scholarly journals Lockdown exit strategies and risk of a second epidemic peak: a stochastic agent-based model of SARS-CoV-2 epidemic in France

2020 ◽  
Author(s):  
Nicolas Hoertel ◽  
Martin Blachier ◽  
Carlos Blanco ◽  
Mark Olfson ◽  
Marc Massetti ◽  
...  
Keyword(s):  
Cumulative Incidence ◽  
Herd Immunity ◽  
Agent Based ◽  
Social Distancing ◽  
Viral Spread ◽  
Population Immunity ◽  
Incidence And Mortality ◽  
Potential Impact ◽  
Parameter Values ◽  
Nationwide Implementation

AbstractMost European countries have responded to the COVID-19 threat by nationwide implementation of barrier measures and lockdown. However, assuming that population immunity will build up through the epidemic, it is likely to rebound once these measures are relaxed, possibly leading to a second or multiple repeated lockdowns. In this report, we present results of epidemiological modelling that has helped inform policy making in France. We used a stochastic agent-based microsimulation model of the COVID-19 epidemic in France, and examined the potential impact of post-quarantine measures, including social distancing, mask-wearing, and shielding of the population the most vulnerable to severe COVID-19 infection, on the disease’s cumulative incidence and mortality, and on ICU-bed occupancy. The model calibrated well and variation of model parameter values had little impact on outcome estimates. While quarantine is effective in containing the viral spread, it would be unlikely to prevent a rebound of the epidemic once lifted, regardless of its duration. Both social distancing and mask-wearing, although effective in slowing the epidemic and in reducing mortality, would also be ineffective in ultimately preventing the overwhelming of ICUs and a second lockdown. However, these measures coupled with shielding of vulnerable people would be associated with better outcomes, including lower cumulative incidence, mortality, and maintaining an adequate number of ICU beds to prevent a second lockdown. Benefits would nonetheless be markedly reduced if these measures were not applied by most people or not maintained for a sufficiently long period, as herd immunity progressively establishes in the less vulnerable population.

scholarly journals Facing the COVID-19 epidemic in NYC: a stochastic agent-based model of various intervention strategies

2020 ◽  
Author(s):  
Nicolas Hoertel ◽  
Martin Blachier ◽  
Carlos Blanco ◽  
Mark Olfson ◽  
Marc Massetti ◽  
...  
Keyword(s):  
Effective Treatment ◽  
Source Code ◽  
Herd Immunity ◽  
Single Step ◽  
Mitigation Measures ◽  
Bed Occupancy ◽  
Agent Based ◽  
Cumulative Mortality ◽  
Viral Spread ◽  
Incidence And Mortality

AbstractGlobal spread of coronavirus disease 2019 (COVID-19) has created an unprecedented infectious disease crisis worldwide. Despite uncertainties about COVID-19, model-based forecasting of competing mitigation measures on its course is urgently needed to inform mitigation policy. We used a stochastic agent-based microsimulation model of the COVID-19 epidemic in New York City and evaluated the potential impact of quarantine duration (from 4 to 16 weeks), quarantine lifting type (1-step lifting for all individuals versus a 2-step lifting according to age), post-quarantine screening, and use of a hypothetical effective treatment against COVID-19 on the disease’s cumulative incidence and mortality, and on ICU-bed occupancy. The source code of the model has been deposited in a public source code repository (GitHub®). The model calibrated well and variation of model parameter values had little impact on outcome estimates. While quarantine is efficient to contain the viral spread, it is unlikely to prevent a rebound of the epidemic once lifted. We projected that lifting quarantine in a single step for the full population would be unlikely to substantially lower the cumulative mortality, regardless of quarantine duration. By contrast, a two-step quarantine lifting according to age was associated with a substantially lower cumulative mortality and incidence, up to 71% and 23%, respectively, as well as lower ICU-bed occupancy. Although post-quarantine screening was associated with diminished epidemic rebound, this strategy may not prevent ICUs from being overcrowded. It may even become deleterious after a 2-step quarantine lifting according to age if the herd immunity effect does not had sufficient time to become established in the younger population when the quarantine is lifted for the older population. An effective treatment against COVID-19 would considerably reduce the consequences of the epidemic, even more so if ICU capacity is not exceeded.

