Fig 1.
The comorbidity rates that predispose individuals (of ages 45–64) to severe cases of COVID-19 among adults in the United States without (blue) and with (pink) disabilities.
Fig 2.
An egocentric network (i.e., ego network) of an example disabled person on (A) day 43 (before the start of contact-limiting) and (B) day 45 (during contact-limiting).
The two ego networks encode contacts for the same disabled person. The label ‘W1’ denotes the weak caregiver on day 43 and the label ‘W2’ denotes the weak caregiver on day 45. In this example, W1 and W2 are different caregivers. We illustrate the different groups (colors) in our model city, the interaction strengths between individuals (line thicknesses), and distances (numbers) from the ego. The edge weights are relative to the strong-contact weight of 1.
Fig 3.
A schematic illustration of our compartmental model of disease transmission.
Susceptible individuals (S), by being exposed to asymptomatic (A) or symptomatically ill (I) individuals, can become exposed (E) with a baseline transmission probability β. One can reduce the risk level of an interaction through the NPI of mask-wearing; this multiplies the risk level by the factor m1/2 (if only one individual in the interaction wears a mask) or the factor m (if both individuals in the interaction wear a mask). Caregiving interactions have a higher risk level (by a factor wc) than the baseline and weak interactions have a lower risk level (by a factor ww) than the baseline. Exposed individuals are not yet contagious; however, they can eventually transition to the asymptomatic state. From the asymptomatic state, an individual can either become symptomatically ill or be removed (R), which encompasses recovery, death, and any other situation in which an individual is no longer infectious. If an individual is symptomatic, they can either be removed or become hospitalized (H). From the hospitalized state, an individual eventually transitions to the removed state. The state-transition parameters that we have not yet mentioned are fixed rates of exponential processes.
Table 1.
The parameter values that we use in our study.
In the “Source” column, literature indicates that we found a value directly from data in the literature, inferred indicates that we inferred a value based on published data in the literature, by definition signifies a value that we set in our model formulation, chosen indicates that a value is unknown but we made a choice in our model, borrowed indicates that we adopted a value directly from a model in the literature, and fit indicates that we use Ottawa case data along with other (i.e., not fit) parameters in this table to estimate a value.
Fig 4.
A comparison of a mean of 100 simulations of our stochastic model of COVID-19 spread with (left) cumulative documented case counts and (right) the 7-day mean of new daily documented cases.
For each day, we calculate the 7-day mean over a sliding window that includes the previous three days, the current day, and the next three days. We fit the parameters by minimizing the ℓ2-error of the model’s count of daily documented cases over the first 90 days. We show the mean of our model in blue and the Ottawa case data in red. The gray window indicates the middle 95% of these 100 simulations. On day 44 (i.e., 24 March 2020), all subpopulations limit contacts and the (D+C+E) mask-wearing scenario begins. The graphs terminate on day 148, when Ottawa had its first reopening.
Fig 5.
Characterization of centrality measures of subpopulations in the networks on which we run our stochastic model of COVID-19 spread.
The violin plots depict empirical probability densities. The initial situation, for which we show day 43 of one simulation, has no contact-limiting. The distanced situation, for which we show day 45 of the same simulation, has contact-limiting in all subpopulations. For each subpopulation, we calculate the distributions of (A) the number of neighbors (i.e., direct contacts), (B) the number of second neighbors (i.e., contacts of contacts), (C) the strength (i.e., total edge weight) of the contacts with neighbors, and (D) eigenvector centrality.
Fig 6.
Characterization of the effects of mask-wearing on centrality measures of subpopulations in the networks on which we run our stochastic model of COVID-19 spread.
The violin plots depict empirical probability densities. The initial situation, for which we show day 43 of one simulation, has no contact-limiting. The distanced situation, for which we show day 45 of the same simulation, has contact-limiting in all subpopulations. We modify edge weights by supposing that masks have the effectiveness that we indicated in Table 1. To indicate the mask-wearing statuses of different scenarios, we use the notation that we defined in Section 2.2. For each subpopulation, we compute (A) the strength distribution and the (B) eigenvector-centrality distribution.
Fig 7.
The mean number of cumulative infections in the general population (blue), essential workers (purple), caregivers (orange), and disabled people (pink) for different contact-limiting and mask-wearing statuses.
The mask-wearing statuses are the same as in Fig 6.
Fig 8.
The effect of the number of caregivers (4, 10, or 25) that are assigned to a given disabled person on the mean fraction of each subpopulation that becomes infected.
The label “DCE PPE” refers to the (D+C+E) mask-wearing scenario.
Fig 9.
The effects of (A) the probability of breaking weak contacts when ill, (B) mask effectiveness on the mean fraction that each subpopulation becomes infected, and (C) the percent of the population that serve as caregivers.
Fig 10.
The fraction of each subpopulation that is infected through day 148 when all of the initially infected individuals are in a single subpopulation.
On day 44, all groups limit contacts and the (D+C+E) mask-wearing scenario begins.
Fig 11.
The infections that are prevented in each subpopulation when one subpopulation is vaccinated with a limited number of vaccines.
(A) Timeline of contact-limiting and reopening in our simulations. After targeted vaccination occurs on day 148, there are no contact-limiting measures, but all individuals wear PPE during non-household interactions. (B) The total number of infections that are avoided between day 148 and day 300 in each subpopulation after vaccinating a limited number of individuals in a given subpopulation. (C) The percent of infections that are avoided in each subpopulation between day 148 and day 300 after vaccinating a limited number of individuals in a given subpopulation.