scholarly journals Temperature Significantly Change COVID-19 Transmission in 429 cities

2020 ◽  
Author(s):  
Mao Wang ◽  
Aili Jiang ◽  
Lijuan Gong ◽  
Lina Lu ◽  
Wenbin Guo ◽  
...  
Keyword(s):  
Minimum Temperature ◽  
Factor Model ◽  
Control Measures ◽  
Maximum Temperature ◽  
Cumulative Number ◽  
The World ◽  
Temperature Group ◽  
Linear Mixture Model ◽  
Maximum Temperatures ◽  
Lower Temperature

AbstractBackgroundThere is no evidence supporting that temperature changes COVID-19 transmission.MethodsWe collected the cumulative number of confirmed cases of all cities and regions affected by COVID-19 in the world from January 20 to February 4, 2020, and calculated the daily means of the average, minimum and maximum temperatures in January. Then, restricted cubic spline function and generalized linear mixture model were used to analyze the relationships.ResultsThere were in total 24,139 confirmed cases in China and 26 overseas countries. In total, 16,480 cases (68.01%) were from Hubei Province. The lgN rose as the average temperature went up to a peak of 8.72°C and then slowly declined. The apexes of the minimum temperature and the maximum temperature were 6.70°C and 12.42°C respectively. The curves shared similar shapes. Under the circumstance of lower temperature, every 1°C increase in average, minimum and maximum temperatures led to an increase of the cumulative number of cases by 0.83, 0.82 and 0.83 respectively. In the single-factor model of the higher-temperature group, every 1°C increase in the minimum temperature led to a decrease of the cumulative number of cases by 0.86.ConclusionThe study found that, to certain extent, temperature could significant change COVID-19 transmission, and there might be a best temperature for the viral transmission, which may partly explain why it first broke out in Wuhan. It is suggested that countries and regions with a lower temperature in the world adopt the strictest control measures to prevent future reversal.

scholarly journals Vulnérabilité Future Des Systèmes De Productions Agricoles Face Aux Changements Climatiques Dans Le 4ème PDA : Cas Des Communes De Djidja Et De Djougou

2021 ◽  
Vol 25 (1) ◽  
pp. 445
Author(s):  
AWO Sourou Malikiyou ◽  
ALE Agbachi Georges ◽  
YABI Ibouraïma
Keyword(s):  
Climate Change ◽  
Minimum Temperature ◽  
Agricultural Production ◽  
Production Systems ◽  
Methodological Approach ◽  
Maximum Temperature ◽  
A Value ◽  
Current Minimum ◽  
Maximum Temperatures ◽  
Changements Climatiques

