Wastewater-based SARS-CoV-2 airport surveillance: key trends at the Cape Town International Airport.

Monitoring of SARS-CoV-2 RNA in wastewater has revealed the role of mobility in the transmission of coronavirus disease (COVID-19), and the surveillance of airport wastewater in cities across the world has demonstrated how travel entry points can give an indication of trends in transmission. This study undertook wastewater surveillance at the Cape Town International Airport (CTIA) to assess the use of a WBE approach to provide supplementary information on the presence of COVID-19 at a key air travel entry point in South Africa. Grab wastewater samples (n = 55) were collected from the CTIA wastewater pump station and analysed using quantitative real-time polymerase chain reaction (qRT-PCR) method. The study found a correlation between the wastewater data and clinical cases reported in the City of Cape Town during various time periods and during the peak of a COVID-19 wave. Highly elevated viral loads in the wastewater were observed at times there was increased mobility through the airport. The study also revealed elevated viral load levels at the airport despite the stricter restrictions and through the lower restrictions. The study findings indicate wastewater surveillance and airports can provide supplementary information to airport authorities to assess the impacts of imposed travel restrictions.

Models of Aviation Noise Impact in the Context of Operation Decrease at Tan Son Nhat Airport.

Air traffic bans in response to the spread of the coronavirus have changed the sound situation of urban areas around airports. This study aimed to investigate the effect of this unprecedented event on the community response to noise before and after the international flight operation at Tan Son Nhat Airport (TSN) in March 2020. The "before" survey was conducted in August 2019, and the two "after" surveys were conducted in June and September 2020. Structural equation models (SEMs) for noise annoyance and insomnia were developed by linking the questionnaire items of the social surveys. The first effort aimed to achieve a common model of noise annoyance and insomnia, corresponding to the situation before and after the change, respectively. Approximately, 1200 responses were obtained from surveys conducted in 12 residential areas around TSN in 2019 and 2020. The average daily flight numbers observed in August 2019 during the two surveys conducted in 2020 were 728, 413, and 299, respectively. The sound pressure levels of the 12 sites around TSN decreased from 45-81 dB (mean = 64, SD = 9.8) in 2019 to 41-76 dB (mean = 60, SD = 9.8) and 41-73 dB (mean = 59, SD = 9.3) in June and September 2020, respectively. The SEM indicated that the residents' health was related to increased annoyance and insomnia.

COVID-19 cases reported in Colorado following screening at selected US airports, January - July 2020.

OBJECTIVE: We sought to estimate the proportion of air travelers who may have been infected with SARS-CoV-2 upon arrival to Colorado by comparing data on Colorado residents screened upon entering the US to COVID-19 cases reported in the state. Data on Colorado's screened passengers arriving into the US between January 17 and July 30, 2020 were compared to Colorado's Electronic Disease Reporting System. We conducted a descriptive analysis of true matches, including age, gender, case status, symptom status, time from arrival to symptom onset (days), and time from arrival to specimen collection date (days). RESULTS: Fourteen confirmed COVID-19 cases in travelers who were diagnosed within 14 days after arriving in Colorado were matched to the 8,272 travelers who underwent screening at 15 designated airports with a recorded destination of Colorado, or 0.2%. Most (N = 13/14 or 93%) of these infected travelers arrived in Colorado in March 2020; 12 (86%) of them were symptomatic. Entry screening for COVID-19 and the sharing of traveler information with the Colorado Department of Public Health and Environment appeared to identify few cases early in the pandemic. Symptom-based entry screening and sharing of traveler information was minimally effective at decreasing travel-associated COVID-19 transmission.

A novel resilience analysis methodology for airport networks system from the perspective of different epidemic prevention and control policy responses.

As the COVID-19 pandemic fades, the aviation industry is entering a fast recovery period. To analyze airport networks' post-pandemic resilience during the recovery process, this paper proposes a Comprehensive Resilience Assessment (CRA) model approach using the airport networks of China, Europe, and the U.S.A as case studies. The impact of COVID-19 on the networks is analyzed after populating the models of these networks with real air traffic data. The results suggest that the pandemic has caused damage to all three networks, although the damages to the network structures of Europe and the U.S.A are more severe than the damage in China. The analysis suggests that China, as the airport network with less network performance change, has a more stable level of resilience. The analysis also shows that the different levels of stringency policy in prevention and control measures during the epidemic directly affected the recovery rate of the network. This paper provides new insights into the impact of the pandemic on airport network resilience.

