scholarly journals Evaluation of Different Types of Face Masks to Limit the Spread of SARS-CoV-2 – A Modeling Study

2021 ◽  
Author(s):  
Brian M. Gurbaxani ◽  
Andrew N. Hill ◽  
Prabasaj Paul ◽  
Pragati V. Prasad ◽  
Rachel B. Slayton
Keyword(s):  
The United States ◽  
Basic Reproductive Number ◽  
Transmission Model ◽  
Source Control ◽  
Reproductive Number ◽  
Medical Procedure ◽  
Different Types ◽  
Protection Efficacy ◽  
N95 Respirators ◽  
Related Mortality

AbstractWe updated a published mathematical model of SARS-CoV-2 transmission with laboratory-derived source and wearer protection efficacy estimates for a variety of face masks to estimate their impact on COVID-19 incidence and related mortality in the United States. When used at already-observed population rates of 80% for those ≥65 years and 60% for those <65 years, face masks are associated with 69% (cloth) to 78% (medical procedure mask) reductions in cumulative COVID-19 infections and 82% (cloth) to 87% (medical procedure mask) reductions in related deaths over a 6-month timeline in the model. If cloth or medical procedure masks’ source control and wearer protection efficacies are boosted about 30% each to 84% and 60% by cloth over medical procedure masking, fitters, or braces, the COVID-19 basic reproductive number of 2.5 could decrease to an effective reproductive number ≤ 1.0, and from 4.0 to ≈ 1.6 for the B.1.1.7 variant.Article Summary LineAdapting a published SARS-CoV-2 transmission model together with updated, laboratory-derived source control and wearer protection efficacy estimates for a variety of face coverings as well as N95 respirators, we demonstrate that community masking as currently practiced has likely reduced cases and deaths and that this benefit can be increased with wider adoption of better performing masks.

scholarly journals Evaluation of Different Types of Face Masks To Limit The Spread of SARS-CoV-2 – A Modeling Study

2021 ◽  
Author(s):  
Brian M. Gurbaxani ◽  
Andrew N. Hill ◽  
Prabasaj Paul ◽  
Pragati V. Prasad ◽  
Rachel B. Slayton
Keyword(s):  
United States ◽  
Mathematical Model ◽  
The United States ◽  
Basic Reproductive Number ◽  
Source Control ◽  
Reproductive Number ◽  
Medical Procedure ◽  
Different Types ◽  
Protection Efficacy ◽  
Related Mortality

Abstract We updated a published mathematical model of SARS-CoV-2 transmission with laboratory-derived source and wearer protection efficacy estimates for a variety of face masks to estimate their impact on COVID-19 incidence and related mortality in the United States. When used at 25 already-observed population rates of 80% for those ≥65 years and 60% for those <65 years, face masks are associated with 69% (cloth) to 78% (medical procedure mask) reductions in cumulative COVID-19 infections and 82% (cloth) to 87% (medical procedure mask) reductions in related deaths over a 6-month timeline in the model, assuming a basic reproductive number of 2.5. If cloth or medical procedure masks’ source control and wearer protection efficacies are boosted about 30% each to 84% and 60% by cloth over medical procedure masking, fitters, or braces, the COVID-19 basic reproductive number of 2.5 could be reduced to an effective reproductive number ≤ 1.0, and from 6.0 to 2.3 for a variant of concern similar to delta (B.1.617.2).

scholarly journals Differential effects of intervention timing on COVID-19 spread in the United States

2020 ◽  
Vol 6 (49) ◽  
pp. eabd6370 ◽  
Author(s):  
Sen Pei ◽  
Sasikiran Kandula ◽  
Jeffrey Shaman
Keyword(s):  
United States ◽  
Disease Transmission ◽  
Human Mobility ◽  
The United States ◽  
Control Measures ◽  
Basic Reproductive Number ◽  
Transmission Model ◽  
Reproductive Number ◽  
Mobility Data ◽  
Nonpharmaceutical Interventions

Assessing the effects of early nonpharmaceutical interventions on coronavirus disease 2019 (COVID-19) spread is crucial for understanding and planning future control measures to combat the pandemic. We use observations of reported infections and deaths, human mobility data, and a metapopulation transmission model to quantify changes in disease transmission rates in U.S. counties from 15 March to 3 May 2020. We find that marked, asynchronous reductions of the basic reproductive number occurred throughout the United States in association with social distancing and other control measures. Counterfactual simulations indicate that, had these same measures been implemented 1 to 2 weeks earlier, substantial cases and deaths could have been averted and that delayed responses to future increased incidence will facilitate a stronger rebound of infections and death. Our findings underscore the importance of early intervention and aggressive control in combatting the COVID-19 pandemic.

