Spatio-temporal epidemiology of emerging viruses—leveraging crowdsourced data and occurrence data to improve early disease detection systems

• Applicants: Dr. Thomas Jänisch; Professor Dr. Alexander Zipf
• Subject Area: Epidemiology and Medical Biometry/Statistics
• Term: since 2021
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 451956976

The on-going SARS-CoV-2 pandemic and lessons learned from the recent large-scale epidemics of Zika virus and Ebola virus underscore the need for public health preparedness to cope with emerging infections. Infectious diseases propagate in space and time and understanding their spatial and temporal distribution is critical to predict their spread and transmission dynamics, but so far, epidemiological analysis has typically focused on the spatial dimension only. Innovative and robust quantitative models are needed to provide a fully integrated approach for early epidemic detection and intervention. Traditionally, public health surveillance data, such as hospital and laboratory records, have been used to create models of disease dynamics. Although these data are reliable, they are difficult and slow to obtain. On the other hand, crowdsourced data, data that can be outsourced from large groups of people through the internet, can be obtained in real time but are subject to credibility issues, limitations in geographic coverage, and biases among social media users. The goal of this project is to combine the advantages of both crowdsourced and official health surveillance data to develop novel algorithms that will allows us to achieve higher information quality to generate more reliable conclusions and more accurate predictions about the spatio-temporal spread of diseases. To achieve this, we will 1) compare the validity and accuracy of crowdsourced data to official surveillance data, 2) detect spatio-temporal patterns of diseases in crowdsourced and official surveillance data, 3) incorporate the effect of human mobility patterns, socio-economic, environmental, and demographic variables on the spatio-temporal movement patterns of diseases at fine-grained scales (i.e. city), and 4) develop new visualization tools for the spatio-temporal spread of diseases. To test these algorithms and visualization tools, we will use SARS-CoV-2, dengue virus, Zika virus, chikungunya virus, yellow fever virus, and Ebola virus as case studies. Overall, the information derived from this project will be highly valuable to deliver early warning information and to control disease spread before public health surveillance data become available.

• DFG Programme: Research Grants
• International Connection: Austria
• Partner Organisation: Fonds zur Förderung der wissenschaftlichen Forschung (FWF)
• Cooperation Partner: Professor Dr. Bernd Resch