Risk of MERS importation and onward transmission: a systematic review and analysis of cases reported to WHO

Drivers and impact of the early silent invasion of SARS-CoV-2 Alpha

SARS-CoV-2 variants of concern (VOCs) circulated cryptically before being identified as a threat, delaying interventions. Here we studied the drivers of such silent spread and its epidemic impact to inform future response planning. We focused on Alpha spread out of the UK. We integrated spatio-temporal records of international mobility, local epidemic growth and genomic surveillance into a Bayesian framework to reconstruct the first three months after Alpha emergence. We found that silent circulation lasted from days to months and decreased with the logarithm of sequencing coverage. Social restrictions in some countries likely delayed the establishment of local transmission, mitigating the negative consequences of late detection. Revisiting the initial spread of Alpha supports local mitigation at the destination in case of emerging events.

Airline strategies during the pandemic: What worked?

An examination is conducted of airline strategies during the covid-19 pandemic using data from the United States. Our findings show that airlines pursued diverse strategies in terms of route entry and retention, pricing, and load factors. At the route level, a more detailed examination is conducted of the performance of a middle-seat blocking strategy designed to increase the safety of air travel. We show that this strategy (i.e., not making middle seats available to passengers) likely resulted in revenue losses for carriers, an estimated US $3,300 per flight. This revenue loss provides an indication as to why the middle seat blocking strategy was discontinued by all US airlines despite ongoing safety concerns.

Evaluating the Impact of the Pandemic Crisis on the Aviation Industry

This paper investigates the intellectual structure of the literature addressing "epidemic/pandemic" and "aviation industry" through a bibliometric approach to the literature from 1991 to 2021. The final count of 856 publications was collected from Web of Science and analyzed by CiteSpace (version 5.8.R1) and VOS Viewer. Visualization tools are used to perform the co-citation, co-occurrence, and thematic-based cluster analysis. The results highlight the most prominent nodes (articles, authors, journals, countries, and institutions) within the literature on "epidemic/pandemic" and "aviation industry." Furthermore, this study conceptualizes and compares the growth of literature before theCOVID-19 pandemic and during the COVID-19 ("hotspot") era. The conclusion is that the aviation industry is an engine for global economics on the road to recovery from COVID-19, in which soft (human) resources can play an integral part.

COVID-19 pandemic and air transportation: Summary of Recent Research, Policy Consideration and Future Research Directions

The COVID-19 pandemic can be considered an unparalleled disruption to the aviation industry in the last century. Starting with an at-that-time inconceivable reduction in the number of flights from March 2020 to May 2020, the aviation industry has been trying to navigate through and out of the crisis. This process is accompanied with a significant number of scientific studies, reporting on the direct and indirect impact of the COVID-19 pandemic on aviation and vice versa. This paper reviews the impacts in context of the recent literature. We have collected nearly 200 well-published papers on the subject in the years 2021/2022 and dissected them into a framework of eight categories, built around: airlines, airports, passengers, workforce, markets, contagion, sustainability, and economics. We highlight the essence of findings in the literature and derive a set of future research directions and policy considerations which we deem important on the way towards pandemic-resilient aviation.

The effects of air transport mobility and global connectivity on viral transmission: Lessons learned from Covid-19 and its variants

We investigate the impact of air travel mobility and global connectivity on viral transmission by tracing the announced arrival time of COVID-19 and its major variants in countries around the world. We find that air travel intensity to a country, "effective distance" as measured by international air traffic, is generally a significant predictor for the announced viral arrival time. The level of healthcare infrastructure in a country is less important at predicting the initial transmission and detection time of a virus. A policy variable, notably the percentage reduction of total inbound seats in response to a viral outbreak, is largely ineffective at delaying viral transmission and discovery time. These findings suggest that air network connectivity is a major contributor to the speed of viral transmission. However, government attempts to delay viral transmission by reducing air network connectivity after the virus is first discovered are largely ineffective.

Emerging and Re-emerging Infectious Diseases in the WHO Eastern Mediterranean Region, 2001-2018

BACKGROUND

Countries in the World Health Organization (WHO) Eastern Mediterranean Region (EMR) are predisposed to highly contagious, severe and fatal, emerging infectious diseases (EIDs), and re-emerging infectious diseases (RIDs). This paper reviews the epidemiological situation of EIDs and RIDs of global concern in the EMR between 2001 and 2018.

METHODS

To do a narrative review, a complete list of studies in the field was we prepared following a systematic search approach. Studies that were purposively reviewed were identified to summarize the epidemiological situation of each targeted disease. A comprehensive search of all published studies on EIDs and RIDs between 2001 and 2018 was carried out through search engines including Medline, Web of Science, Scopus, Google Scholar, and ScienceDirect.

RESULTS

Leishmaniasis, hepatitis A virus (HAV) and hepatitis E virus (HEV) are reported from all countries in the region. Chikungunya, Crimean Congo hemorrhagic fever (CCHF), dengue fever, and H5N1 have been increasing in number, frequency, and expanding in their geographic distribution. Middle East respiratory syndrome (MERS), which was reported in this region in 2012 is still a public health concern. There are challenges to control cholera, diphtheria, leishmaniasis, measles, and poliomyelitis in some of the countries. Moreover, Alkhurma hemorrhagic fever (AHF), and Rift Valley fever (RVF) are limited to some countries in the region. Also, there is little information about the real situation of the plague, Q fever, and tularemia.

CONCLUSION

EIDs and RIDs are prevalent in most countries in the region and could further spread within the region. It is crucial to improve regional capacities and capabilities in preventing and responding to disease outbreaks with adequate resources and expertise.

