Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccines reduced severe coronavirus disease 2019, but variants like Delta and Omicron caused widespread breakthrough infections (BIs). Mexico, offering diverse vaccines, and its Yucatán region, a major travel hub, provide a unique setting to study BIs. We characterized SARS-CoV-2 BIs in Yucatán during the Delta-to-Omicron transition (September 2021-January 2022), assessing disease severity, symptoms, and viral transmission dynamics using epidemiological and genomic data. A case-control study using health system data (n = 13,325) compared outcomes in BIs (n = 5,183) versus unvaccinated infections (UIs; n = 8,142) via logistic regression, also comparing Delta versus Omicron waves. Phylodynamic modeling of 205 BI sequences, contextualized globally (n = 1,152 total), reconstructed the evolutionary history and transmission routes. Vaccination significantly reduced hospitalization (odds ratio [OR] = 0.38) and death (OR = 0.45) in BIs compared to UIs. Omicron infections were less severe than Delta (hospitalization OR = 0.60; death OR = 0.33) and presented with less loss of smell/taste but more upper respiratory symptoms. Phylodynamics revealed numerous introductions (17 Delta, 36 Omicron). Delta BIs in Yucatán originated mainly from within Mexico, Guatemala, Europe, and the United States. Omicron BIs in Yucatán had more diverse origins including from North and South America and Africa, coinciding with eased travel restrictions. Vaccines maintained protection against severe outcomes during the Delta and Omicron waves in Yucatán. Omicron, though less severe, showed enhanced transmissibility with increased introductions linked to relaxed public health measures. Findings highlight the critical role of continued vaccination, genomic surveillance, adaptive policies, and cross-border collaboration for pandemic preparedness and health security. IMPORTANCE:Our understanding of severe acute respiratory syndrome coronavirus 2 breakthrough infections in Latin America is limited, specifically in regions with unique epidemiological dynamics. In this study, we fill a knowledge gap by characterizing these infections in Yucatán, Mexico, a major international travel hub with one of the world's most diverse vaccine rollouts, during the critical transition from the Delta variant to the Omicron variant. The translational importance of our investigation is twofold. First, through case-control data analysis, we provide robust, real-world evidence that vaccination significantly reduced the risk of hospitalization and death, offering crucial data to support ongoing vaccination campaigns against emerging variants. Second, by combining epidemiological data with phylodynamic analysis, we demonstrate a direct link between the easing of public health restrictions and the increased number and diversity of viral introductions that sparked the Omicron wave. This highlights the critical importance of coordinating genomic tracking with public health policy to mitigate the spread of future pandemic threats and strengthen global health security.

Background:In August 1995, necropsies on post-weaning piglets from the CA-CART farm in the province of Cartago, Costa Rica, revealed respiratory lesions, pleuritis, peritonitis, and arthritis. Skin lesions were also observed, progressing to scabs. A subsequent outbreak in 1996 prompted antibiotic administration. Mortality analysis from 1990 to 1995 showed no significant seasonal patterns, but yearly variations were noted. Piglets born in Costa Rica from imported gilts had a higher average mortality rate (10.65%) than the 8.11% mortality rate for piglets born from non-imported gilts and sows or imported sows (p = 0.002). Methods:In March 1996, serum samples were sent for potential PRRS virus (PRRSV) diagnosis, and 10 PRRSV-2 ORF5 sequences collected during a prospective study from 2019-2021 were obtained from various locations in the country. In this article, we seek to investigate the evolutionary and spatio-temporal dynamics of PRRSV-2 in Costa Rica in the context of international swine movements using the phylodynamic framework integrated with the BEAST package. Results and Discussion:The phylodynamic modeling estimated at least two independent PRRSV-2 introductions into the country. The earliest introduction occurred around 1978 (95% highest posterior density interval: 1959.01-1997.54) and led to the viruses circulating in the farm 1CRC with an origin from Japan, possibly via US swine exports. A second cluster, 4CRC, subsequently emerged within Costa Rica from the earlier 1CRC lineage (1990.24; 95% HPD interval = 1976.15-2010.30). Another viral introduction from the US occurred around 1991 (95% highest posterior density interval: 1974.34-2011.35) and established the 3CRC cluster. Though the viral introduction was traced back to the US, the limited genomic surveillance of PRRSV leaves room for considering alternative origins. Conclusion:Our findings provide a high-resolution model of how international swine trade drives the introduction and evolution of a major livestock pathogen, highlighting the critical need for integrating genomic surveillance into biosecurity protocols.

