Peculiar weather patterns effects on air pollution and COVID-19 spread in Tokyo metropolis.

As a pandemic hotspot in Japan, between March 1, 2020-October 1, 2022, Tokyo metropolis experienced seven COVID-19 waves. Motivated by the high rate of COVID-19 incidence and mortality during the seventh wave, and environmental/health challenges we conducted a time-series analysis to investigate the long-term interaction of air quality and climate variability with viral pandemic in Tokyo. Through daily time series geospatial and observational air pollution/climate data, and COVID-19 incidence and death cases, this study compared the environmental conditions during COVID-19 multiwaves. In spite of five State of Emergency (SOEs) restrictions associated with COVID-19 pandemic, during (2020-2022) period air quality recorded low improvements relative to (2015-2019) average annual values, namely: Aerosol Optical Depth increased by 9.13% in 2020 year, and declined by 6.64% in 2021, and 12.03% in 2022; particulate matter PM2.5 and PM10 decreased during 2020, 2021, and 2022 years by 10.22%, 62.26%, 0.39%, and respectively by 4.42%, 3.95%, 5.76%. For (2021-2022) period the average ratio of PM2.5/PM10 was (0.319 ± 0.1640), showing a higher contribution to aerosol loading of traffic-related coarse particles in comparison with fine particles. The highest rates of the daily recorded COVID-19 incidence and death cases in Tokyo during the seventh COVID-19 wave (1 July 2022-1 October 2022) may be attributed to accumulation near the ground of high levels of air pollutants and viral pathogens due to: 1) peculiar persistent atmospheric anticyclonic circulation with strong positive anomalies of geopotential height at 500 hPa; 2) lower levels of Planetary Boundary Layer (PBL) heights; 3) high daily maximum air temperature and land surface temperature due to the prolonged heat waves (HWs) in summer 2022; 4) no imposed restrictions. Such findings can guide public decision-makers to design proper strategies to curb pandemics under persistent stable anticyclonic weather conditions and summer HWs in large metropolitan areas.

Rethinking local resilience for extreme heat events.

OBJECTIVE: This study aims to provide insights into how local resilience structures in England can be leveraged to deliver a whole-of-society approach to managing a national response to extreme heat events during summer months. STUDY DESIGN: A communication based on the literature review of currently available research on health emergency response and extreme heat events in England. METHODS: This communication draws insights from the authors' research programmes, which examined national-level public health emergency response during the COVID-19 pandemic and literature review of the latest available English research on health and extreme heat events. RESULTS: Periods of extreme heat are on the rise in England. Local resilience forums (LRFs), due to their multiagency nature, offer a shared situational awareness and understanding of the need in their local communities. Such information is critical to ensure messaging about heat risks and available resources are tailored to reach specific targeted groups within their communities. Scenario planning and adaptation efforts require a more local articulation which LRFs are well placed to manage. CONCLUSIONS: LRFs are well suited as key structures in the English emergency response to extreme heat events. We suggest that English public health and hospital organisations, working with community partners via the LRFs, must develop their thinking about pressures from adverse weather in the summer months.

Analyzing the impact of vaccinations and weather factors on the COVID-19 pandemic.

