Guest talk by Kaspar Staub, University of Zürich, 18 May 2022


Excess mortality quantifies the overall mortality impact of a pandemic. This presentation summarises recent research that historically contextualises COVID-19 for Switzerland and other countries by comparing past and present pandemics based on equivalent mortality data.

The presentation will be held by dr. Kaspar Staub, who will be a guest researcher at Centre for Research on Pandemics & Society (PANSOC) 16-19 May. Staub is a historian and epidemiologist and works at the University of Zurich, Switzerland. Staub is the leader at the research group “Anthropometry and Historical Epidemiology” at the Institute of Evolutionary Medicine.

The event is free, open for all and part of the brown bag seminar series “Lunsjpåfyll” held by the University Library at OsloMet. The talk takes place 11:30-12:00 at Library P48 (Pilestredet 48), ground floor.

Next Webinar March 31

At 1600 Oslo time, Lianne Tripp, University of Northern British Columbia, will present:

Overlooking the demographic data: COVID-19 in First Nations in Canada

Previous studies on Indigenous populations and COVID-19 have argued for the need to collect COVID-19 data on Indigenous populations because during times of pandemics they experience more severe health outcomes in relation to their non-Indigenous counterparts. Counterintuitively, studies have found that the COVID-19 rates for some countries (such as in Canada) are higher in non-Indigenous population than Indigenous populations. A re-examination of COVID-19 in Canada reveals misinterpretations and misrepresentations of the data. The failure to recognize that the Canadian COVID-19 data for Indigenous populations was collected for First Nations living on reserves only is one misinterpretation. By end of December 2020, the prevalence rates were higher in First Nations populations living on reserves than non-First Nations populations, and COVID-19 mortality rates in First Nations exceeded the rest of the country by the end of April 2021. There was also considerable regional variation in rates of COVID-19 among First Nations communities across the country, where in western Canada the highest rates were observed.  

Dr Tripp is a medical anthropologist, whose research involves the areas of historical demography and epidemiology (infectious diseases). Emphasis is given to combining an empirical approach with a bio-cultural lens on demographic, primary health reports and qualitative information from historical records. Lianne’s publications have dealt with such matters as: colonial health; disease risk; bio-cultural dimensions of epidemics and pandemics; age and sex/gender differentials in disease experience; and health and religiosity. The diseases of focus are cholera, COVID-19,  measles, 1918 pandemic influenza, tuberculosis, undulant fever, whooping cough, and yellow fever.

PANSOC affiliated students and researchers interviewed in Quartz on pandemics & mental health

Journalist Annalisa Merelli in has interviewed our master student Carla Hughes on her research on the 1918 influenza and suicide risks while centre leader Mamelund has shared earlier research and his thoughts on the increases risks of asylum hospitalizations assoctaed with the “Spanish flu” pandemic. Read more in here: Why is the great resignation happening? — Quartz (

Interior of Hospital during the influenza epidemic. The beds are isolated by curtains

Next webinar this Thursday

The next PANSOC webinar will be March 17 at 1600 CET. Margarida Pereira will present: “The 2020 Syndemic of Obesity and COVID-19 in an Urbanized World.”

One-hundred years after one of the largest infectious disease pandemics, the Spanish influenza, the world was hit by the COVID-19 pandemic. As in 1918-20, the most common public health measures in 2020 to control the spread of this highly contagious disease were essentially non-pharmaceutical. The first COVID-19 outbreaks occurred in urban areas, which confirmed that these areas bring together the perfect conditions for fast dissemination of infectious diseases. Also, at an early stage of the COVID-19 pandemic, physicians and scientists observed that individuals with specific comorbidities, and namely with obesity, not only were at higher risk of contracting severe illness but also had increased odds of dying. Hence, urban areas became naturally privileged settings for the uprising of the syndemic of obesity and COVID-19.

Margarida Pereira is a Health Geographer, and her research focuses on the social determinants of health. Currently, Margarida is a postdoctoral fellow at PANSOC and is studying the syndemic relation between obesity and COVID-19 from a social science perspective.

Next webinar on March 10

Tamara Giles-Vernick, Institut Pasteur, will present: “Complex local vulnerabilities and the COVID-19 pandemic in France.”

Who is responsible for health during a pandemic? This long-standing question, debated widely among state and local authorities, international institutions, and health experts, has also come to fore in our Vulnerability Assessment among lay publics in France and four other European countries during the COVID-19 pandemic. This presentation draws on our 177 qualitative interviews (157 Vulnerability Assessments + 20 supplementary interviews) conducted in France in 2021.

Dr. Tamara Giles-Vernick is Director of Research and Unit head of the Anthropology and Ecology of Disease Emergence Unit – the Institut Pasteur’s first social sciences research unit in its 130-year history. Dr. Giles-Vernick currently coordinates SoNAR-Global, a European Commission-funded global social sciences research network for preparedness and response to infectious threats. A specialist in the medical anthropology and history of central and west Africa, her current research focuses on COVID-19 and its consequences, as well as the emergence of zoonotic diseases and epidemics. In addition, she has published on viral hepatitis, Ebola, Buruli ulcer, the historical emergence of HIV in Africa, global health in Africa, the history of influenza pandemics, and environmental history.

Contact for a Zoom link.

New paper out: Predicting Psychological Distress During the COVID-19 Pandemic: Do Socioeconomic Factors Matter?

portrait of researcher Nan Bakkeli

Nan Zou Bakkeli at PANSOC and Consumption Reserch Norway has just published a new paper in the journal “Social Science Computer Review”. You can read it here: Predicting Psychological Distress During the COVID-19 Pandemic: Do Socioeconomic Factors Matter? – Nan Zou Bakkeli, 2022 (

The COVID-19 pandemic has posed considerable challenges to people’s mental health, and the prevalence of anxiety and depression increased substantially during the pandemic. Early detection of potential depression is crucial for timely preventive interventions; therefore, there is a need for depression prediction.

This study was based on survey data collected from 5001 Norwegians (3001 in 2020 and 2000 in 2021). Machine learning models were used to predict depression risk and to select models with the best performance for each pandemic phase. Probability thresholds were chosen based on cost-sensitive analysis, and measures such as accuracy (ACC) and the area under the receiver operating curve (AUC) were used to evaluate the models’ performance.

The study found that decision tree models and regularised regressions had the best performance in both 2020 and 2021. For the 2020 predictions, the highest accuracies were obtained using gradient boosting machines (ACC = 0.72, AUC = 0.74) and random forest algorithm (ACC = 0.71, AUC = 0.75). For the 2021 predictions, the random forest (ACC = 0.76, AUC = 0.78) and elastic net regularisation (ACC = 0.76, AUC = 0.78) exhibited the best performances. Highly ranked predictors of depression that remained stable over time were self-perceived exposure risks, income, compliance with nonpharmaceutical interventions, frequency of being outdoors, contact with family and friends and work–life conflict. While epidemiological factors (having COVID symptoms or having close contact with the infected) influenced the level of psychological distress to a larger extent in the relatively early stage of pandemic, the importance of socioeconomic factors (gender, age, household type and employment status) increased substantially in the later stage.Conclusion: Machine learning models consisting of demographic, socioeconomic, behavioural and epidemiological features can be used for fast ‘first-hand’ screening to diagnose mental health problems. The models may be helpful for stakeholders and healthcare providers to provide early diagnosis and intervention, as well as to provide insight into forecasting which social groups are more vulnerable to mental illness in which social settings.