Bloomberg. (2020). Bloomberg Prognosis: How Covid-19 is Reshaping the Global Healthcare Ecosystem. Retrieved from: https://onlinexperiences.com/scripts/Server.nxp?LASCmd=AI:4;F:QS!10100&ShowUUID=CB925306-099F-4D23-8526-0FDD06AC15C8&LangLocaleID=1033&AffiliateDate=TW

ReconfigBehSci [@SciBeh] (2020, August) I'm familiar with those constraints/arguments- but keeping family members of infected individuals isolated is one of the most basic public health measure - it surely would be legal to mandate, particularly given the role pre/asymptomatic spread? no? Twitter. Retrieved from: https://twitter.com/i/web/status/1295726328994566144

Smith, R., Myrick, J. G., Lennon, R. P., Martin, M. A., Small, M. L., Scoy, L. J. V., & Group, D. R. (2020). Optimizing COVID-19 Health Campaigns: A Person-Centered Approach. PsyArXiv. https://doi.org/10.31234/osf.io/xuefh

The COVID-19 Vaccine Communication Handbook | A practical guide for improving vaccine communication and fighting misinformation. (n.d.). Retrieved January 14, 2021, from https://rri-tools.eu/-/the-covid-19-vaccine-communication-handbook-a-practical-guide-for-improving-vaccine-communication-and-fighting-misinformation

Leblanc-Sirois, Y., Gagnon, M.-È., & Blanchette, I. (2020). Emotions, reasoning, and mental health as predictors of behavior during three phases of the COVID-19 pandemic. PsyArXiv. https://doi.org/10.31234/osf.io/2p39h

Enders, A. M., Uscinski, J. E., Klofstad, C., & Stoler, J. (2020). The different forms of COVID-19 misinformation and their consequences. Harvard Kennedy School Misinformation Review. https://doi.org/10.37016/mr-2020-48

The takeaway: The official definition of a "close contact" for COVID-19 is not foolproof.

The claim: The official definition of a "close contact" - 15 minutes, within six feet - isn't foolproof.

The evidence: In Korea, a person sitting in a restaurant 6.5 meters (>20ft) away from the COVID index case for five minutes was infected, most likely because airflow from the air conditioner carried droplets with COVID-19 from the infected person to the person who became infected (1). How common transmission across large distances occurs is still debated (2). As several indoor outbreaks were attributed to airborne transmission, precautions to prevent airborne COVID transmission are needed (3). Examples include better air filtration/UV to kill virus in the system, increased air flow from outside, avoidance of recirculating interior air, and avoiding overcrowding in interior spaces.

Sources:

1) https://jkms.org/DOIx.php?id=10.3346/jkms.2020.35.e415

2) https://www.sciencedirect.com/science/article/pii/S0166093420302858?via%3Dihub

3) https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7454469/pdf/ciaa939.pdf

Achakulwisut, P. (n.d.). The U.S. Risks Locking In a Climate Health Crisis in Response to COVID. Scientific American. Retrieved December 10, 2020, from https://www.scientificamerican.com/article/the-u-s-risks-locking-in-a-climate-health-crisis-in-response-to-covid/

The takeaway: While minimal protection occurs when a mask is worn in a place where many others are not wearing a mask, community masking is associated with a reduction in COVID cases.

The claim: In a community with modest infection rates, some social distancing, and most people not wearing masks, wearing a surgical mask did not reduce the SARS-CoV-2 infection rate by more than 50%.

The evidence: This study showed that wearing a mask in a community where most people did not wear a mask, did not reduce the risk of getting infected by 50%. Fewer COVID infections were reported in the mask group than in the unmasked group. This study agrees with a meta analysis which showed that masks resulted in a decrease in infections but did not prevent all infections (1) According to the CDC, seven studies have shown community level benefit when masking recommendations were made (2).

When most in the community are not wearing masks, social distancing, and washing hands, wearing a mask alone provides minimal protection to the mask wearer. Community wide masking is associated with a reduction in COVID cases (2).

Sources:

1) https://pubmed.ncbi.nlm.nih.gov/29140516/

2) https://www.cdc.gov/coronavirus/2019-ncov/more/masking-science-sars-cov2.html

Take away: Though the number of COVID deaths prevented and the exact number of years lost due directly to decreases in mental health from lockdowns is at best a rough estimate, several facts are known. Lockdowns decrease mental health, and a decrease in mental health shortens lives too.

