Journal of Travel Medicine

The 2026 Bundibugyo Ebola outbreak in eastern Democratic Republic of the Congo (DRC) has already generated international spread to Uganda, raising concerns about further regional and international dissemination. Using International Air Transport Association origin-destination passenger flows, we assessed relative exposure to Ebola virus disease importation into Europe under six outbreak expansion scenarios reflecting plausible pathways of geographical spread, including cross-border transmission and amplification in highly connected regional capitals. Relative exposure patterns remained largely unchanged under localized transmission in eastern DRC and border-spillover scenarios. Expansion into South Sudan generated a first structural increase in importation pressure to Europe through the connectivity associated with Juba, while hypothetical amplification in Kampala, Kigali, and Kinshasa substantially increased importation pressure and reshaped exposure patterns across Europe. Across all scenarios, France, Italy, and the United Kingdom remained among the most exposed countries. Mobility-informed scenario analyses support preparedness as the geography of the outbreak evolves.

During the 2026 Bundibugyo virus disease outbreak in the Democratic Republic of the Congo and Uganda, we projected potential airline-mediated importation risk using contemporary airline network and an externally calibrated Ebola importation hazard. Effective-distance analyses identified major international hub countries, including Belgium, France, South Africa, Kenya, and the United Arab Emirates, as higher-probability gateways within 30 days. These early projections provide a reproducible framework for real-time international situational awareness, while emphasizing that importation risk does not imply local transmission risk.

The panzootic caused by high pathogenicity avian influenza (HPAI) H5N1 clade 2.3.4.4b has been devastating for animals, globally. Despite global spread, the virus remains absent in Oceania. Herein we report the results of our fourth year of enhanced migratory bird surveillance, coinciding with the spring migration of wild birds in 2025; none of the 847 migratory wild birds or 38 marine mammals were positive for HPAI H5N1, although we did detect LPAI. Surveillance remains a critical tool for HPAI H5N1 response, with early detection and rapid response being critical to mitigate the impacts of this virus on animal, environment and human health.

Background. The 2026 FIFA World Cup will bring an estimated 1--5~million international visitors to 11~US host cities between June~11 and July~19, 2026---the largest tournament in history. Large-scale international gatherings accelerate importation of infectious diseases from diverse source populations. Advance estimation of importation risk is essential for public health preparedness and surveillance prioritization. Methods. We developed a Poisson importation framework applied to five diseases (dengue fever, influenza, malaria, measles, and pertussis) across the 11~US venue cities. Three nested travel models of increasing resolution were constructed: a baseline model using routine June~2024 arrival data; a World Cup--adjusted model incorporating projected visitor growth factors; and a schedule-driven model routing WC fans to specific cities based on match assignments. WHO incidence and BTS T-100 routing fractions were combined with Monte Carlo uncertainty propagation (5,000 Uniform draws on under-reporting and travel-while-infectious parameters) to yield median importation estimates with 95\% uncertainty intervals. Results. Dengue posed the highest importation risk at most venue cities under the schedule-driven model (median $\Lambda > 10$ expected importations from Brazil alone; 95\% uncertainty interval 5.9--33.1), robust across the full literature-supported parameter range; Atlanta was the exception, where malaria probability exceeded dengue, driven by direct travel from West and Central African nations. Influenza ranked second at most cities, coinciding with the Southern Hemisphere winter peak. Pertussis showed broad geographic spread but carries the widest relative uncertainty, as the assumed detection rate sits at the upper bound of the literature range. Background tourism accounted for the dominant share of total importation risk; the World Cup fan increment contributed approximately 8.3\% of projected arrivals for WC-qualified nations. Conclusions. This Poisson importation framework, built entirely from publicly available data, provides reproducible importation risk estimates for mass gathering events. The framework extends to additional diseases, cities, and gatherings, offering a transparent baseline complementary to proprietary modeling systems.

