scholarly journals The impact of social distancing and epicenter lockdown on the COVID-19 epidemic in mainland China: A data-driven SEIQR model study

2020 ◽  
Author(s):  
Yuzhen Zhang ◽  
Bin Jiang ◽  
Jiamin Yuan ◽  
Yanyun Tao
Keyword(s):  
Mainland China ◽  
Cost Effective ◽  
Data Driven ◽  
Favorable Effect ◽  
Reproductive Number ◽  
Model Parameters ◽  
Susceptible Population ◽  
Social Distancing ◽  
Epidemic Size ◽  
The Impact

AbstractThe outbreak of coronavirus disease 2019 (COVID-19) which originated in Wuhan, China, constitutes a public health emergency of international concern with a very high risk of spread and impact at the global level. We developed data-driven susceptible-exposed-infectious-quarantine-recovered (SEIQR) models to simulate the epidemic with the interventions of social distancing and epicenter lockdown. Population migration data combined with officially reported data were used to estimate model parameters, and then calculated the daily exported infected individuals by estimating the daily infected ratio and daily susceptible population size. As of Jan 01, 2020, the estimated initial number of latently infected individuals was 380.1 (95%-CI: 379.8∼381.0). With 30 days of substantial social distancing, the reproductive number in Wuhan and Hubei was reduced from 2.2 (95%-CI: 1.4∼3.9) to 1.58 (95%-CI: 1.34∼2.07), and in other provinces from 2.56 (95%-CI: 2.43∼2.63) to 1.65 (95%-CI: 1.56∼1.76). We found that earlier intervention of social distancing could significantly limit the epidemic in mainland China. The number of infections could be reduced up to 98.9%, and the number of deaths could be reduced by up to 99.3% as of Feb 23, 2020. However, earlier epicenter lockdown would partially neutralize this favorable effect. Because it would cause in situ deteriorating, which overwhelms the improvement out of the epicenter. To minimize the epidemic size and death, stepwise implementation of social distancing in the epicenter city first, then in the province, and later the whole nation without the epicenter lockdown would be practical and cost-effective.

scholarly journals Transmission dynamics and control strategies of COVID-19: a modelling study

2021 ◽  
Vol 102 (2) ◽  
pp. 92-105
Author(s):  
U.T. Mustapha ◽  
◽  
E. Hincal ◽  
A. Yusuf ◽  
S. Qureshi ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Recovery Rate ◽  
Transmission Rate ◽  
Face Mask ◽  
Basic Reproductive Number ◽  
Effective Control ◽  
Reproductive Number ◽  
Model Parameters ◽  
Susceptible Population ◽  
Social Distancing

In this paper a mathematical model is proposed, which incorporates quarantine and hospitalization to assess the community impact of social distancing and face mask among the susceptible population. The model parameters are estimated and fitted to the model with the use of laboratory confirmed COVID-19 cases in Turkey from March 11 to October 10, 2020. The partial rank correlation coefficient is employed to perform sensitivity analysis of the model, with basic reproduction number and infection attack rate as response functions. Results from the sensitivity analysis reveal that the most essential parameters for effective control of COVID-19 infection are recovery rate from quarantine individuals (δ1), recovery rate from hospitalized individuals (δ4), and transmission rate (β). Some simulation results are obtained with the aid of mesh plots with respect to the basic reproductive number as a function of two different biological parameters randomly chosen from the model. Finally, numerical simulations on the dynamics of the model highlighted that infections from the compartments of each state variables decreases with time which causes an increase in susceptible individuals. This implies that avoiding contact with infected individuals by means of adequate awareness of social distancing and wearing face mask are vital to prevent or reduce the spread of COVID-19 infection.

scholarly journals Harnessing testing strategies and public health measures to avert COVID-19 outbreaks during ocean cruises

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Gerardo Chowell ◽  
Sushma Dahal ◽  
Raquel Bono ◽  
Kenji Mizumoto
Keyword(s):  
Public Health ◽  
Cost Effective ◽  
Contact Network ◽  
Social Distancing ◽  
Individual Level ◽  
Health Measures ◽  
Testing Strategies ◽  
Novel Coronavirus ◽  
The Individual ◽  
The Impact