The claim: Anxiety from reactions to COVID-19 will destroy at least seven times more years of life than can be saved by lockdowns.

The evidence: This article references many studies detailing the anxiety surrounding COVID-19 (1-4). These studies indicate that many people have increased stress due to COVID. Nature Public Health Emergency Collection reports that the mental health cost of widespread lockdowns may negate the lives saved by this policy (5). This article lists many articles which describe the effect of stay-at-home orders on mental health. Additionally, the effect of poor mental health on physical outcomes is well-defined. Poor mental health shortens lives. Other factors with COVID such as negative media coverage and dealing with job loss and death are also described as negatively affecting mental health. It is unclear how much of the negative mental health outcomes is directly related to lockdowns and what is contributed to the disease, job loss, future uncertainty, and continuous media coverage.

Several supporting facts used in this article are now outdated or could use clarification. Many assumptions are detailed in this article to estimate the number of years lost due to mental harm caused by lockdowns. One example is the authors used a survey of 1,266 patients to estimate the number of people in the United States who have suffered mental harm from lockdowns. These estimates are challenging to conclusively verify. The authors did choose the conservative estimate for each of their numbers. One example of an outdated number is the predicted number of deaths was 114,228 by August 4th. The actual number of deaths per Johns Hopkins was 157,500 (6).

Based on the facts, anxiety and mental disorders can be deadly. Lockdowns result in an increase in poor mental health. The exact number of years lost due to poor mental health directly resulting from lockdowns is less clear. Poor mental health may also result from constant media coverage, loss of loved ones and fear of the future.

The sources:

1) https://www.psychiatry.org/newsroom/news-releases/new-poll-covid-19-impacting-mental-well-being-americans-feeling-anxious-especially-for-loved-ones-older-adults-are-less-anxious

2) https://www.kff.org/health-reform/report/kff-health-tracking-poll-early-april-2020/

3) https://www.bsgco.com/post/coronavirus-and-americans-mental-health-insights-from-bsg-s-pulse-of-america-poll

4) https://www.kff.org/report-section/kff-health-tracking-poll-late-april-2020-economic-and-mental-health-impacts-of-coronavirus/

5) https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7431738/#

6) https://coronavirus.jhu.edu/us-map

The Takeaway: The combination of closing borders, tightly regulating travel, effective quarantine of all exposed people using cell phone data for enforcement, and universal mask wearing contributed to effectively keeping COVID-19 from infecting most of Taiwan's population.

The claim: Closing borders early, tightly regulating travel, contact tracing, technology-enforced quarantine, universal mask wearing, and Taiwan's previous deadly experience with SARS resulted in control of SARS-CoV-2 in Taiwan.

The evidence: The earlier COVID-19 cases are stopped from entering a country, the fewer cases will be present to spread the disease to others. To illustrate, it is easier to stop a trickle of water than to try to dam up a flood and easier to extinguish a candle than a forest fire. Taiwan closed its borders on January 23rd, 2020 (1). The Philippines closed their borders on February 2nd, 2020 (2). Tightly regulating travel will help to stop cases before they enter the country. Effective quarantining the few cases and contacts of the cases which do enter a country is critical to preventing the spread of the disease within the country. Taiwan used mobile telephone data to enforce quarantine (1). Without quarantine, each infected person will spread COVID-19 to 2-6 additional people based on the R0 (3, 4). Universal masking will help slow the spread of disease (5). Previous experience with controlling a deadly disease will most likely increase compliance to methods to control the disease.

Per Our World in Data website, Taiwan had one of the least stringent government responses to COVID-19 (6). The biweekly number of COVID-19 cases in Taiwan was 23 on October 29, 2020 (7). Neighboring countries had biweekly COVID-19 cases of 372 (China), 28,644 (Philippines), 11,871 (Malaysia), 51 (Vietnam), and 8,142 (Japan). These neighboring countries had more stringent government responses to COVID-19 (6).

Sources:

1) https://focustaiwan.tw/society/202001230011

2)https://www.pharmaceutical-technology.com/features/coronavirus-affected-countries-philippines-measures-impact-tourism-economy/#:~:text=The%20government%20announced%20earlier%20on,barred%20from%20entering%20the%20country.

