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COVID-19 POLICY PAPERS

Posted on April 4, 2020

COVID-19 POLICY PAPERS

CHPI has compiled a list of the key studies that are likely to be informing Canada's policy responses to the COVID-19 pandemic. The list will be updated as new research is published.

ANNOTATED BIBLIOGRAPHY

Liu, Y., Gayle, A. A., Wilder-Smith, A., & Rocklöv, J. (2020). The reproductive number of COVID-19 is higher compared to SARS coronavirus. Journal of Travel Medicine, 27(2), taaa021. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7074654/

Authors review the basic reproduction number (R0) of the COVID-19 virus. R0 is an indication of the transmissibility of a virus, representing the average number of new infections generated by an infectious person in a totally naïve population. For R0 >1, the number infected is likely to increase, and for R0 <1, transmission is likely to die out. The basic reproduction number is a central concept in infectious disease epidemiology, indicating the risk of an infectious agent with respect to epidemic spread. Analysis found the average R0 for COVID-19 to be 3.28, which exceeds WHO estimates from 1.4 to 2.5.

Warwick McKibbin, Roshen Fernando (2020). The Global Macroeconomic Impacts of COVID-19: Seven Scenarios. Australian National University; the Brookings Institution; and Centre of Excellence in Population Ageing Research (CEPAR). https://www.brookings.edu/wp-content/uploads/2020/03/20200302_COVID19.pdf 

In order to better understand possible economic outcomes, this paper explores seven different scenarios of how COVID-19 might evolve in the coming year. The scenarios in this paper demonstrate that even a contained outbreak could significantly impact the global economy in the short run. The model estimates COVID-19 will cause between 15 and 68 million deaths globally in first year; 236,000 to 1,060,000 in the United States; 30,000 to 133,000 in Canada. COVID-19 is expected to reduce global GDP by $US2.4 trillion and $US9trillion in 2020.

Parliamentary Budget Officer (2020). Scenario Analysis: COVID-19 Pandemic and Oil Price Shocks. https://www.pbo-dpb.gc.ca/web/default/files/Documents/Reports/RP-1920-033-S/RP-1920-033-S_en.pdf

PBO estimates that real GDP will decline by 2.5 per cent in the first quarter of 2020 and then again by 25.0 per cent in the second quarter (both at annual rates). For 2020, real GDP growth would be -5.1 per cent, the weakest on record since 1962. The decline in real GDP and the GDP price level combine to reduce the level of nominal GDP—the single broadest measure of the Government’s tax base—by $218 billion in 2020. The unemployment rate will rise to 15.0 per cent in the third quarter. The budget deficit would increase to $26.7 billion in 2019-20 and then to $112.7 billion in 2020-21. Relative to the size of the Canadian economy, the deficit would be 1.2 per cent of GDP in 2019-20 and 5.2 per cent of GDP in 2020-21. Rising budget deficits and lower nominal GDP push the federal debt-to-GDP ratio to 38.1 per cent in 2020-21.

Haiou Li et al (2020). Updated approaches against SARS-CoV-2. Antimicrobial Agents and Chemotherapy Mar 2020, AAC.00483-20; DOI: 10.1128/AAC.00483-20. https://aac.asm.org/content/early/2020/03/18/AAC.00483-20.long 

There is a growing understanding of SARS-CoV-2 (COVID-19) in the virology, epidemiology and clinical management strategies. Scientists are racing towards the development of a vaccine and/or treatment for COVID-19. This paper summarizes the updated potential approaches against COVID-19.

Neil M Ferguson et al (16 March 2020). Impact of non-pharmaceutical interventions (NPIs) to reduce COVID-19 mortality and healthcare demand. Imperial College COVID-19 Response Team. WHO Collaborating Centre for Infectious Disease Modelling MRC Centre for Global Infectious Disease Analysis Abdul Latif Jameel Institute for Disease and Emergency Analytics Imperial College London. https://www.imperial.ac.uk/media/imperial-college/medicine/sph/ide/gida-fellowships/Imperial-College-COVID19-NPI-modelling-16-03-2020.pdf 

