- 著者
- Hiroshi Yoshikura Fumihiko Takeuchi
- 出版者
- 国立感染症研究所 Japanese Journal of Infectious Diseases 編集委員会
- 雑誌
- Japanese Journal of Infectious Diseases (ISSN:13446304)
- 巻号頁・発行日
- pp.JJID.2015.670, (Released:2016-05-09)
- 参考文献数
- 23
- 被引用文献数
- 1 1
The size distribution of the local infection cluster (LIC), a group of patients reported from the same prefecture without interruption in successive weeks, was scale-free for infections that are transmitted from person to person (e.g., measles, rubella, syphilis and HIV/AIDS). For infections that never spread from person to person, the distribution was entirely random. The size distribution for measles, rubella, syphilis and HIV/AIDS could be simulated successfully by random coin tossing with probabilities that were higher for highly populated prefectures.The size distribution of the population in the large municipalities (>120,000) as well as that of LICs were found scale free. As the number of patients per prefecture was correlated with an equation P=kNm, where m was 1.38 for syphilis, 1.63 for HIV/AIDS and 2 for measles or rubella, the frequency distribution of N1.38, N1.6 and N2, where N was population of municipalities, were compared with the frequency distributions of LIC sizes of syphilis, HIV/AIDS, measles and rubella. The frequency distribution of LICs, particularly those of measles and rubella during the large epidemic years, was close to the frequency distribution of Nm. The analysis suggested that LICs were products of stochastic events under the influence of the municipality population size.
- 著者
- Fumihiko Takeuchi Tsuyoshi Sekizuka Akifumi Yamashita Yumiko Ogasawara Katsumi Mizuta Makoto Kuroda
- 出版者
- National Institute of Infectious Diseases, Japanese Journal of Infectious Diseases Editorial Committee
- 雑誌
- Japanese Journal of Infectious Diseases (ISSN:13446304)
- 巻号頁・発行日
- vol.67, no.1, pp.62-65, 2014 (Released:2014-01-22)
- 参考文献数
- 13
- 被引用文献数
- 21 38
Next-generation DNA sequencing technologies have led to a new method of identifying the causative agents of infectious diseases. The analysis comprises three steps. First, DNA/RNA is extracted and extensively sequenced from a specimen that includes the pathogen, human tissue and commensal microorganisms. Second, the sequenced reads are matched with a database of known sequences, and the organisms from which the individual reads were derived are inferred. Last, the percentages of the organisms' genomic sequences in the specimen (i.e., the metagenome) are estimated, and the pathogen is identified. The first and last steps have become easy due to the development of benchtop sequencers and metagenomic software. To facilitate the middle step, which requires computational resources and skill, we developed a cloud-computing pipeline, MePIC: “Metagenomic Pathogen Identification for Clinical specimens.” In the pipeline, unnecessary bases are trimmed off the reads, and human reads are removed. For the remaining reads, similar sequences are searched in the database of known nucleotide sequences. The search is drastically sped up by using a cloud-computing system. The webpage interface can be used easily by clinicians and epidemiologists. We believe that the use of the MePIC pipeline will promote metagenomic pathogen identification and improve the understanding of infectious diseases.