Are social networking platforms the key to effective social distancing for COVID-19? (Preprint)

2020 ◽  
Author(s):  
Viknesh Sounderajah ◽  
Hutan Ashrafian ◽  
Sheraz Markar ◽  
Ara Darzi
Keyword(s):  
Operating System ◽  
Social Networking ◽  
Contact Tracing ◽  
Text Data ◽  
Social Distancing ◽  
Web Mapping ◽  
Viral Spread ◽  
Behavioural Insights ◽  
Mapping Software ◽  
Incidence And Mortality

UNSTRUCTURED If health systems are to effectively employ social distancing measures to in response to further COVID-19 peaks, they must adopt new behavioural metrics that can supplement traditional downstream measures, such as incidence and mortality. Access to mobile digital innovations may dynamically quantify compliance to social distancing (e.g. web mapping software) as well as establish personalised real-time contact tracing of viral spread (e.g. mobile operating system infrastructure through Google-Apple partnership). In particular, text data from social networking platforms can be mined for unique behavioural insights, such as symptom tracking and perception monitoring. Platforms, such as Twitter, have shown significant promise in tracking communicable pandemics. As such, it is critical that social networking companies collaborate with each other in order to (1) enrich the data that is available for analysis, (2) promote the creation of open access datasets for researchers and (3) cultivate relationships with governments in order to affect positive change.

scholarly journals Modeling the Spread and Control of COVID-19

Systems ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 53
Author(s):  
Ashutosh Trivedi ◽  
Nanda Kishore Sreenivas ◽  
Shrisha Rao
Keyword(s):  
Herd Immunity ◽  
Black Box ◽  
Personal Hygiene ◽  
Confined Space ◽  
Real Situation ◽  
Seir Model ◽  
Agent Based ◽  
Social Distancing ◽  
The Difference ◽  
And Control

Data-centric models of COVID-19 have been attempted, but have certain limitations. In this work, we propose an agent-based model of the epidemic in a confined space of agents representing humans. An extension to the SEIR model allows us to consider the difference between the appearance (black-box view) of the spread of disease and the real situation (glass-box view). Our model allows for simulations of lockdowns, social distancing, personal hygiene, quarantine, and hospitalization, with further considerations of different parameters, such as the extent to which hygiene and social distancing are observed in a population. Our results provide qualitative indications of the effects of various policies and parameters, for instance, that lockdowns by themselves are extremely unlikely to bring an end to an epidemic and may indeed make things worse, that social distancing is more important than personal hygiene, and that the growth of infection is significantly reduced for moderately high levels of social distancing and hygiene, even in the absence of herd immunity.

scholarly journals Modeling the Spread and Control of COVID-19

2020 ◽  
Author(s):  
Ashutosh Trivedi ◽  
Nanda Kishore Sreenivas ◽  
Shrisha Rao
Keyword(s):  
Herd Immunity ◽  
Black Box ◽  
Personal Hygiene ◽  
Confined Space ◽  
Real Situation ◽  
Seir Model ◽  
Agent Based ◽  
Social Distancing ◽  
The Difference ◽  
And Control

ABSTRACTData-centric models of COVID-19 have been tried, but have certain limitations. In this work, we propose an agent-based model of the epidemic in a confined space of agents representing humans. An extension to the SEIR model allows us to consider the difference between the appearance (black-box view) of the spread of disease, and the real situation (glass-box view). Our model allows for simulations of lockdowns, social distancing, personal hygiene, quarantine, and hospitalization, with further considerations of different parameters such as the extent to which hygiene and social distancing are observed in a population. Our results give qualitative indications of the effects of various policies and parameters; for instance, that lockdowns by themselves are extremely unlikely to bring an end to an epidemic and may indeed make things worse, that social distancing matters more than personal hygiene, and that the growth of infection comes down significantly for moderately high levels of social distancing and hygiene, even in the absence of herd immunity.

