Readiness for Responding to a Severe Pandemic 100 Years After 1918

Unveiling the role of host kinases at different steps of influenza A virus life cycle

Influenza viruses remain a major public health concern causing contagious respiratory illnesses that result in around 290,000-650,000 global deaths every year. Their ability to constantly evolve through antigenic shifts and drifts leads to the emergence of newer strains and resistance to existing drugs and vaccines. To combat this, there is a critical need for novel antiviral drugs through the introduction of host-targeted therapeutics. Influenza viruses encode only 14 gene products that get extensively modified through phosphorylation by a diverse array of host kinases. Reversible phosphorylation at serine, threonine, or tyrosine residues dynamically regulates the structure, function, and subcellular localization of viral proteins at different stages of their life cycle. In addition, kinases influence a plethora of signaling pathways that also regulate virus propagation by modulating the host cell environment thus establishing a critical virus-host relationship that is indispensable for executing successful infection. This dependence on host kinases opens up exciting possibilities for developing kinase inhibitors as next-generation anti-influenza therapy. To fully capitalize on this potential, extensive mapping of the influenza virus-host kinase interaction network is essential. The key focus of this review is to outline the molecular mechanisms by which host kinases regulate different steps of the influenza A virus life cycle, starting from attachment-entry to assembly-budding. By assessing the contributions of different host kinases and their specific phosphorylation events during the virus life cycle, we aim to develop a holistic overview of the virus-host kinase interaction network that may shed light on potential targets for novel antiviral interventions.

Viral target and metabolism-based rationale for combined use of recently authorized small molecule COVID-19 medicines: Molnupiravir, nirmatrelvir, and remdesivir

The COVID-19 pandemic remains a major health concern worldwide, and SARS-CoV-2 is continuously evolving. There is an urgent need to identify new antiviral drugs and develop novel therapeutic strategies. Combined use of newly authorized COVID-19 medicines including molnupiravir, nirmatrelvir, and remdesivir has been actively pursued. Mechanistically, nirmatrelvir inhibits SARS-CoV-2 replication by targeting the viral main protease (Mpro ), a critical enzyme in the processing of the immediately translated coronavirus polyproteins for viral replication. Molnupiravir and remdesivir, on the other hand, inhibit SARS-CoV-2 replication by targeting RNA-dependent RNA-polymerase (RdRp), which is directly responsible for genome replication and production of subgenomic RNAs. Molnupiravir targets RdRp and induces severe viral RNA mutations (genome), commonly referred to as error catastrophe. Remdesivir, in contrast, targets RdRp and causes chain termination and arrests RNA synthesis of the viral genome. In addition, all three medicines undergo extensive metabolism with strong therapeutic significance. Molnupiravir is hydrolytically activated by carboxylesterase-2 (CES2), nirmatrelvir is inactivated by cytochrome P450-based oxidation (e.g., CYP3A4), and remdesivir is hydrolytically activated by CES1 but covalently inhibits CES2. Additionally, remdesivir and nirmatrelvir are oxidized by the same CYP enzymes. The distinct mechanisms of action provide strong rationale for their combined use. On the other hand, these drugs undergo extensive metabolism that determines their therapeutic potential. This review discusses how metabolism pathways and enzymes involved should be carefully considered during their combined use for therapeutic synergy.

Designing an optimal sequence of non-pharmaceutical interventions for controlling COVID-19

The COVID-19 pandemic has had an unprecedented impact on global health and the economy since its inception in December, 2019 in Wuhan, China. Non-pharmaceutical interventions (NPI) like lockdowns and curfews have been deployed by affected countries for controlling the spread of infections. In this paper, we develop a Mixed Integer Non-Linear Programming (MINLP) epidemic model for computing the optimal sequence of NPIs over a planning horizon, considering shortages in doctors and hospital beds, under three different lockdown scenarios. We analyse two strategies - centralised (homogeneous decisions at the national level) and decentralised (decisions differentiated across regions), for two objectives separately - minimization of infections and deaths, using actual pandemic data of France. We linearize the quadratic constraints and objective functions in the MINLP model and convert it to a Mixed Integer Linear Programming (MILP) model. A major result that we show analytically is that under the epidemic model used, the optimal sequence of NPIs always follows a decreasing severity pattern. Using this property, we further simplify the MILP model into an Integer Linear Programming (ILP) model, reducing computational time up to 99%. Our numerical results show that a decentralised strategy is more effective in controlling infections for a given severity budget, yielding up to 20% lesser infections, 15% lesser deaths and 60% lesser shortages in healthcare resources. These results hold without considering logistics aspects and for a given level of compliance of the population.