AbstractTo ensure the safe operation of schools, workplaces, nursing homes, and other businesses during COVID-19 pandemic there is an urgent need to develop cost-effective public health strategies. Here we focus on the cruise industry which was hit early by the COVID-19 pandemic, with more than 40 cruise ships reporting COVID-19 infections. We apply mathematical modeling to assess the impact of testing strategies together with social distancing protocols on the spread of the novel coronavirus during ocean cruises using an individual-level stochastic model of the transmission dynamics of COVID-19. We model the contact network, the potential importation of cases arising during shore excursions, the temporal course of infectivity at the individual level, the effects of social distancing strategies, different testing scenarios characterized by the test’s sensitivity profile, and testing frequency. Our findings indicate that PCR testing at embarkation and daily testing of all individuals aboard, together with increased social distancing and other public health measures, should allow for rapid detection and isolation of COVID-19 infections and dramatically reducing the probability of onboard COVID-19 community spread. In contrast, relying only on PCR testing at embarkation would not be sufficient to avert outbreaks, even when implementing substantial levels of social distancing measures.

scholarly journals Diverse local epidemics reveal the distinct effects of population density, demographics, climate, depletion of susceptibles, and intervention in the first wave of COVID-19 in the United States

2020 ◽  
Author(s):  
Niayesh Afshordi ◽  
Benjamin Holder ◽  
Mohammad Bahrami ◽  
Daniel Lichtblau
Keyword(s):  
United States ◽  
Population Density ◽  
The United States ◽  
Effective Response ◽  
Susceptible Population ◽  
Local Climate ◽  
Climate Conditions ◽  
Mortality And Morbidity ◽  
Social Distancing ◽  
The Impact

The SARS-CoV-2 pandemic has caused significant mortality and morbidity worldwide, sparing almost no community. As the disease will likely remain a threat for years to come, an understanding of the precise influences of human demographics and settlement, as well as the dynamic factors of climate, susceptible depletion, and intervention, on the spread of localized epidemics will be vital for mounting an effective response. We consider the entire set of local epidemics in the United States; a broad selection of demographic, population density, and climate factors; and local mobility data, tracking social distancing interventions, to determine the key factors driving the spread and containment of the virus. Assuming first a linear model for the rate of exponential growth (or decay) in cases/mortality, we find that population-weighted density, humidity, and median age dominate the dynamics of growth and decline, once interventions are accounted for. A focus on distinct metropolitan areas suggests that some locales benefited from the timing of a nearly simultaneous nationwide shutdown, and/or the regional climate conditions in mid-March; while others suffered significant outbreaks prior to intervention. Using a first-principles model of the infection spread, we then develop predictions for the impact of the relaxation of social distancing and local climate conditions. A few regions, where a significant fraction of the population was infected, show evidence that the epidemic has partially resolved via depletion of the susceptible population (i.e., “herd immunity”), while most regions in the United States remain overwhelmingly susceptible. These results will be important for optimal management of intervention strategies, which can be facilitated using our online dashboard.

scholarly journals Impact of nonpharmaceutical strategies on trends of COVID-19 in São Paulo State

2021 ◽  
Vol 55 ◽  
pp. 48
Author(s):  
Cristiane Ravagnani Fortaleza ◽  
Thomas Nogueira Vilches ◽  
Gabriel Berg de Almeida ◽  
Claudia Pio Ferreira ◽  
Lenice do Rosário de Souza ◽  
...  
Keyword(s):  
Interrupted Time Series ◽  
Sao Paulo ◽  
Reproductive Number ◽  
São Paulo ◽  
Social Distancing ◽  
Paulo State ◽  
São Paulo State ◽  
Time Series Analyses ◽  
The Impact ◽  
Mandatory Use

