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Post-processing elaboration of a machine learning model probability output, applied to classify the time series of ambulances dispatched and calls received by EMS for respiratory and infective issues (see text for details), to distinguish between a scenario of no epidemic diffusion and a scenario of active spreading by generating five possible classes of alert.
Source publication
The pandemic of COVID-19 has posed unprecedented threats to healthcare systems worldwide. Great efforts were spent to fight the emergency, with the widespread use of cutting-edge technologies, especially big data analytics and AI. In this context, the present study proposes a novel combination of geographical filtering and machine learning (ML) for...
Contexts in source publication
Context 1
... to these two features, five 'alert classes' were defined as: The number of classes was set to five in order to keep it minimal, targeting the best possible explicability while still accounting for both confidence level and confidence trend. A graphical representation of this post-processing is reported in Figure 2. ...
Context 2
... number of classes was set to five in order to keep it minimal, targeting the best possible explicability while still accounting for both confidence level and confidence trend. A graphical representation of this post-processing is reported in Figure 2. Post-processing elaboration of a machine learning model probability output, applied to classify the time series of ambulances dispatched and calls received by EMS for respiratory and infective issues (see text for details), to distinguish between a scenario of no epidemic diffusion and a scenario of active spreading by generating five possible classes of alert. ...
Context 3
... graphical representation of this post-processing is reported in Figure 2. Post-processing elaboration of a machine learning model probability output, applied to classify the time series of ambulances dispatched and calls received by EMS for respiratory and infective issues (see text for details), to distinguish between a scenario of no epidemic diffusion and a scenario of active spreading by generating five possible classes of alert. Figure 2. Post-processing elaboration of a machine learning model probability output, applied to classify the time series of ambulances dispatched and calls received by EMS for respiratory and infective issues (see text for details), to distinguish between a scenario of no epidemic diffusion and a scenario of active spreading by generating five possible classes of alert. ...
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Background:
Certain alcohol misuse patients heavily utilise the Emergency Department (ED) and Emergency Medical Services (EMS) and may present with intoxication or long-term sequelae of alcohol misuse. Our study explored reasons for repeated ED/EMS utilisation and sought to understand perpetuating and protective factors for drinking.
Methods:
Fa...
Citations
... From a methodological perspective our approach measures the relationship between the confirmed cases of the disease and the effect of indicators-e.g., determinants-in explaining the incidence patterns. This type of analysis is not only informative for public health policies targeted to different population groups [36,37] but also essential in epidemic contexts to manage early warning systems [38,39]. The classification of territorial units by their propensity to infection can be used for equity in pandemic and public health policies avoiding one-size-fits-all containment measures in favour of geographically-tailored interventions in areas more prone to diffusion [40,41]. ...
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.
