scholarly journals Relaxation of social distancing restrictions: Model estimated impact on COVID-19 epidemic in Manitoba, Canada

PLoS ONE ◽  
2021 ◽  
Vol 16 (1) ◽  
pp. e0244537
Author(s):  
Leigh Anne Shafer ◽  
Marcello Nesca ◽  
Robert Balshaw
Keyword(s):  
Mathematical Model ◽  
Local Governments ◽  
Closed Population ◽  
Social Distancing ◽  
Epidemic Curve ◽  
One Year ◽  
The Impact

Objectives The unprecedented worldwide social distancing response to COVID-19 resulted in a quick reversal of escalating case numbers. Recently, local governments globally have begun to relax social distancing regulations. Using the situation in Manitoba, Canada as an example, we estimated the impact that social distancing relaxation may have on the pandemic. Methods We fit a mathematical model to empirically estimated numbers of people infected, recovered, and died from COVID-19 in Manitoba. We then explored the impact of social distancing relaxation on: (a) time until near elimination of COVID-19 (< one case per million), (b) time until peak prevalence, (c) proportion of the population infected within one year, (d) peak prevalence, and (e) deaths within one year. Results Assuming a closed population, near elimination of COVID-19 in Manitoba could have been achieved in 4–6 months (by July or August) if there were no relaxation of social distancing. Relaxing to 15% of pre-COVID effective contacts may extend the local epidemic for more than two years (median 2.1). Relaxation to 50% of pre-COVID effective contacts may result in a peak prevalence of 31–38% of the population, within 3–4 months of initial relaxation. Conclusion Slight relaxation of social distancing may immensely impact the pandemic duration and expected peak prevalence. Only holding the course with respect to social distancing may have resulted in near elimination before Fall of 2020; relaxing social distancing to 15% of pre-COVID-19 contacts will flatten the epidemic curve but greatly extend the duration of the pandemic.

scholarly journals A simple mathematical model for the evaluation of the long first wave of the COVID-19 pandemic in Brazil

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yuanji Tang ◽  
Tamires D. A. Serdan ◽  
Amanda L. Alecrim ◽  
Diego R. Souza ◽  
Bruno R. M. Nacano ◽  
...  
Keyword(s):  
Mathematical Model ◽  
Growth Rate ◽  
Good Accuracy ◽  
Simple Mathematical Model ◽  
Epidemic Curve ◽  
Local Characteristics ◽  
Stable Period ◽  
Long Duration ◽  
Short Period ◽  
The Impact

AbstractWe propose herein a mathematical model to predict the COVID-19 evolution and evaluate the impact of governmental decisions on this evolution, attempting to explain the long duration of the pandemic in the 26 Brazilian states and their capitals well as in the Federative Unit. The prediction was performed based on the growth rate of new cases in a stable period, and the graphics plotted with the significant governmental decisions to evaluate the impact on the epidemic curve in each Brazilian state and city. Analysis of the predicted new cases was correlated with the total number of hospitalizations and deaths related to COVID-19. Because Brazil is a vast country, with high heterogeneity and complexity of the regional/local characteristics and governmental authorities among Brazilian states and cities, we individually predicted the epidemic curve based on a specific stable period with reduced or minimal interference on the growth rate of new cases. We found good accuracy, mainly in a short period (weeks). The most critical governmental decisions had a significant temporal impact on pandemic curve growth. A good relationship was found between the predicted number of new cases and the total number of inpatients and deaths related to COVID-19. In summary, we demonstrated that interventional and preventive measures directly and significantly impact the COVID-19 pandemic using a simple mathematical model. This model can easily be applied, helping, and directing health and governmental authorities to make further decisions to combat the pandemic.

