A wind speed threshold for increased outdoor transmission of coronavirus: an ecological study

Is aromatic plants environmental health engineering (APEHE) a leverage point of the earth system?

It is important to note that every ecological niche in an ecosystem is significant. This study aims to assess the importance of medicinal and aromatic plants (MAPs) in the ecosystem from multiple perspectives. A primary model of Aromatic Plants Environmental Health Engineering (APEHE) has been designed and constructed. The APEHE system was used to collect aerosol compounds, and it was experimentally verified that these compounds have the potential to impact human health by binding to AKT1 as the primary target, and MMP9 and TLR4 as secondary targets. These compounds may indirectly affect human immunity by reversing drug resistance in drug-resistant bacteria in the nasal cavity. This is mainly achieved through combined mutations in sdhA, scrA, and PEP. Our findings are based on Network pharmacology and molecular binding, drug-resistance rescue experiments, as well as combined transcriptomics and metabolomics experiments. It is suggested that APEHE may have direct or indirect effects on human health. We demonstrate APEHE's numerous potential benefits, such as attenuation and elimination of airborne microorganisms in the environment, enhancing carbon and nitrogen storage in terrestrial ecosystems, promoting the formation of low-level clouds and strengthening the virtuous cycle of Earth's ecosystems. APEHE also supports the development of transdisciplinary technologies, including terpene energy production. It facilitates the creation of a sustainable circular economy and provides additional economic advantages through urban optimisation, as well as fresh insights into areas such as the habitability of other planets. APEHE has the potential to serve as a leverage point for the Earth system. We have created a new research direction.

Do Weather Conditions Still Have an Impact on the COVID-19 Pandemic? An Observation of the Mid-2022 COVID-19 Peak in Taiwan

Since the onset of the COVID-19 pandemic in 2019, the role of weather conditions in influencing transmission has been unclear, with results varying across different studies. Given the changes in border policies and the higher vaccination rates compared to earlier conditions, this study aimed to reassess the impact of weather on COVID-19, focusing on local climate effects. We analyzed daily COVID-19 case data and weather factors such as temperature, humidity, wind speed, and a diurnal temperature range from 1 March to 15 August 2022 across six regions in Taiwan. This study found a positive correlation between maximum daily temperature and relative humidity with new COVID-19 cases, whereas wind speed and diurnal temperature range were negatively correlated. Additionally, a significant positive correlation was identified between the unease environmental condition factor (UECF, calculated as RH*Tmax/WS), the kind of Climate Factor Complex (CFC), and confirmed cases. The findings highlight the influence of local weather conditions on COVID-19 transmission, suggesting that such factors can alter environmental comfort and human behavior, thereby affecting disease spread. We also introduced the Fire-Qi Period concept to explain the cyclic climatic variations influencing infectious disease outbreaks globally. This study emphasizes the necessity of considering both local and global climatic effects on infectious diseases.

The Dynamics of COVID-19 in Hiroshima Prefecture Compared to Japan and Its Association With Meteorological Factors: A Comparative Analysis

Introduction Despite the implementation of countermeasures and mass vaccination programs, the COVID-19 pandemic incidence was a vital public health concern. This study aimed to explore the dynamics of COVID-19 cases and assess the association of COVID-19 pandemic epidemiological data with meteorological factors in Hiroshima Prefecture compared to Japan. Methods We analyzed COVID-19 pandemic data in Japan's Hiroshima Prefecture from January 16, 2020, to May 9, 2023. Meteorological factors were examined at different time frames, and Spearman correlation coefficients were calculated for COVID-19 variables and variants based on GISAID whole genome analysis. Results Hiroshima Prefecture reported 816,788 COVID-19 cases and 1,371 fatalities, with a city-to-rural case ratio of 0.97:1. Infection rates were 17.42% for Japan and 15.83% for Hiroshima. Gender-wise, the ratio was 99:1, and the 30-39 age group in Hiroshima had the highest cases (15.5%). Among all meteorological factors, daily and 14-day average wind speed showed a weak correlation with incidence (-0.1954, P < 0.01; 0.3669 P < 0.01), fatalities (-0.1148, P < 0.01; -0.2232 P < 0.01), and incidence rate (-0.2042, P < 0.01; -0.3751, P < 0.01), respectively. Clade GRA was most frequent (39.7%), and among 61 variants, B.1.1.7, AY.29, and BA.1.1.2 were predominant. Precipitation was associated significantly with the Alpha variant (0.3373, P<0.01), while the Delta variant (0.2934, <0.05) weakly correlated with humidity. Conclusion COVID-19 pandemic trends in Hiroshima Prefecture paralleled Japan's, yet with lower incidence and fatalities compared to most prefectures. Significant associations were found between meteorological factors and COVID-19 metrics, including incidence, fatalities, incidence rate, and mutations in Hiroshima.

