Mobility in times of pandemics: Evidence on the spread of COVID19 in Italy's labour market areas

People inflows as a pandemic trigger: Evidence from a quasi-experimental study

Although it has been established that population density can contribute to the outbreak of the COVID-19 virus, there is no evidence to suggest that economic activities, which imply a significant change in mobility, played a causal role in the unfolding of the pandemic. In this paper, we exploit the particular situation of Sardinia (Italy) in 2020 to examine how changes in mobility due to tourism inflows (a proxy of economic activities) influenced the development of the COVID-19 pandemic. Using a difference-in-differences approach, we identify a strong causal relationship between tourism flows and the emergence of COVID-19 cases in Sardinia. We estimate the elasticity of COVID-19 cases in relation to the share of tourists to be 4.1%, which increases to 5.1% when excluding local residents. Our analysis suggests that, in the absence of tools preventing the spread of infection, changes in population density due to economic activities trigger the pandemic spreading in previously unaffected locations. This work contributes to the debate on the complex relationship between COVID-19 and the characteristics of locations by providing helpful evidence for risk-prevention policies.

Predicting economic resilience of territories in Italy during the COVID-19 first lockdown

This paper aims to predict the economic resilience to crises of territories based on local pre-existing socioeconomic characteristics. Specifically, we consider the case of Italian municipalities during the first wave of the COVID-19 pandemic, leveraging a large-scale dataset of cardholders performing transactions in Point-of-Sales. Based on a set of machine learning classifiers, we show that network-based measures and variables related to the social, economic, demographic and environmental dimensions are relevant predictors of the economic resilience of Italian municipalities to the crisis. In particular, we find accurate classification performance both in balanced and un-balanced scenarios, as well as in the case we restrict the analysis to specific geographical areas. Our analysis predicts that territories with larger income per capita, soil consumption, concentration of real estate activities and commuting network centrality in terms of closeness and Pagerank constitute the set of most affected areas, experiencing the strongest reduction of economic activities during the COVID-19 pandemic. Overall, we provide an application of an early-warning system able to provide timely evidence to policymakers about the detrimental effects generated by natural disasters and severe crisis episodes, thus contributing to optimize public decision support systems.

Ecosystem degradation and the spread of Covid-19

The linkages between the emergence of zoonotic diseases and ecosystem degradation have been widely acknowledged by the scientific community and policy makers. In this paper we investigate the relationship between human overexploitation of natural resources, represented by the Human Appropriation of Net Primary Production Index (HANPP) and the spread of Covid-19 cases during the first pandemic wave in 730 regions of 63 countries worldwide. Using a Bayesian estimation technique, we highlight the significant role of HANPP as a driver of Covid-19 diffusion, besides confirming the well-known impact of population size and the effects of other socio-economic variables. We believe that these findings could be relevant for policy makers in their effort towards a more sustainable intensive agriculture and responsible urbanisation.

The economic impacts of COVID-19 and city lockdown: Early evidence from China

As the first major developing country heavily struck by the COVID-19 pandemic, China adopted the world's most stringent lockdown interventions to contain the virus spread. Using macro- and micro-level data, this paper shows that both the pandemic and lockdown policies have had negative and significant impacts on the economy. Gross regional product (GRP) fell by 9.5 and 0.3 percentage points in cities with and without lockdown interventions, respectively. These impacts represent a dramatic recession from China's average growth of 6.74% before the pandemic. The results indicate that lockdown explains 2.8 percentage points of the GDP loss. We also document significant spill-over effects of the pandemic in adjacent areas but no such effects of lockdown. Reduced labor mobility, land supply, and entrepreneurship are among the most significant mechanisms underpinning the impacts of the pandemic and lockdown. Cities with higher share of secondary industry, higher traffic intensity, lower population density, lower internet access, and lower fiscal capacity suffered more. However, these cities seem to have recovered well from the recession and quickly closed the economic gap in the aftermath of the pandemic and city lockdown. Our findings have broader implications for the global interventions in pandemic containment.