The impact of the COVID-19 pandemic on O-D flow and airport networks in the origin country and in Northeast Asia

The ongoing COVID-19 pandemic has posed a global threat to human health. In order to prevent the spread of this virus, many countries have imposed travel restrictions. This difficult situation has dramatically affected the airline industry by reducing the passenger volume, number of flights, airline flow patterns, and even has changed the entire airport network, especially in Northeast Asia (because it includes the original disease seed). However, although most scholars have used conventional statistical analysis to describe the changes in passenger volume before and during the COVID-19 outbreak, very few of them have applied statistical assessment or time series analysis, and have not even examined how the impact may be different from place to place. Therefore, the purpose of this study was to identify the impact of COVID-19 on the airline industry and affected areas (including the origin-destination flow and the airport network). First, a Clustering Large Applications (CLARA) algorithm was used to group numerous origin-destination (O-D) flow patterns based on their characteristics and to determine if these characteristics have changed the severity of the impact of each cluster during the COVID-19 outbreak. Second, two statistical tests (the paired t-test and the Wilcoxon signed-rank test) were utilized to determine if the entire airport network and the top 30 hub airports changed during COVID-19. Four centrality measurement indices (degree, closeness, eigenvector, and betweenness centrality) of the airports were used to assess the entire network and ranking of individual hub airports. The study data, provided by The Official Aviation Guide (OAG) from December 2019 to April 2020, indicated that during the COVID-19 outbreak, there was a decrease in passenger volume (60%-98.4%) as well as the number of flights (1.5%-82.6%). However, there were no such significant changes regarding the popularity ranking of most airports during the outbreak. Before this occurred (December 2019), most hub airports were in China (April 2020), and this trend remain similar during the COVID-19 outbreak. However, the values of the centrality measurement decreased significantly for most hub airports due to travel restrictions issued by the government.

Evaluating the effectiveness and efficiency of risk communication for maps depicting the hazard of COVID-19

COVID-19 maps convey hazard and risk information to the public, which play an important role in the risk communication for individual protection. The aim of this study is to improve the effectiveness and efficiency of communicating the specific risk of COVID-19 maps. By testing 71 subjects from Wuhan, China, this study explored how color schemes (cool, warm, and mixed colors) and data presentation forms (choropleth maps, graduated symbol maps) influence visual cognition patterns, risk perception, comprehension, and subjective satisfaction. The results indicated that the warm scheme (yellow/red) has significant strengths in visual cognition and understanding, and the choropleth map (vs. the graduated symbol map) has significant strengths in risk expression. On subjective satisfaction, the combination of the mixed scheme (blue/yellow/red) and the choropleth map scored highest mean value. These results have implications for enhancing the focused functions of COVID-19 maps that fit different terms: in the early and medium terms of disease transmission, choropleth maps with warm or cool colors should be considered as a priority design for their better risk perception. When the epidemic conditions are on the upturn, a better reading experience combination of choropleth maps with mixed colors can be considered.

STARTUPS: Founding airlines during COVID-19 - A hopeless endeavor or an ample opportunity for a better aviation system?

The devastating impact of COVID-19 on aviation is unprecedented and undoubted in the recent sci-entific literature, with many studies having dissected different facets of COVID-19-induced changes to the industry. A few studies have stepped further and highlighted that the COVID-19 pandemic could have positive long-term impacts on aviation. Given that traditional air carriers are known to be reluctant for performing high-risk experiments outside their business-as-usual, parts of hope for a better aviation future rests on novel players entering the industry. The pandemic - against common perception and odds - might have created a rare opportunity for airline startups to enter the market. In this study, we first dissect the impact of the COVID-19 pandemic on aviation and how it possibly created a breeding ground for new airlines. We propose a framework of eight facets, STARTUPS, covering flight Suspensions, Talents, Aircraft, Recovery, Travel demand, Uniquity, Policy making, and Strategy. Moreover, we analyze the business model and markets of 46 airline startups, established or becoming active during the pandemic. Our study is concluded with a dis-cussion on the risk factors for airline startups during the COVID-19 pandemic and induced policy challenges. Our analysis, we believe, is complementary to existing studies on COVID-19, leveraging a novel perspective on the pandemic and the aviation industry.

Travel Bubbles in Air Transportation: Myth or Reality?

Aviation has been hit hard by COVID-19, with passengers stranded in remote destinations, airlines filing for bankruptcy, and uncertain demand scenarios for the future. Travel bubbles are discussed as one possible solution, meaning countries which have successfully constrained the spread of COVID-19 gradually increase their mutual international flights, returning to a degree of normality. This study aims to answer the question of whether travel bubbles are indeed observable in flight data for the year 2020. We take the year 2019 as reference and then search for anomalies in countries’ flight bans and recoveries, which could possibly be explained by having successfully implemented a travel bubble. To the best of our knowledge, this study is the first to try to address the identification of COVID-19 travel bubbles in real data. Our methodology and findings lead to several important insights regarding policy making, problems associated with the concept of travel bubbles, and raise interesting avenues for future research.

The effect of aviation responses to the control of imported COVID-19 cases

The outbreak of the COVID-19 pandemic has a lasting and unprecedented negative impact on the global aviation industry. While countries such as China have successfully curbed the domestic outbreak of the virus with various restrictive and preventive measures, the challenge of avoiding imported cases remains. More importantly, it is still unclear to what extent these implemented aviation emergency responses have effectively mitigated the transmission risk of the virus. This paper provides an empirical assessment of aviation responses to the control of imported COVID-19 cases, with a focus on the following three strategies: the "circuit breaker" policy, the "negative Nucleic Acid testing (NAT)", and the "double negative tests" requirement. Non-recursive structural equation models (SEM) with latent variables were applied to detailed international flight data and individual epidemic survey data of Guangzhou, China, between May 1 and November 30, 2020. The results show that the "double negative tests" measure has a positive effect on eliminating the number of SARS-CoV-2 carriers, while the effects of single "circuit breaker" and its co-intervention with "negative NAT" are conterproductive. This study provides important implications to civil aviation agencies in regard to medium and long-term risk control of imported cases. Specifically, although the circuit breaker mechanism was designed to target on the risk control of imported COVID-19 cases, it may be more effective to carefully maintain a timely and reliable pre-boarding screening and testing to curb the number of imported cases.