BackgroundIn World War 1 (WW1) outbreaks of measles were associated with high case fatality rates amongst soldiers. Recent studies have shown that survivors of acute measles can also develop immune amnesia, increasing their susceptibility to other infections. However, the impact of prior measles infection on infectious diseases during WWI remains unclear. MethodsHere, we create a searchable database documenting the medical history of 1,569 individuals from the Australian, New Zealand, and Canadian forces during WW1. ResultsWe use this novel database to show that a recent measles hospitalisation was associated with a higher chance of death for infectious diseases (excluding pandemic influenza like illness), consistent with immune amnesia. Surprisingly, a prior measles infection was associated with a significant reduction in hospitalisations duration from pandemic influenza like illness. ConclusionThese findings highlight the unique interaction between measles and pandemic influenza, contrasting with other infectious diseases, and underscore the significant health burden measles placed on young adults during WW1.

Background Appendicitis is a readily treatable surgical emergency, yet it remains a cause of preventable death among children in resource-limited settings. While recent studies have documented the global burden of pediatric appendicitis, none have systematically examined its geographic clustering or spatial spillover effects. Understanding whether high-mortality countries cluster geographically, and whether neighboring countries influence each other's outcomes, is essential for designing regional surgical capacity strategies. Methods We conducted a spatial analysis of pediatric appendicitis case fatality rates in children aged 0-14 years across 169 countries from 2000 to 2019. Data were obtained from the Global Burden of Disease Study 2023 and World Bank databases. We calculated global Moran's I to assess spatial autocorrelation, used Getis-Ord Gi* to identify local hotspots, and fitted spatial lag and spatial error regression models to quantify spatial spillovers while adjusting for GDP per capita, physician density, and basic sanitation access. Results Global Moran's I was 0.621 in 2000 (p < 0.001), 0.621 in 2010 (p < 0.001), and 0.592 in 2019 (p < 0.001), indicating strong and persistent spatial clustering. Hotspots at 99% confidence were consistently concentrated in sub-Saharan Africa and parts of South Asia, with little change in geographic distribution over two decades. The spatial error model provided the best fit (AIC = 212.6), with a spatial error coefficient ({lambda}) of 0.663 (p < 0.001), suggesting that approximately 66% of residual variation was explained by unobserved regional factors. In the final model, higher GDP per capita ({beta} = -0.497, p < 0.001) and higher physician density ({beta} = -0.568, p < 0.001) were independently associated with lower case fatality, while basic sanitation access showed no significant association (p = 0.284). Conclusions Pediatric appendicitis case fatality exhibits strong and persistent geographic clustering. The substantial spatial spillover effect suggests that regional coordination of surgical capacity building may be more effective than country-by-country investments. Priority should be given to hotspot countries in sub-Saharan Africa and South Asia, with emphasis on surgical workforce expansion rather than broad economic development alone.

Background. Climate change is intensifying extreme weather events (EWEs) with potentially profound consequences for zoonotic disease dynamics, yet the mechanisms linking EWEs to highly pathogenic avian influenza (HPAI) H5N1 outbreaks remain poorly characterized. The ongoing H5N1 panzootic, responsible for infection in over 500 avian and mammalian species, as well as nearly 1000 human cases and 477 deaths worldwide, provides a critical opportunity to evaluate how climate conditions shape spillover risk at landscape scales. Methods. We compiled a county-month dataset of confirmed H5N1 detections across the contiguous United States from 2022 to 2024 and integrated it with satellite-derived climate metrics, storm event data, and wild bird activity data. We trained and validated a gradient boosting machine classifier to predict outbreak risk and characterize predictor relationships. Results. Our model achieved strong discriminative performance (AUC-ROC = 0.856; AUC-PR = 0.237, representing a 7-fold improvement over chance) and high recall (0.726), supporting its utility as an early warning tool. Human population and temperature-related variables were the most influential predictors: cold temperature shocks and prolonged low temperatures were consistently associated with elevated outbreak risk, likely through enhanced environmental viral persistence, wild bird habitat compression, and allostatic stress-driven immunosuppression in reservoir hosts. Among storm variables, high wind coverage elevated risk, potentially via aerosol dispersal of contaminated particulates, while tornado activity showed an inverse relationship, consistent with documented avoidant behavior in migratory birds. Wild bird reservoir density showed a strong positive monotonic relationship with outbreak risk. Conclusions. Our analyses demonstrate that routinely available environmental and infection data can be used to predict HPAI outbreak risk at fine spatiotemporal scales. These findings demonstrate the divergent roles of short- versus long-term environmental exposures in HPAI spillover dynamics, as well as the potential for machine learning-based surveillance tools to inform targeted biosecurity interventions and early warning systems.

