scholarly journals How vaccination and contact isolation might interact to suppress transmission of Covid-19: a DCM study

2021 ◽  
Author(s):  
Karl J. Friston ◽  
Anthony Costello ◽  
Guillaume Flandin ◽  
Adeel Razi
Keyword(s):  
Causal Model ◽  
Herd Immunity ◽  
Contact Tracing ◽  
Effective Vaccine ◽  
Full Potential ◽  
Contact Rates ◽  
The United Kingdom ◽  
Contact Isolation ◽  
Community Transmission ◽  
Dynamic Causal Model

AbstractThis report describes a dynamic causal model that could be used to address questions about the rollout and efficacy of vaccines in the United Kingdom. For example, is suppression of community transmission a realistic aspiration? And, if not, what kind of endemic equilibrium might be achieved? What percentage of the population needs to be vaccinated? And over what timescale? It focuses on the synergies among (i) vaccination, (ii) the supported isolation of contacts of confirmed cases and (iii) restrictions on contact rates (i.e., lockdown and social distancing). To model these mitigations, we used a dynamic causal model that embeds an epidemiological model into agent-based behavioural model. The model structure and parameters were optimised to best explain responses—to the first and subsequent waves—enabling predictions over the forthcoming year under counterfactual scenarios. Illustrative analyses suggest that the full potential of vaccination is realised by increasing the efficacy of contact tracing: for example, under idealised (best case) assumptions—of an effective vaccine and efficient isolation of infected pre-symptomatic cases— suppression of community transmission would require 50% herd immunity by vaccinating 22% by the end of 2021; i.e., 15 million people or about 50,000 per day. With no change in the isolation of contacts, 36% would require vaccination, i.e., 25 million people. These figures should not be read as estimates of the actual number of people requiring vaccination; however, they illustrate the potential of this kind of model to quantify interactions among public health interventions. We anticipate using this model in a few months—to estimate the average effectiveness of vaccines when more data become available.

scholarly journals Dynamic causal modelling of COVID-19

2020 ◽  
Vol 5 ◽  
pp. 89 ◽  
Author(s):  
Karl J. Friston ◽  
Thomas Parr ◽  
Peter Zeidman ◽  
Adeel Razi ◽  
Guillaume Flandin ◽  
...  
Keyword(s):  
Bayesian Model ◽  
Causal Model ◽  
State Of The Art ◽  
Herd Immunity ◽  
Nonlinear Effects ◽  
Model Inversion ◽  
Neuronal Ensembles ◽  
Technical Report ◽  
Per Se ◽  
Dynamic Causal Model

This technical report describes a dynamic causal model of the spread of coronavirus through a population. The model is based upon ensemble or population dynamics that generate outcomes, like new cases and deaths over time. The purpose of this model is to quantify the uncertainty that attends predictions of relevant outcomes. By assuming suitable conditional dependencies, one can model the effects of interventions (e.g., social distancing) and differences among populations (e.g., herd immunity) to predict what might happen in different circumstances. Technically, this model leverages state-of-the-art variational (Bayesian) model inversion and comparison procedures, originally developed to characterise the responses of neuronal ensembles to perturbations. Here, this modelling is applied to epidemiological populations—to illustrate the kind of inferences that are supported and how the model per se can be optimised given timeseries data. Although the purpose of this paper is to describe a modelling protocol, the results illustrate some interesting perspectives on the current pandemic; for example, the nonlinear effects of herd immunity that speak to a self-organised mitigation process.

scholarly journals Mathematical Modeling and Analysis of COVID-19 pandemic in Nigeria

2020 ◽  
Author(s):  
Enahoro Iboi ◽  
Oluwaseun O. Sharomi ◽  
Calistus Ngonghala ◽  
Abba B. Gumel
Keyword(s):  
Nonlinear Differential Equations ◽  
Intervention Strategy ◽  
Mitigation Strategies ◽  
Contact Tracing ◽  
Effective Vaccine ◽  
Deterministic System ◽  
Worst Case ◽  
Social Distancing ◽  
The Public ◽  
Community Transmission

