A Strategy Transfer and Decision Support Approach for Epidemic Control
  in Experience Shortage Scenarios
X. Xiao P. Chen X. Cao K. Liu L. Deng D. Zhao Z. Chen Q. Deng F. Yu H. Zhang
Epidemic outbreaks can cause critical health concerns and severe global
economic crises. For countries or regions with new infectious disease
outbreaks, it is essential to generate preventive strategies by learning
lessons from others with similar risk profiles. A Strategy Transfer and
Decision Support Approach (STDSA) is proposed based on the profile similarity
evaluation. There are four steps in this method: (1) The similarity evaluation
indicators are determined from three dimensions, i.e., the Basis of National
Epidemic Prevention & Control, Social Resilience, and Infection Situation. (2)
The data related to the indicators are collected and preprocessed. (3) The
first round of screening on the preprocessed dataset is conducted through an
improved collaborative filtering algorithm to calculate the preliminary
similarity result from the perspective of the infection situation. (4) Finally,
the K-Means model is used for the second round of screening to obtain the final
similarity values. The approach will be applied to decision-making support in
the context of COVID-19. Our results demonstrate that the recommendations
generated by the STDSA model are more accurate and aligned better with the
actual situation than those produced by pure K-means models. This study will
provide new insights into preventing and controlling epidemics in regions that
lack experience.