Poor air passenger knowledge of COVID-19 symptoms and behaviour undermines strategies aimed at preventing the import of SARS-CoV-2 into the UK.

Air travel mediates transboundary movement of SARS-CoV-2. To prepare for future pandemics, we sought to understand air passenger behaviour and perceived risk during the COVID-19 pandemic. This study of UK adults (n = 2103) quantified knowledge of COVID-19 symptoms, perceived health risk of contracting COVID-19, likelihood of returning to the UK with COVID-19 symptoms, likelihood to obey self-quarantining guidelines, how safe air travellers felt when flying during the pandemic (n = 305), and perceptions towards face covering effectiveness.Overall knowledge of COVID-19 symptoms was poor. Men and younger age groups (18-44) were less informed than women and older age groups (44 +). A significant proportion (21%) of the population would likely travel back to the UK whilst displaying COVID-19 symptoms with many expressing that they would not fully comply with self-isolation guidelines. Overall, males and younger age groups had a reduced perceived personal risk from contracting COVID-19, posing a higher risk of transporting SARS-CoV-2 back to the UK. Poor passenger knowledge and behaviour undermines government guidelines and policies aimed at preventing SARS-CoV-2 entry into the UK. This supports the need for stricter, clearer and more targeted guidelines with point-of-departure viral testing and stricter quarantining upon arrival.

How to avoid a local epidemic becoming a global pandemic.

Here, we combine international air travel passenger data with a standard epidemiological model of the initial 3 mo of the COVID-19 pandemic (January through March 2020; toward the end of which the entire world locked down). Using the information available during this initial phase of the pandemic, our model accurately describes the main features of the actual global development of the pandemic demonstrated by the high degree of coherence between the model and global data. The validated model allows for an exploration of alternative policy efficacies (reducing air travel and/or introducing different degrees of compulsory immigration quarantine upon arrival to a country) in delaying the global spread of SARS-CoV-2 and thus is suggestive of similar efficacy in anticipating the spread of future global disease outbreaks. We show that a lesson from the recent pandemic is that reducing air travel globally is more effective in reducing the global spread than adopting immigration quarantine. Reducing air travel out of a source country has the most important effect regarding the spreading of the disease to the rest of the world. Based upon our results, we propose a digital twin as a further developed tool to inform future pandemic decision-making to inform measures intended to control the spread of disease agents of potential future pandemics. We discuss the design criteria for such a digital twin model as well as the feasibility of obtaining access to the necessary online data on international air travel.

Sensitive SARS-CoV-2 detection, air travel Covid-19 testing, variant determination and fast direct PCR detection, using ddPCR and RT-qPCR methods.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) monitoring in air traffic is important in the prevention of the virus spreading from abroad. The gold standard for SARS-CoV-2 detection is RT-qPCR; however, for early and low viral load detection, a much more sensitive method, such as droplet digital PCR (ddPCR), is required. Our first step was to developed both, ddPCR and RT-qPCR methods, for sensitive SARS-CoV-2 detection. Analysis of ten swab/saliva samples of five Covid-19 patients in different stages of disease showed positivity in 6/10 samples with RT-qPCR and 9/10 with ddPCR. We also used our RT-qPCR method for SARS-CoV-2 detection without the need of RNA extraction, obtaining results in 90-120 minutes. We analyzed 116 self-collected saliva samples from passengers and airport staff arriving from abroad. All samples were negative by RT-qPCR, while 1 was positive, using ddPCR. Lastly, we developed ddPCR assays for SARS-CoV-2 variants identification (alpha, beta, gamma, delta/kappa) that are more economically advantageous when compared to NGS. Our findings demonstrated that saliva samples can be stored at ambient temperature, as we did not observe any significant difference between a fresh sample and the same sample after 24 hours (p = 0.23), hence, saliva collection is the optimal route for sampling airplane passengers. Our results also showed that droplet digital PCR is a more suitable method for detecting virus from saliva, compared to RT-qPCR. Keywords: COVID-19; RT-PCR; ddPCR; SARS-CoV-2; nasopharyngeal swab; saliva.

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.

Infrared image method for possible COVID-19 detection through febrile and subfebrile people screening.