Background COVID-19 waves caused by specific SARS-CoV-2 variants have occurred globally at different times. We focused on Omicron variants to understand the genomic diversity and phylogenetic relatedness of SARS-CoV-2 strains in various regions of Pakistan. Methods We studied 276,525 COVID-19 cases and 1,031 genomes sequenced from December 2021 to August 2022. Sequences were analyzed and visualized using phylogenetic trees. Results The highest case numbers and deaths were recorded in Sindh and Punjab, the most populous provinces in Pakistan. Omicron variants comprised 93% of all genomes, with BA.2 (32.6%) and BA.5 (38.4%) predominating. The first Omicron wave was associated with the sequential identification of BA.1 in Sindh, then Islamabad Capital Territory, Punjab, Khyber Pakhtunkhwa (KP), Azad Jammu Kashmir (AJK), Gilgit-Baltistan (GB) and Balochistan. Phylogenetic analysis revealed Sindh to be the source of BA.1 and BA.2 introductions into Punjab and Balochistan during early 2022. BA.4 was first introduced in AJK and BA.5 in Punjab. Most recent common ancestor (MRCA) analysis revealed relatedness between the earliest BA.1 genome from Sindh with Balochistan, AJK, Punjab and ICT, and that of first BA.1 from Punjab with strains from KPK and GB. Conclusions Phylogenetic analysis provides insights into the introduction and transmission dynamics of the Omicron variant in Pakistan, identifying Sindh as a hotspot for viral dissemination. Such data linked with public health efforts can help limit surges of new infections.

Four seasonal human coronaviruses (sHCoVs) are endemic globally (229E, NL63, OC43, and HKU1), accounting for 5-30% of human respiratory infections. However, the epidemiology and evolution of these CoVs remain understudied due to their association with mild symptomatology. Using a multigene and complete genome analysis approach, we find the evolutionary histories of sHCoVs to be highly complex, owing to frequent recombination of CoVs including within and between sHCoVs, and uncertain, due to the under sampling of non-human viruses. The recombination rate was highest for 229E and OC43 whereas substitutions per recombination event were highest in NL63 and HKU1. Depending on the gene studied, OC43 may have ungulate, canine, or rabbit CoV ancestors. 229E may have origins in a bat, camel, or an unsampled intermediate host. HKU1 had the earliest common ancestor (1809-1899) but fell into two distinct clades (genotypes A and B), possibly representing two independent transmission events from murine-origin CoVs that appear to be a single introduction due to large gaps in the sampling of CoVs in animals. In fact, genotype B was genetically more diverse than all the other sHCoVs. Finally, we found shared amino acid substitutions in multiple proteins along the non-human to sHCoV host-jump branches. The complex evolution of CoVs and their frequent host switches could benefit from continued surveillance of CoVs across non-human hosts.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants continue to emerge, and their identification is important for the public health response to coronavirus disease 2019 (COVID-19). Genomic sequencing provides robust information but may not always be accessible, and therefore, mutation-based polymerase chain reaction (PCR) approaches can be used for rapid identification of known variants. International travelers arriving in Karachi between December 2020 and February 2021 were tested for SARS-CoV-2 by PCR. A subset of positive samples was tested for S-gene target failure (SGTF) on TaqPathTM COVID-19 (Thermo Fisher Scientific) and for mutations using the GSD NovaType SARS-CoV-2 (Eurofins Technologies) assays. Sequencing was conducted on the MinION platform (Oxford Nanopore Technologies). Bayesian phylogeographic inference was performed integrating the patients' travel history information. Of the thirty-five COVID-19 cases screened, thirteen had isolates with SGTF. The travelers transmitted infection to sixty-eight contact cases. The B.1.1.7 lineage was confirmed through sequencing and PCR. The phylogenetic analysis of sequence data available for six cases included four B.1.1.7 strains and one B.1.36 and B.1.1.212 lineage isolate. Phylogeographic modeling estimated at least three independent B.1.1.7 introductions into Karachi, Pakistan, originating from the UK. B.1.1.212 and B.1.36 were inferred to be introduced either from the UK or the travelers' layover location. We report the introduction of SARS-CoV-2 B.1.1.7 and other lineages in Pakistan by international travelers arriving via different flight routes. This highlights SARS-CoV-2 transmission through travel, importance of testing, and quarantine post-travel to prevent transmission of new strains, as well as recording detailed patients' metadata. Such results help inform policies on restricting travel from destinations where new highly transmissible variants have emerged.