Infectious bursal disease virus (IBDV) and H9N2 avian influenza virus (AIV) are significant global threats to poultry health and production. While IBDV induces severe immunosuppression, undermining host defense and vaccine efficacy, H9N2 AIV is characterized by widespread prevalence, persistent shedding, and substantial economic losses. Conventional inactivated vaccines often fail to elicit robust cellular immunity and necessitate multiple booster doses, underscoring the urgent requirement for advanced multivalent vaccination platforms. To address this, we developed a recombinant herpesvirus of turkey (rHVT BAC-VP2-HA) using a bacterial artificial chromosome (BAC) vector system, engineered to co-express the major protective antigen VP2 of IBDV and the hemagglutinin (HA) of H9N2 AIV. Genetic stability and in vitro characterization confirmed that the recombinant exhibited replication kinetics and plaque morphology comparable to parental HVT, with stable antigen expression. In SPF chickens, rHVT BAC-VP2-HA induced strong humoral immune responses against both target antigens, comparable to those elicited by a commercial inactivated vaccine. Crucially, the recombinant virus significantly enhanced cellular immunity, evidenced by markedly elevated CD3+CD8+ T cell responses. Upon challenge, the recombinant conferred high clinical protection (86%) against virulent IBDV, significantly ameliorating bursal pathology and reducing viral loads. Notably, it provided complete (100%) protection against H9N2 AIV, effectively abolishing viral shedding and suppressing viral replication in respiratory tissues. These results demonstrate that rHVT BAC-VP2-HA is a safe and efficacious candidate capable of eliciting humoral and cellular immune responses, offering a promising strategy for the integrated control of major poultry diseases. ImportanceInfectious bursal disease virus (IBDV) and H9N2 avian influenza virus (AIV) are major pathogens that frequently co-circulate in poultry, where IBDV-induced immunosuppression compromises the efficacy of vaccination against other infectious diseases. Conventional inactivated vaccines primarily induce humoral immunity and are often insufficient to prevent viral shedding or provide broad protection against multiple pathogens. In this study, we developed a recombinant herpesvirus of turkeys (HVT) vaccine co-expressing the IBDV VP2 and H9N2 HA antigens and demonstrated that it induces both robust antibody responses and enhanced CD8+ T cell immunity. Notably, this vaccine not only provided effective protection against IBDV but also completely prevented viral shedding following H9N2 challenge. These findings highlight the advantage of HVT-vectored multivalent vaccines in eliciting balanced immune responses and controlling virus transmission, providing important insights for the development of next-generation vaccines against immunosuppressive and respiratory viral co-infections in poultry.

An ongoing outbreak of Bundibugyo virus disease (BVD) in the Democratic Republic of the Congo was deemed a public health emergency of international concern in May 2026. To prevent cross-border importation, many countries, including the United States, Canada, India, Thailand, and Kenya have already proposed containment strategies, and others are likely to follow suit. How well (or poorly) are screening and quarantine containment measures are likely to work? We leverage established epidemiological theory and develop a mathematical model of traveler screening and post-arrival quarantine for BVD to answer this question. We find that traveler screening via symptom screening or molecular testing will miss the majority of infected travelers, and should be complemented by post-arrival quarantine and monitoring of sufficient duration to detect those with long incubation periods. Our findings underscore the limitations of border screening and the importance of complementary measures like post-arrival quarantine to prevent local importation of BVD.

