scholarly journals Effect of temperature and precipitation on the daily new cases and daily new death in seven cities around the globe

2020 ◽  
Author(s):  
Amar Prashad Chaudhary ◽  
Adna Nelson K ◽  
Harish S ◽  
Mydhily S ◽  
Chaitanya KJ ◽  
...  
Keyword(s):  
Minimum Temperature ◽  
Effect Of Temperature ◽  
Maximum Temperature ◽  
Weather Variables ◽  
Average Temperature ◽  
Temperature And Precipitation ◽  
The World

AbstractBackgroundThis study was done to understand the effect of temperature and precipitation in COVID-19.ObjectiveTo study the effect of temperature and precipitation on transmission of COVID-19.To study the effect of temperature and precipitation on daily death of COVID-19.MethodologyWe collected 3 consecutive month data of seven cities around the world which were effected most by the COVID-19. Data included weather variables i.e temperature (average temperature, maximum temperature and minimum temperature), precipitation, daily new cases and daily new death.ConclusionIncrease in average temperature reduces daily death and increase in maximum temperature reduces transmission.

scholarly journals Relationship between Weather Variables and New Daily COVID-19 Cases in Dhaka, Bangladesh

2020 ◽  
Vol 12 (20) ◽  
pp. 8319
Author(s):  
M. Mofijur ◽  
I.M. Rizwanul Fattah ◽  
A.B.M. Saiful Islam ◽  
M.N. Uddin ◽  
S.M. Ashrafur Rahman ◽  
...  
Keyword(s):  
Air Quality ◽  
Minimum Temperature ◽  
Rank Correlation ◽  
Maximum Temperature ◽  
Weather Variables ◽  
Strong Negative Correlation ◽  
Average Temperature ◽  
Climate Indicators ◽  
The Relationship ◽  
Spearman Rank Correlation Test

The present study investigated the relationship between the transmission of COVID-19 infections and climate indicators in Dhaka, Bangladesh, using coronavirus infections data available from the Institute of Epidemiology, Disease Control and Research (IEDCR), Bangladesh. The Spearman rank correlation test was carried out to study the association of seven climate indicators, including humidity, air quality, minimum temperature, precipitation, maximum temperature, mean temperature, and wind speed with the COVID-19 outbreak in Dhaka, Bangladesh. The study found that, among the seven indicators, only two indicators (minimum temperature and average temperature) had a significant relationship with new COVID-19 cases. The study also found that air quality index (AQI) had a strong negative correlation with cumulative cases of COVID-19 in Dhaka city. The results of this paper will give health regulators and policymakers valuable information to lessen the COVID-19 spread in Dhaka and other countries around the world.

scholarly journals Relationship between Climate Variables and New Daily COVID-19 Cases in Dhaka, Bangladesh

2020 ◽  
Author(s):  
M. Mofijur ◽  
Islam Md Rizwanul Fattah ◽  
A. B. M. Saiful Islam ◽  
S.M. Ashrafur Rahman ◽  
Mohammad Asaduzzaman Chowdhury
Keyword(s):  
Air Quality ◽  
Wind Speed ◽  
Minimum Temperature ◽  
Maximum Temperature ◽  
Dhaka City ◽  
Climate Variables ◽  
Average Temperature ◽  
Climate Indicators ◽  
The World ◽  
The Relationship

The present study investigates the relationship between the transmission of COVID-19 infections and climate indicators in Dhaka City, Bangladesh, using coronavirus infections data available from the Institute of Epidemiology, Disease Control and Research (IEDCR), Bangladesh. The Spearman-ranked correlation test was carried out to study the association of seven climate indicators, including humidity, air quality, minimum temperature, precipitation, maximum temperature, mean temperature and wind speed with the COVID-19 outbreak in Dhaka City, Bangladesh. The study found that, among the seven indicators, only three indicators (air quality, minimum temperature and average temperature) have a significant relationship with new COVID-19 cases. The results of this paper will give health regulators and policymakers valuable information to lessen the COVID-19 infection in Dhaka and other countries around the world.

scholarly journals Analyzing the Effect of Temperature on the Outspread of COVID-19 around the Globe

2020 ◽  
Author(s):  
Pratik Das ◽  
Suvendu Manna ◽  
Piyali Basak
Keyword(s):  
Dose Response ◽  
Minimum Temperature ◽  
Recovery Rate ◽  
Effect Of Temperature ◽  
Temperature Changes ◽  
Average Temperature ◽  
Linear Dose ◽  
The World ◽  
Non Linear ◽  
Deadly Disease

The emergence of the pandemic around the world owing to COVID-19 is putting the world into a big threat. Many factors may be involved in the transmission of this deadly disease but not much-supporting data are available. Till now no proper evidences has been reported supporting that temperature changes can affect COVID-19 transmission. This work aims to correlate the effect of temperature with that of Total Cases, Recovery, Death, and Critical cases all around the globe. All the data were collected in April and the maximum and minimum temperature and the average temperature were collected from January to April (i.e the months during which the disease was spread). Regression was conducted to find a non-linear relationship between Temperate and the cases. It was evident that indeed temperature does have a significant effect on the total cases and recovery rate around the globe. It was also evident from the study that the countries with lower temperatures are the hotspots for COVID-19. The Study depicted a non-linear dose-response between temperature and the transmission, indicating the existence of the best temperature for its transmission. This study can indeed put some light on how temperature can be a significant factor in COVID-19 transmission.