Hamlet-Pattern-Based Automated COVID-19 and Influenza Detection Model Using Protein Sequences

SARS-CoV-2 and Influenza-A can present similar symptoms. Computer-aided diagnosis can help facilitate screening for the two conditions, and may be especially relevant and useful in the current COVID-19 pandemic because seasonal Influenza-A infection can still occur. We have developed a novel text-based classification model for discriminating between the two conditions using protein sequences of varying lengths. We downloaded viral protein sequences of SARS-CoV-2 and Influenza-A with varying lengths (all 100 or greater) from the NCBI database and randomly selected 16,901 SARS-CoV-2 and 19,523 Influenza-A sequences to form a two-class study dataset. We used a new feature extraction function based on a unique pattern, HamletPat, generated from the text of Shakespeare's Hamlet, and a signum function to extract local binary pattern-like bits from overlapping fixed-length (27) blocks of the protein sequences. The bits were converted to decimal map signals from which histograms were extracted and concatenated to form a final feature vector of length 1280. The iterative Chi-square function selected the 340 most discriminative features to feed to an SVM with a Gaussian kernel for classification. The model attained 99.92% and 99.87% classification accuracy rates using hold-out (75:25 split ratio) and five-fold cross-validations, respectively. The excellent performance of the lightweight, handcrafted HamletPat-based classification model suggests that it can be a valuable tool for screening protein sequences to discriminate between SARS-CoV-2 and Influenza-A infections.

Augmentation of a Hospital Incident Command System to Support Continued Waves of the COVID-19 Pandemic

Stamford Hospital (SH) is a 305 bed Level II Trauma center located in Fairfield County, the epicenter of the coronavirus disease 2019 (COVID-19) pandemic in Connecticut. The COVID-19 pandemic was a fast paced, rapidly evolving crisis, presenting our leadership team with unique challenges related to resource availability, patient care, and staff safety. The existing Hospital Incident Command System (HICS) was activated in March 2020 to coordinate our operational emergency management planning, response, and recovery capability for this unplanned event. Although HICS lays the groundwork for hospital preparations and protocol implementation, it is not designed to withstand prolonged crisis circumstances. Given the conditions that the COVID-19 pandemic presented, the possibility for future waves became an impending reality, prompting the need for a long-term solution. To establish guidelines that promoted balance between necessary preparations in the case of additional surges of the pandemic and concurrent resumption then maintenance of routine hospital operations, the SH COVID-19 Playbook was created. The Playbook, presented here, is arranged in accordance with the evidence-based 4 S’s (Space, Staff, Systems, and Stuff) strategic critical care planning framework, to address surge capacity management within our hospital’s four main patient care areas and additional supportive services. Through feedback from frontline caregivers and leaders within SH, the Playbook captures our experience, best practices, and insight acquired during the first wave of the pandemic. Established with the intentions of equipping leadership and staff globally, guidelines are presented to aid in the navigation of future pandemic surges and successfully care for COVID-19 patients, ensure staff safety, allow for normal services to operate, and provide optimal communication and support for the community, patients, and staff.

The Global Health Security Index: Another Look

The Global Health Security Index (GHSI) was published in October 2019 (after more than 2 years in preparation); at about the same time that the COVID-19 pandemic (COVID) started. The GHSI was intended to score countries' pandemic preparedness. Within months of the start of the pandemic, articles began to be published that claimed to assess the GHSI's validity. They correlated GHSI scores with countries' COVID per capita death rates. They showed that the better prepared a country, the higher the death rate: a result that was counter to what would have been expected. This article takes another look at the GHSI by exploring the relationship in major European Union countries plus the United Kingdom. The analysis reported here confirms that early on the higher the GHSI score, the higher the COVID per capita death rate (r = 0.52, P < 0.05). But, by the end of 2020, there was no correlation. By July 2021, the correlation was in the expected direction: the higher the GHSI score, the lower the COVID per capita death rate (r = -0.55, P < 0.05); ditto case fatality rate (r = -0.74, P < 0.01). Further, the GHSI was better correlated with excess mortality, the best measure of pandemic impact (r = -0.69, P < 0.01). However, per capita GDP was as good a predictor of excess mortality (r = -0.71, P < 0.01) and the Health System Performance Index of case fatality rate (r = -0.71; P < 0.01). By the end of 2021, the correlation between GHSI scores and COVID per capita death rates had strengthened (r = -0.71; P < 0.01). This exploratory analysis is not intended to produce generalizable conclusions about the effectiveness of countries' COVID pandemic response management, which continues to evolve and hence can only be properly assessed after the pandemic has ended. Nevertheless, the following conclusions would seem to be warranted: 1) there seems to have been a rush to judge, or, at least, to publish, and 2) the validity of any forward looking pandemic preparedness score depends not only on being able to assess countries' capabilities but also being able to forecast what governments will (and will not) do in any given situation, a seemingly quixotic quest.