3) https://pubmed.ncbi.nlm.nih.gov/32234343/

4) https://pubmed.ncbi.nlm.nih.gov/32097725/

5) https://www.nature.com/articles/s41591-020-1132-9#annotations:7jRWRheWEeuY8x_rXDuRjg

6) https://ourworldindata.org/grapher/covid-stringency-index

7) https://ourworldindata.org/grapher/biweekly-confirmed-covid-19-cases

What you need to know about Sweden’s new local coronavirus recommendations. (2020, October 19). https://www.thelocal.se/20201019/what-you-need-to-know-about-swedens-new-local-coronavirus-recommendations

Royal Statistical Society on Twitter. (n.d.). Twitter. Retrieved October 25, 2020, from https://twitter.com/RoyalStatSoc/status/1317133702183456769

Take away: Cloth face masks filter approximately 50% of bacteriophage five times smaller than one SARS-CoV-2 virus. Therefore it is reasonable to assume that masks, including cloth masks, are 50% effective.

The claim: Masks are assumed to be 50% effective.

The evidence: Face masks, including home made face masks, were shown to reduce aerosol exposure (1). Masks made from various materials were shown to filter 50-68% of Bacteriophage CS2 which is 20 nm (2). When NaCl aerosols were used instead of a bacteriophage, penetration by NaCl occurred 9-98% of the time depending on the size of the particles (3). Two well written reviews detail the efficacy of facemasks (4, 5). SARS-CoV-2 virus is ~100 nm in size (6).

Sources: 1 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2440799/

2 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7108646/

3 https://academic.oup.com/annweh/article/54/7/789/202744

4 https://www.preprints.org/manuscript/202004.0203/v1

5 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7497125/#ref23

6 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7224694/#:~:text=SARS%2DCoV%2D2%20is%20an,they%20do%20more%20than%20that.

Abraar Karan: Politics and public health in America—taking a stand for what is right. (2020, October 9). The BMJ. https://blogs.bmj.com/bmj/2020/10/09/abraar-karan-politics-and-public-health-in-america-taking-a-stand-for-what-is-right/

Take away:

If correct numerators and denominators are used, COVID-19 is at least 10 times as deadly as seasonal influenza.

The claim:

The Infection Fatality Ratio for COVID-19 is “approximately 2.5 times higher than the estimated IFR for seasonal influenza.”

The evidence:

Blackburn et al. report an infection fatality ratio among community-living adults of 0.26% (1). If institutionalized adults had been included the ratio would be higher, likely approximating the 0.6% mortality rate among exposed individuals readily calculated by combining official death tolls, the known 30% undercount (2), and a definitive CDC study that found 10 times as many people have been exposed to the novel coronavirus than are reported as cases (3). Among the elderly, Blackburn et al. calculate COVID-19 is 2.5 times deadlier than seasonal flu. This is clearly an underestimate:

1) Blackburn et al. use CDC estimates of case-fatality rates calculated on the basis of all Americans, including the institutionalized, not limited to much healthier community-dwellers.

2) The seasonal influenza case fatality rates reported by the CDC, including the often cited 0.1% overall, are for symptomatic cases. Their denominators are estimated by using the reported number of influenza hospitalizations to guess the burden of clinical illness (4). But antibody studies show that 65%-85% of people infected with influenza never develop symptoms (5). The 0.6% mortality rate calculated here for SARS-CoV-2-exposed individuals is 6 times higher than the 0.1% usually cited for seasonal influenza. Given the overestimation of commonly accepted influenza mortality rates due to failure to take asymptomatic infections into account, SARS-CoV-2 can be seen to be not 2.5 times, or even 6 times, but at least 10 times as lethal as seasonal flu.

Sources:

1 http://www.acpjournals.org/doi/10.7326/M20-5352

2 https://jamanetwork.com/journals/jamainternalmedicine/fullarticle/2767980

3 https://jamanetwork.com/journals/jamainternalmedicine/fullarticle/2768834

4 https://www.cdc.gov/flu/about/burden/how-cdc-estimates.htm

5 https://pubmed.ncbi.nlm.nih.gov/26133025/

Kavanagh, M. M. (2020). US elections and a foreign policy for pandemics. The Lancet Public Health, 5(10), e517–e518. https://doi.org/10.1016/S2468-2667(20)30211-5

Covid-19: Is Behavioural Science The Key To Handle The Pandemic? (n.d.). Retrieved October 10, 2020, from https://www.psychologs.com/article/covid-19-is-behavioral-science-the-key-to-handle-the-pandemic

Take Away: The new scientific paper confirms earlier modeling work and should not be interpreted as a detailed prediction for future deaths due to the ongoing pandemic.