scholarly journals The effect of average temperature on suicide rates in five urban California counties, 1999–⁠2019: an ecological time series analysis

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Sierra Cheng ◽  
Rebecca Plouffe ◽  
Stephanie M. Nanos ◽  
Mavra Qamar ◽  
David N. Fisman ◽  
...  
Keyword(s):  
Public Health ◽  
Suicide Rate ◽  
Minimum Temperature ◽  
The United States ◽  
Additive Models ◽  
Maximum Temperature ◽  
Mortality Data ◽  
Average Temperature ◽  
Suicide Rates ◽  
Average Maximum

Abstract Background Suicide is among the top 10 leading causes of premature morality in the United States and its rates continue to increase. Thus, its prevention has become a salient public health responsibility. Risk factors of suicide transcend the individual and societal level as risk can increase based on climatic variables. The purpose of the present study is to evaluate the association between average temperature and suicide rates in the five most populous counties in California using mortality data from 1999 to 2019. Methods Monthly counts of death by suicide for the five counties of interest were obtained from CDC WONDER. Monthly average, maximum, and minimum temperature were obtained from nCLIMDIV for the same time period. We modelled the association of each temperature variable with suicide rate using negative binomial generalized additive models accounting for the county-specific annual trend and monthly seasonality. Results There were over 38,000 deaths by suicide in California’s five most populous counties between 1999 and 2019. An increase in average temperature of 1 °C corresponded to a 0.82% increase in suicide rate (IRR = 1.0082 per °C; 95% CI = 1.0025–1.0140). Estimated coefficients for maximum temperature (IRR = 1.0069 per °C; 95% CI = 1.0021–1.0117) and minimum temperature (IRR = 1.0088 per °C; 95% CI = 1.0023–1.0153) were similar. Conclusion This study adds to a growing body of evidence supporting a causal effect of elevated temperature on suicide. Further investigation into environmental causes of suicide, as well as the biological and societal contexts mediating these relationships, is critical for the development and implementation of new public health interventions to reduce the incidence of suicide, particularly in the face increasing temperatures due to climate change.

scholarly journals Homogeneity Test and Correction of Daily Temperature and Precipitation Data (1978–2015) in North China

2018 ◽  
Vol 2018 ◽  
pp. 1-17 ◽  
Author(s):  
Lingling Shen ◽  
Li Lu ◽  
Tianjie Hu ◽  
Runsheng Lin ◽  
Ji Wang ◽  
...  
Keyword(s):  
Large Scale ◽  
North China ◽  
Maximum Temperature ◽  
Homogeneity Test ◽  
Climate Data ◽  
Average Temperature ◽  
Temperature And Precipitation ◽  
Daily Data ◽  
Before And After ◽  
Annual Average Temperature

Homogeneity of climate data is the basis for quantitative assessment of climate change. By using the MASH method, this work examined and corrected the homogeneity of the daily data including average, minimum, and maximum temperature and precipitation during 1978–2015 from 404/397 national meteorological stations in North China. Based on the meteorological station metadata, the results are analyzed and the differences before and after homogenization are compared. The results show that breakpoints are present pervasively in these temperature data. Most of them appeared after 2000. The stations with a host of breakpoints are mainly located in Beijing, Tianjin, and Hebei Province, where meteorological stations are densely distributed. The numbers of breakpoints in the daily precipitation series in North China during 1978–2015 also culminated in 2000. The reason for these breakpoints, called inhomogeneity, may be the large-scale replacement of meteorological instruments after 2000. After correction by the MASH method, the annual average temperature and minimum temperature decrease by 0.04°C and 0.06°C, respectively, while the maximum temperature increases by 0.01°C. The annual precipitation declines by 0.96 mm. The overall trends of temperature change before and after the correction are largely consistent, while the homogeneity of individual stations is significantly improved. Besides, due to the correction, the majority series of the precipitation are reduced and the correction amplitude is relatively large. During 1978–2015, the temperature in North China shows a rise trend, while the precipitation tends to decrease.

scholarly journals Impact Assessment of Future Climate Change on Streamflows Upstream of Khanpur Dam, Pakistan using Soil and Water Assessment Tool

Water ◽  
2019 ◽  
Vol 11 (5) ◽  
pp. 1090 ◽  
Author(s):  
Saima Nauman ◽  
Zed Zulkafli ◽  
Abdul Halim Bin Ghazali ◽  
Badronnisa Yusuf
Keyword(s):  
Minimum Temperature ◽  
Assessment Tool ◽  
Baseline Period ◽  
Maximum Temperature ◽  
Future Climate Change ◽  
Temperature Minimum ◽  
Temperature And Precipitation ◽  
Rcp 8.5 ◽  
Water Assessment ◽  
Soil And Water