COVID-19, the Brain, and the Future: Is Infection by the Novel Coronavirus a Harbinger of Neurodegeneration?

The possible impact of viral infections on the development or pathogenesis of neurodegenerative disorders remains largely unknown. However, there have been reports associating the influenza virus pandemic and long-term infection with the Japanese encephalitis virus with the development of post-encephalitic Parkinsonism or von Economo encephalitis. In the last one year plus, there has been a worldwide pandemic arising from infection with the novel coronavirus or severe acute respiratory syndrome coronavirus (SARS-CoV)-2 which causes a severe acute respiratory syndrome that has become associated with central nervous system symptoms or complications. Its possible central nervous system involvement is in line with emerging scientific evidence which shows that the human respiratory coronaviruses can enter the brain, infect neural cells, persist in the brain, and cause activation of myelin-reactive T cells. Currently, there is a dearth of scientific information on the acute or possible long-term impact of infection with SARS-CoV-2 on the development of dementias and/or neurodegenerative diseases. This is not unrelated to the fact that the virus is 'new', and its effects on humans are still being studied. This narrative review examines extant literature for the impact of corona virus infections on the brain; as it considers the possibility that coronavirus disease 2019 (COVID-19) could increase the risk for the development of neurodegenerative diseases or hasten their progression.

A mathematical model of COVID-19 transmission

Disease transmission is studied through disciplines like epidemiology, applied mathematics, and statistics. Mathematical simulation models for transmission have implications in solving public and personal health challenges. The SIR model uses a compartmental approach including dynamic and nonlinear behavior of transmission through three factors: susceptible, infected, and removed (recovered and deceased) individuals. Using the Lambert W Function, we propose a framework to study solutions of the SIR model. This demonstrates the applications of COVID-19 transmission data to model the spread of a real-world disease. Different models of disease including the SIR, SIRmp and SEIRρqr model are compared with respect to their ability to predict disease spread. Physical distancing impacts and personal protection equipment use are discussed with relevance to the COVID-19 spread.

The nucleoside antiviral prodrug remdesivir in treating COVID-19 and beyond with interspecies significance

Infectious pandemics result in hundreds and millions of deaths, notable examples of the Spanish Flu, the Black Death and smallpox. The current pandemic, caused by SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2), is unprecedented even in the historical term of pandemics. The unprecedentedness is featured by multiple surges, rapid identification of therapeutic options and accelerated development of vaccines. Remdesivir, originally developed for Ebola viral disease, is the first treatment of COVID-19 (Coronavirus disease 2019) approved by the United States Food and Drug Administration. As demonstrated by in vitro and preclinical studies, this therapeutic agent is highly potent with a broad spectrum activity against viruses from as many as seven families even cross species. However, randomized controlled trials have failed to confirm the efficacy and safety. Remdesivir improves some clinical signs but not critical parameters such as mortality. This antiviral agent is an ester/phosphorylation prodrug and excessive hydrolysis which increases cellular toxicity. Remdesivir is given intravenously, leading to concentration spikes and likely increasing the potential of hydrolysis-based toxicity. This review has proposed a conceptual framework for improving its efficacy and minimizing toxicity not only for the COVID-19 pandemic but also for future ones caused by remdesivir-sensitive viruses.

Tail risks and infectious disease: Influenza mortality in the U.S., 1900-2018

I use extreme values theory and data on influenza mortality from the U.S. for 1900 to 2018 to estimate the tail risks of mortality. I find that the distribution for influenza mortality rates is heavy-tailed, which suggests that the tails of the mortality distribution are more informative than the events of high frequency (i.e., years of low mortality). I also discuss the implications of my estimates for risk management and pandemic planning.