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 the impact of COVID-19 vaccination in Lebanon: A call to speed-up vaccine roll out

2021 ◽  
Author(s):  
Ghina R Mumtaz ◽  
Fadi El-Jardali ◽  
Mathilda Jabbour ◽  
Aya Harb ◽  
Laith J Abu-Raddad ◽  
...  
Keyword(s):  
Vaccine Coverage ◽  
Vaccination Campaign ◽  
Scale Up ◽  
High Coverage ◽  
Social Distancing ◽  
Epidemic Curve ◽  
Limited Health ◽  
Care Capacity ◽  
The Impact ◽  
Roll Out

Background: Amidst a very difficult economic and political situation, and after a large first SARS-CoV-2 wave near the end of 2020, Lebanon launched its vaccination campaign on 14 February 2021. To date, only 6.7% of the population have received at least one dose of the vaccine, raising serious concerns over the speed of vaccine roll-out and its impact in the event of a future surge. Objective: Using mathematical modeling, we assessed the short-term impact (by end of 2021) of various vaccine roll-out scenarios on SARS-CoV-2 epidemic course in Lebanon. Results: At current immunity levels in the population, estimated by the model at 40% on 15 April 2021, a large epidemic wave is predicted if all social distancing restrictions are gradually eased and variants of concern are introduced. Reaching 80% vaccine coverage by end of 2021 will flatten the epidemic curve and will result in a 37% and 34% decrease in the peak daily numbers of severe/critical disease cases and deaths, respectively; while reaching intermediate coverage of 40% will result in only 10-11% decrease in each. Reaching 80% coverage by end of 2021 will avert 3 times more hospitalizations and deaths over the course of this year compared with 40% coverage. Impact of vaccination was substantially enhanced with rapid scale-up. Reaching 80% vaccine coverage by August would prevent twice as many severe/critical disease cases and deaths than if it were reached by December. Finally, a longer duration over which restrictions are eased resulted in a more favorable impact of vaccination. Conclusion: For vaccination to have an impact on the predicted epidemic course and associated disease burden in Lebanon, vaccination has to be rapid and reach high coverage (at least 70%), while sustaining social distancing measures during roll-out. At current vaccination pace, this is unlikely to be achieved. Concerted efforts need to be put to overcome local challenges and substantially scale up vaccination to avoid a surge that the country, with its multiple crises and limited health-care capacity, is largely unprepared for.

scholarly journals State-level needs for social distancing and contact tracing to contain COVID-19 in the United States 

2020 ◽  
Author(s):  
Weihsueh A. Chiu ◽  
Rebecca Fischer ◽  
Martial L. Ndeffo-Mbah
Keyword(s):  
Large Scale ◽  
District Of Columbia ◽  
State Level ◽  
The United States ◽  
Contact Tracing ◽  
Social Distancing ◽  
Epidemic Curve ◽  
Dynamics And Control ◽  
And Control ◽  
The Impact

Abstract Starting in mid-May 2020, many US states began relaxing social distancing measures that were put in place to mitigate the spread of COVID-19. To evaluate the impact of relaxation of restrictions on COVID-19 dynamics and control, we developed a transmission dynamic model and calibrated it to US state-level COVID-19 cases and deaths. We used this model to evaluate the impact of social distancing, testing and contact tracing on the COVID-19 epidemic in each state. As of July 22, 2020, we found only three states were on track to curtail their epidemic curve. Thirty-nine states and the District of Columbia may have to double their testing and/or tracing rates and/or rolling back reopening by 25%, while eight states require an even greater measure of combined testing, tracing, and distancing. Increased testing and contact tracing capacity is paramount for mitigating the recent large-scale increases in U.S. cases and deaths.

scholarly journals Predicting the evolution and control of the COVID-19 pandemic in Portugal

F1000Research ◽  
2020 ◽  
Vol 9 ◽  
pp. 283 ◽  
Author(s):  
Ricardo J. Pais ◽  
Nuno Taveira
Keyword(s):  
Mathematical Model ◽  
Exponential Growth ◽  
Hospital Care ◽  
Effective Control ◽  
Social Distancing ◽  
And Control ◽  
The Impact

Coronavirus disease 2019 (COVID-19) is a worldwide pandemic that has been affecting Portugal since 2 March 2020. The Portuguese government has been making efforts to contradict the exponential growth through lockdown, social distancing and the usage of masks. However, these measures have been implemented without controlling the compliance degree and how much is necessary to achieve an effective control. To address this issue, we developed a mathematical model to estimate the strength of Government-Imposed Measures (GIM) and predict the impact of the degree of compliance on the number of infected cases and peak of infection. We estimate the peak to be around 650 thousand infected cases with 53 thousand requiring hospital care by the beginning of May if no measures were taken. The model shows that the population compliance of the GIM was gradual between   30% to 75%, contributing to a significant reduction on the infection peak and mortality. Importantly, our simulations show that the infection burden could have been further reduced if the population followed the GIM immediately after their release on 18 March.

