Association between nitrogen dioxide concentration and hypertension hospitalization: A multicenter time-series analysis in Xinxiang, China

Hypertension is a common chronic disease, and air pollution is strongly associated with hypertension hospitalization. However, the association between nitrogen dioxide (NO2)1 concentration and hypertension hospitalization has rarely been studied. We collected daily data on hypertension hospitalizations, air pollutants, and meteorology from January 1, 2016 to October 31, 2021. After controlling for the effects of seasonal and long-term trends, weather conditions, weekdays, holidays, and during the novel coronavirus crown epidemic, a generalized additive model with over discrete Poisson regression was used to simulate the association between NO2 concentration and hypertension hospitalizations while quantifying hypertension hospitalizations, hospital stays, and hospital costs attributable to NO2. The results showed that each 10 μg/m3 increase in NO2 concentration was significantly associated with the relative risk (RR) of hypertension admission in Xinxiang, with the greatest effect at lag04 (RR = 1.107;95% confidence interval, 1.046–1.172). Hypertension hospitalizations attributed to NO2 during the study period accounted for 9.70% (484) of the total hypertension hospitalizations, equivalent to 4202 hospital days and 338.55 thousand United States dollars (USD). Increased NO2 concentration increases the risk of hypertension hospitalization in Xinxiang, which poses a significant health and economic burden to society and patients. The findings of this study provide a basis for developing stricter environmental pollutant standards.

Assessing the viability of estimating baleen whale abundance from tourist vessels

As Southern hemisphere baleen whales recover, they are again becoming dominant consumers in the Southern Ocean. Key to understanding the present and future role of baleen whales in Southern Ocean ecosystems is determining their abundance on foraging grounds. Distance sampling is the standard method for estimating baleen whale abundance but requires specific logistic requirements which are rarely achieved in the remote Southern Ocean. We explore the potential use of tourist vessel-based sampling, as a cost-effective solution for conducting distance sampling surveys for baleen whales. We aimed to determine if tourist vessel-based surveys could be effective in determining baleen whale abundance in the southwest Atlantic sector of the Southern Ocean. We did this in two parts. First, we used tourist vessel tracks to estimate the likely whale sightings per tourist's vessel voyage to understand how many voyages are needed to meet the model requirements. Second, we simulated the abundance and distributions of four baleen whale species for the study area and sampled them with both non-standardized tourist vessel-based surveys and standardized line transect surveys. Data were modeled using a generalized additive model and results were compared to the original simulated baleen whale abundance and distributions. We show that for the southwest Atlantic, 12-22 tourist voyages are likely required to provide an adequate number of sightings to estimate abundance for humpback and fin whales, and relative estimates for blue, sei, Antarctic minke, and southern right whales. Our analysis suggests tourist vessel-based surveys are a viable method for estimating baleen whale abundance in remote regions.

An investigation into the forces shaping the evolution of global shipping alliances

Global shipping alliances have become an important institution in international seaborne trade. Their raison d'être is higher efficiency and lower costs, to the benefit of the consumer. However, experiences from GSA operations during the COVID-19 supply chain crisis show that GSAs may have considerable market power, not quite aligned with the spirit of the lawmaker who has exempted them from antitrust laws. This raises many questions this paper attempts to answer: What drives the formation, stability and dissolution of GSAs? And have external and internal factors, such as government policies, ship sizes and freight rates, had always the same effect on GSAs over time? We decompose industry concentration (HHI) into seven components. This is done based on the Variational Mode Decomposition model. The components are subsequently reconstructed through gray correlation. Next, a Generalized Additive Model is specified, to analyze the relationships between influencing factors and the evolution of GSAs. We look both at the development (trend) of industry concentration, as well as its fluctuations (cyclicality) over time. We show that effects vary over time, with the same factors having different impacts on GSAs at different times. The paper can assist policymakers in their efforts to regulate and supervise container shipping. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

Evolving Drivers of Brazilian SARS-CoV-2 Transmission: A Spatiotemporally Disaggregated Time Series Analysis of Meteorology, Policy, and Human Mobility.

