Browse
Submit Data
Databases
API
Help

Dataset Information

8 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Short-term effects of specific humidity and temperature on COVID-19 morbidity in select US cities.

ABSTRACT: Little is known about the environmental conditions that drive the spatiotemporal patterns of SARS-CoV-2. Preliminary research suggests an association with meteorological parameters. However, the relationship with temperature and humidity is not yet apparent for COVID-19 cases in US cities first impacted. The objective of this study is to evaluate the association between COVID-19 cases and meteorological parameters in select US cities. A case-crossover design with a distributed lag nonlinear model was used to evaluate the contribution of ambient temperature and specific humidity on COVID-19 cases in select US cities. The case-crossover examines each COVID case as its own control at different time periods (before and after transmission occurred). We modeled the effect of temperature and humidity on COVID-19 transmission using a lag period of 7 days. A subset of 8 cities were evaluated for the relationship with meteorological parameters and 5 cities were evaluated in detail. Short-term exposure to humidity was positively associated with COVID-19 transmission in 4 cities. The associations were small with 3 out of 4 cities exhibiting higher COVID19 transmission with specific humidity that ranged from 6 to 9 g/kg. Our results suggest that weather should be considered in infectious disease modeling efforts. Future work is needed over a longer time period and across different locations to clearly establish the weather-COVID19 relationship.

SUBMITTER: Runkle JD

PROVIDER: S-EPMC7280823 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Effect of short-term exposure to air pollution on COVID-19 mortality and morbidity in Iranian cities.

Project description:PurposeThe association between air pollutant (PM2.5, PM10, NO2, and O3) concentrations and daily number of COVID-19 confirmed cases and related deaths were evaluated in three major Iranian cities (Tehran, Mashhad, and Tabriz).MethodsHourly concentrations of air pollutants and daily number of PCR-confirmed cases and deaths of COVID-19 were acquired (February 20th, 2020 to January 4th, 2021). A generalized additive model (GAM) assuming a quasi-Poisson distribution was used to model the associations in each city up to lag-day 7 (for mortality) and 14 (for morbidity). Then, the city-specific estimates were meta-analyzed using a fixed effect model to obtain the overall relative risks (RRs).ResultsA total of 114,964 confirmed cases and 21,549 deaths were recorded in these cities. For confirmed cases, exposure to PM2.5, NO2, and O3 for several lag-days showed significant associations. In case of mortality, meta-analysis estimated that the RRs for PM2.5, PM10, NO2, and O3 concentrations were 1.06 (95% CI: 0.99, 1.13), 1.06 (95% CI: 0.93, 1.19), 1.15 (95% CI: 0.93, 1.38), and 1.07 (95% CI: 0.84, 1.31), respectively. Despite several positive associations with all air pollutants over multiple lag-days, COVID-19 mortality was only significantly associated with NO2 on lag-days 0-1 and 1 with the RRs of 1.35 (95% CI: 1.04, 1.67) and 1.16 (95% CI: 1.02, 1.31), respectively.ConclusionThis study showed that air pollution can be a factor exacerbating COVID-19 infection and clinical outcomes. Actions should be taken to reduce the exposure of the public and particularly patients to ambient air pollutants.Supplementary informationThe online version contains supplementary material available at 10.1007/s40201-021-00736-4.

| S-EPMC8553398 | biostudies-literature

Short-term effects of COVID-19 on the risk of traumatic fractures in China cities.

Project description:This study aimed to investigate the association between COVID-19 and fracture risk and provide a targeted reference for the world through China's experience. A nationally representative sample of COVID-19 prevalence areas selected using stratified random sampling was retrospectively analyzed. Age, sex, fracture site, mechanism of injury, and concurrent fractures of traumatic fracture patients in selected hospitals were collected from 10 January to 10 July 2020. The epidemiologic characteristics of traumatic fractures and the association between COVID-19 and fracture risk were explored using descriptive epidemiological methods and a distributed lag nonlinear model. A total of 67,249 patients (52.3% males, 49.4 ± 19.4 years old) with 68,989 fractures were included. The highest proportion of fractures were in the tibia and fibula (14.9%), followed by the femur (13.6%) and ulna and radius (12.5%). Low-energy fractures accounted for 23.3%. With the increase in newly confirmed COVID-19 cases, fracture risk decreased for children, young and middle-aged adults, elderly men, high-energy fractures, and residents in regions with < 1000 cumulative confirmed COVID-19 cases. Fracture risk decreased sharply in all residents except elderly women, for low-energy fractures, and in regions with > 1000 cumulative confirmed COVID-19 cases when newly confirmed COVID-19 cases increased in China. Primary (home) prevention measures are emphasized to prevent traumatic fractures.

| S-EPMC9020760 | biostudies-literature

Association between temperature, humidity, and COVID-19 outbreaks in Bangladesh.

