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Globally, there has been a commitment to produce and distribute a vaccine within 100 days of the next pandemic. This 100-day target will place pressure on countries to make swift decisions on how to optimise vaccine delivery. We used data from the COVID-19 pandemic to inform mathematical modelling of future pandemics in Indonesia for a wide range of pandemic characteristics. We explored the benefits of vaccination programs with different start dates, rollout capacity, and age-specific prioritisation within a year of the detection of a novel pathogen. Early vaccine availability, public uptake of vaccines, and capacity for consistent vaccine delivery were the key factors influencing vaccine benefit. Monitoring age-specific severity will be essential for optimising vaccine benefit. Our study complements existing pathogen-specific pandemic preparedness plans and contributes a tool for the rapid assessment of future threats in Indonesia and similar middle-income countries.

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Background and Aims: Crimean-Congo hemorrhagic fever (CCHF) is a severe and potentially lethal illness. Tick bites of the Hyalomma genus are the primary source of transmission of CCHF to humans. The virus responsible for CCHF is the CCHF virus (CCHFV). It is a single-stranded negative sensed RNA virus. The virus belongs to the Orthonairoviridae genus within the Nairoviridae family. It occurs in an extensive geographical area spanning the Middle East, western China, southern Asia, southeastern Europe, and much of Africa. The current study aimed to evaluate the pathogenicity and potential risk of CCHFV to cause a public health emergency of international concern. Methods: We searched updated relevant information from PubMed, Google Scholar, and Scopus databases using Crimean-Congo hemorrhagic fever, tick-borne virus, and Nairovirus as keywords. Results: The case fatality rate (CFR) varies by region. It can be more than 30% in some cases. Three segments in the genome of CCHFV (L, M, and S) are different in size and function. It is unknown whether the pathogenicity of CCHFV varied based on the genomic diversity. CCHFV can be transmitted through tick bites, handling of infected ticks, contact with infected humans, contaminated body fluids, and so on. A wide range of severity is associated with CCHF, ranging from a moderate fever with no apparent cause to increased vascular permeability, failure of several organs, bleeding, and shock. Hospitals with high-level isolation units should be the first choice for treating CCHF patients. Individual safety equipment is crucial in healthcare to prevent the spread of the virus. In the farm environment, using integrated pest management techniques, minimizing activity in tick-infested regions, and dressing appropriately in long sleeves and pants will help to reduce the risk of CCHFV infection via tick bites. Conclusion: There are no approved vaccinations or therapeutics for CCHF except supportive therapeutic approaches. Therefore, scientists recommend early ribavirin therapy for cases of high-risk exposures.

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BackgroundThe COVID-19 pandemic was largely driven by genetic mutations of SARS-CoV-2, leading in some instances to enhanced infectiousness of the virus or its capacity to evade the host immune system. To closely monitor SARS-CoV-2 evolution and resulting variants at genomic-level, an innovative pipeline termed SARSeq was developed in Austria.AimWe discuss technical aspects of the SARSeq pipeline, describe its performance and present noteworthy results it enabled during the pandemic in Austria.MethodsThe SARSeq pipeline was set up as a collaboration between private and public clinical diagnostic laboratories, a public health agency, and an academic institution. Representative SARS-CoV-2 positive specimens from each of the nine Austrian provinces were obtained from SARS-CoV-2 testing laboratories and processed centrally in an academic setting for S-gene sequencing and analysis.ResultsSARS-CoV-2 sequences from up to 2,880 cases weekly resulted in 222,784 characterised case samples in January 2021-March 2023. Consequently, Austria delivered the fourth densest genomic surveillance worldwide in a very resource-efficient manner. While most SARS-CoV-2 variants during the study showed comparable kinetic behaviour in all of Austria, some, like Beta, had a more focused spread. This highlighted multifaceted aspects of local population-level acquired immunity. The nationwide surveillance system enabled reliable nowcasting. Measured early growth kinetics of variants were predictive of later incidence peaks.ConclusionWith low automation, labour, and cost requirements, SARSeq is adaptable to monitor other pathogens and advantageous even for resource-limited countries. This multiplexed genomic surveillance system has potential as a rapid response tool for future emerging threats.