Brazil has been severely affected by the COVID-19 pandemic. Temperature and humidity have been purported as drivers of SARS-CoV-2 transmission, but no consensus has been reached in the literature regarding the relative roles of meteorology, governmental policy, and mobility on transmission in Brazil. We compiled data on meteorology, governmental policy, and mobility in Brazil's 26 states and one federal district from June 2020 to August 2021. Associations between these variables and the time-varying reproductive number (R t ) of SARS-CoV-2 were examined using generalized additive models fit to data from the entire 15-month period and several shorter, 3-month periods. Accumulated local effects and variable importance metrics were calculated to analyze the relationship between input variables and R t . We found that transmission is strongly influenced by unmeasured sources of between-state heterogeneity and the near-recent trajectory of the pandemic. Increased temperature generally was associated with decreased transmission and increased specific humidity with increased transmission. However, the impacts of meteorology, policy, and mobility on R t varied in direction, magnitude, and significance across our study period. This time variance could explain inconsistencies in the published literature to date. While meteorology weakly modulates SARS-CoV-2 transmission, daily or seasonal weather variations alone will not stave off future surges in COVID-19 cases in Brazil. Investigating how the roles of environmental factors and disease control interventions may vary with time should be a deliberate consideration of future research on the drivers of SARS-CoV-2 transmission.

Short-term forecasting of COVID-19 using support vector regression: An application using Zimbabwean data.

BACKGROUND: This study aims to show that including pairwise hierarchical interactions of covariates and combining forecasts from individual models improves prediction accuracy. METHODS: The least absolute shrinkage and selection operator via hierarchical pairwise interaction is used in selecting variables that are not correlated and with the greatest predictive power in single forecast models (Gradient boosting method [GBM], Generalized additive models [GAMs], Support vector regression [SVR]) are used in the analysis. The best model was selected based on the mean absolute error (MAE), the best key performance indicator for skewed data. Forecasts from the 5 models were combined using linear quantile regression averaging (LQRA). Box and Whiskers plots are used to diagnose the overall performance of fitted models. RESULTS: Single forecast models (GBM, GAMs, and SVRs) show that including pairwise interactions improves forecast accuracy. The SVR model with interactions based on the radial basis kernel function is the best from single forecast models with the lowest MAE. Combining point forecasts from all the single forecast models using the LQRA approach further reduces the MAE. However, based on the Box and Whiskers plot, the SVR model with pairwise interactions has the smallest range. CONCLUSIONS: Based on the key performance indicators, combining predictions from several individual models improves forecast accuracy. However, overall, the SVM with pairwise hierarchical interactions outperforms all the other models.

The Relationship between the Transmission of Different SARS-CoV-2 Strains and Air Quality: A Case Study in China.

Coronavirus Disease 2019 (COVID-19) has been a global public health concern for almost three years, and the transmission characteristics vary among different virus variants. Previous studies have investigated the relationship between air pollutants and COVID-19 infection caused by the original strain of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, it is unclear whether individuals might be more susceptible to COVID-19 due to exposure to air pollutants, with the SARS-CoV-2 mutating faster and faster. This study aimed to explore the relationship between air pollutants and COVID-19 infection caused by three major SARS-CoV-2 strains (the original strain, Delta variant, and Omicron variant) in China. A generalized additive model was applied to investigate the associations of COVID-19 infection with six air pollutants (PM2.5, PM10, SO2, CO, NO2, and O3). A positive correlation might be indicated between air pollutants (PM2.5, PM10, and NO2) and confirmed cases of COVID-19 caused by different SARS-CoV-2 strains. It also suggested that the mutant variants appear to be more closely associated with air pollutants than the original strain. This study could provide valuable insight into control strategies that limit the concentration of air pollutants at lower levels and would better control the spread of COVID-19 even as the virus continues to mutate.

Risk assessment and prevention and control effective assessment for COVID-19 in Hainan based on population migration

