Demonstration of effectiveness of facemasks and facial coverings to slow the spread of coronavirus transmission


Duke physician Eric Westman was one of the first champions of masking as a means to curtail the spread of coronavirus, working with a local non-profit to provide free masks to at-risk and under-served populations in the greater Durham community.

But he needed to know whether the virus-blocking claims mask suppliers made were true, to assure he wasn’t providing ineffective masks that spread viruses along with false security.

So he turned to colleagues in the Duke Department of Physics: Could someone test various masks for him?

Martin Fischer, Ph.D., a chemist and physicist, stepped up. As director of the Advanced Light Imaging and Spectroscopy facility, he normally focuses on exploring new optical contrast mechanisms for molecular imaging, but for this task, he MacGyvered a relatively inexpensive apparatus from common lab materials that can easily be purchased online.

The setup consisted of a box, a laser, a lens, and a cell phone camera.

In a proof-of-concept study appearing online Aug. 7 in the journal Science Advances, Fischer, Westman and colleagues report that the simple, low-cost technique provided visual proof that face masks are effective in reducing droplet emissions during normal wear.

“We confirmed that when people speak, small droplets get expelled, so disease can be spread by talking, without coughing or sneezing,” Fischer said.

“We could also see that some face coverings performed much better than others in blocking expelled particles.”

Notably, the researchers report, the best face coverings were N95 masks without valves – the hospital-grade coverings that are used by front-line health care workers.

Surgical or polypropylene masks also performed well.

But hand-made cotton face coverings provided good coverage, eliminating a substantial amount of the spray from normal speech.

On the other hand, bandanas and neck fleeces such as balaclavas didn’t block the droplets much at all.

“This was just a demonstration – more work is required to investigate variations in masks, speakers, and how people wear them – but it demonstrates that this sort of test could easily be conducted by businesses and others that are providing masks to their employees or patrons,” Fischer said.

“Wearing a mask is a simple and easy way to reduce the spread of COVID-19,” Westman said. “About half of infections are from people who don’t show symptoms, and often don’t know they’re infected.

They can unknowingly spread the virus when the cough, sneeze and just talk.

“If everyone wore a mask, we could stop up to 99% of these droplets before they reach someone else,” Westman said. “In the absence of a vaccine or antiviral medicine, it’s the one proven way to protect others as well as yourself.”

Westman and Fischer said it’s important that businesses supplying masks to the public and employees have good information about the products they’re providing to assure the best protection possible.

“We wanted to develop a simple, low-cost method that we could share with others in the community to encourage the testing of materials, masks prototypes and fittings,” Fischer said. “The parts for the test apparatus are accessible and easy to assemble, and we’ve shown that they can provide helpful information about the effectiveness of masking.”

Westman said he put the information immediately to use: “We were trying to make a decision on what type of face covering to purchase in volume, and little information was available on these new materials that were being used.”

The masks that he was about to purchase for the “Cover Durham” initiative?

“They were no good,” Westman said. “The notion that ‘anything is better than nothing’ didn’t hold true.”

Under the ongoing COVID-19 pandemic (caused by the SARS-CoV-2 coronavirus), recommendations and common practices regarding face mask use by the general public have varied greatly and are in rapid flux: Mask use by the public in public spaces has been controversial in the US, although as of April 3, 2020, the US Centers for Disease Control and Prevention (CDC) is recommending the public wear cloth masks.

Public mask use is far more prevalent in many Asian countries, which have longer experience with novel coronavirus epidemics; public mask use may have been effective at limiting community spread during the 2003 SARS epidemic (Lau, Tsui, Lau, & Yang, 2004; Wu et al., 2004), and widespread mask use is a prominent feature of the relatively successful COVID-19 response in Taiwan (Wang, Ng, & Brook, 2020), for example. Masks have also been suggested as a method for limiting community transmission by asymptomatic or at least clinically undetected carriers (Chan & Yuen, 2020), who may be a major driver of transmission of COVID-19 (Li et al., 2020).

Various experimental studies suggest that masks may both protect the wearer from acquiring various infections (Davies et al., 2013; Lai, Poon, & Cheung, 2012) or transmitting infection (Dharmadhikari et al., 2012). Medical masks (i.e., surgical masks and N95 respirators) in healthcare workers appear to consistently protect against respiratory infection under metanalysis (MacIntyre et al., 2017; Offeddu, Yung, Low, & Tam, 2017), although clinical trials in the community have yielded more mixed results (Canini et al., 2010; Cowling et al., 2009; MacIntyre et al., 2009).

