scholarly journals Correlation between the Level of Social Distancing and Activity of Influenza Epidemic or COVID-19 Pandemic: A Subway Use-Based Assessment

2021 ◽  
Vol 10 (15) ◽  
pp. 3369
Author(s):  
Hye Seong ◽  
Jin-Wook Hong ◽  
Hak-Jun Hyun ◽  
Jin-Gu Yoon ◽  
Ji-Yun Noh ◽  
...  
Keyword(s):  
Time Series ◽  
Public Transportation ◽  
Viral Infections ◽  
Antiviral Agents ◽  
Social Activity ◽  
Temporal Association ◽  
Influenza Epidemic ◽  
Time Series Regression ◽  
Effective Measure ◽  
Social Distancing

Social distancing is an effective measure to mitigate the spread of novel viral infections in the absence of antiviral agents and insufficient vaccine supplies. Subway utilization density may reflect social activity and the degree of social distancing in the general population.; This study aimed to evaluate the correlations between subway use density and the activity of the influenza epidemic or coronavirus disease 2019 (COVID-19) pandemic using a time-series regression method. The subway use-based social distancing score (S-SDS) was calculated using the weekly ridership of 11 major subway stations. The temporal association of S-SDS with influenza-like illness (ILI) rates or the COVID-19 pandemic activity was analyzed using structural vector autoregressive modeling and the Granger causality (GC) test. During three influenza seasons (2017–2020), the time-series regression presented a significant causality from S-SDS to ILI (p = 0.0484). During the COVID-19 pandemic in January 2020, S-SDS had been suppressed at a level similar to or below the average of the previous four years. In contrast to the ILI rate, there was a negative correlation between COVID-19 activity and S-SDS. GC analysis revealed a negative causal relationship between COVID-19 and S-SDS (p = 0.0098).; S-SDS showed a significant time-series association with the ILI rate but not with COVID-19 activity. When public transportation use is sufficiently suppressed, additional social mobility restrictions are unlikely to significantly affect COVID-19 pandemic activity. It would be more important to strengthen universal mask-wearing and detailed public health measures focused on risk activities, particularly in enclosed spaces.

scholarly journals SHELTER IN PLACE ORDER CONTAINED COVID-19 GROWTH RATE IN GREECE

2020 ◽  
Author(s):  
T. Giannouchos ◽  
A. Giannouchos ◽  
I. Christodoulou ◽  
E. Steletou ◽  
K. Souliotis
Keyword(s):  
Growth Rate ◽  
Time Series ◽  
Regression Analysis ◽  
Exponential Growth ◽  
Fixed Effects ◽  
Interrupted Time Series ◽  
Shopping Centers ◽  
Time Series Regression ◽  
Social Distancing ◽  
Implementation Period

AbstractBackgroundThe Greek authorities implemented the strong social distancing measures within the first few weeks after the first confirmed case of the virus to curtail the COVID-19 growth rate.ObjectivesTo estimate the effect of the two-stage strong social distancing measures, the closure of all non-essential shopping centers and businesses on March 16 and the shelter in place orders (SIPOs) on March 23 on the COVID-19 growth rate in GreeceMethodsWe obtained data on COVID-19 cases in Greece from February 26th through May 4th from publicly available sources. An interrupted time-series regression analysis was used to estimate the effect of the measures on the exponential growth of confirmed COVID-19 cases, controlling for the number of daily testing, and weekly fixed-effects.ResultsThe growth rate of the COVID-19 cases in the pre-policies implementation period was positive as expected (p=0.003). Based on the estimates of the interrupted time-series, our results indicate that the SIPO on March 23 significantly slowed the growth rate of COVID-19 in Greece (p=0.04). However, we did not find evidence on the effectiveness of standalone and partial measures such as the non-essential business closures implemented on March 16 on the COVID-19 spread reduction.DiscussionThe combined social distancing measures implemented by the Greek authorities within the first few weeks after the first confirmed case of the virus reduced the COVID-19 growth rate. These findings provide evidence and highlight the effectiveness of these measures to flatten the curve and to slow the spread of the virus.