OBJECTIVES: A world-wide immunization project was launched at the peak of COVID-19 pandemic to contain and minimize the adverse effects of SARS-CoV-2 virus. We carried out a series of statistical analyses in this paper to determine, confirm and quantify the impact of the vaccinations on COVID-19 cases and mortalities, amidst critical confounding factors-temperature and solar irradiance. METHODS: The experiments in this paper were carried out on the world data, data from 21 countries, and the five major continents. The significance of the 2020-2022 vaccinations on the COVID-19 cases and mortalities response data were evaluated via Hypotheses' tests. Correlation coefficient analyses were carried out to determine the extent of the relationship between vaccination coverage and corresponding COVID-19 mortalities data. The impact of vaccination was quantified. The effects of the weather factors-temperature and solar irradiance, on COVID-19 cases and mortalities data were analyzed. RESULTS: The series of hypotheses tests carried out reveal that vaccinations did not affect cases; however, vaccinations significantly impacted the mean daily mortalities in all five major continents and globally. The correlation coefficient analysis results show vaccination coverage to be highly and negatively correlated with daily mortalities in the world-the five major continents and most of the countries studied in this work. The percentage reduction in mortalities as a result of wider vaccination coverage was indeed significant. Temperature and solar irradiance impacted daily COVID-19 cases and mortalities data during the vaccination and post-vaccination periods. CONCLUSION: Results show that the world-wide vaccination against COVID-19 project had a significant impact in reducing mortalities and minimizing the adverse effects due to COVID-19 globally, in all five (5) major continents of the world and the countries studied in this work, however, temperature and solar irradiance still had effects on COVID-19 response in the vaccination eras.

Impact of non-pharmaceutical interventions, weather, vaccination, and variants on COVID-19 transmission across departments in France.

BACKGROUND: Multiple factors shape the temporal dynamics of the COVID-19 pandemic. Quantifying their relative contributions is key to guide future control strategies. Our objective was to disentangle the individual effects of non-pharmaceutical interventions (NPIs), weather, vaccination, and variants of concern (VOC) on local SARS-CoV-2 transmission. METHODS: We developed a log-linear model for the weekly reproduction number (R) of hospital admissions in 92 French metropolitan departments. We leveraged (i) the homogeneity in data collection and NPI definitions across departments, (ii) the spatial heterogeneity in the timing of NPIs, and (iii) an extensive observation period (14 months) covering different weather conditions, VOC proportions, and vaccine coverage levels. FINDINGS: Three lockdowns reduced R by 72.7% (95% CI 71.3-74.1), 70.4% (69.2-71.6) and 60.7% (56.4-64.5), respectively. Curfews implemented at 6/7 pm and 8/9 pm reduced R by 34.3% (27.9-40.2) and 18.9% (12.04-25.3), respectively. School closures reduced R by only 4.9% (2.0-7.8). We estimated that vaccination of the entire population would have reduced R by 71.7% (56.4-81.6), whereas the emergence of VOC (mainly Alpha during the study period) increased transmission by 44.6% (36.1-53.6) compared with the historical variant. Winter weather conditions (lower temperature and absolute humidity) increased R by 42.2% (37.3-47.3) compared to summer weather conditions. Additionally, we explored counterfactual scenarios (absence of VOC or vaccination) to assess their impact on hospital admissions. INTERPRETATION: Our study demonstrates the strong effectiveness of NPIs and vaccination and quantifies the role of weather while adjusting for other confounders. It highlights the importance of retrospective evaluation of interventions to inform future decision-making.

Disasters collide at the intersection of extreme weather and infectious diseases.

Natural disasters interact to affect the resilience and prosperity of communities and disproportionately affect low income families and communities of colour. However, due to lack of a common theoretical framework, these are rarely quantified. Observing severe weather events (e.g. hurricanes and tornadoes) and epidemics (e.g. COVID-19) unfolding in southeastern US communities led us to conjecture that interactions among catastrophic disturbances might be much more considerable than previously recognized. For instance, hurricane evacuations increase human aggregation, a factor that affects the transmission of acute infections like SARS-CoV-2. Similarly, weather damage to health infrastructure can reduce a community's ability to provide services to people who are ill. As globalization and human population and movement continue to increase and weather events are becoming more intense, such complex interactions are expected to magnify and significantly impact environmental and human health.

Public Health Preparedness in the Era of Weather Modification and Climate Engineering.

The use of technological and chemical means aiming to achieve favorable weather conditions or reduce the risk of weather extremes is known as Weather Modification (WM). The United States of America, the People's Republic of China, Thailand, the United Arab Emirates, and Europe have employed WM in an effort to prevent hurricanes and storms, control precipitations, mitigate deforestation and drought, and enhance agriculture. Recently, the use of WM has been expanded toward decreasing air pollution and creating favorable weather conditions for major political and athletic events. The increasing significance and use of WM call for consideration upon its positive and negative effects on human health, close collaboration among health experts and WM decision makers, and relevant public health emergency contingency planning.