Background: The 2026 FIFA World Cup may bring over one million visitors to North America from around the globe to participate in mass gathering events. The nature of the event and recent news have raised concerns for some that the tournament could lead to infectious disease outbreaks or fuel existing epidemics. Objective: To systematically assess the infectious disease threat posed to the United States by the tournament. Design: A multi-institutional team evaluated pathogen-specific risk across three dimensions: importation, outbreak potential, and impact to identify a priority pathogen list. A systematic screening protocol ensured common criteria and that pathogen information was collected when necessary to inform inclusion. Results: Increased risk from the World Cup is near zero for 63 of 77 evaluated pathogens. Pathogens were predominantly excluded as threats due to low excess importation risk and low outbreak potential if introduced. The remaining priority pathogens fall into five categories: (a) mosquito borne pathogens with the potential for sustained transmission in some host cities, (b) seasonal respiratory viruses, (c) chronic infections with high prevalence outside the United States, (d) pathogens present in the United States with likely increased transmission at World Cup activities, and (e) high-consequence infectious threats. Limitations: Data availability is variable across diseases. Impact calculations may not reflect actual costs to host cities. Disease incidence in World Cup travelers may differ from national incidence rates. Conclusion: While infectious disease outbreaks at the 2026 FIFA World Cup are possible, in an already highly connected world where large gatherings are frequent, the elevated risk from the tournament is not as extreme as it first may seem.

The emergence of a hantavirus variant aboard a commercial cruise ship presents a significant public health concern. This study develops a discrete-time stochastic Susceptible-Exposed-Infectious-Recovered-Dead model to estimate transmission dynamics, hidden exposed infections, and outbreak risk among passengers and crew. Epidemiological parameters and latent disease states were inferred using an Ensemble Adjustment Kalman Filter calibrated to reported case data from WHO and ECDC situation reports. The estimated basic reproduction number was 2.76, with a 95% confidence interval of 2.52-2.99, indicating substantial potential for sustained onboard transmission before strict quarantine measures. Simulations further suggest that several exposed individuals may remain unidentified during the early outbreak phase, creating a hidden reservoir that symptom-based surveillance alone may fail to detect. These findings highlight the importance of rapid surveillance, widespread testing, targeted quarantine, and active monitoring of exposed individuals in confined travel settings. The proposed modeling framework can support timely outbreak assessment and intervention planning for infectious-disease events in similarly dense and spatially constrained populations.

The Hajj is an annual pilgrimage made by millions of Muslims to Mecca in the Kingdom of Saudi Arabia (KSA). The large number of international attendees at the Hajj increases the risk of global infectious disease spread. However, we know very little about the benefits, costs, and cost-effectiveness of testing and quarantining strategies to contain epidemic spread during mass gathering events. In this work we developed a stochastic discrete-time compartmental metapopulation model to simulate international epidemics of infectious pathogens and their potential importation into KSA during the Hajj. We used the model and an epidemic simulation study to evaluate the impact and cost-effectiveness of three testing and quarantining strategies for arriving pilgrims: randomly testing 99% of pilgrims, 80% of pilgrims, or using a symptom-based screening strategy. The simulations lasted 100 days, covering the 30 days before the Hajj and 65 days after the Hajj. Under the conditions assumed in our simulation study, there was strong evidence that testing and quarantining strategies are cost-effective measures for controlling epidemic threats at the Hajj. The median net monetary benefits of intervention strategies ranged from Intl$-41.89M [95% quantile range Intl$-42.37M to Intl$3.18B] to Intl$12.68B [Intl$-8.70B to Intl$13.82B] across scenarios with different pathogen characteristics (based on the natural histories of SARS-CoV-2 and H1N1 Influenza) and epidemic seed locations. Our results were sensitive to the data sources that were used to estimate the number of pilgrims travelling to KSA by origin country, with flight passenger statistics providing biased estimates of pilgrim numbers. Our work provides an adaptable tool to inform infectious disease risk assessments and evaluate the cost-effectiveness of possible disease control measures for the Hajj, and could be extended to other mass gathering events.

