COVID-19-outbreak response combining e-health, serolomics, modeling, artificial intelligence and implementation research (CORESMA)

CORESMA (“COVID-19 Outbreak Response combining E-health, Serolomics, Modelling, Artificial Intelligence and Implementation Research”). This project aims to close existing gaps between clinical, epidemiological and immunological information in order to better respond to the pandemic. European researchers from the Netherlands, Switzerland and Germany, as well as partners from China, Côte d’Ivoire and Nepal are working together to achieve this goal. They intend to obtain real-time clinical data via the SORMAS app developed by the HZI together with national and international partners since 2014, which will allow data on disease outbreaks to be recorded locally and transmitted to health authorities. The focus here is on particularly endangered countries, including Ivory Coast, Ghana and Nigeria. At the same time, investigations will be carried out in Germany and Nepal to determine whether infections with other human corona viruses lead to cross-immunity against the novel SARS-CoV-2. The data collected will help to better assess the transmission of the virus and to evaluate the effectiveness of measures against its spread.Among the biggest challenges in the COVID-19 outbreak are the lack of triangulation of clinical, epidemiological and immunological information for evidence-based response strategies.                      

The overriding aim of CORESMA is to overcome this challenge through field studies and implementation research in populations of low- and-middle income countries to serve in the response to the COVID-19 and other possible new outbreaks. Four technical work packages (WP) address the four main objectives: They are (1)To provide real-time clinical data to improve risk assessment and response, deploying an established mHealth Surveillance Outbreak Response Management and Analysis System (SORMAS) in Nepal, Côte d’Ivoire, Ghana and Nigeria; (2) to implement differential serolomics (multiplex serology) for population serum samples from Germany and Nepal for investigating pre-existing cross or partial immunity against COVID-19 ; (3) to apply comprehensive modeling, sampling and artificial intelligence on data in order to assess predictors for severe outcome, transmission dynamics and intervention effectiveness; and (4) to measure and improve quality of epidemic containment measures through implementation research in countries particularly vulnerable to the COVID-19 epidemic, in order to tailor effective and efficient control measures to health systems realities in Nepal and Côte d’Ivoire.　

COVID-19-Outbreak Response combining E-health, Serolomics, Modelling, Artificial Intelligence and Implementation Research

Methodology

WP1:

• Improvement of disease surveillance system (SORMAS) in Nigeria and Ghana
• Set-up of new disease surveillance system (SORMAS) in Côte d’Ivoire and Nepal
• Analyzing and providing real-time data on spread, transmission and clinical information of COVID-19 infections in humans

WP2:

• Differential serolomics, in order to estimate infection susceptibility. 
• Samples are available for approximately 10,000 individuals and were collected pre-epidemic between 2014-18. A follow up serum collection is intended for 2021-22. 
• Use of a successfully established assay format based on the Luminex platform

WP3:

• Using data provided by SORMAS in a mathematical model to estimate the completeness of reporting and the effectiveness of contact tracing by SORMAS; 
• Assess the possible impact of super-spreaders and investigate the systems potential to track zoonotic sources.
• Using artificial intelligence to classify cases and characterize possible super-spreaders, and
• Using methods from social sciences to study the added value and feasibility of complementing SORMAS with online respondent-driven detection to enhance case finding and increase knowledge on transmission networks.

WP4: 

• Process and outcome evaluation of the introdcution of SORMAS using qualitative (interviews, FGDs) and qualitative (health service provider survey) methods

Selected deliverables of CORESMA are:

WP1:

• An optimized point of entry module in SORMAS which is in use in Ghana and Nigeria
• SORMAS fully implemented at the points of entry in Côte d’Ivoire and Nepal

WP2:

• Development and final technical validation of the multiplex serological immunoassay due
• Production of assay components in large quantities to meet requirements for screening of >10,000 samples and use of the assay in Germany and Nepal
• Data evaluation and explorative analyses for all addressed parameters including sero-prevalence and pre-existing immunity in the German population and co-determinants of susceptibility to (2019-n)CoV infection

WP3:

• Model analysis of effectiveness of contact tracing for reducing case numbers
• Working AI software for risk categorization for Corona virus patients

WP4: 

• Rapid assessment of containment measures in Côte d’Ivoire and Nepal
• In-depth analysis of containment measures and deployment of SORMAS in Côte d’Ivoire and Nepal

Horizon 2020 Project information - CORESMA

Modeling

Apr 2020 — Mar 2023

$3.03M

China
Côte d'Ivoire
Germany
Ghana
Nepal
Nigeria
Switzerland
The Netherlands

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