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The objective of this chapter is to introduce the reader to Spatial Health Econometrics (SHE). In both micro and macro health economics there are phenomena that are characterised by a strong spatial dimension, from hospitals engaging in local competitions in the delivery of health care services, to the regional concentration of health risk factors and needs. SHE allows health economists to incorporate these spatial effects using simple econometric models that take into account these spillover effects. This improves our understanding of issues such as hospital quality, efficiency and productivity and the sustainability of health expenditure of regional and national health care systems, to mention a few.

With the spread of the coronavirus disease across over 100 countries and its status upgraded to that of a pandemic on 11 March 2020 (World Health Organization), increased attention is being placed on the policy measures that may be required to effectively curb the rate of contagion within and across countries. Currently, several governments, such as China, Italy, Spain, Japan and the Republic of Korea, have implemented emergency measures informed by the principle of social distancing to limit the spread of coronavirus (World Health Organization). Ever since the virus was first identified in Wuhan City in December 2019, this succession of uncoordinated policy responses offers a set of natural experiments that should be analysed to understand the successes and failures of containment at the societal level. In this analysis, we focus on the case of Italy, the hardest hit country in Europe (Dong, Du, & Gardner, 2020; World Health Organization). The objective of this short note is to provide an even-handed analysis of the actions taken by the Italian government to cope with the transmission of the virus and to highlight lessons in emergency management that can be learnt for other countries currently facing the onset of the Covid-19 epidemic.

This chapter reviews graphical modeling techniques for estimating large covariance matrices and their inverse. The chapter provides a selective survey of different models and estimators proposed by the graphical modeling literature and offers some practical examples where these methods could be applied in the area of health economics.

Seismometers continuously record a wide range of ground movements not caused by earthquake activity, but rather generated by human activities such as traffic, industrial machinery functioning and industrial processes. In this Chapter we exploit seismic data to predict variations in Gross Domestic Product (GDP) for a set of States in the USA over the period from 2016 to 2021. We measure the noise generated at specific frequencies that are linked to human activity and use it as an indicator of economic activity. We show a remarkable reduction in seismic noise due to a slowdown in traffic and economic activities during the Corona economic crisis. Our results point at seismic data as a valuable source of information that can be used for monitoring regional and national economies.

This chapter aims to provide suggestive evidence on how the Lombardy region dealt with the COVID-19 pandemic in 2020 and discuss future challenges for the Lombardy healthcare system. After an introduction to the wide spread of the virus inside the region, we describe the Lombardy health system so the reader may understand the context in which the virus has taken hold so quickly. The pandemic has heavily stressed the system, mainly because Lombardy experienced an excess of hospital admissions. We have considered the increased mortality rate as a proxy of the proper managing of the COVID-19 pandemic. In addition, we describe the process of treating non-COVID patients, such as those affected by acute myocardial infarction (AMI), stroke and oncological diseases. Despite the pandemic, hospitals have been able to guarantee a high level of performance. A discussion of the future evolution of the healthcare system concludes this chapter.