Meteorological factors and domestic new cases of coronavirus disease (COVID-19) in nine Asian cities: A time-series analysis

AbstractAIMTo investigate the associations of meteorological factors and the daily new cases of coronavirus disease (COVID-19) in nine Asian cities.METHODPearson’s correlation and generalized additive modeling were performed to assess the relationships between daily new COVID-19 cases and meteorological factors (daily average temperature and relative humidity) with the most updated data currently available.RESULTSThe Pearson correlation showed that daily new confirmed cases of COVID-19 were more correlated with the average temperature than with relative humidity. Daily new confirmed cases were negatively correlated with the average temperature in Beijing (r=-0.565, P<0.01), Shanghai (r=-0.471, P<0.01), and Guangzhou (r=-0.530, P<0.01), yet in contrast, positively correlated with that in Japan (r=0.441, P<0.01). In most of the cities (Shanghai, Guangzhou, Hong Kong, Seoul, Tokyo, and Kuala Lumpur), generalized additive modeling analysis showed the number of daily new confirmed cases was positively associated with both average temperature and relative humidity, especially in lagged 3d model, where a positive influence of temperature on the daily new confirmed cases was discerned in 5 cities except in Beijing, Wuhan, Korea, and Malaysia. Nevertheless, the results were inconsistent across cities and lagged time, suggesting meteorological factors were unlikely to greatly influence the COVID-19 epidemic.CONCLUSIONThe associations between meteorological factors and the number of COVID-19 daily cases are inconsistent across cities and lagged time. Large-scale public health measures and expanded regional research are still required until a vaccine becomes available and herd immunity is established.Significance statementWith increasing COVID-19 cases across China and the world, and previous studies showing that meteorological factors may be associated with infectious disease transmission, the saying has it that when summer comes, the epidemic of COVID-19 may simultaneously fade away. We demonstrated the influence of meteorological factors on the daily domestic new cases of coronavirus disease (COVID-19) in nine Asian cities. And we found that the associations between meteorological factors and the number of COVID-19 daily cases are inconsistent across cities and time. We think this important topic may give better clues on prevention, management, and preparation for new events or new changes that could happen in the COVID-19 epidemiology in various geographical regions and as we move towards Summer.

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The Influence of Average Temperature and Relative Humidity on New Cases of COVID-19: Time-Series Analysis (Preprint)

10.2196/preprints.20495 ◽

2020 ◽

Author(s):

Zonglin He ◽

Yiqiao Chin ◽

Shinning Yu ◽

Jian Huang ◽

Casper J P Zhang ◽

...

Keyword(s):

Public Health ◽

Relative Humidity ◽

Large Scale ◽

Meteorological Factors ◽

Pearson Correlation ◽

Herd Immunity ◽

Positive Influence ◽

Average Temperature ◽

Health Measures ◽

Additive Modeling

BACKGROUND The influence of meteorological factors on the transmission and spread of COVID-19 is of interest and has not been investigated. OBJECTIVE This study aimed to investigate the associations between meteorological factors and the daily number of new cases of COVID-19 in 9 Asian cities. METHODS Pearson correlation and generalized additive modeling (GAM) were performed to assess the relationships between daily new COVID-19 cases and meteorological factors (daily average temperature and relative humidity) with the most updated data currently available. RESULTS The Pearson correlation showed that daily new confirmed cases of COVID-19 were more correlated with the average temperature than with relative humidity. Daily new confirmed cases were negatively correlated with the average temperature in Beijing (r=–0.565, P<.001), Shanghai (r=–0.47, P<.001), and Guangzhou (r=–0.53, P<.001). In Japan, however, a positive correlation was observed (r=0.416, P<.001). In most of the cities (Shanghai, Guangzhou, Hong Kong, Seoul, Tokyo, and Kuala Lumpur), GAM analysis showed the number of daily new confirmed cases to be positively associated with both average temperature and relative humidity, especially using lagged 3D modeling where the positive influence of temperature on daily new confirmed cases was discerned in 5 cities (exceptions: Beijing, Wuhan, Korea, and Malaysia). Moreover, the sensitivity analysis showed, by incorporating the city grade and public health measures into the model, that higher temperatures can increase daily new case numbers (beta=0.073, Z=11.594, P<.001) in the lagged 3-day model. CONCLUSIONS The findings suggest that increased temperature yield increases in daily new cases of COVID-19. Hence, large-scale public health measures and expanded regional research are still required until a vaccine becomes widely available and herd immunity is established.