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Background and Aims: Disease X represents the possibility that an unidentified infection may spread globally and start a pandemic. This study explored various aspects of emerging non-polio enteroviruses (NPEVs) as a possible source of "Disease X," an enigmatic agent declared by the World Health Organization, and discussed the potential impact of NPEVs on global public health. Methods: In this perspective article, we collected information from publicly available sources such as Google Scholar, PubMed, and Scopus. We used NPEVs, viral diseases, pandemics, and zoonotic diseases as keywords. We extracted information from the most relevant articles. Results: Notable outbreaks caused by NPEVs include enterovirus D68 (EV-D68) and enterovirus A71 (EV-A71), among many others. With a focus on therapeutic and preventative components, alternate modes of therapy, and the development of broad-spectrum antivirals, this analysis looks at the origin, epidemiology, genetic alterations, transmission dynamics, and disease pathophysiology of NPEVs. The information presented in the review indicates the current risk assessment of NPEVs, taking into account the following factors: the need for research and therapeutic interventions, the diversity of clinical manifestations, the impact of genetic variability on virulence, the persistence of emergence despite vaccination efforts, recurrent outbreaks, and the global impact of these viruses. Conclusion: There is a possibility that NPEVs could trigger global pandemics based on their zoonotic origins and urges for complete readiness, continuous research, cooperation, and a comprehensive strategy to combat emerging infectious diseases in a constantly changing global environment. It is peak time to acknowledge how important it is to abide by safety and health laws to prevent these illnesses.

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Background and Aims: Disease X is included to represent an unknown pathogen that has the potential to spread globally and trigger significant epidemics. The World Health Organization (WHO) and the Global Alliance for Vaccine and Immunization (GAVI) recommend 10 potential disease organisms responsible for disease X. Among them, the Zika virus (ZIKV) is one of the potential organisms. The present work aimed to evaluate the current pathogenicity and potential risk of the ZIKV for public health emergencies. Methods: We performed a brief review of existing literature from available sources using Zika fever, Zika virus, and Flaviviridae as keywords and extracted relevant information for this review. Results: The primary way that the ZIKV transmits to humans is by mosquito bites (Aedes aegypti and Aedes albopictus). Typically, Aedes mosquitoes bite during the day. As an RNA virus, the ZIKV is well-known for its rapid mutation rate. ZIKV strains or variations can arise due to genomic mutations, and they occur through various processes, including replication errors, recombination, and selection pressure. Nowadays, the ZIKV has also evolved into a global pandemic because the four-amino acid sequence fits the envelope protein's 154 glycosylation motif, which may be involved in virulence. Conclusion: There is no specific medicine or vaccine available for the prevention or treatment of ZIKV infection. Therefore, personal protective measures should be the way to prevent ZIKV disease. Therefore, avoiding exposure is the best way to prevent problems from ZIKV to mosquitoes.

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The World Health Organization (WHO) defined Disease X as an upcoming disease with the potential to cause a pandemic. Pathogen X is responsible for Disease X. Marburg virus disease (MVD) is one of the diseases from the priority disease list published by WHO. Marburg virus is a filamentous, negative-sense RNA virus that belongs to the same filovirus family as the lethal Ebola virus. Since the first discovery of this virus in 1967, 17 outbreaks occurred sporadically till 2023. Rousettus aegyptiacus acts as the natural reservoir of the virus. With an average incubation period of 5 to 10 days, its first target is the mononuclear phagocytic system cells. It is highly contagious and can be easily transmitted from animal to human and human to human via direct contact with blood or body fluid, feces, and semen of the infected host. Although Marburg disease has a high case fatality rate of close to 90%, unfortunately, there is no approved vaccines or treatments are available. The most recent outbreak of Marburg virus in Equatorial Guinea and Tanzania in 2023 caused an alert for global health. However, based on the last global pandemic of COVID-19 and the sudden re-emerging of monkeypox around the world, we can assume that the Marburg virus has the potential to cause a global pandemic. Our modern world depends on globalization, which helps the virus transmission among countries. The Marburg virus can easily be transmitted to humans by fruit bats of the Pteropodidae family. This virus causes severe hemorrhagic disease, and there are no specific vaccines and treatments available to combat it. Therefore, community engagement and early supportive care for patients are keys to successfully controlling MVD.

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BACKGROUND: The spread of infectious diseases can be modeled using deterministic or stochastic models. A deterministic approximation of a stochastic model can be appropriate under some conditions, but is unable to capture the discrete nature of populations. We look into the choice of a model from the perspective of decision making. METHOD: We consider an emerging disease (Disease X) in a closed population modeled by a stochastic SIR model or its deterministic approximation. The objective of the decision maker is to minimize the cumulative number of symptomatic infected-days over the course of the epidemic by picking a vaccination policy. We consider four decision making scenarios: based on the stochastic model or the deterministic model, and with or without parameter uncertainty. We also consider different sample sizes for uncertain parameter draws and stochastic model runs. We estimate the average performance of decision making in each scenario and for each sample size. RESULTS: The model used for decision making has an influence on the picked policies. The best achievable performance is obtained with the stochastic model, knowing parameter values, and for a large sample size. For small sample sizes, the deterministic model can outperform the stochastic model due to stochastic effects. Resolving uncertainties may bring more benefit than switching to the stochastic model in our example. CONCLUSION: This article illustrates the interplay between the choice of a type of model, parameter uncertainties, and sample sizes. It points to issues to be considered when optimizing a stochastic model.

