Accounting for Global COVID-19 Diffusion Patterns, January–April 2020

Abstract

Key factors in modeling a pandemic and guiding policy-making include mortality rates associated with infections; the ability of government policies, medical systems, and society to adapt to the changing dynamics of a pandemic; and institutional and demographic characteristics affecting citizens' perceptions and behavioral responses to stringent policies. This paper traces the crosscountry associations between COVID-19 mortality, policy interventions aimed at limiting social contact, and their interactions with institutional and demographic characteristics. We document that, with a lag, more stringent pandemic policies were associated with lower mortality growth rates. The association between stricter pandemic policies and lower future mortality growth is more pronounced in countries with a greater proportion of the elderly population and urban population, greater democratic freedoms, and larger international travel flows. Countries with greater policy stringency in place prior to the first death realized lower peak mortality rates and exhibited lower durations to the first mortality peak. In contrast, countries with higher initial mobility saw higher peak mortality rates in the first phase of the pandemic, and countries with a larger elderly population, a greater share of employees in vulnerable occupations, and a higher level of democracy took longer to reach their peak mortalities. Our results suggest that policy interventions are effective at slowing the geometric pattern of mortality growth, reducing the peak mortality, and shortening the duration to the first peak. We also shed light on the importance of institutional and demographic characteristics in guiding policy-making for future waves of the pandemic.

The Role of Social Connectedness in the Coronavirus Disease (Covid-19) Pandemic Outcome

“…As suchit joins the growing literature on the role of social networks of the COVID-19 outbreak 2020 (Bailey et al, 2018;Kuschler et al 2020;Klepac et al 2020). It also joins the literature on the socio-economic determinants which governed the coronavirus outbreak (Stojkoski et al 2020;Jinjarak et al 2020;Ehlert, 2020;Szulczyk and Cheema 2020). From a methodological perspective the paper contributes to the literature which investigates the impact of the shape of an empirical social network structure on a disease-spreading process, especially those that take into account the process behind COVID-19 (Basnarkov, 2020;Pastor and Vespignani, 2001).…”

Pandemic shocks and fiscal-monetary policies in the Eurozone: COVID-19 dominance during January–June 2020

“…Some of this heterogeneity may be influenced by the different methodologies of local agencies tasked with capturing the contagion and severity of the virus. 2 At the same time, Jinjarak et al (2020) provide evidence that the government pandemic policy interventions (in terms of their strictness, duration, and rapidity), along with initial country characteristics, may have influenced both the mortality growth rates and the empirical shape of the mortality curve (including the duration and peak). 3 Their paper explores several demographic and structural features across a large sample of advanced and emerging economies from 23 January 2020 to 28 April 2020, finding that with a lag, more stringent pandemic policies were associated with lower mortality growth rates.…”

“… Note : The peak new mortality rates of France, Belgium, Ireland, Italy, and Spain were over-predicted. At the same time, those of Greece, Cyprus, Slovak Republic, Finland, and Portugal were under-predicted ( Jinjarak et al , 2020 ) …”

How Timing of Stay-at-home Orders and Mobility Reductions Impacted First-Wave COVID-19 Deaths in US Counties

“…It is clear that both NPIs and behavioral changes have impacted transmission of the virus (Jinjarak et al [2020]; Lin & Meissner [2020]; Kapoor et al [2020]; Abouk & Heydari [2020]; Pei et al [2020]; Du et al [2020]). However, decoupling the relative impacts of these factors presents important challenges as discussed in (Abouk & Heydari [2020]; Courtemanche et al [2020]), and despite the existing evidence, there remains no consensus about the relative impact of these measures on epidemic growth.…”