scholarly journals Covasim: An agent-based model of COVID-19 dynamics and interventions

2021 ◽  
Vol 17 (7) ◽  
pp. e1009149
Author(s):  
Cliff C. Kerr ◽  
Robyn M. Stuart ◽  
Dina Mistry ◽  
Romesh G. Abeysuriya ◽  
Katherine Rosenfeld ◽  
...  
Keyword(s):  
Source Model ◽  
Ease Of Use ◽  
Asia Pacific ◽  
Contact Tracing ◽  
Local Health ◽  
Term Care ◽  
Loss To Follow Up ◽  
Agent Based ◽  
Epidemic Dynamics ◽  
Pharmaceutical Interventions

The COVID-19 pandemic has created an urgent need for models that can project epidemic trends, explore intervention scenarios, and estimate resource needs. Here we describe the methodology of Covasim (COVID-19 Agent-based Simulator), an open-source model developed to help address these questions. Covasim includes country-specific demographic information on age structure and population size; realistic transmission networks in different social layers, including households, schools, workplaces, long-term care facilities, and communities; age-specific disease outcomes; and intrahost viral dynamics, including viral-load-based transmissibility. Covasim also supports an extensive set of interventions, including non-pharmaceutical interventions, such as physical distancing and protective equipment; pharmaceutical interventions, including vaccination; and testing interventions, such as symptomatic and asymptomatic testing, isolation, contact tracing, and quarantine. These interventions can incorporate the effects of delays, loss-to-follow-up, micro-targeting, and other factors. Implemented in pure Python, Covasim has been designed with equal emphasis on performance, ease of use, and flexibility: realistic and highly customized scenarios can be run on a standard laptop in under a minute. In collaboration with local health agencies and policymakers, Covasim has already been applied to examine epidemic dynamics and inform policy decisions in more than a dozen countries in Africa, Asia-Pacific, Europe, and North America.

scholarly journals Covasim: an agent-based model of COVID-19 dynamics and interventions

2020 ◽  
Author(s):  
Cliff C. Kerr ◽  
Robyn M. Stuart ◽  
Dina Mistry ◽  
Romesh G. Abeysuriya ◽  
Gregory Hart ◽  
...  
Keyword(s):  
Source Model ◽  
Contact Tracing ◽  
Local Health ◽  
Transmission Networks ◽  
Loss To Follow Up ◽  
Agent Based ◽  
Resource Needs ◽  
Hygiene Measures ◽  
Health Agencies

The COVID-19 pandemic has created an urgent need for models that can project epidemic trends, explore intervention scenarios, and estimate resource needs. Here we describe the methodology of Covasim (COVID-19 Agent-based Simulator), an open-source model developed to help address these questions. Covasim includes demographic information on age structure and population size; realistic transmission networks in different social layers, including households, schools, workplaces, and communities; age-specific disease outcomes; and intrahost viral dynamics, including viral-load-based transmissibility. Covasim also supports an extensive set of interventions, including non-pharmaceutical interventions, such as physical distancing, hygiene measures, and protective equipment; and testing interventions, such as symptomatic and asymptomatic testing, isolation, contact tracing, and quarantine. These interventions can incorporate the effects of delays, loss-to-follow-up, micro-targeting, and other factors. In collaboration with local health agencies and policymakers, Covasim has already been applied to examine disease dynamics and policy options in Africa, Europe, Oceania, and North America.

scholarly journals A data driven agent-based model that recommends non-pharmaceutical interventions to suppress Coronavirus disease 2019 resurgence in megacities

2021 ◽  
Vol 18 (181) ◽  
pp. 20210112
Author(s):  
Ling Yin ◽  
Hao Zhang ◽  
Yuan Li ◽  
Kang Liu ◽  
Tianmu Chen ◽  
...  
Keyword(s):  
Census Data ◽  
Herd Immunity ◽  
Risk Tolerance ◽  
Contact Tracing ◽  
Agent Based Model ◽  
Agent Based ◽  
Shenzhen City ◽  
Other World ◽  
Work Class ◽  
Pharmaceutical Interventions

