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US CDC Says Wearing Double Mask Reduce COVID by 95%. Sam Leong very Angry, Red Faced

capamerica

Alfrescian
Loyal
Who said masks dont work? Of course they do

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7393808/


Masks Do More Than Protect Others During COVID-19: Reducing the Inoculum of SARS-CoV-2 to Protect the Wearer
Monica Gandhi, MD, MPH,
corresponding author
1 Chris Beyrer, MD, MPH,2 and Eric Goosby, MD1
Author information Article notes Copyright and License information Disclaimer
This article has been cited by other articles in PMC.

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Abstract
Although the benefit of population-level public facial masking to protect others during the COVID-19 pandemic has received a great deal of attention, we discuss for one of the first times the hypothesis that universal masking reduces the “inoculum” or dose of the virus for the mask-wearer, leading to more mild and asymptomatic infection manifestations. Masks, depending on type, filter out the majority of viral particles, but not all. We first discuss the near-century-old literature around the viral inoculum and severity of disease (conceptualized as the LD50 or lethal dose of the virus). We include examples of rising rates of asymptomatic infection with population-level masking, including in closed settings (e.g., cruise ships) with and without universal masking. Asymptomatic infections may be harmful for spread but could actually be beneficial if they lead to higher rates of exposure. Exposing society to SARS-CoV-2 without the unacceptable consequences of severe illness with public masking could lead to greater community-level immunity and slower spread as we await a vaccine. This theory of viral inoculum and mild or asymptomatic disease with SARS-CoV-2 in light of population-level masking has received little attention so this is one of the first perspectives to discuss the evidence supporting this theory.