Objective To assess imported risk of COVID-19 in Hainan province from January 10 to March 7 in 2020, and to assess the effect of "The Normalization Prevention and Control" (measures during the Spring Festival Travel Rush (SFTR) in Hainan in 2021. Methods The daily reported imported cases in Hainan province, the daily reported cases in other 30 province of China, and the Baidu Migration Index were collected to calculated into the Imported Risk Index (IRI) to quantitatively assess the imported risk of Hainan province. Based on the analysis of the relationship between the imported risk index and imported cases, an imported case prediction model was constructed to fit the number of imported cases in "emergency containment" stage in Hainan. And number of imported cases during the Spring Festival Travel rush in 2021 was predicted by this model to compared with the actual number, which was to evaluate the "Normalization Prevention and Control" measures in this model was also used to assess the effect of "Normalization Prevention and Control" measures during the SFTR in 2021. Results Totally 112 imported cases were reported in Hainan. The average IRI was 0.98. Haikou, Sanya and Danzhou have the highest imported risk. Except Haikou, the imported risk index of all cities and counties reached the maximum value around January 24th. The generalized additive model based on the lag 4 days and lag 5 days was best fitted with the actual imported cases number (R2adjust1=83.50%, R2adjust2=82.00%, MRE=17.61%). If "Emergency Containment" strategy was still adopted, there were 10 COVID-19 cases imported into Hainan during the SFTR in 2021. Under the "Normalization Prevention and Control" strategy, virtually no imported cases were found in Hainan. Conclusions Tourism cities such as Haikou and Sanya have high imported risks. Hubei and Guangdong provinces are the main imported provinces. The Generalized Additive Model based on the Imported Risk Index can better fit with the imported cases number of COVID-19 in Hainan Province in "emergency containment". Compared with the "Emergency Containment" strategy, the "Normalization Prevention and Control" strategy adopted during the SFTR in 2021 reduced imported cases in Hainan by about 10. © 2022. China Tropical Medicine. All rights reserved.

Short-term effects of ambient particulate matter (PM1, PM2.5 and PM10) on influenza-like illness in Guangzhou, China.

BACKGROUND: Particulate matter (PM) has been linked to respiratory infections in a growing body of evidence. Studies on the relationship between ILI (influenza-like illness) and PM1 (particulate matter with aerodynamic diameter ≤1 µm) are, however, scarce. The purpose of this study was to investigate the effects of PM on ILI in Guangzhou, China. METHODS: Daily ILI cases, air pollution records (PM1, PM2.5, PM10 and gaseous pollutants), and metrological data between 2014 and 2019 were gathered from Guangzhou, China. To estimate the risk of ILI linked with exposure to PM pollutants, a quasi-Poisson regression was used. Additionally, subgroup analyses stratified by gender, age and season were carried out. RESULTS: For each 10 µg/m3 increase of PM1 and PM2.5 over the past two days (lag01), and PM10 over the past three days (lag02), the relative risks (RR) of ILI were 1.079 (95% confidence interval [CI]: 1.050, 1.109), 1.044 (95% CI: 1.027, 1.062) and 1.046 (95% CI: 1.032, 1.059), respectively. The estimated risks for men and women were substantially similar. The effects of PM pollutants between male and female were basically equivalent. People aged 15-24 years old were more susceptive to PM pollutants. CONCLUSIONS: It implies that PM1, PM2.5 and PM10 are all risk factors for ILI, the health impacts of PM pollutants vary by particle size. Reducing the concentration of PM1 needs to be considered when generating a strategy to prevent ILI.

Significance between air pollutants, meteorological factors, and COVID-19 infections: probable evidences in India.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) disease represents the causative agent with a potentially fatal risk which is having great global human health concern. Earlier studies suggested that air pollutants and meteorological factors were considered as the risk factors for acute respiratory infection, which carries harmful pathogens and affects the immunity. The study intended to explore the correlation between air pollutants, meteorological factors, and the daily reported infected cases caused by novel coronavirus in India. The daily positive infected cases, concentrations of air pollutants, and meteorological factors in 288 districts were collected from January 30, 2020, to April 23, 2020, in India. Spearman's correlation and generalized additive model (GAM) were applied to investigate the correlations of four air pollutants (PM2.5, PM10, NO2, and SO2) and eight meteorological factors (Temp, DTR, RH, AH, AP, RF, WS, and WD) with COVID-19-infected cases. The study indicated that a 10 µg/m3 increase during (Lag0-14) in PM2.5, PM10, and NO2 resulted in 2.21% (95%CI: 1.13 to 3.29), 2.67% (95% CI: 0.33 to 5.01), and 4.56 (95% CI: 2.22 to 6.90) increase in daily counts of Coronavirus Disease 2019 (COVID 19)-infected cases respectively. However, only 1 unit increase in meteorological factor levels in case of daily mean temperature and DTR during (Lag0-14) associated with 3.78% (95%CI: 1.81 to 5.75) and 1.82% (95% CI: -1.74 to 5.38) rise of COVID-19-infected cases respectively. In addition, SO2 and relative humidity were negatively associated with COVID-19-infected cases at Lag0-14 with decrease of 7.23% (95% CI: -10.99 to -3.47) and 1.11% (95% CI: -3.45 to 1.23) for SO2 and for relative humidity respectively. The study recommended that there are significant correlations between air pollutants and meteorological factors with COVID-19-infected cases, which substantially explain the effect of national lockdown and suggested positive implications for control and prevention of the spread of SARS-CoV-2 disease.