Interrupted time series analyses were conducted to measure the impact of social distancing policies (instituted on March 22, 2020) and of subsequent mandatory masking in the community (instituted on May 4, 2020) on the incidence and effective reproductive number of COVID-19 in São Paulo State, Brazil. Overall, the impact of social distancing both on incidence and Rt was greater than the incremental effect of mandatory masking. Those findings may reflect either a small impact of face masking or the loosening of social distancing after mandatory use of masks.

scholarly journals Downsizing of contact tracing during COVID-19 vaccine roll-out

2021 ◽  
Author(s):  
Maria M Martignoni ◽  
Joshua Renault ◽  
Joseph Baafi ◽  
Amy Hurford
Keyword(s):  
Exponential Growth ◽  
Cost Effective ◽  
Quantitative Information ◽  
Vaccination Status ◽  
Outbreak Detection ◽  
Contact Tracing ◽  
Epidemic Spread ◽  
Social Distancing ◽  
The Impact ◽  
Roll Out

Contact tracing is a key component of successful management of COVID-19. Contacts of infected individuals are asked to quarantine, which can significantly slow down (or prevent) community spread. Contact tracing is particularly effective when infections are detected quickly (e.g., through rapid testing), when contacts are traced with high probability, when the initial number of cases is low, and when social distancing and border restrictions are in place. However, the magnitude of the individual contribution of these factors in reducing epidemic spread and the impact of vaccination in determining contact tracing outputs is not fully understood. We present a delayed differential equation model to investigate how vaccine roll-out and the relaxation of social distancing requirements affect contact tracing practises. We provide an analytical criteria to determine the minimal contact tracing efficiency (defined as the the probability of identifying and quarantining contacts of symptomatic individuals) required to keep an outbreak under control, with respect to the contact rate and vaccination status of the population. Additionally, we consider how delays in outbreak detection and increased case importation rates affect the number of contacts to be traced daily. We show that in vaccinated communities a lower contact tracing efficiency is required to avoid uncontrolled epidemic spread, and delayed outbreak detection and relaxation of border restrictions do not lead to a significantly higher risk of overwhelming contact tracing. We find that investing in testing programs, rather than increasing the contact tracing capacity, has a larger impact in determining whether an outbreak will be controllable. This is because early detection activates contact tracing, which will slow, and eventually reverse exponential growth, while the contact tracing capacity is a threshold that will easily become overwhelmed if exponential growth is not curbed. Finally, we evaluate quarantine effectiveness during vaccine roll-out, by considering the proportion of people that will develop an infection while in isolation in relation to the vaccination status of the population and for different viral variants. We show that quarantine effectiveness decreases with increasing proportion of fully vaccinated individuals, and increases in the presence of more transmissible variants. These results suggest that a cost-effective approach during vaccine roll-out is to establish different quarantine rules for vaccinated and unvaccinated individuals, where rules should depend on viral transmissibility. Altogether, our study provides quantitative information for contact tracing downsizing during vaccine roll-out, to guide COVID-19 exit strategies.

scholarly journals Epidemiological Modeling of COVID-19 in Saudi Arabia: Spread Projection, Awareness, and Impact of Treatment

2020 ◽  
Vol 10 (17) ◽  
pp. 5895 ◽  
Author(s):  
Yousef Alharbi ◽  
Abdulrahman Alqahtani ◽  
Olayan Albalawi ◽  
Mohsen Bakouri
Keyword(s):  
Saudi Arabia ◽  
Information Criterion ◽  
Sir Model ◽  
Cumulative Number ◽  
Epidemiological Modeling ◽  
Social Distancing ◽  
Epidemic Size ◽  
Statistical Indices ◽  
First Case ◽  
The Impact

