Dataset Information

The Influence of Average Temperature and Relative Humidity on New Cases of COVID-19: Time-Series Analysis.

ABSTRACT:

Background

The influence of meteorological factors on the transmission and spread of COVID-19 is of interest and has not been investigated.

Objective

This study aimed to investigate the associations between meteorological factors and the daily number of new cases of COVID-19 in 9 Asian cities.

Methods

Pearson correlation and generalized additive modeling (GAM) were performed to assess the relationships between daily new COVID-19 cases and meteorological factors (daily average temperature and relative humidity) with the most updated data currently available.

Results

The Pearson correlation showed that daily new confirmed cases of COVID-19 were more correlated with the average temperature than with relative humidity. Daily new confirmed cases were negatively correlated with the average temperature in Beijing (r=-0.565, P<.001), Shanghai (r=-0.47, P<.001), and Guangzhou (r=-0.53, P<.001). In Japan, however, a positive correlation was observed (r=0.416, P<.001). In most of the cities (Shanghai, Guangzhou, Hong Kong, Seoul, Tokyo, and Kuala Lumpur), GAM analysis showed the number of daily new confirmed cases to be positively associated with both average temperature and relative humidity, especially using lagged 3D modeling where the positive influence of temperature on daily new confirmed cases was discerned in 5 cities (exceptions: Beijing, Wuhan, Korea, and Malaysia). Moreover, the sensitivity analysis showed, by incorporating the city grade and public health measures into the model, that higher temperatures can increase daily new case numbers (beta=0.073, Z=11.594, P<.001) in the lagged 3-day model.

Conclusions

The findings suggest that increased temperature yield increases in daily new cases of COVID-19. Hence, large-scale public health measures and expanded regional research are still required until a vaccine becomes widely available and herd immunity is established.

SUBMITTER: He Z

PROVIDER: S-EPMC7836910 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

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Publications

The Influence of Average Temperature and Relative Humidity on New Cases of COVID-19: Time-Series Analysis.

He Zonglin Z Chin Yiqiao Y Yu Shinning S Huang Jian J Zhang Casper J P CJP Zhu Ke K Azarakhsh Nima N Sheng Jie J He Yi Y Jayavanth Pallavi P Liu Qian Q Akinwunmi Babatunde O BO Ming Wai-Kit WK

JMIR public health and surveillance 20210125 1

<h4>Background</h4>The influence of meteorological factors on the transmission and spread of COVID-19 is of interest and has not been investigated.<h4>Objective</h4>This study aimed to investigate the associations between meteorological factors and the daily number of new cases of COVID-19 in 9 Asian cities.<h4>Methods</h4>Pearson correlation and generalized additive modeling (GAM) were performed to assess the relationships between daily new COVID-19 cases and meteorological factors (daily avera ...[more]

PMID: 33232262

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Project description:The effects of temperature and relative humidity on different types of children's allergic diseases have not been comprehensively evaluated so far. This study aims to assess the impact of temperature and relative humidity variability on children's allergic diseases and to identify the critical time window. We collected outpatient data on allergen testing in children between July 2020 and January 2022 from the Affiliated Children's Hospital of Nanjing Medical University. We defined the 1st, 10th, 90th, and 99th percentiles as extreme cold, moderate cold, moderate hot, and extreme hot for temperature, and as low, moderate high, and extreme high for relative humidity, respectively. A distributed lag nonlinear model (DLNM) combined with a binomial regression model was used to assess the possible nonlinear relationship at different periods. Subgroup analysis by gender and age was conducted. We found that extreme and moderate cold temperatures were positively associated with skin allergies and total allergies (28 days: OR = 4.69, 95% CI: 2.88, 7.63; OR = 3.36, 95% CI: 2.39, 4.73) and (28 days: OR = 3.76, CI: 2.43, 5.81; OR = 2.71, 95% CI: 2.00, 3.68), respectively. Moderate and extreme hot temperatures were negatively associated with food allergies (28 days: OR = 0.13, 95% CI: 0.04, 0.41 and OR = 0.04; 95% CI: 0.01, 0.27). Low relative humidity was negatively associated with respiratory allergies, skin allergies, and total allergic diseases (28 days: OR = 0.26, 95% CI: 0.10, 0.71; OR = 0.29, 95% CI: 0.15, 0.55; and OR = 0.42, 95% CI: 0.26, 0.68). Meanwhile, extreme high relative humidity was negatively associated with respiratory allergies, and positively associated with skin allergies, food allergies, and total allergies (28 days: OR = 0.16, 95%CI: 0.07, 0.37; OR = 3.60, 95% CI: 2.52, 5.14; OR = 15.61, 95% CI: 3.23, 75.56; and OR = 2.33, 95% CI: 1.73, 3.15). A stronger relationship between temperature, relative humidity, and allergic diseases was observed in children under 5 years, specifically girls. Our study provides evidence that temperature and relative humidity variability may be associated with allergic diseases, however, the directionality of the relationship differs by allergic type.