Global efforts to prevent and mitigate the impacts of high pathogenicity avian influenza (HPAI) H5 on domestic animals, humans, and wildlife rely on timely and transparent information that is both accurate and interpretable across countries and sectors. International epidemiological and genomic databases, such as the World Animal Health Information System (WAHIS), the Global Animal Disease Information System (EMPRES-i+), the Global Initiative on Sharing All Influenza Data (GISAID), and the National Center for Technological Bioinformation Virus Portal (NCBI) provide essential information for surveillance, research, and decision-making. To evaluate how well these resources capture recent wildlife impacts, we consolidated information from these databases and complementary public sources including government reports, scientific literature, and news articles, on wildlife mortality associated with HPAI H5 in the Americas from November 2021 to July 2024. The consolidated dataset comprised 615,883 wild birds (287 spp.) and 63,409 wild mammals (39 spp.). In comparison, WAHIS represented 16,902 wild birds (261 spp.) and 6,323 wild mammals (31 spp.) while EMPRES-i+ captured a substantially smaller portion of affected host diversity for both wild birds (105 spp.) and wild mammals (27 spp.). Genomic databases (GISAID and NCBI) represented 7,027 whole genome equivalents of H5 viruses from wild birds (175 spp.) and 371 from wild mammals (26 spp.). These discrepancies indicate that international databases, while essential, provide an incomplete picture of HPAI impacts on wildlife, with significant geographic and taxonomic asymmetries attributable to differences in surveillance capacity, reporting practices, sequencing effort, and data-sharing pathways. Studies and management strategies relying on these resources without complementary validation may therefore mistake data gaps for real-world epidemiological patterns. Strengthening data reporting standards, improving validation procedures, and integrating international databases with national reports, scientific publications, and other sources will enhance the reliability of epidemiological analyses and support more effective One Health surveillance, risk assessment, and conservation action. Author summaryHigh pathogenicity avian influenza (HPAI) H5 viruses, often called bird flu viruses, can cause severe disease in birds and mammals, including humans. Because of their relevance for human health, livestock production, and wildlife conservation, international databases were established to share information on when and where these viruses are detected, which species are affected, and what virus strains are found. These databases are essential tools for governments, scientists, and conservation practitioners working to track outbreaks, understand how these viruses spread and evolve, and guide surveillance and response. In this study, we compiled and compared information on recent HPAI H5 events in wildlife in the Americas available in international databases with information from other public sources, including reports from governments, scientific literature, and news articles. We found important discrepancies in how countries and species affected were represented across sources. As a result, international databases might not fully capture the actual distribution or conservation impact of HPAI H5 on wildlife. Our findings also show why decision-makers and scientists should interpret database-derived patterns carefully. We provide recommendations to improve international databases to address these gaps and better inform One Health risk assessment and wildlife conservation actions.

Temperate regions of the world, such as southern Australia, often experience increased health burden from respiratory pathogens during winter. The ability to forecast short-term trends in cases of these pathogens is of significant interest to public health. Across the 2024 southern hemisphere winter period, the Australia--Aotearoa Consortium for Epidemic Forecasting and Analytics (ACEFA) ran a pilot respiratory virus forecasting initiative in collaboration with the Victorian Department of Health. Each week from the 9th of May 2024 through to 12th September 2024, the consortium solicited 28-day forecasts of daily case incidence for influenza, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and respiratory syncytial virus (RSV) from multiple research groups. Four component model forecasts were contributed by three different research groups, with a fourth group utilising the component forecasts to generate ensemble forecasts (making a total of six models, four component models and two ensembles). Here we statistically evaluated the performance of each forecast and a baseline model against the observed case data. The two ensemble models were found to be frequently the top performing models. All models performed worse than the baseline model around the epidemic peaks for each pathogen.

This novel study detected persistent low level infections of Elephant Endotheliotropic Herpesviruses (EEHV), that can cause highly pathogenic Elephant Hemorrhagic Disease (EHD) in Loxodonta and Elephas, and co-infection of presumed less pathogenic Elephant Gammaherpesviruses (EGHV), in skin nodule biopsies, saliva and tissues collected from 43 wild L. africana (savannah elephant) in Botswana, Kenya, South Africa and Zimbabwe; in saliva from 25 wild L. cyclotis (forest elephant) in Gabon; and in saliva collected over seven years from 7 wild-born L.africana at Six Flags Safari Park, USA; and in saliva, blood and tissues from an additional 200 L. africana in USA zoos. DNA from these samples was extracted in our USA laboratories and amplified by conventional polymerase chain reaction using three-round nested primer sets designed specifically to screen for known EEHV and EGHV genes loci and to discover new species and subtypes. Sanger sequencing of purified DNA from nearly all samples yielded unambiguous positive genetic matches to previously known Loxodonta-associated EEHV2, EEHV3A, EEHV3B, EEHV6, EEHV7A, and EGHV1B, EGHV2, EGHV3B, EGHV4B, EGHV5B and discovered novel types EEHV3C-H and EEHV7B and the prototype EGHV1B. Many of the primer sets used could also have detected known Elephas-associated EEHV1A, EEHV1B, EEHV4, and EEHV5 if present in these samples, but they did not. Our extensive library of EEHV and EGHV sequences from wild and zoo Loxodonta, (as well as from 100 zoo Elephas maximus not discussed in this review), is a significant contribution to the elephant virology community, particularly for comparing subtypes types of EEHV found in pathogenic cases of EHD in zoos as well as determining and comparing species and subtypes of EEHV present in existing zoo herds, and in individual elephants being transported between zoos, and for importation of wild elephants into existing zoo herds.