La variabilité climatique dans les communes de Djidja et de Djougou engendre des conséquences aussi bien sur les niveaux de productivités, de production que sur les revenus des exploitants agricoles. L’objectif de cette recherche est d’étudier la vulnérabilité future des systèmes de productions agricoles face aux changements climatiques dans les Communes de Djidja et de Djougou.L’approche méthodologique utilisée comprend la collecte des données, leur traitement et l’analyse des résultats. Les enquêtes ont été faites dans les villages choisis sur la base de critères bien définis (la taille de la population agricole et son implication dans la production agricole). La méthode de D. Schwartz (1995, p. 94) a permis de constituer l’échantillon de 377 producteurs. Enfin, une projection climatique sur la période 2019-2050/2075 est faite au moyen du logiciel climatique « Climate explorer ».Il ressort des résultats de l’étude que, dans la commune de Djougou, la variation au niveau de la température minimale actuelle (RCP8.5) est comprise entre -1,62°C en 1992 et 2,29°C en 2075. La température maximale quant à elle varie entre -1,40°C en 1994 à 2,18°C en 2075. C’est à partir de 2071 que l’augmentation de la température minimale va dépasser les 2°C et si rien n’est fait cette hausse va s’accroître et devenir permanente. De même, dans la commune de Djidja, la température minimale la plus élevée est observée en 2075 avec des variations de 1 à 2°C pour les RCP4.5 et RCP8.5. Au niveau de la température maximale, l’année la moins chaude est 1992 (-1,33mm/jour) pour RCP8.5 et 1991 (-1,02mm/jour) pour RCP4.5. La même évolution s’observe au niveau des températures maximales. L’année 1992 reste la plus déficitaire avec une chute de -1,60°C et l’année la plus excédentaire sera l’année 2075 avec une hausse de 2,18 mm par jour, sur la période 1992-2080. La corrélation est observée en 2042 avec une valeur de 0,322 mm par jour. L’examen des résultats révèle que les valeurs des paramètres climatiques à savoir précipitations et évaporation sont à la hausse sur la période 1980-2080 dans la commune de Djidja. Suivant la trajectoire actuelle, RCP8.5, les années les plus arrosées sont 2037, 2070 et 2073 avec respectivement des variations égales à 0,17mm et 0,27mm de pluie par jour. Face à ces difficultés, les populations agricoles adoptent des mesures pour contrer les contraintes climatiques.ABSTRACTClimatic variability in the communes of Djidja and Djougou has consequences both on the levels of productivity and production and on the income of farmers. The objective of this research is to study the vulnerability of agricultural production systems to climate change in the Communes of Djidja and Djougou.The methodological approach used includes data collection, processing and analysis of the results. The surveys were carried out in the villages chosen on the basis of well-defined criteria (the size of the agricultural population and its involvement in agricultural production). The method of D. Schwartz (1995, p. 94) made it possible to constitute the sample of 377 producers. Finally, a climate projection over the period 2019-2050 / 2075 is made using the climate software "Climate explorer".The results of the study show that, in the municipality of Djougou, the variation in the current minimum temperature (RCP8.5) is between -1.62 ° C in 1992 and 2.29 ° C in 2075. The maximum temperature varies between -1.40 ° C in 1994 to 2.18 ° C in 2075. It is from 2071 that the increase in the minimum temperature will exceed 2 ° C and if nothing is In fact, this increase will increase and become permanent. Similarly, in the municipality of Djidja, the highest minimum temperature is observed in 2075 withvariations of 1 to 2 ° C for RCP4.5 and RCP8.5. At maximum temperature, the coolest year is 1992 (-1.33mm / day) for RCP8.5 and 1991 (-1.02mm / day) for RCP4.5. The same development can be observed at the level of maximum temperatures. The year 1992 remains the most in deficit with a fall of -1.60 ° C and the year the most in surplus will be the year 2075 with an increase of 2.18mm per day, over the period 1992-2080. The correlation is observed in 2042 with a value of 0.322 mm per day. Examination of the results reveals that the values of climatic parameters, namely precipitation and evaporation, are on the rise over the period 1980-2080 in the municipality of Djidja. Following the current trajectory, RCP8.5, the wettest years are 2037, 2070 and 2073 with respectively variations equal to 0.17mm and 0.27mm of rain per day. Faced with these difficulties, agricultural populations are adopting measures to counter climatic constraints. Keywords: Djidja, Djougou, vulnerability, production system, agriculture, climate change.

Extreme temperatures analysis: A study for Campinas, Brazil

2021 ◽  
Author(s):  
Guilherme Correia ◽  
Ana Maria Ávila
Keyword(s):  
Minimum Temperature ◽  
Temperature Increase ◽  
Heat Waves ◽  
Vulnerable Groups ◽  
Maximum Temperature ◽  
Daily Maximum ◽  
P Value ◽  
Significance Level ◽  
Historical Series ◽  
Maximum Temperatures

<p>Extreme events such as heat waves have adverse effects on human health, especially on vulnerable groups, which can lead to deaths, thus they must be faced as a huge threat. Many studies show general mean temperature increase, notably, minimum temperatures. The scope of this work was to assess daily data of a historical series (1890-2018) available on the Instituto Agronômico de Campinas (IAC), in Campinas, using a suite of indices derived from daily temperature and formulated by the Expert Team on Climate Change Detection and Indices (ETCCDI) and evaluate trends. To compute the extreme indices RClimDex 1.1 was used. The significance test is based on a t  test, with a significance level of 95% (p-value<0,05). Temperature increase is undoubtedly through many indices, especially from 1980, as there is a continuous rise of the temperature. Annual mean maximum temperature rose from 26°C to 29°C, whereas many years consistently have more than 50 days with maximum temperatures as high as 31°C and more than 20% of the days within a year are beyond the 90th percentile of the daily maximum temperatures. Annual mean minimum temperature rose from 14°C to 18°C, whereas many years consistently have more than 150 days with minimum temperatures as high as 18°C and more than 30% of the days within a year are beyond the 90th percentile of the daily minimum temperatures. Therefore, results indicate the increase of minimum temperature is greater than the increase of maximum temperatures.</p>

scholarly journals Impacts of Agroclimatic Variability on Maize Production in the Setsoto Municipality in the Free State Province, South Africa