scholarly journals A unified activities-based approach to the modelling of viral epidemics and COVID-19 as an illustrative example

2020 ◽  
Author(s):  
Yulii D. Shikhmurzaev ◽  
Vladislav D. Shikhmurzaev
Keyword(s):  
Mathematical Model ◽  
Mathematical Models ◽  
Social Interactions ◽  
Clinical Picture ◽  
Simple Mathematical Model ◽  
New Approach ◽  
Social Distancing ◽  
The Uk ◽  
The Impact

AbstractA new approach to formulating mathematical models of increasing complexity to describe the dynamics of viral epidemics is proposed. The approach utilizes a map of social interactions characterizing the population and its activities and, unifying the compartmental and the stochastic viewpoints, offers a framework for incorporating both the patterns of behaviour studied by sociological surveys and the clinical picture of a particular infection, both for the virus itself and the complications it causes. The approach is illustrated by taking a simple mathematical model developed in its framework and applying it to the ongoing pandemic of SARS-CoV-2 (COVID-19), with the UK as a representative country, to assess the impact of the measures of social distancing imposed to control its course.

scholarly journals Endogenous social distancing and its underappreciated impact on the epidemic curve

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Marko Gosak ◽  
Moritz U. G. Kraemer ◽  
Heinrich H. Nax ◽  
Matjaž Perc ◽  
Bary S. R. Pradelski
Keyword(s):  
High Risk Group ◽  
Comparative Statics ◽  
Epidemiological Models ◽  
Social Distancing ◽  
Epidemic Curve ◽  
Social Mixing ◽  
Health Related ◽  
The Impact ◽  
Loss Severity ◽  
Micro Foundations

AbstractSocial distancing is an effective strategy to mitigate the impact of infectious diseases. If sick or healthy, or both, predominantly socially distance, the epidemic curve flattens. Contact reductions may occur for different reasons during a pandemic including health-related mobility loss (severity of symptoms), duty of care for a member of a high-risk group, and forced quarantine. Other decisions to reduce contacts are of a more voluntary nature. In particular, sick people reduce contacts consciously to avoid infecting others, and healthy individuals reduce contacts in order to stay healthy. We use game theory to formalize the interaction of voluntary social distancing in a partially infected population. This improves the behavioral micro-foundations of epidemiological models, and predicts differential social distancing rates dependent on health status. The model’s key predictions in terms of comparative statics are derived, which concern changes and interactions between social distancing behaviors of sick and healthy. We fit the relevant parameters for endogenous social distancing to an epidemiological model with evidence from influenza waves to provide a benchmark for an epidemic curve with endogenous social distancing. Our results suggest that spreading similar in peak and case numbers to what partial immobilization of the population produces, yet quicker to pass, could occur endogenously. Going forward, eventual social distancing orders and lockdown policies should be benchmarked against more realistic epidemic models that take endogenous social distancing into account, rather than be driven by static, and therefore unrealistic, estimates for social mixing that intrinsically overestimate spreading.

scholarly journals Analysis of COVID-19 pandemic trends and its impact on the health system of the main urban centers of Minas Gerais, Brazil.

2020 ◽  
Vol 8 (11) ◽  
pp. 621-633
Author(s):  
Larissa Pereira Caixeta ◽  
Tathiane Ribeiro da Silva ◽  
Douglas Eulálio Antunes
Keyword(s):  
Time Series ◽  
Health Systems ◽  
Kendall Test ◽  
Minas Gerais ◽  
Urban Centers ◽  
Social Distancing ◽  
Epidemic Curve ◽  
Before And After ◽  
Belo Horizonte ◽  
The Impact