Influenza viruses present a significant public health risk, causing substantial illness and death in humans each year. Seasonal flu vaccines must be updated regularly, and their effectiveness often decreases due to mismatches with circulating strains. Furthermore, inactivated vaccines do not provide protection against shifted influenza viruses that have the potential to cause a pandemic. The highly pathogenic avian influenza H5N1 clade 2.3.4.4b is prevalent among wild birds worldwide and is causing a multi-state outbreak affecting poultry and dairy cows in the United States (US) since March 2024. In this study, we have generated a NS1 deficient mutant of a low pathogenic version of the cattle-origin human influenza A/Texas/37/2024 H5N1, namely LPhTXdNS1, and validated its safety, immunogenicity, and protection efficacy in a prime vaccination regimen against wild-type (WT) A/Texas/37/2024 H5N1. The attenuation of LPhTXdNS1 in vitro was confirmed by its reduced replication in cultured cells and inability to control IFN{beta} promoter activation. In C57BL/6J mice, LPhTXdNS1 has reduced viral replication and pathogenicity compared to WT A/Texas/37/2024 H5N1. Notably, LPhTXdNS1 vaccinated mice exhibited high immunogenicity that reach its peak at weeks 3 and 4 post-immunization, leading to robust protection against subsequent lethal challenge with WT A/Texas/37/2024 H5N1. Altogether, we demonstrate that a single dose vaccination with LPhTXdNS1 is safe and able to induce protective immune responses against H5N1. Both safety profile and protection immunity suggest that LPhTXdNS1 holds promise as a potential solution to address the urgent need for an effective vaccine in the event of a pandemic for the treatment of infected animals and humans.

Leishmaniasis, a climate-sensitive zoonotic neglected tropical disease, is transmitted by Phlebotomine sand flies and closely linked to socio-economic inequities. Understanding its spatio-temporal dynamics under environmental and social change is critical for effective control. A machine learning framework (XGBoost) was developed to map the global and European distribution of leishmaniasis, incorporating climatic indicators, land cover, elevation, and socio-economic indices (Human Development Index, AROPE). For Europe, five proven vector species (Phlebotomus perniciosus, P. ariasi, P. perfiliewi, P. neglectus, and P. tobbi) were modelled alongside cutaneous and visceral leishmaniasis. Across both analyses, land use features, particularly shrubland and forest cover, had the greatest explanatory power, reflecting their role in providing microclimates and vertebrate hosts for sand flies. Climatic factors, notably mean temperature of the coldest quarter and humidity of the warmest/driest quarters, were also influential, as these facilitate sand fly survival. Socio-economic predictors consistently improved model performance, confirming the role of poverty and inequity as determinants of disease distribution. Globally, leishmaniasis risk increased by ~17% since the 1990s, with Africa, Asia, and the Americas experiencing the greatest rise. In Europe, modest continental-scale increases (CL +1.28%; VL +2.47%) masked strong sub-national heterogeneity, including northward expansion of visceral leishmaniasis and increases in cutaneous leishmaniasis in southern and eastern regions. Sand fly projections indicated expansion of warm-adapted species (P. ariasi, P. perniciosus, P. neglectus) and contraction of species preferring cooler, more humid niches (P. perfiliewi, P. tobbi). These findings highlight climate change, land use, and inequity as interacting drivers of leishmaniasis, emphasising the need for enhanced surveillance, integrated vector management, and targeted support for vulnerable populations, including refugees and migrants.

