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Epidemiological and economic consequences of government responses to COVID-19

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Epidemiological and economic consequences of government responses to COVID-19

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Epidemiological and economic consequences of government responses to the COVID-19 pandemic

Since the onset of the Covid-19 pandemic, parameterised epidemiological models (‘SIR’ models, for Susceptible, Infected, and Recovered) have been a popular tool to analyse the diseases dynamics (Anderson et al. 2020, Atkeson et al. 2020). These models can be used to shed light on the impact of physical distancing and other public health measures in containing waves of infections (Aschwanden 2020, Ferguson et al. 2020, Davies et al. 2020). SIR models rely on several parameters (for instance, to quantify the impact of physical distancing on the reproduction rate of the virus, or R), so their insights are only as good as the accuracy of their parameters. 

By contrast, our study (Égert et al. 2020) contributes to a burgeoning literature that seeks to quantify the impact of government interventions on disease progression and mobility, employing reduced-form econometric estimates for the Covid-19 pandemic. This literature has already shown that stricter lockdown policies go in tandem with a reduction in Covid-19-related deaths (Conyon et al. 2020). It has found strong evidence in favour of banning mass gatherings as one of the most effective ways of taming the spread of the virus (Ahammer et al. 2020). Similarly, air travel restrictions are found to be effective, especially those imposed on national and international flights and at the early stages of the pandemic (Hubert 2020, Xiong 2020). Stay-at-home requirements and workplace closures can also curb the propagation of the disease (Deb et al. 2020), as can the use of face masks (Chu et al. 2020, Betsch et al. 2020). Nevertheless, the recent empirical literature has said little about the importance of testing and contact tracing policies despite their prominence in SIR models (Aleta et al. 2020), and the protection of the elderly population.  

In most OECD countries, a combination of public health and containment measures, often involving a shutdown of major parts of the economy, was successful in reducing the spread of the pandemic in the first half of 2020. Having lifted many restrictions, the dilemma many policymakers are now facing is how to deal with subsequent waves of infection without inflicting significant damage on economic activity as caused by the measures deployed in response to the first wave. Our study attempts to inform these decisions by examining country experiences at a daily frequency during the period from March to mid-August, with a focus on how the reproduction number, R, and the physical mobility of people (a proxy for economic activity) respond to policy measures. Indicators of containment policies from the Oxford Blavatnik School of Government are complemented with public health indicators assembled for the study.  

Figure 1 shows that, based on estimation results from an econometric model with the log of R and mobility, the combined effect of applying all containment polices – including stay-at-home requirements, workplace and school closures, restricting public gatherings, and imposing limits to international travel – would nearly halve the reproduction number, from an initial R value of about 3.  

Results further suggest that test and trace policies can reduce the spread of the virus. The most comprehensive form of such policies are more than two and a half times more effective in reducing R than more limited forms. Test and trace polices are most effective when the infection rate is not too high (which in estimation is taken to be less than ten new daily cases per million population – a rate which was well exceeded in many countries in March and April). This is a rather unsurprising finding given the difficulties of tracking down all contact persons in a timely manner if the system is overwhelmed with new cases. Overall, the most effective test and trace regime, in an environment of low daily infections, is estimated to be more effective than any other public health intervention and two to three times more effective than most individual containment measures. Policies aiming at shielding the elderly population can also play an important role, as the testing of residents and staff in long-term care facilities and general stay-at-home recommendations for the elderly are associated with fewer infections. The combined effect of these polices on reducing R is estimated to exceed the effect of most individual containment measures. Regarding masks, results show a sizeable and fairly robust negative effect on R from the introduction of mandatory mask wearing in all closed public spaces, although other results (not reported) suggest that extending mask wearing obligations to the outdoors does not appear to add much to reducing the reproduction rate.

The national and global daily death rates are used as proxies for the severity of the pandemic, which by itself could drive spontaneous changes in behaviour irrespective of policy measures. The national cumulative death rate is used to control for the possibility that built-up population immunity could lower R over time. Coefficient estimates on all three variables are statistically significant and their magnitudes imply that spontaneous changes in behaviour and immunity have played an important role in the evolution of R. 

Figure 1 The determinants of the log reproduction rate and physical mobility of people

Epidemiological and economic consequences of government responses to COVID-19 1

Notes: a) This chart decomposes the effect on log R and mobility from selected containment policies and public health policies, while controlling from precautionary motives arising from high death rates. Variables on the containment policies and tracing and testing are obtained from the Oxford Blavatnik School of Government. Variables on testing in care homes, restricting visits to care homes, recommendation for elderly to stay-at-home and mandatory mask wearing are collected by the authors for the purpose of this study. The regression model for (the log of) R is based on a 147-country sample running to 17 August and uses country fixed effects to control for fixed country characteristics such as population density and social norms. The regression model for mobility is based on a 128-country sample for the same time period and also incorporates country fixed effects.  b) The effects of school closures (>=2), stay-at-home requirements (>=1) and workplace closures (>=2) have been combined into one segment labelled ‘Typical lockdown’, this is both because such policies have often been imposed at the same time and, as discussed in the main text, because multi-collinearity means that the sum of the coefficients on these three containment variables are more reliable than any of the individual coefficients. 