Managing health disasters and Civil-Military Cooperation: A case of COVID-19 in Pakistan

As an institution, the Pakistan Army has been playing a significant role in dealing with emergencies and disasters facing the nation. The novel coronavirus disease, Severe Acute Respiratory Syndrome (SARS) COVID-19, was wreaking havoc around the world. The pandemic is a threat to health and has caused severe damage across most aspects of the society. The situation forced the formation of a unique series of civil-military inter-agency relationships to be formed, in order to curb the spread of the pandemic. The Pakistan Army that was neither trained nor equipped to undertake any such health disaster management operation played a significant role in preventing disease and overcoming the disaster. Civil-military cooperation (CIMIC) was the key to the successful response of Pakistan towards COVID-19. The research was based on qualitative interviews that analysed the phenomenon of COVID-19 pandemic, that is, 'health disaster', to elucidate the disaster management practices performed through the framework of CIMIC in Pakistan. With regard to this, the article argued that formulating a comprehensive guideline or framework was necessary to maintain an effective and cooperative relationship between civil and military components. It further demonstrated the need to recognise the constitutive factors that influenced the functionalisation and institutionalisation of CIMIC to manage the highly complex health-related emergencies.

Comparing the socio-economic implications of the 1918 Spanish flu and the COVID-19 pandemic in India: A systematic review of literature

The focus of the present study is to compare and assess the socio‐economic implications of the 1918 influenza pandemic and the COVID‐19 pandemic in India. Both pandemics are similar in the nature of their disease and spread, and have had a far‐reaching impact on society and economies worldwide. To achieve their objective, the researchers adopted the method of systematic literature review (SLR). The findings of the review have been categorised in four subsections: comparison of 1918 influenza and COVID‐19 pandemics in a global context; economic consequences of a pandemic in India; social consequences of a pandemic in India; and the pandemic mitigation measures adopted by India. The findings suggest there are similarities in the socio‐economic implications of the two pandemics and also indicate that developing countries face more severe implications of such pandemics as compared to developed countries. The research findings from the review of literature are followed by the recommendations made by the researchers.

Targeting the DEAD-Box RNA Helicase eIF4A with Rocaglates-A Pan-Antiviral Strategy for Minimizing the Impact of Future RNA Virus Pandemics

The increase in pandemics caused by RNA viruses of zoonotic origin highlights the urgent need for broad-spectrum antivirals against novel and re-emerging RNA viruses. Broad-spectrum antivirals could be deployed as first-line interventions during an outbreak while virus-specific drugs and vaccines are developed and rolled out. Viruses depend on the host’s protein synthesis machinery for replication. Several natural compounds that target the cellular DEAD-box RNA helicase eIF4A, a key component of the eukaryotic translation initiation complex eIF4F, have emerged as potential broad-spectrum antivirals. Rocaglates, a group of flavaglines of plant origin that clamp mRNAs with highly structured 5′ untranslated regions (5′UTRs) onto the surface of eIF4A through specific stacking interactions, exhibit the largest selectivity and potential therapeutic indices among all known eIF4A inhibitors. Their unique mechanism of action limits the inhibitory effect of rocaglates to the translation of eIF4A-dependent viral mRNAs and a minor fraction of host mRNAs exhibiting stable RNA secondary structures and/or polypurine sequence stretches in their 5′UTRs, resulting in minimal potential toxic side effects. Maintaining a favorable safety profile while inducing efficient inhibition of a broad spectrum of RNA viruses makes rocaglates into primary candidates for further development as pan-antiviral therapeutics.

Potential Effects of the COVID-19 Pandemic on Future Birth Rate

Here, we examine the potential effect of the COVID-19 pandemic on future birth rates. This highly contagious disease originated in China, and rapidly spread worldwide, leading to extensive lockdown policies being implemented globally with the aim of containing the infection rates and its serious attendant consequences. Based on previous extant literature, this paper overviews the potential demographic consequences of the current progressively widespread epidemic on conception and fertility as driven by the data obtained during similar prior incidents. In general, epidemics manifest a common pattern as far as their impact on population, which is remarkably similar to natural disasters, i.e., a steep decline in birth rates followed by gradual increases and then followed by a baby boom. Additionally, we have also depicted how economic conditions, mental health, fear, and mortality may also influence future birth rates.

