PandemiX Center Mission and Programmes

Interdisciplinary Center

PandemiX Vision

PandemiX - a Center to study pandemic diseases

The goal of PandemiX is to build a comprehensive understanding of the forces that shape pandemic trajectories from origin to endgame, as we describe and quantify the signature features of pandemics present and past.

We bring together in one Center a diverse team of experts with four core competencies: mathematical modeling of infectious diseases, historical epidemiology, clinical research, and bioinformatics. With a common quantitative focus, Center researchers bring their various skill sets to bear on a set of central questions that frame pandemics as a cyclical process, from its origin through its endgame.

Our work relies on tools and approaches from four distinct core competencies. We will develop new mechanistic transmission models to predict a pandemic’s spread and evolutionary trajectory. But mechanistic models alone are blind; biological data on the pathogen, health outcomes and surveillance data from the human population and the historical and societal context are also necessary. Thus, we will also use Bioinformatics to reveal the genetic traits that characterize an evolving pathogen, providing crucial insight into the evolutionary path of emerging pathogens throughout a pandemic. Clinical research using patient-level data will shed light on pandemic population impact, the effects of vaccination, changes in population immunity, and long-term sequelae. Historical epidemiology combines analysis of archival health data with historical study of the societal and demographic context to elucidate past pandemic signatures. Bringing these competencies together in an interdisciplinary environment is the core of our strategy.

Programmes

We have organized our research plan into six intertwined research programs. Together, these programs will address all aspects of the entire pandemic cycle. All six programs require two or more of our listed core competencies. The program teams will collaborate extensively.

  • The drivers of zoonotic events

    While SARS-CoV-2 is the latest virus to cause a pandemic, an enormous number of other viruses lurk at animal-human interface, well-positioned make the leap. We aim to understand why zoonotic events occur, including the roles of viral replication and evolution in the host organism, immune-response and evasion, human-animal interaction dynamics, receptor binding and internalization in human cells, and potential for pathogen spread. What molecular signatures are necessary? What determines how likely an animal virus is to jump and evolve in a new host? Can we minimize the risk?

    Specific aim

    • To apply bioinformatical analysis and modelling to understand zoonotic events at the molecular, organismal, and population scales

  • Melding transmission patterns, pathogen evolution and public health countermeasures

    We will aim to understand how pathogen-specific signatures such as transmission and immunity patterns affect the course of an outbreak and whether certain features are under selection pressure. How might that affect the evolutionary course of an emerging pathogen and the likelihood that more dangerous variants will emerge? Could it be that superspreading is an evolvable property that can be affected by public health countermeasures?

    Specific aims

    • To develop novel mathematical models of pathogen spread and evolution based on phylogenetics, transmission and social network data
    • To investigate the interplay between transmission and immunity patterns, epidemic spread, mitigation strategies and pathogen evolution


  • When the Center is launched, Denmark will have been through multiple waves dominated by at least three SARS-CoV-2 variants. We will have gathered an enormous amount of data through mass testing and RNA sequencing, and through disease surveillance and records of clinical illness and vaccination. Much remains to be learned from this data.

    One unknown is how pandemic parameters changed over the course of the pandemic. For example, did clinical illness or risk factors for severe disease differ between COVID-19 variants, particularly among children? What are the risk factors for long COVID after mild vs. severe illness, and to what extent does vaccination protect against sequelae after breakthrough infection?

    Other unknowns will require laboratory studies, such as an investigation of the correlates of immunity, which are needed to gauge the intensity and duration of immune responses to both vaccination and infection. To assess how population immunity may develop in the future, we will investigate ‘hybrid vigor immunity’ gained from multiple exposures, through infection and vaccines. We will also investigate the extent of cross protection from common cold coronaviruses such as OC43, and the relationship between COVID-19 sequelae and severity of acute illness and vaccination status.

    Specific aims

    • To investigate clinical, epidemiologic, and immunological aspects of COVID-19 across the pandemic period based on analysis of health, mortality and RNA sequence data and serology (banked blood samples)
    • Understand fundamental aspects of long COVID, in terms of impact, frequency and severity

  • A substantial number of health records survive from the past four centuries that allow us to quantitatively study past pandemics as well as the post-pandemic era of endemic disease patterns. To get a full picture of the range of possible pandemic origins, impact, and endgames, we need to fully explore these data. Important factors in the European context in this time frame were smallpox vaccination, improving hygiene and nutrition, and increasing understanding of disease transmission.

    Specific aims

    • To unearth historic health and demographic data from diverse settings and environments
    • To quantitatively model these data and characterize the signatures of multiple pandemic and endemic diseases


  • The burden of once-pandemic diseases such as plague, measles, smallpox, and pertussis remained high as they entered the endgame, especially among children. In the late 18th century, however, infectious disease mortality began a long decline, which led first to rapid population growth followed in the late 19th and early 20th centuries by dramatically increased life expectancy. These changes were driven both by complex interactions between evolving pathogens and human host immunity and societal factors such as population growth, trade, hygiene, urbanization, migration, cultural practices, and disease control measures. We will investigate the relative importance of the multiple factors that drove these processes.

    Specific aims

    • To characterize the demographic impact of changing patterns of epidemic diseases in the setting of changing societal factors in the 18th and 19th centuries
    • To the build a more complete understanding of the transitions of historic pandemic to epidemic diseases
    • To investigate the role of population age structure in COVID-19 mortality impact


  • Our most recent experience with pandemic endgames comes from influenza in the 20th and 21st centuries. They caused multiple waves of infection over the course of 2-5 years, then subsided into a seasonal pattern that caused severe disease mostly in older adults. Smallpox and measles, characterized by sterilizing immunity, became endemic childhood diseases that manifested as severe epidemics. In contrast, winter seasonal coronaviruses such as OC43 and 229 are likely echoes of past coronavirus pandemics that now only cause mild colds.

    We will attempt to predict the path COVID-19 will take. Two key factors for a virus’ post pandemic fate are the virus’ capacity for antigenic drift and the degree and duration of population immunity. Evolutionary changes in antigenic properties of influenza combined with a partial waning of immunity shapes the pattern of regular winter epidemics.
    However, we do not know how these processes interacted with other aspects of viral adaptation during the transition from pandemic to endemic influenza after past pandemics.

    Specific aims

    • To use dynamic models to explore the transition of past pandemics to endemic, seasonal disease
    • To map possible trajectories of the COVID-19 endgame

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