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The burden associated with ambient PM2.5 and meteorological factors in Guangzhou, China, 2012–2016: A generalized additive modeling of temporal years of life lost

Chemosphere ◽

10.1016/j.chemosphere.2018.08.129 ◽

2018 ◽

Vol 212 ◽

pp. 705-714 ◽

Cited By ~ 5

Author(s):

Xiao Lin ◽

Yu Liao ◽

Yuantao Hao

Keyword(s):

Meteorological Factors ◽

Years Of Life Lost ◽

Generalized Additive Modeling ◽

Additive Modeling

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Phenological pattern reproductive of Attalea speciosa Mart ex Spreng. in pasture area in the central region of Rondônia.

FLORESTA ◽

10.5380/rf.v50i2.63713 ◽

2020 ◽

Vol 50 (2) ◽

pp. 1335 ◽

Cited By ~ 1

Author(s):

Jhonatas Cortes Rosa ◽

Andreza Pereira Mendonça ◽

Claudemir Carlos Ribeiro ◽

Sylviane Beck Ribeiro

Keyword(s):

Relative Humidity ◽

Climatic Factors ◽

Pearson Correlation ◽

Meteorological Variables ◽

Average Temperature ◽

Palm Trees ◽

Monthly Total Precipitation ◽

Monthly Total ◽

Pasture Area ◽

Reproductive Events

The objective of this work was to evaluate the phenology of the babassu (Attalea speciosa Mart ex Spreng.), aiming to subsidize information for non - timber management of the species in pasture area. For 48 months, the phenological patterns of babassu were studied, relating them to meteorological variables such as monthly average temperature, monthly total precipitation, relative humidity of the medium air and photoperiod. A total of 130 adult palm trees from natural regeneration were monitored in a pasture in the municipality of Ji-Paraná, RO, from January 2012 to December 2015. The data were analyzed by means of the activity indexes, the relation of the phenophases and meteorological variables by means of Pearson correlation. The population synchrony index was also evaluated. During the study, the population of Attalea speciosa showed flowering asynchronously in the population. In the process of formation and maturation of the fruits of the babassu, it was possible to observe that the time of total development of the fruits was in average 254 days. In young infructescence, the most influential climatic factors were temperature and photoperiod. In the green infructescence, climatic factors such as precipitation, relative humidity and photoperiod influenced negatively. The presence of mature infructescence occurred throughout the year, with the highest intensities in the month of November, and its activity related to the increase in temperature and duration of days. Phenological monitoring of babassu allowed to identify the intensity and predictability of reproductive events, thus enabling information to be provided to the plans for the sustainable exploitation of the species in pasture in the municipality of Ji-Paraná.

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1475Effects of climate factors on the dengue fever in Paraguay: generalized additive model in 2014-2020

International Journal of Epidemiology ◽

10.1093/ije/dyab168.231 ◽

2021 ◽

Vol 50 (Supplement_1) ◽

Author(s):

Raquel Elizabeth Gomez Gomez

Keyword(s):