The Claim: "A scientific review of the science behind lockdown concludes the policy was a MISTAKE & will have caused MORE deaths from Covid-19"

The Evidence: The scientific process involves replication and confirmation of experiments and studies. A new paper replicates and expands on an early modeling study of the COVID-19 pandemic in England (1). Their findings support the earlier results. However, there are limitations to the replication paper, which does not accurately reflect the current state of the pandemic response and does not make detailed predictions for a second wave of infections and deaths.

A recent expert response to the paper further explains (2):

"It needs to be stressed that all the simulations assume that interventions are only in place for 3 months (18th April – 18th July) and then completely relaxed. This gives rise to a strange set of scenarios where a second wave is allowed to progress in an uncontrolled manner."

“It is this that leads to the counter-intuitive headline finding “that school closures would result in more overall covid-19 deaths than no school closures” – actually what the authors find is that a short period of intense lock-down (including the closure of schools) leads to a large second wave if it is allowed to run with no controls. To be fair the authors do highlight this in the paper, but it is not in the reported press release." -Prof Matt Keeling, Professor of Populations and Disease, University of Warwick

Sources:

(1) https://www.bmj.com/content/371/bmj.m3588

(2) https://www.sciencemediacentre.org/expert-reaction-to-reanalysis-of-model-used-for-imperial-report-9-and-impact-of-school-closures/

Take away: This model excludes the possibility of vaccination. As many vaccines are in stage three clinical trials, the conclusion that more people will die from closing schools, etc. will most likely not be realized.

The claim: School closures and isolation of younger people will increase total number of deaths from second and subsequent waves of COVID-19 when restrictions are lifted.

The evidence: This model predicts more deaths from the combination of place closures such as schools, case isolations, household quarantine, and social distancing of over 70s than for the combination of case isolation, household quarantine, and social distancing for over 70s. The majority of the deaths for the combination of place closures, case isolations, household quarantine, and social distancing of over 70s occur once the restrictions are lifted. This model excludes the possibility of a vaccine reducing the size of the second wave.

At least ten companies have a COVID-19 vaccine in the final stage (Phase III) of clinical trials (1). Therefore a model which excludes vaccination will most likely not be accurate to reality once a vaccine is widely administered.

Source:

1 https://www.who.int/publications/m/item/draft-landscape-of-covid-19-candidate-vaccines

Bozorgmehr, K. (2020). Power of and power over COVID-19 response guidelines. The Lancet, 0(0). https://doi.org/10.1016/S0140-6736(20)32081-X

IZA – Institute of Labor Economics. ‘COVID-19 and the Labor Market’. Accessed 6 October 2020. https://covid-19.iza.org/publications/dp13707/.

IZA – Institute of Labor Economics. ‘COVID-19 and the Labor Market’. Accessed 6 October 2020. https://covid-19.iza.org/publications/dp13683/.

Kaplan, Edward H, Dennis Wang, Mike Wang, Amyn A Malik, Alessandro Zulli, and Jordan H Peccia. ‘Aligning SARS-CoV-2 Indicators via an Epidemic Model: Application to Hospital Admissions and RNA Detection in Sewage Sludge’. Preprint. Infectious Diseases (except HIV/AIDS), 29 June 2020. https://doi.org/10.1101/2020.06.27.20141739.

COVID-19 and the Politics of Reproductive Health: Global Perspectives. (2020, August 18). https://www.youtube.com/watch?v=Kex9O_cwMwo

Covid: Cardiff and Swansea go into local lockdown—BBC News. (n.d.). Retrieved September 29, 2020, from https://www.bbc.co.uk/news/uk-wales-54310400

Learning lessons before launching an inquiry—IfG LIVE 2020 Labour Fringe Programme—YouTube. (n.d.). Retrieved September 29, 2020, from https://www.youtube.com/watch?v=cCZl-naQ6UM

Take away: Mass infection without vaccination to achieve herd immunity will result in millions of deaths based on the observed death rate and may not result in herd immunity due to virus mutation. Historically, vaccination results in less deaths than the disease.

