Maximum Daily Temperature, Precipitation, Ultraviolet Light, and Rates of Transmission of Severe Acute Respiratory Syndrome Coronavirus 2 in the United States

Abstract Background Previous reports have suggested that transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is reduced by higher temperatures and higher humidity. We analyzed case data from the United States to investigate the effects of temperature, precipitation, and ultraviolet (UV) light on community transmission of SARS-CoV-2. Methods Daily reported cases of SARS-CoV-2 across the United States from 22 January 2020 to 3 April 2020 were analyzed. We used negative binomial regression modeling to determine whether daily maximum temperature, precipitation, UV index, and the incidence 5 days later were related. Results A maximum temperature above 52°F on a given day was associated with a lower rate of new cases at 5 days (incidence rate ratio [IRR], 0.85 [0.76, 0.96]; P = .009). Among observations with daily temperatures below 52°F, there was a significant inverse association between the maximum daily temperature and the rate of cases at 5 days (IRR, 0.98 [0.97, 0.99]; P = .001). A 1-unit higher UV index was associated with a lower rate at 5 days (IRR, 0.97 [0.95, 0.99]; P = .004). Precipitation was not associated with a greater rate of cases at 5 days (IRR, 0.98 [0.89, 1.08]; P = .65). Conclusions The incidence of disease declines with increasing temperature up to 52°F and is lower at warmer vs cooler temperatures. However, the association between temperature and transmission is small, and transmission is likely to remain high at warmer temperatures.

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Relations between Temperature and Residential Natural Gas Consumption in the Central and Eastern United States

Journal of Applied Meteorology and Climatology ◽

10.1175/2007jamc1552.1 ◽

2007 ◽

Vol 46 (11) ◽

pp. 1993-2013 ◽

Cited By ~ 17

Author(s):

Reed P. Timmer ◽

Peter J. Lamb

Keyword(s):

United States ◽

Natural Gas ◽

Time Scales ◽

The United States ◽

Maximum Temperature ◽

Daily Maximum Temperature ◽

Daily Maximum ◽

The South ◽

Northern Regions ◽

Gas Consumption

Abstract The increased U.S. natural gas price volatility since the mid-to-late-1980s deregulation generally is attributed to the deregulated market being more sensitive to temperature-related residential demand. This study therefore quantifies relations between winter (November–February; December–February) temperature and residential gas consumption for the United States east of the Rocky Mountains for 1989–2000, by region and on monthly and seasonal time scales. State-level monthly gas consumption data are aggregated for nine multistate subregions of three Petroleum Administration for Defense Districts of the U.S. Department of Energy. Two temperature indices [days below percentile (DBP) and heating degree-days (HDD)] are developed using the Richman–Lamb fine-resolution (∼1° latitude–longitude) set of daily maximum and minimum temperatures for 1949–2000. Temperature parameters/values that maximize DBP/HDD correlations with gas consumption are identified. Maximum DBP and HDD correlations with gas consumption consistently are largest in the Great Lakes–Ohio Valley region on both monthly (from +0.89 to +0.91) and seasonal (from +0.93 to +0.97) time scales, for which they are based on daily maximum temperature. Such correlations are markedly lower on both time scales (from +0.62 to +0.80) in New England, where gas is less important than heating oil, and on the monthly scale (from +0.55 to +0.75) across the South because of low January correlations. For the South, maximum correlations are for daily DBP and HDD indices based on mean or minimum temperature. The percentiles having the highest DBP index correlations with gas consumption are slightly higher for northern regions than across the South. This is because lower (higher) relative (absolute) temperature thresholds are reached in warmer regions before home heating occurs. However, these optimum percentiles for all regions are bordered broadly by surrounding percentiles for which the correlations are almost as high as the maximum. This consistency establishes the robustness of the temperature–gas consumption relations obtained. The reference temperatures giving the highest HDD correlations with gas consumption are lower for the colder northern regions than farther south where the temperature range is truncated. However, all HDD reference temperatures greater than +10°C (+15°C) yield similar such correlations for northern (southern) regions, further confirming the robustness of the findings. This robustness, coupled with the very high correlation magnitudes obtained, suggests that potentially strong gas consumption predictability would follow from accurate seasonal temperature forecasts.

