Detection of COVID-19 patient based on attention segmental recurrent neural network (ASRNN) Archimedes optimization algorithm using ultra-low-dose CT images

In this article, the detection of COVID-19 patient based on attention segmental recurrent neural network (ASRNN) with Archimedes optimization algorithm (AOA) using ultra-low-dose CT (ULDCT) images is proposed. Here, the ultra-low-dose CT images are gathered via real time dataset. The input images are preprocessed with the help of convolutional auto-encoder to recover the ULDCT images quality by removing noises. The preprocessed images are given to generalized additive models with structured interactions (GAMI) for extracting the radiomic features. The radiomic features, such as morphologic, gray scale statistic, Haralick texture are extracted using GAMI-Net. The ASRNN classifier, whose weight parameters optimized with Archimedes optimization algorithm enables COVID-19 ULDCT images classification as COVID-19 or normal. The proposed approach is activated in MATLAB platform. The proposed ASRNN-AOA-ULDCT attains accuracy 22.08%, 24.03%, 34.76%, 34.65%, 26.89%, 45.86%, and 32.14%;precision 23.34%, 26.45%, 34.98%, 27.06%, 35.87%, 34.44%, and 22.36% better than the existing methods, such as DenseNet-HHO-ULDCT, ELM-DNN-ULDCT, EDL-ULDCT, ResNet 50-ULDCT, SDL-ULDCT, CNN-ULDCT, and DRNN-ULDCT, respectively. © 2023 John Wiley & Sons, Ltd.

Vessel traffic influences distribution of Aotearoa New Zealand's endemic dolphin (Cephalorhynchus hectori)

Cetacean tourism and vessel traffic have grown considerably around the world in recent decades. At Akaroa Harbor, Aotearoa New Zealand, recreational vessel traffic, dolphin tourism, and cruise ship presence increased substantially between 2008 and 2020. We examined the relationship between vessel traffic parameters and the presence of Hector's dolphins (Cephalorhynchus hectori) during the austral summer 2019–2020, using automated vessel tracking and autonomous passive acoustic monitoring. Data were collected between December 2019 and May 2020, including the entirety of the first COVID‐19 nationwide lockdown. Generalized additive models revealed that increasing levels of motor vessel traffic, the presence of cruise ships, and high levels of dolphin tour vessel traffic resulted in decreases in acoustic detections of dolphins. Our findings suggest that Hector's dolphins at Akaroa Harbor were displaced from core habitat in response to each of these vessel traffic parameters. We recommend that managers use immediately actionable tools to reduce the impacts of vessels on these dolphins.

Precision recruitment for high-risk participants in a COVID-19 cohort study.

Background: Studies for developing diagnostics and treatments for infectious diseases usually require observing the onset of infection during the study period. However, when the infection base rate incidence is low, the cohort size required to measure an effect becomes large, and recruitment becomes costly and prolonged. We developed a model for reducing recruiting time and resources in a COVID-19 detection study by targeting recruitment to high-risk individuals. Methods: We conducted an observational longitudinal cohort study at individual sites throughout the U.S., enrolling adults who were members of an online health and research platform. Through direct and longitudinal connection with research participants, we applied machine learning techniques to compute individual risk scores from individually permissioned data about socioeconomic and behavioral data, in combination with predicted local prevalence data. The modeled risk scores were then used to target candidates for enrollment in a hypothetical COVID-19 detection study. The main outcome measure was the incidence rate of COVID-19 according to the risk model compared with incidence rates in actual vaccine trials. Results: When we used risk scores from 66,040 participants to recruit a balanced cohort of participants for a COVID-19 detection study, we obtained a 4- to 7-fold greater COVID-19 infection incidence rate compared with similar real-world study cohorts. Conclusion: This risk model offers the possibility of reducing costs, increasing the power of analyses, and shortening study periods by targeting for recruitment participants at higher risk.

Longitudinal analyses of depression, anxiety, and suicidal ideation highlight greater prevalence in the northern Dutch population during the COVID-19 lockdowns.