COVID-19 pandemic and air transportation: Successfully navigating the paper hurricane

This paper aims to analyze and understand the impact of the corona virus disease (COVID-19) on aviation and also the role aviation played in the spread of COVID-19, by reviewing the recent scientific literature. We have collected 110 papers on the subject published in the year 2020 and grouped them according to their major application domain, leading to the following categories: Analysis of the global air transportation system during COVID-19, the impacts on the passenger-centric flight experience, and the long-term impacts on broad aviation. Based on the aggregated reported findings in the literature, this paper concludes with a set of recommendations for future scientific directions; hopefully helping aviation to prepare for a post-COVID-19 world.

Risk Assessment of Importation and Local Transmission of COVID-19 in South Korea: Statistical Modeling Approach

Background

Despite recent achievements in vaccines, antiviral drugs, and medical infrastructure, the emergence of COVID-19 has posed a serious threat to humans worldwide. Most countries are well connected on a global scale, making it nearly impossible to implement perfect and prompt mitigation strategies for infectious disease outbreaks. In particular, due to the explosive growth of international travel, the complex network of human mobility enabled the rapid spread of COVID-19 globally.

Objective

South Korea was one of the earliest countries to be affected by COVID-19. In the absence of vaccines and treatments, South Korea has implemented and maintained stringent interventions, such as large-scale epidemiological investigations, rapid diagnosis, social distancing, and prompt clinical classification of severely ill patients with appropriate medical measures. In particular, South Korea has implemented effective airport screenings and quarantine measures. In this study, we aimed to assess the country-specific importation risk of COVID-19 and investigate its impact on the local transmission of COVID-19.

Methods

The country-specific importation risk of COVID-19 in South Korea was assessed. We investigated the relationships between country-specific imported cases, passenger numbers, and the severity of country-specific COVID-19 prevalence from January to October 2020. We assessed the country-specific risk by incorporating country-specific information. A renewal mathematical model was employed, considering both imported and local cases of COVID-19 in South Korea. Furthermore, we estimated the basic and effective reproduction numbers.

Results

The risk of importation from China was highest between January and February 2020, while that from North America (the United States and Canada) was high from April to October 2020. The R₀ was estimated at 1.87 (95% CI 1.47-2.34), using the rate of α=0.07 for secondary transmission caused by imported cases. The R_t was estimated in South Korea and in both Seoul and Gyeonggi.

Conclusions

A statistical model accounting for imported and locally transmitted cases was employed to estimate R₀ and R_t. Our results indicated that the prompt implementation of airport screening measures (contact tracing with case isolation and quarantine) successfully reduced local transmission caused by imported cases despite passengers arriving from high-risk countries throughout the year. Moreover, various mitigation interventions, including social distancing and travel restrictions within South Korea, have been effectively implemented to reduce the spread of local cases in South Korea.

The effect of the synchronized multi-dimensional policies on imported COVID-19 curtailment in China

As countries are lifting restrictions and resuming international travels, the rising risk of COVID-19 importation remains concerning, given that the SARS-CoV-2 virus could be transmitted unintentionally through the global transportation network. To explore and assess the effective strategies for curtailing the epidemic risk from international importation nationwide, we evaluated "the joint prevention and control" mechanism, which made up of 19 containment policies, on how it impacted the change of medical observation and detection time from border arrival to laboratory confirmation of COVID-19 in its burst in China. Based on 1,314 epidemiological-survey cases from February 29 to May 25, 2020, we found that the synchronized approach of implementing multi-dimensional interventional policies, such as a centralized quarantine and nucleic acid testing (NAT), flight service adjustment and border closure, effectively facilitate early identification of infected case. Specifically, the implementation of the international flight service reduction was found to be associated with a reduction of the mean intervals of diagnosis from arrival to lab-confirmation by 0.44 days maximally, and the border closure was associated with a reduction of the diagnosis interval of imported cases by 0.69 days, from arrival to laboratory confirmation. The study suggests that a timely and synchronized implementation of multi-dimensional policies is compelling in preventing domestic spreading from importation.

On the degree of synchronization between air transport connectivity and COVID-19 cases at worldwide level

COVID-19 is one of the most impactful pandemics in recent history, not only in terms of direct casualties but also regarding socio-economic impact. The goal of our study is to investigate the degree of synchronization between the number of confirmed cases in specific countries, on one hand, and how/at which stage these countries adapted their air transportation operations, on the other hand. We investigate the global air transportation system as a network of countries whose edges represent the existence of direct flights. Aggregated analysis of this country network and its evolving dynamics leads to novel insights regarding the synchronization with the number of confirmed cases; finding that most country borders were likely closed too late. We believe and hope that our analysis leads to a more efficient/effective prevention and control of future epidemics.

Population Movement, City Closure in Wuhan, and Geographical Expansion of the COVID-19 Infection in China in January 2020

Background

The unprecedented outbreak of 2019-nCoV pneumonia infection in Wuhan City caused global concern, the outflowing population from Wuhan was believed to be a main reason for the rapid and large-scale spread of the disease, so the government implemented a city closure measure to prevent its transmission considering the large amount of travelling before the Chinese New Year.

Methods

Based on the daily reported new cases and the population movement data between January 1 and 31, we examined the effects of population outflow from Wuhan on the geographical expansion of the infection in other provinces and cities of China, as well as the impacts of the city closure in Wuhan in different scenarios of closing dates.

Results

We observed a significantly positive association between population movement and the number of the 2019-nCoV cases. The spatial distribution of cases per unit outflow population indicated that some areas with large outflow population might have been underestimated for the infection, such as Henan and Hunan provinces. Further analysis revealed that if the city closure policy was implemented two days earlier, 1420 (95% CI: 1059, 1833) cases could have been prevented, and if two days later, 1462 (95% CI: 1090, 1886) more cases would be possible.

