COCAP. Source: UFZ


COping CAPacity of nations facing systemic crisis – a global intercomparison exploring the SARS-CoV-2 pandemic

The SARS-CoV-2 pandemic is a systemic global crisis. It is characterized by huge economic, social and health impacts, and there is a broad consensus that the next pandemic is also likely to be caused by a SARS type virus. Consequently, a critical research goal is to develop a deeper understanding of how to increase the coping capacity of nations facing such a systemic crisis in the future.

When novel human diseases emerge into naive populations, identification and isolation of infected individuals forms the first line of defense of a society against the invading pathogens. Using epidemiological models, we showed that testing-isolation strategies will typically fail to contain epidemic outbreaks at practicably achievable testing capacities. We identified three key disease characteristics that govern controllability under resource constraints (the basic reproduction number R, the mean latent period, and the non-symptomatic transmission index) and showed that SARS-CoV-2 was extremely difficult to control. This result means that pandemic control will typically only be achievable through non-pharmaceutical intervention (NPI) strategies.

Consequently, COCAP worked in Phase 1 towards a coherent understanding of the overall development of the SARS-CoV-2 pandemic and corresponding NPI responses across many nations. Based on the tools developed and knowledge gained in Phase 1, COCAP’s Phase 2 will explore strategies to increase future coping capacities of nations facing pandemic crises.

At the core of COCAP is a new, streamlined, integrated epidemiological-socio-economic model. The epidemiological component is based on an SIR (Susceptible-Infectious-Recovered) model, but with infection rates that change over time. These dynamic infection rates reflect both the effects of interventions and the natural behavioral responses to infection levels. We estimate these rates from observed infection data and link them to time-varying R-factors, which are used to control the epidemic. By examining the R-factor trajectories over time, we assess how well the epidemic is managed. The economic model is connected to the epidemiological model through the relationship between the current R-factor and economic output which allows us to evaluate the relative economic costs of alternative response strategies.

The vision then is to control an epidemic by steering the R-factor trajectory to keep infection numbers at a tolerable level. The tolerable level of infections is strongly linked to the specific death rates in a country and is therefore nation specific. Many countries differ both in terms of their intervention strategies as well as their levels of risk aversion. Understanding how these different aspects of each nation’s response interact and contribute to its overall performance is a key step in defining sensible intervention strategies for future pandemics. Our coupled model facilitates precisely this kind of decomposition by allowing one to consider counterfactual scenarios with either or both classes of response mechanism active. This kind of scenario analysis helps tease apart the separate the effects of each of these different response mechanisms, in addition to highlighting any synergy occurring between them.

Sub-projects and transfer component

The project is divided into four different sub-projects. Sub-project 1 (SP1) builds a high-resolution QA-controlled global data set and hands over ready-to-use data sets to all other sub-projects. Sub-project 2 (SP2) thoroughly analyzes data to identify relevant nation-specific conditions (called National Framework Conditions, NFCs) and the causal relations impacting the epidemic situation. Sub-project 3 (SP3) builds a modular epidemiological-socio-economic model system, which is robustly parametrized via innovative parametrization and optimization methods. Sub-project 4 (SP4) explores the model system, synthesizes results and suggests intervention packages to control an epidemic. The transfer component is developed by the whole team. It is envisioned to be completely contained within project Phase 2.

Work packages

  • WP 1.1 Advanced data integration and access
  • WP 1.2 Development and implementation of cloud-based information systems

Recent key results

  • Trustable data sources were identified and a cross-domain metadata standard was developed.
  • Automatic data access pipelines have been written in order to permanently feed the common COCAP database
  • Various QA procedures to identify errors, gaps, typos, plausibility checks, were implemented based on common standards.
  • The data warehouse is made accessible through a web-API and corresponding results can be directly stored in the data warehouse.
  • The infrastructure is designed to be extensible for automatic workflows for data analysis and model simulation, which will be integrated within the 2nd project phase.

Links to repositories

Principal Investigator

Work packages

  • WP 2.1 Socioeconomic drivers for COVID trajectories
  • WP 2.2 Statistical Methods for Interpretable Causal Effects

Recent key results

  • To identify key features in excess mortality in comparison to infection numbers we compared national conditions across countries using Isomap dimensions to study differences in excess mortality and reported infection rates during the pandemic in 2020 and 2021. These two outcomes differ significantly: reported infection rates are higher in wealthier countries, while excess mortality rates are higher in middle-income countries.
  • The study of socio-demographic influence on infection numbers and excess mortality at the subnational level showed that at the regional level, socio-demographic factors were not useful for predicting health outcomes due to the varying introduction times and wave patterns of the pandemic. Instead, we tracked weekly excess mortality at a local level and identified five clusters in Europe with similar patterns. This is the first empirical description of COVID-19's spread in Europe, enabling better future analysis of countries' resilience and responses. Our initial findings suggest that the timing of a country’s response is more important than socioeconomic factors, explaining conflicting conclusions in previous research.