This study proposed an infrared image-based method for febrile and subfebrile people screening to comply with the society need for alternative, quick response, and effective methods for COVID-19 contagious people screening. The methodology consisted of: (i) Developing a method based on facial infrared imaging for possible COVID-19 early detection in people with and without fever (subfebrile state); (ii) Using 1206 emergency room (ER) patients to develop an algorithm for general application of the method, and (iii) Testing the method and algorithm effectiveness in 2558 cases (RT-qPCR tested for COVID-19) from 227,261 workers evaluations in five different countries. Artificial intelligence was used through a convolutional neural network (CNN) to develop the algorithm that took facial infrared images as input and classified the tested individuals in three groups: fever (high risk), subfebrile (medium risk), and no fever (low risk). The results showed that suspicious and confirmed COVID-19 (+) cases characterized by temperatures below the 37.5 °C fever threshold were identified. Also, average forehead and eye temperatures greater than 37.5 °C were not enough to detect fever similarly to the proposed CNN algorithm. Most RT-qPCR confirmed COVID-19 (+) cases found in the 2558 cases sample (17 cases/89.5%) belonged to the CNN selected subfebrile group. The COVID-19 (+) main risk factor was to be in the subfebrile group, in comparison to age, diabetes, high blood pressure, smoking and others. In sum, the proposed method was shown to be a potentially important new tool for COVID-19 (+) people screening for air travel and public places in general.

Analyzing Cross-country Pandemic Connectedness During COVID-19 Using a Spatial-Temporal Database: Network Analysis.

Communicable diseases including COVID-19 pose a major threat to public health worldwide. To curb the spread of communicable diseases effectively, timely surveillance and prediction of the risk of pandemics are essential. The aim of this study is to analyze free and publicly available data to construct useful travel data records for network statistics other than common descriptive statistics. This study describes analytical findings of time-series plots and spatial-temporal maps to illustrate or visualize pandemic connectedness. We analyzed data retrieved from the web-based Collaborative Arrangement for the Prevention and Management of Public Health Events in Civil Aviation dashboard, which contains up-to-date and comprehensive meta-information on civil flights from 193 national governments in accordance with the airport, country, city, latitude, and the longitude of flight origin and the destination. We used the database to visualize pandemic connectedness through the workflow of travel data collection, network construction, data aggregation, travel statistics calculation, and visualization with time-series plots and spatial-temporal maps. We observed similar patterns in the time-series plots of worldwide daily flights from January to early-March of 2019 and 2020. A sharp reduction in the number of daily flights recorded in mid-March 2020 was likely related to large-scale air travel restrictions owing to the COVID-19 pandemic. The levels of connectedness between places are strong indicators of the risk of a pandemic. Since the initial reports of COVID-19 cases worldwide, a high network density and reciprocity in early-March 2020 served as early signals of the COVID-19 pandemic and were associated with the rapid increase in COVID-19 cases in mid-March 2020. The spatial-temporal map of connectedness in Europe on March 13, 2020, shows the highest level of connectedness among European countries, which reflected severe outbreaks of COVID-19 in late March and early April of 2020. As a quality control measure, we used the aggregated numbers of international flights from April to October 2020 to compare the number of international flights officially reported by the International Civil Aviation Organization with the data collected from the Collaborative Arrangement for the Prevention and Management of Public Health Events in Civil Aviation dashboard, and we observed high consistency between the 2 data sets. The flexible design of the database provides users access to network connectedness at different periods, places, and spatial levels through various network statistics calculation methods in accordance with their needs. The analysis can facilitate early recognition of the risk of a current communicable disease pandemic and newly emerging communicable diseases in the future.

Using Population Mobility Patterns to Adapt COVID-19 Response Strategies in 3 East Africa Countries.

The COVID-19 pandemic spread between neighboring countries through land, water, and air travel. Since May 2020, ministries of health for the Democratic Republic of the Congo, Tanzania, and Uganda have sought to clarify population movement patterns to improve their disease surveillance and pandemic response efforts. Ministry of Health-led teams completed focus group discussions with participatory mapping using country-adapted Population Connectivity Across Borders toolkits. They analyzed the qualitative and spatial data to prioritize locations for enhanced COVID-19 surveillance, community outreach, and cross-border collaboration. Each country employed varying toolkit strategies, but all countries applied the results to adapt their national and binational communicable disease response strategies during the pandemic, although the Democratic Republic of the Congo used only the raw data rather than generating datasets and digitized products. This 3-country comparison highlights how governments create preparedness and response strategies adapted to their unique sociocultural and cross-border dynamics to strengthen global health security.

