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TRACE-STL

TRACE (Testing Responses through Agent-based Computational Epidemiology) is a collaborative effort by researchers from The Brookings Institution and Washington University in St. Louis to produce a sophisticated computational simulation model to inform policy responses to the COVID-19 pandemic. It draws on the extensive body of evidence about both the current and past epidemics, and is also designed to manage a high degree of remaining uncertainty about some of the parameters it uses.  Previous work focused on U.S. national-level analysis and policy. The version of the simulation model described here has been extended and adapted to represent the St Louis metro area geographically and demographically and is focused on how the COVID-19 pandemic might be effectively contained in St. Louis through summer, 2021. For more details on the model design, see “About TRACE-STL” above.

Key initial results

Overall, we conducted over approximately 50,000 simulations representing over 1,000 different combinations of containment policy options across a range of epidemiological parameters (see “About TRACE-STL” for details). Below, we summarize four key results from our initial analysis of these simulations.

[1] With current infectivity, existing control measures and the planned vaccination campaign are likely sufficient to contain spread

Our simulations show that current containment measures in place in the St. Louis metro region (testing, quarantine, mask protocols, and social distancing) can contain the spread of COVID-19 and continue suppression of the epidemic, if:

  • Infectivity remains low (e.g. new, more contagious variants do not become widespread)
  • The current rates of testing, test positivity, and adherence to control measures are maintained

Fig 1A Total Virus PrevalenceFigure 1a. Proportion of residents with active COVID-19 infections in St. Louis city at each time point during the simulated time period. Each blue line represents an individual simulation run that reflects our best available estimates of current epidemiological and containment conditions, with the thickest line indicating the median one. The dashed line indicates the peak value, which might have implications for things such as strain on health care resources.

Figure 1B Cumulative InfectionsFigure 1b. Cumulative proportion of residents who experienced COVID-19 infections in St. Louis city during the simulated time period. Each blue line represents an individual simulation run that reflects our best available estimates of current epidemiological and containment conditions, with the thickest line indicating the median one with respect to total cumulative count. The dashed line indicates the total value reached during the median run, which might have implication for outcomes such as incidence of severe cases and mortality rates.

In the simulations, continued control and reduction in numbers of new cases can be attained in this scenario even without widespread closures of businesses or schools. For full details of the simulated assumptions, as well as parallel results for the entire St Louis metro region, see the TRACE-STL Dashboard.

[2] If infectivity increases, additional control measures will be needed to prevent a substantial increase in new cases

Our simulations show that significant increases in infectivity, such as would be expected if new COVID variants become widespread, are likely to undermine existing control measures in the short run, resulting in a large additional wave of new infections over the coming several months. Final cumulative infection rates in such scenarios are projected in the simulations to be 6-10 times higher than today’s levels.

Figure 2A Total Virus PrevalenceFigure 2a. Proportion of residents with active COVID-19 infections in St. Louis city at each time point during the simulated time period. Each blue line represents an individual simulation run that reflects our best available estimates of current containment conditions and a greater prevalence of more infectious variants of COVID-19, with the thickest line indicating the median one. The dashed line indicates the peak value, which might have implications for things such as strain on health care resources.

Figure 2B Cumulative InfectionsFigure 2b. Cumulative proportion of residents who experienced COVID-19 infections in St. Louis city during the simulated time period. Each blue line represents an individual simulation run that reflects our best available estimates of current containment conditions and a greater prevalence of more infectious variants of COVID-19, with the thickest line indicating the median one. The dashed line indicates the value reached during the median run, which might have implication for outcomes such as incidence of severe cases and mortality rates.

For full details of the simulated assumptions, as well as parallel results for the entire St Louis metro region, see the TRACE-STL Dashboard.

[3] Multiple robust policy options exist for increased control of COVID spread, even with higher infectivity

Even with much higher infectivity (e.g. from new variants), our simulations find multiple distinct policy options to effectively control the spread of COVID and keep growth in new infections relatively low. Three are detailed here and a potential fourth is discussed separately below.

[a] Increased investment in regional contact tracing capacity

Our simulations find that increasing the region’s capacity to successfully and quickly trace contacts of confirmed or suspected COVID cases can significantly contain spread even with high-infectivity variants. The graphs below show reductions in cumulative infection rate by >60% by increasing contact tracing capacity ten-fold (policy B vs policy A in Figure 3a), while leaving all other policies unchanged.

Figure 3A Cumulative InfectionsFigure 3a.

[b] Increased investment in PCR testing

Our simulations show that increases in the rate of PCR testing in the region would also substantially cut spread of infection. This is true even with all other policies held constant, but is especially effective in combination with increased contact tracing [see policy B vs policy A in Figure 3b]

Figure 3B Cumulative InfectionsFigure 3b.

[c] Restrictions on in-person business activity 

Our simulations show that these policies can significantly curtail spread [gold lines below in Figure 3c], but only when undertaken region-wide across the St Louis metro [top panel of Figure 3c] instead of unilaterally in the City [bottom panel in Figure 3c]

Figure 3C-i Cumulative InfectionsFigure 3C-ii Cumulative Infections

Figure 3c.

Due to the high degree of intermixing of contact across city lines with nearby regions in the metro area, simulation show policies must be region wide to maximize impact.

[4] Effective and widespread mask usage continues to be an important means of control over the spread of COVID-19, especially if new variants become widespread

Our model allows for varying assumptions regarding both the level of adherence to mask-wearing (what fraction of the population regularly wears masks when in contact with other people outside their home) and the assumed “effectiveness” of masks in blocking transmission (relative to an “ideal” of well-fitted, properly worn masks in a clinical setting). Simulations show both of these factors can have significant impact on spread, especially when infectivity of the pathogen is high.

Mask Adherence

Mask Effectiveness

Low

High

Low

43.4

37.4

Moderate

34.4

23.1

High

23.5

11.8

Table 1. Cumulative Percentage of St. Louis City Population Infected After 6 Simulated Months across varying combinations of masking assumptions, holding other control measures to our best estimates of current conditions and assuming a greater prevalence of more infectious variants of COVID-19.

This suggests that investments in maximizing proper use of masks across the population over the coming months may be an important policy goal.

 Additional results and future work

The full set of results from our models can be explored using the interactive TRACE-STL Dashboard, a user-friendly tool that allows for adjustment of and comparison across epidemiological and control conditions as well as the selection of viewing city or region-wide outcomes. In the results presented above and in the Dashboard, we focus on overall infection rates (both over time and cumulative). We do this because they have important public health implications (e.g. for health care capacity and incidences of severe or fatal Covid cases). However, because the TRACE model places simulated individuals in a realistic geographic environment, we have the ability to disaggregate these “top level” numbers to identify where Covid prevalence is higher or lower geographically under different conditions.

These two maps show two different scenarios that illustrate the potential importance of considering the geographic location of infections. In the first, overall prevalence appears to be driven largely by a large number of cases in one part of St. Louis. In the second, there are multiple areas that experience large numbers of Covid infections.

TRACE STL Map 1TRACE-STL Map 2Figure 6a and b. Map of St Louis City, with zip code boundaries and shading showing simulated cumulative infection rates

Disaggregated results and spatial analysis will allow for additional work with TRACE-STL to focus on disparities and equity, as well as potential policy options to address these. Additional results will be added to this page as they become available.