In this review, we discuss the epidemiological dynamics of different viral infections to project how the transition from a pandemic to endemic Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) might take shape. Drawing from theories of disease invasion and transmission dynamics, waning immunity in the face of viral evolution and antigenic drift, and empirical data from influenza, dengue, and seasonal coronaviruses, we discuss the putative periodicity, severity, and age dynamics of SARS-CoV-2 as it becomes endemic. We review recent studies on SARS-CoV-2 epidemiology, immunology, and evolution that are particularly useful in projecting the transition to endemicity and highlight gaps that warrant further research.

Respiratory syncytial virus (RSV) is the most common cause of serious lower respiratory tract illness in infants and children and causes significant disease in the elderly and immunocompromised. Recently there has been an acceleration in the development of candidate RSV vaccines, monoclonal antibodies and therapeutics. However, the effects of RSV genomic variability on the implementation of vaccines and therapeutics remain poorly understood. To address this knowledge gap, the National Institute of Allergy and Infectious Diseases and the Fogarty International Center held a workshop to summarize what is known about the global burden and transmission of RSV disease, the phylogeographic dynamics and genomics of the virus, and the networks that exist to improve the understanding of RSV disease. Discussion at the workshop focused on the implications of viral evolution and genomic variability for vaccine and therapeutics development in the context of various immunization strategies. This paper summarizes the meeting, highlights research gaps and future priorities, and outlines what has been achieved since the meeting took place. It concludes with an examination of what the RSV community can learn from our understanding of SARS-CoV-2 genomics and what insights over sixty years of RSV research can offer the rapidly evolving field of COVID-19 vaccines.

There is a dearth of information on COVID-19 disease dynamics in Africa. To fill this gap, we investigated the epidemiology and genetic diversity of SARS-CoV-2 lineages circulating in the continent. We retrieved 5229 complete genomes collected in 33 African countries from the GISAID database. We investigated the circulating diversity, reconstructed the viral evolutionary divergence and history, and studied the case and death trends in the continent. Almost a fifth (144/782, 18.4%) of Pango lineages found worldwide circulated in Africa, with five different lineages dominating over time. Phylogenetic analysis revealed that African viruses cluster more closely with those from Europe. We also identified two motifs that could function as integrin-binding sites and N-glycosylation domains. These results shed light on the epidemiological and evolutionary dynamics of the circulating viral diversity in Africa. They also emphasize the need to expand surveillance efforts in Africa to help inform and implement better public health measures.

