scholarly journals Short-term effects of ambient temperature and relative humidity on the risk of COVID-19 and SARS in Guangzhou, China: A time-series analysis

2020 ◽  
Author(s):  
Hui Wang ◽  
Chun Chen ◽  
Qiaoxuan Lin ◽  
Tiegang Li
Keyword(s):  
Relative Humidity ◽  
Ambient Temperature ◽  
Meteorological Factors ◽  
Severe Disease ◽  
Early Warning Systems ◽  
Optimal Range ◽  
Distributed Lag ◽  
Mean Temperature ◽  
Non Linear ◽  
Coronavirus Infection

Abstract Coronavirus infection has exerted a severe disease burden on the world, especially the newly emerged SARS-CoV-2 that has caused worldwide pandemic. It is possible meteorological factors can influence the transmission of coronavirus. The aim of this study was to evaluate the effect of meteorological factors on COVID-19 and SARS, and to provide evidence for disease control and prevention. Data of COVID-19 and SARS cases and daily mean temperature, relative humidity and other meteorological factors in Guangzhou in 2003 and 2020 were collected. Using a distributed lag non-linear model approach, we assessed the relationship between ambient temperature, relative humidity and the risks of COVID-19 and SARS. The numbers of cases for COVID-19 and SARS during the study period were 347 and 1072, respectively. There was a dome-shaped relation between mean temperature and COVID-19, with a threshold of 14.50°C (RR=1.48, 95%CI: 1.01, 2.16) and the optimal range was 12.40-16.40°C. A similar association was found between mean temperature and SARS occurrence, with a threshold of 18.40°C (RR=1.02, 95%CI: 1.00, 1.04), and the optimal range was 15.30-19.30°C. Besides, there were non-linear negative relationships between both RH and COVID-19, SARS. In addition, the largest overall effect of RH on COVID-19 and SARS were obtained at 52% and 45%, yielding relative risk of 7.47 (95%CI: 1.66, 33.55) and 47.56 (95%CI: 11.49, 196.95), respectively. The optimal ranges were below 77.00% and below 82.70%, respectively. Meteorological parameters should be taken into consideration while developing early warning systems and risk strategies for controlling and preventing coronavirus infection.

scholarly journals Using a distributed lag non-linear model to identify impact of temperature variables on haemorrhagic fever with renal syndrome in Shandong Province

2018 ◽  
Vol 146 (13) ◽  
pp. 1671-1679 ◽  
Author(s):  
Qinqin Xu ◽  
Runzi Li ◽  
Shannon Rutherford ◽  
Cheng Luo ◽  
Yafei Liu ◽  
...  
Keyword(s):  
Linear Model ◽  
Meteorological Factors ◽  
Global Climate ◽  
Meteorological Data ◽  
Shandong Province ◽  
Haemorrhagic Fever ◽  
Distributed Lag ◽  
Mean Temperature ◽  
Non Linear

AbstractHaemorrhagic fever with renal syndrome (HFRS) is transmitted to humans mainly by rodents and this transmission could be easily influenced by meteorological factors. Given the long-term changes in climate associated with global climate change, it is important to better identify the effects of meteorological factors of HFRS in epidemic areas. Shandong province is one of the most seriously suffered provinces of HFRS in China. Daily HFRS data and meteorological data from 2007 to 2012 in Shandong province were applied. Quasi-Poisson regression with the distributed lag non-linear model was used to estimate the influences of mean temperature and Diurnal temperature range (DTR) on HFRS by sex, adjusting for the effects of relative humidity, precipitation, day-of-the-week, long-term trends and seasonality. A total of 6707 HFRS cases were reported in our study. The two peaks of HFRS were from March to June and from October to December, particularly, the latter peak in 2012. The estimated effects of mean temperature and DTR on HFRS were non-linear. The immediate and strong effect of low temperature and high DTR on HFRS was found. The lowest temperature −8.86°C at lag 0 days indicated the largest related relative risk (RRs) with the reference (14.85 °C), respectively, 1.46 (95% CI 1.11–1.90) for total cases, 1.33 (95% CI 1.00–1.78) for the males and 1.76 (95% CI 1.12–2.79) for the females. Highest DTR was associated with a higher risk on HFRS, the largest RRs (95% CI) were obtained when DTR = 15.97 °C with a reference at 8.62 °C, with 1.26 (0.96–1.64) for total cases and 1.52 (0.97–2.38) for the female at lag 0 days, 1.22 (1.05–1.41) for the male at lag 5 days. Non-linear lag effects of mean temperature and DTR on HFRS were identified and there were slight differences for different sexes.

scholarly journals The Impacts of Climatic Factors and Vegetation on Hemorrhagic Fever with Renal Syndrome Transmission in China: A Study of 109 Counties

2019 ◽  
Vol 16 (18) ◽  
pp. 3434 ◽  
Author(s):  
Junyu He ◽  
Yong Wang ◽  
Di Mu ◽  
Zhiwei Xu ◽  
Quan Qian ◽  
...  
Keyword(s):  
Relative Humidity ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Climatic Factors ◽  
Meta Analysis ◽  
National Level ◽  
Hemorrhagic Fever ◽  
Early Warning Systems ◽  
Mean Temperature ◽  
Non Linear

