Lagged meteorological impacts on COVID-19 incidence among high-risk counties in the United States-a spatiotemporal analysis

Short-term associations between precipitation and gastrointestinal illness-related hospital admissions: A multi-city study in Texas

The ongoing climate change crisis presents challenges to the global public health system. The risk of gastrointestinal illness (GI) related hospitalization increases following extreme weather events but is largely under-reported and under-investigated. This study assessed the association between precipitation and GI-related hospital admissions in four major cities in Texas. Daily data on GI-related hospital admissions and precipitation from 2004 to 2014 were captured from the Texas Department of State Health Services and the National Climate Data Center. Distributed lagged nonlinear modeling approaches were employed to examine the association between precipitation and GI-related hospital admissions. Results showed that the cumulative risk ratios (RRs) of GI-related hospital admissions were elevated in the 2 weeks following precipitation events; however, there were differences observed across study locations. The cumulative RR of GI-related hospitalizations was significantly higher when the amount of daily precipitation ranged from 3.3 mm to 13.5 mm in Dallas and from 6.0 mm to 24.5 mm in Houston. Yet, substantial increases in the cumulative RRs of GI-related hospitalizations were not observed in Austin or San Antonio. Age-specific and cause-specific GI-related hospitalizations were also found to be associated with precipitation events following the same pattern. Among them, Houston depicted the largest RR for overall GI and subgroup GI by age and cause, particularly for the overall GI among children aged 6 and under (RR = 1.35; 95 % CI = 1.11, 1.63), diarrhea-caused GI among children aged 6 and under (RR = 1.38, 95 % CI = 1.13, 1.69), and other-caused GI among children age 6 and under (RR = 1.46; 95 % CI = 1.12, 1.80). The findings underscore the need for public health interventions and adaptation strategies to address climate change-related health outcomes such as GI illness associated with extreme precipitation events.

A Bayesian spatio-temporal study of the association between meteorological factors and the spread of COVID-19

BACKGROUND

The spread of COVID-19 has brought challenges to health, social and economic systems around the world. With little to no prior immunity in the global population, transmission has been driven primarily by human interaction. However, as with common respiratory illnesses such as influenza some authors have suggested COVID-19 may become seasonal as immunity grows. Despite this, the effects of meteorological conditions on the spread of COVID-19 are poorly understood. Previous studies have produced contrasting results, due in part to limited and inconsistent study designs.

METHODS

This study investigates the effects of meteorological conditions on COVID-19 infections in England using a Bayesian conditional auto-regressive spatio-temporal model. Our data consists of daily case counts from local authorities in England during the first lockdown from March-May 2020. During this period, legal restrictions limiting human interaction remained consistent, minimising the impact of changes in human interaction. We introduce a lag from weather conditions to daily cases to accommodate an incubation period and delays in obtaining test results. By modelling spatio-temporal random effects we account for the nature of a human transmissible virus, allowing the model to isolate meteorological effects.

RESULTS

Our analysis considers cases across England's 312 local authorities for a 55-day period. We find relative humidity is negatively associated with COVID-19 cases, with a 1% increase in relative humidity corresponding to a reduction in relative risk of 0.2% [95% highest posterior density (HPD): 0.1-0.3%]. However, we find no evidence for temperature, wind speed, precipitation or solar radiation being associated with COVID-19 spread. The inclusion of weekdays highlights systematic under reporting of cases on weekends with between 27.2-43.7% fewer cases reported on Saturdays and 26.3-44.8% fewer cases on Sundays respectively (based on 95% HPDs).

CONCLUSION

By applying a Bayesian conditional auto-regressive model to COVID-19 case data we capture the underlying spatio-temporal trends present in the data. This enables us to isolate the main meteorological effects and make robust claims about the association of weather variables to COVID-19 incidence. Overall, we find no strong association between meteorological factors and COVID-19 transmission.

