Relationship of meteorological factors and human brucellosis in Hebei province, China.

Relationship of meteorological factors and human brucellosis in Hebei province, China.

Cao, Long-Ting;Liu, Hong-Hui;Li, Juan;Yin, Xiao-Dong;Duan, Yu;Wang, Jing;
The Science of the total environment 2019 pp. 135491
217
cao2019relationshipthe

Abstract

Brucellosis has always been one of the major public health problems in China. Investigating the influencing factors of brucellosis is conducive to its prevention and control. The incidence trend of brucellosis shows an obvious seasonality, suggesting that there may be a correlation between brucellosis and meteorological factors, but related studies were few. We aimed to use the autoregressive integrated moving average (ARIMA) model to analyze the relationship between meteorological factors and brucellosis.The data of monthly incidence of brucellosis and meteorological factors in Hebei province from January 2004 to December 2015 were collected from the Chinese Public Health Science Data Center and Chinese meteorological data website. An ARIMA model incorporated with covariables was conducted to estimate the effects of meteorological variables on brucellosis.There was a highest peak from May to July every year and an upward trend during the study period. Atmospheric pressure, wind speed, mean temperature, and relative humidity had significant effects on brucellosis. The ARIMA(1,0,0)(1,1,0) model with the covariates of atmospheric pressure, wind speed and mean temperature was the optimal model. The results showed that the atmospheric pressure with a 2-month lag (β = -0.004, p = 0.037), the wind speed with a 1-month lag (β = 0.030, p = 0.035), and the mean temperature with a 2-month lag (β = -0.003, p = 0.034) were significant predictors.Our study suggests that atmospheric pressure, wind speed, mean temperature, and relative humidity have a significant impact on brucellosis. Further understanding of its mechanism would help facilitate the monitoring and early warning of brucellosis.

Citation

ID: 69631
Ref Key: cao2019relationshipthe
Use this key to autocite in SciMatic or Thesis Manager

References

Blockchain Verification

Account:
NFT Contract Address:
0x95644003c57E6F55A65596E3D9Eac6813e3566dA
Article ID:
69631
Unique Identifier:
S0048-9697(19)35485-3
Network:
Scimatic Chain (ID: 481)
Loading...
Blockchain Readiness Checklist
Authors
Abstract
Journal Name
Year
Title
5/5
Creates 1,000,000 NFT tokens for this article
Token Features:
  • ERC-1155 Standard NFT
  • 1 Million Supply per Article
  • Transferable via MetaMask
  • Permanent Blockchain Record
Blockchain QR Code
Scan with Saymatik Web3.0 Wallet

Saymatik Web3.0 Wallet