Presentation Rates for Acute Pharyngitis in the Emergency Room Are Influenced by Extreme Weather Events

OBJECTIVE

Extreme weather events are becoming more prevalent with the increasing pace of climate change. These events negatively impact human health and put considerable strain on health care resources, including emergency departments. Within otolaryngology, acute pharyngitis is a common reason for emergency room visits (ERV). Therefore, we aimed to investigate the impact of extreme meteorological conditions on ERV rates related to acute pharyngitis.

STUDY DESIGN

Retrospective time-series study.

SETTING

ERVs related to acute pharyngitis (n = 1511) were identified at a tertiary care hospital in Vienna, Austria, between 2015 and 2018.

METHODS

The effects of single-day and prolonged (3-day) extreme weather events on ERVs were analyzed using a distributed lag nonlinear model. Relative risk (RR) and cumulative relative risk (cRR) were calculated over a lag period of 14 days. RR refers to the risk for pharyngitis-related ERV at extreme conditions (1st, 5th, 95th, or 99th percentile) compared to the risk at median conditions.

RESULTS

Same-day RR (lag0) was elevated more than 3-fold after prolonged extremely low mean temperatures (P = .028). Furthermore, same-day RR after single-day and prolonged extremely high relative humidity was elevated by 51% (P = .024) and 46% (P = .036), respectively. Significant delayed effects on cRR were observed for extreme mean temperatures, relative humidity, and mean wind speeds within 8 days and for extreme atmospheric pressure within 14 days.

CONCLUSION

Extreme weather events impact ERV rates for acute pharyngitis. Extremely low temperatures, high relative humidity, high atmospheric pressure, and low and high wind speeds were risk-promoting factors.

Causal effect of air pollution and meteorology on the COVID-19 pandemic: A convergent cross mapping approach

Environmental factors have been suspected to influence the propagation and lethality of COVID-19 in the global population. However, most of the studies have been limited to correlation analyses and did not use specific methods to address the dynamic of the causal relationship between the virus and its external drivers. This work focuses on inferring and understanding the causal effect of critical air pollutants and meteorological parameters on COVID-19 by using an Empirical Dynamic Modeling approach called Convergent Cross Mapping. This technique allowed us to identify the time-delayed causation and the sign of interactions. Considering its remarkable urban environment and mortality rate during the pandemic, Quito, Ecuador, was chosen as a case study. Our results show that both urban air pollution and meteorology have a causal impact on COVID-19. Even if the strength and the sign of the causality vary over time, a general trend can be drawn. NO2, SO2, CO and PM2.5 have a positive causation for COVID-19 infections (ρ > 0.35 and ∂ > 9.1). Contrary to current knowledge, this study shows a rapid effect of pollution on COVID-19 cases (1 < lag days <24) and a negative impact of O3 on COVID-19-related deaths (ρ = 0.53 and ∂ = -0.3). Regarding the meteorology, temperature (ρ = 0.24 and ∂ = -0.4) and wind speed (ρ = 0.34 and ∂ = -3.9) tend to mitigate the epidemiological consequences of SARS-CoV-2, whereas relative humidity seems to increase the excess deaths (ρ = 0.4 and ∂ = 0.05). A causal network is proposed to synthesize the interactions between the studied variables and to provide a simple model to support the management of coronavirus outbreaks.

Effects of hydrometeorological and other factors on SARS-CoV-2 reproduction number in three contiguous countries of tropical Andean South America: a spatiotemporally disaggregated time series analysis

Background

The COVID-19 pandemic has caused societal disruption globally, and South America has been hit harder than other lower-income regions. This study modeled the effects of six weather variables on district-level SARS-CoV-2 reproduction numbers (Rt ) in three contiguous countries of tropical Andean South America (Colombia, Ecuador, and Peru), adjusting for environmental, policy, healthcare infrastructural and other factors.

Methods

Daily time-series data on SARS-CoV-2 infections were sourced from the health authorities of the three countries at the smallest available administrative level. Rt values were calculated and merged by date and unit ID with variables from a unified COVID-19 dataset and other publicly available sources for May-December, 2020. Generalized additive models were fitted.

