OpenABM-Covid19-An agent-based model for non-pharmaceutical interventions against COVID-19 including contact tracing.
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ABSTRACT:
SUBMITTER: Hinch R
PROVIDER: S-EPMC8328312 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Contagious respiratory diseases, such as COVID-19, depend on sufficiently prolonged exposures for the successful transmission of the underlying pathogen. It is important that organizations evaluate the efficacy of non-pharmaceutical interventions aimed at mitigating viral transmission among their personnel. We have developed a operational risk assessment simulation framework that couples a spatial agent-based model of movement with an agent-based SIR model to assess the relative risks of different intervention strategies. By applying our model on MIT's Stata center, we assess the impacts of three possible dimensions of intervention: one-way vs unrestricted movement, population size allowed onsite, and frequency of leaving designated work location for breaks. We find that there is no significant impact made by one-way movement restrictions over unrestricted movement. Instead, we find that reducing the frequency at which individuals leave their workstations combined with lowering the number of individuals admitted below the current recommendations lowers the likelihood of highly connected individuals within the contact networks that emerge, which in turn lowers the overall risk of infection. We discover three classes of possible interventions based on their epidemiological effects. By assuming a direct relationship between data on secondary attack rates and transmissibility in the agent-based SIR model, we compare relative infection risk of four respiratory illnesses, MERS, SARS, COVID-19, and Measles, within the simulated area, and recommend appropriate intervention guidelines.
| S-EPMC9007058 | biostudies-literature
Project description:Prolonging non-pharmaceutical interventions (NPIs) used in the control of pandemics can cause a devastating effect on the overall economic and social welfare levels. Therefore, policymakers are facing a difficult duty in terms of implementing economically and socially sustainable and acceptable measures. The aim of this study is to investigate the effectiveness of NPIs implemented to control the COVID-19 pandemic. To this end, eight NPI measures were analysed, and their effects on the number of cases were investigated for France, Spain, China, and South Korea. In the study, the treatment effect of these mechanisms on the daily increase rate of the total number of cases during a certain period was analysed by using logarithmic linear regression with a dummy variables model. The findings indicate that the measures are effective against the spread of the pandemic at different levels. The findings also suggest that the most effective measure in decreasing the number of cases is workplace closure. An analysis comparing the effectiveness of countrywide measures and regional measures shows that school closing is the most effective measure to decrease the number of cases when implemented countrywide as opposed to regional implementation.
| S-EPMC8251211 | biostudies-literature
Project description:The COVID-19 epidemic created, at the time of writing the paper, highly unusual and uncertain socio-economic conditions. The world economy was severely impacted and business-as-usual activities severely disrupted. The situation presented the necessity to make a trade-off between individual health and safety on one hand and socio-economic progress on the other. Based on the current understanding of the epidemiological characteristics of COVID-19, a broad set of control measures has emerged along dimensions such as restricting people's movements, high-volume testing, contract tracing, use of face masks, and enforcement of social-distancing. However, these interventions have their own limitations and varying level of efficacy depending on factors such as the population density and the socio-economic characteristics of the area. To help tailor the intervention, we develop a configurable, fine-grained agent-based simulation model that serves as a virtual representation, i.e., a digital twin of a diverse and heterogeneous area such as a city. In this paper, to illustrate our techniques, we focus our attention on the Indian city of Pune in the western state of Maharashtra. We use the digital twin to simulate various what-if scenarios of interest to (1) predict the spread of the virus; (2) understand the effectiveness of candidate interventions; and (3) predict the consequences of introduction of interventions possibly leading to trade-offs between public health, citizen comfort, and economy. Our model is configured for the specific city of interest and used as an in-silico experimentation aid to predict the trajectory of active infections, mortality rate, load on hospital, and quarantine facility centers for the candidate interventions. The key contributions of this paper are: (1) a novel agent-based model that seamlessly captures people, place, and movement characteristics of the city, COVID-19 virus characteristics, and primitive set of candidate interventions, and (2) a simulation-driven approach to determine the exact intervention that needs to be applied under a given set of circumstances. Although the analysis presented in the paper is highly specific to COVID-19, our tools are generic enough to serve as a template for modeling the impact of future pandemics and formulating bespoke intervention strategies.
