Background: Time series analysis is suitable for investigations of relatively direct and short-term effects of exposures on outcomes. In environmental epidemiology studies, this method has been one of the standard approaches to assess impacts of environmental factors on acute non-infectious diseases (e.g. cardiovascular deaths), with conventionally generalized linear or additive models (GLM and GAM). However, the same manner of practices of this method is observed with infectious diseases despite of the substantial differences from non-infectious diseases which may result in analytical challenges. Methods: Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guideline, systematic review was conducted to elucidate important issues in assessing the associations between environmental factors and infectious diseases using time series analysis with GLM or GAM. Published studies in relation to associations between weather factors, and malaria, cholera, dengue, or influenza were targeted. Findings: Issues regarding the estimation of susceptible population and exposure lag times, adequacy of seasonal adjustments, the presence of strong autocorrelations, and a lack of smaller observation time unit of outcomes (i.e. daily data) were raised from our review. These concerns may be attributed to the features specific to infectious diseases, such as transmissions among individuals and complicated causal mechanisms. Conclusion: The consequence of not taking adequate measures to address these issues is distortion of the appropriate risk quantifications of exposures factors. The future studies are required careful attentions to details, and recommended to examine alternative models or methods that improve studies with time series regression analysis for environmental determinants of infectious diseases.