Evaluating the COVID-19 impact in Italian regions via multi criteria analysis.

Italy was the first European country to be significantly impacted by the COVID-19 pandemic. The lack of similar previous experiences and the initial uncertainty regarding the new virus resulted in an unpredictable health crisis with 243,506 total confirmed cases and 34,997 deaths between February and July 2020. Despite the panorama of precariousness and the impelling calamity, the country successfully managed many aspects of the early stages of the health and socio-economic crisis. Nevertheless, many disparities can be identified at the regional level. The study aims to determine which aspects of regional management were considered more important by the citizens regarding economic and health criteria. A survey was designed to gather responses from the population on the Italian regions' response and provide a ranking of the regions. The 29-item online survey was provided to 352 individuals, and the collected data were analyzed using the Analytic Hierarchy Process methodology. The results show a general agreement in considering of greater relevance the healthcare policies rather than the economic countermeasures adopted by regional governments. Our analysis associated a weight of 64% to the healthcare criteria compared to the economic criteria with a weight of 36%. In addition to the results obtained from the Analytic Hierarchy Process, the sample's composition was analyzed to provide an overall assessment of the Italian regions. To do so, we collected objective data for each region and multiplied them by the overall weight obtained for each sub-criteria. Looking at the propensity to vaccination or the belief in a relation between COVID-19 and 5G according to age and educational qualification helps understand how public opinion is structured according to cultural and anthropological differences.

'Fight the parasite': raising awareness of cystic echinococcosis in primary school children in endemic countries.

BACKGROUND: Cystic echinococcosis (CE) is a widespread zoonosis and a significant economic concern and cause of morbidity in humans. A scarcity of education on the sources of CE infection and containment measures is considered to be a key factor responsible for persistent transmission within communities. Recently, edutainment approaches have captured the attention of health education (HE) professionals due to the benefits of integrating cognitive and emotional learning processes. METHODS: A study was carried out in Sardinia, Italy, between 2020 and 2022, amid the SARS-Covid-19 pandemic. The project, designed to involve primary school children (via remote or face-to-face learning depending on the evolving Covid-19 containment measures) consisted of four distinct phases: (i) creation of material for school children and teachers focused on cystic echinococcosis; (ii) pre-intervention evaluation of CE knowledge (i.e. True False Don't Know [TFD] pre-intervention questionnaire based on CE-related knowledge and practices); (iii) edutainment activity (e.g. interactive lessons enhanced by the comic booklet and the "Fight the parasite" cartoon video, hands-on educational activities and drawing activities on CE); and (iv) post-intervention evaluation of CE knowledge (via TFD post-intervention questionnaire [same questionnaire as used for the pre-intervention assessment] on CE-related knowledge and practices) and on-site edutainment tour in primary schools taking part to the project. RESULTS: The percentage of correct answers increased from 65% for the questionnaire administered pre-intervention to 87.9% for the same questionnaire administered post-intervention (χ2 = 648.12, df = 1, P < 0.0001), while the percentage of uncertain answers (i.e. 'I don't know') decreased from 23% pre-intervention to 5% post-intervention (χ2 = 603.44, df = 1, P < 0.0001). These differences indicate a significantly enhanced understanding of CE among participating school children after the intervention. CONCLUSIONS: The results of the present survey indicate that the use of digital educational tools, the use of video animations as a model for science communication, as well as other participatory teaching methods, enabled children to retain key knowledge of the routes of CE transmission and ways to prevent it.

Dynamical intervention planning against COVID-19-like epidemics.

COVID-19 has got us to face a new situation where, for the lack of ready-to-use vaccines, it is necessary to support vaccination with complex non-pharmaceutical strategies. In this paper, we provide a novel Mixed Integer Nonlinear Programming formulation for fine-grained optimal intervention planning (i.e., at the level of the single day) against newborn epidemics like COVID-19, where a modified SIR model accounting for heterogeneous population classes, social distancing and several types of vaccines (each with its efficacy and delayed effects), allows us to plan an optimal mixed strategy (both pharmaceutical and non-pharmaceutical) that takes into account both the vaccine availability in limited batches at selected time instants and the need for second doses while keeping hospitalizations and intensive care occupancy below a threshold and requiring that new infections die out at the end of the planning horizon. In order to show the effectiveness of the proposed formulation, we analyze a case study for Italy with realistic parameters.

Infodemics: A new challenge for public health.

The COVID-19 information epidemic, or "infodemic," demonstrates how unlimited access to information may confuse and influence behaviors during a health emergency. However, the study of infodemics is relatively new, and little is known about their relationship with epidemics management. Here, we discuss unresolved issues and propose research directions to enhance preparedness for future health crises.

The mathematics of multiple lockdowns.

While vaccination is the optimal response to an epidemic, recent events have obliged us to explore new strategies for containing worldwide epidemics, like lockdown strategies, where the contacts among the population are strongly reduced in order to slow down the propagation of the infection. By analyzing a classical epidemic model, we explore the impact of lockdown strategies on the evolution of an epidemic. We show that repeated lockdowns have a beneficial effect, reducing the final size of the infection, and that they represent a possible support strategy to vaccination policies.