Project description:This study aimed to determine the association between average temperature and humidity with COVID-19 pandemic in Bangladesh. This study included 9,455 confirmed cases during the observation period (March 08, 2020 to May 03, 2020). The peak spread of COVID-19 occurred at an average temperature of 26 °C. We find, under a linear regression framework, high temperature and high humidity significantly reduce the transmission of COVID-19, respectively. It indicates that the arrival of summer and rainy season in Bangladesh can effectively reduce the transmission of the COVID-19. However, it is unlikely that the COVID-19 pandemic diminishes by summer since Bangladesh still have a high coronavirus transmission. Therefore, other public health interventions such as social distancing are still important for blocking the COVID-19 transmission.

| S-EPMC7447231 | biostudies-literature

Transmissibility of COVID-19 in 11 major cities in China and its association with temperature and humidity in Beijing, Shanghai, Guangzhou, and Chengdu.

Project description:BackgroundThe new coronavirus disease COVID-19 began in December 2019 and has spread rapidly by human-to-human transmission. This study evaluated the transmissibility of the infectious disease and analyzed its association with temperature and humidity to study the propagation pattern of COVID-19.MethodsIn this study, we revised the reported data in Wuhan based on several assumptions to estimate the actual number of confirmed cases considering that perhaps not all cases could be detected and reported in the complex situation there. Then we used the equation derived from the Susceptible-Exposed-Infectious-Recovered (SEIR) model to calculate R0 from January 24, 2020 to February 13, 2020 in 11 major cities in China for comparison. With the calculation results, we conducted correlation analysis and regression analysis between R0 and temperature and humidity for four major cities in China to see the association between the transmissibility of COVID-19 and the weather variables.ResultsIt was estimated that the cumulative number of confirmed cases had exceeded 45 000 by February 13, 2020 in Wuhan. The average R0 in Wuhan was 2.7, significantly higher than those in other cities ranging from 1.8 to 2.4. The inflection points in the cities outside Hubei Province were between January 30, 2020 and February 3, 2020, while there had not been an obvious downward trend of R0 in Wuhan. R0 negatively correlated with both temperature and humidity, which was significant at the 0.01 level.ConclusionsThe transmissibility of COVID-19 was strong and importance should be attached to the intervention of its transmission especially in Wuhan. According to the correlation between R0 and weather, the spread of disease will be suppressed as the weather warms.

| S-EPMC7348130 | biostudies-literature

Estimating Short- and Long-Term Associations Between Air Quality Index and COVID-19 Transmission: Evidence From 257 Chinese Cities.

Project description:Objectives: To evaluate the long- and short-term effects of air pollution on COVID-19 transmission simultaneously, especially in high air pollution level countries. Methods: Quasi-Poisson regression was applied to estimate the association between exposure to air pollution and daily new confirmed cases of COVID-19, with mutual adjustment for long- and short-term air quality index (AQI). The independent effects were also estimated and compared. We further assessed the modification effect of within-city migration (WM) index to the associations. Results: We found a significant 1.61% (95%CI: 0.51%, 2.72%) and 0.35% (95%CI: 0.24%, 0.46%) increase in daily confirmed cases per 1 unit increase in long- and short-term AQI. Higher estimates were observed for long-term impact. The stratifying result showed that the association was significant when the within-city migration index was low. A 1.25% (95%CI: 0.0.04%, 2.47%) and 0.41% (95%CI: 0.30%, 0.52%) increase for long- and short-term effect respectively in low within-city migration index was observed. Conclusions: There existed positive associations between long- and short-term AQI and COVID-19 transmission, and within-city migration index modified the association. Our findings will be of strategic significance for long-run COVID-19 control.

| S-EPMC8333027 | biostudies-literature

Coronavirus COVID-19 outbreak and control: Effect of temperature, relative humidity, and lockdown implementation.

Project description:Meteorological parameters are important factors that have an influence on infectious diseases. The present study aimed to explore the correlation between the spread of COVID-19, temperature, and relative humidity. The effect of human-imposed control parameters in the form of lockdown on the dissipation of COVID-19 was also analysed. Data were collected on the three study variables - temperature, relative humidity, and lockdown period - from nine of the most infected cities worldwide as well as information on changes in the number of COVID-19 patients from the beginning to a specific point in the lockdown period. A generalised regression model was applied to explore the effect of temperature and relative humidity on the change in daily new cases of COVID-19. The regression analysis did not find any significant correlation between temperature, humidity, and change in number of COVID-19 cases. Analysis of the cities with wide-ranging temperature variations showed a negative correlation of COVID-19 transmission (P=0.079) with temperature, but a relatively non-significant correlation with relative humidity (P=0.198). The number of total deaths was also higher in low-temperature countries compared with high-temperature countries. The specific growth rate in COVID-19 cases was decreased by more than 66% after implementation of a lockdown. This growth rate was exponentially decreased over time through the proper implementation of lockdown. Analysis of the real-case scenario and application of predictive models showed that for New York, Lombardy, and Madrid more than 120 days of strict lockdown was required for complete control of the transmission of COVID-19.

| S-EPMC7755572 | biostudies-literature

Effects of temperature and humidity on the spread of COVID-19: A systematic review.