scholarly journals Protease Inhibitors as Antiviral Agents

1998 ◽  
Vol 11 (4) ◽  
pp. 614-627 ◽  
Author(s):  
A. K. Patick ◽  
K. E. Potts
Keyword(s):  
Protease Inhibitors ◽  
Viral Infections ◽  
Critical Role ◽  
Antiviral Agents ◽  
Antiviral Agent ◽  
Structural Proteins ◽  
Viral Protease ◽  
Virus Particles ◽  
Viral Proteases ◽  
Viral Polyprotein

SUMMARY Currently, there are a number of approved antiviral agents for use in the treatment of viral infections. However, many instances exist in which the use of a second antiviral agent would be beneficial because it would allow the option of either an alternative or a combination therapeutic approach. Accordingly, virus-encoded proteases have emerged as new targets for antiviral intervention. Molecular studies have indicated that viral proteases play a critical role in the life cycle of many viruses by effecting the cleavage of high-molecular-weight viral polyprotein precursors to yield functional products or by catalyzing the processing of the structural proteins necessary for assembly and morphogenesis of virus particles. This review summarizes some of the important general features of virus-encoded proteases and highlights new advances and/or specific challenges that are associated with the research and development of viral protease inhibitors. Specifically, the viral proteases encoded by the herpesvirus, retrovirus, hepatitis C virus, and human rhinovirus families are discussed.

scholarly journals Nanobiocide Based-Silver Nanomaterials Upon Coronaviruses: Approaches for Preventing Viral Infections

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Kamyar Khoshnevisan ◽  
Hassan Maleki ◽  
Hadi Baharifar
Keyword(s):  
Viral Entry ◽  
Viral Infections ◽  
Respiratory Infections ◽  
Antiviral Agents ◽  
Long Term Stability ◽  
Silver Nanomaterials ◽  
Inhibitory Mechanisms ◽  
Low Toxicity ◽  
Nano Graphene

Abstract The effectiveness of silver nanomaterials (AgNMs), as antiviral agents, has been confirmed in humans against many different types of viruses. Nanobiocides-based AgNMs can be effectively applied to eliminate coronaviruses (CoVs), as the cause of various diseases in animals and humans, particularly the fatal human respiratory infections. Mostly, these NMs act effectively against CoVs, thanks to the NMs’ fundamental anti-viral structures like reactive oxygen species (ROS), and photo-dynamic and photo-thermal abilities. Particularly, the antiviral activity of AgNMs is clarified under three inhibitory mechanisms including viral entry limitation, attachment inhibition, and viral replication limitation. It is believed that nanobiocide with other possible materials such as TiO2, silica and, carbon NMs exclusively nano-graphene materials can emerge as a more effective disinfectant for long-term stability with low toxicity than common disinfectants. Nanobiocides also can be applied for the prevention and treatment of viral infections specifically against COVID-19. Graphic Abstract

scholarly journals A Vision-Based Social Distancing and Critical Density Detection System for COVID-19

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4608
Author(s):  
Dongfang Yang ◽  
Ekim Yurtsever ◽  
Vishnu Renganathan ◽  
Keith A. Redmill ◽  
Ümit Özgüner
Keyword(s):  
Visual Cues ◽  
Detection System ◽  
Critical Density ◽  
Region Of Interest ◽  
Spatial Awareness ◽  
Real Time System ◽  
Effective Measure ◽  
Social Distancing ◽  
Record Data ◽  
Real World Datasets

Social distancing (SD) is an effective measure to prevent the spread of the infectious Coronavirus Disease 2019 (COVID-19). However, a lack of spatial awareness may cause unintentional violations of this new measure. Against this backdrop, we propose an active surveillance system to slow the spread of COVID-19 by warning individuals in a region-of-interest. Our contribution is twofold. First, we introduce a vision-based real-time system that can detect SD violations and send non-intrusive audio-visual cues using state-of-the-art deep-learning models. Second, we define a novel critical social density value and show that the chance of SD violation occurrence can be held near zero if the pedestrian density is kept under this value. The proposed system is also ethically fair: it does not record data nor target individuals, and no human supervisor is present during the operation. The proposed system was evaluated across real-world datasets.

scholarly journals Time series regression studies in environmental epidemiology

2013 ◽  
Vol 42 (4) ◽  
pp. 1187-1195 ◽  
Author(s):  
Krishnan Bhaskaran ◽  
Antonio Gasparrini ◽  
Shakoor Hajat ◽  
Liam Smeeth ◽  
Ben Armstrong
Keyword(s):  
Time Series ◽  
Environmental Epidemiology ◽  
Time Series Regression

scholarly journals Relationship between daily suicide counts and temperature in England and Wales