Pandemics and the emerging stock markets

In this study, we examine the response of emerging stock markets due to the uncertainty of pandemics and epidemics (UPE), including the COVID-19 pandemic. We demonstrate this by evaluating the stock return predictability of 24 emerging market stocks using the new datasets on uncertainty due to pandemics as well as the global fear index for the COVID-19 pandemic. We partition the data sample into periods before and after the announcement of the COVID-19 pandemic and employ panel data techniques that account for salient features of both the series and predictive model. We found that emerging stock markets are more vulnerable to UPE than developed market stocks. Put differently, developed stock markets provide a better hedge against UPE than emerging stock markets. We also find that incorporating the UPE indicator in the valuation of stocks, particularly during pandemics, is crucial for investment decisions.

The Seattle Flu Study: a multiarm community-based prospective study protocol for assessing influenza prevalence, transmission and genomic epidemiology

INTRODUCTION

Influenza epidemics and pandemics cause significant morbidity and mortality. An effective response to a potential pandemic requires the infrastructure to rapidly detect, characterise, and potentially contain new and emerging influenza strains at both an individual and population level. The objective of this study is to use data gathered simultaneously from community and hospital sites to develop a model of how influenza enters and spreads in a population.

METHODS AND ANALYSIS

Starting in the 2018-2019 season, we have been enrolling individuals with acute respiratory illness from community sites throughout the Seattle metropolitan area, including clinics, childcare facilities, Seattle-Tacoma International Airport, workplaces, college campuses and homeless shelters. At these sites, we collect clinical data and mid-nasal swabs from individuals with at least two acute respiratory symptoms. Additionally, we collect residual nasal swabs and data from individuals who seek care for respiratory symptoms at four regional hospitals. Samples are tested using a multiplex molecular assay, and influenza whole genome sequencing is performed for samples with influenza detected. Geospatial mapping and computational modelling platforms are in development to characterise the regional spread of influenza and other respiratory pathogens.

ETHICS AND DISSEMINATION

The study was approved by the University of Washington's Institutional Review Board (STUDY00006181). Results will be disseminated through talks at conferences, peer-reviewed publications and on the study website (www.seattleflu.org).

Influenza

Influenza continues to baffle humans by its constantly changing nature. The twenty-first century has witnessed considerable advances in the understanding of the influenza viral pathogenesis, its synergy with bacterial infections and diagnostic methods. However, challenges continue: to find a less expensive and more reliable point-of-care test for use in developing countries, to produce more efficacious antiviral drugs, to explore ways to combat emerging antiviral resistance and to develop vaccines that can either be produced in a shorter production time or can overcome the need for annual matching with the circulating influenza strains. Most importantly for India, as a nation that suffered the highest mortality in the influenza pandemic 1918, there is an urgent need to gear up our existing preparedness for the next pandemic which is capable to hit at any moment in time.

Neurological emergencies associated with COVID-19: stroke and beyond

Novel coronavirus disease (COVID-19) was declared a global pandemic on March 1, 2020. Neurological manifestations are now being reported worldwide, including emergent presentation with acute neurological changes as well as a comorbidity in hospitalized patients. There is limited knowledge on the neurologic manifestations of COVID-19 at present, with a wide array of neurological complications reported, ranging from ischemic stroke to acute demyelination and encephalitis. We report five cases of COVID-19 presenting to the ER with acute neurological symptoms, over the course of 1 month. This includes two cases of ischemic stroke, one with large-vessel occlusion and one with embolic infarcts. The remainders of the cases include acute tumefactive demyelination, isolated cytotoxic edema of the corpus callosum with subarachnoid hemorrhage, and posterior reversible encephalopathy syndrome (PRES).

Nursing during Times of Pandemic: from Courage on the Front Line to Heroic Courage in Banksy's Mural

On 12 May 2020, the world commemorated thebicentennial of the birth of Florence Nightingale,and to date, multiple publications in press media,social networks, and journal articles have relatedher biography and accounted for her contributionand legacy, not only to nursing, but to publichealth. It is precisely within this context of healthcontingency due to the COVID-19 pandemic thatthe relevance of Nightingale gains greater senseon aspects as simple, but as necessary, likehandwashing and measures of basic health.