This perspective outlines a unique angle on why universal public masking during the COVID-19 pandemic should be one of the most important pillars of disease control. Our theory is based on the likelihood of masking reducing the viral inoculum to which the mask-wearer is exposed, leading to higher rates of mild or asymptomatic infection with COVID-19. No prior perspective has specifically focused on this link between population-level facial masking, the viral inoculum, and increasing rates of asymptomatic infection with SARS-CoV-2.
On April 3, 2020, the Centers for Disease Control and Prevention issued recommendations on wearing cloth face coverings by the public to reduce community spread.1 The World Health Organization did not recommend population-level face masking in April,2 but changed their guidance on June 5, 2020,3 when the extent of transmission from pre-symptomatic or even asymptomatic individuals was clear.4, 5 One recent model showed that population-level masking is one of the most efficacious interventions to reduce further spread of SARS-CoV-2, allowing for less-stringent lock-down requirements in countries adopting this strategy.6 Countries worldwide have had a range of responses to the recommendation on universal masking, with many countries (and US states)7 issuing mandates and enforcement strategies.8 Countries accustomed to universal population-level masking since the SARS epidemic in 2003 adopted the intervention more readily.9
There are two likely reasons for the effectiveness of facial masks: The first—to prevent the spread of viral particles from asymptomatic individuals to others—has received a great deal of attention.10, 11 However, the second theory—that reducing the inoculum of virus to which a mask-wearer is exposed will result in milder disease1227—has received less attention and is the focus of our perspective which compiles virologic, epidemiologic and ecologic evidence.
Masks, depending on the material and design, filter out a majority of viral particles, but not all.28 The theory that exposure to a lower inoculum or dose of any virus (whether respiratory, gastrointestinal or sexually transmitted) can make subsequent illness far less likely to be severe1227 has been propounded for some time. Indeed, the concept of the 50% lethal dose (LD50), the virus dose at which 50% of exposed hosts die, determined via controlled experiments in which a range of exposure doses are administered to animals to calculate a dose-mortality curve, was first described in 1938.18 Other studies have examined the LD50—or the dose that leads to severe disease or death—for a variety of viruses in hosts or animal models.17, 21, 2934
These studies have limitations, since experiments to examine the dose of virus to achieve its LD50 have necessarily not been conducted in humans. Studies to experimentally examine the dose of virus associated with different levels of diseases severity in humans have been limited to non-lethal viruses. In one experiment in preparation for vaccine development, healthy human volunteers exposed to different doses of wild-type influenza A virus developed more severe symptoms at higher inocula of administered virus.34 Giving SARS-CoV-2 in a range of doses to humans experimentally would be unethical, but an animal model has tested this theory of masking attenuating disease severity. In a frequently cited study showing that hamsters are less likely to contract SARS-CoV-2 infection with a surgical mask partition, those hamsters that did contract COVID-19 with simulated masking had milder manifestations of infection.27
Increasing rates of asymptomatic and mild infection with COVID-19 have been seen over time during the pandemic in settings adopting population-level masking. A systematic review of earlier studies, before facial masking was widely practiced, placed the proportion of asymptomatic infection with SARS-CoV-2 at 15%.35 A more recent narrative review of 16 different studies estimated the rate of asymptomatic infection at 40–45%.36 The CDC has now (since article submission) also placed the rate of asymptomatic infection at 40% - the reference is as follows and could this new reference be placed here: “Centers for Disease Control and Prevention (CDC). COVID-19 Pandemic Planning Scenarios. July 10, 2020. https://www.cdc.gov/coronavirus/2019-ncov/hcp/planning-scenarios.html.” Closed settings, such as cruise ships, can be particularly illustrative when examining phenotypes associated with SARS-CoV-2. For example, one of the earliest estimates of the rate of asymptomatic infection due to SARS-CoV-2 was in the 20% range from a report of a COVID-19 outbreak on the Diamond Princess cruise ship.37 In a more recent report from a different cruise ship outbreak, all passengers were issued surgical masks and all staff provided N95 masks after the initial case of COVID-19 on the ship was detected.38 In this closed setting with masking, where 128 of 217 passengers and staff eventually tested positive for SARS-CoV-2 via RT-PCR, the majority of infected patients on the ship (81%) remained asymptomatic,38 compared with 18% in the cruise ship outbreak without masking.37
A report from a pediatric hemodialysis unit in Indiana, where all patients and staff were masked, demonstrated that staff rapidly developed antibodies to SARS-CoV-2 after exposure to a single symptomatic patient with COVID-19. In the setting of masking, however, none of the new infections was symptomatic.39 And in a recent outbreak in a seafood processing plant in Oregon where all workers were issued masks each day at work, the rate of asymptomatic infection among the 124 infected was 95%.40, 41 An outbreak in a Tyson chicken plant in Arkansas with masking also showed a 95% asymptomatic rate of infection.42, 43
One model showed a correlation between population-level masking and number of COVID-19 cases in various countries, but an even stronger correlation with suppression of COVID-related death rates.9 However, it should be acknowledged that this model could not account for all confounders that led to such low death rates in the regions examined. This group showed that, if 80% of the population wears a moderately effective mask, nearly half of the projected deaths over the next two months could be prevented.9 Countries accustomed to masking since the 2003 SARS-CoV pandemic, including Japan, Hong Kong (Fig. 1a),44 Taiwan, Thailand, South Korea, and Singapore,9 and those who newly embraced masking early on in the COVID-19 pandemic, such as the Czech Republic,46 have fared well in terms of rates of severe illness and death. Indeed, even when cases have resurged in these areas with population-based masking upon re-opening (e.g., South Korea, Singapore, Hong Kong, Taiwan), the case-fatality rate has remained low,47 which is suggestive of this viral inoculum theory.
[IMG alt="An external file that holds a picture, illustration, etc.
Object name is 11606_2020_6067_Fig1_HTML.jpg"]https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7393808/bin/11606_2020_6067_Fig1_HTML.jpg[/IMG]
Figure 1

a Busy Hong Kong Street on May 14, 2020, demonstrating universal public masking;44 only five deaths reported in Hong Kong from COVID-19. b Georgia Tech football game with fans wearing masks packed in a campus stadium in the midst of the 1918 influenza pandemic.45
Although asymptomatic infection can be problematic in terms of increasing spread,4 it can also be beneficial.14 Higher rates of asymptomatic infection with SARS-CoV-2 lead to higher rates of exposure, as was seen with antibody testing campaigns in Japan48 or the surveillance study in the pediatric hemodialysis unit in Indiana.39 Exposing society to SARS-CoV-2 without the unacceptable consequences of severe illness could lead to greater community-level immunity49 and slow down spread as we await a vaccine. However, the level of effective antibody and T cell immune responses to different manifestations of COVID-19 has not yet been determined. Monitoring for upticks in illness, not asymptomatic cases, could herald a need to re-enforce more stringent social distancing measures in a society which has adopted universal public masking going forward.
For this particular pillar of pandemic control to work in the USA, leading politicians will need to endorse and model mask-wearing. The USA has embraced universal public masking before, during the 1918 Spanish influenza pandemic (Fig. (Fig.1b1b),45 but the CDC recommendation made on April 3, 2020, for public masking due to COVID-19 has been unevenly followed.7 The efforts to preserve life must be balanced against the catastrophic consequences of shutting down economies, which ultimately will lead to more suffering, poverty, and death than the virus itself, especially for the working poor. Although universal public masking can certainly protect others, the “inoculum” theory argues for a major protective effect for the individual and will allow for the preservation of life, along with other COVID-19 control measures, as society re-opens. This perspective puts forth another advantage of population-level facial masking for pandemic control with SARS-CoV-2 based on an old but enduring theory18 regarding viral inoculum, clinical manifestations in the host, and protection.
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Acknowledgments
 