BACKGROUND: The pandemic of the coronavirus disease 2019 (COVID-19) has led to an increased burden on mental health. AIMS: To investigate the development of major depressive disorder (MDD), generalized anxiety disorder (GAD), and suicidal ideation in the Netherlands during the first fifteen months of the pandemic and three nation-wide lockdowns. METHOD: Participants of the Lifelines Cohort Study -a Dutch population-based sample-reported current symptoms of MDD and GAD, including suicidal ideation, according to DSM-IV criteria. Between March 2020 and June 2021, 36,106 participants (aged 18-96) filled out a total of 629,811 questionnaires across 23 time points. Trajectories over time were estimated using generalized additive models and analyzed in relation to age, sex, and lifetime history of MDD/GAD. RESULTS: We found non-linear trajectories for MDD and GAD with a higher number of symptoms and prevalence rates during periods of lockdown. The point prevalence of MDD and GAD peaked during the third hard lockdown at 2.88 % (95 % CI: 2.71 %-3.06 %) and 2.92 % (95 % CI: 2.76 %-3.08 %), respectively, in March 2021. Women, younger adults, and participants with a history of MDD/GAD reported significantly more symptoms. For suicidal ideation, we found a significant linear increase over time in younger participants. For example, 20-year-old participants reported 4.14× more suicidal ideation at the end of June 2021 compared to the start of the pandemic (4.64 % (CI: 3.09 %-6.96 %) versus 1.12 % (CI: 0.76 %-1.66 %)). LIMITATIONS: Our findings should be interpreted in relation to the societal context of the Netherlands and the public health response of the Dutch government during the pandemic, which may be different in other regions in the world. CONCLUSIONS: Our study showed greater prevalence of MDD and GAD during COVID-19 lockdowns and a continuing increase in suicidal thoughts among young adults suggesting that the pandemic and government enacted restrictions impacted mental health in the population. Our findings provide actionable insights on mental health in the population during the pandemic, which can guide policy makers and clinical care during future lockdowns and epi/pandemics.

Vessel traffic influences distribution of Aotearoa New Zealand's endemic dolphin (Cephalorhynchus hectori)

Cetacean tourism and vessel traffic have grown considerably around the world in recent decades. At Akaroa Harbor, Aotearoa New Zealand, recreational vessel traffic, dolphin tourism, and cruise ship presence increased substantially between 2008 and 2020. We examined the relationship between vessel traffic parameters and the presence of Hector's dolphins (Cephalorhynchus hectori) during the austral summer 2019-2020, using automated vessel tracking and autonomous passive acoustic monitoring. Data were collected between December 2019 and May 2020, including the entirety of the first COVID-19 nationwide lockdown. Generalized additive models revealed that increasing levels of motor vessel traffic, the presence of cruise ships, and high levels of dolphin tour vessel traffic resulted in decreases in acoustic detections of dolphins. Our findings suggest that Hector's dolphins at Akaroa Harbor were displaced from core habitat in response to each of these vessel traffic parameters. We recommend that managers use immediately actionable tools to reduce the impacts of vessels on these dolphins.

COVID-19 Pandemic vis-à-vis Zamboanga Peninsula Aviation: A Predictive Analysis

The use of time-series analysis to examine aviation data trends through time comes crucial in planning its future. The prophet is an additive model that fits non-linear patterns. It functions best with historical data from various seasons and time series with significant seasonal impacts. This research looked closely into the aviation data in Zamboanga Peninsula, Jolo, and Tawi-Tawi to give a clearer picture of its impact on the sector and forecast passenger and aircraft movement in the coming months to see whether the impact of the opening in the aviation industry can be sustained. The final data comprise 51 data points for flight arrivals and departures and 51 data points for passenger arrivals and departures. Data show the decline in passengers and aircrafts arriving and departing in major airports in Zamboanga Peninsula, Jolo, and Tawi-Tawi during the pandemic. However, an increasing trend was observed years after the pandemic hit the region. Findings during the training and testing phase revealed that different models attained varied results;however, there are models which attained a higher degree of accuracy as depicted in the RMSE and R2. This indicates that predicting passenger and aircraft movement using models with higher accuracy is similar to real data thus, it is viable in predicting future values. Forecasting results further show a gradually increasing trend of aircraft and passenger arrivals in the major airports in Zamboanga Peninsula, Jolo, and Tawi-Tawi despite some observed smaller forecasted values. © 2022 IEEE.