Project description:BackgroundThe COVID-19 pandemic has caused significant global public health challenges, and impacted HIV testing and reporting worldwide. We aimed to estimate the impact of COVID-19 polices on identifying HIV/AIDS cases in China from 2020 to 2022.MethodsWe used an interrupted time series (ITS) design and seasonal autoregressive integrated moving average intervention (SARIMA Intervention) model. Monthly reported data on HIV/AIDS cases were extracted from the National Bureau of Disease Control and Prevention of China from January 2004 to August 2022. Data on Stringency Index (SI) and Economic Support Index (ESI) from January 22, 2020 to August 31, 2022 were extracted from the Oxford COVID-19 Government Response Tracker (OxCGRT). Using these, a SARIMA-Intervention model was constructed to evaluate the association between COVID-19 polices and monthly reported HIV/AIDS case numbers from January 2004 to August 2022 using auto.arima () function from R. The absolute percentage errors (APEs) compared the expected numbers generated by the SARIMA-Intervention model with actual numbers of HIV/AIDS, and was the primary outcome of this study. A second counterfactual model estimated HIV/AIDS case numbers if COVID-19 hadn't occurred in December 2019, and the mean difference between actual and predicted numbers were calculated. All statistical analyses were performed in R software (version 4.2.1) and EmpowerStats 2.0 and a P < 0.05 was considered statistically significant.FindingsThe SARIMA-Intervention model indicated HIV/AIDS monthly reported cases were inversely and significantly correlated with stricter lockdown and COVID-19 related polices (Coefficient for SI = -231.24, 95% CI: -383.17, -79.32) but not with economic support polices (Coefficient for ESI = 124.27, 95% CI: -309.84, 558.38). APEs of the SARIMA-Intervention model for prediction of HIV/AIDS cases from January 2022 through August 2022, were -2.99, 5.08, -13.64, -34.04, -2.76, -1.52, -1.37 and -2.47 respectively, indicating good accuracy and underreporting of cases during COVID-19. The counterfactual model estimates between January 2020 and August 2022 an additional 1314 HIV/AIDS cases should have been established monthly if COVID-19 hadn't occurred.InterpretationThe COVID-19 pandemic influenced the allocation and acquisition of medical resources which impacted accurate monthly reporting of HIV in China. Interventions that promote continuous HIV testing and ensure the adequate provision of HIV services including remote delivery of HIV testing services (HIV self-testing) and online sexual counseling services are necessary during pandemics in future.FundingMinistry of Science and Technology of the People's Republic of China (The grant number: 2020YFC0846300) and Fogarty International Center, National Institutes of Health, USA (The grant number: G11TW010941).

Dataset Information

The Influence of Average Temperature and Relative Humidity on New Cases of COVID-19: Time-Series Analysis.

Background

Objective

Methods

Results

Conclusions

Publications

The Influence of Average Temperature and Relative Humidity on New Cases of COVID-19: Time-Series Analysis.

Similar Datasets

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