Geospatial analysis in the United States reveals the changing roles of temperature on COVID-19 transmission

Environmental factors are known to affect outbreak patterns of infectious disease, but their impacts on the spread of COVID-19 along with the evolution of this relationship over time intervals and in different regions are unclear. This study utilized 3 years of data on COVID-19 cases in the continental United States from 2020 to 2022 and the corresponding weather data. We used regression analysis to investigate weather impacts on COVID-19 spread in the mainland United States and estimate the changes of these impacts over space and time. Temperature exhibited a significant and moderately strong negative correlation for most of the US while relative humidity and precipitation experienced mixed relationships. By regressing temperature factors with the spreading rate of waves, we found temperature change can explain over 20% of the spatial-temporal variation in the COVID-19 spreading, with a significant and negative response between temperature change and spreading rate. The pandemic in the continental United States during 2020-2022 was characterized by seven waves, with different transmission rates and wave peaks concentrated in seven time periods. When repeating the analysis for waves in the seven periods and nine climate zones, we found temperature impacts evolve over time and space, possibly due to virus mutation, changes in population susceptibility, social behavior, and control measures. Temperature impacts became weaker in 6 of 9 climate zones from the beginning of the epidemic to the end of 2022, suggesting that COVID-19 has increasingly adapted to wider weather conditions.

A distributed lag non-linear time-series study of ambient temperature and healthcare-associated infections in Hefei, China

Little is known about the effects of temperature on healthcare-associated infections (HAIs). A distributed lag non-linear model was used to estimate the association between ambient temperature and HAIs in Hefei, China. In total, 9,592 HAIs were included. The effect of low temperature (-0.1°C, 2.5th percentile) was significant on the current day (RR = 1.108, 95%CI:1.003-1.222), and then appeared on the 4th day (RR = 1.045, 95%CI:1.007-1.084) and the 5th day (RR = 1.033, 95%CI:1.006-1.061). The cumulative lag effects of low temperature lasted from the 5th to 10th days (RR = 1.123-1.143), and a long-term cumulative lag effect was observed on the 14th day (RR = 1.157, 95%CI:1.001-1.338). The lag effect of high temperature (31.0°C, 97.5th percentile) was not statistically significant. However, the effects of temperatures on HAIs were not significant among gender or age subgroups. This study suggests that the low temperatures have acute and lag effects on HAIs in Hefei, China.

The association of demographic and socioeconomic factors with COVID-19 during pre- and post-vaccination periods: A cross-sectional study of Virginia

Sociodemographic factors have been found to be associated with the transmission of coronavirus disease 2019 (COVID-19), yet most studies focused on the period before the proliferation of vaccination and obtained inconclusive results. In this cross-sectional study, the infections, deaths, incidence rates, case fatalities, and mortalities of Virginia's 133 jurisdictions during the pre-vaccination and post-vaccination periods were compared, and their associations with demographic and socioeconomic factors were studied. The cumulative infections and deaths and medians of incidence rates, case fatalities, and mortalities of COVID-19 in 133 Virginia jurisdictions were significantly higher during the post-vaccination period than during the pre-vaccination period. A variety of demographic and socioeconomic risk factors were significantly associated with COVID-19 prevalence in Virginia. Multiple linear regression analysis suggested that demographic and socioeconomic factors contributed up to 80% of the variation in the infections, deaths, and incidence rates and up to 53% of the variation in the case fatalities and mortalities of COVID-19 in Virginia. The demographic and socioeconomic determinants differed during the pre- and post-vaccination periods. The developed multiple linear regression models could be used to effectively characterize the impact of demographic and socioeconomic factors on the infections, deaths, and incidence rates of COVID-19 in Virginia.

Effect of Seasonal Land Surface Temperature Variation on COVID-19 Infection Rate: A Google Earth Engine-Based Remote Sensing Approach

This study aims to identify the effect of seasonal land surface temperature variation on the COVID-19 infection rate. The study area of this research is Bangladesh and its 8 divisions. The Google Earth Engine (GEE) platform has been used to extract the land surface temperature (LST) values from MODIS satellite imagery from May 2020 to July 2021. The per-day new COVID-19 cases data has also been collected for the same date range. Descriptive and statistical results show that after experiencing a high LST season, the new COVID-19 cases rise. On the other hand, the COVID-19 infection rate decreases when the LST falls in the winter. Also, rapid ups and downs in LST cause a high number of new cases. Mobility, social interaction, and unexpected weather change may be the main factors behind this relationship between LST and COVID-19 infection rates.

Associative evidence for the potential of humidification as a non-pharmaceutical intervention for influenza and SARS-CoV-2 transmission

BACKGROUND

Both influenza and SARS-CoV-2 viruses show a strong seasonal spreading in temperate regions. Several studies indicated that changes in indoor humidity could be one of the key factors explaining this.

OBJECTIVE

The purpose of this study is to quantify the association between relevant epidemiological metrics and humidity in both influenza and SARS-CoV-2 epidemic periods.