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Multiscale Variability in North American Summer Maximum Temperatures and Modulations from the North Atlantic Simulated by an AGCM

Journal of Climate ◽

10.1175/jcli-d-17-0392.1 ◽

2018 ◽

Vol 31 (7) ◽

pp. 2549-2562 ◽

Cited By ~ 5

Author(s):

Nicolas Vigaud ◽

M. Ting ◽

D.-E. Lee ◽

A. G. Barnston ◽

Y. Kushnir

Keyword(s):

United States ◽

North America ◽

North Atlantic ◽

Seasonal Cycle ◽

The United States ◽

Maximum Temperature ◽

Daily Maximum Temperature ◽

Northeastern United States ◽

The North ◽

The North Atlantic

Six recurrent thermal regimes are identified over continental North America from June to September through a k-means clustering applied to daily maximum temperature simulated by ECHAM5 forced by historical SSTs for 1930–2013 and validated using NCEP–DOE AMIP-II reanalysis over the 1980–2009 period. Four regimes are related to a synoptic wave pattern propagating eastward in the midlatitudes with embedded ridging anomalies that translate into maximum warming transiting along. Two other regimes, associated with broad continental warming and above average temperatures in the northeastern United States, respectively, are characterized by ridging anomalies over North America, Europe, and Asia that suggest correlated heat wave occurrences in these regions. Their frequencies are mainly related to both La Niña and warm conditions in the North Atlantic. Removing all variability beyond the seasonal cycle in the North Atlantic in ECHAM5 leads to a significant drop in the occurrences of the regime associated with warming in the northeastern United States. Superimposing positive (negative) anomalies mimicking the Atlantic multidecadal variability (AMV) in the North Atlantic translates into more (less) warming over the United States across all regimes, and does alter regime frequencies but less significantly. Regime frequency changes are thus primarily controlled by Atlantic SST variability on all time scales beyond the seasonal cycle, rather than mean SST changes, whereas the intensity of temperature anomalies is impacted by AMV SST forcing, because of upper-tropospheric warming and enhanced stability suppressing rising motion during the positive phase of the AMV.

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A Single-Station Approach to Model Output Statistics Temperature Forecast Error Assessment

Weather and Forecasting ◽

10.1175/waf893.1 ◽

2005 ◽

Vol 20 (6) ◽

pp. 1006-1020 ◽

Cited By ~ 16

Author(s):

Andrew A. Taylor ◽

Lance M. Leslie

Keyword(s):

United States ◽

The United States ◽

Maximum Temperature ◽

Daily Maximum Temperature ◽

Forecast Errors ◽

Model Output ◽

Error Characteristics ◽

Temperature Forecast ◽

Model Output Statistics

Abstract Error characteristics of model output statistics (MOS) temperature forecasts are calculated for over 200 locations around the continental United States. The forecasts are verified on a station-by-station basis for the year 2001. Error measures used include mean algebraic error (bias), mean absolute error (MAE), relative frequency of occurrence of bias and MAE values, and the daily forecast errors themselves. A case study examining the spatial and temporal evolution of MOS errors is also presented. The error characteristics presented here, together with the case study, provide a more detailed evaluation of MOS performance than may be obtained from regionally averaged error statistics. Knowledge concerning locations where MOS forecasts have large errors or biases and why those errors or biases exist is of great value to operational forecasters. Not only does such knowledge help improve their forecasts, but forecaster performance is often compared to MOS predictions. Examples of biases in MOS forecast errors are illustrated by examining two stations in detail. Significant warm and cold biases are found in maximum temperature forecasts for Los Angeles, California (LAX), and minimum temperature forecasts for Las Vegas, Nevada (LAS), respectively. MAE values for MOS temperature predictions calculated in this study suggest that coastal stations tend to have lower MAE values and lower variability in their errors, while forecasts with high MAE and error variability are more frequent in the interior of the United States. Therefore, MAE values from samples of MOS forecasts are directly proportional to the variance in the observations. Additionally, it is found that daily maximum temperature forecast errors exhibit less variability during the summer months than they do over the rest of the year, and that forecasts for any one station rarely follow a consistent temporal pattern for more than two or three consecutive days. These inconsistent error patterns indicate that forecasting temperatures based on recent trends in MOS forecast errors at an individual station is usually not a good strategy. As shown in earlier studies by other authors and demonstrated again here, MOS temperature forecasts are often inaccurate in the vicinity of strong temperature gradients, for locations affected by shallow cold air masses, or for stations in regions of anomalously warm or cold temperatures. Finally, a case study is presented examining the spatial and temporal distributions of MOS temperature forecast errors across the United States from 13 to 15 February 2001. During this period, two surges of cold arctic air moved south into the United States. In contrast to error trends at individual stations, nationwide spatial and temporal patterns of MOS forecast errors could prove to be a powerful forecasting tool. Nationwide plots of errors in MOS forecasts would be useful if made available in real time to operational forecasters.