Sniffer dogs performance is stable over time in detecting COVID-19 positive samples and agrees with the rapid antigen test in the field.

Rapid antigen diagnostic (RAD) tests have been developed for the identification of the SARS-CoV-2 infection. However, they require nasopharyngeal or nasal swab, which is invasive, uncomfortable, and aerosolising. The use of saliva test was also proposed but has not yet been validated. Trained dogs may efficiently smell the presence of SARS-CoV-2 in biological samples of infected people, but further validation is needed both in laboratory and in field. The present study aimed to (1) assess and validate the stability over a specific time period of COVID-19 detection in humans' armpit sweat by trained dogs thanks to a double-blind laboratory test-retest design, and (2) assess this ability when sniffing people directly. Dogs were not trained to discriminate against other infections. For all dogs (n. 3), the laboratory test on 360 samples yielded 93% sensitivity and 99% specificity, an 88% agreement with the Rt-PCR, and a moderate to strong test-retest correlation. When sniffing people directly (n. 97), dogs' (n. 5) overall sensitivity (89%) and specificity (95%) were significantly above chance level. An almost perfect agreement with RAD results was found (kappa 0.83, SE 0.05, p = 0.001). Therefore, sniffer dogs met appropriate criteria (e.g., repeatability) and WHO's target product profiles for COVID-19 diagnostics and produced very promising results in laboratory and field settings, respectively. These findings support the idea that biodetection dogs could help reduce the spread of the virus in high-risk environments, including airports, schools, and public transport.

Effect of Predeparture Testing on Postarrival SARS-CoV-2-Positive Test Results Among International Travelers - CDC Traveler-Based Genomic Surveillance Program, Four U.S. Airports, March-September 2022.

Beginning December 6, 2021, all international air passengers boarding flights to the United States were required to show either a negative result from a SARS-CoV-2 viral test taken ≤1 day before departure or proof of recovery from COVID-19 within the preceding 90 days (1). As of June 12, 2022, predeparture testing was no longer mandatory but remained recommended by CDC (2,3). Various modeling studies have estimated that predeparture testing the day before or the day of air travel reduces transmission or importation of SARS-CoV-2 by 31%-76% (4-7). Postarrival SARS-CoV-2 pooled testing data from CDC's Traveler-based Genomic Surveillance program were used to compare SARS-CoV-2 test results among volunteer travelers arriving at four U.S. airports during two 12-week periods: March 20-June 11, 2022, when predeparture testing was required, and June 12-September 3, 2022, when predeparture testing was not required. In a multivariable logistic regression model, pooled nasal swab specimens collected during March 20-June 11 were 52% less likely to be positive for SARS-CoV-2 than were those collected during June 12-September 3, after adjusting for COVID-19 incidence in the flight's country of origin, sample pool size, and collection airport (adjusted odds ratio [aOR] = 0.48, 95% CI = 0.39-0.58) (p<0.001). These findings support predeparture testing as a tool for reducing travel-associated SARS-CoV-2 transmission and provide important real-world evidence that can guide decisions for future outbreaks and pandemics.

Agent-Based Simulation for Infectious Disease Modelling over a Period of Multiple Days, with Application to an Airport Scenario.

With the COVID-19 pandemic, the role of infectious disease spreading in public places has been brought into focus more than ever. Places that are of particular interest regarding the spread of infectious diseases are international airport terminals, not only for the protection of staff and ground crew members but also to help minimize the risk of the spread of infectious entities such as COVID-19 around the globe. Computational modelling and simulation can help in understanding and predicting the spreading of infectious diseases in any such scenario. In this paper, we propose a model, which combines a simulation of high geometric detail regarding virus spreading with an account of the temporal progress of infection dynamics. We, thus, introduce an agent-based social force model for tracking the spread of infectious diseases by modelling aerosol traces and concentration of virus load in the air. We complement this agent-based model to have consistency over a period of several days. We then apply this model to investigate simulations in a realistic airport setting with multiple virus variants of varying contagiousness. According to our experiments, a virus variant has to be at least twelve times more contagious than the respective control to result in a level of infection of more than 30%. Combinations of agent-based models with temporal components can be valuable tools in an attempt to assess the risk of infection attributable to a particular virus and its variants.

Preparing for the Next Pandemic: An After-Action Review of Infection Prevention and Control Audit Strategies Within Airport and Hotel Quarantine Program.

