Impact assessment of full and partial stay-at-home orders, face mask usage, and contact tracing: An agent-based simulation study of COVID-19 for an urban region

Abstract

Purpose Social intervention strategies to mitigate COVID-19 are examined using an agent-based simulation model. Outbreak in a large urban region, Miami-Dade County, Florida, USA is used as a case study. Results are intended to serve as a planning guide for decision makers. Methods The simulation model mimics daily social mixing behavior of the susceptible and infected generating the spread. Data representing demographics of the region, virus epidemiology, and social interventions shapes model behavior. Results include daily values of infected, reported, hospitalized, and dead. Results Results show that early implementation of complete stay-at-home order is effective in flattening and reversing the infection growth curve in a short period of time. Whereas, using Florida's Phase II plan alone could result in 75% infected and end of pandemic via herd immunity. Universal use of face masks reduced infected by 20%. A further reduction of 66% was achieved by adding contact tracing with a target of identifying 50% of the asymptomatic and pre-symptomatic. Conclusions In the absence of a vaccine, the strict stay-at-home order, though effective in curbing a pandemic outbreak, leaves a large proportion of the population susceptible. Hence, there should be a strong follow up plan of social distancing, use of face mask, contact tracing, testing, and isolation of infected to minimize the chances of large-scale resurgence of the disease. However, as the economic cost of the complete stay-at-home-order is very high, it can perhaps be used only as an emergency first response, and the authorities should be prepared to activate a strong follow up plan as soon as possible. The target level for contact tracing was shown to have a nonlinear impact on the reduction of the percentage of population infected. Increase in contact tracing target from 20% to 30% appeared to provide the largest incremental benefit.

The Effect of the COVID-19 Pandemic on Riding Lesson Barns and Summer Camps in Ontario

“…However, the advantage of outdoor settings allows the dilution of respiratory droplets much quicker than in indoor environments, which in turn decreases the risk of spread by almost 80% [ 14 ]. Regardless, repeated studies have shown the effectiveness of facemasks in reducing the spread of infection [ 8 , 15 , 16 ], and riding facilities should consider requiring all students and staff to wear facemasks at least when not mounted on a horse. However, knowledge on the spread of COVID-19 is still evolving, and physical distancing rules could be considered based on the factors of the activity undertaken and the ventilation level [ 7 ].…”

Impact of Vaccine Prioritization Strategies on Mitigating COVID-19: An Agent-Based Simulation Study using an Urban Region in the United States

“…As in the model presented in [19], each day our model tracks the following for each person: 1) hourly movements for building such a model for another region can be found in [19]. The methodology begins by generating the individual people according to the U.S. census data that gives population attributes including age (see Table A1 in [19]) and occupational distribution (see Table A4 in [19]). Thereafter, it generates the households based on their composition characterized by the number of adults and children (see Table A2 in [19]).…”

“…The methodology begins by generating the individual people according to the U.S. census data that gives population attributes including age (see Table A1 in [19]) and occupational distribution (see Table A4 in [19]). Thereafter, it generates the households based on their composition characterized by the number of adults and children (see Table A2 in [19]). The model also generates, per census data, schools (see Table A3 in [19]) and the workplaces and other community locations (see Table A4 in [19]).…”

“…However, it is assumed that if a susceptible does not gather any additional force of infection (i.e., does not come in contact with any infected) for two consecutive days, the cumulative force of infection for the susceptible reduces to zero. At the end of each day, The infected people are considered to follow a disease natural history as shown in Figure 3, parameters of which can be found in Table A6 of [19]. The model assumes that the recovered cases become fully immune to further COVID-19 infections.…”

“…The function is truncated at the average length of the infectiousness period (which is considered to be 9.5 days). Asymptomatic cases are assumed to follow a similar infectiousness intensity profile but scaled by a factor in the force of infection calculation (1) (see Table A7 in [19]). The AB model estimates the probability of infection for a susceptible using the accumulated value of daily force of infection ( ), which is calculated as follows.…”