scholarly journals Low-Impact Social Distancing Interventions to Mitigate Local Epidemics of SARS-CoV-2

2020 ◽  
Author(s):  
Michael L. Jackson
Keyword(s):  
Nursing Home ◽  
Hospital Admissions ◽  
Herd Immunity ◽  
Nursing Home Residents ◽  
King County ◽  
Rapid Testing ◽  
School Closures ◽  
Agent Based ◽  
Social Distancing ◽  
Hospital Occupancy

AbstractBackgroundAfter many jurisdictions have implemented intensive social distancing to suppress SARS-CoV-2 transmission, the challenge now is to mitigate the ongoing COVID-19 epidemic without overburdening economic and social activities. This report explores “low-impact” interventions to mitigate SARS-CoV-2 with a minimum of social and economic disruption.MethodsAn agent-based model simulated the population of King County, Washington, with agents that interact in homes, schools, workplaces, and other community sites. SARS-CoV-2 transmission probabilities were estimated by fitting simulated to observed hospital admissions from February – May 2020. Interventions considered were (a) encouraging telecommuting; (b) reducing contacts to seniors and nursing home residents; (c) modest reductions to contacts outside of the home; (d) encouraging self-isolation of persons with COVID-19 symptoms; (e) rapid testing and household quarantining.ResultsIndividual interventions are not expected to have a large impact on COVID-19 hospitalizations. No intervention reduced COVID-19 hospitalizations by more than 12.7% (95% confidence interval [CI], 12.0% to 13.3%). Removing all interventions would result in nearly 42,000 COVID- 19 hospitalizations between June 2020 and January 2021, with peak hospital occupancy exceeding available beds 6-fold. Combining the interventions is predicted to reduce total hospitalizations by 48% (95% CI, 47-49%), with peak COVID-19 hospital occupancy of 70% of total beds. Targeted school closures can further reduce the peak occupancy.ConclusionsCombining low-impact interventions may mitigate the course of the COVID-19 epidemic, keeping hospital burden within the capacity of the healthcare system. Under this approach SARS-CoV-2 can spread through the community, moving toward herd immunity, while minimizing social and economic disruption.

Partial unlock for COVID-19-like epidemics can save 1-3 million lives worldwide (Preprint)

2020 ◽  
Author(s):  
Robert Shuler ◽  
Theodore Koukouvitis ◽  
Dyske Suematsu
Keyword(s):  
Herd Immunity ◽  
Seasonal Effects ◽  
Social Distancing ◽  
Public Response ◽  
Replication Factor ◽  
Population Immunity ◽  
Animal Trials ◽  
Highly Sensitive ◽  
Low Levels ◽  
Initial Peak

BACKGROUND A large percentage of deaths in an epidemic or pandemic can be due to overshoot of herd immunity, either from the initial peak or from planned or unplanned exit from lockdown or social distancing conditions. OBJECTIVE We study partial unlock or reopening interaction with seasonal effects in a managed epidemic to quantify overshoot effects on small and large unlock steps and discover robust strategies for reducing overshoot. METHODS We simulate partial unlock of social distancing for epidemics over a range of replication factor, immunity duration and seasonality factor for strategies targeting immunity thresholds using overshoot optimization. RESULTS Seasonality change must be taken into account as one of the steps in an easing sequence, and a two step unlock, including seasonal effects, minimizes overshoot and deaths. It may cause undershoot, which causes rebounds and assists survival of the pathogen CONCLUSIONS Partial easing levels, even low levels for economic relief while waiting on a vaccine, have population immunity thresholds based on the reduced replication rates and may experience overshoot as well. We further find a two step strategy remains highly sensitive to variations in case ratio, replication factor, seasonality and timing. We demonstrate a three or more step strategy is more robust, and conclude that the best possible approach minimizes deaths under a range of likely actual conditions which include public response. CLINICALTRIAL No human or animal trials were involved.