Before herd immunity against Coronavirus disease 2019 (COVID-19) is achieved by mass vaccination, science-based guidelines for non-pharmaceutical interventions are urgently needed to reopen megacities. This study integrated massive mobile phone tracking records, census data and building characteristics into a spatially explicit agent-based model to simulate COVID-19 spread among 11.2 million individuals living in Shenzhen City, China. After validation by local epidemiological observations, the model was used to assess the probability of COVID-19 resurgence if sporadic cases occurred in a fully reopened city. Combined scenarios of three critical non-pharmaceutical interventions (contact tracing, mask wearing and prompt testing) were assessed at various levels of public compliance. Our results show a greater than 50% chance of disease resurgence if the city reopened without contact tracing. However, tracing household contacts, in combination with mandatory mask use and prompt testing, could suppress the probability of resurgence under 5% within four weeks. If household contact tracing could be expanded to work/class group members, the COVID resurgence could be avoided if 80% of the population wear facemasks and 40% comply with prompt testing. Our assessment, including modelling for different scenarios, helps public health practitioners tailor interventions within Shenzhen City and other world megacities under a variety of suppression timelines, risk tolerance, healthcare capacity and public compliance.

scholarly journals Using Non-Pharmaceutical Interventions and High Isolation of Asymptomatic Carriers to Contain the Spread of SARS-CoV-2 in Nursing Homes

2021 ◽  
Author(s):  
Alec J. Schmidt ◽  
Yury García ◽  
Diego Pinheiro ◽  
Tom Reichert ◽  
Miriam A. Nuño
Keyword(s):  
Attack Rate ◽  
Group Living ◽  
Daily Basis ◽  
Contact Tracing ◽  
Term Care ◽  
Care Facilities ◽  
Degree Of Control ◽  
Antigen Tests ◽  
Pharmaceutical Interventions

ABSTRACTObjectiveUsing a pandemic influenza model modified for COVID-19, this study investigated the degree of control over pre-symptomatic transmission that common non-pharmaceutical interventions (NPIs) would require to reduce the spread in long-term care facilities.MethodsWe created a stochastic compartmental SEIR model with Poisson-distributed transition states that compared the effect of R0, common NPIs, and isolation rates of pre-symptomatic carriers primarily on attack rate, peak cases, and timing in a 200-resident nursing home. Model sensitivity was assessed with 1st order Sobol’ indices.ResultsThe most rigorous NPIs decreased the peak number of infections by 4.3 and delayed the peak by 9.7 days in the absence of pre-symptomatic controls. Reductions in attack rate were not likely, even with rigorous application of all defined NPIs, unless pre-symptomatic carriers were identified and isolated at rates exceeding 76%. Attack rate was most sensitive to the pre-symptomatic isolation rate (Sobol’ index > 0.7) and secondarily to R0.ConclusionsCommon NPIs delayed and reduced epidemic peaks. Reducing attack rates ultimately required efficient isolation of pre-symptomatic cases, including rapid antigen tests on a nearly daily basis. This must be accounted for in testing and contact tracing plans for group living settings.

scholarly journals Discontinuous epidemic transition due to limited testing

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Davide Scarselli ◽  
Nazmi Burak Budanur ◽  
Marc Timme ◽  
Björn Hof
Keyword(s):  
Exponential Growth ◽  
Growth Period ◽  
High Impact ◽  
Contact Tracing ◽  
Epidemic Dynamics ◽  
Slowing Down ◽  
Containment Measures ◽  
Second Wave ◽  
Limited Testing ◽  
Pharmaceutical Interventions

AbstractHigh impact epidemics constitute one of the largest threats humanity is facing in the 21st century. In the absence of pharmaceutical interventions, physical distancing together with testing, contact tracing and quarantining are crucial in slowing down epidemic dynamics. Yet, here we show that if testing capacities are limited, containment may fail dramatically because such combined countermeasures drastically change the rules of the epidemic transition: Instead of continuous, the response to countermeasures becomes discontinuous. Rather than following the conventional exponential growth, the outbreak that is initially strongly suppressed eventually accelerates and scales faster than exponential during an explosive growth period. As a consequence, containment measures either suffice to stop the outbreak at low total case numbers or fail catastrophically if marginally too weak, thus implying large uncertainties in reliably estimating overall epidemic dynamics, both during initial phases and during second wave scenarios.

scholarly journals Model-M: An agent-based epidemic model of a middle-sized municipality