Conclusions

Our findings suggest that population movement might be one important trigger for the transmission of 2019-nCoV infection in China, and the policy of city closure is effective to control the epidemic.

COVID-19 predictability in the United States using Google Trends time series

During the unprecedented situation that all countries around the globe are facing due to the Coronavirus disease 2019 (COVID-19) pandemic, which has also had severe socioeconomic consequences, it is imperative to explore novel approaches to monitoring and forecasting regional outbreaks as they happen or even before they do so. To that end, in this paper, the role of Google query data in the predictability of COVID-19 in the United States at both national and state level is presented. As a preliminary investigation, Pearson and Kendall rank correlations are examined to explore the relationship between Google Trends data and COVID-19 data on cases and deaths. Next, a COVID-19 predictability analysis is performed, with the employed model being a quantile regression that is bias corrected via bootstrap simulation, i.e., a robust regression analysis that is the appropriate statistical approach to taking against the presence of outliers in the sample while also mitigating small sample estimation bias. The results indicate that there are statistically significant correlations between Google Trends and COVID-19 data, while the estimated models exhibit strong COVID-19 predictability. In line with previous work that has suggested that online real-time data are valuable in the monitoring and forecasting of epidemics and outbreaks, it is evident that such infodemiology approaches can assist public health policy makers in addressing the most crucial issues: flattening the curve, allocating health resources, and increasing the effectiveness and preparedness of their respective health care systems.

COVID-19 predictability in the United States using Google Trends time series

During the unprecedented situation that all countries around the globe are facing due to the Coronavirus disease 2019 (COVID-19) pandemic, which has also had severe socioeconomic consequences, it is imperative to explore novel approaches to monitoring and forecasting regional outbreaks as they happen or even before they do so. To that end, in this paper, the role of Google query data in the predictability of COVID-19 in the United States at both national and state level is presented. As a preliminary investigation, Pearson and Kendall rank correlations are examined to explore the relationship between Google Trends data and COVID-19 data on cases and deaths. Next, a COVID-19 predictability analysis is performed, with the employed model being a quantile regression that is bias corrected via bootstrap simulation, i.e., a robust regression analysis that is the appropriate statistical approach to taking against the presence of outliers in the sample while also mitigating small sample estimation bias. The results indicate that there are statistically significant correlations between Google Trends and COVID-19 data, while the estimated models exhibit strong COVID-19 predictability. In line with previous work that has suggested that online real-time data are valuable in the monitoring and forecasting of epidemics and outbreaks, it is evident that such infodemiology approaches can assist public health policy makers in addressing the most crucial issues: flattening the curve, allocating health resources, and increasing the effectiveness and preparedness of their respective health care systems.

Association of Search Query Interest in Gastrointestinal Symptoms With COVID-19 Diagnosis in the United States: Infodemiology Study

BACKGROUND

Coronavirus disease (COVID-19) is a novel viral illness that has rapidly spread worldwide. While the disease primarily presents as a respiratory illness, gastrointestinal symptoms such as diarrhea have been reported in up to one-third of confirmed cases, and patients may have mild symptoms that do not prompt them to seek medical attention. Internet-based infodemiology offers an approach to studying symptoms at a population level, even in individuals who do not seek medical care.

OBJECTIVE

This study aimed to determine if a correlation exists between internet searches for gastrointestinal symptoms and the confirmed case count of COVID-19 in the United States.

METHODS

The search terms chosen for analysis in this study included common gastrointestinal symptoms such as diarrhea, nausea, vomiting, and abdominal pain. Furthermore, the search terms fever and cough were used as positive controls, and constipation was used as a negative control. Daily query shares for the selected symptoms were obtained from Google Trends between October 1, 2019 and June 15, 2020 for all US states. These shares were divided into two time periods: pre-COVID-19 (prior to March 1) and post-COVID-19 (March 1-June 15). Confirmed COVID-19 case numbers were obtained from the Johns Hopkins University Center for Systems Science and Engineering data repository. Moving averages of the daily query shares (normalized to baseline pre-COVID-19) were then analyzed against the confirmed disease case count and daily new cases to establish a temporal relationship.

RESULTS

The relative search query shares of many symptoms, including nausea, vomiting, abdominal pain, and constipation, remained near or below baseline throughout the time period studied; however, there were notable increases in searches for the positive control symptoms of fever and cough as well as for diarrhea. These increases in daily search queries for fever, cough, and diarrhea preceded the rapid rise in number of cases by approximately 10 to 14 days. The search volumes for these terms began declining after mid-March despite the continued rises in cumulative cases and daily new case counts.

CONCLUSIONS

Google searches for symptoms may precede the actual rises in cases and hospitalizations during pandemics. During the current COVID-19 pandemic, this study demonstrates that internet search queries for fever, cough, and diarrhea increased prior to the increased confirmed case count by available testing during the early weeks of the pandemic in the United States. While the search volumes eventually decreased significantly as the number of cases continued to rise, internet query search data may still be a useful tool at a population level to identify areas of active disease transmission at the cusp of new outbreaks.

Tracing and analysis of 288 early SARS-CoV-2 infections outside China: A modeling study

BACKGROUND

In the early months of 2020, a novel coronavirus disease (COVID-19) spread rapidly from China across multiple countries worldwide. As of March 17, 2020, COVID-19 was officially declared a pandemic by the World Health Organization. We collected data on COVID-19 cases outside China during the early phase of the pandemic and used them to predict trends in importations and quantify the proportion of undetected imported cases.

