Modeling SARS-CoV-2 transmission at a winter destination resort region with high outside visitation
This article has been Reviewed by the following groups
Listed in
- Evaluated articles (ScreenIT)
Abstract
Travel destinations, particularly large resorts in otherwise small communities, risk infectious disease outbreaks from an influx of visitors who may import infections during peak seasons. The COVID-19 pandemic highlighted this risk in the context of global travel and has raised questions about appropriate interventions to curb the potential spread of infectious disease at tourist destinations. In Colorado, the initial outbreaks of SARS-CoV-2 in the state occurred in ski communities, leading to large economic losses from closures and visitor restrictions. In this study, we modeled SARS-CoV-2 transmission during the 2020-21 season in a ski region of Colorado to determine optimal combinations of intervention strategies that would keep the region below a predetermined threshold of SARS-CoV-2 infection density. This analysis used an age-stratified, deterministic SEIR compartmental model of disease transmission, calibrated to cellphone-based mobility data, to simulate infection trajectories during the winter ski season. Under three national infection levels corresponding to high, medium, and low viral importation risk, we estimated the potential impact of interventions including policy and behavior changes, visitor restriction strategies, and case investigation/contact tracing, in order to quantify the relative and absolute impacts of these interventions in the context of the COVID-19 pandemic. Our results suggest that, in the context of low viral importation risk, case investigation/contact tracing and policy and behavior changes may be sufficient to stay below predetermined infection thresholds without visitor restrictions. However, if viral importation risk is high, visitor restrictions and/or screening for infected visitors would be needed to avoid lockdown-like control scenarios and large outbreaks in tourist communities. These findings provide important guidance to tourist destinations for balancing policy impact in future infectious disease outbreaks.
Article activity feed
-
SciScore for 10.1101/2021.08.18.21262227: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:These results are sensitive to a number of limitations and assumptions, some of which are typical of SIR models (i.e., uniform mixing and an inability to characterize the impact of clustered outbreaks or super-spreading events), and others unique to this study. First, the DIRT was estimated as the last level of infection density before a lockdown should be introduced to prevent the state from reaching ICU capacity, a catastrophic …
SciScore for 10.1101/2021.08.18.21262227: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:These results are sensitive to a number of limitations and assumptions, some of which are typical of SIR models (i.e., uniform mixing and an inability to characterize the impact of clustered outbreaks or super-spreading events), and others unique to this study. First, the DIRT was estimated as the last level of infection density before a lockdown should be introduced to prevent the state from reaching ICU capacity, a catastrophic outcome. In an ideal situation, infection rates would never approach the DIRT, especially given the high rate of morbidity and mortality that occurs at this level of transmission. However, the same model framework can be used for different infection thresholds and the results are expected to be consistent regardless of the defined threshold. Second, across all 12 scenarios, each simulation presumes that the people in the region maintain the same level of transmission control throughout the entire duration of the ski season, regardless of the infection trajectory. In reality, policies and behaviors are likely to evolve as the situation evolves. Known as the “roller coaster” effect, 19 people tend to become more vigilant when infections rise and let down their guard when infections fall. While it is possible to parameterize a model with adaptive behavior,20 empirically distinguishing voluntary behavior from policy and other factors remains challenging.21 Third, the TC parameter may be difficult to interpret as it does not discriminate between the diffe...
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
Results from scite Reference Check: We found no unreliable references.
-
