Pandemic Data Quality Modelling: A Bayesian Approach (preprint)

When pandemics like COVID-19 spread around the world, the rapidly evolving situation compels officials and executives to take prompt decisions and adapt policies depending on the current state of the disease. In this context, it is crucial for policymakers to have always a firm grasp on what is the current state of the pandemic, and to envision how the number of infections and possible deaths is going to evolve over the next weeks. However, as in many other situations involving compulsory registration of sensitive data from multiple collectors, cases might be reported with errors, often with delays deferring an up-to-date view of the state of things. Errors in collecting new cases affect the overall mortality, resulting in excess deaths reported by official statistics only months later. In this paper, we provide tools for evaluating the quality of pandemic mortality data. We accomplish this through a Bayesian approach accounting for the excess mortality pandemics might bring with respect to the normal level of mortality in the population.

Integrated Analysis of Behavioral and Health Data: A Comparative Study of COVID19 Data in Israel and Italy (preprint)

The response to the COVID19 pandemic has been highly variable, both in terms of between-nations variation and within the same nation, at different waves. In this context, governments applied different mitigation policy responses with varying impact on social and economic measures over time. This article examines the effect of mobility restriction measures in Italy and Israel and compares the association between health and population mobility data. Facing the pandemic, Israel and Italy implemented different policy measures and experienced different public activity patterns. The analysis we conducted is a staged approach using Bayesian Networks and Structural Equations Models to investigate these patterns. The goal is to assess the impact of pandemic management and mitigation policies on pandemic spread and population activity. We propose a methodology that first models data from health registries and Google mobility data and then shows how decision makers can conduct scenario analysis to help support pandemic management policies.

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 (preprint)

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.

Are health indicators able to describe the ability to cope of Health Systems with COVID-19 epidemic? (preprint)

The Coronavirus Disease 19 epidemic is an infectious disease which was declared as a pandemic and hit all the Countries, all over the world, from the beginning of the year 2020. Despite the emergency vigilance plans, in all the Countries, Health Systems experienced a different ratio of lethality, admissions to intensive care units and managing quarantine of positive patients. The aim of this study is to investigate if some health indicators might have been useful to understand the capacity of Italian National Health Service to manage the COVID 19 epidemic. We will compare data in two different Italian regions in the Northern part of Italy (Lombardy and Veneto) with the national data to understand if different health strategies might be significant to explain different patterns of COVID 19 epidemic in Italy. The two regions have two different health policies to face CoViD-2019 epidemic. To face epidemic like this one the answer should be outside hospitals but this means to have general practitioners well-trained and enough healthcare personnel working outside hospitals.

Modelling provincial Covid-19 epidemic data in Italy using an adjusted time-dependent SIRD model (preprint)

In this paper we develop a predictive 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 from local newspaper websites. This integration is mainly concerned with COVID-19 cause specific death data which are not available at NUTS-3 level from open official data data channels. An adjusted time-dependent SIRD model is used to predict the behavior of the epidemic, specifically the number of susceptible, infected, deceased and recovered people. Predictive model performance is evaluated using comparison with real data.

COVID-Pro in Italy: a dashboard for a province-based analysis (preprint)

This paper presents an dashboard developed to analyse the outbreak of the Covid-19 infection in Italy considering daily NUTS-3 data on positive cases provided by the Italian Ministry of Health and on deaths derived from Italian regional authorities' official press conferences. Descriptive time series plots are provided together with a map describing the spatial distribution of province cumulative cases and rates. A section on a proposed time-dependent adjusted SIRD model for NUTS-3 regions is also provided in the dashboard.