Principal Investigator

Work packages

  • WP 3.1 Coupled epidemiological-socio-economic model
  • WP 3.2 Core nation case studies and rate parameter trajectories

Recent key results

  • We developed a coupled epidemiological-socio-economic model and associated methods for fitting the coupled model to data via nonlinear least squares minimization (Calabrese et al. 2024). Specifically, we successfully combined an SIR-type epidemiological model with a time-varying disease transmission rate, a behavioral model capturing the autonomous response of individuals based on the average degree of risk aversion in the population, and a sector-specific economic model for Germany. The coupled model serves as the centerpiece of the COCAP project, and its development required substantial integration of efforts across the COCAP consortium. A key feature is that the model facilitates scenario analysis by transferring NPI effects from a reference nation to a focal nation. As a first proof of concept (Calabrese et al. 2024), we took Germany as our focal nation during Spring 2020, and New Zealand and Switzerland as reference nations with contrasting NPI strategies that we compared with Germany’s NPI response. Our results suggested that, while New Zealand's more aggressive strategy would have yielded modest epidemiological gains in Germany, it would have resulted in substantially higher economic costs while dramatically reducing social contacts. In contrast, Switzerland’s more lenient strategy would have prolonged the first wave in Germany, but would have also have increased relative costs. This case study served to demonstrate the potential of our approach to yield novel, multifaceted comparisons of response strategies across nations.
  • A new modelling framework to assess critical characteristics of newly emerged diseases was developed (Demers et al., 2023). It overcomes the limitations of typical ordinary differential equation compartmental models by decoupling symptom status from model compartments to allow a more realistic representation of symptom onset and pre-symptomatic transmission. Looking backwards, this framework clearly shows that even the original variant of SARS-CoV-2 was extremely difficult to control, while later variants (delta and omicron) were substantially less controllable, with omicron representing a “perfect storm” of the three key factors (basic reproduction number, the mean latent period, and the degree of non-symptomatic transmission).
  • A new general Bayesian estimation framework for models with dynamics that change at particular points in time represents a significant advance over existing statistical methods for simultaneously inferring both system dynamics and changepoints. First, it allows for the use of informative prior distributions, making it easier to estimate challenging parameters without treating them as fixed. Second, the Bayesian approach accounts for estimation uncertainty in all parameters, including changepoints, and provides credible intervals. Third, it identifies when significant changes in the system dynamics occurred, helping to determine which NPI changepoints truly impacted disease dynamics. Finally, though we developed this framework with our coupled epidemiological model in mind, the statistical framework itself is much more general and can be used for models covering a large range of dynamical systems.

Principal Investigator

Work packages

  • WP 4.1 Development of strategies and scenarios
  • WP 4.2 Evaluation of strategies and scenarios & synthesis

Recent key results

  • A novel framework for analyzing and modeling behavioral adaptation was developed to cope with the observed interplay of autonomous and policy-induced adaptation, being a "moving target”. The framework describes a number of behavioral mechanisms, including "compliance," which is analyzed in detail in cooperation with the LOKI project.
    An empirical application to the German case suggested that mobility patterns changed significantly due to both autonomous and policy-induced adaptation, with potentially weaker effects over time due to decreasing risk signals, diminishing risk perceptions, and an erosion of trust in the government. These findings are discussed for their relevance to modeling, laying the foundation for constructing convincing counterfactual scenarios for strategy analysis.
  • In a scenario analysis for Germany we evaluate the success of response strategies. We identified four ideal type response strategies in analysing time-dependent courses of infection rates across nations:
    (1) “Zero Covid”, which deploys NPIs with the aim to reduce transmissions to zero in a defined geographical area.
    (2) “Proactive defense”, which aims to control infection numbers at a low level by staying “ahead of the curve”, e.g., through tracing and isolating every infection.
    (3) “Reactive-discretionary”, which aims to control infection numbers at a tolerable level, while ensuring the social acceptability of measures by balancing cost of NPIs in society.
    (4) “Herd immunity”, which involves minimal intervention from policy makers, often with the aim of preserving economic activities and societal liberties.

Principal Investigator

COCAP aims to help countries develop effective strategies for handling future pandemics, especially during critical non-pharmaceutical intervention (NPI) phases. To achieve this, we are sharing our results with various national and international institutions and decision-makers through two main activities:

  • International Data Sharing: We will provide over 40 countries with access to our comprehensive, quality-assured data and model workflows for creating intervention strategies. This will be done in collaboration with leading health organizations like the WHO and ECDC, with the first release planned for mid-2024.
  • National and Sub-National Collaboration: Working with our sister project LOKI, which focuses on COVID-19 in Germany, we will make the COCAP model available at the local level. This collaboration allows for precise interventions based on detailed infection and spread models. If successful, we aim to extend this service internationally, possibly through a start-up.

These efforts will enhance each nation's ability to respond effectively to future pandemic crises.

Further Information

The following partners and associated partners are involved in the project:

COCAP Partners

COCAP Associated Partners

COCAP partners COCAP Associated Partners

Prof. Dr. Sabine Attinger , UFZ

Dr. Jan Bumberger , UFZ

Dr. Michael Bussmann , CASUS, HZDR

Prof. Dr. Justin M. Calabrese , CASUS, HZDR

Prof. Dr. Dr. h.c. Clemens Fuest , ifo Institute, LMU Munich

Prof. Dr. Erik Gawel , UFZ

Dr. Guido Kraemer , RSC4Earth, University of Leipzig

Prof. Dr. Miguel D. Mahecha , RSC4Earth, University of Leipzig

Prof. Dr. Michael Meyer-Hermann , HZI

Dr. rer. nat. Sadeeb Simon Ottenburger , CEDIM, KIT

Prof. Dr. Andreas Peichl , ifo Institute, LMU Munich

Prof. Dr. Martin F. Quaas , iDiv, Leipzig University

Wolfgang Raskob , CEDIM, KIT

Prof. Dr. Melanie Schienle , KIT

Marc Stöckli , ifo Institute, LMU Munich

Prof. Dr. Georg Teutsch , UFZ

Dr. Jakob Zscheischler , UFZ

COCAP Stakeholders

Our IVF Campaign Partners

The following projects were funded in the same research campaign of the HGF:

Please also see the main website on research campaigns at Helmholtz for further information: