COVID-19 spreading patterns but no clear correlation between population size and the number of cases or deaths per 100 k habitants. Although virus spread beyond these initial cases may provide additional data for analysis, this study is an initial step in understanding the relationship between COVID-19 and urban configuration.

Machine Learning and Geo-Based Multi-Criteria Decision Support Systems in Analysis of Complex Problems

Many complex problems require a multi-criteria decision, such as the COVID-19 pandemic that affected nearly all activities in the world. In this regard, this study aims to develop a multi-criteria decision support system considering the sustainability, feasibility, and success rate of possible approaches. Therefore, two models have been developed: Geo-AHP (applying geo-based data) and BN-Geo-AHP using probabilistic techniques (Bayesian network). The ranking method of Geo-APH is generalized, and the equations are provided in a way that adding new elements and variables would be possible by experts. Then, to improve the ranking, the application of the probabilistic technique of a Bayesian network and the role of machine learning for database and weight of each parameter are explained, and the model of BN-Geo-APH has been developed. In the next step, to show the application of the developed Geo-AHP and BN-Geo-AHP models, we selected the new pandemic of COVID-19 that affected nearly all activities, and we used both models for analysis. For this purpose, we first analyzed the available data about COVID-19 and previous studies about similar virus infections, and then we ranked the main approaches and alternatives in confronting the pandemic of COVID-19. The analysis of approaches with the selected alternatives shows the first ranked approach is massive vaccination and the second ranked is massive swabs or other tests. The third is the use of medical masks and gloves, and the last ranked is the lockdown, mostly due to its major negative impact on the economy and individuals.

Effect of lockdown on wastewater characteristics: a comparison of two large urban areas

The effect of the lockdown imposed to limit the spread of SARS-CoV-2 in France between March 14 and May 11, 2020 on the wastewater characteristics of two large urban areas (with between 250,000 and 300,000 inhabitants) was studied. The number of outward and inward daily commuters was extracted from national census databases related to the population and their commuting habits. For urban area A, with the larger number of daily inward commuters (110,000, compared to 53,000 for B), lockdown was observed to have an effect on the monthly load averages of chemical oxygen demand, biochemical oxygen demand, total Kjeldahl nitrogen, total suspended solids and total phosphorus, all of which decreased (confidence level of 95%). This decrease, which varied between 20% and 40% and reached 45% for COD, can be related to the cessation of catering and activities such as hairdressing, which generate large amounts of graywater. The ammonium loads, due to the use of toilets before leaving for work and after returning from work, remained constant. In the case of urban area B, lockdown had no noticeable effect. More data would be necessary in the long term to analyze the effect of changes in the balance between ammonia and carbon sources on the operation of wastewater treatment plants.