Leongsam

High Order Twit / Low SES subject
Admin
Asset
Now I wonder why we all wearing masks? Must be because they work.

https://www.medrxiv.org/content/10.1101/2020.10.08.20209510v1


Global projections of potential lives saved from COVID-19 through universal mask use
Emmanuela Gakidou, IHME COVID-19 Forecasting Team
doi: https://doi.org/10.1101/2020.10.08.20209510
This article is a preprint and has not been peer-reviewed [what does this mean?]. It reports new medical research that has yet to be evaluated and so should not be used to guide clinical practice.


Abstract
Background Social distancing mandates have been effective at reducing the health impacts of COVID-19. The ensuing economic downturns and unemployment increases have led many nations to progressively relax mandates. As COVID-19 transmission and deaths rise in many low and middle-income countries (LMICs), with continuing widespread transmission elsewhere, policymakers are searching for options to reduce COVID-19 mortality without re-imposing strict social distancing mandates.
Methods Using a Bayesian meta-regression of 40 studies measuring the impact of mask use on respiratory viral infections, we estimated the reduction in transmission associated with the use of cloth or paper masks used in a general population setting. We used data from daily surveys conducted by Facebook, YouGov, and Premise, on the proportion of people reporting always wearing a mask outside their home for nearly all countries. We predicted deaths and infections until January 1st 2021 under a reference and universal mask use scenario using a deterministic transmission dynamics model with categories for susceptible, exposed, infected and recovered (SEIR). In the reference scenario, we assume continued easing of mandates but with action to re-impose mandates for a period of six weeks, at a level of eight daily deaths per million population. The universal mask scenario assumed scaling up of mask use to 95% over a one-week period.
Findings Use of simple masks can reduce transmission of COVID-19 by 40% (95% uncertainty interval [UI] 20% – 54%). Universal mask use would lead to a reduction of 815,600 deaths (95% UI 430,600 to 1,491,000 deaths) between August 26th 2020 and January 1st 2021, the difference between the predicted 3.00 million deaths (95% UI 2.20 to 4.52 million) in the reference and 2.18 million deaths (95% UI 1.71 to 3.14 million) in the universal mask scenario over this time period. Mask use was estimated at 59.0% of people globally on August 18th, ranging from 41.9% in North Africa and the Middle East to 79.2% in Latin America and the Caribbean. The effect of universal mask use is greatest in countries such as India (158,832 fewer deaths in universal mask scenario, 95% UI 75,152 to 282,838 deaths), the United States of America (93,495 fewer deaths; 95% UI 59,329 to 150,967 deaths), and Russia (68,531 fewer deaths; 95% UI 34,249 to 145,960 deaths).
Interpretation The rising toll of the COVID-19 pandemic can be substantially reduced by the universal adoption of masks. This low-cost policy, whether customary or mandated, has enormous health benefits and likely large economic benefits as well, by delaying the need for re-imposition of social distancing mandates.
Evidence before this study One meta-analysis of 21 studies reported a pooled reduction in the risk of respiratory virus infection of 47% (95% CI 36-79%) from a subset of eight studies reporting on mask use in non-health workers but it did not distinguish type of mask. Another meta-analysis reported on 26 studies of mask use in health workers and three studies in non-healthcare settings, reported a pooled effect of a 66% (55-74%) reduction in infections and a reduction by 44% (21-60%) in the three non-healthcare setting studies. Several survey series regularly measure self-reported mask use but results from these different sources have not previously been pooled to derive daily estimates of mask use over the course of the epidemic. Global models of the impact of scaled up mask use have to our knowledge not been published.
Added value of this study We combined the studies on mask use identified in the two meta-analyses and added one further study. In a Bayesian meta-regression approach, we derived the effect of simple cloth or paper masks used outside of a healthcare setting. In the meta-regression we make use of all the information provided by all of these studies, rather than subsetting to just those studies that provided the direct comparison of interest. Pooling estimates on the prevalence of self-reported mask use from three survey series provides up-to-date information on trends in mask use in almost all countries. We use extensive survey data covering nearly every country in the world to assess recent trends and current mask use. We then use an SEIR transmission dynamics model with good predictive validity to assess the potential of scaled up mask use to reduce global mortality from COVID-19.
Implications of all the available evidence Universal mask use can save many lives and avoid or, at least, delay the need for re-imposition of social mandates (such as stay-at-home orders, curfews, etc.), which would also contribute to ameliorating the negative effects of COVID-19 on the economy and unemployment. Until an affordable vaccine becomes universally available, mandating mask use is the most attractive policy option available to all countries, particularly if an expected increased transmission risk occurs in the Northern hemisphere’s fall and winter. Simple face coverings are cheap and effective; one of the few available interventions that is widely available to everyone. Countries with currently low mask use will need to determine the optimal balance between encouraging the use of masks through advocacy and information about their benefits, and governance of a compulsory use associated with penalties for non-compliance.
Competing Interest Statement
The authors have declared no competing interest.
Funding Statement
This study was funded by the Bill & Melinda Gates Foundation and Bloomberg Philanthropies. The funders of the study had no role in study design, data collection, data analysis, data interpretation, writing of the final report, or decision to publish. The corresponding author had full access to all of the data in the study and had final responsibility for the decision to submit for publication.
Author Declarations
I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.
Yes
The details of the IRB/oversight body that provided approval or exemption for the research described are given below:
We only use data collected by other entities.
All necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived.
Yes
I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