scholarly journals Effect of specific non-pharmaceutical intervention policies on SARS-CoV-2 transmission in the counties of the United States

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Bingyi Yang ◽  
Angkana T. Huang ◽  
Bernardo Garcia-Carreras ◽  
William E. Hart ◽  
Andrea Staid ◽  
...  
Keyword(s):  
United States ◽  
Home Visiting ◽  
Leisure Activities ◽  
The United States ◽  
Causal Effects ◽  
Basic Reproductive Number ◽  
Reproductive Number ◽  
County Level ◽  
Metapopulation Model ◽  
Pharmaceutical Interventions

AbstractNon-pharmaceutical interventions (NPIs) remain the only widely available tool for controlling the ongoing SARS-CoV-2 pandemic. We estimated weekly values of the effective basic reproductive number (Reff) using a mechanistic metapopulation model and associated these with county-level characteristics and NPIs in the United States (US). Interventions that included school and leisure activities closure and nursing home visiting bans were all associated with a median Reff below 1 when combined with either stay at home orders (median Reff 0.97, 95% confidence interval (CI) 0.58–1.39) or face masks (median Reff 0.97, 95% CI 0.58–1.39). While direct causal effects of interventions remain unclear, our results suggest that relaxation of some NPIs will need to be counterbalanced by continuation and/or implementation of others.

Modeling and stability analysis of the spread of novel coronavirus disease COVID-19

2021 ◽  
pp. 2150035
Author(s):  
A. George Maria Selvam ◽  
Jehad Alzabut ◽  
D. Abraham Vianny ◽  
Mary Jacintha ◽  
Fatma Bozkurt Yousef
Keyword(s):  
Fractional Order ◽  
Disease Transmission ◽  
Equilibrium Points ◽  
Sufficient Conditions ◽  
Basic Reproductive Number ◽  
Phase Portraits ◽  
Transmission Model ◽  
Reproductive Number ◽  
Discrete Version ◽  
Novel Coronavirus

Towards the end of 2019, the world witnessed the outbreak of Severe Acute Respiratory Syndrome Coronavirus-2 (COVID-19), a new strain of coronavirus that was unidentified in humans previously. In this paper, a new fractional-order Susceptible–Exposed–Infected–Hospitalized–Recovered (SEIHR) model is formulated for COVID-19, where the population is infected due to human transmission. The fractional-order discrete version of the model is obtained by the process of discretization and the basic reproductive number is calculated with the next-generation matrix approach. All equilibrium points related to the disease transmission model are then computed. Further, sufficient conditions to investigate all possible equilibria of the model are established in terms of the basic reproduction number (local stability) and are supported with time series, phase portraits and bifurcation diagrams. Finally, numerical simulations are provided to demonstrate the theoretical findings.

scholarly journals Emerging infectious pathogens of wildlife

2001 ◽  
Vol 356 (1411) ◽  
pp. 1001-1012 ◽  
Author(s):  
A. Dobson ◽  
J. Foufopoulos
Keyword(s):  
United States ◽  
Anthropogenic Activities ◽  
Disease Outbreaks ◽  
The United States ◽  
Host Population ◽  
Basic Reproductive Number ◽  
Reproductive Number ◽  
Emerging Pathogens ◽  
Emergent Pathogen ◽  
Insight Into

The first part of this paper surveys emerging pathogens of wildlife recorded on the ProMED Web site for a 2–year period between 1998 and 2000. The majority of pathogens recorded as causing disease outbreaks in wildlife were viral in origin. Anthropogenic activities caused the outbreaks in a significant majority of cases. The second part of the paper develops some matrix models for quantifying the basic reproductive number, R 0 , for a variety of potential types of emergent pathogen that cause outbreaks in wildlife. These analyses emphasize the sensitivity of R 0 to heterogeneities created by either the spatial structure of the host population, or the ability of the pathogens to utilize multiple host species. At each stage we illustrate how the approach provides insight into the initial dynamics of emergent pathogens such as canine parvovirus, Lyme disease, and West Nile virus in the United States.