Turning to the policy drivers of mobility, empirical results suggest that seven of the eight categories of containment policies have a negative effect on mobility. There is a clear ranking of coefficients, so that the more stringent application of a particular policy tends to reduce mobility to a greater extent. For example, the most severe form of workplace closure (a score of three) has nine times the effect on mobility as that of the mildest form (a score of one). These findings suggest that moving to the more stringent forms of workplace closure, stay-at-home requirements, and school closure has large negative effects on mobility and hence economic activity, although it is difficult to detect any corresponding benefit from further reductions in R. In contrast, for policies such as the cancellation of public events and travel restrictions, the most limited application of the policy has no significant effect on mobility. Applying all containment policies in their most severe forms would reduce mobility by more than half relative to normal, with 50% of this reduction accounted for by workplace closures and stay-at-home requirements. Alternative estimations, not reported here, explored the effect of mask-wearing on mobility. The estimated positive coefficient estimates suggest that mandating mask wearing in public transports and shops raises mobility, possibly by reducing concerns about infection. The national daily death rate from the virus is again included to proxy general awareness of the virus and its effect in voluntarily reducing mobility due to an increase in natural caution. A national daily death rate running at around 15 per million – similar to the rate experienced by some of the major OECD countries going into the lockdown in March – is estimated to reduce mobility by 10%, independently of any government-mandated polices. 

These estimates are consistent with much of the above literature. Containment policies can successfully reduce the spread of the virus, but most have a substantial impact on mobility and, by implication, economic activity; in particular stay-at-home requirements, workplace closures, and school closures. Most importantly, unlike earlier empirical studies, the results strongly support the view that testing, combined with effective contact tracing are key components of the post-lockdown strategy, especially at relatively low level of infections (OECD 2020). This corroborates a recent outbreak modelling study (Hellewell et al. 2020), which found that contact tracing and isolation would only contain outbreaks of Covid-19 if very high levels of contact tracing were achieved. This is also consistent with the view that testing and tracing is most effective in a low-infection environment, because contact tracing becomes increasingly difficult with higher levels of new daily infections (Hellewell et al. 2020). Furthermore, estimation results suggest that mask-wearing and the protection of the elderly population in general and those in care homes in particular might play an important role in combating the virus. 

Authors’ note: More technical details and robustness checks are provided in Égert et al. (2020).

References

Ahammer, A, M Halla and M Lackner (2020), “Mass Gathering Contributed to Early COVID-19 Spread: Evidence from US Sports”, Covid Economics 30, 19 June.

Aleta et al. (2020), “Modelling the impact of testing, contact tracing and household quarantine on second waves of Covid-19”, Nature Human Behaviour, 5 August.

Anderson, R M, H Heesterbeek, D Klinkenberg and T D Hollingsworth (2020), “How will country-based mitigation measures influence the course of the COVID-19 epidemic?”, The Lancet 395(10228): 931-934.

Aschwanden, C (2020), “How ‘superspreading’ events drive most COVID-19 spread”, Scientific American, June 23.

Atkeson, A (2020), “What will be the Economic Impact of COVID-19 in the US? Estimate of Disease Scenario”, NBER Working Paper 26867, March.

Betsch, C et al. (2020), “Social and behavioral consequences of mask policies during the COVID-19 pandemic”, Proceedings of the National Academy of Sciences 117(36): 21851-21853. 

Chu et al. (2020), “Physical distancing, face masks and eye protection to prevent person-to-person transmission of SARS-COV-2 and Covid-19: A systematic review and meta-analysis”, The Lancet 395: 1973-1987.

Conyon, M J, L He and S Thomsen (2020), “Lockdowns and Covid-19 Deaths in Scandinavia”, Covid Economics 26, 5 June.

Davies, N G, A J Kucharski, R M Eggo, A Gimma and W J Edmunds (2020), “Effects of non-pharmaceutical interventions on Covid-19 cases, death and demand for hospital services in the UK: A modelling study”, The Lancet Public Health 5(7).

Deb, P, D Furceri, J D Ostry and N Tawk (2020a), “The Effect of Containment Measures on the Covid-19 Pandemic”, Covid Economics 19, 18 May.

Égert, B, Y Guillemette, F Murtin and D Turner (2020), “Walking the tightrope: Avoiding a lockdown while containing the virus”, OECD Economics Department working paper 1633.

Ferguson, N et al. (2020), “Impact of non-pharmaceutical interventions (NPIs) to reduce COVID-19 mortality and healthcare demand”, Imperial College COVID-19 Response Team, London.

Hellewell, J et al. (2020), “Feasibility of controlling COVID-19 outbreaks by isolation of cases and contacts”, The Lancet Global Health 8(4): e488-e496.

Hubert, O (2020), “Spacial Distancing: Air Traffic, Covid-19 Propagation and the Cost of Efficiency of Air Travel Restrictions”, Covid Economics 24, 1 June. 

OECD (2020), “Testing for COVID-19: A way to lift confinement restrictions”.

Xiong, C, S Yu, M Yang, W Luo and L Zhang (2020), “Mobile device data reveal the dynamics in a positive relationship between human mobility and COVID-19 infections”, Proceedings of the National Academy of Sciences 117(44) 27087-27089.

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