BACKGROUND: Effective infection prevention and control (IPAC) practices within the New South Wales (NSW) Airport Operations and Hotel Quarantine Program (Quarantine Program) were required to mitigate the risk of COVID-19 being transmitted to staff, other guests, contractors, and the community. METHOD: The Quarantine Program relied on complex logistical arrangements and an end-to-end process that included all steps from the time travelers boarded the returning flight until completion of the quarantine period. This required compliance with relevant IPAC standards historically reserved for health care and the implementation of a quality assurance audit framework. RESULTS: The Clinical Excellence Commission (CEC) as the NSW Health Pillar for quality and safety and the lead in IPAC provided training and resources coupled with an IPAC quality audit framework after program commencement. CONCLUSION: This approach ensured a clear governance structure and a regular review process to minimize risk and support continuous improvement within the program.

Automated System for Multiplexing Detection of COVID-19 and Other Respiratory Pathogens.

OBJECTIVE: Infectious diseases are global health challenge, impacted the communities worldwide particularly in the midst of COVID-19 pandemic. The need of rapid and accurate automated systems for detecting pathogens of concern has always been critical. Ideally, such systems shall detect a large panel of pathogens simultaneously regardless of well-equipped facilities and highly trained operators, thus realizing on-site diagnosis for frontline healthcare providers and in critical locations such as borders and airports. METHODS & RESULTS: Avalon Automated Multiplex System, AAMST, is developed to automate a series of biochemistry protocols to detect nucleic acid sequences from multiple pathogens in one test. Automated processes include isolation of nucleic acids from unprocessed samples, reverse transcription and two rounds of amplifications. All procedures are carried out in a microfluidic cartridge performed by a desktop analyzer. The system was validated with reference controls and showed good agreement with their laboratory counterparts. In total 63 clinical samples, 13 positives including those from COVID-19 patients and 50 negative cases were detected, consistent with clinical diagnosis using conventional laboratory methods. CONCLUSIONS: The proposed system has demonstrated promising utility. It would benefit the screening and diagnosis of COVID-19 and other infectious diseases in a simple, rapid and accurate fashion. Clinical and Translational Impact Statement- A rapid and multiplex diagnostic system proposed in this work can clinically help to control spread of COVID-19 and other infectious agents as it can provide timely diagnosis, isolation and treatment to patients. Using the system at remoted clinical sites can facilitate early clinical management and surveillance.

Exploring the Transmission Path, Influencing Factors and Risk of Aerosol Transmission of SARS-CoV-2 at Xi'an Xianyang International Airport.

SARS-CoV-2 genetic sequence results collected from native COVID-19 cases who waited or saw relatives off at Xi'an Xianyang International Airport were highly consistent with the imported cases. In order to explore the routes of transmission and influencing factors that may cause the transmission of SARS-CoV-2 at the airport, a field simulation experiment of aerosol diffusion was adopted based on epidemiological survey data and a detailed field investigation of airport structure and ventilation. The results showed that the inbound passengers waited for approximately 3 h in the rest area on the first level of the international arrival area (Zone E). During the period, masks were removed for eating and drinking, resulting in the viral aerosols rising from the first level to the second level with hot air. After deplaning, the inbound passengers handled the relevant procedures and passed through the corridor on the second floor. The local side wall of the corridor adopted fan coil air conditioning, combined with fresh air supply and personnel walking, resulting in airflow flowing to Zone E. After merging with diffused air containing virus aerosol from the first floor, it continued to spread upward to the connected third-layer area. There was a local suspended ceiling on the top of the third floor, but it was approximately 4 m high and connected to the corridor from Terminal 2 to Terminal 3. When the virus aerosol diffused above the Terminal 2-Terminal 3 corridor, where the temperature was low and the air diffused downward, it could cause an infection risk for people passing through the corridor. In addition, the investigation found that the exhaust pipes of the nucleic acid sampling rooms at the international arrival corridor were directly discharged outdoors without treatment. Only one exhaust pipe and poor ventilation in the bathroom in Zone E had a risk of viral aerosol diffusion. Therefore, the international arrival area should be set up alone or separated from the other areas by hard isolation to avoid the existence of communication between different areas that could cause viral aerosols to diffuse with airflow. The toilet ventilation should be increased to avoid the accumulation of viral aerosols at high concentrations. The exhaust pipes of the toilet and the nucleic acid sampling rooms should be equipped with disinfection and efficient filtration devices, and high-altitude emission should be adopted to reduce the risk of virus aerosol diffusion.