AbstractA novel Coronavirus (COVID-19), caused by SARS-CoV-2, emerged from the Wuhan city of China at the end of 2019, causing devastating public health and socio-economic burden around the world. In the absence of a safe and effective vaccine or antiviral for use in humans, control and mitigation efforts against COVID-19 are focussed on using non-pharmaceutical interventions (aimed at reducing community transmission of COVID-19), such as social (physical)-distancing, community lockdown, use of face masks in public, isolation and contact tracing of confirmed cases and quarantine of people suspected of being exposed to COVID-19. We developed a mathematical model for understanding the transmission dynamics and control of COVID-19 in Nigeria, one of the main epicenters of COVID-19 in Africa. Rigorous analysis of the Kermack-McKendrick-type compartmental epidemic model we developed, which takes the form of a deterministic system of nonlinear differential equations, reveal that the model has a continuum of disease-free equilibria which is locally-asymptotically stable whenever a certain epidemiological threshold, called the control reproduction (denoted by ), is less than unity. The epidemiological implication of this result is that the pandemic can be effectively controlled (or even eliminated) in Nigeria if the control strategies implemented can bring (and maintain) the epidemiological threshold () to a value less than unity. The model, which was parametrized using COVID-19 data published by Nigeria Centre for Disease Control (NCDC), was used to assess the community-wide impact of various control and mitigation strategies in the entire Nigerian nation, as well as in two states (Kano and Lagos) within the Nigerian federation and the Federal Capital Territory (FCT Abuja). It was shown that, for the worst-case scenario where social-distancing, lockdown and other community transmission reduction measures are not implemented, Nigeria would have recorded a devastatingly high COVID-19 mortality by April 2021 (in hundreds of thousands). It was, however, shown that COVID-19 can be effectively controlled using social-distancing measures provided its effectiveness level is at least moderate. Although the use of face masks in the public can significantly reduce COVID-19 in Nigeria, its use as a sole intervention strategy may fail to lead to the realistic elimination of the disease (since such elimination requires unrealistic high compliance in face mask usage in the public, in the range of 80% to 95%). COVID-19 elimination is feasible in both the entire Nigerian nation, and the States of Kano and Lagos, as well as the FCT, if the public face masks use strategy (using mask with moderate efficacy, and moderate compliance in its usage) is complemented with a social-distancing strategy. The lockdown measures implemented in Nigeria on March 30, 2020 need to be maintained for at least three to four months to lead to the effective containment of COVID-19 outbreaks in the country. Relaxing, or fully lifting, the lockdown measures sooner, in an effort to re-open the economy or the country, may trigger a deadly second wave of the pandemic.

scholarly journals A twelve-month projection to September 2022 of the Covid-19 epidemic in the UK using a Dynamic Causal Model

2021 ◽  
Author(s):  
Cam Bowie ◽  
Karl Friston
Keyword(s):  
Public Health ◽  
Causal Model ◽  
Hospital Admissions ◽  
Economic Cost ◽  
Model Parameters ◽  
Health Measures ◽  
Contact Rates ◽  
The Future ◽  
The Uk ◽  
Dynamic Causal Model

Objectives Predicting the future UK Covid-19 epidemic allows other countries to compare their epidemic with one unfolding without public health measures except a vaccine programme. Methods A Dynamic Causal Model (DCM) is used to estimate the model parameters of the epidemic such as vaccine effectiveness and increased transmissibility of alpha and delta variants, the vaccine programme roll-out and changes in contact rates. The model predicts the future trends in infections, long-Covid, hospital admissions and deaths. Results Two dose vaccination given to 66% of the UK population prevents transmission following infection by 44%, serious illness by 86% and death by 93%. Despite this, with no other public health measures used, cases will increase from 37 million to 61 million, hospital admission from 536,000 to 684,000 and deaths from 136,000 to 142,000 over twelve months. Discussion Vaccination alone will not control the epidemic. Relaxation of mitigating public health measures carries several risks including overwhelming the health services, the creation of vaccine resistant variants and the economic cost of huge numbers of acute and chronic cases.