scholarly journals Transmissibility of swine flu at Fort Dix, 1976

2007 ◽  
Vol 4 (15) ◽  
pp. 755-762 ◽  
Author(s):  
Justin Lessler ◽  
Derek A.T Cummings ◽  
Steven Fishman ◽  
Amit Vora ◽  
Donald S Burke
Keyword(s):  
United States ◽  
New Jersey ◽  
Pandemic Influenza ◽  
The United States ◽  
Basic Reproductive Number ◽  
World Health ◽  
United States Government ◽  
Swine Flu ◽  
Reproductive Number ◽  
Serial Interval

Abstarct The 1976 outbreak of A/New Jersey/76 influenza in Fort Dix is a rare example of an influenza virus with documented human to human transmission that failed to spread widely. Despite extensive epidemiological investigation, no attempt has been made to quantify the transmissibility of this virus. The World Health Organization and the United States Government view containment of emerging influenza strains as central to combating pandemic influenza. Computational models predict that it may be possible to contain an emergent pandemic influenza if virus transmissibility is low. The A/New Jersey/76 outbreak at the United States Army Training Center at Fort Dix, New Jersey in January 1976 caused 13 hospitalizations, 1 death and an estimated 230 cases. To characterize viral transmission in this epidemic, we estimated the basic reproductive number and serial interval using deterministic epidemic models and stochastic simulations. We estimated the basic reproductive number for this outbreak to be 1.2 (supported interval 1.1–1.4), the serial interval to be 1.9 days (supported interval 1.6–3.8 days), and that the virus had at least six serial human to human transmissions. This places the transmissibility of A/New Jersey/76 virus at the lower end of circulating flu strains, well below the threshold for control.

MODELING AND ANALYSIS OF AN SEIR MODEL WITH DIFFERENT TYPES OF NONLINEAR TREATMENT RATES

2013 ◽  
Vol 21 (03) ◽  
pp. 1350023 ◽  
Author(s):  
B. DUBEY ◽  
ATASI PATRA ◽  
P. K. SRIVASTAVA ◽  
UMA S. DUBEY
Keyword(s):  
Stability Analysis ◽  
Epidemic Model ◽  
Basic Reproductive Number ◽  
Reproductive Number ◽  
Acquired Immunity ◽  
Treatment Rate ◽  
Seir Model ◽  
Modeling And Analysis ◽  
Different Types ◽  
Disease Free

In this study, an SEIR epidemic model is proposed for treatment of infectives considering the development of acquired immunity in recovered individuals. We employed two different types of treatment functions. Stability analysis for disease-free as well as endemic equilibria is performed. It is observed that the existence of unique endemic equilibrium depends on the basic reproductive number R0as well as on treatment rate. Numerical simulations are performed on the proposed models to support and analyze theoretical findings.

scholarly journals Population density and basic reproductive number of COVID-19 across United States counties

PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0249271
Author(s):  
Karla Therese L. Sy ◽  
Laura F. White ◽  
Brooke E. Nichols
Keyword(s):  
United States ◽  
Population Density ◽  
Disease Transmission ◽  
Household Income ◽  
The United States ◽  
Basic Reproductive Number ◽  
Reproductive Number ◽  
Main Mode ◽  
Contact Rates ◽  
Mode Of Transportation

The basic reproductive number (R0) is a function of contact rates among individuals, transmission probability, and duration of infectiousness. We sought to determine the association between population density and R0 of SARS-CoV-2 across U.S. counties. We conducted a cross-sectional analysis using linear mixed models with random intercept and fixed slopes to assess the association of population density and R0, and controlled for state-level effects using random intercepts. We also assessed whether the association was differential across county-level main mode of transportation percentage as a proxy for transportation accessibility, and adjusted for median household income. The median R0 among the United States counties was 1.66 (IQR: 1.35–2.11). A population density threshold of 22 people/km2 was needed to sustain an outbreak. Counties with greater population density have greater rates of transmission of SARS-CoV-2, likely due to increased contact rates in areas with greater density. An increase in one unit of log population density increased R0 by 0.16 (95% CI: 0.13 to 0.19). This association remained when adjusted for main mode of transportation and household income. The effect of population density on R0 was not modified by transportation mode. Our findings suggest that dense areas increase contact rates necessary for disease transmission. SARS-CoV-2 R0 estimates need to consider this geographic variability for proper planning and resource allocation, particularly as epidemics newly emerge and old outbreaks resurge.