Hemorrhagic fever with renal syndrome (HFRS) is a rodent-borne infectious disease caused by hantaviruses. About 90% of global cases were reported in China. We collected monthly data on counts of HFRS cases, climatic factors (mean temperature, rainfall, and relative humidity), and vegetation (normalized difference vegetation index (NDVI)) in 109 Chinese counties from January 2002 to December 2013. First, we used a quasi-Poisson regression with a distributed lag non-linear model to assess the impacts of these four factors on HFRS in 109 counties, separately. Then we conducted a multivariate meta-analysis to pool the results at the national level. The results of our study showed that there were non-linear associations between the four factors and HFRS. Specifically, the highest risks of HFRS occurred at the 45th, 30th, 20th, and 80th percentiles (with mean and standard deviations of 10.58 ± 4.52 °C, 18.81 ± 17.82 mm, 58.61 ± 6.33%, 198.20 ± 22.23 at the 109 counties, respectively) of mean temperature, rainfall, relative humidity, and NDVI, respectively. HFRS case estimates were most sensitive to mean temperature amongst the four factors, and the lag patterns of the impacts of these factors on HFRS were heterogeneous. Our findings provide rigorous scientific support to current HFRS monitoring and the development of early warning systems.

scholarly journals Effect of meteorological factors on the activity of influenza in Chongqing, China, 2012–2019

PLoS ONE ◽  
2021 ◽  
Vol 16 (2) ◽  
pp. e0246023
Author(s):  
Li Qi ◽  
Tian Liu ◽  
Yuan Gao ◽  
Dechao Tian ◽  
Wenge Tang ◽  
...  
Keyword(s):  
Wind Speed ◽  
Relative Humidity ◽  
Meteorological Factors ◽  
Meteorological Data ◽  
Warning System ◽  
Absolute Humidity ◽  
Influenza Surveillance ◽  
Average Wind Speed ◽  
Mean Temperature ◽  
Influenza Activity

Background The effects of multiple meteorological factors on influenza activity remain unclear in Chongqing, the largest municipality in China. We aimed to fix this gap in this study. Methods Weekly meteorological data and influenza surveillance data in Chongqing were collected from 2012 to 2019. Distributed lag nonlinear models (DLNMs) were conducted to estimate the effects of multiple meteorological factors on influenza activity. Results Inverted J-shaped nonlinear associations between mean temperature, absolute humidity, wind speed, sunshine and influenza activity were found. The relative risks (RRs) of influenza activity increased as weekly average mean temperature fell below 18.18°C, average absolute humidity fell below 12.66 g/m3, average wind speed fell below 1.55 m/s and average sunshine fell below 2.36 hours. Taking the median values as the references, lower temperature, lower absolute humidity and windless could significantly increase the risks of influenza activity and last for 4 weeks. A J-shaped nonlinear association was observed between relative humidity and influenza activity; the risk of influenza activity increased with rising relative humidity with 78.26% as the break point. Taking the median value as the reference, high relative humidity could increase the risk of influenza activity and last for 3 weeks. In addition, we found the relationship between aggregate rainfall and influenza activity could be described with a U-shaped curve. Rainfall effect has significantly higher RR than rainless effect. Conclusions Our study shows that multiple meteorological factors have strong associations with influenza activity in Chongqing, providing evidence for developing a meteorology-based early warning system for influenza to facilitate timely response to upsurge of influenza activity.

scholarly journals Association of Short-Term Exposure to Meteorological Factors and Risk of Hand, Foot, and Mouth Disease: A Systematic Review and Meta-Analysis

2020 ◽  
Vol 17 (21) ◽  
pp. 8017
Author(s):  
Zhihui Liu ◽  
Yongna Meng ◽  
Hao Xiang ◽  
Yuanan Lu ◽  
Suyang Liu
Keyword(s):  
Risk Factors ◽  
Systematic Review ◽  
Relative Humidity ◽  
Ambient Temperature ◽  
Meteorological Factors ◽  
Meta Analysis ◽  
Foot And Mouth Disease ◽  
Short Term ◽  
Short Term Exposure ◽  
Lag Model

(1) Background: Inconsistencies were observed in studies on the relationship between short-term exposure to meteorological factors and the risk of hand, foot, and mouth disease (HFMD). This systematic review and meta-analysis was aimed to assess the overall effects of meteorological factors on the incidence of HFMD to help clarify these inconsistencies and serve as a piece of evidence for policy makers to determine relevant risk factors. (2) Methods: Articles published as of 24 October 2020, were searched in the four databases, namely, PubMed, Web of Science, Embase, and MEDLINE. We applied a meta-analysis to assess the impact of ambient temperature, relative humidity, rainfall, wind speed, and sunshine duration on the incidence of HFMD. We conducted subgroup analyses by exposure metrics, exposure time resolution, regional climate, national income level, gender, and age as a way to seek the source of heterogeneity. (3) Results: Screening by the given inclusion and exclusion criteria, a total of 28 studies were included in the analysis. We observed that the incidence of HFMD based on the single-day lag model is significantly associated with ambient temperature, relative humidity, rainfall, and wind speed. In the cumulative lag model, ambient temperature and relative humidity significantly increased the incidence of HFMD as well. Subgroup analysis showed that extremely high temperature and relative humidity significantly increased the risk of HFMD. Temperate regions, high-income countries, and children under five years old are major risk factors for HFMD. (4) Conclusions: Our results suggest that various meteorological factors can increase the incidence of HFMD. Therefore, the general public, especially susceptible populations, should pay close attention to weather changes and take protective measures in advance.