Spatiotemporal Autoregressive Partially Linear Varying Coefficient Models

With increasingly abundant data that relate to both space and time becoming available, spatiotemporal modeling is receiving much attention in the literature. This paper study develops a class of spatiotemporal autoregressive par-tially linear varying-coefficient models that are sufficiently flexible to simultaneously capture the spatiotemporal dependence and nonstationarity often encountered in practice. When spatial observations are observed over time and exhibit dynamic and nonstationary behaviors, our models become particularly useful. We develop a numerically stable and computationally efficient estimation procedure, using the tensor-product splines over triangular prisms to approximate the coefficient func-tions. The estimators of both the constant coefficients and the varying coefficients are consistent. We also show that the estimators of the constant coefficients are asymptotically normal, which enables us to construct confidence intervals and make inferences. The method's performance is evaluated using Monte Carlo experiments, and applied to model and forecast the spread of COVID-19 at the county level in the United States.

Statistical modelling of COVID-19 data: Putting generalized additive models to work

Over the course of the COVID-19 pandemic, Generalized Additive Models (GAMs) have been successfully employed on numerous occasions to obtain vital data-driven insights. In this article we further substantiate the success story of GAMs, demonstrating their flexibility by focusing on three relevant pandemic-related issues. First, we examine the interdepency among infections in different age groups, concentrating on school children. In this context, we derive the setting under which parameter estimates are independent of the (unknown) case-detection ratio, which plays an important role in COVID-19 surveillance data. Second, we model the incidence of hospitalizations, for which data is only available with a temporal delay. We illustrate how correcting for this reporting delay through a nowcasting procedure can be naturally incorporated into the GAM framework as an offset term. Third, we propose a multinomial model for the weekly occupancy of intensive care units (ICU), where we distinguish between the number of COVID-19 patients, other patients and vacant beds. With these three examples, we aim to showcase the practical and ‘off-the-shelf’ applicability of GAMs to gain new insights from real-world data. [ FROM AUTHOR] Copyright of Statistical Modelling: An International Journal is the property of Sage Publications, Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Introduction to the special issue on Data Science for COVID-19

An introduction to this Special Issue on Data Science for COVID-19 is included in this paper. It contains a general overview about methods and applications of nonparametric inference and other flexible data science methods for the COVID-19 pandemic. Specifically, some methods existing before the COVID-19 outbreak are surveyed, followed by an account of survival analysis methods for COVID-related times. Then, several nonparametric tools for the estimation of certain COVID rates are revised, along with the forecasting of most relevant series counts, and some other related problems. Within this setup, the papers published in this special issue are briefly commented in this introductory article. [ FROM AUTHOR] Copyright of Journal of Nonparametric Statistics is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Impact of Short-Term Air Pollution on Respiratory Infections: A Time-Series Analysis of COVID-19 Cases in California during the 2020 Wildfire Season.

The 2020 California wildfire season coincided with the peak of the COVID-19 pandemic affecting many counties in California, with impacts on air quality. We quantitatively analyzed the short-term effect of air pollution on COVID-19 transmission using county-level data collected during the 2020 wildfire season. Using time-series methodology, we assessed the relationship between short-term exposure to particulate matter (PM2.5), carbon monoxide (CO), nitrogen dioxide (NO2), and Air Quality Index (AQI) on confirmed cases of COVID-19 across 20 counties impacted by wildfires. Our findings indicate that PM2.5, CO, and AQI are positively associated with confirmed COVID-19 cases. This suggests that increased air pollution could worsen the situation of a health crisis such as the COVID-19 pandemic. Health policymakers should make tailored policies to cope with situations that may increase the level of air pollution, especially during a wildfire season.

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.

Impact of Short-Term Air Pollution on Respiratory Infections: A Time-Series Analysis of COVID-19 Cases in California during the 2020 Wildfire Season

The 2020 California wildfire season coincided with the peak of the COVID-19 pandemic affecting many counties in California, with impacts on air quality. We quantitatively analyzed the short-term effect of air pollution on COVID-19 transmission using county-level data collected during the 2020 wildfire season. Using time-series methodology, we assessed the relationship between short-term exposure to particulate matter (PM2.5), carbon monoxide (CO), nitrogen dioxide (NO2), and Air Quality Index (AQI) on confirmed cases of COVID-19 across 20 counties impacted by wildfires. Our findings indicate that PM2.5, CO, and AQI are positively associated with confirmed COVID-19 cases. This suggests that increased air pollution could worsen the situation of a health crisis such as the COVID-19 pandemic. Health policymakers should make tailored policies to cope with situations that may increase the level of air pollution, especially during a wildfire season.

Atmospheric pollutants response to the emission reduction and meteorology during the COVID-19 lockdown in the north of Africa (Morocco).

