COVID-19 Literature Situation Report May 27, 2020

• [pre-print, not peer reviewed] Yan et al. use SafeGraph smart device location data and show that the representative American in states that have facemask mandates spends 20-30 minutes less time at home and makes an increased number of visits to commercial locations following the mandate. The authors suggest that this is due to substitution behavior and could lead to a resurgence of cases. 

Yan et al. (May 27, 2020). Do Face Masks Create a False Sense of Security A COVID-19 Dilemma. Pre-print downloaded May 27 from https://doi.org/10.1101/2020.05.23.20111302 

• [pre-print, not peer reviewed] Statewide stay-at-home policies induced “mandate effects” of 4.1%-5.9% declines in travel and visits to non-essential businesses relative to pre-COVID-19 levels for the first four states to introduce such policies (CA, IL, NJ, NY).  
• By the time of statewide stay-at-home mandates in many states, residents had already reduced travel by considerable amounts relative to pre-COVID-19 levels. 
• A 1% reduction in visits to non-essential businesses is associated with 9.2 fewer deaths per 100 million per day. This corresponds to 74,000 lives saved nationwide and an economic benefit of $249-$745 billion from deaths averted by observed behavioral changes in March and April. 

Sears et al. (May 26, 2020). Are We #stayinghome to Flatten the Curve. Pre-print downloaded May 27 from https://doi.org/10.1101/2020.05.23.20111211  

• [pre-print, not peer reviewed] Rosenfeld et al. use a new model (Covasim) to simulate population-level SARS-CoV-2 transmission expected with reopening and the extent to which measures including testing, tracing, and quarantine can prevent exponential growth in King County, WA. 
• Distancing and other measures have reduced the transmission potential of COVID-19 on April 25th to 33% (30-37%) of early-pandemic levels. Increased testing, contact tracing to home and work, and better compliance with household quarantine beginning on May 26th could counterbalance a two-fold increase in work and community transmission from the level estimated on April 25th. 

Rosenfeld et al. (May 25, 2020). Modeling Countermeasures for a Balanced Reopening in King County, Washington. IDM. Report downloaded May 27 from https://covid.idmod.org/data/Modeling_countermeasures_for_balanced_reopening_King_County_Washington.pdf 

• [pre-print, not peer reviewed] Takenouchi et al used whole viral genome sequencing to determine that 9 COVID-19 patients at a tertiary medical center had probable community infection instead of being associated with infection clusters at the medical center.  

Takenouchi et al. (May 26, 2020). Clinical Utility of SARS-CoV-2 Whole Genome Sequencing in Deciphering Source of Infection. Pre-print downloaded May 27 from https://doi.org/10.1101/2020.05.21.20107599  

• Jin et al. found that among SARS-CoV-2 infection cases (n=89), serum IgM was significantly higher among those with prolonged viral RNA shedding (>30 days) at weeks 4 and 5 after symptom onset, IgM levels were similar between prolonged and non-prolonged shedders from weeks 6 to 8, and declined almost to the reference level in both groups at week 8. 
• Serum IgM persisted at a high level during the acute illness and up to 8 weeks after symptom onset. 

Jin et al. (May 18, 2020). Correlation between viral RNA shedding and serum antibodies in COVID-19 patients. Clinical Microbiology and Infection. https://doi.org/10.1016/j.cmi.2020.05.022  

• [pre-print, not peer reviewed] SARS-CoV-2 RNA was detectable in blood samples at low viral loads up to 20 days after symptom onset in 27 of 212 (12.7%) COVID-19 cases. However, there was no evidence of infectious SARS-CoV-2 based on inoculating PCR-positive sera into cell culture. 

Andersson et al. (May 26, 2020). SARS-CoV-2 RNA Detected in Blood Samples from Patients with COVID-19 Is Not Associated with Infectious Virus. Pre-print downloaded May 27 from https://doi.org/10.1101/2020.05.21.20105486  

• Zhang et al. report the efficacy of therapeutic plasma exchange (TPE) in three severe COVID-19 patients with acute respiratory distress syndrome in China. Approximately 10 days after TPE, all three patients had met discharge criteria. The results suggested TPE might be a possible strategy to attenuate circulating cytokines and inflammatory mediators. 