scholarly journals Impact of Meteorological Factors and Southern Oscillation Index on Scrub Typhus Incidence in Guangzhou, Southern China, 2006–2018

2021 ◽  
Vol 8 ◽  
Author(s):  
Jianyun Lu ◽  
Yanhui Liu ◽  
Xiaowei Ma ◽  
Meixia Li ◽  
Zhicong Yang
Keyword(s):  
High Risk ◽  
Relative Humidity ◽  
Southern Oscillation Index ◽  
Scrub Typhus ◽  
Meteorological Factors ◽  
Southern Oscillation ◽  
Southern China ◽  
Meteorological Variables ◽  
Linear Relationships ◽  
Non Linear

Background: Scrub typhus was epidemic in the western Pacific Ocean area and East Asia, scrub typhus epidemic in densely populated areas in southern China. To better understand the association between meteorological variables, Southern Oscillation Index (SOI), and scrub typhus incidence in Guangzhou was benefit to the control and prevention.Methodology/Principal Findings: We collected weekly data for scrub typhus cases and meteorological variables in Guangzhou, and Southern Oscillation Index from 2006 to 2018, and used the distributed lag non-linear models to evaluate the relationships between meteorological variables, SOI and scrub typhus. The median value of each variable was set as the reference. The high-risk occupations were farmer (51.10%), house worker (17.51%), and retiree (6.29%). The non-linear relationships were observed with different lag weeks. For example, when the mean temperature was 27.7°C with1-week lag, the relative risk (RR) was highest as 1.08 (95% CI: 1.01–1.17). The risk was the highest when the relative humidity was 92.0% with 9-week lag, with the RR of 1.10 (95% CI: 1.02–1.19). For aggregate rainfall, the highest RR was 1.06 (95% CI: 1.03–1.11), when it was 83.0 mm with 4-week lag. When the SOI was 19 with 11-week lag, the highest RR was 1.06 (95% CI: 1.01–1.12). Most of the extreme effects of SOI and meteorological factors on scrub typical cases were statistically significant.Conclusion/Significance: The high-risk occupations of scrub typhus in Guangzhou were farmer, house worker, and retiree. Meteorological factors and SOI played an important role in scrub typhus occurrence in Guangzhou. Non-linear relationships were observed in almost all the variables in our study. Approximately, mean temperature, and relative humidity positively correlated to the incidence of scrub typhus, on the contrary to atmospheric pressure and weekly temperature range (WTR). Aggregate rainfall and wind velocity showed an inverse-U curve, whereas the SOI appeared the bimodal distribution. These findings can be helpful to facilitate the development of the early warning system to prevent the scrub typhus.

scholarly journals Impact of Weekly Climatic Variables on Weekly Malaria Incidence throughout Thailand: A Country-Based Six-Year Retrospective Study

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Manas Kotepui ◽  
Kwuntida Uthaisar Kotepui
Keyword(s):  
Relative Humidity ◽  
Malaria Incidence ◽  
Rank Correlation ◽  
Early Warning Systems ◽  
Climatic Data ◽  
Warning Systems ◽  
Awareness Campaigns ◽  
Spearman’S Rank Correlation ◽  
Mean Temperature ◽  
The Mean

Purpose. This study aimed to evaluate climatic data, including mean temperature, relative humidity, and rainfall, and their association with malaria incidence throughout Thailand from 2012 to 2017. The correlation of climatic parameters including temperature, relative humidity, and rainfall in each province and the weekly malaria incidence was analyzed using Spearman’s rank correlation. The results showed that the mean temperature correlated with malaria incidence (p value < 0.05) in 44 provinces in Thailand. These correlations were frequently found in the western and southern parts of Thailand. Relative humidity correlated with malaria incidence (p value < 0.05) in 35 provinces. These correlations were frequently found in the northern and northeastern parts of Thailand. Rainfall correlated with malaria incidence (p value < 0.05) in 38 provinces. These correlations were frequently found in the northern parts and some western parts of Thailand. The impacts of the mean temperature, relative humidity, and rainfall were observed frequently in specific provinces, including Chiang Mai, Chiang Rai, Trat, Kanchanaburi, Ubonratchathani, and Si Sa Ket. This is the first study to report areas where climatic data are associated with malaria incidence throughout Thailand from 2012 to 2017. These results can map out the climatic change process over time and across the country, which is the foundation for effective early warning systems for malaria, public health awareness campaigns, and the adoption of proper adaption measures that will help in malaria detection, diagnosis, and treatment.

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