All are wearing masks because it has been made the law in countries that have vested interests, either directly or indirectly, in prolonging the pandemic as long as possible.

Countries which have been able to formulate independent policy regarding masks have not made it mandatory. The best example is NZ where I am. I have not worn a mask since day one of this charade and neither has anyone else I know.

Saying "they work" 1000 times a day does not mean they actually do. All it means is you have learned to recite a mantra.
 

capamerica

Alfrescian
Loyal
Oh dear, it would seem masks work. Who knew?

https://hartfordhealthcare.org/about-us/news-press/news-detail?articleid=27691&publicId=395

Masks Save Lives: Duke Study Confirms Which Ones Work Best
NewsDukeMasks.jpg

August 11, 2020



Of all the mask studies, the latest by Duke University researchers affirming the fitted N95 as king in the fight against COVID-19, the most important remains the study of all studies by the University of Washington’s Institute for Health Metrics and Evaluation.

The IHME, a research center that has provided projections on hospitalizations and deaths during the COVID-19 pandemic, performed a meta-analysis earlier this summer of mask studies from the United States, China and Germany that confirmed what most medical experts have advocated for months:

If 95 percent of people wear cloth masks when within 6 feet of other people in public, it will reduce COVID-19 transmission by at least 30 percent. So if every infected person transmits the virus to 30 percent fewer people, it improves the chances of subduing COVID-19’s spread in the United States.

“It’s as important as ever to wash your hands, wear a mask and don’t touch your face,” said Keith Grant, APRN, head of infectious disease for Hartford HealthCare. “Those are still the basic ways to avoid COVID-19 infection.”

Masks work. The meta-analysis assumed all masks in public use are cloth, not the even more effective N95 respirators worn by healthcare professionals. The new Duke study, published Aug. 7 in the journal Science Advances, rated a fitted N95 and a three-layer surgical mask as the top two protectors in simple tests using using a cardboard box with a lens, a laser and a phone’s camera to track particles released from a person’s mouth when speaking. (Do not use an N95 mask with a valve. Here’s why.) Three of the next four top performers in the test included cotton.

Test subjects were asked to repeat the same phrase into the box without a mask, then repeat with each mask. Every mask was tested 10 times. (See a schematic, courtesy of Duke, of the test setup below.)