Association between meteorological indicators and COVID-19 pandemic in Pakistan.

This study was designed to investigate the impact of meteorological indicators (temperature, rainfall, and humidity) on total COVID-19 cases in Pakistan, its provinces, and administrative units from March 10, 2020, to August 25, 2020. The correlation analysis showed that COVID-19 cases and temperature showed a positive correlation. It implies that the increase in COVID-19 cases was reported due to an increase in the temperature in Pakistan, its provinces, and administrative units. The generalized Poisson regression showed that the rise in the expected log count of COVID-19 cases was 0.024 times for a 1 °C rise in the average temperature in Pakistan. Second, the correlation between rainfall and COVID-19 cases was negative in Pakistan. However, the regression coefficient between the expected log count of COVID-19 cases and rainfall was insignificant in Pakistan. Third, the correlation between humidity and the total COVID-19 cases was negative, which implies that the increase in humidity is beneficial to stop the transmission of COVID-19 in Pakistan, its provinces, and administrative units. The reduction in the expected log count of COVID-19 cases was 0.008 times for a 1% increase in the humidity per day in Pakistan. However, humidity and COVID-19 cases were positively correlated in Sindh province. It is required to create awareness among the general population, and the government should include the causes, symptoms, and precautions in the educational syllabus. Moreover, people should adopt the habit of hand wash, social distancing, personal hygiene, mask-wearing, and the use of hand sanitizers to control the COVID-19.

Ozone pollution in London and Edinburgh: spatiotemporal characteristics, trends, transport and the impact of COVID-19 control measures.

Air pollution remains the most serious environmental health issue in the United Kingdom while also carrying non-trivial economic costs. The COVID-19 lockdown periods reduced anthropogenic emissions and offered unique conditions for air pollution research. This study sources fine-granularity geo-spatial air quality and meteorological data for the capital cities of two UK countries (i.e. England's capital London and Scotland's capital Edinburgh) from the UK Automatic Urban and Rural Network (AURN) spanning 2016-2022 to assess long-term trends in several criteria pollutants (PM10, PM2.5, SO2, NO2, O3, and CO) and the changes in ozone pollution during the pandemic period. Unlike other studies conducted thus far, this research integrates several tools in trend estimation, including the Mann-Kendall test, the Theil-Sen estimator with bootstrap resampling, and the generalized additive model (GAM). Moreover, several investigations, including cluster trajectory analysis, pollution rose plots, and potential source contribution function (PSCF), are also employed to identify potential origin sources for air masses carrying precursors and estimate their contributions to ozone concentrations at receptor sites and downwind areas. The main findings reveal that most of the criteria pollutants show a decreasing trend in both geographies over the seven-year period, except for O3, which presents a significant ascending trend in London and a milder ascending trend in Edinburgh. However, O3 concentrations have significantly decreased during the year 2020 in both urban areas, despite registering sharp increases during the first lockdown period. In turn, these findings indicate on one hand that the O3 generation process is in the VOC-limited regime in both UK urban areas and, on the other hand, confirm previous findings that, when stretching the analysis period, diminishing ozone levels can lead to NOx reduction even in VOC-controlled geographies. Trajectory analysis reveals that northern Europe, particularly Norway and Sweden, is a principal ozone pollution source for Edinburgh, whereas, for London, mainland Europe (i.e., the Benelux countries) is another significant source. The results have important policy implications, revealing that effective and efficient NOx abatement measures spur ozone pollution in the short-term, but the increase can be transient. Moreover, policymakers in London and Edinburgh should consider that both local and transboundary sources contribute to local ozone pollution.

Interaction Effect Between Hemoglobin and Hypoxemia on COVID-19 Mortality: an observational study from Bogotá, Colombia.