Do We Learn From Errors? The Economic Impact of Differentiated Policy Restrictions in Italy

This paper investigates the economic impact of the three tiers risk framework implemented in Italy against the COVID-19 pandemic during the Autumn of 2020. Exploiting a large-scale dataset encompassing daily credit card transactions mediated by a large Italian bank, we estimate a set of panel event study models to disentangle the impact of restrictions with low, medium and high stringency levels in terms of consumption reduction. We show that space-time differentiated policies tend to produce stronger welfare losses for progressively more stringent restrictions in specific sectors targeted by these policies such as Retail and Restaurants. However, when we compare provinces implementing the same level of policy stringency, we show that territories with higher income per capita and larger concentration of manufacturing and service activities experience both significantly worse economic and epidemiological performances. Overall, our results suggest that policy makers should properly account for local socio-economic characteristics when designing tailored restrictions entailing an equal and homogeneous impact across territories.

Gaps in mobility data and implications for modelling epidemic spread: A scoping review and simulation study

Reliable estimates of human mobility are important for understanding the spatial spread of infectious diseases and the effective targeting of control measures. However, when modelling infectious disease dynamics, data on human mobility at an appropriate temporal or spatial resolution are not always available, leading to the common use of model-derived mobility proxies. In this study we reviewed the different data sources and mobility models that have been used to characterise human movement in Africa. We then conducted a simulation study to better understand the implications of using human mobility proxies when predicting the spatial spread and dynamics of infectious diseases. We found major gaps in the availability of empirical measures of human mobility in Africa, leading to mobility proxies being used in place of data. Empirical data on subnational mobility were only available for 17/54 countries, and in most instances, these data characterised long-term movement patterns, which were unsuitable for modelling the spread of pathogens with short generation times (time between infection of a case and their infector). Results from our simulation study demonstrated that using mobility proxies can have a substantial impact on the predicted epidemic dynamics, with complex and non-intuitive biases. In particular, the predicted times and order of epidemic invasion, and the time of epidemic peak in different locations can be underestimated or overestimated, depending on the types of proxies used and the country of interest. Our work underscores the need for regularly updated empirical measures of population movement within and between countries to aid the prevention and control of infectious disease outbreaks. At the same time, there is a need to establish an evidence base to help understand which types of mobility data are most appropriate for describing the spread of emerging infectious diseases in different settings.

Using geospatial social media data for infectious disease studies: a systematic review

Geospatial social media (GSM) data has been increasingly used in public health due to its rich, timely, and accessible spatial information, particularly in infectious disease research. This review synthesized 86 research articles that use GSM data in infectious diseases published between December 2013 and March 2022. These articles cover 12 infectious disease types ranging from respiratory infectious diseases to sexually transmitted diseases with spatial levels varying from the neighborhood, county, state, and country. We categorized these studies into three major infectious disease research domains: surveillance, explanation, and prediction. With the assistance of advanced statistical and spatial methods, GSM data has been widely and deeply applied to these domains, particularly in surveillance and explanation domains. We further identified four knowledge gaps in terms of contextual information use, application scopes, spatiotemporal dimension, and data limitations and proposed innovation opportunities for future research. Our findings will contribute to a better understanding of using GSM data in infectious diseases studies and provide insights into strategies for using GSM data more effectively in future research.

From the lockdown to the new normal: individual mobility and local labor market characteristics following the COVID-19 pandemic in Italy

Italy was among the first countries to introduce drastic measures to reduce individual mobility in order to slow the diffusion of COVID-19. The first measures imposed by the central authorities on March 8, 2020, were unanticipated and highly localized, focusing on 26 provinces. Additional nationwide measures were imposed after one day, and were removed only after June 3. Looking at these watershed moments of the pandemic, this paper explores the impact of the adoption of localized restrictions on changes in individual mobility in Italy using a spatial discontinuity approach. Results show that these measures lowered individual mobility by 7 percentage points on top of the reduction in mobility recorded in the adjacent untreated areas. The study also fills a gap in the literature in that it looks at the changes in mobility after the nationwide restrictions were lifted and shows how the recovery in mobility patterns is related to various characteristics of local labour markets. Areas with a higher proportion of professions exposed to diseases, more suitable for flexible work arrangements, and with a higher share of fixed-term contracts before the pandemic are characterised by a smaller increase in mobility after re-opening.