Diagnostic accuracy of a SARS-CoV-2 rapid antigen test among military and civilian personnel of an Air Force airport in central Italy.

BACKGROUND: Most SARS-CoV-2 rapid antigen detection tests (RADTs) validation studies have been performed on specimens from COVID-19 patients and negative controls or from mostly symptomatic individuals. Herein we evaluated the diagnostic accuracy of AFIAS COVID-19 Ag, hereinafter denominated as AFIAS, during a COVID-19 screening program surveillance testing conducted among personnel of an Italian military airport. METHODS: Nasopharyngeal swabs (NPSs) were collected from study participants and were analysed by both AFIAS and RT-PCR assay. A questionnaire collecting demographic and exposure data were administered to all participants. AFIAS accuracy parameters including Cohen's kappa (K) were determined. RESULTS: Overall, from November 2020 to April 2021, 1294 (NPSs) were collected from 1183 participants (88.6% males, 11.4% females; mean age were 41.3, median age 42). Forty-nine NPSs (3.78%) were positive by RT-PCR, while 54 NPSs were positive by AFIAS. Overall baseline sensitivity, specificity, positive and negative predictive values were 0.633, 0.981, 0.574, 0.985, respectively and K was 0.585 (moderate). AFIAS sensitivity tended to be higher for NPSs with higher viral load. A higher sensitivity (0.944) compared to the overall baseline sensitivity (0.633) was also found for NPSs from participants with COVID-19 compatible symptoms, for which K was 0.891 (almost perfect). Instead, AFIAS sensitivity was quite poor for NPSs from asymptomatic participants. Most false negative NPSs in this group had moderate viral load. CONCLUSION: Overall, AFIAS showed high specificity but only moderate sensitivity, mainly because of the high proportion of asymptomatic participants. However, AFIAS showed good sensitivity for NPSs with high viral load and nearly optimal accuracy parameters for NPSs from participants with COVID-19 compatible symptoms. Thus, taking into consideration its performance features, this test can be useful for COVID-19 case identification and management as well as for infection control.

The impact of the COVID-19 outbreak on the air traffic noise at the Hannover airport region.

In this work, the impact of the COVID-19 outbreak on the environmental noise generated by the air traffic at the Hannover Airport, Germany, is assessed. For this purpose, a comparative study of the air traffic noise in the years 2019 and 2020 is conducted by means of publicly available measurement data and computational simulations. Based on environmental noise directives defined by the responsible German authorities, the comparative study is conducted in terms of A-weighted equivalent sound pressure level metrics computed for the six months of the forecast years with the largest number of flights. In comparison with the year of 2019, the measurement data indicates that the L , L, and L were reduced in average by 2.4, 4.2, and 3.7 dBA, respectively, in the year 2020. Furthermore, the results based on the computational simulations show that the isocontour areas of the L = 60 dBA and L = 55 dBA noise protection zones defined by the German federal government were reduced by ≈ 40.29 % and ≈ 54.08 %, respectively, in the year of 2020.

The impact of air travel on the precocity and severity of COVID-19 deaths in sub-national areas across 45 countries.

Human travel fed the worldwide spread of COVID-19, but it remains unclear whether the volume of incoming air passengers and the centrality of airports in the global airline network made some regions more vulnerable to earlier and higher mortality. We assess whether the precocity and severity of COVID-19 deaths were contingent on these measures of air travel intensity, adjusting for differences in local non-pharmaceutical interventions and pre-pandemic structural characteristics of 502 sub-national areas on five continents in April-October 2020. Ordinary least squares (OLS) models of precocity (i.e., the timing of the 1st and 10th death outbreaks) reveal that neither airport centrality nor the volume of incoming passengers are impactful once we consider pre-pandemic demographic characteristics of the areas. We assess severity (i.e., the weekly death incidence of COVID-19) through the estimation of a generalized linear mixed model, employing a negative binomial link function. Results suggest that COVID-19 death incidence was insensitive to airport centrality, with no substantial changes over time. Higher air passenger volume tends to coincide with more COVID-19 deaths, but this relation weakened as the pandemic proceeded. Different models prove that either the lack of airports in a region or total travel bans did reduce mortality significantly. We conclude that COVID-19 importation through air travel followed a 'travel as spark' principle, whereby the absence of air travel reduced epidemic risk drastically. However, once some travel occurred, its impact on the severity of the pandemic was only in part associated with the number of incoming passengers, and not at all with the position of airports in the global network of airline connections.

