Evaluating targeted COVID-19 vaccination strategies with agent-based modeling
Fig 3
Time-varying model inputs and indicators of model performance.
Panels (a-d) show model inputs, and (e-i) compare model outputs to observed data. In (e-i), points and cross-hairs indicate observed values, solid lines median trends, and faded lines sample trajectories. Horizontal gridline values are plotted above October 2020. (a) Seasonal forcing has a 6-month period, peaking in January and July each year; we also considered an alternative model with no seasonal forcing, see Section E in S1 Text. for details. (b) Detection and reporting probabilities by disease outcome. (c) Simulated first, second and third vaccine doses distributed statewide in Florida, used to calibrate the model (but not for evaluating strategies). (d) Societal risk perception, which drives personal protective behaviors in the model, is fitted so that cumulative reported cases in the model match empirical case data for FL (black dots in panel e). For approximately the month of April 2020, non-essential businesses were closed in the state, and thus are closed during this period in the model (gray “lockdown” shaded region). Not shown: schools in the model close during the summers and during spring 2020, and are 50% and 80% open during the 2020–2021 and 2021–2022 school years, respectively. (e-i) Simulated data closely track empirical data for incidence of reported cases (e), daily hospital admissions (f), excess deaths (g), seroprevalence (h), and the fraction of infections that occurred in vaccinees (i). Results in (e-g) are scaled to show values per 10,000 individuals, and VOC waves are labeled as alpha (α), delta (δ) and omicron (o). For empirical seroprevalance data in (h), horizontal bars indicate the dates covered by each data point and vertical lines indicate the 95% CI).