Respiratory illness caused by respiratory syncytial virus (RSV) is increasingly recognised as a major cause of childhood morbidity and mortality globally. This increased awareness has led to the development of candidate RSV vaccines, monoclonal antibodies, and therapeutics, some of which have reached phase 2 and 3 trials. We recently organised an international workshop titled RSV Genomic Diversity and the Development of a Globally Effective RSV Intervention, at the Fogarty International Centre, National Institutes of Health (Bethesda, MD, USA) in September, 2019. A common theme that emerged was that planning the implementation of potential interventions requires a deeper understanding of RSV molecular epidemiology and the host immune response. Although several international efforts to improve our understanding are ongoing, the effectiveness of this research is hampered by the lack of a consistent approach. In this Comment, we highlight the rationale for standardising key aspects of RSV research (panel). PanelPriorities in standardising respiratory syncytial virus research •A standardised nomenclature for virus strains to facilitate processing and analysis of sequencing samples •A standard genotyping system to be able to relate strain type to clinical presentation and understand changes in viral transmission subsequent to the introduction of any intervention •Agreement on laboratory assays used to quantify viral load and correlates of protection •Mechanisms to make sequencing data available in a timely manner, which fairly acknowledges the providers of that information •Minimum necessary clinical data to be associated with viral sequence data •Uptake of standardised clinical case definitions of disease to establish a baseline to understand how interventions affect disease severity once introduced •A standardised nomenclature for virus strains to facilitate processing and analysis of sequencing samples •A standard genotyping system to be able to relate strain type to clinical presentation and understand changes in viral transmission subsequent to the introduction of any intervention •Agreement on laboratory assays used to quantify viral load and correlates of protection •Mechanisms to make sequencing data available in a timely manner, which fairly acknowledges the providers of that information •Minimum necessary clinical data to be associated with viral sequence data •Uptake of standardised clinical case definitions of disease to establish a baseline to understand how interventions affect disease severity once introduced

The prospect of universal influenza vaccines is generating much interest and research at the intersection of immunology, epidemiology, and viral evolution. While the current focus is on developing a vaccine that elicits a broadly cross-reactive immune response in clinical trials, there are important downstream questions about global deployment of a universal influenza vaccine that should be explored to minimize unintended consequences and maximize benefits. Here, we review and synthesize the questions most relevant to predicting the population benefits of universal influenza vaccines and discuss how existing information could be mined to begin to address these questions. We review three research topics where computational modeling could bring valuable evidence: immune imprinting, viral evolution, and transmission. We address the positive and negative consequences of imprinting, in which early childhood exposure to influenza shapes and limits immune responses to future infections via memory of conserved influenza antigens. However, the mechanisms at play, their effectiveness, breadth of protection, and the ability to “reprogram” already imprinted individuals, remains heavily debated. We describe instances of rapid influenza evolution that illustrate the plasticity of the influenza virus in the face of drug pressure and discuss how novel vaccines could introduce new selective pressures on the evolution of the virus. We examine the possible unintended consequences of broadly protective (but infection-permissive) vaccines on the dynamics of epidemic and pandemic influenza, compared to conventional vaccines that have been shown to provide herd immunity benefits. In conclusion, computational modeling offers a valuable tool to anticipate the benefits of ambitious universal influenza vaccine programs, while balancing the risks from endemic influenza strains and unpredictable pandemic viruses. Moving forward, it will be important to mine the vast amount of data generated in clinical studies of universal influenza vaccines to ensure that the benefits and consequences of these vaccine programs have been carefully modeled and explored.