Hyperlocal Postcode Based Crowdsourced Surveillance Systems in the COVID-19 Pandemic Response

The SARS-CoV-2 pandemic has rapidly saturated healthcare resources across the globe and has led to a restricted screening process, hindering efforts at comprehensive case detection. This has not only facilitated community spread but has also resulted in an underestimation of the true incidence of disease, a statistic which is useful for policy making aimed at controlling the current pandemic and in preparing for future outbreaks. In this perspective, we present a crowdsourced platform developed by us for the true estimation of all SARS-CoV-2 infections in the community, through active self-reporting and layering other authentic datasets. The granularity of data captured by this system could prove to be useful in assisting governments to identify SARS-CoV-2 hotspots in the community facilitating lifting of restrictions in a controlled fashion.

New York State Emergency Preparedness and Response to Influenza Pandemics 1918-2018

Emergency health preparedness and response efforts are a necessity in order to safeguard the public against major events, such as influenza pandemics. Since posting warnings of the epidemic influenza in 1918, to the mass media communications available a century later, state, national and global public health agencies have developed sophisticated networks, tools, detection methods, and preparedness plans. These progressive measures guide health departments and clinical providers, track patient specimens and test reports, monitor the spread of disease, and evaluate the most threatening influenza strains by means of risk assessment, to be able to respond readily to a pandemic. Surge drills and staff training were key aspects for New York State preparedness and response to the 2009 influenza pandemic, and the re-evaluation of preparedness plans is recommended to ensure readiness to address the emergence and spread of a future novel virulent influenza strain.

FluChip-8G Insight: HA and NA subtyping of potentially pandemic influenza A viruses in a single assay

BACKGROUND

Global influenza surveillance in humans and animals is a critical component of pandemic preparedness. The FluChip-8G Insight assay was developed to subtype both seasonal and potentially pandemic influenza viruses in a single assay with a same day result. FluChip-8G Insight uses whole gene segment RT-PCR-based amplification to provide robustness against genetic drift and subsequent microarray detection with artificial neural network-based data interpretation.

OBJECTIVES

The objective of this study was to verify and validate the performance of the FluChip-8G Insight assay for the detection and positive identification of human and animal origin non-seasonal influenza A specimens.

METHODS

We evaluated the ability of the FluChip-8G Insight technology to type and HA and NA subtype a sample set consisting of 297 results from 180 unique non-seasonal influenza A strains (49 unique subtypes).

RESULTS

FluChip-8G Insight demonstrated a positive percent agreement ≥93% for 5 targeted HA and 5 targeted NA subtypes except for H9 (88%), and negative percent agreement exceeding 95% for all targeted subtypes.

CONCLUSIONS

The FluChip-8G Insight neural network-based algorithm used for virus identification performed well over a data set of 297 naïve sample results, and can be easily updated to improve performance on emerging strains without changing the underlying assay chemistry.

Experimental H1N1pdm09 infection in pigs mimics human seasonal influenza infections

Pigs are anatomically, genetically and physiologically comparable to humans and represent a natural host for influenza A virus (IAV) infections. Thus, pigs may represent a relevant biomedical model for human IAV infections. We set out to investigate the systemic as well as the local immune response in pigs upon two subsequent intranasal infections with IAV H1N1pdm09. We detected decreasing numbers of peripheral blood lymphocytes after the first infection. The simultaneous increase in the frequencies of proliferating cells correlated with an increase in infiltrating leukocytes in the lung. Enhanced perforin expression in αβ and γδ T cells in the respiratory tract indicated a cytotoxic T cell response restricted to the route of virus entry such as the nose, the lung and the bronchoalveolar lavage. Simultaneously, increasing frequencies of CD8αα expressing αβ T cells were observed rapidly after the first infection, which may have inhibited uncontrolled inflammation in the respiratory tract. Taking together, the results of this study demonstrate that experimental IAV infection in pigs mimics major characteristics of human seasonal IAV infections.

The 1918 Influenza Pandemic: Looking Back, Looking Forward

In commemoration of the centennial of the 1918 influenza pandemic, the American Journal of Epidemiology has convened a collection of 12 articles that further illuminate the epidemiology of that pandemic and consider whether we would be more prepared if an equally deadly influenza virus were to emerge again. In the present commentary, we place these 12 articles in the context of a growing body of work on the archeo-epidemiology of past pandemics, the socioeconomic and geographic drivers of influenza mortality and natality impact, and renewed interest in immune imprinting mechanisms and the development of novel influenza vaccines. We also highlight persisting mysteries in the origins and severity of the 1918 pandemic and the need to preserve rapidly decaying information that may provide treasure troves for future generations.