Suggested by: Christian Neuwirth (Z_GIS – Spatial Simulation)
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| Source: The Economist |
Short description:
Covid-19 is the first digitally documented pandemic in history, presenting a unique opportunity to learn how to best deal with similar crises in the future.
The large global variability in cumulative excess mortality indicates that countries were not equally successful in handling covid-19 outbreaks. Empirical investigations showed that predictors like population age, gross domestic product, quality of the healthcare system or availability of vaccination have an impact on national excess mortality. In contrast to those variables, the effect of epidemic arrival times was rarely considered.
Hypothesis: Locations that were hit early on and unprepared were facing more severe outbreaks (longer duration without vaccination, lack of non-pharmaceutical interventions, additional winter outbreak etc.). Accordingly, early arrival is associated with high epidemic prevalence, reproduction, and excess mortality.
Method: 1) Construct a Global Mobility Network (GMN) from worldwide air travel data (Data: Open Sky COVID-19 Flight Dataset and/or Flight Radar API) that reproduces the situation at the time of outbreak (early 2020); 2) Implement a SIR network simulation model as presented by Brockmann & Helbing (2013); 3) Run outbreak simulations (Wuhan as expected source) to get estimated arrival times (ranks); 4) Validate results by comparing modeled arrivals (ranks) with reported arrival times; 5) Correlate modeled arrivals with reported excess mortality.
References/Suggested reading:
- Brockmann, D., Helbing, D., 2013. The hidden geometry of complex, network-driven contagion phenomena. science 342, 1337–1342.
Start: ASAP
Prerequisites/qualifications:
Interest in spatial statistics and/or spatial simulation, basic knowledge of R or Python
Please contact Christian Neuwirth in case of interest: christian.neuwirth@plus.ac.at
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