Climate and air quality change due to COVID-19 lockdown (LCD) are extremely concerned subjects of several research recently. The contribution of meteorological factors and emission reduction to air pollution change over the north of Morocco has been investigated in this study using the framework generalized additive models, that have been proved to be a robust technique for the environmental data sets, focusing on main atmospheric pollutants in the region including ozone (O3), nitrogen dioxide (NO2), sulfur dioxide (SO2), particulate matter (PM2.5 and PM10), secondary inorganic aerosols (SIA), nom-methane volatile organic compounds and carbon monoxide (CO) from the regional air pollution dataset of the Copernicus Atmosphere Monitoring Service. Our results, indicate that secondary air pollutants (PM2.5, PM10 and O3) are more influenced by metrological factors and the other air pollutants reported by this study (NO2 and SO2). We show a negative effect for PBHL, total precipitation and NW10M on PM (PM2.5 and PM10 ), this meteorological parameters contribute to decrease in PM2.5 by 9, 2 and 9% respectively, before LCD and 8, 1 and 5% respectively during LCD. However, a positive marginal effect was found for SAT, Irradiance and RH that contribute to increase PM2.5 by 9, 12 and 18% respectively, before LCD and 17, 54 and 34% respectively during LCD. We found also that meteorological factors contribute to O3, PM2.5, PM10 and SIA average mass concentration by 22, 5, 3 and 34% before LCD and by 28, 19, 5 and 42% during LCD respectively. The increase in meteorological factors marginal effect during LCD shows the contribution of photochemical oxidation to air pollution due to increase in atmospheric oxidant (O3 and OH radical) during LCD, which can explain the response of PM to emission reduction. This study indicates that PM (PM2.5, PM10) has more controlled by SO2 due to the formation of sulfate particles especially under high oxidants level. The positive correlation between westward wind at 10 m (WW10M), Northward Wind at 10 m (NW10M) and PM indicates the implication of sea salt particles transported from Mediterranean Sea and Atlantic Ocean. The Ozone mass concentration shows a positive trend with Irradiance, Total and SAT during LCD; because temperature and irradiance enhance tropospheric ozone formation via photochemical reaction.This study shows the contribution of atmospheric oxidation capacity to air pollution change. Supplementary Information: The online version contains supplementary material available at 10.1007/s00477-022-02224-z.

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

Generalized Additive Models to Capture the Death Rates in Canada COVID-19

To capture the death rates and strong weekly, biweekly and probably monthly patterns in the Canada COVID-19, we utilize the generalized additive models in the absence of direct statistically based measurement of infection rates. By examining the death rates of Canada in general and Quebec, Ontario and Alberta in particular, that there are substantial overdispersion relative to the Poisson so that the negative binomial distribution is an appropriate choice for the analysis. Generalized additive models (GAMs) are one of the main modeling tools for data analysis. © 2022, Springer Nature Switzerland AG.

Modeling the number of people infected with SARS-COV-2 from wastewater viral load in Northwest Spain.

The quantification of the SARS-CoV-2 RNA load in wastewater has emerged as a useful tool to monitor COVID-19 outbreaks in the community. This approach was implemented in the metropolitan area of A Coruña (NW Spain), where wastewater from a treatment plant was analyzed to track the epidemic dynamics in a population of 369,098 inhabitants. Viral load detected in the wastewater and the epidemiological data from A Coruña health system served as main sources for statistical models developing. Regression models described here allowed us to estimate the number of infected people (R2 = 0.9), including symptomatic and asymptomatic individuals. These models have helped to understand the real magnitude of the epidemic in a population at any given time and have been used as an effective early warning tool for predicting outbreaks in A Coruña municipality. The methodology of the present work could be used to develop a similar wastewater-based epidemiological model to track the evolution of the COVID-19 epidemic anywhere in the world where centralized water-based sanitation systems exist.

Unified statistical inference for a nonlinear dynamic functional/longitudinal data model

Studying massive functional/longitudinal data, we adopt a flexible nonlinear dynamic regression method named the Semi-Varying Coefficient Additive Model, in which the response can be a functional/longitudinal variable, and the explanatory variables can be a mixture of functional/longitudinal and scalar variables. With the aid of an initial B-spline approximation, a local linear smoothing is proposed to estimate the unknown functional effects in the model. Existing methods of statistical inference for sparse data and dense data are significantly different. We therefore develop the asymptotic theories of the resultant pilot estimation based local linear estimators (PEBLLE) on a unified framework of sparse, dense and ultra-dense cases of data. Remarkably, we obtain the oracle properties as if other functions were known in advance. Extensive Monte Carlo simulation studies investigating the finite sample performance of the proposed methodologies confirm our asymptotic results. We further illustrate our methodologies by analyzing COVID-19 data from China.