The first case of COVID-19 originated in Wuhan, China, after which it spread across more than 200 countries. By 21 July 2020, the rapid global spread of this disease had led to more than 15 million cases of infection, with a mortality rate of more than 4.0% of the total number of confirmed cases. This study aimed to predict the prevalence of COVID-19 and to investigate the effect of awareness and the impact of treatment in Saudi Arabia. In this paper, COVID-19 data were sourced from the Saudi Ministry of Health, covering the period from 31 March 2020 to 21 July 2020. The spread of COVID-19 was predicted using four different epidemiological models, namely the susceptible–infectious–recovered (SIR), generalized logistic, Richards, and Gompertz models. The assessment of models’ fit was performed and compared using four statistical indices (root-mean-square error (RMSE), R squared (R2), adjusted R2 ( Radj2), and Akaike’s information criterion (AIC)) in order to select the most appropriate model. Modified versions of the SIR model were utilized to assess the influence of awareness and treatment on the prevalence of COVID-19. Based on the statistical indices, the SIR model showed a good fit to reported data compared with the other models (RMSE = 2790.69, R2 = 99.88%, Radj2 = 99.98%, and AIC = 1796.05). The SIR model predicted that the cumulative number of infected cases would reach 359,794 and that the pandemic would end by early September 2020. Additionally, the modified version of the SIR model with social distancing revealed that there would be a reduction in the final cumulative epidemic size by 9.1% and 168.2% if social distancing were applied over the short and long term, respectively. Furthermore, different treatment scenarios were simulated, starting on 8 July 2020, using another modified version of the SIR model. Epidemiological modeling can help to predict the cumulative number of cases of infection and to understand the impact of social distancing and pharmaceutical intervention on the prevalence of COVID-19. The findings from this study can provide valuable information for governmental policymakers trying to control the spread of this pandemic.

Evaluation of Turkish social distancing measures on the spread of COVID-19 (Preprint)

2020 ◽  
Author(s):  
Irfan Civcir
Keyword(s):  
Linear Model ◽  
Model Parameters ◽  
Social Distancing ◽  
Slowing Down ◽  
Epidemic Size ◽  
Johns Hopkins University ◽  
The Government ◽  
Log Linear

UNSTRUCTURED The coronavirus disease (COVID-19) affecting across the globe. The government of different countries has adopted various policies to contain this epidemic and the most common were social distancing and lockdown. We use a simple log-linear model with intercept and trend break to evaluate whether the measures are effective preventing/slowing down the spread of the disease in Turkey. We estimate the model parameters from the Johns Hopkins University (2020) epidemic data between 15th March and 26th April 2020. Our analysis revealed that the measures can slow down the outbreak. We can reduce the epidemic size and prolong the time to arrive at the epidemic peak by seriously following the measures suggested by the authorities.

Cost-effective solutions for water quality improvement in the Dommel River supported by sewer–WWTP–river integrated modelling

2013 ◽  
Vol 68 (5) ◽  
pp. 965-973 ◽  
Author(s):  
Lorenzo Benedetti ◽  
Jeroen Langeveld ◽  
Arjen F. van Nieuwenhuijzen ◽  
Jarno de Jonge ◽  
Jeroen de Klein ◽  
...  
Keyword(s):  
Control Strategies ◽  
Treatment Plant ◽  
Water System ◽  
Cost Effective ◽  
Integrated Modelling ◽  
Model Parameters ◽  
Urban Wastewater ◽  
Real Time Control ◽  
Worst Case ◽  
The Impact

This project aims at finding cost-efficient sets of measures to meet the Water Framework Directive (WFD) derived goals for the Dommel River (The Netherlands). Within the project, both acute and long-term impacts of the urban wastewater system on the chemical and ecological quality of the river are studied with a monitoring campaign in the urban wastewater system (wastewater treatment plant and sewers) and in the receiving surface water system. An integrated model, which proved to be a powerful tool to analyse the interactions within the integrated urban wastewater system, was first used to evaluate measures in the urban wastewater system using the existing infrastructure and new real-time control strategies. As the latter resulted to be beneficial but not sufficient, this paper investigated the use of additional infrastructural measures to improve the system cost-effectively and have it meet the Directive's goals. Finally, an uncertainty analysis was conducted to investigate the impact of uncertainty in the main model assumptions and model parameters on the performance robustness of the selected set of measures. Apart from some extreme worst-case scenarios, the proposed set of measures turned out to be sufficiently robust. Due to the substantial savings obtained with the results of this project, the pay-back time of the whole monitoring and modelling work proved to be less than 5 months. This illustrates the power of mathematical modelling for decision support in the context of complex urban water systems.

