This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIRx Med, is properly cited. The complete bibliographic information, a link to the original publication on https://med.jmirx.org/, as well as this copyright and license information must be included.

Face mask mandates have been instrumental in the reduction of transmission of airborne COVID-19. Thus, the question arises whether comparatively mild measures should be kept in place after the pandemic to reduce other airborne diseases such as influenza.

In this study, we aim to simulate the quantitative impact of face masks on the rate of influenza illnesses in the United States.

Using the Centers for Disease Control and Prevention data from 2010 to 2019, we used a series of differential equations to simulate past influenza seasons, assuming that people wore face masks. This was achieved by introducing a variable to account for the efficacy and prevalence of masks and then analyzing its impact on influenza transmission rate in a susceptible-exposed-infected-recovered model fit to the actual past seasons. We then compared influenza rates in this hypothetical scenario with the actual rates over the seasons.

Our results show that several combinations of mask efficacy and prevalence can substantially reduce the burden of seasonal influenza. Across all the years modeled, a mask prevalence of 0.2 (20%) and assumed moderate inward and outward mask efficacy of 0.45 (45%) reduced influenza infections by >90%.

A minority of individuals wearing masks substantially reduced the number of influenza infections across seasons. Considering the efficacy rates of masks and the relatively insignificant monetary cost, we highlight that it may be a viable alternative or complement to influenza vaccinations.

In March 2020, the World Health Organization officially declared COVID-19 a global pandemic, as it extended beyond borders and reached various parts of the world [

Noting the success achieved by this nonpharmaceutical measure, we ask if similar but less stringent measures should be kept in place after the COVID-19 pandemic to deal with influenza, which is another pertinent airborne disease.

To gain an in-depth and quantitative understanding of face masks’ impact on the reduction in influenza activity, we simulate how past influenza seasons 2010/2011 to 2018/2019 would have played out had people worn masks. The simulations were developed using deterministic compartmental models with the incorporation of variables to account for the impact of masks. Using publicly available influenza infection data for the past seasons from the Centers for Disease Control and Prevention (CDC), the influenzas transmission rates model for each season (2010/2011 to 2018/2019) was calibrated. We then simulated the seasons factoring in different scenarios of mask prevalence as well as inward-outward filtration efficacy of masks.

Susceptible-exposed-infected-recovered (SEIR) models are a standard disease modeling technique in epidemiology. The population is compartmentalized into various groups: susceptible, exposed, infected, and recovered. Susceptible is the population susceptible to the disease. The exposed population are infected but have not been detected by testing. Infected is the population who have been confirmed to be infected and can transmit the disease. Recovered is the population who are recovered. To develop the SEIR model, the relationship between these groups is then mathematically characterized by differential equations. In our model, we used a basic SEIR model with a time-dependent transmission rate that is described by the following equations (

Variables used in equations.

Variable | Parameter |

S | Susceptible |

E | Exposed |

I | Infected |

R | Recovered |

β | Probability of disease transmission per contact times the number of contacts per unit time |

δ | Rate of progression from exposed to infectious or inverse of the incubation period |

γ | Rate of progression from infected to recovered or the inverse of the generation time |

N | Total population (S + E + I + R) |

Since the flu fatality rates are insignificant in relation to the total population [

The transmission rate β(t) is described as the number of contacts an infected individual has per timestep, multiplied by the probability of disease transmission in a contact. Thus, as only

In regard to influenza, all parameters of the SEIR model except the time-dependent transmission rate (β(t)) are publicly available via CDC data [

We estimated β(t) by fitting the model to the scaled past infection data.

To account for mask use, a simplified version of the model used by Eikenberry et al [

_{pre}

_{eff}

_{eff}

Consequently, we assumed that the reduction of the chance of infection when both individuals in a contact wear a mask is 1 _{eff}_{eff}

To incorporate

We will now look at the data used to fit

The CDC FluView application [

For any season, let _{i}_{i}_{i}_{i}

_{i}

Therefore, _{i}_{i}

For each season, we calculated the scaling factor

To estimate the time-dependent transmission rate, we fit a seasonal function of the form:

to the scaled data for each season, similar to the approaches by Towers and Feng [

Timesteps

Least squares fitting using the LMFIT Python library [

Results of the transmission rate fitting to data of past flu seasons. Actual infection data and prediction for influenza seasons 2010/2011 to 2018.

We simulated the past influenza seasons with the estimated transmission rate

From May to December 2020, mask use during the COVID-19 pandemic in the United States ranged from 50% to 70% [

Reduction of total infections over all seasons pertaining to total infected mask wearing population and (meffi), and total susceptible mask wearing population (meffs) at mask prevalence levels (mpre) = 0.2 (20%), 0.4 (40%), 0.6 (60%) and 0.8 (80%).

Our simulations showed that the “mask suggested scenario,” with relatively low mask prevalence of around 0.2 (20%) and assumed moderate inward and outward efficacy of 0.45 (45%), would have substantially reduced influenza infections by >90% over several past seasons. The “mask mandate scenario,” with 0.5 (50%) mask prevalence combined with an efficacy of 0.35 (35%), led to >95% reduction in influenza illnesses across seasons (

Simulated weekly infections for mask suggested scenarios (left) and mask mandate scenarios (right).

The findings show that when mask prevalence is high, for example, over 0.6 (60%), low mask efficacies (caused by masks worn too long, that are loose-fitting, etc) are sufficient to fully contain the flu. With that, it appears that a minority of disciplined mask wearers is sufficient to prevent most infection.

Currently, vaccinations are the prominent way to protect against influenza, having been available on a large scale since 1945 [

The economic burden of seasonal influenza in the United States is about US $6.3 to US $25.3 billion [

The limitations of our approach include no stratification by age or contact scenario, significant uncertainties in mask use and efficacy, and disregard of other nonpharmaceutical interventions.

Centers for Disease Control and Prevention

susceptible-exposed-infected-recovered

None declared.