Prescription fill patterns for benzodiazepine and opioid drugs during the COVID-19 pandemic in the United States.

BACKGROUND: COVID-19 and resulting mitigation measures in the United States (US) brought about limited access to medical care that has been linked to increases in mental health problems, excessive substance use, and drug overdoses. The increase in co-prescription of benzodiazepines and opioids may indicate population-level changes in health behaviors that can be exacerbated by limited access, hence necessitating the tracking of these drugs during COVID-19. We evaluated the impact of the declaration of COVID-19 as a US national emergency on prescription patterns in 2020. METHODS: Prescriptions of benzodiazepines and opioids were analyzed using data aggregated on a weekly basis across 38 states over the January 2019-December 2020 period. Data were from Bamboo Health Prescription Drug Monitoring Program and covered all individuals regardless of insurance status. Generalized additive models estimated the effects of the March 13, 2020 declaration on proportion of prescriptions to all controlled substances by comparing volumes before to after the week of March 13 in 2020 (range: January 27-May 24) and comparing this trend to its 2019 counterpart. RESULTS: When comparing the January 27-March 9 period to the March 16-May 24 period in 2020, there was a statistically significant 2.0% increase in the proportion of benzodiazepine dispensations to all controlled substances, and a significant 1.7% mean decrease in proportion of opioid dispensations to all controlled substances. A significant return approaching pre-declaration levels was observed only for opioids (beginning week of May 18, 2020). CONCLUSIONS: The results suggest significant impacts of the COVID-19 pandemic on dispensations of benzodiazepines and opioids across the US. Continued monitoring of prescription trends and maintenance of adequate and accessible access to mental healthcare are important for understanding public health crises related to substance use.

A statistical model for the dynamics of COVID-19 infections and their case detection ratio in 2020.

The case detection ratio of coronavirus disease 2019 (COVID-19) infections varies over time due to changing testing capacities, different testing strategies, and the evolving underlying number of infections itself. This note shows a way of quantifying these dynamics by jointly modeling the reported number of detected COVID-19 infections with nonfatal and fatal outcomes. The proposed methodology also allows to explore the temporal development of the actual number of infections, both detected and undetected, thereby shedding light on the infection dynamics. We exemplify our approach by analyzing German data from 2020, making only use of data available since the beginning of the pandemic. Our modeling approach can be used to quantify the effect of different testing strategies, visualize the dynamics in the case detection ratio over time, and obtain information about the underlying true infection numbers, thus enabling us to get a clearer picture of the course of the COVID-19 pandemic in 2020.

Opposite impact of emission reduction during the COVID-19 lockdown period on the surface concentrations of PM2.5 and O3 in Wuhan, China.

To prevent the spread of the COVID-19 epidemic, the Chinese megacity Wuhan has taken emergent lockdown measures starting on January 23, 2020. This provided a natural experiment to investigate the response of air quality to such emission reductions. Here, we decoupled the influence of meteorological and non-meteorological factors on main air pollutants using generalized additive models (GAMs), driven by data from the China National Environmental Monitoring Center (CNEMC) network. During the lockdown period (Jan. 23 - Apr. 8, 2020), PM2.5, PM10, NO2, SO2, and CO concentrations decreased significantly by 45 %, 49 %, 56 %, 39 %, and 18 % compared with the corresponding period in 2015-2019, with contributions by S(meteos) of 15 %, 17 %, 13 %, 10 %, and 6 %. This indicates an emission reduction of NOx at least 43 %. However, O3 increased by 43 % with a contribution by S(meteos) of 6 %. In spite of the reduced volatile organic compound (VOC) emissions by 30 % during the strict lockdown period (Jan. 23 - Feb. 14, 2020), which likely reduced the production of O3, O3 concentrations increased due to a weakening of the titration effect of NO. Our results suggest that conventional emission reduction (NOx reduction only) measures may not be sufficient to reduce (or even lead to an increase of) surface O3 concentrations, even if reaching the limit, and VOC-specific measures should also be taken.