The claim: Herd immunity from widespread disease instead of vaccination will lead to many people dying.

The evidence: Approximately 50-67% of a given population is estimated to need to be infected for herd immunity to COVID-19 to exist which will result in millions of deaths. This is supported by additional publications (1, 2). This number assumes that the virus will not mutate to the point where re-infection is possible. If mutation occurs, COVID could become established in the general population similar to influenza or the common cold (3). A third publication estimates a needed infected percentage of 29-74% (4). These publications support the statement that millions will die if herd immunity is achieved via infection without vaccination. Historically, vaccination results in fewer deaths/disease on a population level than the disease for which the vaccine is designed to prevent (5-7).

Sources:

1 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7314002/

2 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7262166/pdf/JMV-9999-na.pdf

3 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7164482/

4 https://www.cambridge.org/core/services/aop-cambridge-core/content/view/A1480DAE803D4CD4A3E9F79B82309584/S1935789320001913a.pdf/covid19_reflections.pdf

5 https://pubmed.ncbi.nlm.nih.gov/28708957/

6 https://pubmed.ncbi.nlm.nih.gov/29668817/

7 https://pubmed.ncbi.nlm.nih.gov/12531323/

Take away: People are infectious for only part of the time they test positive. The tests for COVID-19 were granted emergency status by the FDA so some debate concerning the most ideal number of cycles is to be expected. It is worth noting that the FDA has the disclaimer "Negative results do not preclude 2019-nCoV infection and should not be used as the sole basis for treatment or other patient management decisions. Negative results must be combined with clinical observations, patient history, and epidemiological information (2)."

The claim: Up to 90 percent of people diagnosed with coronavirus may not be carrying enough of it to infect anyone else

The evidence: Per Walsh et al. (1), SARS-CoV-2 virus (COVID-19) is most likely infectious if the number of PCR cycles is <24 and the symptom onset to test is <8 days. RT-PCR detects the RNA, not the infectious virus. Therefore, setting the cycle threshold at 37-40 cycles will most likely result in detecting some samples with virus which is not infectious. As the PCR tests were granted emergency use by the FDA (samples include 2-9), it is not surprising that some debate exists currently about where the cycle threshold should be. Thresholds need to be set and validated for dozens of PCR tests currently in use. If identifying only infectious individuals is the goal, a lower cycle number may be justified. If detection of as many cases as possible to get closer to the most accurate death rate is the goal, setting the cycle threshold at 37-40 makes sense. A lower threshold will result in fewer COVID-19 positive samples being identified. It is worth noting that the emergency use approval granted by the FDA includes the disclaimer that a negative test does not guarantee that a person is not infected with COVID-19. RNA degrades easily. If samples are not kept cold or properly processed, the virus can degrade and result in a false negative result.

Source: 1 https://academic.oup.com/cid/advance-article/doi/10.1093/cid/ciaa638/5842165

2 https://www.fda.gov/media/134922/download

3 https://www.fda.gov/media/138150/download

4 https://www.fda.gov/media/137120/download

5 https://www.fda.gov/media/136231/download

6 https://www.fda.gov/media/136472/download

7 https://www.fda.gov/media/139279/download

8 https://www.fda.gov/media/136314/download

9 https://www.fda.gov/media/140776/download

Casoria, F., Galeotti, F., & Villeval, M. C. (2020). Perceived Social Norm and Behavior Quickly Adjusted to Legal Changes During the COVID-19 Pandemic (SSRN Scholarly Paper ID 3681204). Social Science Research Network. https://doi.org/10.2139/ssrn.3681204

Lewandowsky, Stephan, Simon Dennis, Amy Perfors, Yoshihisa Kashima, Joshua White, Paul Michael Garrett, Daniel R. Little, and Muhsin Yesilada. ‘Public Acceptance of Privacy-Encroaching Policies to Address the COVID-19 Pandemic in the United Kingdom’. Preprint. PsyArXiv, 4 September 2020. https://doi.org/10.31234/osf.io/njwmp.