Objectives: In this study, related to COVID-19, we characterized the epidemiologic, trends and the impact of new coronavirus on the health systems of the main urban centers in Minas Gerais, Brazil. Methods: A retrospective time series encompassing data associated with COVID-19 disease, from March to July of 2020, were approached for verifying the trends of social distancing rate and number of daily deaths by means of Mann-Kendall test. The Binomial test was performed to analyzing the differences between percentages of two periods (before and after pandemic) with the goal to measure the impact of disease on health systems. Results: Although the social distancing rates for the main urban centers of Minas Gerais presented declining trend along the time series, Juiz de Fora had the best rate and, consequently, flattened the epidemic curve for new cases of the disease, besides of to notify the lowest number of deaths (Mann-Kendall [Belo Horizonte]: -0.77, p<0.001; Mann-Kendall [Juiz de Fora]: -0.74, p<0.001; Mann-Kendall [Uberlandia]: 0.29, p<0.001). The number of oncologic treatments in Belo Horizonte (April 2019 vs April 2020= -41.5%; p<0.001) and clinical treatments in Uberlandia (March 2019 vs March 2020= -51.7%; p<0.0001) have reduced drastically before and after pandemic. Conclusions: Therefore, the implementation of a higher social distancing rate could flatten the epidemic curve avoiding an increase in deaths number and to reduce the impact of COVID-19 on health systems preventing the collapse of them.

scholarly journals State-level impact of social distancing and testing on COVID-19 in the United States

2020 ◽  
Author(s):  
Weihsueh A. Chiu ◽  
Rebecca Fischer ◽  
Martial L. Ndeffo-Mbah
Keyword(s):  
United States ◽  
Large Scale ◽  
Disease Incidence ◽  
State Level ◽  
The United States ◽  
Contact Tracing ◽  
Reproductive Number ◽  
Social Distancing ◽  
Epidemic Curve ◽  
The Impact

Abstract Social distancing measures have been implemented in the United States (US) since March 2020, to mitigate the spread of SARS-CoV-2, the causative agent of COVID-19. However, by mid-May most states began relaxing these measures to support the resumption of economic activity, even as disease incidence continued to increase in many states. To evaluate the impact of relaxing social distancing restrictions on COVID-19 dynamics and control in the US, we developed a transmission dynamic model and calibrated it to US state-level COVID-19 cases and deaths from March to June 20th, 2020, using Bayesian methods. We used this model to evaluate the impact of reopening, social distancing, testing, contact tracing, and case isolation on the COVID-19 epidemic in each state. We found that using stay-at-home orders, most states were able to curtail their COVID-19 epidemic curve by reducing and achieving an effective reproductive number below 1. But by June 20th, 2020, only 19 states and the District of Columbia were on track to curtail their epidemic curve with a 75% confidence, at current levels of reopening. Of the remaining 31 states, 24 may have to double their current testing and/or contact tracing rate to curtail their epidemic curve, and seven need to further restrict social contact by 25% in addition to doubling their testing and contact tracing rates. When social distancing restrictions are being eased, greater state-level testing and contact tracing capacity remains paramount for mitigating the risk of large-scale increases in cases and deaths.

scholarly journals A Mathematical Model for the Prediction of the Impact of Coronavirus(COVID­19) and Social Distancing Effect

2020 ◽  
Vol 15 ◽  
Keyword(s):  
Mathematical Model ◽  
Data Fitting ◽  
Asymptotically Stable ◽  
Globally Asymptotically Stable ◽  
Social Distancing ◽  
The World ◽  
Unique Style ◽  
The Right ◽  
The Impact ◽  
Disease Free

The spread of coronavirus across the world has become a major pandemic following the Spanishflu of 1918. A mathematical model of the spread of the coronavirus with social distancing effect is studied. Amathematical model of the spread of the virus form Wuhan in China to the rest of the world is suggested andanalyzed. Another mathematical model with quarantine and social distancing factors is proposed and analyzed.Stability analysis for both models were carried out and data fitting was performed to predict the possible extinctionof the disease. The disease free equilibria of both models were locally and globally asymptotically stable. Themodels suggest that with interventions such as lock downs and social distancing the extinction of the coronaviruscan be achieved. Increasing social distancing could reduce the number of new cases by up to 30%. The paperpresents a unique style of considering both theoretical and data analysis which is rarely studied in the literature.Questions arising from this study for further research include the right time to apply interventions and the state ofpreparedness in case of similar pandemics.

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