Relative Humidity ◽

Dengue Fever ◽

Additive Model ◽

Climate Factors ◽

Final Model ◽

Generalized Additive Modeling ◽

Increasing Trend ◽

The Mean ◽

Additive Modeling ◽

Dengue Control

Abstract Background Dengue fever is disease transmitted by mostly Aedes spp. This study aims investigate the association between climate factors and dengue incidence in Paraguay, considered as an endemic disease since 2009. Methods We extracted incidence of dengue by week from 2014-2020 national health surveillance, Paraguay. The climate factors, including rainfall, sunshine, minimum temperate, air pressure, relative humidity and wind were extracted from Directorate of Meteorology and Hydrology and aggregated as a weekly data. Generalized additive modeling was performed, adjusted by seasonality and population. Lags between 0-10 weeks were chosen for according to rho statistics of Spearman’s test. Results A total of dengue fever was 40,593 in study period. The mean cumulative incidence per 10,000 populations was 22.37 (standard deviation: 93.27). All six climate factors and seasonality were significant in the final model with the adjusted R-square 18.6%. Rainfall (relative risk [RR]: 0.51), relative humidity (RR: 0.25) and wind (RR: 0.19) showed negative trends with the increase of dengue while atmospheric pressure (RR: 9.32) and sunshine (RR: 0.12) showed positive associations. Minimum temperature showed increasing trend until 15ºC (1ºC increase in 4-fold incidence). The lag of each factor was selected between 2 to 10 weeks. Conclusion Climate factors showed associations with dengue fever in Paraguay. Such climate factors should be considered along with the dengue surveillance in endemic areas for effective dengue control. Key messages Climate factors showed significant dynamic associations with dengue incidence in Paraguay.

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Effects of Meteorological Factors on PM10 Pollution in Yantai Urban Areas

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.178-181.328 ◽

2012 ◽

Vol 178-181 ◽

pp. 328-331

Author(s):

Jian Guo Song ◽

Ming Chang ◽

Xin Zhi Wang ◽

Wei Liu

Keyword(s):

Relative Humidity ◽

Frequency Distribution ◽

Urban Areas ◽

Meteorological Factors ◽

Air Pressure ◽

The Other ◽

Average Temperature ◽

The North

This paper makes analysis and statistics about the frequency distribution of average temperature, pressure, humidity and wind conditions between moderate pollution days of PM10(API>200) and conventional days from 2008 to 2010 in Yantai. The result shows that the frequency of PM10 pollution which occurred in winter is close to the sum of the other seasons. PM10 pollution days appears easily under such conditions: the average temperature below 10°C, average air pressure is higher than 101.0kPa, relative humidity is less than 70%, or average weed speed of 3-7m/s with the north-south wind.

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EFFECTS OF METEOROLOGICAL VARIABLES ON Pinus caribaea GROWTH IN DIFFERENT AGES AT THE AMAZON REGION.

FLORESTA ◽

10.5380/rf.v52i1.78392 ◽

2022 ◽

Vol 52 (1) ◽

pp. 113

Author(s):

Gabriel Miranda Lima de Lima ◽

Nei Sebastião Braga Gomes ◽

Thiago Augusto da Cunha ◽

Afonso Figueiredo Filho

Keyword(s):

Relative Humidity ◽

Pearson Correlation ◽

Meteorological Variables ◽

Pinus Caribaea ◽

Annual Increment ◽

Positive Correlation ◽

Average Temperature ◽

Different Ages ◽

One Year ◽

The Impact

This study compares the impact of five meteorological variables on the diametric growth of Pinus caribaea var. hondurensis Barrett & Golfari in Vilhena, Rondônia. One thousand nine hundred sixty-eight trees were evaluated and classified at different ages: 600 trees were one year old; 600 trees were two years old; 768 trees were 13 years. The diameter measurement at the soil level (SL) was conducted in young stands between one and two years old. In the stand with 13 years old, the diameter was measured at 1.3 m (DCH). Using a Pressler borer, 50 increment cores were removed at DCH to measure the tree rings in LINTAB™ 6. The diametric growth was evaluated through the Periodic Increment (PI) for young stands and Current Annual Increment (CAI) for adult stands. The following variables were considered: average temperature (°C), precipitation (mm), solar radiation (Kj m-²), real evapotranspiration (mm), and maximum relative humidity (%). The Pearson Correlation Coefficient (r) proposed by Callegari-Jacques and the coefficient of variation (CV%) were used to establish the relationship between growth and meteorological variables. For young stands, the variables with higher positive correlation were real evapotranspiration and maximum relative humidity. However, the variable with a higher positive correlation in adult stands was average temperature, demonstrating a strong correlation until the sixth year of the species.