Effects of air pollution and weather on the initial COVID-19 outbreaks in United States, Italy, Spain, and China: A comparative study.

Contrasting effects have been identified in association of weather (temperature and humidity) and pollutant gases with COVID-19 infection, which could be derived from the influence of lockdowns and season change. The influence of pollutant gases and climate during the initial phases of the pandemic, before the closures and the change of season in the northern hemisphere, is unknown. Here, we used a spatial-temporal Bayesian zero-inflated-Poisson model to test for short-term associations of weather and pollutant gases with the relative risk of COVID-19 disease in China (first outbreak) and the countries with more cases during the initial pandemic (the United States, Spain and Italy), considering also the effects of season and lockdown. We found contrasting association between pollutant gases and COVID-19 risk in the United States, Italy, and Spain, while in China it was negatively associated (except for SO2 ). COVID-19 risk was positively associated with specific humidity in all countries, while temperature presented a negative effect. Our findings showed that short-term associations of air pollutants with COVID-19 infection vary strongly between countries, while generalized effects of temperature (negative) and humidity (positive) with COVID-19 was found. Our results show novel information about the influence of pollution and weather on the initial outbreaks, which contribute to unravel the mechanisms during the beginning of the pandemic.

Associations between indoor relative humidity and global COVID-19 outcomes.

Globally, the spread and severity of COVID-19 have been distinctly non-uniform. Seasonality was suggested as a contributor to regional variability, but the relationship between weather and COVID-19 remains unclear and the focus of attention has been on outdoor conditions. Because humans spend most of their time indoors and because most transmission occurs indoors, we here, instead, investigate the hypothesis that indoor climate-particularly indoor relative humidity (RH)-may be the more relevant modulator of outbreaks. To study this association, we combined population-based COVID-19 statistics and meteorological measurements from 121 countries. We rigorously processed epidemiological data to reduce bias, then developed and experimentally validated a computational workflow to estimate indoor conditions based on outdoor weather data and standard indoor comfort conditions. Our comprehensive analysis shows robust and systematic relationships between regional outbreaks and indoor RH. In particular, we found intermediate RH (40-60%) to be robustly associated with better COVID-19 outbreak outcomes (versus RH < 40% or >60%). Together, these results suggest that indoor conditions, particularly indoor RH, modulate the spread and severity of COVID-19 outbreaks.

Forecasting HFMD Cases Using Weather Variables and Google Search Queries in Sabah, Malaysia.

HFMD is a viral-mediated infectious illness of increasing public health importance. This study aimed to develop a forecasting tool utilizing climatic predictors and internet search queries for informing preventive strategies in Sabah, Malaysia. HFMD case data from the Sabah State Health Department, climatic predictors from the Malaysia Meteorological Department, and Google search trends from the Google trends platform between the years 2010-2018 were utilized. Cross-correlations were estimated in building a seasonal auto-regressive moving average (SARIMA) model with external regressors, directed by measuring the model fit. The selected variables were then validated using test data utilizing validation metrics such as the mean average percentage error (MAPE). Google search trends evinced moderate positive correlations to the HFMD cases (r0-6weeks: 0.47-0.56), with temperature revealing weaker positive correlations (r0-3weeks: 0.17-0.22), with the association being most intense at 0-1 weeks. The SARIMA model, with regressors of mean temperature at lag 0 and Google search trends at lag 1, was the best-performing model. It provided the most stable predictions across the four-week period and produced the most accurate predictions two weeks in advance (RMSE = 18.77, MAPE = 0.242). Trajectorial forecasting oscillations of the model are stable up to four weeks in advance, with accuracy being the highest two weeks prior, suggesting its possible usefulness in outbreak preparedness.

