COVID-19 in Norway: A real-time analytical pipeline for preparedness, planning and response during the COVID-19 pandemic in Norway

Koronaviruspandemien er en trussel mot folks helse over hele verden. Den har allerede påført mange samfunn stor skade. Politikere trenger data av høy kvalitet om spredningen av denne infeksjonen for å kunne velge gode tiltak. Vi bruker både matematiske modeller og samler data om symptomer fra befolkningen for å gi et detaljert bilde av epidemien i Norge. En viktig måte å begrense spredningen på er å redusere person-til-person kontakt. Vi samler data om folks bevegelser i samfunnet med bruk av mobiltelefoner. Dette, sammen med data om symptomer på sykdom i den generelle befolkningen, gjør oss i stand til å estimere smittsomheten av korona-viruset og gi gode prognoser for spredningen og antall sykehusinnleggelser på kort sikt. Modellene gir politikerne viktige opplysninger ved å vise hvordan ulike tiltak, for eksempel prioritering av vaksiner og isolering kan redusere smittespredningen. Det kjøres langtidsscenarioer for å gi innsikt om epidemiens mulige utvikling som benyttes i risikovurderinger. Metodene vi bruker er i forskningsfronten når det gjelder statistisk vitenskap, og vil ha betydning for å styrke beredskap mot andre infeksjonssykdommer i årene framover. Vi samler inn data i eksisterende befolkningsundersøkelser, slik som Den norske mor, far og barn-undersøkelsen (MoBa) og Den norske influensastudien (NorFlu), og vi kobler data mot registerinformasjon om COVID-19 forekomst, for å forstå hvorfor noen personer har høyere risiko for infeksjon og alvorlig sykdom enn andre mennesker. Disse undersøkelsene inkluderer viktige opplysninger basert på tidligere genetiske og immunologiske analyser. Slik informasjon, sammen med data om utdanning, yrke og tidligere sykdom gir forståelse for hvordan SARS-CoV-2 vil påvirke befolkningens helse. Vi analyserer også hele den norske befolkningen med utgangspunkt i det sentrale personregisteret. Til dette registeret kobler vi informasjon fra pasientregisteret, primærhelseregisteret, meldesystemet for infeksjonssykdommer og dødsårsaksregisteret for å kunne beskrive og forstå konsekvensene av epidemien i Norge.

The project has delivered tools, instruments and results that have supported the Norwegian policy response and management throughout the pandemic. Outcomes include modelling-derived regional and national situational awareness (reproduction numbers, estimates of cumulative infections, hospitalisations and ICU patients) and 3-week projections of the healthcare burden for Norway. The results are published in weekly modelling reports. We have delivered modelling reports to support commissions to NIPH from the Ministry of Health and Care Services on relevant policy issues. To this end, we have developed novel methodology in terms of stochastic metapopulation models calibrated using novel Bayesian inference techniques, and a high-granular individual-based dual-strain model. We have used data from Norwegian registries and data collected in large cohorts during the pandemic to provide time-critical insights about increased occurrence of menstrual disturbances in 18- to 30-year-old women after COVID-19 vaccination; we have studied the association between tobacco use and covid-19 infection, public adherence during the covid-19 pandemic, and quantified the effectiveness of mRNA booster vaccination against covid-19 disease caused by the Omicron variant. Data collected from the cohorts on symptoms, testing, isolation and quarantine have been employed in NIPH’s surveillance and presented in weekly reports. The project has generic value for future preparedness through an improved scientific understanding of risk factors and effects of covid-19 infection and vaccination. We have delivered versatile mathematical tools and gained critical experience with operative modelling during a crisis. Data harvesting in cohorts will form the basis of further studies into the long-term effects of covid-19.

The rapidly expanding COVID-19 pandemic poses an imminent threat to the human population and the world economy. There is a need for novel data and high quality analyses of the evolving epidemic in order to guide policies. The overarching purpose of our project is to deliver real-time relevant output to guide policies on the actions to combat the ongoing Covid-19 epidemic in Norway. Our vehicle for these outputs is an advanced mathematical model that incorporates population movements from telephone surveillance as well as a series of epidemiological observations, including data from large cohorts and nation-wide registries. The model can simulate and assess the effects of actions that decrease person-to-person contact in the whole population, as well as in subgroups. Mathematical modelling will make projections of scenarios with different mitigation measures. We will combine dedicated analytical capacities with real time data harvesting, and we will interpret trends, the future course of the pandemic, and explore the possibilities for action to guide decision makers despite remaining uncertainty. We will use repeated data on symptoms from more than 200 000 participants in The Norwegian Influenza Cohort (NorFlu) and the Norwegian Mother, Father and Child Cohort Study (MoBa) with extensive information on previous diseases, exposures and life styles, and whole-genome genotyping. We will examine if underlying genetic predisposition to various diseases influence the susceptibility for COVID-19, or the progression of the disease. Using registry data on the whole Norwegian populations, we will include sociodemographic variables from Statistics Norway and data from health registries that include the newly odes for Covid-19 infection in primary and secondary health care. This will enable us to describe the burden of disease caused by this epidemic and analyse long-term consequences.