The onset of the SARS-COV-2 (or COVID-19) pandemic led to a complete lockdown of society in many countries. So there I was, an applied mathematician, in confinement, following the news coverage of extreme situations in hospitals in Italy and New York, and at the same time hearing confusing and contradictory statements from politicians and leaders in various countries.
This situation motivated me to contribute with knowledge-based information to support decision-makers. So, in my home office, I started developing an epidemic model and gathering data on the COVID-19 related hospitalisations and deaths in Norway. I coupled the model to my data-assimilation library, and at the end of March, after about two weeks of work, I was able to model and predict the SARS-COV-2 pandemic evolution in Norway. I used the data-assimilation system to calibrate model parameters, including the time-dependent effective-reproductive-number, R. I then provided reports with model predictions to the Norwegian authorities to explain the unstable situation and the pandemic's dependency on R.
On 10 April, I invited an international group of colleagues working in data assimilation to use the model system on data from their respective countries. Nearly everyone jumped at the opportunity, and from then, we have worked together as an international team. We modelled the SARS-COV-2 pandemic in four European countries, Norway, England, the Netherlands, and France; the province of Quebec in Canada; the South American countries Argentina and Brazil; and the four US states Alabama, North Carolina, California, and New York. These countries and states all have vastly different developments of the epidemic, and we could accurately model the SARS-CoV-2 outbreak in all of them.
The joint work led to significant learnings regarding the use of data assimilation in epidemic modelling. We compiled the results into a large manuscript and submitted it to the journal "Fundamentals of Data Science" and we are currently finalising the revisions. We also made the paper available from MedRxiv.
We used a SEIR (susceptible, exposed, infectious, recovered) model with age-classes and compartments of sick, hospitalised, and deceased (Figure 1).