scholarly journals Expected impact of lockdown in Île-de-France and possible exit strategies

2020 ◽  
Author(s):  
Laura Di Domenico ◽  
Giulia Pullano ◽  
Chiara E. Sabbatini ◽  
Pierre-Yves Boëlle ◽  
Vittoria Colizza
Keyword(s):  
Substantial Reduction ◽  
Case Finding ◽  
System Capacity ◽  
Basic Reproductive Number ◽  
Transmission Model ◽  
Reproductive Number ◽  
Health Crisis ◽  
Social Distancing ◽  
Response Planning ◽  
Exit Strategies

More than half of the global population is currently under strict forms of social distancing, with more than 90 countries in lockdown, including France. Estimating the expected impact of the lockdown, and the potential effectiveness of different exit strategies is critical to inform decision makers on the management of the COVID-19 health crisis. We use a stochastic age-structured transmission model integrating data on age profile and social contacts in the Île-de-France region to (i) assess the current epidemic situation, (ii) evaluate the expected impact of the lockdown implemented in France on March 17, 2020, and (iii) estimate the effectiveness of possible exit strategies. The model is calibrated on hospital admission data of the region before lockdown and validated on syndromic and virological surveillance data. Different types and durations of social distancing interventions are simulated, including a progressive lifting of the lockdown targeted on specific classes of individuals (e.g. allowing a larger proportion of the population to go to work, while protecting the elderly), and large-scale testing. We estimate the basic reproductive number at 3.0 [2.8, 3.2] (95% confidence interval) prior to lockdown and the population infected by COVID-19 as of April 5 to be in the range 1% to 6%. The average number of contacts is predicted to be reduced by 80% during lockdown, leading to a substantial reduction of the reproductive number (RLD =0.68 [0.62-0.73]). Under these conditions, the epidemic curve reaches ICU system capacity and slowly decreases during lockdown. Lifting the lockdown with no exit strategy would lead to a second wave largely overwhelming the healthcare system. Extensive case-finding, testing and isolation are required to envision social distancing strategies that gradually relax current constraints (larger fraction of individuals going back to work, progressive reopening of activities), while keeping schools closed and seniors isolated. As France faces the first wave of COVID-19 pandemic in lockdown, intensive forms of social distancing are required in the upcoming months due to the currently low population immunity. Extensive case-finding and isolation would allow the partial release of the socio-economic pressure caused by extreme measures, while avoiding healthcare demand exceeding capacity. Response planning needs to urgently prioritize the logistics and capacity for these interventions.

scholarly journals Estimating the reproductive number R0 of SARS-CoV-2 in the United States and eight European countries and implications for vaccination

2020 ◽  
Author(s):  
Ruian Ke ◽  
Ethan Obie Romero-Severson ◽  
Steven Sanche ◽  
Nick Hengartner
Keyword(s):  
United States ◽  
Herd Immunity ◽  
The United States ◽  
Control Measures ◽  
European Countries ◽  
Basic Reproductive Number ◽  
Reproductive Number ◽  
Individual Level ◽  
Epidemic Period ◽  
Induced Immunity

SARS-CoV-2 rapidly spread from a regional outbreak to a global pandemic in just a few months. Global research efforts have focused on developing effective vaccines against SARS-CoV-2 and the disease it causes, COVID-19. However, some of the basic epidemiological parameters, such as the exponential epidemic growth rate and the basic reproductive number, R0, across geographic areas are still not well quantified. Here, we developed and fit a mathematical model to case and death count data collected from the United States and eight European countries during the early epidemic period before broad control measures were implemented. Results show that the early epidemic grew exponentially at rates between 0.19-0.29/day (epidemic doubling times between 2.4-3.6 days). We discuss the current estimates of the mean serial interval, and argue that existing evidence suggests that the interval is between 6-8 days in the absence of active isolation efforts. Using parameters consistent with this range, we estimated the median R0 value to be 5.8 (confidence interval: 4.7-7.3) in the United States and between 3.6 and 6.1 in the eight European countries. This translates to herd immunity thresholds needed to stop transmission to be between 73% and 84%. We further analyze how vaccination schedules depends on R0, the duration of vaccine-induced immunity to SARS-CoV-2, and show that individual-level heterogeneity in vaccine induced immunity can significantly affect vaccination schedules.

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