LOKI

Local Early Warning System for Control of Infection Outbreaks

Project content and objectives

With the emergence of infectious pathogens with pandemic potential, such as the coronavirus pandemic caused by SARS-CoV-1 in 2002/2003 or avian influenza A/H5N1 in 2005, national pandemic plans were developed for the healthcare system in Germany. However, a control system for outbreaks of infection that helps health authorities to develop individual targets and effective measures to deal with a pandemic under local conditions has not yet been implemented. The experience of the SARS-CoV-2 pandemic shows the need for a practical and user-friendly platform for monitoring local outbreaks, taking into account specific regional structures.

The aim of the LOKI-pandemics project is therefore to develop a platform with which public health departments can control local infection rates by introducing tailored non-pharmaceutical measures.

In order to be able to detect and assess outbreaks at a local level at an early stage, the project will analyze infection-relevant data using various methods ranging from artificial intelligence to epidemiological modelling. The platform offers the possibility to interactively simulate various scenarios. Health authority employees can view various scenarios with and without measures in order to illustrate the impact of measures on the incidence of infection, see how outbreaks develop and develop possible containment strategies.

Project structure

The LOKI-pandemics project is divided into four sub-projects.

Sub-project 1 – Data collection

Sub-project 1 deals with the collection and processing of data from various sources. These are relevant for use in the subsequent sub-projects and form the basis for the development of LOKI and the underlying models. The aim is to integrate several data sources in a structured and easily usable way into a data pool for models and forecasts. This includes data collection, structuring and primary analysis. In addition, the potential for integrating new data sources is analyzed. This data is either publicly accessible or is generated through cooperation with the pilot health authorities. Sub-project 1 also deals with data protection issues. A major limitation in the assessment and prediction of epidemic dynamics is the availability, quality and origin of the data. The integration of a large number of independent data sources, their standardization and the automation of their processing are essential for downstream statistical analyses and accurate predictions. Quality control mechanisms are set up to ensure high accuracy of data and metadata with minimal manual maintenance. At the same time, the data is aggregated, combined and converted into standardized formats to ensure a reliable and predictable data source for automated workflows.

Epidemiological models must be calibrated for predictions. However, reliable estimation of model parameters from insufficient data is often impossible. This problem is exacerbated as the level of detail of the models increases. Directly measurable model parameters derived from data provide valuable information and their use improves the reliability of predictions. Primary data sources include aggregated public surveillance data, case-based data from local health authorities, data on clinical and hospital-related parameters (in collaboration with the Lean European Open Survey on SARS-CoV-2 [LEOSS]a prospective European multicenter cohort study), population-based studies and seroprevalence surveys (https://serohub.net/en/, https://hzi-c19-antikoerperstudie.de/). In addition, subproject 1 integrates existing data on vaccinations, wastewater samples, contact networks, mobility and aerosol dynamics. In addition, an evidence synthesis of the existing literature is carried out to integrate previous estimates of the parameters. The evidence synthesis will focus on SARS-CoV-2 in the first phase and later integrate ongoing work (https://respinow.de/) on other respiratory pathogens.

These data and the synthesized evidence on the model parameters are provided in a structured form for downstream subprojects and used directly in pattern recognition approaches, mechanical models and in model calibration and uncertainty analysis. In order to promote synergies with existing international projects, subproject 1 will attempt to include and compare the current platform in an overarching review of similar global projects to build modeling and forecasting platforms for use by health authorities.

Several areas of the sub-project deal with sensitive data. The collected data is analyzed for its potential to violate privacy and the risk of inference is investigated. It also ensures that the data analysis products or algorithms do not disclose any private data and are not susceptible to inference attacks. Where necessary, local mechanisms are used and developed. The data is to be aggregated and the analysis coarsened to ensure that the privacy of the individual is protected.

Aggregating a variety of data sources will provide a detailed picture of the pandemic and its parameters, enabling unprecedented accuracy of prediction in a sophisticated, semi-automated way that is accessible to public health workers and other relevant professionals.

The aim is to integrate several data sources into a data pool for models and forecasts in a structured and easily usable form (for use in subsequent sub-projects).

Sub-project 2 – Analysis

Subproject 2 “Analysis” deals with the development of models that are intended to represent the real spread of infectious diseases and provides methods for parameter estimation, uncertainty quantification and optimization. Parameter estimation attempts to draw conclusions about the real values of various parameters (e.g. the number of daily contacts). Uncertainty quantification can be used to calculate how likely certain results are if certain aspects of the model are not fully known or are fuzzy or subject to fluctuations. The purpose of optimization is to achieve the best possible results in accordance with a predefined target.

Different types of models are used. The first class of models ranges from classical SIR-type models based on ordinary differential equations to models that include integro-differential equations and coupled regional or metapopulation models. In addition, LOKI-pandemics will provide agent-based models as well as models based on classical statistics and machine learning. To investigate the infection dynamics, these models are used either independently or with methods for parameter estimation and optimization. To ensure that the models can be used for future epidemiologically relevant outbreaks, the scientists in subproject 2 will also set up a workflow for calibrating and integrating real-time data into the models.

Subproject 2 will provide retrospective analyses of the Sars-CoV-2 pandemic in order to draw conclusions from past developments in the outbreak. These conclusions will help to increase the depth of models for the spread of infectious diseases or to develop new models for potential future outbreaks of respiratory viral diseases. In addition, retrospective analyses will help to evaluate the impact of regionally adopted, non-pharmaceutical measures, such as those introduced in Germany, in order to be able to use these findings for future outbreak containment.

