HYBRID EVENT: You can participate in person at Singapore or Virtually from your home or work.

3rd Edition of

International Public Health Conference

March 21-23, 2024 | Singapore

IPHC 2022

Xiaoqin Wang

Speaker at Public Health Conference 2022 - Xiaoqin Wang
University of Gavle, Sweden

Title : Prolonged effect of the early Swedish measure on public health and economic outcomes during the first wave of COVID 19


Background and purpose: In combating the negative impact of the COVID-19 pandemic, the Swedish approach was far more relaxed than the approach commonly adopted by its neighboring countries, Norway, Finland and Denmark. Notably, the two approaches differed remarkably during the initial period of the pandemic: the common approach had a far swifter and stronger early measure than the Swedish approach. Here, we analyze the long-term influence of the early measure of these approaches on public health and economic outcomes during the first wave of the pandemic.

Challenges and solutions: pandemic progression was a complex stochastic process in which measures yielded outcomes and outcomes in turn influenced subsequent measures. In this context, the measure taken during a certain period not only had a short-term influence on the immediate outcome in this period but also a long-term influence on the outcomes in the subsequent periods. In recent decades, one of the most exiting methodological developments in statistics is sequential causal inference, which addresses such complex stochastic processes. (Hernan & Robin (2020), Causal Inference: What If. Chapman & Hall/CRC, Boca Raton; Wang & Yin(2020), New G-Formula for the Sequential Causal Effect and Blip Effect of Treatment in Sequential Causal Inference, Annals of Statistics, 48, 138-160.)

Achievements: The Swedish approach performed far worse than the common approach for the public health outcomes, and the poor performance was largely due to its early measure during the initial period of the pandemic. The Swedish approach performed worse than the common approach for unemployment, but its early measure led to less poor performance for unemployment than for the public health outcomes


Xiaoqin Wang is an associate professor at the University of Gävle, Sweden. She specializes in causal inference, analysis of missing data, Bayesian analysis, longitudinal study with time dependent covariates, applied statistics including study design, case-control study, and survival analysis.