ABSTRACT
Small, Elan, Caleb Phillips, William Bunzel, Lakota Cleaver, Nishant Joshi, Laurel Gardner, Rony Maharjan, and James Marvel. Prior ambulatory mild coronavirus disease 2019 does not increase risk of acute mountain sickness. High Alt Med Biol. 00:000-000, 2023. Background: Given its long-term morbidity, understanding how prior coronavirus disease 2019 (COVID-19) may affect acute mountain sickness (AMS) susceptibility is important for preascent risk stratification. The objective of this study was to examine if prior COVID-19 impacts risk of AMS. Materials and Methods: This was a prospective observational study conducted in Lobuje (4,940 m) and Manang (3,519 m), Nepal, from April to May 2022. AMS was defined by the 2018 Lake Louise Questionnaire criteria. COVID-19 severity was defined using the World Health Organization-developed criteria. Results: In the Lobuje cohort of 2,027, 46.2% of surveyed individuals reported history of COVID-19, with 25.7% AMS point-prevalence. There was no significant relationship between prior ambulatory mild COVID-19 and AMS (p = 0.6) or moderate AMS (p = 1.0). In the Manang cohort of 908, 42.8% reported history of COVID-19, with 14.7% AMS point-prevalence. There was no significant relationship between prior ambulatory mild COVID-19 and AMS (p = 0.3) or moderate AMS (p = 0.4). Average months since COVID-19 was 7.4 (interquartile range [IQR] 3-10) for Lobuje, 6.2 (IQR 3-6) for Manang. Both cohorts rarely exhibited moderate COVID-19 history. Conclusions: Prior ambulatory mild COVID-19 was not associated with increased risk of AMS and should not preclude high-altitude travel.
ABSTRACT
Emerging mobility technologies are changing the transportation system landscape. This is especially evident at airports, such as the Dallas-Fort Worth International Airport (DFW). Without careful analysis, these changes could lead to inefficient and costly airport operations. This paper presents a modeling framework that integrates travel mode encoding, demand projection, and microsimulation to enable airports to develop, simulate, and evaluate curbside traffic managements policies and measure their impact. The framework is utilized to analyze several traffic scenarios and policies for DFW: a baseline scenario which represents DFW traffic pattern as observed in 2018 and projected to 2045, a transit network company (TNC) electrification policy, a TNC queuing policy, a policy that increased transit ridership, a bus-only policy which considers the use of only buses inside DFW, an autonomous vehicle (AV) policy which investigates the impact of autonomous vehicle (AV) adoption on airport operations, and an example COVID-19 scenario which models the impact of the COVID19 pandemic. The simulations’ results demonstrate that: increasing the DFW transit ridership postpones the need for airport curbside expansion the most;encouraging shared-mobility with the bus-only policy produces the most savings in curbside congestion delays;automation and electrification for all passenger vehicle trips to/from DFW generates the most saving in fuel consumption and emissions;and uncontrolled AV adoption incurs the highest increase in fuel consumption, delay, and emissions and could require immediate airport capacity extension. Without policy intervention or investment in additional infrastructure capacity, these results predict the current operations would face significant congestion on high demand days starting as early as 2028. While derived in close partnership with DFW, the methodology presented here can be generalized to any airport.