Name
Abstract | ||
|---|---|---|
Bacillary dysentery is an important infectious disease caused by shigella dysenteriae. Here, we characterized the dynamic temporal trend of bacillary dysentery, and identified climate-related risk factors and their roles in bacillary dysentery transmission in Harbin city, China. A database is integrated monthly climate factors and incidence rates of Harbin city from 1986 to 1990. In this study, three consecutive months' climate data are used to predicted one month's incidence. One popular algorithm, The Least Absolute Shrinkage and Selectionator operator (lasso), a shrinkage and selection method for linear regression is applied to select related climate factors and build prediction model. Through this study, monthly accumulative precipitation, daily maximum precipitation, the daily maximum precipitation of one month before and monthly mean minimum temperature were found to result in the highest relative risk for bacillary dysentery. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/BIBM.2013.6732701 | 2013 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE (BIBM) |
Keywords | Field | DocType |
Bacillary dysentery, climate factors, prediction model, Lasso, linear regression | Cellular biophysics,Demography,Incidence (epidemiology),Regression analysis,Computer science,Relative risk,China,Bioinformatics,Linear regression,Bacillary dysentery,Precipitation | Conference |
Volume | Issue | ISSN |
null | null | 2156-1125 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hao Zhang | 1 | 97 | 15.19 |
| Chunpu Zou | 2 | 0 | 1.01 |
| Fengfeng Shao | 3 | 0 | 0.34 |
| Guo-Zheng Li | 4 | 368 | 42.62 |