Gaussian Parameters Correlate with the Spread of COVID-19 Pandemic: The Italian Case

Until today, numerous models have been formulated to predict the spreading of Covid-19. Among them, the actively discussed susceptible-infected-removed (SIR) model is one of the most reliable. Unfortunately, many factors (i.e., social behaviors) can influence the outcomes as well as the occurrence of multiple contributions corresponding to multiple waves. Therefore, for a reliable evaluation of the conversion rates, data need to be continuously updated and analyzed. In this work, we propose a model using Gaussian functions, coming from the solution of an ordinary differential equation representing a logistic model, able to describe the growth rate of infected, deceased and recovered people in Italy. We correlate the Gaussian parameters with the number of people affected by COVID-19 as a function of the large-scale anti-contagion control measures strength, and also of vaccines effects adopted to reach herd immunity. The superposition of gaussian curves allow modeling the growth rate of the total cases, deceased and recovered people and reproducing the corresponding cumulative distribution and probability density functions. Moreover, we try to predict a time interval in which all people will be infected or vaccinated (with at least one dose) and/or the time end of pandemic in Italy when all people have been infected or vaccinated with two doses.

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Lessons from mainland China’s epidemic experience about the growth rules of infected and recovered cases of COVID-19 worldwide

10.1101/2020.04.16.20067454 ◽

2020 ◽

Author(s):

Chuanliang Han ◽

Yimeng Liu ◽

Jiting Tang ◽

Yuyao Zhu ◽

Carlo Jaeger ◽

...

Keyword(s):

Growth Rate ◽

Strong Evidence ◽

Logistic Model ◽

Mainland China ◽

Linear Connection ◽

Control Measures ◽

Logistic Growth ◽

Strong Positive Correlation ◽

The Novel ◽

Novel Coronavirus

AbstractThe novel coronavirus disease (COVID-19) that emerged at the end of 2019 has been controlled in mainland China so far, while it is still spreading globally. When the pandemic will end is a question of great concern. A logistic model depicting the growth rules of infected and recovered cases in mainland China may shed some light on this question. We extended this model to 31 countries outside China experiencing serious COVID-2019 outbreaks. The model well explained the data in our study (R2 ≥ 0.95). For infected cases, the semi-saturation period (SSP) ranges from 63 to 170 days (March 3 to June 18). The logistic growth rate of infected cases is positively correlated with that of recovered cases, and the same holds for the SSP. According to the linear connection between the growth rules for infected and recovered cases identified from the Chinese data, we predicted that the SSP of the recovered cases outside China ranges from 82 to 196 days (March 22 to July 8). More importantly, we found a strong positive correlation between the SSP of infected cases and the timing of government’s response, providing strong evidence for the effectiveness of rapid epidemic control measures in various countries.

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Fundamental principles of epidemic spread highlight the immediate need for large-scale serological surveys to assess the stage of the SARS-CoV-2 epidemic

10.1101/2020.03.24.20042291 ◽

2020 ◽

Cited By ~ 73

Author(s):

José Lourenço ◽

Robert Paton ◽

Craig Thompson ◽

Paul Klenerman ◽

Sunetra Gupta

Keyword(s):

Protective Immunity ◽

Large Scale ◽

Severe Disease ◽

Herd Immunity ◽

Control Measures ◽

Infectious Period ◽

Second Phase ◽

Social Distancing ◽

The Uk ◽

Epidemic Wave

AbstractThe spread of a novel pathogenic infectious agent eliciting protective immunity is typically characterised by three distinct phases: (I) an initial phase of slow accumulation of new infections (often undetectable), (II) a second phase of rapid growth in cases of infection, disease and death, and (III) an eventual slow down of transmission due to the depletion of susceptible individuals, typically leading to the termination of the (first) epidemic wave. Before the implementation of control measures (e.g. social distancing, travel bans, etc) and under the assumption that infection elicits protective immunity, epidemiological theory indicates that the ongoing epidemic of SARS-CoV-2 will conform to this pattern.Here, we calibrate a susceptible-infected-recovered (SIR) model to data on cumulative reported SARS-CoV-2 associated deaths from the United Kingdom (UK) and Italy under the assumption that such deaths are well reported events that occur only in a vulnerable fraction of the population. We focus on model solutions which take into consideration previous estimates of critical epidemiological parameters such as the basic reproduction number (R0), probability of death in the vulnerable fraction of the population, infectious period and time from infection to death, with the intention of exploring the sensitivity of the system to the actual fraction of the population vulnerable to severe disease and death.Our simulations are in agreement with other studies that the current epidemic wave in the UK and Italy in the absence of interventions should have an approximate duration of 2-3 months, with numbers of deaths lagging behind in time relative to overall infections. Importantly, the results we present here suggest the ongoing epidemics in the UK and Italy started at least a month before the first reported death and have already led to the accumulation of significant levels of herd immunity in both countries. There is an inverse relationship between the proportion currently immune and the fraction of the population vulnerable to severe disease.This relationship can be used to determine how many people will require hospitalisation (and possibly die) in the coming weeks if we are able to accurately determine current levels of herd immunity. There is thus an urgent need for investment in technologies such as virus (or viral pseudotype) neutralization assays and other robust assays which provide reliable read-outs of protective immunity, and for the provision of open access to valuable data sources such as blood banks and paired samples of acute and convalescent sera from confirmed cases of SARS-CoV-2 to validate these. Urgent development and assessment of such tests should be followed by rapid implementation at scale to provide real-time data. These data will be critical to the proper assessment of the effects of social distancing and other measures currently being adopted to slow down the case incidence and for informing future policy direction.Disclaimer(a) This material is not final and is subject to be updated any time. (b) Code used will be made available as soon as possible. (c) Contact for press enquiries: Cairbre Sugrue, [email protected], +44 (0)7502 203 769.

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Assessment of quality of life in patients with the effects of transient ischemic stroke

East European Journal of Neurology ◽

10.33444/2411-5797.2016.6(12).37-39 ◽

2016 ◽

pp. 37-39

Author(s):

A. Babirad

Keyword(s):

Quality Of Life ◽

Ischemic Stroke ◽

Large Scale ◽

Cerebrovascular Diseases ◽

Control Measures ◽

Sociological Research ◽

Cerebrovascular Events ◽

Sf 36 ◽

Angioplasty And Stenting

Cerebrovascular diseases are a problem of the world today, and according to the forecast, the problem of the near future arises. The main risk factors for the development of ischemic disorders of the cerebral circulation include oblique and aging, arterial hypertension, smoking, diabetes mellitus and heart disease. An effective strategy for the prevention of cerebrovascular events is based on the implementation of large-scale risk control measures, including the use of antiagregant and anticoagulant therapy, invasive interventions such as atheromectomy, angioplasty and stenting. In this connection, the efforts of neurologists, cardiologists, angiosurgery, endocrinologists and other specialists are the basis for achieving an acceptable clinical outcome. A review of the SF-36 method for assessing the quality of life in patients with the effects of transient ischemic stroke is presented. The assessment of quality of life is recognized in world medical practice and research, an indicator that is also used to assess the quality of the health system and in general sociological research.

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Estimating asymptomatic, undetected and total cases for the COVID-19 outbreak in Wuhan: a mathematical modeling study

BMC Infectious Diseases ◽

10.1186/s12879-021-06078-8 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Xi Huo ◽

Jing Chen ◽

Shigui Ruan

Keyword(s):

Disease Transmission ◽

Large Scale ◽

Transmission Rate ◽

Control Strategies ◽

Herd Immunity ◽

Antibody Prevalence ◽

Screening Process ◽

Vaccination Programs ◽

Prevalence Level ◽

The Impact

Abstract Background The COVID-19 outbreak in Wuhan started in December 2019 and was under control by the end of March 2020 with a total of 50,006 confirmed cases by the implementation of a series of nonpharmaceutical interventions (NPIs) including unprecedented lockdown of the city. This study analyzes the complete outbreak data from Wuhan, assesses the impact of these public health interventions, and estimates the asymptomatic, undetected and total cases for the COVID-19 outbreak in Wuhan. Methods By taking different stages of the outbreak into account, we developed a time-dependent compartmental model to describe the dynamics of disease transmission and case detection and reporting. Model coefficients were parameterized by using the reported cases and following key events and escalated control strategies. Then the model was used to calibrate the complete outbreak data by using the Monte Carlo Markov Chain (MCMC) method. Finally we used the model to estimate asymptomatic and undetected cases and approximate the overall antibody prevalence level. Results We found that the transmission rate between Jan 24 and Feb 1, 2020, was twice as large as that before the lockdown on Jan 23 and 67.6% (95% CI [0.584,0.759]) of detectable infections occurred during this period. Based on the reported estimates that around 20% of infections were asymptomatic and their transmission ability was about 70% of symptomatic ones, we estimated that there were about 14,448 asymptomatic and undetected cases (95% CI [12,364,23,254]), which yields an estimate of a total of 64,454 infected cases (95% CI [62,370,73,260]), and the overall antibody prevalence level in the population of Wuhan was 0.745% (95% CI [0.693%,0.814%]) by March 31, 2020. Conclusions We conclude that the control of the COVID-19 outbreak in Wuhan was achieved via the enforcement of a combination of multiple NPIs: the lockdown on Jan 23, the stay-at-home order on Feb 2, the massive isolation of all symptomatic individuals via newly constructed special shelter hospitals on Feb 6, and the large scale screening process on Feb 18. Our results indicate that the population in Wuhan is far away from establishing herd immunity and provide insights for other affected countries and regions in designing control strategies and planing vaccination programs.

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Universal Masking to Control Healthcare-Associated Transmission of SARS-CoV-2

Infection Control and Hospital Epidemiology ◽

10.1017/ice.2021.127 ◽

2021 ◽

pp. 1-24

Author(s):

Eliza R. Thompson ◽

Faith S. Williams ◽

Pat A. Giacin ◽

Shay Drummond ◽

Eric Brown ◽

...

Keyword(s):

Large Scale ◽

Infection Prevention ◽

Index Patient ◽

Field Investigation ◽

Point Prevalence ◽

Control Measures ◽

Prevention Measures ◽

Infection Control Measures ◽

Va Health Care System ◽

Healthcare Associated

Abstract Objective: To assess extent of a healthcare-associated outbreak of SARS-CoV-2 and evaluate effectiveness of infection control measures, including universal masking Design: Outbreak investigation including 4 large-scale point-prevalence surveys Setting: Integrated VA Health Care System with 2 facilities and 330 beds Participants: Index patient and 250 exposed patients and staff Methods: We identified exposed patients and staff and classified them as probable and confirmed cases based on symptoms and testing. We performed a field investigation and assessment of patient and staff interactions to develop probable transmission routes. Infection prevention interventions implemented included droplet and contact precautions, employee quarantine, and universal masking with medical and cloth facemasks. Four point-prevalence surveys of patient and staff subsets were conducted using real-time reverse-transcriptase polymerase chain reaction for SARS-CoV-2. Results: Among 250 potentially exposed patients and staff, 14 confirmed cases of Covid-19 were identified. Patient roommates and staff with prolonged patient contact were most likely to be infected. The last potential date of transmission from staff to patient was day 22, the day universal masking was implemented. Subsequent point-prevalence surveys in 126 patients and 234 staff identified 0 patient cases and 5 staff cases of Covid-19, without evidence of healthcare-associated transmission. Conclusions: Universal masking with medical facemasks was effective in preventing further spread of SARS-CoV-2 in our facility in conjunction with other traditional infection prevention measures.