scholarly journals Determination of COVID-19 parameters for an agent-based model: Easing or tightening control strategies

2020 ◽  
Author(s):  
Ali Najmi ◽  
Farshid Safarighouzhdi ◽  
Eric J. Miller ◽  
Raina MacIntyre ◽  
Taha H. Rashidi
Keyword(s):  
Control Strategies ◽  
Mathematical Structure ◽  
Disease Suppression ◽  
Estimation Methods ◽  
Agent Based Models ◽  
Determine Parameter ◽  
Agent Based ◽  
Social Distancing ◽  
Compliance Level ◽  
Parameter Values

Different agent-based models have been developed to estimate the spread progression of coronavirus disease 2019 (COVID-19) and to evaluate different control strategies to control outbreak of the infectious disease. While there are several estimation methods for the disease-specific parameters of COVID-19, they have been used for aggregate level models such as SIR and not for agent-based models. We propose a mathematical structure to determine parameter values of agent-based models considering the mutual effects of parameters. Then, we assess the extent to which different control strategies can intervene the transmission of COVID-19. Accordingly, we consider scenarios of easing social distancing restrictions, opening businesses, speed of enforcing control strategies and quarantining family members of isolated cases on the disease progression. We find the social distancing compliance level in the Sydney greater metropolitan area to be around 85%. Then we elaborate on consequences of easing the compliance level in the disease suppression. We also show that tight social distancing levels should be considered when the restrictions on businesses and activity participations are easing.

scholarly journals Temporal dynamics of viral load and false negative rate influence the levels of testing necessary to combat COVID-19 spread

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Katherine F. Jarvis ◽  
Joshua B. Kelley
Keyword(s):  
Viral Load ◽  
Temporal Dynamics ◽  
Reproduction Number ◽  
False Negative ◽  
False Negative Rate ◽  
Agent Based Model ◽  
Agent Based ◽  
Viral Spread ◽  
Negative Rate

AbstractColleges and other organizations are considering testing plans to return to operation as the COVID-19 pandemic continues. Pre-symptomatic spread and high false negative rates for testing may make it difficult to stop viral spread. Here, we develop a stochastic agent-based model of COVID-19 in a university sized population, considering the dynamics of both viral load and false negative rate of tests on the ability of testing to combat viral spread. Reported dynamics of SARS-CoV-2 can lead to an apparent false negative rate from ~ 17 to ~ 48%. Nonuniform distributions of viral load and false negative rate lead to higher requirements for frequency and fraction of population tested in order to bring the apparent Reproduction number (Rt) below 1. Thus, it is important to consider non-uniform dynamics of viral spread and false negative rate in order to model effective testing plans.

scholarly journals The importance of supplementary immunisation activities to prevent measles outbreaks during the COVID-19 pandemic in Kenya

BMC Medicine ◽  
2021 ◽  
Vol 19 (1) ◽  
Author(s):  
C. N. Mburu ◽  
◽  
J. Ojal ◽  
R. Chebet ◽  
D. Akech ◽  
...  
Keyword(s):  
Vaccination Coverage ◽  
Herd Immunity ◽  
African Region ◽  
Population Immunity ◽  
Measles Outbreak ◽  
Increased Risk ◽  
Measles Vaccination ◽  
Serological Data ◽  
Measles Outbreaks ◽  
Measles Vaccination Coverage

