Covasim can be tailored to the local context by using detailed data on the population (such as the population age distribution and number of contacts between people) and the epidemic (such as diagnosed cases and reported deaths). Different interventions, including contact tracing, are applied on a scaled-down version of New York City, USA, and the parameters that lead to a controlled epidemic are determined. A simple webapp for Covasim has been developed, based on Vue.js (for the frontend), ScirisWeb (for communicating between the frontend and the backend), Flask (for running the backend), and Gunicorn/NGINX (for running the server); this webapp is available at app.covasim.org. Visualization, Intuitively, most distributional assumptions mean that larger errors imply a lower log-likelihood. The length of time between the start of viral shedding and symptom onset is assumed to follow a log-normal distribution with a mean of 1.1 days (Table 1). In practice, each agent has several dozen states, and there are typically hundreds of thousands of agents. Covasim model structure, including infection (exposure), disease progression, and final outcomes. Calibrating any model to the COVID-19 epidemic is an inherently difficult task: not only is there significant uncertainty around the reported data, but there are also many possible combinations of parameter values that could give rise to these data. Flaxman S, Mishra S, Gandy A, Unwin H, Coupland H, Mellan T, et al. Investigation, of interaction, but the distribution of interaction rates from agent toagent. Here we describe the methodology of Covasim (COVID-19 Agent-based Simulator), an open-source model developed to help address these questions. By default, we set the viral load of the high stage to be twice as high as the low stage and to last for either 30% of the infectious duration or 4 days, whichever is shorter. Some are simple [2 ], while others can be very complex[3 -5]. Yellow shading indicates that an individual is infectious and can transmit the disease to other susceptible agents. RESEARCH ARTICLE Covasim: An agent-based model of COVID-19 dynamics and interventions Cliff C. Kerr ID 1*, Robyn M. Stuart ID 2,3, Dina Mistry ID 1, Romesh G. Abeysuriya ID 3, Katherine . Evaluating How Smartphone Contact Tracing Technology Can Reduce the Spread of Infectious Diseases: The Case of COVID-19. All plotting outputs are configurable, and results can also be saved in Excel, JSON, or NumPy formats for further processing. Note that Covasim depends on a number of user-installed Python packages that can be installed automatically via pip install. Project administration, [view 2022 Aug;140:104315. doi: 10.1016/j.autcon.2022.104315. FOIA doi: 10.1088/1478-3975/abf5b4. For example, human contact patterns are intractably complex, and the algorithms that Covasim uses to approximate these are necessarily quite simplified. In partnership with local stakeholders, Covasim has been used to answer policy and research questions in more than a dozen countries, including India, the United States, Vietnam, and Australia. Additional flexibility, including waning efficacy and differential effectiveness across variants, will be incorporated as trial results become available. This definition of Re is nearly identical to the definition of the "instantaneous reproductive number" in Gostic et al. The preceding examples illustrate some aspects of Covasims core functionality that are used in most applications. We use a non-Markovian age-of-infection model that is capable of handling long and variable time delays without changing its model topology. The coronavirus disease 2019 (COVID-19) has resulted in an ongoing pandemic worldwide. Digital contact tracing can be approximated in Covasim as a standard contact-tracing intervention with zero delays, with the caveat that tracing multiple steps (i.e., contacts of contacts) within a single day would require a custom intervention. This folder contains Jupyter notebooks for nine tutorials that walk you through using Covasim, from absolute basics to advanced topics such as calibration and creating custom populations. doi:10.1371/journal.pcbi.1009149, Editor: Manja Marz, bioinformatics, GERMANY, Received: February 11, 2021; Accepted: June 5, 2021; Published: July 26, 2021. We wanted to capture the benefits of agent-based modeling (in particular, the ability of such models to simulate the kinds of microscale policies being used to respond to the COVID-19 pandemic), whilst making use of recent advances in software tools and computational methods to minimize the complexity and computation time typically associated with such models. Covasim: an agent-based model of COVID-19 dynamics and interventions Cl i C . 2.4.2.2 Schools. Covasim has built-in implementations of the common interventions described below, as well as the ability for users to create their own interventions, which can either be derived from the base intervention class, or be simple functions that modify the simulation object. Children are assigned to schools and adults to workplaces, each with a user-specified number of fixed daily contacts (by default, Poisson-distributed with means of 20 for schools and 16 for workplaces, chosen to match the mean values for SynthPops networks). Our model is guided by the epidemiological characteristics of COVID-19 and the agent modeling method, and based on the interaction mechanism between the risk of COVID-19 outbreak, individual epidemiological influencing factors and macro-intervention behavior, and the optimization of the algorithm in the agent modeling. Writing review & editing, Roles The recommended citation is: Covasim's immunity module (including vaccines and variants) is described here: The Covasim webapp is available at http://app.covasim.org, and the repository for it is available here. Data curation, Contact tracing corresponds to notifying individuals that they have had contact with a confirmed case, so that they can be quarantined, tested, or otherwise change their behavior. Covasim includes country-specific demographic information on age structure and population size . 