Purpose: We aimed to assess the effect of hemoglobin (Hb) concentration and oxygenation index on COVID-19 patients' mortality risk. Patients and Methods: We retrospectively reviewed sociodemographic and clinical characteristics, laboratory findings, and clinical outcomes from patients admitted to a tertiary care hospital in Bogotá, Colombia, from March to July 2020. We assessed exploratory associations between oxygenation index and Hb concentration at admission and clinical outcomes. We used a generalized additive model (GAM) to evaluate the observed nonlinear relations and the classification and regression trees (CART) algorithm to assess the interaction effects. Results: We included 550 patients, of which 52% were male. The median age was 57 years old, and the most frequent comorbidity was hypertension (29%). The median value of SpO2/FiO2 was 424, and the median Hb concentration was 15 g/dL. The mortality was 15.1% (83 patients). Age, sex, and SpO2/FiO2, were independently associated with mortality. We described a nonlinear relationship between Hb concentration and neutrophil-to-lymphocyte ratio with mortality and an interaction effect between SpO2/FiO2 and Hb concentration. Patients with a similar oxygenation index had different mortality likelihoods based upon their Hb at admission. CART showed that patients with SpO2/FiO2 < 324, who were less than 81 years with an NLR >9.9, and Hb > 15 g/dl had the highest mortality risk (91%). Additionally, patients with SpO2/FiO2 > 324 but Hb of < 12 g/dl and a history of hypertension had a higher mortality likelihood (59%). In contrast, patients with SpO2/FiO2 > 324 and Hb of > 12 g/dl had the lowest mortality risk (9%). Conclusion: We found that a decreased SpO2/FiO2 increased mortality risk. Extreme values of Hb, either low or high, showed an increase in the likelihood of mortality. However, Hb concentration modified the SpO2/FiO2 effect on mortality; the probability of death in patients with low SpO2/FiO2 increased as Hb increased.

Analysis of the meteorological factors affecting the short-term increase in O3 concentrations in nine global cities during COVID-19.

Surface ozone (O3) is a major air pollutant around the world. This study investigated O3 concentrations in nine cities during the Coronavirus disease 2019 (COVID-19) lockdown phases. A statistical model, named Generalized Additive Model (GAM), was also developed to assess different meteorological factors, estimate daily O3 release during COVID-19 lockdown and determine the relationship between the two. We found that: (1) Daily O3 significantly increased in all selected cities during the COVID-19 lockdown, presenting relative increases from -5.7% (in São Paulo) to 58.9% (in Guangzhou), with respect to the average value for the same period in the previous five years. (2) In the GAM model, the adjusted coefficient of determination (R2) ranged from 0.48 (Sao Paulo) to 0.84 (Rome), and it captured 51-85% of daily O3 variations. (3) Analyzing the expected O3 concentrations during the lockdown, using GAM fed by meteorological data, showed that O3 anomalies were dominantly controlled by meteorology. (4) The relevance of different meteorological variables depended on the cities. The positive O3 anomalies in Beijing, Wuhan, Guangzhou, and Delhi were mostly associated with low relative humidity and elevated maximum temperature. Low wind speed, elevated maximum temperature, and low relative humidity were the leading meteorological factors for O3 anomalies in London, Paris, and Rome. The two other cities had different leading factor combinations.

A Statistical Non-Parametric data analysis for COVID-19 incidence data.

BACKGROUND: The impact of COVID-19 on the Global scale is tremendously drastic. There are several types of research going on across the world simultaneously to understand and overcome this dire pandemic outbreak. This paper is purely a statistical study on a distinct set of datasets regarding COVID-19 in India. The motivation of this study is to provide an insight into the rapid growth of confirmed COVID-19 cases in India. METHODS: The rapid growth of COVID-19 cases in India started in March 2020. The main objective of this paper is to provide a solid statistical model for the policymaker to handle this kind of pandemic situation in the near future with nonlinear data. In this paper, the data was got from 1st April to 29th November 2020. To come up with a solid statistical model, various nonlinear data such as confirmed COVID-19 cases, maximum temperature, minimum temperature, the total population (state-wise), the total area in km2 (state-wise), and the total rural and urban population count (state-wise) have been analyzed. In this paper, six different Generalized Additive Models (GAM) was identified after a thorough analysis of other researchers' (Xie and Zhu, 2020; Prata et al., 2020) findings. RESULTS: In all perspectives, the results were identified and analyzed. The GAM model regarding total COVID-19 confirmed cases, total population, and the total rural population provides the best average fit of R2 value of 0.934. As the population value is quite high, the author has concise it using logarithm to provide the best p-value of 0.000542 and 0.001407 for a relation between the total number of COVID-19 cases regarding the total population and total rural population respectively.