Climate ◽  
2020 ◽  
Vol 8 (12) ◽  
pp. 147
Author(s):  
Abubakar Hadisu Bello ◽  
Mary Scholes ◽  
Solomon W. Newete
Keyword(s):  
South Africa ◽  
Minimum Temperature ◽  
Negative Impact ◽  
Free State ◽  
Maximum Temperature ◽  
Strong Positive Correlation ◽  
Maize Production ◽  
Free State Province ◽  
Growing Period ◽  
Maximum Temperatures

The majority of people in South Africa eat maize, which is grown as a rain-fed crop in the summer rainfall areas of the country, as their staple food. The country is usually food secure except in drought years, which are expected to increase in severity and frequency. This study investigated the impacts of rainfall and minimum and maximum temperatures on maize yield in the Setsoto municipality of the Free State province of South Africa from 1985 to 2016. The variation of the agroclimatic variables, including the Palmer stress diversity index (PSDI), was investigated over the growing period (Oct–Apr) which varied across the four target stations (Clocolan, Senekal, Marquard and Ficksburg). The highest coefficients of variance (CV) recorded for the minimum and maximum temperatures and rainfall were 16.2%, 6.2% and 29% during the growing period. Non-parametric Mann Kendal and Sen’s slope estimator were used for the trend analysis. The result showed significant positive trends in minimum temperature across the stations except for Clocolan where a negative trend of 0.2 to 0.12 °C year−1 was observed. The maximum temperature increased significantly across all the stations by 0.04–0.05 °C year−1 during the growing period. The temperature effects were most noticeable in the months of November and February when leaf initiation and kernel filling occur, respectively. The changes in rainfall were significant only in Ficksburg in the month of January with a value of 2.34 mm year−1. Nevertheless, the rainfall showed a strong positive correlation with yield (r 0.46, p = < 0.05). The overall variation in maize production is explained by the contribution of the agroclimatic parameters; the minimum temperature (R2 0.13–0.152), maximum temperature (R2 0.214–0.432) and rainfall (R2 0.17–0.473) for the growing period across the stations during the study period. The PSDI showed dry years and wet years but with most of the years recording close to normal rainfall. An increase in both the minimum and maximum temperatures over time will have a negative impact on crop yield.

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 Studies in the dynamics of disinfection. X. The effect of lethal temperatures on standard cultures of Bact. coli. III. on the variation of the rate of disinfection with temperature at pH 7·0, including the calculation of a new and constant temperature coefficient

1947 ◽  
Vol 45 (3) ◽  
pp. 333-341
Author(s):  
R. C. Jordan ◽  
S. E. Jacobs ◽  
H. E. F. Davies
Keyword(s):  
Minimum Temperature ◽  
Logistic Equation ◽  
Maximum Temperature ◽  
Temperature Coefficients ◽  
The Past ◽  
Lethal Temperatures ◽  
Two Temperatures ◽  
Maximum Temperatures ◽  
The Relationship ◽  
Very High

1. The 99·99 % mortality time (t) has been used as a measure of the rate of disinfection of standard cultures of Bact. coli by heat under carefully controlled conditions, and the relationship between this rate and temperature (T) over the range 47–55° C. has been examined.2. From the form of the relationship a minimum temperature of about 44° C. for the reaction was indicated, but the formula t (T — α)b = a, which has been used for the calculation of biological temperature coefficients in the past, was quite inadequate to express the relationship when an acceptable value for the maximum temperature (α) was employed.3. The formula t × θT = A more usually employed in bacteriological work, fitted the data reasonably well except at the highest temperature. The very high value of 897 for Q10 was obtained.4. On theoretical grounds, the above formula could not apply at temperatures near the minimum, and also it appeared likely to break down when high temperatures were used.5. It was shown that the full graph of log (t — 10) against temperature should be sigmoid and asymptotic to two temperatures, a minimum and a maximum, the latter being defined as the temperature at which 99·99 % mortality would be produced in 10 min.6. The graph of the Pearl-Verhulst logistic equation is of this type and, with 44 and 56° C. as the minimum and maximum temperatures, it provided an excellent fit to the data, especially at the highest temperature used.