Metcalf, C. J. E., Morris, D. H., & Park, S. W. (2020). Mathematical models to guide pandemic response. Science, 369(6502), 368–369. https://doi.org/10.1126/science.abd1668

Angulo, M. T., Castaños, F., Velasco-Hernandez, J. X., & Moreno, J. A. (2020). A simple criterion to design optimal nonpharmaceutical interventions for epidemic outbreaks. MedRxiv, 2020.05.19.20107268. https://doi.org/10.1101/2020.05.19.20107268

Benvenisti, E. (2020). The WHO – Destined to Fail?: Political Cooperation and the COVID-19 Pandemic (SSRN Scholarly Paper ID 3638948). Social Science Research Network. https://papers.ssrn.com/abstract=3638948

u/nick_chater (2020) Behavioural Policy Challenge: when does compulsion help? reddit. Retrieved from: https://www.reddit.com/r/BehSciAsk/comments/hzci8g/behavioural_policy_challenge_when_does_compulsion/

Van Bavel, J. J., & Myer, A. (2020). National identity predicts public health support during a global pandemic [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/ydt95

Zheng, Q., Jones, F. K., Leavitt, S. V., Ung, L., Labrique, A. B., Peters, D. H., Lee, E. C., & Azman, A. S. (2020). HIT-COVID, a global database tracking public health interventions to COVID-19. Scientific Data, 7(1), 286. https://doi.org/10.1038/s41597-020-00610-2

Lozano, R., Fullman, N., Mumford, J. E., Knight, M., Barthelemy, C. M., Abbafati, C., Abbastabar, H., Abd-Allah, F., Abdollahi, M., Abedi, A., Abolhassani, H., Abosetugn, A. E., Abreu, L. G., Abrigo, M. R. M., Haimed, A. K. A., Abushouk, A. I., Adabi, M., Adebayo, O. M., Adekanmbi, V., … Murray, C. J. L. (2020). Measuring universal health coverage based on an index of effective coverage of health services in 204 countries and territories, 1990–2019: A systematic analysis for the Global Burden of Disease Study 2019. The Lancet, 0(0). https://doi.org/10.1016/S0140-6736(20)30750-9

Lancet, T. (2020). Research and higher education in the time of COVID-19. The Lancet, 396(10251), 583. https://doi.org/10.1016/S0140-6736(20)31818-3

Chang, R., & Velasco, A. (2020). Economic Policy Incentives to Preserve Lives and Livelihoods (Working Paper No. 27020; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w27020

The Trump administration must stop sidelining the CDC. (2020). Nature, 583(7818), 660–660. https://doi.org/10.1038/d41586-020-02231-6

Beytía, P., & Infante, C. C. (2020). Digital Pathways, Pandemic Trajectories. Using Google Trends to Track Social Responses to COVID-19 [Preprint]. SocArXiv. https://doi.org/10.31235/osf.io/yndb7

Andy Slavitt @ 🏡 on Twitter: “COVID UPDATE July 13: There are successful examples of taking on COVID-19. And there is one story like no others. New York. 1/” / Twitter. (n.d.). Twitter. Retrieved July 17, 2020, from https://twitter.com/ASlavitt/status/1282838738121502720

Marcus, J. (2020, August 15). The Fun Police Should Stand Down. The Atlantic. https://www.theatlantic.com/ideas/archive/2020/08/containing-the-pandemic-isnt-a-job-for-cops/615298/

Holtz, D., Zhao, M., Benzell, S. G., Cao, C. Y., Rahimian, M. A., Yang, J., Allen, J., Collis, A., Moehring, A., Sowrirajan, T., Ghosh, D., Zhang, Y., Dhillon, P. S., Nicolaides, C., Eckles, D., & Aral, S. (2020). Interdependence and the cost of uncoordinated responses to COVID-19. Proceedings of the National Academy of Sciences, 117(33), 19837–19843. https://doi.org/10.1073/pnas.2009522117

Rodela, T. T., Tasnim, S., Mazumder, H., Faizah, F., Sultana, A., & Hossain, M. M. (2020). Economic Impacts of Coronavirus Disease (COVID-19) in Developing Countries [Preprint]. SocArXiv. https://doi.org/10.31235/osf.io/wygpk

