Disease transmission network can provide important information for individuals to protect themselves and to support governments to prevent and control infectious diseases. Current studies on disease transmission network mostly focus on scenarios in small, confined areas. We propose to construct disease transmission network using health status time series computed based on health insurance claims. We adopted Granger causality tests to identify potential links from the health status time series from all pairs between target groups and other individuals. We evaluated our approach by predicting future health care seeking activates for similar diseases based on past health care seeking activates of neighbors in the disease network. Compared to baseline models that use only personal historical data, including the estimated transmission network can improve prediction performance.