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 Policy and Law Assessment of COVID-19 Based on Smooth Transition Autoregressive Model

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Jieqi Lei ◽  
Xuyuan Wang ◽  
Yiming Zhang ◽  
Lian Zhu ◽  
Lin Zhang
Keyword(s):  
Prevention And Control ◽  
Control Measures ◽  
Smooth Transition ◽  
Cumulative Number ◽  
Star Model ◽  
The World ◽  
Transition Points ◽  
And Control ◽  
The Impact ◽  
Smooth Transition Autoregressive

As of the end of October 2020, the cumulative number of confirmed cases of COVID-19 has exceeded 45 million and the cumulative number of deaths has exceeded 1.1 million all over the world. Faced with the fatal pandemic, countries around the world have taken various prevention and control measures. One of the important issues in epidemic prevention and control is the assessment of the prevention and control effectiveness. Changes in the time series of daily new confirmed cases can reflect the impact of policies in certain regions. In this paper, a smooth transition autoregressive (STAR) model is applied to investigate the intrinsic changes during the epidemic in certain countries and regions. In order to quantitatively evaluate the influence of the epidemic control measures, the sequence is fitted to the STAR model; then, comparisons between the dates of transition points and those of releasing certain policies are applied. Our model well fits the data. Moreover, the nonlinear smooth function within the STAR model reveals that the implementation of prevention and control policies is effective in some regions with different speeds. However, the ineffectiveness is also revealed and the threat of a second wave had already emerged.

scholarly journals Impact of Aligning Climatological Day on Gridding Daily Maximum–Minimum Temperature and Precipitation over Canada

2011 ◽  
Vol 50 (8) ◽  
pp. 1654-1665 ◽  
Author(s):  
Ron F. Hopkinson ◽  
Daniel W. McKenney ◽  
Ewa J. Milewska ◽  
Michael F. Hutchinson ◽  
Pia Papadopol ◽  
...  
Keyword(s):  
Minimum Temperature ◽  
Maximum Temperature ◽  
Daily Minimum Temperature ◽  
Daily Maximum Temperature ◽  
Daily Maximum ◽  
Daily Minimum ◽  
Temperature And Precipitation ◽  
Daily Data ◽  
National University ◽  
Maximum Temperatures

AbstractOn 1 July 1961, the climatological day was redefined to end at 0600 UTC at all principal climate stations in Canada. Prior to that, the climatological day at principal stations ended at 1200 UTC for maximum temperature and precipitation and 0000 UTC for minimum temperature and was similar to the climatological day at ordinary stations. Hutchinson et al. reported occasional larger-than-expected residuals at 50 withheld stations when the Australian National University Spline (ANUSPLIN) interpolation scheme was applied to daily data for 1961–2003, and it was suggested that these larger residuals were in part due to the existence of different climatological days. In this study, daily minimum and maximum temperatures at principal stations were estimated using hourly temperatures for the same climatological day as local ordinary climate stations for the period 1953–2007. Daily precipitation was estimated at principal stations using synoptic precipitation data for the climatological day ending at 1200 UTC, which, for much of the country, was close to the time of the morning observation at ordinary climate stations. At withheld principal stations, the climatological-day adjustments led to the virtual elimination of large residuals in maximum and minimum temperature and a marked reduction in precipitation residuals. Across all 50 withheld stations the climatological day adjustments led to significant reductions, by around 12% for daily maximum temperature, 15% for daily minimum temperature, and 22% for precipitation, in the residuals reported by Hutchinson et al.

scholarly journals Nature of transmission of Covid19 in India

2020 ◽  
Author(s):  
Anushree Roy ◽  
Sayan Kar
Keyword(s):  
Population Density ◽  
Minimum Temperature ◽  
Maximum Temperature ◽  
Weak Correlation ◽  
Average Maximum ◽  
Average Maximum Temperature ◽  
Average Relative Humidity ◽  
Maximum Temperatures ◽  
Number Of Individuals

AbstractWe examine available data on the number of individuals infected by the Covid-19 virus, across several different states in India, over the period January 30, 2020 to April 10, 2020. It is found that the growth of the number of infected individuals N(t) can be modeled across different states with a simple linear function N(t) = γ + αt beyond the date when reasonable number of individuals were tested (and when a countrywide lockdown was imposed). The slope α is different for different states. Following recent work by Notari (arxiv:2003.12417), we then consider the dependency of the α for different states on the average maximum and minimum temperatures, the average relative humidity and the population density in each state. It turns out that like other countries, the parameter α, which determines the rate of rise of the number of infected individuals, seems to have a weak correlation with the average maximum temperature of the state. In contrast, any significant variation of α with humidity or minimum temperature seems absent with almost no meaningful correlation. Expectedly, α increases (slightly) with increase in the population density of the states; however, the degree of correlation here too is negligible. These results seem to barely suggest that a natural cause like a hot summer (larger maximum temperatures) may contribute towards reducing the transmission of the virus, though the role of minimum temperature, humidity and population density remains somewhat obscure from the inferences which may be drawn from presently available data.