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Temperature-related climate extremes in the Potsdam observation record

Geografie ◽

10.37040/geografie2008113040372 ◽

2008 ◽

Vol 113 (4) ◽

pp. 372-382

Author(s):

Zbigniew W. Kundzewicz ◽

Damian Józefczyk

Keyword(s):

Climate Extremes ◽

Daily Temperature ◽

Maximum Temperature ◽

Daily Minimum Temperature ◽

Daily Maximum Temperature ◽

Daily Maximum ◽

Maximum Daily Temperature ◽

Daily Minimum ◽

Maximum Temperatures ◽

Minimum Daily Temperature

This paper examines temperature-related climate extremes in the unique long-term gapfree record at the Secular Meteorological Station in Potsdam. Increasing tendencies in daily minimum temperature in winter and daily maximum temperature in summer, as well as monthly means of daily minimum temperatures in winter months and of daily maximum temperatures in summer months are illustrated. Also the numbers of hot days and of summer days (with maximum daily temperature exceeding 30 °C and 25 °C, respectively) have been increasing. In agreement with warming of winter minimum temperatures, the numbers of frost days (with minimum daily temperature below 0 °C) and of ice days (with maximum daily temperature below 0 °C) have been decreasing. However, low correlation coefficient and huge scatter illustrate strong natural variability, so that the occurrence of extremes departs from the general underlying tendency.

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Ambient temperature and subsequent COVID-19 mortality in the OECD countries and individual United States

Scientific Reports ◽

10.1038/s41598-021-87803-w ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Costas A. Christophi ◽

Mercedes Sotos-Prieto ◽

Fan-Yun Lan ◽

Mario Delgado-Velandia ◽

Vasilis Efthymiou ◽

...

Keyword(s):

United States ◽

Ambient Temperature ◽

Negative Binomial ◽

Negative Binomial Regression ◽

Weather Conditions ◽

Oecd Countries ◽

Epidemiological Studies ◽

Sociodemographic Factors ◽

Sensitivity Analyses ◽

Inverse Association

AbstractEpidemiological studies have yielded conflicting results regarding climate and incident SARS-CoV-2 infection, and seasonality of infection rates is debated. Moreover, few studies have focused on COVD-19 deaths. We studied the association of average ambient temperature with subsequent COVID-19 mortality in the OECD countries and the individual United States (US), while accounting for other important meteorological and non-meteorological co-variates. The exposure of interest was average temperature and other weather conditions, measured at 25 days prior and 25 days after the first reported COVID-19 death was collected in the OECD countries and US states. The outcome of interest was cumulative COVID-19 mortality, assessed for each region at 25, 30, 35, and 40 days after the first reported death. Analyses were performed with negative binomial regression and adjusted for other weather conditions, particulate matter, sociodemographic factors, smoking, obesity, ICU beds, and social distancing. A 1 °C increase in ambient temperature was associated with 6% lower COVID-19 mortality at 30 days following the first reported death (multivariate-adjusted mortality rate ratio: 0.94, 95% CI 0.90, 0.99, p = 0.016). The results were robust for COVID-19 mortality at 25, 35 and 40 days after the first death, as well as other sensitivity analyses. The results provide consistent evidence across various models of an inverse association between higher average temperatures and subsequent COVID-19 mortality rates after accounting for other meteorological variables and predictors of SARS-CoV-2 infection or death. This suggests potentially decreased viral transmission in warmer regions and during the summer season.

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Observed Trends in Indices of Daily Temperature Extremes in South America 1960–2000

Journal of Climate ◽

10.1175/jcli3589.1 ◽

2005 ◽

Vol 18 (23) ◽

pp. 5011-5023 ◽

Cited By ~ 294

Author(s):

L. A. Vincent ◽

T. C. Peterson ◽

V. R. Barros ◽

M. B. Marino ◽

M. Rusticucci ◽

...