While medical-grade masks should be prioritized for healthcare providers, homemade cloth masks may still afford significant, although variable and generally lesser, protection (Davies et al., 2013; van der Sande, Teunis, & Sabel, 2008), but clinical trials in the community remain lacking.

Given the flux in recommendations, and uncertainty surrounding the possible community-wide impact of mass face masks (especially homemade cloth masks) on COVID-19 transmission, we have developed a multi-group Kermack-McKendrick-type compartmental mathematical model, extending prior work geared towards modeling the COVID-19 pandemic (e.g. Ferguson et al., 2020, Li et al., 2020, Tracht et al., 2010), as well as models previously used to examine masks in a potential influenza pandemic (Brienen, Timen, Wallinga, Van Steenbergen, & Teunis, 2010; Tracht et al., 2010).

This initial framework suggests that masks could be effective even if implemented as a singular intervention/mitigation strategy, but especially in combination with other non-pharmaceutical interventions that decrease community transmission rates.

Whether masks can be useful, even in principle, depends on the mechanisms for transmission for SARS-CoV-2, which are likely a combination of droplet, contact, and possible airborne (aerosol) modes.

The traditional model for respiratory disease transmission posits infection via infectious droplets (generally 5–10 μm) that have a short lifetime in the air and infect the upper respiratory tract, or finer aerosols, which may remain in the air for many hours (Leung et al.,2020 ), with ongoing uncertainties in the relative importance of these modes (and in the conceptual model itself Bourouiba, 2020) for SARS-CoV-2 transmission (Bourouiba, 2020; Han, Lin, Ni, & You, 2020).

The WHO (World Health Organization, 2020, p. 27) has stated that SARS-CoV-2 transmission is primarily via coarse respiratory droplets and contact routes. An experimental study (van Doremalen et al., 2020) using a nebulizer found SARS-CoV-2 to remain viable in aerosols (<5 μm) for 3 h (the study duration), but the clinical relevance of this setup is debatable (World Health Organization, 2020, p. 27).

One out of three symptomatic COVID-19 patients caused extensive environmental contamination in (Ong et al., 2020), including of air exhaust outlets, though the air itself tested negative.

Face masks can protect against both coarser droplet and finer aerosol transmission, though N95 respirators are more effective against finer aerosols, and may be superior in preventing droplet transmission as well (MacIntyre et al., 2017).

Metanalysis of studies in healthy healthcare providers (in whom most studies have been performed) indicated a strong protective value against clinical and respiratory virus infection for both surgical masks and N95 respirators (Offeddu et al., 2017).

Case control data from the 2003 SARS epidemic suggests a strong protective value to mask use by community members in public spaces, on the order of 70% (Lau et al., 2004; Wu et al., 2004).

Experimental studies in both humans and manikins indicate that a range of masks provide at least some protective value against various infectious agents (Davies et al., 2013; Driessche et al., 2015; Stockwell et al., 2018; van der Sande et al., 2008; Leung et al.,2020 ).

Medical masks were potentially highly effective as both source control and primary prevention under tidally breathing and coughing conditions in manikin studies (Lai et al., 2012; Patel, Skaria, Mansour, & Smaldone, 2016), with higher quality masks (e.g. N95 respirator vs. surgical mask) offering greater protection (Patel et al., 2016).

It is largely unknown to what degree homemade masks (typically made from cotton, teacloth, or other polyesther fibers) may protect against droplets/aerosols and viral transmission, but experimental results by Davies et al. 7 suggest that while the homemade masks were less effective than surgical masks, they were still markedly superior to no mask.

A clinical trial in healthcare workers (MacIntyre et al., 2015) showed relatively poor performance for cloth masks relative to medical masks.

Mathematical modeling has been influential in providing deeper understanding on the transmission mechanisms and burden of the ongoing COVID-19 pandemic, contributing to the development of public health policy and understanding.