Duke Study Schematic


Here’s the full list, with each mask identified by a number in parenthesis corresponding to the photo above:

1. Fitted N95, no valve (14 in photo)
2. 3-layer surgical mask (1)
3. Cotton-polypropylene-cotton mask (5)
4. 2-layer polypropylene apron mask (4)
5. 2-layer cotton, pleated style mask (13)
6. 2-layer cotton, pleated style mask (7)
7. Valved N95 mask (2)
8. 2-layer cotton, Olson style mask (8)
9. 1-layer Maxima AT mask (6)
10. 1-layer cotton, pleated style mask (10)
11. 2-layer cotton, pleated style mask (9)
12. Knitted mask (3)
13. Double-layer bandana (12)
14. Gaiter-style neck fleece (11)

Here are the results, provided by Duke University:

Duke Study Results


The study’s big loser, the neck gaiter, is too thin to offer much protection, the researchers concluded.

It’s possible the fabric breaks up bigger particles into smaller particles that can remain airborne longer. Bandanas and knitted masks were similarly ineffective.

But the conclusions, as in so many other studies, have left little doubt that wearing a mask is our best defense against the spread of COVID-19.
 

capamerica

Alfrescian
Loyal
Is that why are all wearing them? U dont say

https://www.nature.com/articles/s41591-020-0869-5


Temporal dynamics in viral shedding and transmissibility of COVID-19
Nature Medicine volume 26, pages672–675(2020)Cite this article
Matters Arising to this article was published on 17 August 2020
An Author Correction to this article was published on 07 August 2020
This article has been updated
Abstract
We report temporal patterns of viral shedding in 94 patients with laboratory-confirmed COVID-19 and modeled COVID-19 infectiousness profiles from a separate sample of 77 infector–infectee transmission pairs. We observed the highest viral load in throat swabs at the time of symptom onset, and inferred that infectiousness peaked on or before symptom onset. We estimated that 44% (95% confidence interval, 30–57%) of secondary cases were infected during the index cases’ presymptomatic stage, in settings with substantial household clustering, active case finding and quarantine outside the home. Disease control measures should be adjusted to account for probable substantial presymptomatic transmission.
Main
SARS-CoV-2, the causative agent of COVID-19, spreads efficiently, with a basic reproductive number of 2.2 to 2.5 determined in Wuhan1,2. The effectiveness of control measures depends on several key epidemiological parameters (Fig. 1a), including the serial interval (duration between symptom onsets of successive cases in a transmission chain) and the incubation period (time between infection and onset of symptoms). Variation between individuals and transmission chains is summarized by the incubation period distribution and the serial interval distribution, respectively. If the observed mean serial interval is shorter than the observed mean incubation period, this indicates that a significant portion of transmission may have occurred before infected persons have developed symptoms. Significant presymptomatic transmission would probably reduce the effectiveness of control measures that are initiated by symptom onset, such as isolation, contact tracing and enhanced hygiene or use of face masks for symptomatic persons.
Fig. 1: Transmission of infectious diseases.
figure1
a, Schematic of the relation between different time periods in the transmission of infectious disease. b, Human-to-human transmission pairs of SAR-CoV-2 virus (N = 77). We assumed a maximum exposure window of 21 days prior to symptom onset of the secondary cases. Detailed information on the transmission pairs and the source of information is summarized in Supplementary Tables 2 and 3. c, Estimated serial interval distribution (top), inferred infectiousness profile (middle) and assumed incubation period (bottom) of COVID-19.
Full size image
SARS (severe acute respiratory syndrome) was notable, because infectiousness increased around 7–10 days after symptom onset3,4. Onward transmission can be substantially reduced by containment measures such as isolation and quarantine (Fig. 1a)5. In contrast, influenza is characterized by increased infectiousness shortly around or even before symptom onset6.
In this study, we compared clinical data on virus shedding with separate epidemiologic data on incubation periods and serial intervals between cases in transmission chains, to draw inferences on infectiousness profiles.
Among 94 patients with laboratory-confirmed COVID-19 admitted to Guangzhou Eighth People’s Hospital, 47/94 (50%) were male, the median age was 47 years and 61/93 (66%) were moderately ill (with fever and/or respiratory symptoms and radiographic evidence of pneumonia), but none were classified as ‘severe’ or ‘critical’ on hospital admission (Supplementary Table 1).
A total of 414 throat swabs were collected from these 94 patients, from symptom onset up to 32 days after onset. We detected high viral loads soon after symptom onset, which then gradually decreased towards the detection limit at about day 21. There was no obvious difference in viral loads across sex, age groups and disease severity (Fig. 2).
Fig. 2: Temporal patterns of viral shedding.
figure2
Viral load (threshold cycle (Ct) values) detected by RT–PCR (PCR with reverse transcription) in throat swabs from patients infected with SARS-CoV-2 (N = 94), overall and stratified by disease severity, sex, age group and link to Hubei province. The detection limit was Ct = 40, which was used to indicate negative samples. The thick lines show the trend in viral load, using smoothing splines. We added some noise to the data points to avoid overlaps.
Full size image
Separately, based on 77 transmission pairs obtained from publicly available sources within and outside mainland China (Fig. 1b and Supplementary Table 2), the serial interval was estimated to have a mean of 5.8 days (95% confidence interval (CI), 4.8–6.8 days) and a median of 5.2 days (95% CI, 4.1–6.4 days) based on a fitted gamma distribution, with 7.6% negative serial intervals (Fig. 1c). Assuming an incubation period distribution of mean 5.2 days from a separate study of early COVID-19 cases1, we inferred that infectiousness started from 12.3 days (95% CI, 5.9–17.0 days) before symptom onset and peaked at symptom onset (95% CI, –0.9–0.9 days) (Fig. 1c). We further observed that only <0.1% of transmission would occur before 7 days, 1% of transmission would occur before 5 days and 9% of transmission would occur before 3 days prior to symptom onset. The estimated proportion of presymptomatic transmission (area under the curve) was 44% (95% CI, 30–57%). Infectiousness was estimated to decline quickly within 7 days. Viral load data were not used in the estimation but showed a similar monotonic decreasing pattern.
