Integrated Analysis of Behavioural and Health COVID-19 Data Combining Bayesian Networks and Structural Equation Models.

The response to the COVID-19 pandemic has been highly variable. Governments have applied different mitigation policies with varying effect on social and economic measures, over time. This article presents a methodology for examining the effect of mobility restriction measures and the association between health and population activity data. As case studies, we refer to the pre-vaccination experience in Italy and Israel. Facing the pandemic, Israel and Italy implemented different policy measures and experienced different population behavioral patterns. Data from these countries are used to demonstrate the proposed methodology. The analysis we introduce in this paper is a staged approach using Bayesian Networks and Structural Equations Models. The goal is to assess the impact of pandemic management and mitigation policies on pandemic spread and population activity. The proposed methodology models data from health registries and Google mobility data and then shows how decision makers can conduct scenario analyses to help design adequate pandemic management policies.

Dynamic Mixed Data Analysis and Visualization.

One of the consequences of the big data revolution is that data are more heterogeneous than ever. A new challenge appears when mixed-type data sets evolve over time and we are interested in the comparison among individuals. In this work, we propose a new protocol that integrates robust distances and visualization techniques for dynamic mixed data. In particular, given a time t∈T={1,2,…,N}, we start by measuring the proximity of n individuals in heterogeneous data by means of a robustified version of Gower's metric (proposed by the authors in a previous work) yielding to a collection of distance matrices {D(t),∀t∈T}. To monitor the evolution of distances and outlier detection over time, we propose several graphical tools: First, we track the evolution of pairwise distances via line graphs; second, a dynamic box plot is obtained to identify individuals which showed minimum or maximum disparities; third, to visualize individuals that are systematically far from the others and detect potential outliers, we use the proximity plots, which are line graphs based on a proximity function computed on {D(t),∀t∈T}; fourth, the evolution of the inter-distances between individuals is analyzed via dynamic multiple multidimensional scaling maps. These visualization tools were implemented in the Shinny application in R, and the methodology is illustrated on a real data set related to COVID-19 healthcare, policy and restriction measures about the 2020-2021 COVID-19 pandemic across EU Member States.

Modeling Provincial Covid-19 Epidemic Data Using an Adjusted Time-Dependent SIRD Model.

In this paper, we develop a forecasting model for the spread of COVID-19 infection at a provincial (i.e., EU NUTS-3) level in Italy by using official data from the Italian Ministry of Health integrated with data extracted from daily official press conferences of regional authorities and local newspaper websites. This data integration is needed as COVID-19 death data are not available at the NUTS-3 level from official open data channels. An adjusted time-dependent SIRD model is used to predict the behavior of the epidemic; specifically, the number of susceptible, infected, deceased, recovered people and epidemiological parameters. Predictive model performance is evaluated using comparison with real data.

Are Epidemiological Estimates Able to Describe the Ability of Health Systems to Cope with COVID-19 Epidemic?

INTRODUCTION: The coronavirus disease 2019 (COVID-19) epidemic is an infectious disease which was declared a pandemic and hit countries worldwide from the beginning of the year 2020. Despite the emergency vigilance plans, health systems in all countries experienced a different ratio of lethality, amount of admissions to intensive care units and quarantine management of positive patients. The aim of this study is to investigate whether some epidemiological estimates could have been useful in understanding the capacity of the Italian Regional Health Services to manage the COVID-19 epidemic. METHODS: We have compared data between two different Italian regions in the Northern part of Italy (Lombardy and Veneto) and the national data to determine whether different health strategies might be significant in explaining dissimilar patterns of the COVID-19 epidemic in Italy. Data have been extracted from a public database and were available only in an aggregated form. RESULTS: The regions in question displayed two different health policies to face the COVID-19 epidemic: while Veneto's health service was largely territorially oriented, Lombardy's strategy was more hospital-centered. DISCUSSION: The key to facing epidemics like this one consists in identifying solutions outside of hospitals. This however requires there be well-trained general practitioners and enough healthcare personnel working outside hospitals.