scholarly journals COVID-19 War: United Kingdom’s Strategy During the First Wave

2020 ◽  
Vol 2 (3) ◽  
pp. 355-358
Author(s):  
Mrigesh Bhatia ◽  
Charusheela Bhatia ◽  
Vilomi Bhatia
Keyword(s):  
Personal Protective Equipment ◽  
Diagnostic Tests ◽  
Herd Immunity ◽  
Contact Tracing ◽  
Policy Response ◽  
Protective Equipment ◽  
The United Kingdom ◽  
Equipment Availability ◽  
The Uk ◽  
Second Wave

This opinion piece is a reflection on the UK government’s policy response to the war against the COVID-19 pandemic. In the initial stages, concerns were raised with respect to a lack of effective personal protective equipment, availability of ventilators and diagnostic tests. The early defective strategy based on the flawed assumption of building herd immunity in the population was quickly replaced with isolation and social distancing. Subsequently, testing and contact tracing were adopted which too has been criticised for being ‘too little, too late’. With the possibility of the second wave, the concern is the extent to which the United Kingdom has learnt lessons from the first wave and is in a position to effectively respond to the second wave of COVID-19.

scholarly journals Counting the Costs of COVID-19: Why Future Treatment Option Values Matter

2020 ◽  
Vol 7 (6) ◽  
pp. 36
Author(s):  
Adrian Kent
Keyword(s):  
Herd Immunity ◽  
Life Quality ◽  
Quality Adjusted Life Year ◽  
Recent Analysis ◽  
Effective Vaccine ◽  
Medical Interventions ◽  
Social Interventions ◽  
The United Kingdom ◽  
Cast Doubt

I critique a recent analysis (Miles, Stedman & Heald, 2020) of COVID-19 lockdown costs and benefits, focussing on the United Kingdom (UK). Miles et al. (2020) argue that the March-June UK lockdown was more costly than the benefit of lives saved, evaluated using the NICE threshold of £30000 for a quality-adjusted life year (QALY) and that the costs of a lockdown for 13 weeks from mid-June would be vastly greater than any plausible estimate of the benefits, even if easing produced a second infection wave causing over 7000 deaths weekly by mid-September.   I note here two key problems that significantly affect their estimates and cast doubt on their conclusions. Firstly, their calculations arbitrarily cut off after 13 weeks, without costing the epidemic end state. That is, they assume indifference between mid-September states of 13 or 7500 weekly deaths and corresponding infection rates. This seems indefensible unless one assumes that (a) there is little chance of any effective vaccine or improved medical or social interventions for the foreseeable future, (b) notwithstanding temporary lockdowns, COVID-19 will very likely propagate until herd immunity. Even under these assumptions it is very questionable. Secondly, they ignore the costs of serious illness, possible long-term lowering of life quality and expectancy for survivors. These are uncertain, but plausibly at least as large as the costs in deaths.In summary, policy on tackling COVID-19 cannot be rationally made without estimating probabilities of future medical interventions and long-term illness costs. More work on modelling these uncertainties is urgently needed.

scholarly journals Dynamic causal modelling of COVID-19

2020 ◽  
Vol 5 ◽  
pp. 89 ◽  
Author(s):  
Karl J. Friston ◽  
Thomas Parr ◽  
Peter Zeidman ◽  
Adeel Razi ◽  
Guillaume Flandin ◽  
...  
Keyword(s):  
Bayesian Model ◽  
Causal Model ◽  
State Of The Art ◽  
Herd Immunity ◽  
Nonlinear Effects ◽  
Model Inversion ◽  
Neuronal Ensembles ◽  
Technical Report ◽  
Per Se ◽  
Dynamic Causal Model

This technical report describes a dynamic causal model of the spread of coronavirus through a population. The model is based upon ensemble or population dynamics that generate outcomes, like new cases and deaths over time. The purpose of this model is to quantify the uncertainty that attends predictions of relevant outcomes. By assuming suitable conditional dependencies, one can model the effects of interventions (e.g., social distancing) and differences among populations (e.g., herd immunity) to predict what might happen in different circumstances. Technically, this model leverages state-of-the-art variational (Bayesian) model inversion and comparison procedures, originally developed to characterise the responses of neuronal ensembles to perturbations. Here, this modelling is applied to epidemiological populations—to illustrate the kind of inferences that are supported and how the model per se can be optimised given timeseries data. Although the purpose of this paper is to describe a modelling protocol, the results illustrate some interesting perspectives on the current pandemic; for example, the nonlinear effects of herd immunity that speak to a self-organised mitigation process.

scholarly journals The Effect of Vaccination Rates on the Infection of COVID-19 under the Vaccination Rate below the Herd Immunity Threshold

2021 ◽  
Vol 18 (14) ◽  
pp. 7491
Author(s):  
Yi-Tui Chen
Keyword(s):  
Infection Rate ◽  
Herd Immunity ◽  
The United States ◽  
Vaccination Rate ◽  
Infection Rates ◽  
Vaccination Rates ◽  
The United Kingdom ◽  
The World ◽  
Almost All ◽  
The Impact

Although vaccination is carried out worldwide, the vaccination rate varies greatly. As of 24 May 2021, in some countries, the proportion of the population fully vaccinated against COVID-19 has exceeded 50%, but in many countries, this proportion is still very low, less than 1%. This article aims to explore the impact of vaccination on the spread of the COVID-19 pandemic. As the herd immunity of almost all countries in the world has not been reached, several countries were selected as sample cases by employing the following criteria: more than 60 vaccine doses per 100 people and a population of more than one million people. In the end, a total of eight countries/regions were selected, including Israel, the UAE, Chile, the United Kingdom, the United States, Hungary, and Qatar. The results find that vaccination has a major impact on reducing infection rates in all countries. However, the infection rate after vaccination showed two trends. One is an inverted U-shaped trend, and the other is an L-shaped trend. For those countries with an inverted U-shaped trend, the infection rate begins to decline when the vaccination rate reaches 1.46–50.91 doses per 100 people.

scholarly journals Uncovering two phases of early intercontinental COVID-19 transmission dynamics

2020 ◽  
Vol 27 (8) ◽  
Author(s):  
Jing Yang ◽  
Juan Li ◽  
Shengjie Lai ◽  
Corrine W Ruktanonchai ◽  
Weijia Xing ◽  
...  
Keyword(s):  
Phylogenetic Analyses ◽  
International Travel ◽  
Contact Tracing ◽  
Genetic Sequencing ◽  
Response Strategies ◽  
The World ◽  
Containment Measures ◽  
The Usa ◽  
Community Transmission ◽  
Two Phases

Abstract Background The COVID-19 pandemic has posed an ongoing global crisis, but how the virus spread across the world remains poorly understood. This is of vital importance for informing current and future pandemic response strategies. Methods We performed two independent analyses, travel network-based epidemiological modelling and Bayesian phylogeographic inference, to investigate the intercontinental spread of COVID-19. Results Both approaches revealed two distinct phases of COVID-19 spread by the end of March 2020. In the first phase, COVID-19 largely circulated in China during mid-to-late January 2020 and was interrupted by containment measures in China. In the second and predominant phase extending from late February to mid-March, unrestricted movements between countries outside of China facilitated intercontinental spread, with Europe as a major source. Phylogenetic analyses also revealed that the dominant strains circulating in the USA were introduced from Europe. However, stringent restrictions on international travel across the world since late March have substantially reduced intercontinental transmission. Conclusions Our analyses highlight that heterogeneities in international travel have shaped the spatiotemporal characteristics of the pandemic. Unrestricted travel caused a large number of COVID-19 exportations from Europe to other continents between late February and mid-March, which facilitated the COVID-19 pandemic. Targeted restrictions on international travel from countries with widespread community transmission, together with improved capacity in testing, genetic sequencing and contact tracing, can inform timely strategies for mitigating and containing ongoing and future waves of COVID-19 pandemic.

