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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

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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.

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| S-EPMC9020760 | biostudies-literature

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| S-EPMC7447231 | biostudies-literature

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| 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.

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| 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.

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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

Global short-term forecasting of COVID-19 cases.

Project description:The continuously growing number of COVID-19 cases pressures healthcare services worldwide. Accurate short-term forecasting is thus vital to support country-level policy making. The strategies adopted by countries to combat the pandemic vary, generating different uncertainty levels about the actual number of cases. Accounting for the hierarchical structure of the data and accommodating extra-variability is therefore fundamental. We introduce a new modelling framework to describe the pandemic's course with great accuracy and provide short-term daily forecasts for every country in the world. We show that our model generates highly accurate forecasts up to seven days ahead and use estimated model components to cluster countries based on recent events. We introduce statistical novelty in terms of modelling the autoregressive parameter as a function of time, increasing predictive power and flexibility to adapt to each country. Our model can also be used to forecast the number of deaths, study the effects of covariates (such as lockdown policies), and generate forecasts for smaller regions within countries. Consequently, it has substantial implications for global planning and decision making. We present forecasts and make all results freely available to any country in the world through an online Shiny dashboard.

| S-EPMC8024267 | biostudies-literature

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