2007 ◽  
Vol 191 (2) ◽  
pp. 106-112 ◽  
Author(s):  
Lisa A. Page ◽  
Shakoor Hajat ◽  
R. Sari Kovats
Keyword(s):  
Time Series ◽  
Seasonal Fluctuation ◽  
Daily Temperature ◽  
Short Term ◽  
Time Series Regression ◽  
England And Wales ◽  
Increased Risk ◽  
Hot Weather ◽  
The Impact ◽  
The Relationship

BackgroundSeasonal fluctuation in suicide has been observed in many populations. High temperature may contribute to this, but the effect of short-term fluctuations in temperature on suicide rates has not been studied.AimsTo assess the relationship between daily temperature and daily suicide counts in England and Wales between 1 January 1993 and 31 December 2003 and to establish whether heatwaves are associated with increased mortality from suicide.MethodTime-series regression analysis was used to explore and quantify the relationship between daily suicide counts and daily temperature. The impact of two heatwaves on suicide was estimated.ResultsNo spring or summer peak in suicide was found. Above 18 °, each 1 ° increase in mean temperature was associated with a 3.8 and 5.0% rise in suicide and violent suicide respectively. Suicide increased by 46.9% during the 1995 heatwave, whereas no change was seen during the 2003 heat wave.ConclusionsThere is increased risk of suicide during hot weather.

Time Series Regression in Professional Road Cycling

2020 ◽  
pp. 689-703
Author(s):  
Arie-Willem de Leeuw ◽  
Mathieu Heijboer ◽  
Mathijs Hofmijster ◽  
Stephan van der Zwaard ◽  
Arno Knobbe
Keyword(s):  
Time Series ◽  
Time Series Regression ◽  
Road Cycling

scholarly journals Real-Time Prediction of the COVID-19 Epidemic in Thailand using Simple Model-Free Method and Time Series Regression Model

2021 ◽  
Vol 18 (14) ◽  
Author(s):  
Rati WONGSATHAN
Keyword(s):  
Genetic Algorithm ◽  
Time Series ◽  
Regression Model ◽  
Regression Models ◽  
Gaussian Function ◽  
Logistic Function ◽  
Hyperbolic Tangent ◽  
Time Series Regression ◽  
Model Free ◽  
Tangent Function

The novel coronavirus 2019 (COVID-19) pandemic was declared a global health crisis. The real-time accurate and predictive model of the number of infected cases could help inform the government of providing medical assistance and public health decision-making. This work is to model the ongoing COVID-19 spread in Thailand during the 1st and 2nd phases of the pandemic using the simple but powerful method based on the model-free and time series regression models. By employing the curve fitting, the model-free method using the logistic function, hyperbolic tangent function, and Gaussian function was applied to predict the number of newly infected patients and accumulate the total number of cases, including peak and viral cessation (ending) date. Alternatively, with a significant time-lag of historical data input, the regression model predicts those parameters from 1-day-ahead to 1-month-ahead. To obtain optimal prediction models, the parameters of the model-free method are fine-tuned through the genetic algorithm, whereas the generalized least squares update the parameters of the regression model. Assuming the future trend continues to follow the past pattern, the expected total number of patients is approximately 2,689 - 3,000 cases. The estimated viral cessation dates are May 2, 2020 (using Gaussian function), May 4, 2020 (using a hyperbolic function), and June 5, 2020 (using a logistic function), whereas the peak time occurred on April 5, 2020. Moreover, the model-free method performs well for long-term prediction, whereas the regression model is suitable for short-term prediction. Furthermore, the performances of the regression models yield a highly accurate forecast with lower RMSE and higher R2 up to 1-week-ahead. HIGHLIGHTS COVID-19 model for Thailand during the first and second phases of the epidemic The model-free method using the logistic function, hyperbolic tangent function, and Gaussian function  applied to predict the basic measures of the outbreak Regression model predicts those measures from one-day-ahead to one-month-ahead The parameters of the model-free method are fine-tuned through the genetic algorithm  GRAPHICAL ABSTRACT

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