Risk Factors Associated with Passengers with Imported Dengue Fever at International Airports in Taiwan.

Dengue fever (DF) is a mosquito-borne disease prevalent in the tropics (e.g., sub-Saharan Africa, Asia, and Central and South America) and a common cause of febrile illness in travelers. The high incidence of imported DF in Taiwan has led to a domestic outbreak. This study explored the risk factors associated with individuals given diagnoses of imported DF at international airports in Taiwan. The results may serve as a reference for DF prevention. In this retrospective study, data from the symptom notification system database of the Taiwan Centers for Disease Control (TCDC) were used. These data concerned travelers who returned to Taiwan from DF-endemic areas with suspected DF symptoms. The epidemiological characteristics of the cases were analyzed, and 28 variables related to DF infection were included in the multivariate logistic regression analysis. In 2018-2019, there were 8656 cases (451 positive and 8205 negative cases). The results revealed DF symptoms and a 16-30-day stay in endemic areas to be independent risk factors and the presence of three respiratory symptoms and <10 days of short-term travel to be protective factors. These results may enable the accurate assessment of symptoms in travelers with DF as well as the risk factors associated with imported DF, lowering the risk of indigenous DF outbreaks caused by imported DF.

Marginal reduction in surface NO2 attributable to airport shutdown: A machine learning regression-based approach.

Emissions from aviation and airport-related activities degrade surface air quality but received limited attention relative to regular transportation sectors like road traffic and waterborne vessels. Statistically, assessing the impact of airport-related emissions remains a challenge due to the fact that its signal in the air quality time series data is largely dwarfed by meteorology and other emissions. Flight-ban policy has been implemented in a number of cities in response to the COVID-19 spread since early 2020, which provides an unprecedented opportunity to examine the changes in air quality attributable to airport closure. It would also be interesting to know whether such an intervention produces extra marginal air quality benefits, in addition to road traffic. Here we investigated the impact of airport-related emissions from a civil airport on nearby NO2 air quality by applying machine learning predictive model to observational data collected from this unique quasi-natural experiment. The whole lockdown-attributable change in NO2 was 16.7 µg/m3, equals to a drop of 73% in NO2 with respect to the business-as-usual level. Meanwhile, the airport flight-ban aviation-attributable NO2 was 3.1 µg/m3, accounting for a marginal reduction of 18.6% of the overall NO2 change that driven by the whole lockdown effect. The airport-related emissions contributed up to 24% of the local ambient NO2 under normal conditions. Additionally, the average impact of airport-related emissions on the nearby air quality was ∼0.01 ± 0.001 µg/m3 NO2 per air-flight. Our results highlight that attention needs to be paid to such a considerable emission source in many places where regular air quality regulatory measures were insufficient to bring NO2 concentration into compliance with the health-based limit.

Diversity of West Nile and Usutu virus strains in mosquitoes at an international airport in Austria.

Increased globalization and international transportation have resulted in the inadvertent introduction of exotic mosquitoes and new mosquito-borne diseases. International airports are among the possible points of entry for mosquitoes and their pathogens. We established a mosquito and mosquito-borne diseases monitoring programme at the largest international airport in Austria and report the results for the first two years, 2018 and 2019. This included weekly monitoring and sampling of adult mosquitoes, and screening them for the presence of viral nucleic acids by standard molecular diagnostic techniques. Additionally, we surveyed the avian community at the airport, as birds are potentially amplifying hosts. In 2018, West Nile virus (WNV) was detected in 14 pools and Usutu virus (USUV) was detected in another 14 pools of mosquitoes (minimum infection rate [MIR] of 6.8 for each virus). Of these 28 pools, 26 consisted of female Culex pipiens/torrentium, and two contained male Culex sp. mosquitoes. Cx. pipiens/torrentium mosquitoes were the most frequently captured mosquito species at the airport. The detected WNV strains belonged to five sub-clusters within the sub-lineage 2d-1, and all detected USUV strains were grouped to at least seven sub-clusters among the cluster Europe 2; all strains were previously shown to be endemic in Austria. In 2019, all mosquito pools were negative for any viral nucleic acids tested. Our study suggests that airports may serve as foci of arbovirus activity, particularly during epidemic years, and should be considered when designing mosquito control and arbovirus monitoring programmes.

