Simulating phase transitions and control measures for network epidemics caused by infections with presymptomatic, asymptomatic, and symptomatic stages

Buckley

2020

Preprint

Self Cite

Mask wearing is integral to reducing the spread of SARS-CoV-2. Information on prevalence of face mask usage is required both to model disease spread and to improve compliance with mask usage through targeted messaging. We sought to (1) estimate the prevalence of mask usage in the most populous county of Vermont (Chittenden County;>25% of state population) and to (2) assess the effect of age and sex on mask use. We monitored the entrances to eight different business types and visually assessed individuals′ age, gender, and mask use from a distance. We collected 1004 observations from 16 May through 30 May 2020 as businesses began to reopen following an extended state-wide lock down. We analyzed these data using a Bayesian random effects logistic regression model. We found that overall 75.5% of individuals used a mask with significant effects of age and gender on mask usage. Females were more likely to wear masks than males (83.8%, n=488 vs. 67.6%, n=516 mask usage, respectively); the odds of a male wearing a mask was 53% that of the female odds. Across age groups, the elderly were most likely to wear a mask (91.4%, n=209) followed by young adults (74.8%, n=246), middle-aged adults (70.7%, n=519) and children (53.3%, n=30). The odds of an elderly person wearing a mask were 16.7 times that of a child, while the odds for young adults and middle-aged adults were ≈3 times greater than a child. Highest mask usage was in elder females (96.3%, n=109) and lowest mask usage was in male children (43.8%, n=16).

Section: Introductionmentioning

confidence: 99%

Prevalence of mask wearing in northern Vermont in response to SARS-CoV-2

Buckley

2020

Preprint

Self Cite

“…In these studies, face mask use below a threshold percentage of people results in widespread infection of the population in the absence of other mitigation strategies. 5 However, the likelihood of an individual engaging in protective behaviors such as wearing a face mask may vary, with demographic differences among individuals. For example, a meta-analysis of protective behaviors in response to respiratory epidemics found significantly lower compliance among males than among females, 6 as have studies of face mask use during the COVID-19 pandemic.…”

mentioning

confidence: 99%

Prevalence of Face Mask Wearing in Northern Vermont in Response to the COVID-19 Pandemic

2021

Self Cite

Objectives Information on the prevalence of face mask use to reduce the spread of SARS-CoV-2 is needed to model disease spread and to assess the effectiveness of policies that encourage face mask use. We sought to (1) estimate the prevalence of face mask use in northern Vermont and (2) assess the effect of age and sex on the likelihood of face mask use. Methods We monitored the entrances to public businesses and visually assessed age, sex, and face mask use. We collected 1004 observations during May 16-30, 2020. We calculated estimates of overall face mask use and odds ratios (ORs) for effects by age and sex. Results Of 1004 observations, 758 (75.5%) sampled people used a face mask. Our census-weighted estimate was 74.1%. A higher percentage of females than males wore face masks (83.8% vs 67.6%). The odds of face mask use were lower among males than among females (OR = 0.52; 95% CI, 0.37-0.73). Face mask use generally decreased with decreasing age: 91.4% among adults aged >60, 70.7% among adults aged 26-60, 74.8% among people aged 15-25, and 53.3% among people aged ≤14. The OR of an adult aged >60 wearing a face mask was 14.70 times higher, for young people aged 15-25 was 2.72 times higher, and for adults aged 26-60 was 2.99 times higher than for people aged ≤14. Females aged >60 had the highest percentage of face mask use (96.3%) and males aged ≤14 had the lowest (43.8%). Conclusions Educational efforts promoting the use of face masks should be targeted at males and younger age groups to limit the spread of SARS-CoV-2.

“…These models represent a clear departure from the homogeneity assumptions of the traditional well-mixed compartment models and have already been used in the context of COVID-19 epidemic. Specifically, it has been shown that utilizing accurate social network models can provide better insights into the spread of the new coronavirus disease [51] , [52] , [53] , [54] , improve the forecast of the evolution of the epidemic [55] , [56] and help to design efficient strategies to control COVID-19 outbreaks [57] .…”

Section: Introductionmentioning

confidence: 99%

Socio-demographic and health factors drive the epidemic progression and should guide vaccination strategies for best COVID-19 containment

et al. 2021

We propose and study an epidemiological model on a social network that takes into account heterogeneity of the population and different vaccination strategies. In particular, we study how the COVID-19 epidemics evolves and how it is contained by different vaccination scenarios by taking into account data showing that older people, as well as individuals with comorbidities and poor metabolic health, and people coming from economically depressed areas with lower quality of life in general, are more likely to develop severe COVID-19 symptoms, and quicker loss of immunity and are therefore more prone to reinfection. Our results reveal that the structure and the spatial arrangement of subpopulations are important epidemiological determinants. In a healthier society the disease spreads more rapidly but the consequences are less disastrous as in a society with more prevalent chronic comorbidities. If individuals with poor health are segregated within one community, the epidemic outcome is less favorable. Moreover, we show that, contrary to currently widely adopted vaccination policies, prioritizing elderly and other higher-risk groups is beneficial only if the supply of vaccine is high. If, however, the vaccination availability is limited, and if the demographic distribution across the social network is homogeneous, better epidemic outcomes are achieved if healthy people are vaccinated first. Only when higher-risk groups are segregated, like in elderly homes, their prioritization will lead to lower COVID-19 related deaths. Accordingly, young and healthy individuals should view vaccine uptake as not only protecting them, but perhaps even more so protecting the more vulnerable socio-demographic groups.