In sensitivity analysis, using the same estimating procedure but holding constant the start of infectiousness from 5, 8 and 11 days before symptom onset, infectiousness was shown to peak at 2 days before to 1 day after symptom onset, and the proportion of presymptomatic transmission ranged from 37% to 48% (Extended Data Fig. 1).
Finally, simulation showed that the proportion of short serial intervals (for example, <2 days) would be larger if infectiousness were assumed to start before symptom onset (Extended Data Fig. 2). Given the 7.6% negative serial intervals estimated from the infector–infectee paired data, start of infectiousness at least 2 days before onset and peak infectiousness at 2 days before to 1 day after onset would be most consistent with this observed proportion (Extended Data Fig. 3).
Here, we used detailed information on the timing of symptom onsets in transmission pairs to infer the infectiousness profile of COVID-19. We showed substantial transmission potential before symptom onset. Of note, most cases were isolated after symptom onset, preventing some post-symptomatic transmission. Even higher proportions of presymptomatic transmission of 48% and 62% have been estimated for Singapore and Tianjin, where active case finding was implemented7. Places with active case finding would tend to have a higher proportion of presymptomatic transmission, mainly due to quick quarantine of close contacts and isolation, thus reducing the probability of secondary spread later on in the course of illness. In a rapidly expanding epidemic wherein contact tracing/quarantine and perhaps even isolation are no longer feasible, or in locations where cases are not isolated outside the home, we should therefore observe a lower proportion of presymptomatic transmission.
Our analysis suggests that viral shedding may begin 5 to 6 days before the appearance of the first symptoms. After symptom onset, viral loads decreased monotonically, consistent with two recent studies8,9. Another study from Wuhan reported that virus was detected for a median of 20 days (up to 37 days among survivors) after symptom onset10, but infectiousness may decline significantly 8 days after symptom onset, as live virus could no longer be cultured (according to Wölfel and colleagues11). Together, these results support our findings that the infectiousness profile may more closely resemble that of influenza than of SARS (Fig. 1a), although we did not have data on viral shedding before symptom onset6,12. Our results are also supported by reports of asymptomatic and presymptomatic transmission13,14.
For a reproductive number of 2.5 (ref. 2), contact tracing and isolation alone are less likely to be successful if more than 30% of transmission occurred before symptom onset, unless >90% of the contacts can be traced15. This is more likely achievable if the definition of contacts covers 2 to 3 days prior to symptom onset of the index case, as has been done in Hong Kong and mainland China since late February. Even when the control strategy is shifting away from containment to mitigation, contact tracing would still be an important measure, such as when there are super-spreading events that may occur in high-risk settings including nursing homes or hospitals. With a substantial proportion of presymptomatic transmission, measures such as enhanced personal hygiene and social distancing for all would likely be the key instruments for community disease control.
Our study has several limitations. First, symptom onset relies on patient recall after confirmation of COVID-19. The potential recall bias would probably have tended toward the direction of under-ascertainment, that is, delay in recognizing first symptoms. As long as these biases did not differ systematically between infector and infectee, the serial interval estimate would not be substantially affected. However, the incubation period would have been overestimated, and thus the proportion of presymptomatic transmission artifactually inflated. Second, shorter serial intervals than those reported here have been reported, but such estimates lengthened when restricted to infector–infectee pairs with more certain transmission links16. Finally, the viral shedding dynamics were based on data for patients who received treatment according to nationally promulgated protocols, including combinations of antivirals, antibiotics, corticosteroids, immunomodulatory agents and Chinese medicine preparations, which could have modified the shedding dynamical patterns.
In conclusion, we have estimated that viral shedding of patients with laboratory-confirmed COVID-19 peaked on or before symptom onset, and a substantial proportion of transmission probably occurred before first symptoms in the index case. More inclusive criteria for contact tracing to capture potential transmission events 2 to 3 days before symptom onset should be urgently considered for effective control of the outbreak.
Methods
Sources of data
Guangzhou Eighth People’s Hospital in Guangdong, China was designated as one of the specialized hospitals for treating patients with COVID-19 at both city and provincial levels on 20 January 2020. After that, many people with COVID-19 were admitted via fever clinics, the hospital emergency room or after confirmation of cases from community epidemiological investigation carried out by the Guangzhou Center for Disease Control and Prevention, or transferred from other hospitals. The first confirmed patient with COVID-19 was admitted on 21 January 2020, but in the initial phase, patients suspected to have COVID-19 were also admitted. We identified all suspected and confirmed COVID-19 cases admitted from 21 January 2020 to 14 February 2020 and collected throat swabs in each case. Patients included those who traveled from Wuhan or Hubei to Guangzhou as well as locals, with cases ranging from asymptomatic, mild to moderate at admission.