Coronavirus disease 2019 (COVID-19) presents an unprecedented international public health challenge. Policy has frequently been informed by mathematical models of infectious disease transmission, particularly in this outbreak [1.Ferguson N. et al.Report 9: Impact of Non-Pharmaceutical Interventions (NPIs) to Reduce COVID19 Mortality and Healthcare Demand. Imperial College London, 2020Google Scholar]. Such models show that epidemics such as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection are driven by the interaction of three dynamic variables (Figure 1A ): the number or density of infectious individuals; previously unexposed susceptibles; and recovered (partly or fully immune) individuals [2.Metcalf C.J.E. et al.Understanding herd immunity.Trends Immunol. 2015; 36: 753-755Abstract Full Text Full Text PDF PubMed Scopus (98) Google Scholar]. While a time series of estimated cases arises directly from epidemic surveillance, susceptible and immune classes can only be directly identified by serology, which is less routinely carried out. Thus, mathematical models are typically structured around the likely phases of infectiousness, with parameters being estimated by fitting models to reported numbers of cases or deaths (Figure 1B). From these parameter estimates, researchers can project the expected trajectory of cases, opening the way to characterizing the urgency of interventions and their likely impact. However, as we illustrate below using a toy model (Figure 1), if such inference is based only on cases, it may lack robustness for a novel pathogen, as both disease parameters and surveillance intensity remain unclear. Therefore, direct estimation of the susceptible fraction by using serology or other immunological measures to identify the proportion of the population that is susceptible could greatly clarify our understanding of epidemic dynamics and control [3.Metcalf C.J.E. et al.Use of serological surveys to generate key insights into the changing global landscape of infectious disease.Lancet. 2016; 388: 728-730Abstract Full Text Full Text PDF PubMed Scopus (167) Google Scholar,4.Metcalf C.J.E. et al.Opportunities and challenges of a World Serum Bank – authors' reply.Lancet. 2017; 389: 252Abstract Full Text Full Text PDF PubMed Scopus (9) Google Scholar]. By using data on reported numbers of cases or deaths, mathematical models allow estimation of infectious disease parameters such as the magnitude of transmission, or duration of infection that will govern the time course of the outbreak. This is achieved by identifying the combinations of parameters that result in a projected numbers of cases (or deaths) that best matches the observed. However, cases are generally under-reported, infections may vary in terms of their detectability (i.e., children may be less symptomatic [5.Verity R. et al.Estimates of the severity of coronavirus disease 2019: a model-based analysis.Lancet Infect. Dis. 2020; 20: 669-677Abstract Full Text Full Text PDF PubMed Scopus (2296) Google Scholar]), and case definitions may change over the epidemic time course [6.Tsang T.K. et al.Effect of changing case definitions for COVID-19 on the epidemic curve and transmission parameters in mainland China: a modelling study.Lancet Public Health. 2020; 5: E289-E296Abstract Full Text Full Text PDF PubMed Scopus (144) Google Scholar]. Challenges in identifying cause of death, and variability in mortality across different groups can lead to similar issues. This can make it challenging to pin down parameters which define the growth in the number of infections and timing of the peak of an outbreak. For instance, even if only under-reporting is at play, different combinations of parameters can yield the same trajectory of cases in the short term (Figure 1B). This is important because the trajectory associated with parameters that match the numbers of cases over the short term might deviate considerably over the longer term. Slight differences in the magnitude of transmission, or the speed at which infectious individuals recover, can compound into substantially large differences in terms of the degree to which the size of the susceptible population is depleted and thus, the number of cases that will occur. Furthermore, if under-reporting changes over time (e.g., via increased testing), the true drivers of dynamics are further obscured. Measurement of immunological features such as serological status provides a crucial extra layer of information to address this problem. In the depicted example (Figure 1), a model fitted to early case data could erroneously indicate that a 75% reduction in transmission would be needed to avert a second wave of infection – yet in reality (i.e., according to the true parameters used in the simulation), transmission would need to be reduced by only 50%. The discrepancy arises because the true magnitude of transmission (used in the simulation) is lower than that estimated based on reported cases. In turn, a lower magnitude of transmission translates into a smaller fraction by which transmission must be reduced to ensure that the number of infections generated per infected individual is <1 (the condition for the outbreak to decline in size). Repeated estimates of susceptibility through time could both ensure greater precision in estimation of the magnitude of transmission: predictions from Fit1 and Fit2 differ substantially in terms of susceptibility, even early on during the outbreak (Figure 1). Furthermore, such measures give us more power to dissect complexities such as behavioral and seasonal changes in transmission, or age-specific heterogeneities in immunity and transmission. There are, of course, many important caveats. Serology too has an error rate, and we are still uncertain as to how SARS-CoV-2 serology can be interpreted in terms of immunity (including the degree to which it wanes or is partial), and recent work suggests potentially rapid loss of seropositive status, particularly in asymptomatic individuals [7.Long Q.-X. et al.Clinical and immunological assessment of asymptomatic SARS-CoV-2 infections.Nat. Med. 2020; (Published online June 18, 2020. https://doi.org/10.1038/s41591-020-0965-6)Crossref Scopus (2027) Google Scholar]. Cell-mediated immunity may be of greater importance than antibodies for some infections, making serological measurements less likely to reflect relevant immune status, although this aspect of immunity is still unclear for SARS-CoV-2. Yet, since being seropositive is indicative of having been infected at a point in the past, serology measures an integral of past infection. This means that an appropriate sample tested serologically has greater power to capture the state of the system than a test for active infection, which only provides a snapshot of the present moment. In other words, serological data can dramatically narrow down the range of plausible epidemic scenarios by calibrating the model to empirical observations of susceptible depletion, while by contrast, this information is simply missing in traditional case-based surveillance. To conclude, while testing of active infections is and should remain a priority, more widely available serological data will provide powerful discrimination between different sets of parameters and plausible epidemic trajectories, as illustrated in Figure 1. Increasingly, serological tests are becoming available, enabling the identification of individuals bearing antibodies suggestive of past infection [8.Amanat F. et al.A (2020) serological assay to detect SARS-CoV-2 seroconversion in humans.Nat. Med. 2020; (Published online May 12)https://doi.org/10.1038/s41591-020-0913-5Crossref PubMed Scopus (1179) Google Scholar,11.Rosado J. et al.Serological signatures of SARS-CoV-2 infection: implications for antibody-based diagnostics.medRxiv. 2020; (Posted June 29)https://doi.org/10.1101/2020.05.07.20093963Crossref Scopus (0) Google Scholar]; this can allow us to complete our window into the drivers of outbreaks beyond a measure of infection, to include susceptible and recovered individuals (Figure 1). As serology becomes more widespread in our efforts to meet the current pandemic, there is significant potential to lay the foundations towards making serology a routine part of public health. This could enhance various aspects of vigilance, from situational awareness of vaccine preventable infections [9.Winter A.K. et al.Revealing measles outbreak risk with a nested IgG serosurvey in Madagascar.Am. J. Epidemiol. 2018; 187: 2219-2226Crossref PubMed Scopus (16) Google Scholar] to pandemic preparedness [10.Mina M.J. et al.A global lmmunological observatory to meet a time of pandemics.Elife. 2020; 9e58989Crossref PubMed Scopus (1) Google Scholar]. This article does not necessarily represent the views of the National Institutes of Health or US Government.