Neural Additive Models: Interpretable Machine Learning with Neural Nets

Deep neural networks (DNNs) are powerful black-box predictors that have achieved impressive performance on a wide variety of tasks. However, their accuracy comes at the cost of intelligibility: it is usually unclear how they make their decisions. This hinders their applicability to high stakes decision-making domains such as healthcare. We propose Neural Additive Models (NAMs) which combine some of the expressivity of DNNs with the inherent intelligibility of generalized additive models. NAMs learn a linear combination of neural networks that each attend to a single input feature. These networks are trained jointly and can learn arbitrarily complex relationships between their input feature and the output. Our experiments on regression and classification datasets show that NAMs are more accurate than widely used intelligible models such as logistic regression and shallow decision trees. They perform similarly to existing state-of-the-art generalized additive models in accuracy, but are more flexible because they are based on neural nets instead of boosted trees. To demonstrate this, we show how NAMs can be used for multitask learning on synthetic data and on the COMPAS recidivism data due to their composability, and demonstrate that the differentiability of NAMs allows them to train more complex interpretable models for COVID-19. Source code is available at neural-additive-models.github.io. © 2021 Neural information processing systems foundation. All rights reserved.

Relationship between Meteorological and Air Quality Parameters and COVID-19 in Casablanca Region, Morocco.

The aim of this study was to investigate the relationship between meteorological parameters, air quality and daily COVID-19 transmission in Morocco. We collected daily data of confirmed COVID-19 cases in the Casablanca region, as well as meteorological parameters (average temperature, wind, relative humidity, precipitation, duration of insolation) and air quality parameters (CO, NO2, 03, SO2, PM10) during the period of 2 March 2020, to 31 December 2020. The General Additive Model (GAM) was used to assess the impact of these parameters on daily cases of COVID-19. A total of 172,746 confirmed cases were reported in the study period. Positive associations were observed between COVID-19 and wind above 20 m/s and humidity above 80%. However, temperatures above 25° were negatively associated with daily cases of COVID-19. PM10 and O3 had a positive effect on the increase in the number of daily confirmed COVID-19 cases, while precipitation had a borderline effect below 25 mm and a negative effect above this value. The findings in this study suggest that significant associations exist between meteorological factors, air quality pollution (PM10) and the transmission of COVID-19. Our findings may help public health authorities better control the spread of COVID-19.

Smoothed Bernstein Online Aggregation for Short-Term Load Forecasting in IEEE DataPort Competition on Day-Ahead Electricity Demand Forecasting: Post-COVID Paradigm

We present a winning method of the IEEE DataPort Competition on Day-Ahead Electricity Demand Forecasting: Post-COVID Paradigm. The day-ahead load forecasting approach is based on a novel online forecast combination of multiple point prediction models. It contains four steps: i) data cleaning and preprocessing, ii) a new holiday adjustment procedure, iii) training of individual forecasting models, iv) forecast combination by smoothed Bernstein Online Aggregation (BOA). The approach is flexible and can quickly adjust to new energy system situations as they occurred during and after COVID-19 shutdowns. The ensemble of individual prediction models ranges from simple time series models to sophisticated models like generalized additive models (GAMs) and high-dimensional linear models estimated by lasso. They incorporate autoregressive, calendar, and weather effects efficiently. All steps contain novel concepts that contribute to the excellent forecasting performance of the proposed method. It is especially true for the holiday adjustment procedure and the fully adaptive smoothed BOA approach. Author

The occurrence of COVID-19 is associated with air quality and relative humidity.