In-flight transmission of wild-type SARS-CoV-2 and the outbreak potential of imported clusters of COVID-19: a review of published evidence.

International air travel has been highlighted as a concern since the beginning of the COVID-19 pandemic with respect to importation of cases. We summarise the available evidence for in-flight transmission of wild type SARS-CoV-2 during 2020, and for imported COVID-19 clusters to cause outbreaks. This paper provides a data baseline prior to the emergence of new mutations causing SARS-CoV-2 variants of concern, whose characteristics may increase the potential risk of in-flight transmission and imported outbreaks. The evidence on in-flight transmission of wild-type SARS-CoV-2 is limited, and is described in a small number of published reports. Most of the available evidence pertains to the early phase of the COVID-19 pandemic, during a period without non-pharmaceutical interventions such as distancing and in-flight mask wearing. There is considerable potential for outbreaks of COVID-19 from imported cases or clusters when public health guidance around quarantine of travellers and self-isolation of cases is not adhered to. Risks can be mitigated by measures such as: avoiding non-essential travel, targeted testing and quarantine of travellers from high incidence regions or regions of concern, managed quarantine processes, and protocols for rapid investigation and control of transmission from a possible variant of concern. Measures should be dynamically assessed and proportionate to the level of risk.

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.

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.

Identifying mitigation strategies for COVID-19 superspreading on flights using models that account for passenger movement.

BACKGROUND: Despite commercial airlines mandating masks, there have been multiple documented events of COVID-19 superspreading on flights. Conventional models do not adequately explain superspreading patterns on flights, with infection spread wider than expected from proximity based on passenger seating. An important reason for this is that models typically do not consider the movement of passengers during the flight, boarding, or deplaning. Understanding the risks for each of these aspects could provide insight into effective mitigation measures. METHODS: We modeled infection risk from seating and fine-grained movement patterns - boarding, deplaning, and inflight movement. We estimated infection model parameters from a prior superspreading event. We validated the model and the impact of interventions using available data from three flights, including cabin layout and seat locations of infected and uninfected passengers, to suggest interventions to mitigate COVID-19 superspreading events during air travel. Specifically, we studied: 1) London to Hanoi with 201 passengers, including 13 secondary infections among passengers; 2) Singapore to Hangzhou with 321 passengers, including 12 to 14 secondary infections; 3) a non-superspreading event on a private jet in Japan with 9 passengers and no secondary infections. RESULTS: Our results show that the inclusion of passenger movement better explains the infection spread patterns than conventional models do. We also found that FFP2/N95 mask usage would have reduced infection by 95-100%, while cloth masks would have reduced it by only 40-80%. Results indicate that leaving the middle seat vacant is effective in reducing infection, and the effectiveness increases when combined with good quality masks. However, with a good mask, the risk is quite low even without the middle seats being empty. CONCLUSIONS: Our results suggest the need for more stringent guidelines to reduce aviation-related superspreading events of COVID-19.

The Carbon Costs of In-Person Versus Virtual Medical Conferences for the Pharmaceutical Industry: Lessons from the Coronavirus Pandemic.

BACKGROUND: Many in-person congresses have shifted to a virtual format owing to coronavirus disease 2019 (COVID-19). We assessed carbon emissions savings associated with virtual attendance at international medical congresses for a mid-sized pharmaceutical company, to identify which aspects are driving the carbon cost. METHODS: We assessed carbon emissions that were the responsibility of company attendees (including their guests) for the most attended congresses by employees (American Society of Clinical Oncology [ASCO], European Neuroendocrine Tumor Society [ENETS], European Society for Medical Oncology [ESMO], World Congress for NeuroRehabilitation [WCNR]). For in-person estimates, we considered travel, accommodation and congress attendance; for online estimates, we considered office and internet-related energy use. Emissions were defined using recognised data sources. RESULTS: For 1723 anticipated in-person attendees, calculated total carbon emissions were 3,262,574 kgCO2e (mean per in-person company attendee, 1894 kgCO2e: ASCO, 4172; ESMO, 1479; WCNR, 1153; ENETS, 1009). For context, the average UK resident's annual carbon footprint is 5600 kgCO2e. Travel accounted for 91-96% of total emissions, mainly through long distance and business-class air travel. Calculated total carbon emissions associated with 1839 virtual attendees were 19,095 kgCO2e (mean per virtual company attendee, 10.4 kgCO2e; equivalent to approximately 0.3-1.1% of in-person attendance emissions across all four congresses assessed). CONCLUSION: Carbon emissions associated with virtual attendance were two orders of magnitude lower than for in-person attendance, and therefore the benefits of in-person attendance at medical congresses must be balanced against the carbon cost. Due diligence around who should attend and how they should travel to face-to-face meetings, and consideration of hybrid and domestic satellite options could be part of a balanced solution to reducing carbon emissions.