scholarly journals Establishment, contagiousness, and initial spread of SARS-CoV-2 in Canada

FACETS ◽  
2021 ◽  
Vol 6 (1) ◽  
pp. 180-194
Author(s):  
Martin Krkošek ◽  
Madeline Jarvis-Cross ◽  
Kiran Wadhawan ◽  
Isha Berry ◽  
Jean-Paul R. Soucy ◽  
...  
Keyword(s):  
Latent Period ◽  
Bayesian Framework ◽  
Contact Tracing ◽  
Transmission Model ◽  
Infectious Period ◽  
Median Reduction ◽  
Community Transmission

This study empirically quantifies dynamics of SARS-CoV-2 establishment and early spread in Canada. We developed a transmission model that was simulation tested and fitted in a Bayesian framework to timeseries of new cases per day prior to physical distancing interventions. A hierarchical version was fitted to all provinces simultaneously to obtain average estimates for Canada. Across scenarios of a latent period of 2–4 d and an infectious period of 5–9 d, the R0 estimate for Canada ranges from a minimum of 3.0 (95% CI: 2.3–3.9) to a maximum of 5.3 (95% CI: 3.9–7.1). Among provinces, the estimated commencement of community transmission ranged from 3 d before to 50 d after the first reported case and from 2 to 25 d before the first reports of community transmission. Among parameter scenarios and provinces, the median reduction in transmission needed to obtain R0 < 1 ranged from 46% (95% CI: 43%–48%) to 89% (95% CI: 88%–90%). Our results indicate that local epidemics of SARS-CoV-2 in Canada entail high levels of stochasticity, contagiousness, and observation delay, which facilitates rapid undetected spread and requires comprehensive testing and contact tracing for its containment.

scholarly journals A renewal equation model to assess roles and limitations of contact tracing for disease outbreak control

2021 ◽  
Vol 8 (4) ◽  
Author(s):  
Francesca Scarabel ◽  
Lorenzo Pellis ◽  
Nicholas H. Ogden ◽  
Jianhong Wu
Keyword(s):  
Deterministic Model ◽  
Infected Individual ◽  
Equation Model ◽  
Renewal Equation ◽  
Contact Tracing ◽  
Calendar Time ◽  
Individual Level ◽  
Total Incidence ◽  
Community Transmission ◽  
The Individual

We propose a deterministic model capturing essential features of contact tracing as part of public health non-pharmaceutical interventions to mitigate an outbreak of an infectious disease. By incorporating a mechanistic formulation of the processes at the individual level, we obtain an integral equation (delayed in calendar time and advanced in time since infection) for the probability that an infected individual is detected and isolated at any point in time. This is then coupled with a renewal equation for the total incidence to form a closed system describing the transmission dynamics involving contact tracing. We define and calculate basic and effective reproduction numbers in terms of pathogen characteristics and contact tracing implementation constraints. When applied to the case of SARS-CoV-2, our results show that only combinations of diagnosis of symptomatic infections and contact tracing that are almost perfect in terms of speed and coverage can attain control, unless additional measures to reduce overall community transmission are in place. Under constraints on the testing or tracing capacity, a temporary interruption of contact tracing may, depending on the overall growth rate and prevalence of the infection, lead to an irreversible loss of control even when the epidemic was previously contained.

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