scholarly journals Assessment of Social Distancing for Controlling COVID-19 in Korea: An Age-Structured Modeling Approach

2020 ◽  
Vol 17 (20) ◽  
pp. 7474
Author(s):  
Yongin Choi ◽  
James Slghee Kim ◽  
Heejin Choi ◽  
Hyojung Lee ◽  
Chang Hyeong Lee
Keyword(s):  
Mathematical Model ◽  
Age Groups ◽  
Epidemiological Data ◽  
The Elderly ◽  
Model Parameters ◽  
Social Distancing ◽  
First Case ◽  
Age Structured ◽  
Epidemiological Characteristics ◽  
The Impact

The outbreak of the novel coronavirus disease 2019 (COVID-19) occurred all over the world between 2019 and 2020. The first case of COVID-19 was reported in December 2019 in Wuhan, China. Since then, there have been more than 21 million incidences and 761 thousand casualties worldwide as of 16 August 2020. One of the epidemiological characteristics of COVID-19 is that its symptoms and fatality rates vary with the ages of the infected individuals. This study aims at assessing the impact of social distancing on the reduction of COVID-19 infected cases by constructing a mathematical model and using epidemiological data of incidences in Korea. We developed an age-structured mathematical model for describing the age-dependent dynamics of the spread of COVID-19 in Korea. We estimated the model parameters and computed the reproduction number using the actual epidemiological data reported from 1 February to 15 June 2020. We then divided the data into seven distinct periods depending on the intensity of social distancing implemented by the Korean government. By using a contact matrix to describe the contact patterns between ages, we investigated the potential effect of social distancing under various scenarios. We discovered that when the intensity of social distancing is reduced, the number of COVID-19 cases increases; the number of incidences among the age groups of people 60 and above increases significantly more than that of the age groups below the age of 60. This significant increase among the elderly groups poses a severe threat to public health because the incidence of severe cases and fatality rates of the elderly group are much higher than those of the younger groups. Therefore, it is necessary to maintain strict social distancing rules to reduce infected cases.

scholarly journals Modeling Tuberculosis Among Healthcare Workers

2019 ◽  
Vol 5 (2) ◽  
pp. 186-196
Author(s):  
T.S. Faniran ◽  
A.O. Falade ◽  
T.O. Alakija
Keyword(s):  
Sensitivity Analysis ◽  
Equilibrium Point ◽  
Healthcare Workers ◽  
Compliance Rate ◽  
Transmission Dynamics ◽  
Infectious Period ◽  
Model Parameters ◽  
Asymptotically Stable ◽  
Susceptible Population ◽  
The Impact

AbstractA mathematical model for transmission dynamics of tuberculosis among healthcare workers is formulated. Tuberculosis is an airborne disease caused by Mycobacterium tuberculosis bacteria that affect the lungs of a host. Previous research had concentrated on mathematical modeling of transmission dynamics of tuberculosis without considering the impact of compliance rate to particulate respirator by healthcare workers on the transmission. Therefore, how compliance rate to particulate respirator reduces the transmission of tuberculosis is an active question, and we develop a new system of ordinary differential equations that explicitly explores the impact of compliance rate to particulate respirator by healthcare workers upon transmission. Rigorous analysis of the model shows that the disease-free equilibrium point is locally asymptotically stable when the basic reproduction number, Ro < 1. This is established through the analysis of characteristic equation. Basic reproduction, Ro is the number of new cases that an existing case generates on average over the infectious period in a susceptible population. We also show that the endemic equilibrium point is locally asymptotically stable for Ro > 1, by using Routh-Hurwitz criteria for stability. Sensitivity analysis is carried out to determine the relative importance of the model parameters to the disease transmission. The result of the sensitivity analysis shows that the most sensitive parameter is β (Human-to-human transmission rate), followed by Λ (Human recruitment rate). Also, the result shows that increase in ψ (compliance rate to particulate respirator by healthcare workers) leads to decrease in Ro which reduces tuberculosis spread among healthcare workers.

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