All models are efficiently and modularly implemented so that they can provide real-time results in the form of visualizations of the further development of an outbreak event and are easily interchangeable in the near future as newer models are developed. Regionally resolved models can also be used to incorporate regional aspects of disease spread or control. Sub-project 2 will also enable the simulation of scenarios with and without non-pharmaceutical measures. This allows the effects of these measures on the incidence of infections or hospitalizations to be shown. The results help to evaluate the outbreak events, taking into account the uncertainty quantification, and support the decision to introduce non-pharmaceutical measures and their optimization. Numerical optimization methods are used to recommend appropriate measures.

The workflow of subproject 2 is designed to be as general as possible so that the transfer to new pathogens helps to reduce the costs of preparing for new pandemics. The lessons learned from Sars-CoV-2 as part of the sub-project will thus make it possible to better prepare decision-makers and the German population for potentially imminent pandemics.

The aim of subproject 2 is to provide modular and efficiently implemented models for the spread of infectious diseases and to provide methods for parameter estimation, uncertainty quantification and optimization. In addition, a workflow is created that is as automatic and general as possible, enabling the calculation of different scenarios for the spread of infection.

Sub-project 3 – Implementation

Sub-project 3 comprises the technical implementation and realization of the platform. The modular components developed in sub-project 2 are combined here to form a common platform. This is based on a cloud-based infrastructure with a direct connection to a supercomputer for computing-intensive operations and access to large storage capacity. In addition to the calculation of user-controlled simulation scenarios, visualization and visual analytics methods are an essential aspect of the LOKI platform.

The focus of the platform is therefore the web application with interactive components that visualize the model results and enable easy access to the results. The collected data and the results of data analyses, simulations and forecasts are presented to the target audience in a concise, pragmatic and comprehensible form.

By combining visualization with interaction and automated algorithms, the web application supports decision-makers in the evaluation of possible political measures. The algorithms simplify the classification of complex phenomena and the recognition of new patterns in the data. Diagrams and graphs can be displayed in detail and changed interactively.

Seamless integration into existing systems is crucial for a realistic, cost- and time-efficient introduction of the platform and adaptation to targeted user groups. A dedicated API will allow other systems to gain access to the platform’s key results and aggregated and simulated data to facilitate the reuse of our algorithms and data in other systems. In addition, active efforts are being made in the opposite direction to utilize and technically integrate additional data sources.

Information security aspects are an integral part of the entire platform to ensure that sensitive data is analyzed while maintaining privacy. The solutions are primarily defined by technical and legal considerations, which require cooperation with legal experts and concern the design of the platform’s security architecture. As the platform will provide various forms of access to potentially sensitive data, it is critical to develop novel security architectures that can balance data usage with privacy and security considerations.

The ambitious goal of an integrated platform for various methods of data analysis, data storage and user interaction requires modularity and flexibility in the software architecture. Only then will the generated platform serve to combat the pandemic beyond the duration of the project and be generalizable to various respiratory infections.

The aim of this sub-project is the technical implementation and realization of the platform with the development of the web application for users as the central component. From here, user-controlled simulation scenarios can be initiated and results visualized and analysed. Interfaces for integrating the platform into existing systems, linking to external data sources and high standards of information security make the platform open and flexible to use.

Sub-project 4 – Transfer into practice
Sub-project 4 is responsible for transferring the platform into practice. This means that this sub-project and the responsible employees form the interface between the platform developers and the cooperating pilot health authorities.

In order to fulfil this function, this sub-project organizes and structures the exchange and communication between the development and application of LOKI pandemics.

By involving users in the development process of the platform, valuable practical experience and suggestions can be incorporated directly into the further development of the application. This allows the platform to be adapted to the individual needs of a health authority. Various formats are used to set up and coordinate communication between development and application. Regular web conferences, for example, ensure direct contact with the developers and enable participating pilot health authorities to share their experiences.

Following the introduction of the platform, the employees of sub-project 4 will train and advise the pilot health authorities. The training courses are offered in various formats. Training materials are also being developed and made available to the pilot health authorities.

Another task of sub-project 4 is to evaluate the platform. The aim is to demonstrate the benefits and quality of the application for daily practice. Employees can use user feedback during development to evaluate and optimize the platform.

The aforementioned tasks of the sub-project are supplemented by the provision of user support to enable personal exchange with the pilot offices and the rapid clarification of open questions. The employees of sub-project 4 are regularly available for questions, ideas and suggestions via a telephone hotline.

The aim of sub-project 4 is to make the platform user-friendly and practice-oriented in cooperation with the health authorities and the developers.

The focus here is particularly on ensuring the transfer of the platform into the practice of the health authorities. Successful transfer is to be achieved through training and support options, but also through the coordination of exchange forums.

Project managers and partners

  • Prof. Dr. Michael Meyer-Hermann (Project Manager) – Helmholtz Centre for Infection Research
  • Dr. Martin Kühn (Deputy Project Manager, Head of TP2) – German Aerospace Center
  • Dr. Berit Lange (Head of TP1) Helmholtz Centre for Infection Research
  • Prof. Dr. Steffen Krause from the University of the Federal Armed Forces
  • Jens Henrik Göbbert (Head of TP3) – Forschungszentrum Jülich
  • Sybille Somogyi (Head of TP4) – Academy for Public Health