Analysis assumes the absence of a COVID-19 vaccine and assesses the potential role of a number of non-pharmaceutical interventions (NPIs). Authors conclude that the effectiveness of any one intervention in isolation is likely to be limited, requiring multiple interventions to be combined to have a substantial impact on transmission. Two fundamental strategies are possible: (a) mitigation, which focuses on slowing but not necessarily stopping epidemic spread – reducing peak healthcare demand while protecting those most at risk of severe disease from infection, and (b) suppression, which aims to reverse epidemic growth, reducing case numbers to low levels and maintaining that situation indefinitely. Each policy has major challenges. Optimal mitigation policies combining home isolation of suspect cases, home quarantine of those living in the same household as suspect cases, and social distancing of the elderly and others at most risk of severe disease) might reduce peak healthcare demand by 2/3 and deaths by half. However, the resulting mitigated epidemic would still likely result in hundreds of thousands of deaths and health systems (most notably intensive care units) being overwhelmed many times over. For countries able to achieve it, this leaves suppression as the preferred policy option. In the UK and US context, suppression will minimally require a combination of social distancing of the entire population, home isolation of cases and household quarantine of their family members. This may need to be supplemented by school and university closures, though it should be recognised that such closures may have negative impacts on health systems due to increased absenteeism. The major challenge of suppression is that this type of intensive intervention package – or something equivalently effective at reducing transmission – will need to be maintained until a vaccine becomes available (potentially 18 months or more) – given that transmission will quickly rebound if interventions are relaxed. Intermittent social distancing – triggered by trends in disease surveillance – may allow interventions to be relaxed temporarily in relative short time windows, but measures will need to be reintroduced if or when case numbers rebound. While experience in China and now South Korea show that suppression is possible in the short term, it remains to be seen whether it is possible long-term, and whether the social and economic costs of the interventions adopted thus far can be reduced.

Martin S. Eichenbaum et al (2020). The Macroeconomics of Epidemics. NBER Working Paper No. 26882. http://www.nber.org/papers/w26882 

Analysis finds optimal containment policy increases the severity of the recession but saves roughly half a million lives in the U.S.

Roy M Anderson et al (2020). How will country-based mitigation measures influence the course of the COVID-19 epidemic? The Lancet Journal, COMMENT| VOLUME 395, ISSUE 10228, P931-934, MARCH 21, 2020. https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(20)30567-5/fulltext#articleInformation 

Authors conclude Governments will not be able to minimise both deaths from COVID-19 and the economic impact of viral spread. Keeping mortality as low as possible will be the highest priority, hence governments must put in place measures to ameliorate the inevitable economic downturn. Most countries are likely to have spread of COVID-19, at least in the early stages, before any mitigation measures have an impact. What has happened in China shows that quarantine, social distancing, and isolation of infected populations can contain the epidemic.

Shai Shalev-Shwartz and Amnon Shashua (2020). Can we Contain Covid-19 without Locking-down the Economy? CBMM Memo No. 104 March 26, 2020. https://cbmm.mit.edu/publications/can-we-contain-covid-19-without-locking-down-economy 

This paper presents an analysis of a risk-based selective quarantine model where the population is divided into low and high-risk groups. The high-risk group is quarantined until the low-risk group achieves herd-immunity. It answers the question of whether this model an contain the number of low-risk people that require severe ICU care (such as life support systems). Embedded in the reasoning is the idea of selective quarantine where the ”high-risk” group is quarantined and the other is allowed to spread the virus under certain distancing protocols. The underlying premise is that a full population wide quarantine is not a solution in itself — it is merely a step to buy time followed by a more managed (non brute-force) approach. The managed phase is to create herd immunity of the low-risk group in a controlled manner while keeping the economy going. It is all about keeping the health system in check and not overwhelming its capacity to handle severe cases. The question is whether it can be estimated in advance, through sampling, that the number of severe cases arising from the low-risk group would not overwhelm the system? The research concludes that this selective quarantine approach will work if the percentage of positive cases among the low-risk (non-quarantined) population, is not very small, relative to the ratio between the known number of severe cases from existing data and the capacity of the system (number of respiratory systems for example).

Kin On KWOK et al (2020). Herd immunity – estimating the level required to halt the COVID-19 epidemics in affected countries. Article in Press. Journal of Infection. DOI: https://doi.org/10.1016/j.jinf.2020.03.027; https://www.journalofinfection.com/article/S0163-4453(20)30154-7/fulltext 

There have been serious debates about how to react to the spread of COVID-19, particularly by European countries, such as Italy, Spain, Germany, France and the UK, e.g. from closing schools and universities to locking down entire cities and countries. An alternative strategy would be to allow the virus to spread to increase the population herd immunity, but at the same time protecting the elderly and those with multiple comorbidities, who are the most vulnerable to this virus.

Wolfgang Bock et al (2020). Mitigation and herd immunity strategy for COVID-19 is likely to fail. medRxiv 2020.03.25.20043109; doi: https://doi.org/10.1101/2020.03.25.20043109; https://www.medrxiv.org/content/10.1101/2020.03.25.20043109v1

On the basis of a semi-realistic SIR microsimulation for Germany and Poland, the analysis finds that the R0 parameter interval for which the COVID-19 epidemic stays overcritical but below the capacity limit of the health care system to reach herd immunity is so narrow that a successful implementation of this strategy is likely to fail. 

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