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BACKGROUND: Solutions have been proposed to accelerate the development and rollout of vaccines against a hypothetical disease with epidemic or pandemic potential called Disease X. This may involve resolving uncertainties regarding the disease and the new vaccine. However the value for public health of collecting this information will depend on the time needed to perform research, but also on the time needed to produce vaccine doses. We explore this interplay, and its effect on the decision on whether or not to perform research. METHOD: We simulate numerically the emergence and transmission of a disease in a population using a susceptible-infected-recovered (SIR) compartmental model with vaccination. Uncertainties regarding the disease and the vaccine are represented by parameter prior distributions. We vary the date at which vaccine doses are available, and the date at which information about parameters becomes available. We use the expected value of perfect information (EVPI) and the expected value of partially perfect information (EVPPI) to measure the value of information. RESULTS: As expected, information has less or no value if it comes too late, or (equivalently) if it can only be used too late. However we also find non trivial dynamics for shorter durations of vaccine development. In this parameter area, it can be optimal to implement vaccination without waiting for information depending on the respective durations of dose production and of clinical research. CONCLUSION: We illustrate the value of information dynamics in a Disease X outbreak scenario, and present a general approach to properly take into account uncertainties and transmission dynamics when planning clinical research in this scenario. Our method is based on numerical simulation and allows us to highlight non trivial effects that cannot otherwise be investigated.

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INTRODUCTION: The World Health Organization (WHO)'s Research and Development (R&D) Blueprint for Action to Prevent Epidemics, a plan of action, highlighted several infectious diseases as crucial targets for prevention. These infections were selected based on a thorough assessment of factors such as transmissibility, infectivity, severity, and evolutionary potential. In line with this blueprint, the VACCELERATE Site Network approached infectious disease experts to rank the diseases listed in the WHO R&D Blueprint according to their perceived risk of triggering a pandemic. VACCELERATE is an EU-funded collaborative European network of clinical trial sites, established to respond to emerging pandemics and enhance vaccine development capabilities. METHODS: Between February and June 2023, a survey was conducted using an online form to collect data from members of the VACCELERATE Site Network and infectious disease experts worldwide. Participants were asked to rank various pathogens based on their perceived risk of causing a pandemic, including those listed in the WHO R&D Blueprint and additional pathogens. RESULTS: A total of 187 responses were obtained from infectious disease experts representing 57 countries, with Germany, Spain, and Italy providing the highest number of replies. Influenza viruses received the highest rankings among the pathogens, with 79 % of participants including them in their top rankings. Disease X, SARS-CoV-2, SARS-CoV, and Ebola virus were also ranked highly. Hantavirus, Lassa virus, Nipah virus, and henipavirus were among the bottom-ranked pathogens in terms of pandemic potential. CONCLUSION: Influenza, SARS-CoV, SARS-CoV-2, and Ebola virus were found to be the most concerning pathogens with pandemic potential, characterised by transmissibility through respiratory droplets and a reported history of epidemic or pandemic outbreaks.

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Although the development and clinical application of SARS-CoV-2 vaccines during the COVID-19 pandemic demonstrated unprecedented vaccine success in a short time frame, it also revealed a limitation of current vaccines in their inability to provide broad-spectrum or universal protection against emerging variants. Broad-spectrum vaccines, therefore, remain a dream and challenge for vaccinology. This review will focus on current and future efforts in developing universal vaccines targeting different viruses at the genus and/or family levels, with a special focus on henipaviruses, influenza viruses, and coronaviruses. It is evident that strategies for developing broad-spectrum vaccines will be virus-genus or family specific, and it is almost impossible to adopt a universal approach for different viruses. On the other hand, efforts in developing broad-spectrum neutralizing monoclonal antibodies have been more successful and it is worth considering broad-spectrum antibody-mediated immunization, or "universal antibody vaccine," as an alternative approach for early intervention for future disease X outbreaks.

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As the world is still fighting to combat the coronavirus disease 2019 (COVID-19) virus, the United Republic of Tanzania has been confronting yet another bacterial infection called leptospirosis (LS). It is caused by the spirochete bacteria of genus Leptospira, and has been known to infect several people, already claiming a number of lives. It infects 1 million people annually with ~60 000 deaths having a fatality rate of 6.85% worldwide. COVID has profusely burdened the healthcare system worldwide within the past 2 years; it has sabotaged medical management and brought down resources, which has now made it difficult for any country to withstand another pandemic. LS has overburdened the medical care system of Tanzania abjectly; it is now imperative not to overlook environmental factors, like a flood, the presence of rodents, unsatisfactory socioeconomic conditions in areas where dogs reside, substandard wastewater and garbage disposal facilities, or any other factor which might lead to further spread of LS and put Tanzania in jeopardy.