Mathematical transmission models are increasingly used to guide public health interventions for infectious diseases, particularly in the context of emerging pathogens; however, the contribution of modeling to the growing issue of antimicrobial resistance (AMR) remains unclear. Here, we systematically evaluate publications on population-level transmission models of AMR over a recent period (2006–2016) to gauge the state of research and identify gaps warranting further work. We performed a systematic literature search of relevant databases to identify transmission studies of AMR in viral, bacterial, and parasitic disease systems. We analyzed the temporal, geographic, and subject matter trends, described the predominant medical and behavioral interventions studied, and identified central findings relating to key pathogens. We identified 273 modeling studies; the majority of which (> 70%) focused on 5 infectious diseases (human immunodeficiency virus (HIV), influenza virus, Plasmodium falciparum (malaria), Mycobacterium tuberculosis (TB), and methicillin-resistant Staphylococcus aureus (MRSA)). AMR studies of influenza and nosocomial pathogens were mainly set in industrialized nations, while HIV, TB, and malaria studies were heavily skewed towards developing countries. The majority of articles focused on AMR exclusively in humans (89%), either in community (58%) or healthcare (27%) settings. Model systems were largely compartmental (76%) and deterministic (66%). Only 43% of models were calibrated against epidemiological data, and few were validated against out-of-sample datasets (14%). The interventions considered were primarily the impact of different drug regimens, hygiene and infection control measures, screening, and diagnostics, while few studies addressed de novo resistance, vaccination strategies, economic, or behavioral changes to reduce antibiotic use in humans and animals. The AMR modeling literature concentrates on disease systems where resistance has been long-established, while few studies pro-actively address recent rise in resistance in new pathogens or explore upstream strategies to reduce overall antibiotic consumption. Notable gaps include research on emerging resistance in Enterobacteriaceae and Neisseria gonorrhoeae; AMR transmission at the animal-human interface, particularly in agricultural and veterinary settings; transmission between hospitals and the community; the role of environmental factors in AMR transmission; and the potential of vaccines to combat AMR.

