Fig 1.
Pipeline overview description.
Data is collected as patients attend health institutions. Health professionals register patients’ personal, epidemiological and geo-location data to the Clinical Monitoring System (CMS) that is blended with socio-economical and household data. Using these data we estimate the number of Exposed (blue), Infectious (red) and Recovered (green) individuals. All the pre-processed data is used to calibrate our Stochastic Agent Based model, COMORBUSS. From bottom left to right: a schematic representation of the social dynamics of COMORBUSS, producing contacts between individuals in different social contexts. The colored circles represent the state of individuals, and lines represent relevant physical contacts capable of producing contagions. Once calibrated the model is used to estimate the effectiveness of NPIs.
Fig 2.
A non exhaustive classification of parameters used for a COMORBUSS simulation. Further description of parameters can be found on Section 5 in S1 File, while a complete list of parameters and their values can be found in the Git repository.
Fig 3.
Combination of NPIs measures in comparison to the baseline model settings.
Left panel: Case increase under different scenarios with unvaccinated teachers and staff. Right panel: Case increase under different scenarios with vaccinated teachers and staff. The effective teaching hours in hours/week and case increase in school population with respect to baseline are displayed for each NPIs combination. In case the active monitoring is also applied, the mean and standard deviation over 60 realizations for the effective teaching hours are shown. The proportional increase in the number of cases is displayed as violin plots (median, lower and upper quartiles), with kernel density estimates for distributions.
Fig 4.
Population fraction infected at the end of the simulation period (77 days) under varying vaccination coverage.
Fig 5.
Airborne transmission model inside school environment.
The classroom is an enclosed space in which airborne transmission has a high chance of occurrence. Contaminated particles are spread over the classroom, allowing long range infections. The fresh air rate flow Λ quantifies the classroom ventilation. The quanta concentration C varies in the environment depending on the breathing activity.
Fig 6.
The icons distinguish the non-pharmaceutical interventions evaluated in this study. In scenarios involving masks, the mask penetration factor pm is uniform for all individuals, except for teachers wearing PFF2 masks.
Fig 7.
Sensitivity analysis across mask penetration factor pm.
Cases increase in school population (solid lines) versus the mask penetration (mean values over 60 realizations for each pm value).
Fig 8.
Sensitivity analysis across ventilation Λ.
Cases increase in school population (mean and standard deviation) as a function of classroom ventilation rate. Dashed lines indicate the recommended ventilation rates: Λ1 = 0.8 h−1 (unoccupied room), Λ2 = 3.8 h−1 (half occupied room) and Λ3 = 6.6 h−1 (fully occupied room), following ASHRAE standard for an average classroom in Maragogi.
Fig 9.
Population infected in case of increase in susceptibility.
For each intervention scenario, we show the distribution in the percentile of the population infected provided the susceptibility of the population is increased uniformly by a multiplying factor.