scholarly journals Predicting the number of reported and unreported cases for the COVID-19 epidemics in China, South Korea, Italy, France, Germany and United Kingdom

2020 ◽  
Author(s):  
Zhihua Liu ◽  
Pierre Magal ◽  
Glenn Webb
Keyword(s):  
United Kingdom ◽  
South Korea ◽  
Turning Point ◽  
Control Measures ◽  
Cumulative Number ◽  
Biological Parameters ◽  
Final Size ◽  
Cumulative Data ◽  
The World ◽  
Multiple Groups

1SummaryBackgroundThe novel coronavirus (SARS-CoV-2) is currently causing concern in the medical, epidemiological and mathematical communities as the virus is rapidly spreading around the world. Internationally, there are more than 1 200 000 cases detected and confirmed in the world on April 6. The asymptomatic and mild symptomatic cases are just going to be really crucial for us to understand what is driving this epidemic to transmit rapidly. Combining a mathematical model of severe (SARS-CoV-transmission with data from China, South Korea, Italy, France, Germany and United Kingdom, we provide the epidemic predictions of the number of reported and unreported cases for the SARS-CoV-2 epidemics and evaluate the effectiveness of control measures for each country.MethodsWe combined a mathematical model with data on cumulative confirmed cases from China, South Korea, Italy, France, Germany and United Kingdom to provide the epidemic predictions and evaluate the effectiveness of control measures. We divide infectious individuals into asymptomatic and symptomatic infectious individuals. The symptomatic infectious phase is also divided into reported (severe symptoms) and unreported (mild symptoms) cases. In fact, there exists a period for the cumulative number of reported cases to grow (approximately) exponentially in the early phase of virus transmission which is around the implementation of the national prevention and control measures. We firstly combine the date of the implementation of the measures with the daily and cumulative data of the reported confirmed cases to find the most consistent period for the cumulative number of reported cases to grow − approximately exponentially with the formula χ1 exp(χ2t) χ3, thus we can determine the parameters χ1, χ2, χ3 in this formula and then determine the parameters and initial conditions for our model by using this formula and the plausible biological parameters for SARS-CoV-2 based on current evidence.We then provide the epidemic predictions, evaluate the effectiveness of control measures by simulations of our model.FindingsBased on the simulations using multiple groups of parameters (d1, d2, N), here [d1, d2] is the consistent period for the cumulative number of reported cases to grow approximately exponentially with the formula χ1 exp(χ2t) χ3 and N is the date at which public intervention measures became effective, we found that the ranges of the turning point, the final size of reported and unreported cases are respectively Feb.6 − 7, 67 000 − 69 000 and 45 000 − 46 000 for China, Feb.29−Mar.1, 9 000 − 9 400and 2 250 − 2 350 for South Korea, Mar.24 − 26, 156 000 − 177 000, and 234 000 − 265 000 for Italy, Mar.30−Apr.9, 104 000 − 212 000, and 177 000 − 318 000 for France, Mar.30−Apr.20, 141 000 − 912 000, and 197 000 − 1 369 000 for Germany, Apr.1−May12, 140 000 − 473 000, and 210 000 − 709 000 for UnitedKingdom. Our prediction relies on the cumulative data of the reported confirmed cases. As more data become available, the ranges become smaller and smaller, that means the prediction becomes better and better. It is evident that our estimates and simulations have shown good correspondence with the distribution of the cumulative data available of the reported confirmed cases for each country and in particularly, the curves plotted by using different parameter groups (d1, d2, N) for reported and unreported cases tend to be consistent in China and South Korea (see (e) in Figures 2-3). For Italy, France, Germany and United Kingdom, the prediction can be updated to higher accuracy with on-going day by day reported case data (see Figures 4-7).InterpretationWe used the plausible biological parameters f, ν, η for SARS-CoV-2 based on current evidence which might be refined as more comprehensive data become available. Our prediction also relies on the cumulative data of the reported confirmed cases. Using multiple groups of parameters (d1, d2, N), we have attempted to make the best possible prediction using the available data. We found that with more cumulative data available, the curves plotted by using different parameter groups (d1, d2, N) for reported and unreported cases will be closer and closer, and finally tend to be consistent. This shows that when we have no enough cumulative data available, we need to use all possible parameter groups to predict the range of turning point, the final size of reported and unreported cases. When we have enough cumulative data, for example, when we get the data after the turning point, we only need to use any one of these parameter groups to get a prediction with high accuracy.FundingNSFC (Grant No. 11871007), NSFC and CNRS (Grant No. 11811530272) and the Fundamental Research Funds for the Central Universities.

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