| S-EPMC7845792 | biostudies-literature
Project description:Contact tracing is currently one of the most effective measures to contain the COVID-19 pandemic. In order to identify persons that would otherwise not be known or remembered and to keep the time delay when reporting an infection and when contacting people as short as possible, digital contact tracing using smartphones seems to be a reasonable measure additional to manual contact tracing. Although first modelling studies predicted a positive effect in terms of prompt contact tracing, no empirically reliable data are as yet available, neither on the population-wide benefit nor on the potential risks of contact tracing apps. Risk-benefit assessment of such an app includes investigating whether such an app fulfils its purpose, as also research on the effectiveness, risks and side effects, and implementation processes (e.?g. planning and inclusion of different participants). The aim of this article was to give an overview of possible public health benefits as well as technical, social, legal and ethical aspects of a contact-tracing app in the context of the COVID-19 pandemic. Furthermore, conditions for the widest possible use of the app are presented.
| S-EPMC7536383 | biostudies-literature
Project description:Against the current COVID-19 pandemic, governments worldwide have devised a variety of non-pharmaceutical interventions to mitigate it. However, it is generally difficult to estimate the joint impact of different control strategies. In this paper, we tackle this question with an extended epidemic SEIR model, informed by a socio-political classification of different interventions. First, we inquire the conceptual effect of mitigation parameters on the infection curve. Then, we illustrate the potential of our model to reproduce and explain empirical data from a number of countries, to perform cross-country comparisons. This gives information on the best synergies of interventions to control epidemic outbreaks while minimising impact on socio-economic needs. For instance, our results suggest that, while rapid and strong lockdown is an effective pandemic mitigation measure, a combination of social distancing and early contact tracing can achieve similar mitigation synergistically, while keeping lower isolation rates. This quantitative understanding can support the establishment of mid- and long-term interventions, to prepare containment strategies against further outbreaks. This paper also provides an online tool that allows researchers and decision makers to interactively simulate diverse scenarios with our model.
| S-EPMC8139462 | biostudies-literature
Project description:To evaluate the joint impact of childhood vaccination rates and masking policies, in schools and workplaces, on community transmission and severe outcomes due to COVID-19. We utilized a stochastic, agent-based simulation of North Carolina, to evaluate the impact of 24 health policy decisions on overall incidence of disease, COVID-19 related hospitalization, and mortality from July 1, 2021-July 1, 2023. Universal mask removal in schools in January 2022 could lead to a 38.1-47%, 27.6-36.2%, and 15.9-19.7% increase in cumulative infections for ages 5-9, 10-19, and the total population, respectively, depending on the rate of vaccination of children relative to the adult population. Additionally, without increased vaccination uptake in the adult population, a 25% increase in child vaccination uptake from 50% to 75% uptake and from 75% to 100% uptake relative to the adult population, leads to a 22% and 18% or 28% and 33% decrease in peak hospitalizations in 2022 across scenarios when masks are removed either January 1st or March 8th 2022, respectively. Increasing vaccination uptake for the entire eligible population can reduce peak hospitalizations in 2022 by an average of 89% and 92% across all masking scenarios compared to the scenarios where no children are vaccinated. High vaccination uptake among both children and adults is necessary to mitigate the increase in infections from mask removal in schools and workplaces.
| S-EPMC8669851 | biostudies-literature
Project description:Leber hereditary optic neuropathy (LHON) is a monogenic but multifactorial disease vulnerable to environmental triggers. Little is known about how LHON onset changed during the COVID-19 pandemic and how non-pharmaceutical interventions (NPHIs) against COVID-19 impact LHON onset. One hundred and forty-seven LHON patients with the m.11778G>A mutation complaining of vision loss were involved between January 2017 and July 2022. The onset time points, age of onset, and possible risk factors were evaluated. Analyses were conducted among 96 LHON patients in the Pre-COVID-19 group and 51 in the COVID-19 group. The median (IQR) age of onset decreased significantly from 16.65 (13.739, 23.02) in pre-COVID-19 to 14.17 (8.87, 20.29) during COVID-19. Compared with the Pre-COVID-19 group, the COVID-19 group exhibited bimodal distribution with an additional peak at six; the first quarter of 2020 also witnessed a relatively denser onset, with no subsequent second spike. NPHIs against COVID-19 significantly changed patients' lifestyles, including higher secondhand smoke exposure (p < 0.001), adherence to masks (p < 0.001), reduction in time spent outdoors for leisure (p = 0.001), and prolonged screen time (p = 0.007). Multivariate logistic regression revealed that secondhand smoke exposure and mask-wearing were independent risk factors of younger LHON onset. Lower age of onset of LHON appeared after the breakout of the COVID-19 pandemic, and novel risk factors were detected, including secondhand exposure and long mask-wearing. Carriers of LHON mtDNA mutations, especially teenagers or children, should be advised to avoid secondhand smoke exposure and there are possible adverse outcomes of longer mask-wearing.