2021 ◽  
Author(s):  
Ludek Berec ◽  
Tomas Diviak ◽  
Ales Kubena ◽  
Rene Levinsky ◽  
Roman Neruda ◽  
...  
Keyword(s):  
Epidemic Model ◽  
Realistic Model ◽  
Contact Tracing ◽  
Protective Measures ◽  
Social Contacts ◽  
Seir Model ◽  
Agent Based ◽  
Individual Level ◽  
Epidemic Simulation ◽  
Pharmaceutical Interventions

This report presents a technical description of our agent-based epidemic model of a particular middle-sized municipality. We have developed a realistic model with 56 thousand inhabitants and 2.7 million of social contacts. These form a multi-layer social network that serves as a base of our epidemic simulation. The disease is modeled by our extended SEIR model with parameters fitted to real epidemics data for Czech Republic. The model is able to simulate a whole range of non-pharmaceutical interventions on individual level, such as protective measures and physical distancing, testing, contact tracing, isolation and quarantine. The effect of government-issued measures such as contact restrictions in different environments (schools, restaurants, vendors, etc.) can also be simulated. The model is implemented in Python and is available as open source at: www.github.com/epicity-cz/model-m/releases

scholarly journals OpenABM-Covid19 - an agent-based model for non-pharmaceutical interventions against COVID-19 including contact tracing

2020 ◽  
Author(s):  
Robert Hinch ◽  
William J M Probert ◽  
Anel Nurtay ◽  
Michelle Kendall ◽  
Chris Wymatt ◽  
...  
Keyword(s):  
Economic Activity ◽  
Contact Tracing ◽  
Agent Based ◽  
Health Measures ◽  
The World ◽  
The Uk ◽  
Spread Of Infection ◽  
The Impact ◽  
Age Stratification ◽  
Pharmaceutical Interventions

SARS-CoV-2 has spread across the world, causing high mortality and unprecedented restrictions on social and economic activity. Policymakers are assessing how best to navigate through the ongoing epidemic, with models being used to predict the spread of infection and assess the impact of public health measures. Here, we present OpenABM-Covid19: an agent-based simulation of the epidemic including detailed age-stratification and realistic social networks. By default the model is parameterised to UK demographics and calibrated to the UK epidemic, however, it can easily be re-parameterised for other countries. OpenABM-Covid19 can evaluate non-pharmaceutical interventions, including both manual and digital contact tracing. It can simulate a population of 1 million people in seconds per day allowing parameter sweeps and formal statistical model-based inference. The code is open-source and has been developed by teams both inside and outside academia, with an emphasis on formal testing, documentation, modularity and transparency. A key feature of OpenABM-Covid19 is its Python interface, which has allowed scientists and policymakers to simulate dynamic packages of interventions and help compare options to suppress the COVID-19 epidemic.

scholarly journals Development of the reproduction number from coronavirus SARS-CoV-2 case data in Germany and implications for political measures

2020 ◽  
Author(s):  
Sahamoddin Khailaie ◽  
Tanmay Mitra ◽  
Arnab Bandyophadhyay ◽  
Marta Schips ◽  
Pietro Mascheroni ◽  
...  
Keyword(s):  
Health Care ◽  
Health Care System ◽  
Reproduction Number ◽  
Contact Tracing ◽  
Local Health ◽  
Federal States ◽  
Viral Spread ◽  
Number Of Patients ◽  
Pharmaceutical Interventions ◽  
Care System

AbstractThe novel Coronavirus SARS-CoV-2 (COVID-19) has induced a world-wide pandemic and subsequent non-pharmaceutical interventions (NPI) in order to control the spreading of the virus. NPIs are considered to be critical in order to at least delay the peak number of infected individuals and to prevent the health care system becoming overwhelmed by the number of patients to treat in hospitals or in intensive care units (ICUs). However, there is also increasing concern that the NPIs in place would increase mortality because of other diseases, increase the frequency of suicide and increase the risk of an economic recession with unforeseeable implications. It is therefore instrumental to evaluate the necessity of NPIs and to monitor the progress of containment of virus spreading.We used a data-driven estimation of the evolution of the reproduction number for viral spreading in Germany as well as in all its federal states. Based on an extended infection-epidemic model, parameterized with data from the Robert Koch Institute and, alternatively, with parameters stemming from a fit to the initial phase of COVID-19 spreading in different regions of Italy, we found that the reproduction number was decreased to a range below 1 in all federal states. The development in Germany suggests that NPIs can be partially released based on an established new culture of social distancing, face masks and mutual care within the population. However, any release of measures delays reaching low incidence numbers. The strategy to reduce daily new cases to a sufficiently low level to be controlled by contact tracing and testing turned out to work in Germany. This requires a responsible behaviour of the population, optimised contact tracing techniques and extended testing capacities in contact clusters.Author summaryAs of mid-June Germany was able to control the pandemic to an extent that the health care system was not overwhelmed and the daily new reported infections appear under control. We analysed the evolution of the reproduction number during the epidemic in Germany and the efficiency of non-pharmaceutical interventions (NPIs) in containing viral spread. The results suggest that the cultural change induced by NPIs in interpersonal interactions and distancing allows for a partial release of political measures. However, this requires a functional case isolation and contact tracing of new cases by local health departments, early identification of contact clusters and consequent isolation of those clusters.