The association between meteorological factors and COVID-19 is important for the prevention and control of COVID-19. However, similar studies are relatively rare in China. This study aims to investigate the association between COVID-19 and meteorological factors, such as average temperature, relative humidity, and air quality index (AQI), and average wind speed. We collected the daily confirmed cases of COVID-19 and meteorological factors in Shanghai China from January 10, 2020 to March 31, 2020. A generalized additive model was fitted to quantify the associations between meteorological factors and COVID-19 during the study period. A negative association between average temperature and daily confirmed cases of COVID-19 was found on lag 13 days. In addition, we observed a significant positive correlation between meteorological factors (AQI, relative humidity) and daily confirmed cases of COVID-19. A 10 increase in AQI (lag1/7/8/9/10 days) was correlated with a 4.2%-9.0% increase in the daily confirmed cases of COVID-19. A 1% increase in relative humidity (lag1/4/7/8/9/10 days) was correlated with 1.7%-3.7% increase in the daily confirmed cases of COVID-19. However, the associations between average wind speed and the daily confirmed cases of COVID-19 is complex in different lag days. In summary, meteorological factors could affect the occurrence of COVID-19. Reducing the effects of meteorological factors on COVID-19 may be an important public health action for the prevention and control of COVID-19.

Quantifying Neutralizing Antibodies in Patients with COVID-19 by a Two-Variable Generalized Additive Model.

Considering the urgent demand for faster methods to quantify neutralizing antibody titers in patients with coronavirus (CoV) disease 2019 (COVID-19), developing an analytical model or method to replace the conventional virus neutralization test (NT) is essential. Moreover, a "COVID-19 immunity passport" is currently being proposed as a certification for people who travel internationally. Therefore, an enzyme-linked immunosorbent assay (ELISA) was designed to detect severe acute respiratory syndrome CoV 2 (SARS-CoV-2)-neutralizing antibodies in serum, which is based on the binding affinity of SARS-CoV-2 viral spike protein 1 (S1) and the viral spike protein receptor-binding domain (RBD) to antibodies. The RBD is considered the major binding region of neutralizing antibodies. Furthermore, S1 covers the RBD and several other regions, which are also important for neutralizing antibody binding. In this study, we assessed 144 clinical specimens, including those from patients with PCR-confirmed SARS-CoV-2 infections and healthy donors, using both the NT and ELISA. The ELISA results analyzed by spline regression and the two-variable generalized additive model precisely reflected the NT value, and the correlation between predicted and actual NT values was as high as 0.917. Therefore, our method serves as a surrogate to quantify neutralizing antibody titer. The analytic method and platform used in this study present a new perspective for serological testing of SARS-CoV-2 infection and have clinical potential to assess vaccine efficacy. IMPORTANCE Herein, we present a new approach for serological testing for SARS-CoV-2 antibodies using innovative laboratory methods that demonstrate a combination of biology and mathematics. The traditional virus neutralization test is the gold standard method; however, it is time-consuming and poses a risk to medical personnel. Thus, there is a demand for methods that rapidly quantify neutralizing antibody titers in patients with COVID-19 or examine vaccine efficacy at a biosafety level 2 containment facility. Therefore, we used a two-variable generalized additive model to analyze the results of the enzyme-linked immunosorbent assay and found the method to serve as a surrogate to quantify neutralizing antibody titers. This methodology has potential for clinical use in assessing vaccine efficacy.

Impacts of COVID-19 on Air Quality through Traffic Reduction.

In 2020, the first case of COVID-19 was confirmed in Korea, and social distancing was implemented to prevent its spread. This reduced the movement of people, and changes in air quality were expected owing to reduced emissions. In the present paper, the impact of traffic volume change caused by COVID-19 on air quality in Seoul, Korea, is examined. Two regression analyses were performed using the generalized additive model (GAM), assuming a Gaussian distribution; the relationships between (1) the number of confirmed COVID-19 cases in 2020-2021 and the rate of change in the traffic volume in Seoul, and (2) the traffic volume and the rate of change in the air quality in Seoul from 2016 to 2019 were analyzed. The regression results show that traffic decreased by 0.00431% per COVID-19 case; when traffic fell by 1%, the PM10, PM2.5, CO, NO2, O3, and SO2 concentrations fell by 0.48%, 0.94%, 0.39%, 0.74%, 0.16%, and -0.01%, respectively. This mechanism accounts for air quality improvements in PM10, PM2.5, CO, NO2, and O3 in Seoul during 2020-2021. From these results, the majority of the reduction in pollutant concentrations in 2020-2021 appears to be the result of a long-term declining trend rather than COVID-19.