Objective：To study how to model zero-inflated count data, and apply it to handle the data about respiratory infection. Methods：Zero-inflated model with 2 parts，zero and Poisson distribution, were transformed to conditional zero-inflated model. Logit and Log linkage function were used to promote the explanation of the result. Results：The results of significant risk factors were those who were younger and live lower in the ZTP part; the risk factors were younger exerciser, having history of chronic respiratory system disease and ill body conditions in the LOGIT part. Conclusion：The conditional zero-inflated model could be easier to explain the affected factors.