Global patterns and drivers of influenza decline during the COVID-19 pandemic.

OBJECTIVES: The influenza circulation reportedly declined during the COVID-19 pandemic in many countries. The occurrence of this change has not been studied worldwide nor its potential drivers. METHODS: The change in the proportion of positive influenza samples reported by country and trimester was computed relative to the 2014-2019 period using the FluNet database. Random forests were used to determine predictors of change from demographical, weather, pandemic preparedness, COVID-19 incidence, and pandemic response characteristics. Regression trees were used to classify observations according to these predictors. RESULTS: During the COVID-19 pandemic, the influenza decline relative to prepandemic levels was global but heterogeneous across space and time. It was more than 50% for 311 of 376 trimesters-countries and even more than 99% for 135. COVID-19 incidence and pandemic preparedness were the two most important predictors of the decline. Europe and North America initially showed limited decline despite high COVID-19 restrictions; however, there was a strong decline afterward in most temperate countries, where pandemic preparedness, COVID-19 incidence, and social restrictions were high; the decline was limited in countries where these factors were low. The "zero-COVID" countries experienced the greatest decline. CONCLUSION: Our findings set the stage for interpreting the resurgence of influenza worldwide.

The effect of weather data on the spread of COVID-19 in Jordan.

This study aims to analyze the correlation between the daily confirmed COVID-19 cases in Jordan and metrological parameters including the average daily temperature (°C), maximum ambient temperature (°C), relative humidity (%), wind speed (m/s), pressure (kPa), and average daily solar radiation (W/m2). This covers the first and the second waves in Jordan. The data were obtained from both the Jordanian Ministry of health and the Jordan Metrological Department. In this work, the Spearman correlation test was used for data analysis, since the normality assumption was not fulfilled. It was found that the most effective weather parameters on the active cases of COVID-19 in the initial wave transmission was the average daily solar radiation (r = - 0.503; p = 0.000), while all other tests for other parameters failed. In the second wave of COVID-19 transmission, it was found that the most effective weather parameter on the active cases of COVID-19 was the maximum temperature (r = 0.394; p = 0.028). This was followed by wind speed (r = 0.477; p = 0.007), pressure (r = - 0.429; p = 0.016), and average daily solar radiation (r = - 0.757; p = 0.000). Furthermore, the independent variable importance of multilayer perceptron showed that wind speed has a direct relationship with active cases. Conversely, areas characterized by low values of pressure and daily solar radiation exposure have a high rate of infection. Finally, a global sensitivity analysis using Sobol analysis showed that daily solar radiation has a high rate of active cases that support the virus' survival in both wave transmissions.

Cascading disasters and mental health inequities: Winter Storm Uri, COVID-19 and post-traumatic stress in Texas.

Previous research on health effects of extreme weather has emphasized heat events even though cold-attributable mortality exceeds heat-attributable mortality worldwide. Little is known about the mental health effects of cold weather events, which often cascade to produce secondary impacts like power outages, leaving a knowledge gap in context of a changing climate. We address that gap by taking a novel "cascading disaster health inequities" approach to examine winter storm-associated post-traumatic stress (PTS) using survey data (n = 790) collected in eight Texas metro areas following Winter Storm Uri in 2021, which occurred against the backdrop of COVID-19. The incidence of storm-related PTS was 18%. Being Black (odds ratio [OR]: 6.6), Hispanic (OR: 3.5), or of another non-White race (OR: 4.2) was associated with greater odds of PTS compared to being White, which indicates substantial racial/ethnic inequities in mental health impacts (all p < 0.05). Having a disability also increased odds of PTS (OR: 4.4) (p < 0.05). Having piped water outages (OR: 1.9) and being highly impacted by COVID-19 (OR: 3.3) increased odds of PTS (both p < 0.05). When modelling how COVID-19 and outages cascaded, we compared householders to those with no outages and low COVID-19 impacts. PTS was more likely (p < 0.05) if householders had a water or power outage and high COVID-19 impacts (OR: 4.4) and if they had water and power outages and high COVID-19 impacts (OR: 7.7). Findings provide novel evidence of racial/ethnic inequities and cascading effects with regard to extreme cold events amid the COVID-19 pandemic.