Zhang et al. (May 26, 2020). Efficacy of therapeutic plasma exchange in severe COVID-19 patients. British Journal of Haematology. https://doi.org/10.1111/bjh.16890  

• [pre-print, not peer reviewed] Randad et al. evaluated the correlation of results obtained in 167 saliva samples vs. 324 serum samples and found that SARS-CoV-2 appears to trigger a humoral immune response resulting in the almost simultaneous rise of IgG, IgM and IgA levels in serum and in saliva, consistent with the stimulation of existing cross-reactive B cells. 
• SARS-CoV-2 antibody testing in saliva may play a role in large-scale sero-surveillance for COVID-19. 

Randad et al. (May 26, 2020). COVID-19 Serology at Population Scale SARS-CoV-2-Specific Antibody Responses in Saliva. Pre-print downloaded May 27 from https://doi.org/10.1101/2020.05.24.20112300 

• [pre-print, not peer reviewed] Gan et al. diluted three SARS-CoV-2 positive samples (2 with high viral load and 1 with low viral load) with 8,094 negative samples or double distilled water to form RNA pools. SARS-CoV-2 was detected in pools from high viral load samples up to 1/1000 dilution, while the low viral load sample could only be observed at a low dilution ratio. Based on these findings, pooling samples for surveillance may lead to false negatives for samples with low viral load. 

Gan et al. (May 26, 2020). Sample Pooling as a Strategy of SARS-COV-2 Nucleic Acid Screening Increases the False-Negative Rate. Pre-print downloaded May 27 from https://doi.org/10.1101/2020.05.18.20106138 

• [pre-print, not peer reviewed] Lee et al. evaluated the effect of cancer-related treatments on the risk for COVID-19 among >1.8 million patients living with cancer in the UK, US and Sweden. They found that individuals with cancer had a significantly increased risk (aOR 1.62, 95%CI 1.37-1.91) of COVID-19 compared to the general community. Those treated with chemotherapy or immunotherapy were particularly at risk (aOR: 2.42; 95%CI: 1.81-3.25). 

Lee et al. (May 26, 2020). Cancer and Risk of COVID-19 through a General Community Survey. Pre-print downloaded May 27 from https://doi.org/10.1101/2020.05.20.20103762 

• [pre-print, not peer reviewed] Neil et al. use a Bayesian technique called “virtual evidence” to test the sensitivity of the infection fatality rate (IFR) to two significant sources of uncertainty: survey quality and COVID-19 death counts. This approach estimates an IFR that is higher than widely reported values. For Santa Clara, CA and Chelsea, MA, the most likely IFR values are 0.3-0.4%.  

Neil et al. (May 26, 2020). Bayesian Network Analysis of Covid-19 Data Reveals Higher Infection Prevalence Rates and Lower Fatality Rates than Widely Reported. Pre-print downloaded May 27 from https://doi.org/10.1101/2020.05.25.20112466 

• [pre-print, not peer reviewed] Kohanovski et al. applied an SEIR model to infer the effective start dates of non-pharmaceutical interventions and found that they often differ from the official dates. They hypothesize that late effects may be due to low compliance or prolonged time to adopt and organize for a lockdown. Early effects may be due to early adoption of social distancing and similar behavioral adaptations. These results highlight the complex interaction between personal, regional, and global determinants of behavioral response to an epidemic.  

Kohanovski et al. (May 26, 2020). Inferring the Effective Start Dates of Non-Pharmaceutical Interventions during COVID-19 Outbreaks. Pre-print downloaded May 27 from https://doi.org/10.1101/2020.05.24.20092817 

• [pre-print, not peer reviewed] Online COVID-19 symptom checkers and chatbots are being widely used in response to the global pandemic. Munsch et al. evaluated 10 online COVID-19 symptom checkers using COVID-19 case reports and found them to vary widely in their predictive capabilities, suggesting that quality assessment of these tools is necessary before they are implemented. 

Munsch et al. (May 26, 2020). A Benchmark of Online COVID-19 Symptom Checkers. Pre-print downloaded May 27 from https://doi.org/10.1101/2020.05.22.20109777  

• In 4 states with stay-at-home orders (CO, MN, OH, and VA), cumulative hospitalizations for COVID-19 deviated from projected exponential growth rates after these orders became effective. The deviation started 2 to 4 days sooner than the median effective date of each state’s order and may reflect the use of a median incubation period for symptom onset and time to hospitalization to establish this date. Factors that potentially decreased the hospitalization include school closures, social distancing guidelines, and general pandemic awareness.  

Sen et al. (May 27, 2020). Association of Stay-at-Home Orders With COVID-19 Hospitalizations in 4 States. JAMA. https://doi.org/10.1001/jama.2020.9176