Filoviruses pose a threat to individuals and the global community as pathogens of pandemic potential. The scientific community faces an ongoing challenge of developing effective vaccines with unpredictable outbreaks concentrated in countries with lower healthcare resources. Given these limitations, it is important to ensure that existing filovirus research is used as efficiently as possible. To enable rapid identification and use of this research, we have developed evidence maps of existing filovirus publications to enable further analysis and synthesis. We systematically identified and categorised existing immunological and clinical publications on Bundibugyo (BDBV), Marburg (MARV), Sudan (SUDV) and Ebola (EBOV) viruses. We captured studies that reported on animal or human immune responses to infection, outcome of infection, or human vaccine safety data. Initial searches of PubMed, Embase and Europe PMC were run between November 2024 and January 2025 and the MARV, SUDV and EBOV searches were updated on 1 August 2025. A BDBV search was conducted on 18 May 2026 in response to the WHO declaration of a Public Health Emergency on 17 May 2026. The initial searches retrieved 208, 1646, 534 and 3963 manuscripts for BDBV, MARV, SUDV and EBOV, respectively. After screening using an a priori exclusion criteria, 49 BDBV, 198 MARV, 149 SUDV and 850 EBOV publications were included on each evidence map. These maps provide a comprehensive, transparent and reproducible structure to categorise existing studies of filovirus vaccination and immunity. They allow rapid identification of the totality of available evidence and the existing experimental tools to support vaccine development for these priority pathogens.

Using a commercially available H5 serology assay, we identified a 7.4% (n=5/68) anti-H5 seroreactivity rate among hunters in Northern Canada. All participants reported close contact with wild birds.

Novel respiratory pathogens have pandemic potential, making epidemiologic surveillance of acute lower respiratory tract infections (acute LRTI) a global public health priority. Monitoring acute LRTI among international travelers provides an important underutilized opportunity to complement existing surveillance systems, although reliable denominator data on travel volume are often unavailable. Using GeoSentinel data from 2015-2019, capturing syndromic and etiologic LRTI cases, we modeled baseline epidemiology in travelers by comparing generalized linear mixed models (GLMMs) using out-of-sample metrics. A She-whart control-chart framework, accounting for increases in travel volume under non-epidemic conditions, was applied to detect deviations from expected trends. The preferred hybrid autoregressive model incorpo-rated country-specific fixed effects, random seasonal effects, and a latent temporal autocorrelation structure, and was evaluated for goodness-of-fit in pre-pandemic (2015-2019) and post-pandemic (2023-2024) periods before retrospective application to 2020 data to identify early COVID-19 signals. The hybrid autoregressive GLMM performed best for modeling baseline epidemiology. Applied retrospectively to early 2020 data from 64 countries, the framework detected an early syndromic signal in China under the conservative assumption of up to a threefold increase in travel volume, consistent with COVID-19 emergence. A conservative signal was also detected in Italy, though driven primarily by influenza A and B rather than novel syndromic cases. Combining traveler surveillance with this statistical framework--integrating GLMMs for baseline modeling and Shewhart charts for outbreak detection--may support early detection of acute LRTI outbreaks despite absent denominator data, positioning GeoSentinel as a valuable complementary network for global health security and pandemic preparedness. SignificanceGlobal interconnectivity accelerates spread of pathogens, increasing pandemic potential. Travel medicine networks are underutilized for outbreak detection of respiratory diseases. Absence of denominator data, complex seasonal and autocorrelated baselines can mask early signaling of outbreaks. We used a flexible baseline model and robust control charts to signal increased reporting without denominator data. By retrospectively applying this statistical framework to surveillance data from international travelers returning from 64 countries, we identified an increase in influenza-like illness among travelers from China by week 5 of 2020, well before the WHO officially declared COVID-19 a pandemic. We demonstrated that traveler surveillance can operate as a scalable, proactive early-warning system, strengthening global health and enabling identification of threats before they escalate into international crises.