The samples were tested by N-gene-specific quantitative RT–PCR assay as previously described17. To understand the temporal dynamics of viral shedding and exclude non-confirmed COVID-19 cases, we selected 94 patients who had at least one positive result (cycle threshold (Ct) value < 40) in their throat samples. Serial samples were collected from some but not all patients for clinical monitoring purposes.
We collected information reported on possible human-to-human transmission pairs of patients with laboratory-confirmed COVID-19 from publicly available sources, including announcements made by government health agencies and media reports in mainland China and countries/regions outside China. A transmission pair was defined as two confirmed COVID-19 cases identified in the epidemiologic investigation by showing a clear epidemiologic link with each other, such that one case (infectee) was highly likely to have been infected by the other (infector), by fulfilling the following criteria: (1) the infectee did not report a travel history to an area affected by COVID-19 or any contact with other confirmed or suspected COVID-19 cases except for the infector within 14 days before symptom onset; (2) the infector and infectee were not identified in a patient cluster where other COVID-19 cases had also been confirmed; and (3) the infector and infectee pair did not share a common source of exposure to a COVID-19 case or a place where there were COVID-19 case(s) reported. We excluded possible transmission pairs without a clear exposure history reported prior to symptom onset. Data of possible transmission pairs of COVID-19 were extracted, including age, sex, location, date of symptom onset, type or relationship between the pair cases and time of contact of the cases.
Statistical analysis
We analyzed two separate data sets—clinical and epidemiologic—to assess presymptomatic infectiousness. First, we assessed longitudinal viral shedding data from patients with laboratory-confirmed COVID-19 starting from symptom onset, where viral shedding during the first few days after illness onset could be compared with the inferred infectiousness. Second, the serial intervals from clear transmission chains, combined with information on the incubation period distribution, were used to infer the infectiousness profile, as described in the following.
We present SARS-CoV-2 viral loads in the throat swabs of each patient by day of symptom onset. To aid visualization, a smoothing spline was fitted to the Ct values to summarize the overall trend. Specifically, a generalized additive model, E(Y) = β0 + s(t), with an identity link was fitted, where Y are the Ct values, β0 is the intercept and s(t) is a cubic spline evaluated at t days after symptom onset. We also compared the viral load by disease severity, age, sex and travel history from Hubei.
We fitted a gamma distribution to the transmission pairs data to estimate the serial interval distribution. We used a published estimate of the incubation period distribution to infer infectiousness with respect to symptom onset from the first 425 patients with COVID-19 in Wuhan with detailed exposure history1. We considered that infected cases would become infectious at a certain time point before or after illness onset (tS1). Infectiousness—that is, transmission probability to a secondary case—would then increase until reaching its peak (Fig. 1). The transmission event would occur at time tI with a probability described by the infectiousness profile βc(tI − tS1) relative to the illness onset date, assuming a gamma distribution β(t) with a time shift c to allow for start of infectiousness c days prior to symptom onset; that is, βc(t) = β(t + c). The secondary case would then show symptoms at time tS2, after the incubation period that is assumed to follow a lognormal distribution g(tS2 − tI). Hence the observed serial intervals distribution f(tS2 − tS1) would be the convolution between the infectiousness profile and incubation period distribution. We constructed a likelihood function based on the convolution, which was fitted to the observed serial intervals, allowing for the start of infectiousness around symptom onset and window of symptom onset (tS1l, tS1u), given by
L(tS1u,tS1l,tS2|θ)=∫tS1ltS1u∫−∞tS2βc(tI−tS1)g(tS2−tI)dtIdtS1L(tS1u,tS1l,tS2|θ)=∫tS1ltS1u⁡∫−∞tS2⁡βc(tI−tS1)g(tS2−tI)dtIdtS1
A normalization factor can be added to account for the uncertainty in the symptom-onset dates of the index cases. Assuming a uniform distribution, the likelihood would differ only by a multiplicative constant and give the same estimates.
Parameters θ, including the gamma distribution parameters and the start of infectiousness, were estimated using maximum likelihood. The 95% CIs were obtained by bootstrapping with 1,000 replications. We also performed sensitivity analyses by fixing the start of infectiousness from days 5, 8 and 11 before symptom onset and inferred the infectiousness profile.
As an additional check, we simulated the expected serial intervals assuming the same aforementioned incubation period but two different infectiousness profiles, where infectiousness started on the same day and from 2 days before symptom onset, respectively. A recent study isolated live infectious SARS-CoV-2 virus from patients with COVID-19 up to 8 days after symptom onset11, thus we assumed the same duration of infectiousness. We also assumed that infectiousness peaked on the day of symptom onset. The timing of transmission to secondary cases was simulated according to the infectiousness profile using a lognormal and exponential distribution, respectively, where the serial intervals were estimated as the sum of the onset to transmission interval and the incubation period. We drew random samples for the transmission time relative to symptom onset of the infector TI ≈ βc(t), and also the incubation period Tinc ≈ f(t), then the simulated serial interval was TI + Tinc. We also performed simulation considering combinations of different infectiousness profiles, with start of infectiousness 7 days before to 3 days after symptom onset, and peak infectiousness also 7 days before to 3 days after symptom onset. We present the distribution of the serial intervals and proportion of negative serial intervals over 10,000 simulations.
All statistical analyses were conducted in R version 3.6.3 (R Development Core Team).
 