Keyword(s):

Climate Change ◽

South America ◽

Climate Extremes ◽

Daily Temperature ◽

Maximum Temperature ◽

Daily Minimum Temperature ◽

Daily Maximum Temperature ◽

Daily Maximum ◽

Temperature Extremes ◽

Increasing Trends

Abstract A workshop on enhancing climate change indices in South America was held in Maceió, Brazil, in August 2004. Scientists from eight southern countries brought daily climatological data from their region for a meticulous assessment of data quality and homogeneity, and for the preparation of climate change indices that can be used for analyses of changes in climate extremes. This study presents an examination of the trends over 1960–2000 in the indices of daily temperature extremes. The results indicate no consistent changes in the indices based on daily maximum temperature while significant trends were found in the indices based on daily minimum temperature. Significant increasing trends in the percentage of warm nights and decreasing trends in the percentage of cold nights were observed at many stations. It seems that this warming is mostly due to more warm nights and fewer cold nights during the summer (December–February) and fall (March–May). The stations with significant trends appear to be located closer to the west and east coasts of South America.

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Estimated transmissibility and impact of SARS-CoV-2 lineage B.1.1.7 in England

Science ◽

10.1126/science.abg3055 ◽

2021 ◽

Vol 372 (6538) ◽

pp. eabg3055 ◽

Cited By ~ 82

Author(s):

Nicholas G. Davies ◽

Sam Abbott ◽

Rosanna C. Barnard ◽

Christopher I. Jarvis ◽

Adam J. Kucharski ◽

...

Keyword(s):

United States ◽

Severe Acute Respiratory Syndrome ◽

Reproduction Number ◽

The United States ◽

Control Measures ◽

Transmission Model ◽

Educational Institutions ◽

Stringent Control ◽

Dynamic Transmission Model ◽

Credible Intervals

A severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variant, VOC 202012/01 (lineage B.1.1.7), emerged in southeast England in September 2020 and is rapidly spreading toward fixation. Using a variety of statistical and dynamic modeling approaches, we estimate that this variant has a 43 to 90% (range of 95% credible intervals, 38 to 130%) higher reproduction number than preexisting variants. A fitted two-strain dynamic transmission model shows that VOC 202012/01 will lead to large resurgences of COVID-19 cases. Without stringent control measures, including limited closure of educational institutions and a greatly accelerated vaccine rollout, COVID-19 hospitalizations and deaths across England in the first 6 months of 2021 were projected to exceed those in 2020. VOC 202012/01 has spread globally and exhibits a similar transmission increase (59 to 74%) in Denmark, Switzerland, and the United States.

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Near-real time mapping of Keetch-Byram drought index in the south-eastern United States

International Journal of Wildland Fire ◽

10.1071/wf02013 ◽

2002 ◽

Vol 11 (4) ◽

pp. 281 ◽

Cited By ~ 17

Author(s):

Michael J. Janis ◽

Michael B. Johnson ◽

Gloria Forthun

Keyword(s):

United States ◽

Drought Index ◽

Maximum Temperature ◽

Daily Maximum Temperature ◽

Mean Annual Precipitation ◽

Eastern United States ◽

The South ◽

South Eastern ◽

Input Parameters ◽

Weather Stations

High spatial resolution maps of daily Keetch-Byram Drought Index (KBDI) are constructed for the south-eastern United States. KBDI is a cumulative algorithm for estimating fire potential from meteorological information, including daily maximum temperature, daily total precipitation, and mean annual precipitation. With few input parameters, the KBDI is attractive for providing estimates of fire potential at a large number of locations. The Southeast Regional Climate Center (SERCC) applies the original algorithms over daily time steps to maximize the response time in the event of rapidly increasing fire potential. Algorithms are applied to a network of 261 weather stations across the south-eastern United States to provide regional contour maps of KBDI as well as maps of week-to-week KBDI difference. Though uniformity and spatial density of weather stations and the consistency of input parameters are potential hurdles, it is shown that careful compilation of meteorological databases makes KBDI a tractable and valuable monitoring tool for automated fire-potential monitoring.

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Covid-19 pandemic: What is the truth?