METHODS AND FINDINGS

Two hundred and eighty-eight cases have been confirmed out of China from January 3 to February 13, 2020. We collected and synthesized all available information on these cases from official sources and media. We analyzed importations that were successfully isolated and those leading to onward transmission. We modeled their number over time, in relation to the origin of travel (Hubei province, other Chinese provinces, other countries) and interventions. We characterized the importation timeline to assess the rapidity of isolation and epidemiologically linked clusters to estimate the rate of detection. We found a rapid exponential growth of importations from Hubei, corresponding to a doubling time of 2.8 days, combined with a slower growth from the other areas. We predicted a rebound of importations from South East Asia in the successive weeks. Time from travel to detection has considerably decreased since first importation, from 14.5 ± 5.5 days on January 5, 2020, to 6 ± 3.5 days on February 1, 2020. However, we estimated 36% of detection of imported cases. This study is restricted to the early phase of the pandemic, when China was the only large epicenter and foreign countries had not discovered extensive local transmission yet. Missing information in case history was accounted for through modeling and imputation.

CONCLUSIONS

Our findings indicate that travel bans and containment strategies adopted in China were effective in reducing the exportation growth rate. However, the risk of importation was estimated to increase again from other sources in South East Asia. Surveillance and management of traveling cases represented a priority in the early phase of the epidemic. With the majority of imported cases going undetected (6 out of 10), countries experienced several undetected clusters of chains of local transmissions, fueling silent epidemics in the community. These findings become again critical to prevent second waves, now that countries have reduced their epidemic activity and progressively phase out lockdown.

Online behavioural patterns for Coronavirus disease 2019 (COVID-19) in the United Kingdom

The current coronavirus (COVID-19) pandemic offers a unique opportunity to conduct an infodemiological study examining patterns in online searching activity about a specific disease and how this relates to news media within a specific country. Google Trends quantifies volumes of online activity. The relative search volume was obtained for ‘Coronavirus’, ‘handwashing’, ‘face mask’ and symptom related keywords, for the United Kingdom, from the date of the first confirmed case until numbers peaked in April. The relationship between online search traffic and confirmed case numbers was examined. Search volumes varied over time; peaks appear related to events in the progression of the epidemic which were reported in the media. Search activity on ‘Coronavirus’ correlated well against confirmed case number as did ‘face mask’ and symptom-related keywords. User-generated online data sources such as Google Trends may aid disease surveillance, being more responsive to changes in disease occurrence than traditional disease reporting. The relationship between media coverage and online searching activity is rarely examined, but may be driving online behavioural patterns.

The Predictive Capacity of Air Travel Patterns During the Global Spread of the COVID-19 Pandemic: Risk, Uncertainty and Randomness

Air travel has a decisive role in the spread of infectious diseases at the global level. We present a methodology applied during the early stages of the COVID-19 pandemic that uses detailed aviation data at the final destination level in order to measure the risk of the disease spreading outside China. The approach proved to be successful in terms of identifying countries with a high risk of infected travellers and as a tool to monitor the evolution of the pandemic in different countries. The high number of undetected or asymptomatic cases of COVID-19, however, limits the capacity of the approach to model the full dynamics. As a result, the risk for countries with a low number of passengers from Hubei province appeared as low. Globalization and international aviation connectivity allow travel times that are much shorter than the incubation period of infectious diseases, a fact that raises the question of how to react in a potential new pandemic.

Tracking COVID-19 in Europe: Infodemiology Approach

BACKGROUND

Infodemiology (ie, information epidemiology) uses web-based data to inform public health and policy. Infodemiology metrics have been widely and successfully used to assess and forecast epidemics and outbreaks.

OBJECTIVE

In light of the recent coronavirus disease (COVID-19) pandemic that started in Wuhan, China in 2019, online search traffic data from Google are used to track the spread of the new coronavirus disease in Europe.

METHODS

Time series from Google Trends from January to March 2020 on the Topic (Virus) of "Coronavirus" were retrieved and correlated with official data on COVID-19 cases and deaths worldwide and in the European countries that have been affected the most: Italy (at national and regional level), Spain, France, Germany, and the United Kingdom.

RESULTS

Statistically significant correlations are observed between online interest and COVID-19 cases and deaths. Furthermore, a critical point, after which the Pearson correlation coefficient starts declining (even if it is still statistically significant) was identified, indicating that this method is most efficient in regions or countries that have not yet peaked in COVID-19 cases.

CONCLUSIONS

In the past, infodemiology metrics in general and data from Google Trends in particular have been shown to be useful in tracking and forecasting outbreaks, epidemics, and pandemics as, for example, in the cases of the Middle East respiratory syndrome, Ebola, measles, and Zika. With the COVID-19 pandemic still in the beginning stages, it is essential to explore and combine new methods of disease surveillance to assist with the preparedness of health care systems at the regional level.

The impact of news exposure on collective attention in the United States during the 2016 Zika epidemic

In recent years, many studies have drawn attention to the important role of collective awareness and human behaviour during epidemic outbreaks. A number of modelling efforts have investigated the interaction between the disease transmission dynamics and human behaviour change mediated by news coverage and by information spreading in the population. Yet, given the scarcity of data on public awareness during an epidemic, few studies have relied on empirical data. Here, we use fine-grained, geo-referenced data from three online sources—Wikipedia, the GDELT Project and the Internet Archive—to quantify population-scale information seeking about the 2016 Zika virus epidemic in the U.S., explicitly linking such behavioural signal to epidemiological data. Geo-localized Wikipedia pageview data reveal that visiting patterns of Zika-related pages in Wikipedia were highly synchronized across the United States and largely explained by exposure to national television broadcast. Contrary to the assumption of some theoretical epidemic models, news volume and Wikipedia visiting patterns were not significantly correlated with the magnitude or the extent of the epidemic. Attention to Zika, in terms of Zika-related Wikipedia pageviews, was high at the beginning of the outbreak, when public health agencies raised an international alert and triggered media coverage, but subsequently exhibited an activity profile that suggests nonlinear dependencies and memory effects in the relation between information seeking, media pressure, and disease dynamics. This calls for a new and more general modelling framework to describe the interaction between media exposure, public awareness and disease dynamics during epidemic outbreaks.

