Recent global spread of new viruses awaked us to the threat of infectious diseases. The Japanese infectious disease surveillance scheme was established to detect infectious disease outbreaks during their early stages. Under this scheme, incident reports on infectious diseases are collected from 47 prefectures in Japan. However, publication of aggregate reports based on the scheme is delayed by about 2 weeks after data collection. Alternative data sources and real-time tools to monitor infectious disease are therefore required. We investigated whether websites operated by Twitter and local newspapers are promising data sources for monitoring the incidence of influenza. It was found that the Twitter corpus is a promising data source for monitoring national morbidity of influenza, and content from the websites of many local newspapers seems to reflect current local influenza morbidity. We then performed a time series analysis to construct a suitable model for predicting future morbidity. As the result of time series analyses, the nearest neighbor method, which used together Internet-based data with retrospective Japanese infectious disease surveillance data, was found to yield the best fit predictions. The obtained suitable model for predicting future morbidity had been made available on our website, which presents sophisticated visualizations for users to easily ascertain future morbidity. The site can also help medical personnel quickly ascertain trends in infectious diseases morbidity and warn the general population of impending outbreaks of diseases.