Effect of ambient air pollutants and meteorological variables on COVID-19 incidence.

OBJECTIVE: To determine whether ambient air pollutants and meteorological variables are associated with daily COVID-19 incidence. DESIGN: A retrospective cohort from January 25 to February 29, 2020. SETTING: Cities of Wuhan, Xiaogan, and Huanggang, China. PATIENTS: The COVID-19 cases detected each day. METHODS: We collected daily data of COVID-19 incidence, 8 ambient air pollutants (particulate matter of ≤2.5 µm [PM2.5], particulate matter ≤10 µm [PM10], sulfur dioxide [SO2], carbon monoxide [CO], nitrogen dioxide [NO2], and maximum 8-h moving average concentrations for ozone [O3-8h]) and 3 meteorological variables (temperature, relative humidity, and wind) in China's 3 worst COVID-19-stricken cities during the study period. The multivariate Poisson regression was performed to understand their correlation. RESULTS: Daily COVID-19 incidence was positively associated with PM2.5 and humidity in all cities. Specifically, the relative risk (RR) of PM2.5 for daily COVID-19 incidences were 1.036 (95% confidence interval [CI], 1.032-1.039) in Wuhan, 1.059 (95% CI, 1.046-1.072) in Xiaogan, and 1.144 (95% CI, 1.12-1.169) in Huanggang. The RR of humidity for daily COVID-19 incidence was consistently lower than that of PM2.5, and this difference ranged from 0.027 to 0.111. Moreover, PM10 and temperature also exhibited a notable correlation with daily COVID-19 incidence, but in a negative pattern The RR of PM10 for daily COVID-19 incidence ranged from 0.915 (95% CI, 0.896-0.934) to 0.961 (95% CI, 0.95-0.972, while that of temperature ranged from 0.738 (95% CI, 0.717-0.759) to 0.969 (95% CI, 0.966-0.973). CONCLUSIONS: Our data show that PM2.5 and humidity are substantially associated with an increased risk of COVID-19 and that PM10 and temperature are substantially associated with a decreased risk of COVID-19.

Meteorological factors and non-pharmaceutical interventions explain local differences in the spread of SARS-CoV-2 in Austria.

The drivers behind regional differences of SARS-CoV-2 spread on finer spatio-temporal scales are yet to be fully understood. Here we develop a data-driven modelling approach based on an age-structured compartmental model that compares 116 Austrian regions to a suitably chosen control set of regions to explain variations in local transmission rates through a combination of meteorological factors, non-pharmaceutical interventions and mobility. We find that more than 60% of the observed regional variations can be explained by these factors. Decreasing temperature and humidity, increasing cloudiness, precipitation and the absence of mitigation measures for public events are the strongest drivers for increased virus transmission, leading in combination to a doubling of the transmission rates compared to regions with more favourable weather. We conjecture that regions with little mitigation measures for large events that experience shifts toward unfavourable weather conditions are particularly predisposed as nucleation points for the next seasonal SARS-CoV-2 waves.

Flexibility in a Crisis: How Strong Relational Coordination and Lean Literacy Helped Us Weather the COVID Storm.

Our city was significantly impacted by the initial COVID-19 outbreak in the United States. We describe how members of our Quality and Safety team were able to leverage skills in relational coordination and process improvement to respond to rapidly changing needs in a flexible and effective way.

A panel regression analysis for the COVID-19 epidemic in the United States.