Despite its importance for public health policy-makers, understanding the impact of media coverage on collective attention during disease outbreaks remains an elusive research task, due to the lack of available data, especially at high spatial granularity. In this paper, we study the dynamics of collective attention received by the 2016 Zika epidemic in the USA and its interplay with the media coverage of the outbreak, at level of US states and cities. We measure the attention to Zika through geo-localized Wikipedia page view data, and we compare it with mentions of Zika in US news outlets and TV shows. We also compare the collective attention received by the outbreak with the incidence of Zika reported by the US Centers for Disease Control and Prevention in each state. We find that the attention dynamics was highly synchronized across states, irrespective of the local risk of transmission of the virus. By building a linear regression model, we show that the dynamics of collective attention is highly predictable, even at state level, only based on the national media coverage received by the outbreak.

A realistic two-strain model for MERS-CoV infection uncovers the high risk for epidemic propagation

Middle East Respiratory Syndrome Coronavirus (MERS-CoV) causes severe acute respiratory illness with a case fatality rate (CFR) of 35,5%. The highest number of MERS-CoV cases are from Saudi-Arabia, the major worldwide hotspot for this disease. In the absence of neither effective treatment nor a ready-to-use vaccine and with yet an incomplete understanding of its epidemiological cycle, prevention and containment measures can be derived from mathematical models of disease epidemiology. We constructed 2-strain models to predict past outbreaks in the interval 2012–2016 and derive key epidemiological information for Macca, Madina and Riyadh. We approached variability in infection through three different disease incidence functions capturing social behavior in response to an epidemic (e.g. Bilinear, BL; Non-monotone, NM; and Saturated, SAT models). The best model combination successfully anticipated the total number of MERS-CoV clinical cases for the 2015–2016 season and accurately predicted both the number of cases at the peak of seasonal incidence and the overall shape of the epidemic cycle. The evolution in the basic reproduction number (R₀) warns that MERS-CoV may easily take an epidemic form. The best model correctly captures this feature, indicating a high epidemic risk (1≤R₀≤2,5) in Riyadh and Macca and confirming the alleged co-circulation of more than one strain. Accurate predictions of the future MERS-CoV peak week, as well as the number of cases at the peak are now possible. These results indicate public health agencies should be aware that measures for strict containment are urgently needed before new epidemics take off in the region.

There is currently no way to anticipate MERS-CoV epidemic outbreaks and strategies for disease prediction and containment are largely undermined by the limited knowledge of its epidemiological cycle. Not an effective treatment nor a vaccine for MERS-CoV exist to date. Instead, using three two-strain mathematical models that incorporate human social behavior as different disease incidence functions (e.g. bilinear, non-monotone and saturated), the best model combinations successfully anticipate the occurrence of the peak week in the season and the incidence at the peak. Our results confirm there are currently 2 strains co-circulating in the most populated regions in Saudi Arabia and highlight the high risk for large epidemic outbreaks, while the role of super-spreaders appears irrelevant for disease spread.

A database of geopositioned Middle East Respiratory Syndrome Coronavirus occurrences

As a World Health Organization Research and Development Blueprint priority pathogen, there is a need to better understand the geographic distribution of Middle East Respiratory Syndrome Coronavirus (MERS-CoV) and its potential to infect mammals and humans. This database documents cases of MERS-CoV globally, with specific attention paid to zoonotic transmission. An initial literature search was conducted in PubMed, Web of Science, and Scopus; after screening articles according to the inclusion/exclusion criteria, a total of 208 sources were selected for extraction and geo-positioning. Each MERS-CoV occurrence was assigned one of the following classifications based upon published contextual information: index, unspecified, secondary, mammal, environmental, or imported. In total, this database is comprised of 861 unique geo-positioned MERS-CoV occurrences. The purpose of this article is to share a collated MERS-CoV database and extraction protocol that can be utilized in future mapping efforts for both MERS-CoV and other infectious diseases. More broadly, it may also provide useful data for the development of targeted MERS-CoV surveillance, which would prove invaluable in preventing future zoonotic spillover.

Measurement(s)	Middle East Respiratory Syndrome • geographic location
Technology Type(s)	digital curation
Factor Type(s)	geographic distribution of Middle East Respiratory Syndrome Coronavirus (MERS-CoV) • year
Sample Characteristic - Organism	Middle East respiratory syndrome-related coronavirus
Sample Characteristic - Location	Earth (planet)

Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.11108801

The use and reporting of airline passenger data for infectious disease modelling: a systematic review

BackgroundA variety of airline passenger data sources are used for modelling the international spread of infectious diseases. Questions exist regarding the suitability and validity of these sources.AimWe conducted a systematic review to identify the sources of airline passenger data used for these purposes and to assess validation of the data and reproducibility of the methodology.MethodsArticles matching our search criteria and describing a model of the international spread of human infectious disease, parameterised with airline passenger data, were identified. Information regarding type and source of airline passenger data used was collated and the studies' reproducibility assessed.ResultsWe identified 136 articles. The majority (n = 96) sourced data primarily used by the airline industry. Governmental data sources were used in 30 studies and data published by individual airports in four studies. Validation of passenger data was conducted in only seven studies. No study was found to be fully reproducible, although eight were partially reproducible.LimitationsBy limiting the articles to international spread, articles focussed on within-country transmission even if they used relevant data sources were excluded. Authors were not contacted to clarify their methods. Searches were limited to articles in PubMed, Web of Science and Scopus.ConclusionWe recommend greater efforts to assess validity and biases of airline passenger data used for modelling studies, particularly when model outputs are to inform national and international public health policies. We also recommend improving reporting standards and more detailed studies on biases in commercial and open-access data to assess their reproducibility.