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. The simulation gives the number of total COVID-19 cases. This distinguishes it from SynthPops where enrollment or employment varies depending on the given data. You signed in with another tab or window. J Process Control. If nothing happens, download Xcode and try again. Writing review & editing, Roles While agent-based models, including Covasim, are difficult to deploy widely enough, and calibrate quickly enough, to be a feasible replacement for compartmental models, they can provide a mechanistic understanding of the COVID-19 epidemic in ways that compartmental models cannot. Covasim was developed for Python 3.8 using the SciPy (scipy.org) ecosystem [81]. https://github.com/institutefordiseasemodeling/covasim, github.com/institutefordiseasemodeling/covasim, https://www.kingcounty.gov/depts/health/covid-19/data.aspx, http://spiral.imperial.ac.uk/handle/10044/1/77482, https://www.imperial.ac.uk/mrc-global-infectious-disease-analysis/covid-19/report-34-ifr/, https://www.healthknowledge.org.uk/public-health-textbook, Corrections, Expressions of Concern, and Retractions. For example, choosing to implement Covasim in Python instead of C++ or Java significantly reduced development time and increased simplicity for users and developers; however, it imposed a large penalty on performance. Investigation, Supervision, The logical flow of a single Covasim run is as follows. Please enable it to take advantage of the complete set of features! Scripts to automatically scrape data (including demographics and COVID epidemiology data), Had testing and contact tracing programs been rapidly scaled up (by 50% and five fold respectively), we estimated the number of infections would have been approximately halved. The simulation gives the number of total COVID-19 cases. A tag already exists with the provided branch name. Second, a population of agents is created, including age, sex, and comorbidities for each agent, drawing from location-specific data distributions where available; then, agents are then connected into contact networks based on their age and other statistical properties (Section 2.4). All core numerical algorithms in the Covasim integration loopspecifically, calculating intra-host viral load, per-person susceptibility and transmissibility, and which contacts of an infected person become infected themselvesare implemented as highly optimized 32-bit array operations in Numba. This paper describes the methodology underlying Covasim, and provides several examples illustrating its use, including an application to Seattle where Covasim scenarios were used to inform a rapid policy decision, with subsequent validation of these findings by real-world data. Interventions (described in the text) are shown as dashed vertical lines. States with a dashed border are considered symptomatic with respect to symptomatic versus asymptomatic testing. Though treatments for COVID-19 have so far had only modest results in clinical trials [71], they can be implemented in Covasim as interventions that reduce the probability of progressing to severe disease or death. No, Is the Subject Area "Agent-based modeling" applicable to this article? Pharmaceutical interventions, especially vaccines, are an increasingly important part of public health responses to COVID-19. The labor force is drawn using employment rates by age, and non-teachers are assigned to workplaces using data on establishment sizes. Unable to load your collection due to an error, Unable to load your delegates due to an error, Effect of (i) removing lockdown from day 21 (, Introducing contact tracing with a different percentage of the population being smartphone users from day 27. A pre-built version of Covasim, including the webapp, is also available on Docker Hub (hub.docker.com). To facilitate easy adaptation to different contexts, Covasim comes pre-loaded with data on country age distributions and household sizes as reported by the UN Population Division 2019 (population.un.org). Dynamical models are commonly validated by comparing their projections against data on what actually happened, as shown in the case study (Fig 11). Cliff C. Kerr, On each timestep (which corresponds to a single day by default), the order of operations is: dynamic rescaling (Section 2.6.2); applying health system constraints (Section 2.6.3); updating the state of each agent, including disease progression (Section 2.2); importation events (Section 2.6.4); applying interventions (Section 2.5); calculating disease transmission events across each infectious agents contact network (Section 2.3); collating outputs into results arrays (Section 2.6.5); and applying analyzers (Section 2.6.7). An agent-based model (ABM) is a computational model for simulating the actions and interactions of autonomous agents (both individual or collective entities such as organizations or groups) in order to understand the behavior of a system and what governs its outcomes. [8] and Zhao and Chen [9], compartments are further divided to provide more nuance in simulating progression through different disease states, and have been deployed to study the effects of various population-wide interventions such as social