Most mathematical models of the COVID-19 pandemic can broadly be divided into either population-based, SIR (Kermack-McKendrick)-type models, driven by (potentially stochastic) differential equations (Li et al., 2020; Tang et al., 2020; Wu, Leung, & Leung, 2020; Kucharski et al., 2020; Calafiore, Novara, & Possieri, 2020; Simha, Prasad, & Narayana, 2020; Dehning et al., 2020; Nesteruk, 2020; Zhang et al., 2020; Anastassopoulou, Russo, Tsakris, & Siettos, 2020; Moore & Okyere, 2004), or agent-based models (Biswas, Khaleque, & Sen, 2020; Chang, Harding, Zachreson, Cliff, & Prokopenko, 2020; Ferguson et al., 2020; Ruiz Estrada & Koutronas, 2020; Wilder et al., 2020), in which individuals typically interact on a network structure and exchange infection stochastically.

One difficulty of the latter approach is that the network structure is time-varying and can be difficult, if not impossible, to construct with accuracy. Population-based models, alternatively, may risk being too coarse to capture certain real-world complexities.

Many of these models, of course, incorporate features from both paradigms, and the right combination of dynamical, stochastic, data-driven, and network-based methods will always depend on the question of interest.

In Li et al. (2020) imposed a metapopulation structure onto an SEIR-model to account for travel between major cities in China. Notably, they include compartments for both documented and undocumented infections.

Their model suggests that as many as 86% of all cases went undetected in Wuhan before travel restrictions took effect on January 23, 2020. They additionally estimated that, on a per person basis, asymptomatic individuals were only 55% as contagious, yet were responsible for 79% of new infections, given their increased prevalence.

The importance of accounting for asymptomatic individuals has been confirmed by other studies (Calafiore et al., 2020; Ferguson et al., 2020; Moriarty, 2020; Verity et al., 2020). In their model-based assessment of case-fatality ratios, Verity et al. (2020) estimated that 40–50% of cases went unidentified in China, as of February 8, 2020, while in the case of the Princess Diamond cruise ship, 46.5% of individuals who tested positive for COVID-19 were asymptomatic (Moriarty, 2020).

Further, Calafiore et al. (2020), using a modified SIR-model, estimated that, on average, cases in Italy went underreported by a factor of 63, as of March 30, 2020.

Several prior mathematical models, motivated by the potential for pandemic influenza, have examined the utility of mask wearing by the general public.

These include a relatively simple modification of an SIR-type model by Brienen et al. (2010), while Tracht et al. (2010) considered a more complex SEIR model that explicitly disaggregated those that do and do not use masks.

The latter concluded that, for pandemic H1N1 influenza, modestly effective masks (20%) could halve total infections, while if masks were just 50% effective as source control, the epidemic could be essentially eliminated if just 25% of the population wore masks.

We adapt these previously developed SEIR model frameworks for transmission dynamics to explore the potential community-wide impact of public use of face masks, of varying efficacy and compliance, on the transmission dynamics and control of the COVID-19 pandemic.

In particular, we develop a two-group model, which stratifies the total population into those who habitually do and do not wear face masks in public or other settings where transmission may occur.

This model takes the form of a deterministic system of nonlinear differential equations, and explicitly includes asymptomatically-infectious humans. We examine mask effectiveness and coverage (i.e., the fraction of the population that habitually wears masks) as our two primary parameters of interest.

We explore possible nonlinearities in mask coverage and effectiveness and the interaction of these two parameters; we find that the product of mask effectiveness and coverage level strongly predicts the effect of mask use on epidemiologic outcomes.

Thus, homemade cloth masks are best deployed en masse to benefit the population at large. There is also a potentially strong nonlinear effect of mask use on the epidemiologic outcomes of cumulative death and peak hospitalizations.

We note a possible temporal effect: Delaying mass mask adoption too long may undermine its efficacy. Moreover, we perform simulated case studies using mortality data for New York and Washington state.

These case studies likewise suggest a beneficial role to mass adoption of even poorly effective masks, with the relative benefit likely greater in Washington state, where baseline transmission is less intense.

The absolute potential for saving lives is still, however, greater under the more intense transmission dynamics in New York state. Thus, early adoption of masks is useful regardless of transmission intensities, and should not be delayed even if the case load/mortality seems relatively low.

In summary, the benefit to routine face mask use by the general public during the COVID-19 pandemic remains uncertain, but our initial mathematical modeling work suggests a possible strong potential benefit to near universal adoption of even weakly effective homemade masks that may synergize with, not replace, other control and mitigation measures.

reference link :

Duke University


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