Surgical Neurology International ◽

10.25259/sni_1008_2021 ◽

2021 ◽

Vol 12 ◽

pp. 591

Author(s):

Russell L. Blaylock

Keyword(s):

United States ◽

Young People ◽

Severe Acute Respiratory Syndrome ◽

Early Treatment ◽

The United States ◽

Treatment Protocols ◽

Scientific Support ◽

Control And Prevention ◽

History Of ◽

Irreparable Damage

The ongoing “pandemic” involving the severe acute respiratory syndrome coronavirus 2 virus (SARS-CoV-2) has several characteristics that make it unique in the history of pandemics. This entails not only the draconian measures that some countries and individual states within the United States and initiated and made policy, most of which are without precedent or scientific support, but also the completely unscientific way the infection has been handled. For the 1st time in medical history, major experts in virology, epidemiology, infectious diseases, and vaccinology have not only been ignored, but are also demonized, marginalized and in some instances, become the victim of legal measures that can only be characterized as totalitarian. Discussions involving various scientific opinions have been eliminated, top scientists have been frightened into silence by threats to their careers, physicians have lost their licenses, and the concept of early treatment has been virtually eliminated. Hundreds of thousands of people have died needlessly as a result of, in my opinion and the opinion of others, poorly designed treatment protocols, mostly stemming from the Center for Disease Control and Prevention, which have been rigidly enforced among all hospitals. The economic, psychological, and institutional damage caused by these unscientific policies is virtually unmeasurable. Whole generations of young people will suffer irreparable damage, both physical and psychological, possibly forever. The truth must be told.

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From People to Panthera: Natural SARS-CoV-2 Infection in Tigers and Lions at the Bronx Zoo

mBio ◽

10.1128/mbio.02220-20 ◽

2020 ◽

Vol 11 (5) ◽

Cited By ~ 2

Author(s):

Denise McAloose ◽

Melissa Laverack ◽

Leyi Wang ◽

Mary Lea Killian ◽

Leonardo C. Caserta ◽

...

Keyword(s):

United States ◽

Severe Acute Respiratory Syndrome ◽

Disease Transmission ◽

Emerging Infectious Diseases ◽

Genomic Data ◽

The United States ◽

Cellular Damage ◽

Animal Reservoir ◽

Live Virus ◽

The World

ABSTRACT Despite numerous barriers to transmission, zoonoses are the major cause of emerging infectious diseases in humans. Among these, severe acute respiratory syndrome (SARS), Middle East respiratory syndrome (MERS), and ebolaviruses have killed thousands; the human immunodeficiency virus (HIV) has killed millions. Zoonoses and human-to-animal cross-species transmission are driven by human actions and have important management, conservation, and public health implications. The current SARS-CoV-2 pandemic, which presumably originated from an animal reservoir, has killed more than half a million people around the world and cases continue to rise. In March 2020, New York City was a global epicenter for SARS-CoV-2 infections. During this time, four tigers and three lions at the Bronx Zoo, NY, developed mild, abnormal respiratory signs. We detected SARS-CoV-2 RNA in respiratory secretions and/or feces from all seven animals, live virus in three, and colocalized viral RNA with cellular damage in one. We produced nine whole SARS-CoV-2 genomes from the animals and keepers and identified different SARS-CoV-2 genotypes in the tigers and lions. Epidemiologic and genomic data indicated human-to-tiger transmission. These were the first confirmed cases of natural SARS-CoV-2 animal infections in the United States and the first in nondomestic species in the world. We highlight disease transmission at a nontraditional interface and provide information that contributes to understanding SARS-CoV-2 transmission across species. IMPORTANCE The human-animal-environment interface of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is an important aspect of the coronavirus disease 2019 (COVID-19) pandemic that requires robust One Health-based investigations. Despite this, few reports describe natural infections in animals or directly link them to human infections using genomic data. In the present study, we describe the first cases of natural SARS-CoV-2 infection in tigers and lions in the United States and provide epidemiological and genetic evidence for human-to-animal transmission of the virus. Our data show that tigers and lions were infected with different genotypes of SARS-CoV-2, indicating two independent transmission events to the animals. Importantly, infected animals shed infectious virus in respiratory secretions and feces. A better understanding of the susceptibility of animal species to SARS-CoV-2 may help to elucidate transmission mechanisms and identify potential reservoirs and sources of infection that are important in both animal and human health.

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