SARS-CoV-2 Testing Before International Airline Travel, December 2020 to May 2021.

Although there have been several case reports and simulation models of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission associated with air travel, there are limited data to guide testing strategy to minimize the risk of SARS-CoV-2 exposure and transmission onboard commercial aircraft. Among 9853 passengers with a negative SARS-CoV-2 polymerase chain reaction test performed within 72 hours of departure from December 2020 through May 2021, five (0.05%) passengers with active SARS-CoV-2 infection were identified with rapid antigen tests and confirmed with rapid molecular test performed before and after an international flight from the United States to Italy. This translates to a case detection rate of 1 per 1970 travelers during a time of high prevalence of active infection in the United States. A negative molecular test for SARS-CoV-2 within 72 hours of international airline departure results in a low probability of active infection identified on antigen testing during commercial airline flight.

Cryptic transmission of SARS-CoV-2 and the first COVID-19 wave.

Considerable uncertainty surrounds the timeline of introductions and onsets of local transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) globally1-7. Although a limited number of SARS-CoV-2 introductions were reported in January and February 2020 (refs.8,9), the narrowness of the initial testing criteria, combined with a slow growth in testing capacity and porous travel screening10, left many countries vulnerable to unmitigated, cryptic transmission. Here we use a global metapopulation epidemic model to provide a mechanistic understanding of the early dispersal of infections and the temporal windows of the introduction of SARS-CoV-2 and onset of local transmission in Europe and the USA. We find that community transmission of SARS-CoV-2 was likely to have been present in several areas of Europe and the USA by January 2020, and estimate that by early March, only 1 to 4 in 100 SARS-CoV-2 infections were detected by surveillance systems. The modelling results highlight international travel as the key driver of the introduction of SARS-CoV-2, with possible introductions and transmission events as early as December 2019 to January 2020. We find a heterogeneous geographic distribution of cumulative infection attack rates by 4 July 2020, ranging from 0.78% to 15.2% across US states and 0.19% to 13.2% in European countries. Our approach complements phylogenetic analyses and other surveillance approaches and provides insights that can be used to design innovative, model-driven surveillance systems that guide enhanced testing and response strategies.

Transmission of SARS-CoV-2 during a 2-h domestic flight to Okinawa, Japan, March 2020.

BACKGROUND: Coronavirus disease (COVID-19), caused by severe acute respiratory syndrome coronavirus (SARS-CoV-2), has rapidly spread globally. Potentially infected individuals travel on commercial aircraft. Thus, this study aimed to investigate and test the association between the use of face masks, physical distance, and COVID-19 among passengers and flight attendants exposed to a COVID-19 passenger in a domestic flight. METHODS: This observational study investigated passengers and flight attendants exposed to COVID-19 on March 23, 2020, on board a flight to Naha City, Japan. Secondary attack rates were calculated. Whole-genome sequencing of SARS-CoV-2 was used to identify the infectious linkage between confirmed cases in this clustering. The association between confirmed COVID-19 and proximity of passengers' seats to the index case and/or the use of face masks was estimated using logistic regression. RESULTS: Fourteen confirmed and six probable cases were identified among passengers and flight attendants. The secondary attack rate was 9.7%. Twelve of 14 SARS-CoV-2 genome sequences in confirmed cases were identical to that of the index case or showed only one nucleotide mutation. Risk factors for infection included not using a face mask (adjusted odds ratio [aOR]: 7.29, 95% confidence interval [95% CI]: 1.86-28.6), partial face mask use (aOR: 3.0, 95% CI: 0.83-10.8), and being seated within two rows from the index patient (aOR: 7.47, 95% CI: 2.06-27.2). CONCLUSION: SARS-CoV-2 was transmitted on the airplane. Nonuse of face masks was identified as an independent risk factor for contracting COVID-19 on the airplane.