Abstract Background The COVID-19 pandemic has disrupted routine measles immunisation and supplementary immunisation activities (SIAs) in most countries including Kenya. We assessed the risk of measles outbreaks during the pandemic in Kenya as a case study for the African Region. Methods Combining measles serological data, local contact patterns, and vaccination coverage into a cohort model, we predicted the age-adjusted population immunity in Kenya and estimated the probability of outbreaks when contact-reducing COVID-19 interventions are lifted. We considered various scenarios for reduced measles vaccination coverage from April 2020. Results In February 2020, when a scheduled SIA was postponed, population immunity was close to the herd immunity threshold and the probability of a large outbreak was 34% (8–54). As the COVID-19 contact restrictions are nearly fully eased, from December 2020, the probability of a large measles outbreak will increase to 38% (19–54), 46% (30–59), and 54% (43–64) assuming a 15%, 50%, and 100% reduction in measles vaccination coverage. By December 2021, this risk increases further to 43% (25–56), 54% (43–63), and 67% (59–72) for the same coverage scenarios respectively. However, the increased risk of a measles outbreak following the lifting of all restrictions can be overcome by conducting a SIA with ≥ 95% coverage in under-fives. Conclusion While contact restrictions sufficient for SAR-CoV-2 control temporarily reduce measles transmissibility and the risk of an outbreak from a measles immunity gap, this risk rises rapidly once these restrictions are lifted. Implementing delayed SIAs will be critical for prevention of measles outbreaks given the roll-back of contact restrictions in Kenya.

scholarly journals A versatile web app for identifying the drivers of COVID-19 epidemics

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Wayne M. Getz ◽  
Richard Salter ◽  
Ludovica Luisa Vissat ◽  
Nir Horvitz
Keyword(s):  
Model Fitting ◽  
Likelihood Estimation ◽  
Stochastic Simulations ◽  
Social Distancing ◽  
Epidemic Curve ◽  
Policies And Measures ◽  
Incidence And Mortality ◽  
Credible Intervals ◽  
Web App ◽  
Second Wave

Abstract Background No versatile web app exists that allows epidemiologists and managers around the world to comprehensively analyze the impacts of COVID-19 mitigation. The http://covid-webapp.numerusinc.com/ web app presented here fills this gap. Methods Our web app uses a model that explicitly identifies susceptible, contact, latent, asymptomatic, symptomatic and recovered classes of individuals, and a parallel set of response classes, subject to lower pathogen-contact rates. The user inputs a CSV file of incidence and, if of interest, mortality rate data. A default set of parameters is available that can be overwritten through input or online entry, and a user-selected subset of these can be fitted to the model using maximum-likelihood estimation (MLE). Model fitting and forecasting intervals are specifiable and changes to parameters allow counterfactual and forecasting scenarios. Confidence or credible intervals can be generated using stochastic simulations, based on MLE values, or on an inputted CSV file containing Markov chain Monte Carlo (MCMC) estimates of one or more parameters. Results We illustrate the use of our web app in extracting social distancing, social relaxation, surveillance or virulence switching functions (i.e., time varying drivers) from the incidence and mortality rates of COVID-19 epidemics in Israel, South Africa, and England. The Israeli outbreak exhibits four distinct phases: initial outbreak, social distancing, social relaxation, and a second wave mitigation phase. An MCMC projection of this latter phase suggests the Israeli epidemic will continue to produce into late November an average of around 1500 new case per day, unless the population practices social-relaxation measures at least 5-fold below the level in August, which itself is 4-fold below the level at the start of July. Our analysis of the relatively late South African outbreak that became the world’s fifth largest COVID-19 epidemic in July revealed that the decline through late July and early August was characterised by a social distancing driver operating at more than twice the per-capita applicable-disease-class (pc-adc) rate of the social relaxation driver. Our analysis of the relatively early English outbreak, identified a more than 2-fold improvement in surveillance over the course of the epidemic. It also identified a pc-adc social distancing rate in early August that, though nearly four times the pc-adc social relaxation rate, appeared to barely contain a second wave that would break out if social distancing was further relaxed. Conclusion Our web app provides policy makers and health officers who have no epidemiological modelling or computer coding expertise with an invaluable tool for assessing the impacts of different outbreak mitigation policies and measures. This includes an ability to generate an epidemic-suppression or curve-flattening index that measures the intensity with which behavioural responses suppress or flatten the epidemic curve in the region under consideration.

Sign in / Sign up

Export Citation Format

Share Document