Stay-​At-Home Orders, Social Distancing and Trust. COVID-19 and the Labor Market. (n.d.). IZA – Institute of Labor Economics. Retrieved August 5, 2020, from https://covid-19.iza.org/publications/dp13234/

https://twitter.com/edyong209/status/1256351239039856642

Mishra, V., & Dexter, J. P. (2020). Comparison of Readability of Official Public Health Information About COVID-19 on Websites of International Agencies and the Governments of 15 Countries. JAMA Network Open, 3(8), e2018033–e2018033. https://doi.org/10.1001/jamanetworkopen.2020.18033

Farboodi, M., Jarosch, G., & Shimer, R. (2020). Internal and External Effects of Social Distancing in a Pandemic (Working Paper No. 27059; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w27059

Ziedan, E., Simon, K. I., & Wing, C. (2020). Effects of State COVID-19 Closure Policy on NON-COVID-19 Health Care Utilization (Working Paper No. 27621; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w27621

Ding, W., Levine, R., Lin, C., & Xie, W. (2020). Social Distancing and Social Capital: Why U.S. Counties Respond Differently to COVID-19 (Working Paper No. 27393; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w27393

Dave, D. M., Friedson, A. I., Matsuzawa, K., McNichols, D., & Sabia, J. J. (2020). Did the Wisconsin Supreme Court Restart a COVID-19 Epidemic? Evidence from a Natural Experiment (Working Paper No. 27322; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w27322

University of Exeter. (n.d.). Retrieved 12 August 2020, from https://www.exeter.ac.uk/news/homepage/title_808437_en.html

5 myths about face masks under the microscope. (n.d.). World Economic Forum. Retrieved 12 August 2020, from https://www.weforum.org/agenda/2020/08/coronavirus-face-masks-myths/

Gettys, T. (n.d.). ‘Listening to experts is elitist’: Idaho Republicans move to strip power from public health officials. Retrieved 12 August 2020, from https://www.rawstory.com/2020/08/listening-to-experts-is-elitist-idaho-republicans-move-to-strip-power-from-public-health-officials/

Aum, S., Lee, S. Y. (Tim), & Shin, Y. (2020). Inequality of Fear and Self-Quarantine: Is There a Trade-off between GDP and Public Health? (Working Paper No. 27100; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w27100

Berger, D. W., Herkenhoff, K. F., & Mongey, S. (2020). An SEIR Infectious Disease Model with Testing and Conditional Quarantine (Working Paper No. 26901; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w26901

Eichenbaum, M. S., Rebelo, S., & Trabandt, M. (2020). The Macroeconomics of Testing and Quarantining (Working Paper No. 27104; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w27104

Stock, James H. ‘Data Gaps and the Policy Response to the Novel Coronavirus’. Working Paper. Working Paper Series. National Bureau of Economic Research, March 2020. https://doi.org/10.3386/w26902.

Malani, A., Soman, S., Asher, S., Novosad, P., Imbert, C., Tandel, V., Agarwal, A., Alomar, A., Sarker, A., Shah, D., Shen, D., Gruber, J., Sachdeva, S., Kaiser, D., & Bettencourt, L. M. A. (2020). Adaptive Control of COVID-19 Outbreaks in India: Local, Gradual, and Trigger-based Exit Paths from Lockdown (Working Paper No. 27532; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w27532

Take away: As with most things in nature, there are always exceptions – transmission occurring at greater distances than 3 ft and evidence of aerosolization have been reported.

Discussion: In scientific terms, this virus is still very new so the data supporting an optimal physical distance to prevent transmission remains scarce. In the absence of data, public health agencies have used what they understand about this virus and similar viruses to infer a “best” answer. Public health agencies try to simplify the recommendation to a single answer, but the reality is much more complex.

According to reports the WHO bases their recommendation for 1 meter (~3 ft) distancing off of an understanding that SARS-CoV-2 behaves like similar respiratory viruses that are primarily transmitted via larger droplets (as opposed to smaller aerosols). Assuming most spread is via droplets, the WHO reportedly follows the results of a 1934 study indicating most respiratory droplets fall to the ground within 3 feet.

However, as with most things in nature, there are always exceptions – transmission occurring at greater distances than 3 ft and evidence of aerosolization have been reported.

The evidence basis for the CDCs guidance for 6 feet of separation is less clear, but probably reflects lower risk tolerance, or greater weight to evidence of aerosolization or wider droplet spread.