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This Review initiates a wide-ranging discussion over 2023 by selecting and exploring core themes to be investigated more deeply in papers submitted to the Vaccines Special Issue on the "Future of Epidemic and Pandemic Vaccines to Serve Global Public Health Needs". To tackle the SARS-CoV-2 pandemic, an acceleration of vaccine development across different technology platforms resulted in the emergency use authorization of multiple vaccines in less than a year. Despite this record speed, many limitations surfaced including unequal access to products and technologies, regulatory hurdles, restrictions on the flow of intellectual property needed to develop and manufacture vaccines, clinical trials challenges, development of vaccines that did not curtail or prevent transmission, unsustainable strategies for dealing with variants, and the distorted allocation of funding to favour dominant companies in affluent countries. Key to future epidemic and pandemic responses will be sustainable, global-public-health-driven vaccine development and manufacturing based on equitable access to platform technologies, decentralised and localised innovation, and multiple developers and manufacturers, especially in low- and middle-income countries (LMICs). There is talk of flexible, modular pandemic preparedness, of technology access pools based on non-exclusive global licensing agreements in exchange for fair compensation, of WHO-supported vaccine technology transfer hubs and spokes, and of the creation of vaccine prototypes ready for phase I/II trials, etc. However, all these concepts face extraordinary challenges shaped by current commercial incentives, the unwillingness of pharmaceutical companies and governments to share intellectual property and know-how, the precariousness of building capacity based solely on COVID-19 vaccines, the focus on large-scale manufacturing capacity rather than small-scale rapid-response innovation to stop outbreaks when and where they occur, and the inability of many resource-limited countries to afford next-generation vaccines for their national vaccine programmes. Once the current high subsidies are gone and interest has waned, sustaining vaccine innovation and manufacturing capability in interpandemic periods will require equitable access to vaccine innovation and manufacturing capabilities in all regions of the world based on many vaccines, not just "pandemic vaccines". Public and philanthropic investments will need to leverage enforceable commitments to share vaccines and critical technology so that countries everywhere can establish and scale up vaccine development and manufacturing capability. This will only happen if we question all prior assumptions and learn the lessons offered by the current pandemic. We invite submissions to the special issue, which we hope will help guide the world towards a global vaccine research, development, and manufacturing ecosystem that better balances and integrates scientific, clinical trial, regulatory, and commercial interests and puts global public health needs first.

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

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Objective: The objective of this theoretical paper is to identify conceptual solutions for securing, predicting, and improving vaccine production and supply chains. Method: The case study, action research, and review method is used with secondary data - publicly available open access data. Results: A set of six algorithmic solutions is presented for resolving vaccine production and supply chain bottlenecks. A different set of algorithmic solutions is presented for forecasting risks during a Disease X event. A new conceptual framework is designed to integrate the emerging solutions in vaccine production and supply chains. The framework is constructed to improve the state-of-the-art by intersecting the previously isolated disciplines of edge computing; cyber-risk analytics; healthcare systems, and AI algorithms. Conclusion: For healthcare systems to cope better during a disease X event than during Covid-19, we need multiple highly specific AI algorithms, targeted for solving specific problems. The proposed framework would reduce production and supply chain risk and complexity in a Disease X event.

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The technological possibilities and future public health importance of metagenomic sequencing have received extensive attention, but there has been little discussion about the policy and regulatory issues that need to be addressed if metagenomic sequencing is adopted as a key technology for biosurveillance. In this article, we introduce metagenomic monitoring as a possible path to eventually replacing current infectious disease monitoring models. Many key enablers are technological, whereas others are not. We therefore highlight key policy challenges and implementation questions that need to be addressed for "widespread metagenomic monitoring" to be possible. Policymakers must address pitfalls like fragmentation of the technological base, private capture of benefits, privacy concerns, the usefulness of the system during nonpandemic times, and how the future systems will enable better response. If these challenges are addressed, the technological and public health promise of metagenomic sequencing can be realized.

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Disease X is a hypothetical unknown disease that has the potential to cause an epidemic or pandemic outbreak in the future. Nanosensors are attractive portable devices that can swiftly screen disease biomarkers on site, reducing the reliance on laboratory-based analyses. However, conventional data analytics limit the progress of nanosensor research. In this Perspective, we highlight the integral role of machine learning (ML) algorithms in advancing nanosensing strategies toward Disease X detection. We first summarize recent progress in utilizing ML algorithms for the smart design and fabrication of custom nanosensor platforms as well as realizing rapid on-site prediction of infection statuses. Subsequently, we discuss promising prospects in further harnessing the potential of ML algorithms in other aspects of nanosensor development and biomarker detection.

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