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 2023

Li Yin

Speaker at International Public Health Conference 2023 - Li Yin
Karolinska Institutet, Sweden

Title : Improving the decision-making for measures in a future pandemic


Background and purpose: In combating COVID-19, different strategies led to different public health and economic outcomes of COVID-19. For instance, Sweden took mild measures, yielding poor general mortality and COVID-19 mortality, in contrast to the other Nordic countries. Here, we discuss challenges in the decision-making for a future pandemic and the possible improvement.

Challenges in decision-making for measures in combating pandemic

  1. Only table data is immediately available. With table data, one usually performs descriptive analysis, which provides immediate evidence for decision-making but does not adjust for population characteristics and updating pandemic situation.
  2. Pandemic progression is complex: measure influences the pandemic outcome, which in turn influences the subsequent measures and pandemic outcomes. It is one of the most challenging analyses to provide statistical evidence for decision-making, such as the long-term influence of measure on a remote outcome.
  3. Pandemic progression is hardly repetitive: every wave is different due to the new variant of disease and the effectiveness of vaccination. Therefore, it is nearly impossible to develop an optimal decision-making mechanism for new measures.
  4. Politics, economics, culture, and resource play important roles in decision-making and are always challenging. They do not influence the pandemic outcome directly but via the decision. Thus, they are not the evidence for decision-making in combating a pandemic.

Here, we focus on how to acquire evidence, based on which the decision is made.

Possible solution and illustration:  Instead of the impossible optimal decision-making mechanism (challenge 3), we illustrate how to provide statistical evidence for decision-making (challenges 1 and 2). We can use the sequential causal inference to study the short-term and long-term influences of measure on pandemic outcomes. (Reference: Wang, X. and Yin, L. (2020). New G-Formula for the Sequential Causal Effect and Blip Effect of Treatment in Sequential Causal Inference. Annals of Statistics 48, 138-160).

We illustrate how to apply the sequential causal inference to table data by showing that the very early Swedish measure led to significant and sizable long-term influence on general mortality and COVID-19 mortality in comparison to the common measure adopted by the other Nordic countries. This evidence was not revealed by descriptive analysis but could have improved the decision-making for measures in the second wave.  (Reference: Wang, X., Wallentin, Y.F. and Yin, L. (2022). The statistical evidence missing from the Swedish decision-making of COVID-19 strategy during the early period: A longitudinal observational analysis.  Social Science and Medicine – Public Health. Volume 18, 101083).

Audience Take Away:

  1. Possible way to improve the decision-making for measures in a future pandemic.
  2. Pro and cons for using table data to provide timely statistical evidence for decision-making.
  3. General methodology of estimating the long-term influence of measure on public health and economic outcomes.


Li Yin is a senior statistician at Karolinska Institutet, Sweden. He specializes in causal inference and missing data, study design, observational study, and Epidemiology