distancing and testing on COVID-19 transmission. SynthPops generate individuals within households using data on the distribution of ages, household sizes, and the age of reference individuals per household for a given population. Burnet Institute, Melbourne, Victoria, Australia, Affiliation: Writing review & editing, Affiliations Yes To date, Covasim has been used by researchers and public health officials in over a dozen countries. However, this default value is too low for high-transmission contexts such as New York City or Lombardy [51], and may be too high for low-transmission contexts such as Indias first wave [52]. Conceptualization, [47]. Conceptualization, This can be used to reflect both (a) reductions in transmissibility per contact, such as through mask wearing, personal protective equipment, hand-washing, and maintaining physical distance; and (b) reductions in the number of contacts at home, school, work, or in the community. The vertical size of each tree is proportional to the total number of infections. Lauren George, Department of Mathematical Sciences, University of Copenhagen, Copenhagen, Denmark, with every agent now counting as two. Methodology, Questions or comments can be directed to [email protected], or on this project's In this paper, we describe a COVID-19 model, called Covasim (COVID-19 Agent-based Simulator), that we developed to help answer these questions. The most basic intervention in Covasim is to reduce transmissibility () starting on a given day. Department of Mathematical Sciences, University of Copenhagen, Copenhagen, Denmark, Covasim is a stochastic agent-based simulator for performing COVID-19 analyses. Testing Different COVID-19 Vaccination Strategies Using an Agent-Based Modeling Approach. Supplementary information: Software, While methods 2 and 3 are implemented in Covasim, they have the disadvantage that they introduce significant temporal blurring, due to the potentially long infectious period (and, for method 3, the long recovery period). Models for examining COVID-19 transmission and control measures can be broadly divided into two main types: compartmental models and agent-based models (also called individual-based or microsimulation models), with the former generally being simpler and faster, while the latter are generally more complex, detailed, and computationally expensive. A simulation-deep reinforcement learning (SiRL) approach for epidemic control optimization. Initially, when the epidemic is small, there is no scaling performed: one agent corresponds to one person. State estimation of the time-space propagation of COVID-19 using a distributed parameter observer based on a SEIR-type model. Here we provide a case study of how Covasim was used to inform a policy decision in King County (the local government area that includes the city of Seattle), Washington, USA; a full description of the methodology used is given in [25]. Covasim includes country-specific demographic information on age structure and population size . The Institute for Biohealth Innovation and transmitted securely. See the contributing and code of conduct READMEs for more information. The https:// ensures that you are connecting to the Dots () represent omitted entries. As shown in Fig 6, these software optimizations allow Covasim to achieve high levels of performance, despite being implemented purely in Python. The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021. Careers. Friday, Oct. 2, 3:00 p.m. Covasim: an open-source agent-based model of COVID-19 dynamics and interventions Software, 2021 Jun 23;18(4). Supervision, Second, they are unsuitable for answering questions that depend on details of behavior at the individual level, such as superspreading events, transmission within multigenerational households, school classroom cohorting, and contact tracing. At the time of writing, these data are available for over 4,000 unique locations, including most countries in the world (administrative level 0), all US states and many administrative level 1 (i.e., subnational) regions in Europe, and some administrative level 2 regions in Europe and the US (i.e., US counties). However, there are significant modeling challenges due to the large number of vaccine candidates under investigation, coupled with the considerable uncertainty regarding their propertiessuch as the extent to which they block acquisition and transmission as well as symptoms, how much protection is conferred by a single dose, the extent to which immunity wanes over time, and their effectiveness against different COVID-19 strains [70]. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. COVID-19 Agent-based Simulator (Covasim): a model for exploring coronavirus dynamics and interventions simulation model abm stochastic epidemiology agent-based npi coronavirus covid-19 covid contact-tracing Updated 17 days ago Python AB-CE / abce Star 155 Code Issues Pull requests Each column c of the contact matrix is treated as an age distribution of the household contacts for a person in the age group c. The ages of other household members are then sampled conditional on the age of the reference person for the household. Federal government websites often end in .gov or .mil. Evidence is mixed as to whether transmissibility is lower if the infectious individual does not have symptoms [55]. Finally, we predicted that had the additional restrictions not been implemented, by the end of the year, daily infection rates would have been roughly three times as high as actually occurred (Fig 11E). (Note that using actual testing data for this period, rather than assuming a constant number of tests, would have resulted in an even more