Objective
Only 150 years ago, one in five Europeans died in infancy, life expectancy was 40 years, and the leading causes of death were infectious diseases: tuberculosis, smallpox, measles, pertussis, diphtheria, cholera, typhoid fever, scarlet fever. But in just a few decades beginning about 1880, life expectancy rose dramatically as infectious disease mortality plummeted. This “2nd epidemiologic transition”, in which chronic diseases began replacing infections as leading causes of death, occurred well in advance of antibiotics and most vaccines. Many factors have been proposed to explain it, including improved nutrition, sanitation, clean drinking water, better housing and the emergence of social support systems.
Little has been done, however, to systematically rescue and quantitatively study historic health data and rigorously investigate the epidemiologic transition. I lay out here an ambitious, novel, interdisciplinary and feasible proposal to do just that. In the process, I will broaden my research scope from statistical modeling of historic pandemic influenza to all historic infections, understand the historical context in which the transition occurred, and master new concepts in dynamic disease modeling. Danish historic medical data are uniquely detailed and reach far back in time, and are uniquely suited for quantitative studies of long time series of morbidity and mortality, with the promise to further illuminate the epidemiology of important diseases including smallpox, cholera, and measles.
After 25 years abroad as a senior researcher at the National Institutes of Health and Professor of Global Health in the U.S. I now wish to return to my native Denmark. I had the honor this year to be elected to the Royal Danish Academy of Sciences and Letters, and receive funding to be a visiting professor at the University of Copenhagen, and trust this signals the beginning of my successful re-integration to European academia.
Fields of science
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
- social sciencessociologydemographymortality
- medical and health scienceshealth sciencespublic healthepidemiologypandemics
- medical and health scienceshealth sciencesinfectious diseasesRNA virusesinfluenza
- medical and health sciencesbasic medicinepharmacology and pharmacypharmaceutical drugsvaccines
- medical and health sciencesbasic medicinepharmacology and pharmacypharmaceutical drugsantibiotics
Programme(s)
Funding Scheme
MSCA-IF-EF-RI - RI – Reintegration panelCoordinator
1165 Kobenhavn
Denmark