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The Role of Risk Preferences in Responses to Messaging About COVID-19 Vaccine Take-Up

Social Psychological and Personality Science ◽

10.1177/1948550621999622 ◽

2021 ◽

pp. 194855062199962

Author(s):

Jennifer S. Trueblood ◽

Abigail B. Sussman ◽

Daniel O’Leary

Keyword(s):

Large Scale ◽

Risk Preferences ◽

Herd Immunity ◽

Disease Spread ◽

Short Term ◽

Policy Makers ◽

Health Crisis ◽

Flu Vaccine ◽

Nationally Representative

Development of an effective COVID-19 vaccine is widely considered as one of the best paths to ending the current health crisis. While the ability to distribute a vaccine in the short-term remains uncertain, the availability of a vaccine alone will not be sufficient to stop disease spread. Instead, policy makers will need to overcome the additional hurdle of rapid widespread adoption. In a large-scale nationally representative survey ( N = 34,200), the current work identifies monetary risk preferences as a correlate of take-up of an anticipated COVID-19 vaccine. A complementary experiment ( N = 1,003) leverages this insight to create effective messaging encouraging vaccine take-up. Individual differences in risk preferences moderate responses to messaging that provides benchmarks for vaccine efficacy (by comparing it to the flu vaccine), while messaging that describes pro-social benefits of vaccination (specifically herd immunity) speeds vaccine take-up irrespective of risk preferences. Findings suggest that policy makers should consider risk preferences when targeting vaccine-related communications.

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Heterogeneity and effectiveness analysis of COVID-19 prevention and control in major cities in China through time-varying reproduction number estimation

Scientific Reports ◽

10.1038/s41598-020-79063-x ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Qing Cheng ◽

Zeyi Liu ◽

Guangquan Cheng ◽

Jincai Huang

Keyword(s):

Prevention And Control ◽

Large Scale ◽

Reproduction Number ◽

Correlation Method ◽

Control Measures ◽

Time Varying ◽

Short Period ◽

Significant Difference ◽

Prevention And Control Measures ◽

And Control

AbstractBeginning on December 31, 2019, the large-scale novel coronavirus disease 2019 (COVID-19) emerged in China. Tracking and analysing the heterogeneity and effectiveness of cities’ prevention and control of the COVID-19 epidemic is essential to design and adjust epidemic prevention and control measures. The number of newly confirmed cases in 25 of China’s most-affected cities for the COVID-19 epidemic from January 11 to February 10 was collected. The heterogeneity and effectiveness of these 25 cities’ prevention and control measures for COVID-19 were analysed by using an estimated time-varying reproduction number method and a serial correlation method. The results showed that the effective reproduction number (R) in 25 cities showed a downward trend overall, but there was a significant difference in the R change trends among cities, indicating that there was heterogeneity in the spread and control of COVID-19 in cities. Moreover, the COVID-19 control in 21 of 25 cities was effective, and the risk of infection decreased because their R had dropped below 1 by February 10, 2020. In contrast, the cities of Wuhan, Tianmen, Ezhou and Enshi still had difficulty effectively controlling the COVID-19 epidemic in a short period of time because their R was greater than 1.

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Assessing the Response of Snow Avalanche Runout Altitudes to Climate Fluctuations Using Hierarchical Modeling: Application to 61 Winters of Data in France

Journal of Climate ◽

10.1175/2010jcli3312.1 ◽

2010 ◽

Vol 23 (12) ◽

pp. 3157-3180 ◽

Cited By ~ 42

Author(s):

N. Eckert ◽

H. Baya ◽

M. Deschatres

Keyword(s):

Climate Change ◽

Large Scale ◽

Hierarchical Modeling ◽

Control Measures ◽

Level Shift ◽

Snow Avalanches ◽

Winter Climate ◽

Climate Fluctuations ◽

High Magnitude ◽

Proposed Model

Abstract Snow avalanches are natural hazards strongly controlled by the mountain winter climate, but their recent response to climate change has thus far been poorly documented. In this paper, hierarchical modeling is used to obtain robust indexes of the annual fluctuations of runout altitudes. The proposed model includes a possible level shift, and distinguishes common large-scale signals in both mean- and high-magnitude events from the interannual variability. Application to the data available in France over the last 61 winters shows that the mean runout altitude is not different now than it was 60 yr ago, but that snow avalanches have been retreating since 1977. This trend is of particular note for high-magnitude events, which have seen their probability rates halved, a crucial result in terms of hazard assessment. Avalanche control measures, observation errors, and model limitations are insufficient explanations for these trends. On the other hand, strong similarities in the pattern of behavior of the proposed runout indexes and several climate datasets are shown, as well as a consistent evolution of the preferred flow regime. The proposed runout indexes may therefore be usable as indicators of climate change at high altitudes.