SARS-CoV-2 molecular diagnosis at airports to minimize travel-related COVID-19 spread.

Following the identification of SARS-CoV-2, screening for air travel helped mitigate spread, yet lessons learned from a case study of air travel within Canada display enhanced techniques to better identify infected individuals, informing future responsive screening. While international travel bans limit infectious spread beyond a country's borders, such measures are hardly sustainable economically and infrequently address domestic travel. Here, we describe a case study from Canada, where a diagnostic laboratory at point of travel conducted real-time PCR-based detection of SARS-CoV-2 in support of existing interventions, including clinical and epidemiological questionnaires, and temperature checks. All mining workers departing from a populated urban area flying to one of two sites (Site A and B) in a remote northern Canadian region, which we deemed "at-risk", because healthcare services are limited and vulnerable to epidemics. Data collected between June and November 2020 on 15,873 clinical samples, indicate that molecular diagnosis allowed for identification of 13 infected individuals, who would have otherwise been missed by using solely nonpharmaceutical interventions. Overall, no outbreaks, COVID-19-related or other, were detected at the point of travel up to December 2021 since the implementation of the laboratory, suggesting this screening process is an effective means to protect at-risk communities. The success of this study suggests a process more practical than travel bans or an unfocused screening of air travelers everywhere.

National emissions inventory and future trends in greenhouse gases and other air pollutants from civil airports in China.

Civil aviation is an important source of air pollutants, but this field has received insufficient attention in China. Based on the standard emissions model of the International Civil Aviation Organization (ICAO) and actual flight information from 241 airports, this study estimated a comprehensive emissions inventory for 2010-2020 by considering the impacts of mixing layer height. The results showed that annual pollutant emissions rapidly trended upward along with population and economic growth; however, the emissions decreased owing to the impacts of the COVID-19 pandemic. In 2020, the emissions of carbon monoxide (CO), nitrogen oxides (NOX), particulate matter (PM), methane (CH4), nitrous oxide (N2O), carbon dioxide (CO2), and water vapor (H2O) were 34.34, 65.73, 0.10, 0.34, 0.40, 14,706.26, and 5733.11 Gg, respectively. The emissions of total volatile organic compounds (VOCs) from China's civil airports in 2020 were estimated at 17.20 Gg; the major components were formic acid (1.70 Gg), acetic acid (1.62 Gg), 1-butylene (1.03 Gg), acetone (0.96 Gg), and acetaldehyde (0.93 Gg). The distribution of pollutant emissions was consistent with the level of economic development, mainly in Beijing, Guangzhou, and Shanghai. In addition, we estimated future pollution trends for the aviation industry under four scenarios. Under the comprehensive scenario, which considered the impacts of economic growth, passenger turnover, cargo turnover, COVID-19, and technological efficiency, the levels of typical pollutants were expected to increase by nearly 1.51-fold from 2010 to 2035.

Effect of Omicron on the prevalence of COVID-19 in international travelers at the Mexico city international airport. December 16th, 2021 to January 31st, 2022.

INTRODUCTION: SARS-CoV-2 continues to have a high rate of contagion worldwide. The new variant of concern, Omicron, has mutations that decrease the effectiveness of vaccines and evade antibodies from previous infections resulting in a fourth wave of the pandemic. It was identified in Mexico in December 2021. METHODS: The Traveler's Preventive Care Clinic from the Faculty of Medicine UNAM at Mexico City International Airport has performed rapid antigen and PCR SARS CoV2 tests since January 2021 to comply with the new travel requirements. Demographic and clinical characteristics were collected from each passenger and the fourth wave of the pandemic in Mexico mainly caused by Omicron was analyzed in the travelers. RESULTS: A total of 5176 travelers attended the clinic between the second half of December 2021 and January 2022. Ten percent of all the tests performed were positive (13% of PCR and 9.3% of antigens, p = 0.001). Most of the SARS CoV2 positive cases were asymptomatic (78%), with a ratio of 3.5:1 over the symptomatic. By age groups, this ratio was higher for those under 20 years old (8.7:1). DISCUSSION: This study shows the rapid escalation of positivity that occurred in Mexico, detected in travelers, from the second half of December 2020 and throughout the month of January 2021. The incidence of COVID-19 was extremely high in travelers who were mostly asymptomatic for the period under study.