Findings

Relative humidity and solar radiation were inversely associated with SARS-CoV-2 Rt . Days with radiation above 1000 kJ/m2 saw a 1.3% reduction in Rt , and those with humidity above 50% recorded a 0.9% reduction in Rt . Transmission was highest in densely populated districts, and lowest in districts with poor healthcare access and on days with lowest population mobility. Wind speed, temperature, region, aggregate government policy response, and population age structure had little impact. The fully adjusted model explained 4.3% of Rt variance.

Interpretation

Dry atmospheric conditions of low humidity increase district-level SARS-CoV-2 reproduction numbers, while higher levels of solar radiation decrease district-level SARS-CoV-2 reproduction numbers - effects that are comparable in magnitude to population factors like lockdown compliance. Weather monitoring could be incorporated into disease surveillance and early warning systems in conjunction with more established risk indicators and surveillance measures.

Funding

NASA's Group on Earth Observations Work Programme (16-GEO16-0047).

SARS-CoV-2 in outdoor air following the third wave lockdown release, Portugal, 2021

Aiming to contribute with more data on the presence of SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) in outdoor environments, we performed air sampling in outdoor terraces from restaurants in three major cities of Portugal in April 2021, following the third wave lockdown release in the country. Air samples (n=19) were collected in 19 restaurant terraces during lunch time. Each air sample was collected using a Coriolis Compact air sampler, followed by RNA extraction and real-time quantitative PCR for the detection of viral RNA. Viral viability was also assessed through RNAse pre-treatment of samples. Only one of the 19 air samples was positive for SARS-CoV-2 RNA, with 7337 gene copies m^-3 for the genomic region N2, with no viable virus in this sample. The low number of positive samples found in this study is not surprising, as sampling took place in outdoor settings where air circulation is optimal, and aerosols are rapidly dispersed by the air currents. These results are consistent with previous reports stating that transmission of SARS-CoV-2 in outdoor spaces is low, although current evidence shows an association of exposures in settings where drinking and eating is possible on-site with an increased risk in acquiring SARS-CoV-2 infection. Moreover, the minimal infectious dose for SARS-CoV-2 still needs to be determined so that the real risk of infection in different environments can be accurately established.

Prerequisite for COVID-19 Prediction: A Review on Factors Affecting the Infection Rate

Since the year 2020, coronavirus disease 2019 (COVID-19) has emerged as the dominant topic of discussion in the public and research domains. Intensive research has been carried out on several aspects of COVID-19, including vaccines, its transmission mechanism, detection of COVID-19 infection, and its infection rate and factors. The awareness of the public related to the COVID-19 infection factors enables the public to adhere to the standard operating procedures, while a full elucidation on the correlation of different factors to the infection rate facilitates effective measures to minimize the risk of COVID-19 infection by policy makers and enforcers. Hence, this paper aims to provide a comprehensive and analytical review of different factors affecting the COVID-19 infection rate. Furthermore, this review analyses factors which directly and indirectly affect the COVID-19 infection risk, such as physical distance, ventilation, face masks, meteorological factor, socioeconomic factor, vaccination, host factor, SARS-CoV-2 variants, and the availability of COVID-19 testing. Critical analysis was performed for the different factors by providing quantitative and qualitative studies. Lastly, the challenges of correlating each infection risk factor to the predicted risk of COVID-19 infection are discussed, and recommendations for further research works and interventions are outlined.

Evaluating the effectiveness of Hong Kong's border restriction policy in reducing COVID-19 infections

This study evaluates the effectiveness of Hong Kong’s strict border restrictions with mainland China in curbing the transmission of COVID-19. Combining big data from Baidu Population Migration with traditional meteorological data and census data for over 200 Chinese cities, we utilize an advanced quantitative approach, namely synthetic control modeling, to produce a counterfactual “synthetic Hong Kong” without a strict border restriction policy. We then simulate infection trends under the hypothetical scenarios and compare them to actual infection numbers. Our counterfactual synthetic control model demonstrates a lower number of COVID-19 infections than the actual scenario, where strict border restrictions with mainland China were implemented from February 8 to March 6, 2020. Moreover, the second synthetic control model, which assumes a border reopen on 7 May 2020 demonstrates nonpositive effects of extending the border restriction policy on preventing and controlling infections. We conclude that the border restriction policy and its further extension may not be useful in containing the spread of COVID-19 when the virus is already circulating in the local community. Given the substantial economic and social costs, and as precautionary measures against COVID-19 becomes the new normal, countries can consider reopening borders with neighbors who have COVID-19 under control. Governments also need to closely monitor the changing epidemic situations in other countries in order to make prompt and sensible amendments to their border restriction policies.