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Predicting the local COVID-19 outbreak around the world with meteorological conditions: a model-based qualitative study

BMJ Open ◽

10.1136/bmjopen-2020-041397 ◽

2020 ◽

Vol 10 (11) ◽

pp. e041397

Author(s):

Biqing Chen ◽

Hao Liang ◽

Xiaomin Yuan ◽

Yingying Hu ◽

Miao Xu ◽

...

Keyword(s):

Wind Speed ◽

Relative Humidity ◽

Qualitative Study ◽

Meteorological Factors ◽

Weather Data ◽

Epidemic Control ◽

Average Temperature ◽

Term Model ◽

The Future ◽

Future Outbreak

ObjectivesThis study aims to investigate the relationship between daily weather and transmission rate of SARS-CoV-2, and to develop a generalised model for future prediction of the COVID-19 spreading rate for a certain area with meteorological factors.DesignA retrospective, qualitative study.Methods and analysisWe collected 382 596 records of weather data with four meteorological factors, namely, average temperature, relative humidity, wind speed, and air visibility, and 15 192 records of epidemic data with daily new confirmed case counts (1 587 209 confirmed cases in total) in nearly 500 areas worldwide from 20 January 2020 to 9 April 2020. Epidemic data were modelled against weather data to find a model that could best predict the future outbreak.ResultsSignificant correlation of the daily new confirmed case count with the weather 3 to 7 days ago were found. SARS-CoV-2 is easy to spread under weather conditions of average temperature at 5 to 15°C, relative humidity at 70% to 80%, wind speed at 1.5 to 4.5 m/s and air visibility less than 10 statute miles. A short-term model with these four meteorological variables was derived to predict the daily increase in COVID-19 cases; and a long-term model using temperature to predict the pandemic in the next week to month was derived. Taken China as a discovery dataset, it was well validated with worldwide data. According to this model, there are five viral transmission patterns, ‘restricted’, ‘controlled’, ‘natural’, ‘tropical’ and ‘southern’. This model’s prediction performance correlates with actual observations best (over 0.9 correlation coefficient) under natural spread mode of SARS-CoV-2 when there is not much human interference such as epidemic control.ConclusionsThis model can be used for prediction of the future outbreak, and illustrating the effect of epidemic control for a certain area.

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A retrospective study: meteorological factors and COVID-19

10.21203/rs.3.rs-28151/v1 ◽

2020 ◽

Author(s):

Cai Chen ◽

Xiyuan Li ◽

Xiangwei Meng ◽

Zhixiang Ma ◽

Wei Li ◽

...

Keyword(s):

Relative Humidity ◽

Correlation Coefficient ◽

Meteorological Factors ◽

Rank Correlation ◽

Effect Of Temperature ◽

Maximal Effect ◽

Global Epidemic ◽

Average Temperature ◽

Daily Average ◽

Daily Average Temperature

Abstract Background: With the outbreak of novel coronavirus, the global epidemic prevention form is severe. Purpose: This paper aimed to investigate the association between meteorological factors (temperature, precipitation and relative humidity) and the daily new cases in Wuhan. Methods: generalized linear model was built to evaluate the link between daily average temperature and the new cases COVID-19. Spearman rank correlation coefficient was used to investigate the association between temperature, relative humidity, precipitation and the daily new cases COVID-19. Result: The correlation coefficient for daily average temperature, relative humidity, precipitation and NCP were 0.11, -0.083 and 0.17, respectively. The maximal effect of temperature on the new cases NCP appeared on Lag0. Conclusion: The variation of temperature had an effect on the daily new cases.

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