Project description:BackgroundFaced with the global pandemic of COVID-19, declared by World Health Organization (WHO) on March 11th 2020, and the need to better understand the seasonal behavior of the virus, our team conducted this systematic review to describe current knowledge about the emergence and replicability of the virus and its connection with different weather factors such as temperature and relative humidity.MethodsThe review was registered with the PROSPERO database. The electronic databases PubMed, Scopus, Web of Science, Cochrane Library, LILACS, OpenGrey and Google Scholar were examined with the searches restricted to the years 2019 and 2020. Risk of bias assessment was performed using the Joanna Briggs Institute (JBI) Critical Appraisal Checklist tool. The GRADE tool was used to assess the certainty of the evidence.ResultsThe initial screening identified 517 articles. After examination of the full texts, seventeen studies met the review's eligibility criteria. Great homogeneity was observed in the findings regarding the effect of temperature and humidity on the seasonal viability and transmissibility of COVID-19. Cold and dry conditions were potentiating factors on the spread of the virus. After quality assessment, two studies had a high risk of bias, eleven studies were scored as moderate risk of bias, and four studies were classified as low risk of bias. The certainty of evidence was graded as low for both outcomes evaluated.ConclusionConsidering the existing scientific evidence, warm and wet climates seem to reduce the spread of COVID-19. However, these variables alone could not explain most of the variability in disease transmission. Therefore, the countries most affected by the disease should focus on health policies, even with climates less favorable to the virus. Although the certainty of the evidence generated was classified as low, there was homogeneity between the results reported by the included studies.

| S-EPMC7500589 | biostudies-literature

How do temperature, humidity, and air saturation state affect the COVID-19 transmission risk?

Project description:Environmental parameters have a significant impact on the spread of respiratory viral diseases (temperature (T), relative humidity (RH), and air saturation state). T and RH are strongly correlated with viral inactivation in the air, whereas supersaturated air can promote droplet deposition in the respiratory tract. This study introduces a new concept, the dynamic virus deposition ratio (α), that reflects the dynamic changes in viral inactivation and droplet deposition under varying ambient environments. A non-steady-state-modified Wells-Riley model is established to predict the infection risk of shared air space and highlight the high-risk environmental conditions. Findings reveal that a rise in T would significantly reduce the transmission of COVID-19 in the cold season, while the effect is not significant in the hot season. The infection risk under low-T and high-RH conditions, such as the frozen seafood market, is substantially underestimated, which should be taken seriously. The study encourages selected containment measures against high-risk environmental conditions and cross-discipline management in the public health crisis based on meteorology, government, and medical research.

| S-EPMC9366825 | biostudies-literature

Association between ambient temperature and COVID-19 infection in 122 cities from China.

Project description:BackgroundCoronavirus disease 2019 (COVID-19) has become a severe public health problem globally. Both epidemiological and laboratory studies have shown that ambient temperature could affect the transmission and survival of coronaviruses. This study aimed to determine whether the temperature is an essential factor in the infection caused by this novel coronavirus.MethodsDaily confirmed cases and meteorological factors in 122 cities were collected between January 23, 2020, to February 29, 2020. A generalized additive model (GAM) was applied to explore the nonlinear relationship between mean temperature and COVID-19 confirmed cases. We also used a piecewise linear regression to determine the relationship in detail.ResultsThe exposure-response curves suggested that the relationship between mean temperature and COVID-19 confirmed cases was approximately linear in the range of <3 °C and became flat above 3 °C. When mean temperature (lag0-14) was below 3 °C, each 1 °C rise was associated with a 4.861% (95% CI: 3.209-6.513) increase in the daily number of COVID-19 confirmed cases. These findings were robust in our sensitivity analyses.ConclusionsOur results indicate that mean temperature has a positive linear relationship with the number of COVID-19 cases with a threshold of 3 °C. There is no evidence supporting that case counts of COVID-19 could decline when the weather becomes warmer, which provides useful implications for policymakers and the public.

| S-EPMC7142675 | biostudies-literature

Short-term exposure to air pollution and COVID-19 in India: spatio-temporal analysis of relative risk from 20 metropolitan cities

Project description:The present study investigates the possible association between major air pollutants and COVID-19. We hypothesized that the post-lockdown surge in air pollution is the major cause of the increment in COVID-19 cases and deaths. The statistical results showed that pollutant concentrations of PM2.5 (20%), PM10(24%), SO2 (12%), and O3 (19%) were raised. So, we attempted to quantify the relative risk due to all major air pollutants by fitting generalized additive models. The results suggest that the pollution concentration escalated the COVID-19 cases and deaths. The pooled study suggests that for every 10 μg/m3 increment in pollutant concentration, an increment of COVID-19 cases is observed for PM2.5 (3%), PM10 (1%), SO2 (7.7%), and O3 (10%). Similarly, there is an increment in COVID-19 deaths for PM2.5 (2.8%), PM10 (1%), SO2 (4.5%), and O3 (7.2%). The spatial maps of relative risk revealed the most vulnerable regions due to each pollutant, thus steering the policymakers to implement region-specific mitigation strategies. Supplementary Information The online version contains supplementary material available at 10.1007/s41324-023-00512-6.

| S-EPMC10010960 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data