Due to a combination of ecological, political, and demographic factors, the emergence of novel pathogens has been increasingly observed in animals and humans in recent decades. Enhancing global capacity to study and interpret infectious disease surveillance data, and to develop data-driven computational models to guide policy, represents one of the most cost-effective, and yet overlooked, ways to prepare for the next pandemic. Epidemiological and behavioral data from recent pandemics and historic scourges have provided rich opportunities for validation of computational models, while new sequencing technologies and the 'big data' revolution present new tools for studying the epidemiology of outbreaks in real time. For the past two decades, the Division of International Epidemiology and Population Studies (DIEPS) of the NIH Fogarty International Center has spearheaded two synergistic programs to better understand and devise control strategies for global infectious disease threats. The Multinational Influenza Seasonal Mortality Study (MISMS) has strengthened global capacity to study the epidemiology and evolutionary dynamics of influenza viruses in 80 countries by organizing international research activities and training workshops. The Research and Policy in Infectious Disease Dynamics (RAPIDD) program and its precursor activities has established a network of global experts in infectious disease modeling operating at the research-policy interface, with collaborators in 78 countries. These activities have provided evidence-based recommendations for disease control, including during large-scale outbreaks of pandemic influenza, Ebola and Zika virus. Together, these programs have coordinated international collaborative networks to advance the study of emerging disease threats and the field of computational epidemic modeling. A global community of researchers and policy-makers have used the tools and trainings developed by these programs to interpret infectious disease patterns in their countries, understand modeling concepts, and inform control policies. Here we reflect on the scientific achievements and lessons learnt from these programs (h-index = 106 for RAPIDD and 79 for MISMS), including the identification of outstanding researchers and fellows; funding flexibility for timely research workshops and working groups (particularly relative to more traditional investigator-based grant programs); emphasis on group activities such as large-scale modeling reviews, model comparisons, forecasting challenges and special journal issues; strong quality control with a light touch on outputs; and prominence of training, data-sharing, and joint publications.

In August 2016, the World Health Organization (WHO) convened the "Eighth meeting on development of influenza vaccines that induce broadly protective and long-lasting immune responses" to discuss the regulatory requirements and pathways for licensure of next-generation influenza vaccines, and to identify areas where WHO can promote the development of such vaccines. Participants included approximately 120 representatives of academia, the vaccine industry, research and development funders, and regulatory and public health agencies. They reviewed the draft WHO preferred product characteristics (PPCs) of vaccines that could address prioritized unmet public health needs and discussed the challenges facing the development of such vaccines, especially for low- and middle-income countries (LMIC). They defined the data desired by public-health decision makers globally and explored how to support the progression of promising candidates into late-stage clinical trials and for all countries. This report highlights the major discussions of the meeting.

Background Since its initial detection in April 2009, the A/H1N1pdm influenza virus has spread rapidly in humans, with over 5,700 human deaths. However, little is known about the evolutionary dynamics of H1N1pdm and its geographic and temporal diversification. Methods Phylogenetic analysis was conducted upon the concatenated coding regions of whole-genome sequences from 290 H1N1pdm isolates sampled globally between April 1 – July 9, 2009, including relatively large samples from the US states of Wisconsin and New York. Results At least 7 phylogenetically distinct viral clades have disseminated globally and co-circulated in localities that experienced multiple introductions of H1N1pdm. The epidemics in New York and Wisconsin were dominated by two different clades, both phylogenetically distinct from the viruses first identified in California and Mexico, suggesting an important role for founder effects in determining local viral population structures. Conclusions Determining the global diversity of H1N1pdm is central to understanding the evolution and spatial spread of the current pandemic, and to predict its future impact on human populations. Our results indicate that H1N1pdm has already diversified into distinct viral lineages with defined spatial patterns.