Assessing the Methods, Tools, and Statistical Approaches in Google Trends Research: Systematic Review

Background

In the era of information overload, are big data analytics the answer to access and better manage available knowledge? Over the last decade, the use of Web-based data in public health issues, that is, infodemiology, has been proven useful in assessing various aspects of human behavior. Google Trends is the most popular tool to gather such information, and it has been used in several topics up to this point, with health and medicine being the most focused subject. Web-based behavior is monitored and analyzed in order to examine actual human behavior so as to predict, better assess, and even prevent health-related issues that constantly arise in everyday life.

Objective

This systematic review aimed at reporting and further presenting and analyzing the methods, tools, and statistical approaches for Google Trends (infodemiology) studies in health-related topics from 2006 to 2016 to provide an overview of the usefulness of said tool and be a point of reference for future research on the subject.

Methods

Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines for selecting studies, we searched for the term “Google Trends” in the Scopus and PubMed databases from 2006 to 2016, applying specific criteria for types of publications and topics. A total of 109 published papers were extracted, excluding duplicates and those that did not fall inside the topics of health and medicine or the selected article types. We then further categorized the published papers according to their methodological approach, namely, visualization, seasonality, correlations, forecasting, and modeling.

Results

All the examined papers comprised, by definition, time series analysis, and all but two included data visualization. A total of 23.1% (24/104) studies used Google Trends data for examining seasonality, while 39.4% (41/104) and 32.7% (34/104) of the studies used correlations and modeling, respectively. Only 8.7% (9/104) of the studies used Google Trends data for predictions and forecasting in health-related topics; therefore, it is evident that a gap exists in forecasting using Google Trends data.

Conclusions

The monitoring of online queries can provide insight into human behavior, as this field is significantly and continuously growing and will be proven more than valuable in the future for assessing behavioral changes and providing ground for research using data that could not have been accessed otherwise.

Shifting patterns of seasonal influenza epidemics

Seasonal waves of influenza display a complex spatiotemporal pattern resulting from the interplay of biological, sociodemographic, and environmental factors. At country level many studies characterized the robust properties of annual epidemics, depicting a typical season. Here we analyzed season-by-season variability, introducing a clustering approach to assess the deviations from typical spreading patterns. The classification is performed on the similarity of temporal configurations of onset and peak times of regional epidemics, based on influenza-like-illness time-series in France from 1984 to 2014. We observed a larger variability in the onset compared to the peak. Two relevant classes of clusters emerge: groups of seasons sharing similar recurrent spreading patterns (clustered seasons) and single seasons displaying unique patterns (monoids). Recurrent patterns exhibit a more pronounced spatial signature than unique patterns. We assessed how seasons shift between these classes from onset to peak depending on epidemiological, environmental, and socio-demographic variables. We found that the spatial dynamics of influenza and its association with commuting, previously observed as a general property of French influenza epidemics, apply only to seasons exhibiting recurrent patterns. The proposed methodology is successful in providing new insights on influenza spread and can be applied to incidence time-series of different countries and different diseases.

Pulmonary Diseases in Refugees and Migrants in Europe

More than 2 million people fleeing conflict, persecution, and poverty applied for asylum between 2015 and 2016 in the European Union. Due to this, medical practitioners in recipient countries may be facing a broader spectrum of conditions and unusual presentations not previously encountered, including a wide range of infections with pulmonary involvement. Tuberculosis is known to be more common in migrants and has been covered broadly in other publications. The scope of this review was to provide an overview of exotic infections with pulmonary involvement that could be encountered in refugees and migrants and to briefly describe their epidemiology, diagnosis, and management. As refugees and migrants travel from numerous countries and continents, it is important to be aware of the various organisms that might cause disease according to the country of origin. Some of these diseases are very rare and geographically restricted to certain regions, while others have a more cosmopolitan distribution. Also, the spectrum of severity of these infections can vary from very benign to severe and even life-threatening. We will also describe infectious and noninfectious complications that can be associated with HIV infection as some migrants might originate from high HIV prevalence countries in sub-Saharan Africa. As the diagnosis and treatment of these diseases can be challenging in certain situations, patients with suspected infection might require referral to specialized centers with experience in their management. Additionally, a brief description of noncommunicable pulmonary diseases will be provided.

An Opportunistic Pathogen Afforded Ample Opportunities: Middle East Respiratory Syndrome Coronavirus

The human coronaviruses (CoV) include HCoV-229E, HCoV-OC43, HCoV-NL63, and HCoV-HKU1, some of which have been known for decades. The severe acute respiratory syndrome (SARS) CoV briefly emerged into the human population but was controlled. In 2012, another novel severely human pathogenic CoV—the Middle East Respiratory Syndrome (MERS)-CoV—was identified in the Kingdom of Saudi Arabia; 80% of over 2000 human cases have been recorded over five years. Targeted research remains key to developing control strategies for MERS-CoV, a cause of mild illness in its camel reservoir. A new therapeutic toolbox being developed in response to MERS is also teaching us more about how CoVs cause disease. Travel-related cases continue to challenge the world’s surveillance and response capabilities, and more data are needed to understand unexplained primary transmission. Signs of genetic change have been recorded, but it remains unclear whether there is any impact on clinical disease. How camels came to carry the virus remains academic to the control of MERS. To date, human-to-human transmission has been inefficient, but virus surveillance, characterisation, and reporting are key to responding to any future change. MERS-CoV is not currently a pandemic threat; it is spread mainly with the aid of human habit and error.

Estimating and modelling the transmissibility of Middle East Respiratory Syndrome CoronaVirus during the 2015 outbreak in the Republic of Korea

Background

Emerging respiratory infections represent a significant public health threat. Because of their novelty, there are limited measures available to control their early spread. Learning from past outbreaks is important for future preparation. The Middle Eastern Respiratory Syndrome CoronaVirus (MERS‐CoV ) 2015 outbreak in the Republic of Korea (ROK) provides one such opportunity.