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A shift from science to politics : a content analysis of COVID-19 Videos on YouTube (Preprint)

10.2196/preprints.33948 ◽

2021 ◽

Author(s):

Chyun-Fung Shi ◽

Matthew C So ◽

Sophie Stelmach ◽

Arielle Earn ◽

David J D Earn ◽

...

Keyword(s):

Public Health ◽

Social Media ◽

Content Analysis ◽

Large Scale ◽

Control Measures ◽

Mitigation Measures ◽

Health Campaigns ◽

Public Attention ◽

Conflicting Information ◽

Social Media Platforms

BACKGROUND The COVID-19 pandemic is the first pandemic where social media platforms relayed information on a large scale, enabling an “infodemic” of conflicting information which undermined the global response to the pandemic. Understanding how the information circulated and evolved on social media platforms is essential for planning future public health campaigns. OBJECTIVE This study investigated what types of themes about COVID-19 were most viewed on YouTube during the first 8 months of the pandemic, and how COVID-19 themes progressed over this period. METHODS We analyzed top-viewed YouTube COVID-19 related videos in English from from December 1, 2019 to August 16, 2020 with an open inductive content analysis. We coded 536 videos associated with 1.1 billion views across the study period. East Asian countries were the first to report the virus, while most of the top-viewed videos in English were from the US. Videos from straight news outlets dominated the top-viewed videos throughout the outbreak, and public health authorities contributed the fewest. Although straight news was the dominant COVID-19 video source with various types of themes, its viewership per video was similar to that for entertainment news and YouTubers after March. RESULTS We found, first, that collective public attention to the COVID-19 pandemic on YouTube peaked around March 2020, before the outbreak peaked, and flattened afterwards despite a spike in worldwide cases. Second, more videos focused on prevention early on, but videos with political themes increased through time. Third, regarding prevention and control measures, masking received much less attention than lockdown and social distancing in the study period. CONCLUSIONS Our study suggests that a transition of focus from science to politics on social media intensified the COVID-19 infodemic and may have weakened mitigation measures during the first waves of the COVID-19 pandemic. It is recommended that authorities should consider co-operating with reputable social media influencers to promote health campaigns and improve health literacy. In addition, given high levels of globalization of social platforms and polarization of users, tailoring communication towards different digital communities is likely to be essential.

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Probabilistic downscaling of precipitation data in a subtropical mountain area: a two-step approach

Nonlinear Processes in Geophysics ◽

10.5194/npg-18-223-2011 ◽

2011 ◽

Vol 18 (2) ◽

pp. 223-234 ◽

Cited By ~ 8

Author(s):

R. Haas ◽

K. Born

Keyword(s):

Complex Terrain ◽

Large Scale ◽

Distribution Functions ◽

Data Availability ◽

Cumulative Distribution ◽

Second Step ◽

Mountain Area ◽

Investigation Area ◽

Distribution Parameters ◽

Mountain Environment

Abstract. In this study, a two-step probabilistic downscaling approach is introduced and evaluated. The method is exemplarily applied on precipitation observations in the subtropical mountain environment of the High Atlas in Morocco. The challenge is to deal with a complex terrain, heavily skewed precipitation distributions and a sparse amount of data, both spatial and temporal. In the first step of the approach, a transfer function between distributions of large-scale predictors and of local observations is derived. The aim is to forecast cumulative distribution functions with parameters from known data. In order to interpolate between sites, the second step applies multiple linear regression on distribution parameters of observed data using local topographic information. By combining both steps, a prediction at every point of the investigation area is achieved. Both steps and their combination are assessed by cross-validation and by splitting the available dataset into a trainings- and a validation-subset. Due to the estimated quantiles and probabilities of zero daily precipitation, this approach is found to be adequate for application even in areas with difficult topographic circumstances and low data availability.

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