redbull313

Alfrescian
Loyal
All are wearing masks because it has been made the law in countries that have vested interests, either directly or indirectly, in prolonging the pandemic as long as possible.

Countries which have been able to formulate independent policy regarding masks have not made it mandatory. The best example is NZ where I am. I have not worn a mask since day one of this charade and neither has anyone else I know.

Saying "they work" 1000 times a day does not mean they actually do. All it means is you have learned to recite a mantra.

dumb fuck. watching you humiliated just made my whole weekend. fuck you.
 

capamerica

Alfrescian
Loyal
There is no need to respond to meaningless posts because the data speaks for itself.

I am surprised you did not continue to try your lies anymore

I've got 10000 more studies to show masks work and there is a reason humans everywhere are wearing them.
 

Leongsam

High Order Twit / Low SES subject
Admin
Asset
I am surprised you did not continue to try your lies anymore

I've got 10000 more studies to show masks work and there is a reason humans everywhere are wearing them.

The problem is that all your studies are sponsored by big pharma. Try 100,000 perhaps if the numbers are large enough some fools will start to believe the rubbish the studies contain.
 

QANONSG

Alfrescian
Loyal
Fauci is an idiot


Dr Fauci is a dead man. We have tried to assassinate him on many occasions but he has a full time security detail protecting him. This man has done so much work on the vaccine and is the one man responsible for the rollout of so many vaccinations. Disgusting. He is stopping people from dying and we will not stand for this. How can Fauci save so many people? what is wrong with him? We want to see many more deaths.
 

capamerica

Alfrescian
Loyal
The problem is that all your studies are sponsored by big pharma. Try 100,000 perhaps if the numbers are large enough some fools will start to believe the rubbish the studies contain.

Anytime you want to continue your masks are ineffective crap, just let me know. Standing by and ready.
 
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