Even with further study, there may never be a clear answer for optimal physical distancing. This is because, (1) the area of high risk for transmission is probably dependent on the specific conditions of the interaction (e.g. loud talking, windy environment), and (2) the “optimal” distance is based on risk tolerance. There is no single distance between individuals where risk of transmission drops off precipitously to zero.

All evidence indicates that greater distances are safer but, for example, consider how restrictive a physical distancing recommendation of >50 ft would be. In the end, because we can’t control how far others stand away from us, we ask governments to consider these tradeoffs and deliver a “best” answer to guide their citizenry.

Br, F., & mayr. (n.d.). Trusting the experts takes more than belief – Humanities & Social Change. Retrieved 8 August 2020, from https://hscif.org/trusting-the-experts-takes-more-than-belief/

Fetzer, T. R., Witte, M., Hensel, L., Jachimowicz, J., Haushofer, J., Ivchenko, A., Caria, S., Reutskaja, E., Roth, C. P., Fiorin, S., Gómez, M., Kraft-Todd, G., Götz, F. M., & Yoeli, E. (2020). Global Behaviors and Perceptions at the Onset of the COVID-19 Pandemic (Working Paper No. 27082; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w27082

Lin, Z., & Meissner, C. M. (2020). Health vs. Wealth? Public Health Policies and the Economy During Covid-19 (Working Paper No. 27099; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w27099

Were Urban Cowboys Enough to Control COVID-19? Local Shelter-​In-Place Orders and Coronavirus Case Growth. COVID-19 and the Labor Market. (n.d.). IZA – Institute of Labor Economics. Retrieved August 4, 2020, from https://covid-19.iza.org/publications/dp13262/

An Economic Model of the COVID-19 Epidemic: The Importance of Testing and Age-​Specific Policies. COVID-19 and the Labor Market. (n.d.). IZA – Institute of Labor Economics. Retrieved August 4, 2020, from https://covid-19.iza.org/publications/dp13265/

Sweden Did Not Take Herd Immunity Approach Against Coronavirus Pandemic. (2020, July 29). Debunking Denialism. https://debunkingdenialism.com/2020/07/29/sweden-did-not-take-herd-immunity-approach-against-coronavirus-pandemic/

Covid State of Play: Jonathan Zittrain & Margaret Bourdeaux with Beth Cameron & KJ Seung. (2020, July 29). https://www.youtube.com/watch?v=FVn9kn88C_Q

Coronavirus: Can kids spread COVID-19? Your questions answered. (n.d.). Indystar. Retrieved August 2, 2020, from https://www.usatoday.com/story/news/health/2020/07/17/coronavirus-can-kids-spread-covid-19-spreadquestions-answered/5450062002/

https://plantfacedclothing.com/collections/style-picks/products/eat-plants-goth-roses-tee-pitch-black?variant=31758201487421

Chatam House (2020) We may have a vaccine in the next 12 months...And already we are seeing the rise of an anti-vaccination movement. Retrieved from: https://twitter.com/SciBeh/status/1289219157968293889

Glover, A., Heathcote, J., Krueger, D., & Ríos-Rull, J.-V. (2020). Health versus Wealth: On the Distributional Effects of Controlling a Pandemic (Working Paper No. 27046; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w27046

Akbarpour, M., Cook, C., Marzuoli, A., Mongey, S., Nagaraj, A., Saccarola, M., Tebaldi, P., Vasserman, S., & Yang, H. (2020). Socioeconomic Network Heterogeneity and Pandemic Policy Response (Working Paper No. 27374; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w27374

Dave, D. M., Friedson, A. I., Matsuzawa, K., & Sabia, J. J. (2020). When Do Shelter-in-Place Orders Fight COVID-19 Best? Policy Heterogeneity Across States and Adoption Time (Working Paper No. 27091; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w27091

Gupta, S., Montenovo, L., Nguyen, T. D., Rojas, F. L., Schmutte, I. M., Simon, K. I., Weinberg, B. A., & Wing, C. (2020). Effects of Social Distancing Policy on Labor Market Outcomes (Working Paper No. 27280; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w27280

Kaptchuk, G., Goldstein, D. G., Hargittai, E., Hofman, J., & Redmiles, E. M. (2020). How good is good enough for COVID19 apps? The influence of benefits, accuracy, and privacy on willingness to adopt. ArXiv:2005.04343 [Cs]. http://arxiv.org/abs/2005.04343