Objectives

We demonstrated through quantitative methodologies how to estimate MERS‐CoV's transmissibility and identified the effective countermeasures that stopped its spread.

Methods

Using the outbreak data, statistical methods were employed to estimate the basic reproductive number R₀, the average number of secondary cases produced by a typical primary case during its entire infectious period in a fully susceptible population. A transmission dynamics model was also proposed to estimate R₀ and to identify the most effective countermeasures. The consistency between results will provide cross‐validation of the approaches.

Results

R₀ ranged from 2.5 with 95% confidence interval (CI): [1.7, 3.1] (using the sequential Bayesian method) to 7.2 with 95% CI: [5.3, 9.4] (using the Nowcasting method). Estimates from transmission model were higher but overlapped with these. Personal protection and rapid confirmation of cases were identified as the most important countermeasures.

Conclusions

Our estimates were in agreement with others from the ROK outbreak, albeit significantly higher than estimates based on other small outbreaks and sporadic cases of MERS‐CoV. The large‐scale outbreak in the ROK was jointly due to the high transmissibility in the healthcare‐associated setting and the Korean culture‐associated contact behaviour. Limiting such behaviour by rapidly identifying and isolating cases and avoiding high‐risk contacts effectively stopped further transmission.

Measuring distance through dense weighted networks: The case of hospital-associated pathogens

Hospital networks, formed by patients visiting multiple hospitals, affect the spread of hospital-associated infections, resulting in differences in risks for hospitals depending on their network position. These networks are increasingly used to inform strategies to prevent and control the spread of hospital-associated pathogens. However, many studies only consider patients that are received directly from the initial hospital, without considering the effect of indirect trajectories through the network. We determine the optimal way to measure the distance between hospitals within the network, by reconstructing the English hospital network based on shared patients in 2014–2015, and simulating the spread of a hospital-associated pathogen between hospitals, taking into consideration that each intermediate hospital conveys a delay in the further spread of the pathogen. While the risk of transferring a hospital-associated pathogen between directly neighbouring hospitals is a direct reflection of the number of shared patients, the distance between two hospitals far-away in the network is determined largely by the number of intermediate hospitals in the network. Because the network is dense, most long distance transmission chains in fact involve only few intermediate steps, spreading along the many weak links. The dense connectivity of hospital networks, together with a strong regional structure, causes hospital-associated pathogens to spread from the initial outbreak in a two-step process: first, the directly surrounding hospitals are affected through the strong connections, second all other hospitals receive introductions through the multitude of weaker links. Although the strong connections matter for local spread, weak links in the network can offer ideal routes for hospital-associated pathogens to travel further faster. This hold important implications for infection prevention and control efforts: if a local outbreak is not controlled in time, colonised patients will appear in other regions, irrespective of the distance to the initial outbreak, making import screening ever more difficult.

Shared patients can spread hospital-associated pathogens between hospitals, together forming a large network in which all hospitals are connected. We set out to measure the distance between hospitals in such a network, best reflecting the risk of a hospital-associated pathogen spreading from one to the other. The central problem is that this risk may not be a directly reflected by the weight of the direct connections between hospitals, because the pathogen could arrive through a longer indirect route, first causing a problem in an intermediate hospital. We determined the optimal balance between connection weights and path length, by testing different weighting factors between them against simulated spread of a pathogen. We found that while strong connections are important risk factor for a hospital’s direct neighbours, weak connections offer ideal indirect routes for hospital-associated pathogens to travel further faster. These routes should not be underestimated when designing control strategies.

Clinical determinants of the severity of Middle East respiratory syndrome (MERS): a systematic review and meta-analysis

Background

While the risk of severe complications of Middle East respiratory syndrome (MERS) and its determinants have been explored in previous studies, a systematic analysis of published articles with different designs and populations has yet to be conducted. The present study aimed to systematically review the risk of death associated with MERS as well as risk factors for associated complications.

Methods

PubMed and Web of Science databases were searched for clinical and epidemiological studies on confirmed cases of MERS. Eligible articles reported clinical outcomes, especially severe complications or death associated with MERS. Risks of admission to intensive care unit (ICU), mechanical ventilation and death were estimated. Subsequently, potential associations between MERS-associated death and age, sex, underlying medical conditions and study design were explored.

Results

A total of 25 eligible articles were identified. The case fatality risk ranged from 14.5 to 100%, with the pooled estimate at 39.1%. The risks of ICU admission and mechanical ventilation ranged from 44.4 to 100% and from 25.0 to 100%, with pooled estimates at 78.2 and 73.0%, respectively. These risks showed a substantial heterogeneity among the identified studies, and appeared to be the highest in case studies focusing on ICU cases. We identified older age, male sex and underlying medical conditions, including diabetes mellitus, renal disease, respiratory disease, heart disease and hypertension, as clinical predictors of death associated with MERS. In ICU case studies, the expected odds ratios (OR) of death among patients with underlying heart disease or renal disease to patients without such comorbidities were 0.6 (95% Confidence Interval (CI): 0.1, 4.3) and 0.6 (95% CI: 0.0, 2.1), respectively, while the ORs were 3.8 (95% CI: 3.4, 4.2) and 2.4 (95% CI: 2.0, 2.9), respectively, in studies with other types of designs.

Conclusions

The heterogeneity for the risk of death and severe manifestations was substantially high among the studies, and varying study designs was one of the underlying reasons for this heterogeneity. A statistical estimation of the risk of MERS death and identification of risk factors must be conducted, particularly considering the study design and potential biases associated with case detection and diagnosis.

Electronic supplementary material

The online version of this article (doi:10.1186/s12889-016-3881-4) contains supplementary material, which is available to authorized users.