From the information known at this point, several facts are pertinent. First, it belongs to the same family of coronaviruses that caused the Severe Acute Respiratory Syndrome (SARS) outbreak in 2003 and the Middle East Respiratory Syndrome (MERS) outbreak in 2012. Second, the mortality rate (number of deaths relative to number of cases), which is as yet imprecisely estimated, is probably in the range of 1%-3.4%-significantly lower than 10% for SARS and 34% for MERS (Table 1, first column), but substantially higher than the mortality rate for seasonal flu, which is less than 0.1%. 2 Third, even though it emerged from animal hosts, it now spreads through human-to-human contact. The infection rate of COVID-19 appears to be higher than that for the seasonal flu and MERS, with the range of possible estimates encompassing the infection rates of SARS and Ebola (Table 1, second column).

This paper analyzes the impact of production complexity and its adaptability on the level of output and on its rate of growth. We develop an endogenous growth model where increased complexity raises the rate of economic growth but has an ambiguous effect on the level of output. Our empirical measure of production adaptability captures the proximity of production sectors within the product space, which we modify to reflect intra-industry trade and the international fragmentation of production. We test the model against a sample of 89 countries over the two decades to 2009 and find that its main predictions are validated.

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We measure the economic risk of COVID-19 at a geo-spatially detailed resolution. In addition to data about the current prevalence of confirmed cases, we use data from 2014-2018 and a conceptual disaster risk model to compute measures for exposure, vulnerability, and resilience of the local economy to the shock of the epidemic. Using a battery of proxies for these four concepts, we calculate the hazard, the principal components of exposure and vulnerability to it, and of the economy's resilience (i.e. its ability of the recover rapidly from the shock). We find that the economic risk of this pandemic is particularly high in most of Sub-Saharan Africa, South Asia, and Southeast Asia. These results are consistent when comparing an ad hoc equal weighting algorithm for the four components of the index, an algorithm that assumes equal hazard for all countries, and one based on estimated weights using previous aggregated disability-adjusted life years losses associated with communicable diseases. Policy Implications • Most of the economic risks from COVID-19 are in countries and regions that do not get much global attention in this pandemic-Sub-Saharan Africa, South Asia, and Central Asia.

We develop a method to map global network production and vertical trade. Based on product‐level trade data across a matrix of 75 countries, an index measures the intensity of bilateral vertical trade and a force‐directed algorithm lays it out for visualization as a world map of production networks. Three major hubs in the global networks are identified: the USA, Germany and China–Japan. Outside Asia and apart from Mexico, mainly because of its maquiladoras network ties to the USA, we find that developing countries are not yet fully part of the global production networks.