METHODS

The atmospheric dew point temperature serves as a proxy for indoor relative humidity. This study considered the weekly mortality rate in the Netherlands between 1995 and 2019 to determine the correlation between the dew point and the spread of influenza. During influenza epidemic periods in the Netherlands, governmental restrictions were absent; therefore, there is no need to control this confounder. During the SARS-CoV-2 pandemic, governmental restrictions strongly varied over time. To control this effect, periods with a relatively constant governmental intervention level were selected to analyze the reproduction rate. We also examine SARS-CoV-2 deaths in the nursing home setting, where health policy and social factors were less variable. Viral transmissibility was measured by computing the ratio between the estimated daily number of infectious persons in the Netherlands and the lagged mortality figures in the nursing homes.

RESULTS

For both influenza and SARS-CoV-2, a significant correlation was found between the dew point temperature and the aforementioned epidemiological metrics. The findings are consistent with the anticipated mechanisms related to droplet evaporation, stability of virus in the indoor environment, and impairment of the natural defenses of the respiratory tract in dry air.

SIGNIFICANCE

This information is helpful to understand the seasonal pattern of respiratory viruses and motivate further study to what extent it is possible to alter the seasonal pattern by actively intervening in the adverse role of low humidity during fall and winter in temperate regions.

IMPACT

A solid understanding and quantification of the role of humidity on the transmission of respiratory viruses is imperative for epidemiological modeling and the installation of non-pharmaceutical interventions. The results of this study indicate that improving the indoor humidity by humidifiers could be a promising technology for reducing the spread of both influenza and SARS-CoV-2 during winter and fall in the temperate zone. The identification of this potential should be seen as a strong motivation to invest in further prospective testing of this non-pharmaceutical intervention.

Regional Association between Mean Air Temperature and Case Numbers of Multiple SARS-CoV-2 Lineages throughout the Pandemic

The association between mean air temperature and new SARS-CoV-2 case numbers throughout the ongoing coronavirus disease 2019 (COVID-19) pandemic was investigated to identify whether diverse SARS-CoV-2 lineages may exhibit diverse environmental behaviors. The number of new COVID-19 daily cases in the province of Verona was obtained from the Veneto Regional Healthcare Service, whilst the mean daily air temperature during the same period was retrieved from the Regional Agency for Ambient Prevention and Protection of Veneto. A significant inverse correlation was found between new COVID-19 daily cases and mean air temperature in Verona up to Omicron BA.1/BA.2 predominance (correlation coefficients between −0.79 and −0.41). The correlation then became positive when the Omicron BA.4/BA.5 lineages were prevalent (r = 0.32). When the median value (and interquartile range; IQR) of new COVID-19 daily cases recorded during the warmer period of the year in Verona (June–July) was compared across the three years of the pandemic, a gradual increase could be seen over time, from 1 (IQR, 0–2) in 2020, to 22 (IQR, 11–113) in 2021, up to 890 (IQR, 343–1345) in 2022. These results suggest that measures for preventing SARS-CoV-2 infection should not be completely abandoned during the warmer periods of the year.

Methods Used in the Spatial and Spatiotemporal Analysis of COVID-19 Epidemiology: A Systematic Review

The spread of the COVID-19 pandemic was spatially heterogeneous around the world; the transmission of the disease is driven by complex spatial and temporal variations in socioenvironmental factors. Spatial tools are useful in supporting COVID-19 control programs. A substantive review of the merits of the methodological approaches used to understand the spatial epidemiology of the disease is hardly undertaken. In this study, we reviewed the methodological approaches used to identify the spatial and spatiotemporal variations of COVID-19 and the socioeconomic, demographic and climatic drivers of such variations. We conducted a systematic literature search of spatial studies of COVID-19 published in English from Embase, Scopus, Medline, and Web of Science databases from 1 January 2019 to 7 September 2021. Methodological quality assessments were also performed using the Joanna Briggs Institute (JBI) risk of bias tool. A total of 154 studies met the inclusion criteria that used frequentist (85%) and Bayesian (15%) modelling approaches to identify spatial clusters and the associated risk factors. Bayesian models in the studies incorporated various spatial, temporal and spatiotemporal effects into the modelling schemes. This review highlighted the need for more local-level advanced Bayesian spatiotemporal modelling through the multi-level framework for COVID-19 prevention and control strategies.