scholarly journals OpenABM-Covid19—An agent-based model for non-pharmaceutical interventions against COVID-19 including contact tracing

2021 ◽  
Vol 17 (7) ◽  
pp. e1009146
Author(s):  
Robert Hinch ◽  
William J. M. Probert ◽  
Anel Nurtay ◽  
Michelle Kendall ◽  
Chris Wymant ◽  
...  
Keyword(s):  
Computational Models ◽  
Contact Tracing ◽  
Agent Based ◽  
Health Measures ◽  
The World ◽  
The Uk ◽  
Spread Of Infection ◽  
The Impact ◽  
Age Stratification ◽  
Pharmaceutical Interventions

SARS-CoV-2 has spread across the world, causing high mortality and unprecedented restrictions on social and economic activity. Policymakers are assessing how best to navigate through the ongoing epidemic, with computational models being used to predict the spread of infection and assess the impact of public health measures. Here, we present OpenABM-Covid19: an agent-based simulation of the epidemic including detailed age-stratification and realistic social networks. By default the model is parameterised to UK demographics and calibrated to the UK epidemic, however, it can easily be re-parameterised for other countries. OpenABM-Covid19 can evaluate non-pharmaceutical interventions, including both manual and digital contact tracing, and vaccination programmes. It can simulate a population of 1 million people in seconds per day, allowing parameter sweeps and formal statistical model-based inference. The code is open-source and has been developed by teams both inside and outside academia, with an emphasis on formal testing, documentation, modularity and transparency. A key feature of OpenABM-Covid19 are its Python and R interfaces, which has allowed scientists and policymakers to simulate dynamic packages of interventions and help compare options to suppress the COVID-19 epidemic.

scholarly journals Investigating Dynamics of COVID-19 Spread and Containment with Agent-Based Modeling

2021 ◽  
Vol 11 (12) ◽  
pp. 5367
Author(s):  
Amirarsalan Rajabi ◽  
Alexander V. Mantzaris ◽  
Ece C. Mutlu ◽  
Ozlem O. Garibay
Keyword(s):  
Disease Model ◽  
Agent Based Modeling ◽  
Contact Tracing ◽  
Test Bed ◽  
Policy Makers ◽  
Agent Based ◽  
Contact Behavior ◽  
Epidemic Curve ◽  
Behavioral Adaptations ◽  
Travel Restrictions

Governments, policy makers, and officials around the globe are working to mitigate the effects of the COVID-19 pandemic by making decisions that strive to save the most lives and impose the least economic costs. Making these decisions require comprehensive understanding of the dynamics by which the disease spreads. In traditional epidemiological models, individuals do not adapt their contact behavior during an epidemic, yet adaptive behavior is well documented (i.e., fear-induced social distancing). In this work we revisit Epstein’s “coupled contagion dynamics of fear and disease” model in order to extend and adapt it to explore fear-driven behavioral adaptations and their impact on efforts to combat the COVID-19 pandemic. The inclusion of contact behavior adaptation endows the resulting model with a rich dynamics that under certain conditions reproduce endogenously multiple waves of infection. We show that the model provides an appropriate test bed for different containment strategies such as: testing with contact tracing and travel restrictions. The results show that while both strategies could result in flattening the epidemic curve and a significant reduction of the maximum number of infected individuals; testing should be applied along with tracing previous contacts of the tested individuals to be effective. The results show how the curve is flattened with testing partnered with contact tracing, and the imposition of travel restrictions.

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