scholarly journals Impact of Ambient Temperature and Relative Humidity on the Incidence of Hand-Foot-Mouth Disease in Wuhan, China

2020 ◽  
Vol 17 (2) ◽  
pp. 428 ◽  
Author(s):  
Jiayuan Hao ◽  
Zhiyi Yang ◽  
Wenwen Yang ◽  
Shuqiong Huang ◽  
Liqiao Tian ◽  
...  
Keyword(s):  
Air Pollution ◽  
Time Series ◽  
Relative Humidity ◽  
Time Series Analysis ◽  
Meteorological Factors ◽  
Mouth Disease ◽  
Series Analysis ◽  
Non Linear ◽  
Effects Of Temperature ◽  
The Relationship

Background: Few studies have previously explored the relationship between hand, foot, and mouth disease (HFMD) and meteorological factors with the effect modification of air pollution, and these studies had inconsistent findings. We therefore applied a time-series analysis assessing the effects of temperature and humidity on the incidence of HFMD in Wuhan, China to deepen our understanding of the relationship between meteorological factors and the risk of HFMD. Methods: Daily HFMD cases were retrieved from Hubei Provincial Center for Disease Control and Prevention from 1 February 2013 to 31 January 2017. Daily meteorological data including 24 h average temperature, relative humidity, wind velocity, and atmospheric pressure were obtained from Hubei Meteorological Bureau. Data on Air pollution was collected from 10 national air-monitoring stations in Wuhan city. We adopted a distributed lag non-linear model (DLNM) combined with Poisson regression and time-series analysis to estimate the effects of temperature and relative humidity on the incidence HFMD. Results: We found that the association between temperature and HFMD incidence was non-linear, exhibiting an approximate “M” shape with two peaks occurring at 2.3 °C (RR = 1.760, 95% CI: 1.218–2.542) and 27.9 °C (RR = 1.945, 95% CI: 1.570–2.408), respectively. We observed an inverted “V” shape between relative humidity and HFMD. The risk of HFMD reached a maximum value at a relative humidity of 89.2% (RR = 1.553, 95% CI: 1.322–1.824). The largest delayed cumulative effects occurred at lag 6 for temperature and lag 13 for relative humidity. Conclusions: The non-linear relationship between meteorological factors and the incidence of HFMD on different lag days could be used in the early targeted warning system of infectious diseases, reducing the possible outbreaks and burdens of HFMD among sensitive populations.

scholarly journals Time Series Analysis of Climate and Air Pollution Factors Associated with Atmospheric Nitrogen Dioxide Concentration in Japan

2020 ◽  
Vol 17 (24) ◽  
pp. 9507
Author(s):  
Takeshi Miyama ◽  
Hiroshi Matsui ◽  
Kenichi Azuma ◽  
Chika Minejima ◽  
Yasuyuki Itano ◽  
...  
Keyword(s):  
Air Pollution ◽  
Time Series ◽  
Relative Humidity ◽  
Time Series Analysis ◽  
Nitrogen Dioxide ◽  
Rural Areas ◽  
Air Pollutant ◽  
Climate Conditions ◽  
Series Analysis ◽  
Urban Rural

Nitrogen dioxide (NO2) is an air pollutant discharged from combustion of human activities. Nitrous acid (HONO), measured as NO2, is thought to impact respiratory function more than NO2. HONO and NO2 have an equilibrium relationship, and their reaction is affected by climate conditions. This study was conducted to discuss the extent of HONO contained in NO2, depending on the level of urbanization. Whether climate conditions that promote HONO production enhanced the level of NO2 measured was investigated using time series analysis. Climate and outdoor air pollution data measured in April 2009–March 2017 in urban (Tokyo, Osaka, and Aichi) and rural (Yamanashi) areas in Japan were used for the analysis. Air temperature had a trend of negative associations with NO2, which might indicate the decomposition of HONO in the equilibrium between HONO and NO2. The associations of relative humidity with NO2 did not have consistent trends by prefecture: humidity only in Yamanashi was positively associated with NO2. In high relative humidity conditions, the equilibrium goes towards HONO production, which was observed in Yamanashi, suggesting the proportion of HONO in NO2 might be low/high in urban/rural areas.

Nonlinear Time Series Analysis with R

2018 ◽  
Author(s):  
Ray Huffaker ◽  
Marco Bittelli ◽  
Rodolfo Rosa
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Particle Physics ◽  
Linear Time ◽  
Nonlinear Time Series ◽  
Nonlinear Time Series Analysis ◽  
Series Analysis ◽  
Linear Behavior ◽  
Non Linear ◽  
Scientific Principles

In the process of data analysis, the investigator is often facing highly-volatile and random-appearing observed data. A vast body of literature shows that the assumption of underlying stochastic processes was not necessarily representing the nature of the processes under investigation and, when other tools were used, deterministic features emerged. Non Linear Time Series Analysis (NLTS) allows researchers to test whether observed volatility conceals systematic non linear behavior, and to rigorously characterize governing dynamics. Behavioral patterns detected by non linear time series analysis, along with scientific principles and other expert information, guide the specification of mechanistic models that serve to explain real-world behavior rather than merely reproducing it. Often there is a misconception regarding the complexity of the level of mathematics needed to understand and utilize the tools of NLTS (for instance Chaos theory). However, mathematics used in NLTS is much simpler than many other subjects of science, such as mathematical topology, relativity or particle physics. For this reason, the tools of NLTS have been confined and utilized mostly in the fields of mathematics and physics. However, many natural phenomena investigated I many fields have been revealing deterministic non linear structures. In this book we aim at presenting the theory and the empirical of NLTS to a broader audience, to make this very powerful area of science available to many scientific areas. This book targets students and professionals in physics, engineering, biology, agriculture, economy and social sciences as a textbook in Nonlinear Time Series Analysis (NLTS) using the R computer language.

Sign in / Sign up

Export Citation Format

Share Document