CoViD-19, learning from the past: A wavelet and cross-correlation analysis of the epidemic dynamics looking to emergency calls and Twitter trends in Italian Lombardy region.

The first case of Coronavirus Disease 2019 in Italy was detected on February the 20th in Lombardy region. Since that date, Lombardy has been the most affected Italian region by the epidemic, and its healthcare system underwent a severe overload during the outbreak. From a public health point of view, therefore, it is fundamental to provide healthcare services with tools that can reveal possible new health system stress periods with a certain time anticipation, which is the main aim of the present study. Moreover, the sequence of law decrees to face the epidemic and the large amount of news generated in the population feelings of anxiety and suspicion. Considering this whole complex context, it is easily understandable how people "overcrowded" social media with messages dealing with the pandemic, and emergency numbers were overwhelmed by the calls. Thus, in order to find potential predictors of possible new health system overloads, we analysed data both from Twitter and emergency services comparing them to the daily infected time series at a regional level. Particularly, we performed a wavelet analysis in the time-frequency plane, to finely discriminate over time the anticipation capability of the considered potential predictors. In addition, a cross-correlation analysis has been performed to find a synthetic indicator of the time delay between the predictor and the infected time series. Our results show that Twitter data are more related to social and political dynamics, while the emergency calls trends can be further evaluated as a powerful tool to potentially forecast new stress periods. Since we analysed aggregated regional data, and taking into account also the huge geographical heterogeneity of the epidemic spread, a future perspective would be to conduct the same analysis on a more local basis.

Monitoring emergency calls and social networks for COVID-19 surveillance. To learn for the future: The outbreak experience of the Lombardia region in Italy.

On 18th February the first Italian case of Coronavirus Induced Disease 2019 (COVID19) due to secondary transmission outside China was identified in Codogno, Lombardia region. In the following days the number of cases started to rise not only in Lombardia but also in other Italian regions, although Lombardia remained and it is still the most affected region in Italy. At the moment, 234801 cases have been identified in Italy, out of which 90070 in Lombardia region. The (Severe Acute Respiratory Syndrome Coronavirus 2) SARS CoV 2 outbreak in Italy has been characterized by a massive spread of news coming from both official and unofficial sources leading what has been defined as infodemia, an over-abundance of information - some accurate and some not - that has made hard for people to find trustworthy sources and reliable guidance needed. Infodemia on SARS CoV 2 created the perfect field to build uncertainty in the population, which was scared and not prepared to face this outbreak. It is understandable how the rapid increase of the cases' number , the massive spread of news and the adoption of laws to face this outbreak led to a feeling of anxiety in the population whose everyday life changed very quickly. A way to assess the dynamic burden of social anxiety is a context analysis of major social networks activities over the Internet. To this aim Twitter represents a possible ideal tool since the focused role of the tweets according to the more urgent needs of information and communication rather than general aspects of social projection and debate as in the case of Facebook, which could provide slower responses for the fast individual and social context evolution dynamics.  Aim of the paper is to analyse the most common reasons for calling and outcomes. Furthermore, the joint analysis with Twitter trends related to emergency services might be useful to understand possible correlations with epidemic trends and predict new outbreaks.

Modelling bias in combining small area prevalence estimates from multiple surveys.

Combining information from multiple surveys can improve the quality of small area estimates. Customary approaches, such as the multiple-frame and statistical matching methods, require individual level data, whereas in practice often only multiple aggregate estimates are available. Commercial surveys usually produce such estimates without clear description of the methodology that is used. In this context, bias modelling is crucial, and we propose a series of Bayesian hierarchical models which allow for additive biases. Some of these models can also be fitted in a classical context, by using a mixed effects framework. We apply these methods to obtain estimates of smoking prevalence in local authorities across the east of England from seven surveys. All the surveys provide smoking prevalence estimates and confidence intervals at the local authority level, but they vary by time, sample size and transparency of methodology. Our models adjust for the biases in commercial surveys but incorporate information from all the sources to provide more accurate and precise estimates.