The COVID-19 social media infodemic.

We address the diffusion of information about the COVID-19 with a massive data analysis on Twitter, Instagram, YouTube, Reddit and Gab. We analyze engagement and interest in the COVID-19 topic and provide a differential assessment on the evolution of the discourse on a global scale for each platform and their users. We fit information spreading with epidemic models characterizing the basic reproduction number [Formula: see text] for each social media platform. Moreover, we identify information spreading from questionable sources, finding different volumes of misinformation in each platform. However, information from both reliable and questionable sources do not present different spreading patterns. Finally, we provide platform-dependent numerical estimates of rumors' amplification.

Human mobility in response to COVID-19 in France, Italy and UK.

The COVID-19 pandemic is one of the defining events of our time. National Governments responded to the global crisis by implementing mobility restrictions to slow down the spread of the virus. To assess the impact of those policies on human mobility, we perform a massive comparative analysis on geolocalized data from 13 M Facebook users in France, Italy, and the UK. We find that lockdown generally affects national mobility efficiency and smallworldness-i.e., a substantial reduction of long-range connections in favor of local paths. The impact, however, differs among nations according to their mobility infrastructure. We find that mobility is more concentrated in France and UK and more distributed in Italy. In this paper we provide a framework to quantify the substantial impact of the mobility restrictions. We introduce a percolation model mimicking mobility network disruption and find that node persistence in the percolation process is significantly correlated with the economic and demographic characteristics of countries: areas showing higher resilience to mobility disruptions are those where Value Added per Capita and Population Density are high. Our methods and findings provide important insights to enhance preparedness for global critical events and to incorporate resilience as a relevant dimension to estimate the socio-economic consequences of mobility restriction policies.

Time, space and social interactions: exit mechanisms for the Covid-19 epidemics.

We develop a minimalist compartmental model to study the impact of mobility restrictions in Italy during the Covid-19 outbreak. We show that, while an early lockdown shifts the contagion in time, beyond a critical value of lockdown strength the epidemic tends to restart after lifting the restrictions. We characterize the relative importance of different lockdown lifting schemes by accounting for two fundamental sources of heterogeneity, i.e. geography and demography. First, we consider Italian Regions as separate administrative entities, in which social interactions between age classes occur. We show that, due to the sparsity of the inter-Regional mobility matrix, once started, the epidemic spreading tends to develop independently across areas, justifying the adoption of mobility restrictions targeted to individual Regions or clusters of Regions. Second, we show that social contacts between members of different age classes play a fundamental role and that interventions which target local behaviours and take into account the age structure of the population can provide a significant contribution to mitigate the epidemic spreading. Our model aims to provide a general framework, and it highlights the relevance of some key parameters on non-pharmaceutical interventions to contain the contagion.

Economic and social consequences of human mobility restrictions under COVID-19.

In response to the coronavirus disease 2019 (COVID-19) pandemic, several national governments have applied lockdown restrictions to reduce the infection rate. Here we perform a massive analysis on near-real-time Italian mobility data provided by Facebook to investigate how lockdown strategies affect economic conditions of individuals and local governments. We model the change in mobility as an exogenous shock similar to a natural disaster. We identify two ways through which mobility restrictions affect Italian citizens. First, we find that the impact of lockdown is stronger in municipalities with higher fiscal capacity. Second, we find evidence of a segregation effect, since mobility contraction is stronger in municipalities in which inequality is higher and for those where individuals have lower income per capita. Our results highlight both the social costs of lockdown and a challenge of unprecedented intensity: On the one hand, the crisis is inducing a sharp reduction of fiscal revenues for both national and local governments; on the other hand, a significant fiscal effort is needed to sustain the most fragile individuals and to mitigate the increase in poverty and inequality induced by the lockdown.

The COVID-19 Infection in Italy: A Statistical Study of an Abnormally Severe Disease.

We statistically investigate the Coronavirus Disease 19 (COVID-19) pandemic, which became particularly invasive in Italy in March 2020. We show that the high apparent lethality or case fatality ratio (CFR) observed in Italy, as compared with other countries, is likely biased by a strong underestimation of the number of infection cases. To give a more realistic estimate of the lethality of COVID-19, we use the actual (March 2020) estimates of the infection fatality ratio (IFR) of the pandemic based on the minimum observed CFR and analyze data obtained from the Diamond Princess cruise ship, a good representation of a "laboratory" case-study from an isolated system in which all the people have been tested. From such analyses, we derive more realistic estimates of the real extent of the infection as well as more accurate indicators of how fast the infection propagates. We then isolate the dominant factors causing the abnormal severity of the disease in Italy. Finally, we use the death count-the only data estimated to be reliable enough-to predict the total number of people infected and the interval of time when the infection in Italy could end.