This study explored the roles of epidemic-spread-related behaviors, vaccination status and weather factors during the COVID-19 epidemic in 50 U.S. states since March 2020. Data from March 1, 2020 to February 5, 2022 were incorporated into panel model. The states were clustered by the k-means method. In addition to discussing the whole time period, we also took multiple events nodes into account and analyzed the data in different time periods respectively by panel linear regression method. In addition, influence of cluster grouping and different incubation periods were been discussed. Non-segmented analysis showed the rate of people staying at home and the vaccination dose per capita were significantly negatively correlated with the daily incidence rate, while the number of long-distance trips was positively correlated. Weather indicators also had a negative effect to a certain extent. Most segmental results support the above view. The vaccination dose per capita was unsurprisingly proved to be the most significant factor especially for epidemic dominated by Omicron strains. 7-day was a more robust incubation period with the best model fit while weather had different effects on the epidemic spread in different time period. The implementation of prevention behaviors and the promotion of vaccination may have a successful control effect on COVID-19, including variants' epidemic such as Omicron. The spread of COVID-19 also might be associated with weather, albeit to a lesser extent.

Year-long wastewater monitoring for SARS-CoV-2 signals in combined and separate sanitary sewers.

COVID-19 wastewater-based epidemiology has been performed in catchments of various sizes and sewer types with many short-term studies available and multi-seasonal studies emerging. The objective of this study was to compare weekly observations of SARS-CoV-2 genes in municipal wastewater across multiple seasons for different systems as a factor of sewer type (combined, separate sanitary) and system size. Sampling occurred following the first wave of SARS-CoV-2 cases in the study region (June 2020) and continued through the third wave (May 2021), the period during which clinical testing was widely available and different variants dominated clinical cases. The strongest correlations were observed between wastewater N1 concentrations and the cumulative clinical cases reported in the 2 weeks prior to wastewater sampling, followed by the week prior, new cases, and the week after wastewater sampling. Sewer type and size did not necessarily explain the strength of the correlations, indicating that other non-sewer factors may be impacting the observations. In-system sampling results for the largest system sampled are presented for 1 month. Removing wet weather days from the data sets improved even the flow-normalized correlations for the systems, potentially indicating that interpreting results during wet weather events may be more complicated than simply accounting for dilution. PRACTITIONER POINTS: SARS-CoV-2 in wastewater correlated best with total clinical cases reported in 2 weeks before wastewater sampling at the utility level. Study performed when clinical testing was widespread during the year after the first COVID-19 wave in the region. Sewer type and size did not necessarily explain correlation strength between clinical cases and wastewater-based epidemiology results. Removing wet weather days improved correlations for 3/4 utilities studied, including both separate sanitary and combined sewers.

The Annual Global Incidence Rate of Extreme Weather Event Disasters Appears Positively Correlated with World GDP, 1961-2020.

OBJECTIVE: This study compared the per capita annual global incidence rate of disasters caused by natural hazards with the annual world real gross domestic product, GDP (per global capita), as reported during 1961 through 2020. METHODS: Sixty (60) values for the world real GDP per global capita (in constant 2015 $USD) were compared to corresponding annual values for global incidence rates for five natural disaster subgroups and then for a total of twelve individual disaster types that comprise the subgroups; each expressed as an annual global incidence rate (in terms of annual incidence per 100,000 persons). Calculations of multiple linear regression, ANOVA, and Pearson's correlation coefficient were performed for comparing population-adjusted values for GDP to corresponding values. RESULTS: Four out of five hydrological and meteorological disasters were found to have a positive correlation with GDP. Results of the analysis revealed a relatively high degree of correlation between world GDP and the annual incidence of flood and storm disasters (P = 6.21 × 10-10 and P = 4.23 × 10-4, respectively). The annual incidence of heat waves and cold weather disasters also appeared to correlate with GDP (P = .002 and P = .019, respectively). In comparison, wet landslides indicated no such correlation (P = .862). No significant associations were found among the seven other individual biological, climatological, and geophysical disasters and GDP. CONCLUSION: The global incidence of four extreme weather (hydrometeorological) disasters appear to be positively associated with world real GDP during 1961-2020. These findings contradict previous postulates that the risk of disaster incidence is inversely associated with the capacity of the population.