The study aims to evaluate the long-term changes in meteorological parameters and to quantify their impacts on water resources of the Haro River watershed located on the upstream side of Khanpur Dam in Pakistan. The climate data was obtained from the NASA Earth Exchange Global Daily Downscaled Projection (NEX-GDDP) for MIROC-ESM model under two Representative Concentration Pathway (RCP) scenarios. The model data was bias corrected and the performance of the bias correction was assessed statistically. Soil and Water Assessment Tool was used for the hydrological simulation of watershed followed by model calibration using Sequential Uncertainty Fitting version-2. The study is useful for devising strategies for future management of Khanpur Dam. The study indicated that in the future, at Murree station (P-1), the maximum temperature, minimum temperature and precipitation were anticipated to increase from 3.1 °C (RCP 4.5) to 4.0 °C (RCP 8.5), 3.2 °C (RCP 4.5) to 4.3 °C (RCP 8.5) and 8.6% to 13.5% respectively, in comparison to the baseline period. Similarly, at Islamabad station (P-2), the maximum temperature, minimum temperature and precipitation were projected to increase from 3.3 °C (RCP 4.5) to 4.1 °C (RCP 8.5), 3.3 °C (RCP 4.5) to 4.2 °C (RCP 8.5) and 14.0% to 21.2% respectively compared to baseline period. The streamflows at Haro River basin were expected to rise from 8.7 m3/s to 9.3 m3/s.

scholarly journals Effects of Climate on Incidence of Campylobacter spp. in Humans and Prevalence in Broiler Flocks in Denmark

2004 ◽  
Vol 70 (12) ◽  
pp. 7474-7480 ◽  
Author(s):  
Mary Evans Patrick ◽  
Lasse Engbo Christiansen ◽  
Michael Wainø ◽  
Steen Ethelberg ◽  
Henrik Madsen ◽  
...  
Keyword(s):  
Relative Humidity ◽  
Linear Models ◽  
Public Health Problem ◽  
Effect Of Temperature ◽  
Maximum Temperature ◽  
Future Research ◽  
Combined Effects ◽  
Average Temperature ◽  
Effects Of Temperature ◽  
Single Predictor

ABSTRACT Campylobacter infections are increasing and pose a serious public health problem in Denmark. Infections in humans and broiler flocks show similar seasonality, suggesting that climate may play a role in infection. We examined the effects of temperature, precipitation, relative humidity, and hours of sunlight on Campylobacter incidence in humans and broiler flocks by using lag dependence functions, locally fitted linear models, and cross validation methods. For humans, the best model included average temperature and sunlight 4 weeks prior to infection; the maximum temperature lagged at 4 weeks was the best single predictor. For broilers, the average and maximum temperatures 3 weeks prior to slaughter gave the best estimate; the average temperature lagged at 3 weeks was the best single predictor. The combined effects of temperature and sunlight or the combined effects of temperature and relative humidity predicted the incidence in humans equally well. For broiler flock incidence these factors explained considerably less. Future research should focus on elements within the broiler environment that may be affected by climate, as well as the interaction of microclimatic factors on and around broiler farms. There is a need to quantify the contribution of broilers as a source of campylobacteriosis in humans and to further examine the effect of temperature on human incidence after this contribution is accounted for. Investigations should be conducted into food consumption and preparation practices and poultry sales that may vary by season.

scholarly journals Use of Weather Variables to Quantify Sorghum Ergot Potential in South Africa

Plant Disease ◽  
1998 ◽  
Vol 82 (1) ◽  
pp. 26-29 ◽  
Author(s):  
N. W. McLaren ◽  
B. C. Flett
Keyword(s):  
Minimum Temperature ◽  
Pollen Viability ◽  
Breakdown Point ◽  
Field Trials ◽  
Maximum Temperature ◽  
Daily Maximum Temperature ◽  
Daily Maximum ◽  
Weather Variables ◽  
Data Set ◽  
Study Results

Quantification of resistance to ergot requires that the observed ergot severity within a sorghum line be compared with expected ergot severity (ergot potential) to compensate for differences in environmental favorability for the disease among flowering dates and seasons. The ergot potential required to induce the onset of disease is referred to as the ergot breakdown point of that line. In earlier studies, the ergot potential of a specific flowering date was defined as the mean ergot severity in all sorghum heads over all lines in the nursery which commenced flowering on that date in a genetically broad-based sorghum nursery. In this study, results of field trials enabled accurate prediction of ergot potential by using a multiple regression analysis which included three weather variables—namely, pre-flowering minimum temperature (mean of days 23 to 27 pre-flowering), mean daily maximum temperature, and mean daily maximum relative humidity (mean of days 1 to 5 post-flowering; R2 = 0.90; P = 0.91E-5). Evaluation of predicted and observed ergot severity in an independent data set gave an index of agreement of d = 0.94 and R2 = 0.84 (P = 0.106E-4), showing that ergot severity, assuming the presence of viable inoculum, can be accurately predicted. Low pre-flowering minimum temperature was associated with reduced pollen viability, which appeared to be the primary factor predisposing lines to ergot.

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