In 2017, WHO convened a working group of global experts to develop the Preferred Product Characteristics (PPC) for Next-Generation Influenza Vaccines. PPCs are intended to encourage innovation in vaccine development. They describe WHO preferences for parameters of vaccines, in particular their indications, target groups, implementation strategies, and clinical data needed for assessment of safety and efficacy. PPCs are shaped by the global unmet public health need in a priority disease area for which WHO encourages vaccine development. These preferences reflect WHO’s mandate to promote the development of vaccines with high public health impact and suitability in Low- and Middle-Income Countries (LMIC). The target audience is all entities intending to develop or to achieve widespread adoption of a specific influenza vaccine product in these settings. The working group determined that existing influenza vaccines are not well suited for LMIC use. While many developed country manufactures and research funders prioritize influenza vaccine products for use in adults and the elderly, most LMICs do not have sufficiently strong health systems to deliver vaccines to these groups. Policy makers from LMICs are expected to place higher value on vaccines indicated for prevention of severe illness, however the clinical development of influenza vaccines focuses on demonstrating prevention of any influenza illness. Many influenza vaccine products do not meet WHO standards for programmatic suitability of vaccines, which introduces challenges when vaccines are used in low-resource settings. And finally, current vaccines do not integrate well with routine immunization programs in LMICs, given age of vaccine licensure, arbitrary expiration dates timed for temperate country markets, and the need for year-round immunization in countries with prolonged influenza seasonality. While all interested parties should refer to the full PPC document for details, in this article we highlight data needs for new influenza vaccines to better demonstrate the value proposition in LMICs.

Segmental demyelination is the term applied to the patchy breakdown of myelin sheaths limited to individual segments with relative sparing of axis cylinders. It appears to be quite distinct from W allerian degeneration, in which breakdown of myelin and axis cylinder take place simultaneously distal to the site of axis cylinder interruption. Segmental demyelination is a cardinal feature of multiple sclerosis, post-infectious encephalomyelitis, acute idiopathic polyneuropathy (Guillain-Barre Syndrome), and human diphtheritic neuritis (1, 6, 4). It may be conveniently studied, uncomplicated by inflammatory changes, in peripheral nen·es of guinea pigs with experimental diphtheritic neuritis. After our clinical, immunologic, histopathologic, and biochemical studies of experimental diphtheritic neuritis were completed (1:3, 9), it seemed appropriate to utilize phase and electron microscopy in an attempt to define Schwarm cell changeR during this demyelinative process in greater detail. In spite of its significance as a model of the type of myelin breakdown seen in human demyelinative disease, no previous electron microscopic studies of experimental diphtheritic neuritis have been reported.

During an electron microscopic study of experimental demyelination, many variations in the contour of the myelin sheath were observed in normal guinea pig sciatic nerves. These variations consisted of loops and folds of compact myelin which indented the axoplasm or protruded into the Schwann cell cytoplasm. Depending on the plane of section they were seen as isolated ovoids of myelin within axoplasm or Schwann cell cytoplasm. Under the light microscope, these loops and ovoids closely resembled myelin forms seen in early demyelinative lesions and it was necessary to define their structure and distribution before proceeding with the pathological study. Variations in myelin sheath contour are not described in the classical histological studies of Ranvier (16), Nageotte (14), and Cajal (1). In their figures, the myelin sheath is shown as a smooth, axoplasm filled, cylindrical tube with interruptions at Schmidt-Lantermann incisures and the nodes of Ranvier. A slight indentation is present in the region of the nucleus. Juxtanodal irregularities of the myelin sheath are illustrated as preparative artifacts although other observers (12, 2) described round, osmophilic structures located between the myelin sheath and the Schwann cell membrane near the nucleus and elsewhere along the length of normal peripheral nerve fibers. Ridging of the myelin sheath adjacent to the node was noted by several investigators including Hess and Young (ll); one of their figures also shows a large fold of myelin indenting axoplasm near a node shown in longitudinal section, but it was not described or discussed. A few electron micrographs in several subsequent studies of peripheral nerve ultrastructure show similar myelin loops and folds (7, 18). However, their fine structure and distribution were not discussed. Hess and Lansing described an isolated ovoid with the lamellar structure of myelin within the Schwann cell cytoplasm of guinea pig sciatic nerve and tentatively identified it as an Elzholz