accurate prediction of diagnoses, though of course these data were not available at the time the prediction was made). Covasim can also be used to explore the potential impact of different interventions, including social distancing, school closures, testing, contact tracing, quarantine, and vaccination. These speed and memory use results are comparable to OpenABM-Covid19, despite the latter being implemented in C [23]. Copyright: 2021 Kerr et al. Comparison of population options in Covasim. Data curation, If nothing happens, download GitHub Desktop and try again. Report 13: Estimating the number of infections and the impact of non-pharmaceutical interventions on COVID-19 in 11 European countries. Mathematical models have played an important role in helping countries around the world decide how to best tackle the COVID-19 pandemic. All children and young adults aged between 6 and 22 are assigned to schools and universities, and all adults between 22 and 65 are assigned to workplaces. Methods Countries have adopted non-pharmaceutical interventions (NPI) to slow down the spread. Online ahead of print. Vaccines in Covasim are modeled by adjusting individuals susceptibility to infection and probability of developing symptoms after being infected; both of these modifications affect the overall probability of progressing to severe disease and death. Values were validated from model fits to data on numbers of cases, numbers of people hospitalized and in intensive care, and numbers of deaths from Washington and Oregon states. These tests would occur if changes are made to VitoKit (iOS or Android [TBD]) Resources for . Autom Constr. However, the limitation of this approach is that it introduces a discretization of results: model outputs can only be produced in increments of the scaling factor, so relatively rare events, such as deaths, may not have sufficient granularity to reflect the epidemic behavior at a small scale. The series will focus on the effects of the SARS-CoV-2 virus and COVID-19 disease. By default, Covasim calculates the loss using normalized absolute error. During the scenario period, we assumed that the number of tests conducted per day would remain constant at the average value from the previous 7 days (Fig 11A). These include projections of indicators such as numbers of infections and peak hospital demand. Covasim also includes an estimate of the epidemic doubling time, computed similarly to the "rule of 69.3" [74], specifically: This shows a slightly more detailed example, including creating an intervention and saving to disk. In general, interventions are modeled as changes to parameter values. Edward A. Wenger, Once a certain threshold is reached, however (by default, 5% of the population is non-susceptible), the non-susceptible agents in the model are downsampled and a corresponding scaling factor is introduced (by default, a factor of 1.2 is used). These interventions can incorporate the effects of delays, loss-to-follow-up, micro-targeting, and other factors. where T is the doubling time, w is the window length over which to compute the doubling time (3 days by default), and ni(t) is the cumulative number of infections at time t. In addition to interventions, Covasim also includes a library of "analyzers". Micha Jastrzbski, K err 1 * , R oby n M .S tua rt 2 ,3 , D i n a M i s try 1 , R omes h G . We use individual viral load to model these differences in infectivity. These interventions include physical interventions (mobility restrictions and masks), diagnostic interventions (testing, contact tracing, and quarantine), and pharmaceutical interventions (vaccination). After an initial 45 days of uncontrolled epidemic spread, the following interventions are applied: March 26, close schools and reduce work and community contacts to 70% of their original values; April 10, reduce work and community to 30% of their original values; May 5, reopen work and community to 80% of their original values; May 20, begin testing 10% of people with COVID-like illness each day, and trace the contacts of people who test positive. All individuals are present in the household network, including some with no household connections. Romesh G. Abeysuriya, A second common definition of Re ("method 2") is to first determine the total number of people who became infectious on day t, then count the total number of people these people went on to infect, and then divide the latter by the former. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Competing interests: The authors have declared that no competing interests exist. (B) Full listing of the code for this simulation, showing the intervention definition (lines 36), and a compact way of creating the simulations, running them in parallel, and plotting them (lines 811). In addition to these core outputs, Covasim includes several outputs for additional analysis. GitHub page. These design choices are intended to allow users to start running simple Covasim analyses quickly, while providing flexibility later if more detailed data become available or if the modeling questions become more nuanced. Like interventions, in principle they can access and modify any aspect of the simulation state. where s refers to the type of data observed (such cumulative confirmed cases or number of deaths); t is the time index; ws is the weight associated with s; and are the counts from the data and model, respectively, for this time series at this time index; and f is the loss, objective, or goodness-of-fit function (e.g., normalized absolute error, mean absolute error, mean squared error, or the Poisson test statistic [75]).
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