| S-EPMC10298648 | biostudies-literature
Project description:ObjectivesTo evaluate which non-pharmaceutical interventions (NPIs) have been more and less effective in controlling the COVID-19 pandemic.MethodsWe performed a systematic review of published and unpublished empirical studies, either observational or interventional, analysing the comparative effectiveness of NPIs against the COVID-19 pandemic. We searched Embase/Medline and medRxiv to identify the relevant literature.ResultsWe identified 34 studies. During the first wave of the COVID-19 pandemic, school closing was the most effective NPI, followed by workplace closing, business and venue closing and public event bans. Public information campaigns and mask wearing requirements were also effective in controlling the pandemic while being less disruptive for the population than other NPIs. There was no evidence on the effectiveness of public transport closure, testing and contact tracing strategies and quarantining or isolation of individuals. Early implementation was associated with a higher effectiveness in reducing COVID-19 cases and deaths, while general stringency of the NPIs was not.ConclusionsIn this systematic review, we found that school closing, followed by workplace closing, business and venue closing and public event bans were the most effective NPIs in controlling the spread of COVID-19. An early response and a combination of specific social distancing measures are effective at reducing COVID-19 cases and deaths. Continuous monitoring of NPIs effectiveness is needed in order to adapt decision making.
| S-EPMC8214911 | biostudies-literature
Project description:Background and objectiveThe outbreak of the current pandemic begun from the first individual of a 55-year old from Hubei province in China, the disease instigated by the new coronavirus spreading across the world. Scientists presently speculate this coronavirus, SARS-CoV-2, originated in a bat and by one way or another jumped to another creature, potentially the pangolin, which at that point gave it to people. The ailment is currently spreading between individuals with no animal delegate. Researchers are struggling to follow the infection back to where it started to become familiar with its spread. In the event that, for example, specialists can locate the soonest cases, they might have the option to distinguish the creature have where the infection hides. In March and April 2020, researchers detailed that this virus created normally. Coronavirus has been become of the serious global phenomena in the recent years and has negative effects in the entire world health and economy. The virus is believed to have been associated with a host animal which human contracted. Subsequently, human-to-human infection began. Through migration as humans have become complex with easy mobility the disease has traveled to the entire continent. Now, numerous scientist are going on in the hope of obtaining medication and vaccination to prevent the spread of the disease and mortality of the disease. It is important that we obtain quantitative and qualitative information about the etiology of this disease which is crucial. Mathematical modeling is capable of providing qualitative information on many parameters that guides the decision making of health practitioners. In this work we focus the optimal control of COVID-19 with the help of Non Pharmaceutical Interventions (NPIs). To find the role of factors/parameters in the transmission of the syndrome we find R0; the ratio of reproduction for the proposed model.MethodsTo find the role of parameters in the transmission of the syndrome we find R0; the ratio of reproduction for the proposed model. On the basis of sensitivity indices of the parameters we apply Non Pharmaceutical Interventions(NPIs) to control the sensitive parameters and hence formulate the optimal control mode. With the help of Hamiltonian and Lagrangian we minimize the density of contaminated stuff and infected human population.ResultsWe focus the optimal control of COVID-19 with the help of Non Pharmaceutical Interventions(NPIs). On the basis of sensitivity indices of the parameters we apply Non Pharmaceutical Interventions(NPIs) to control the sensitive parameters and hence formulate the optimal control model. The major NPIs are, STAY HOME, SANITIZER (wash hands), EARLY CASE DETECTION (PCR Test) and FACE MASK. These NPIs helps in mitigation and reducing the size of outbreak of the disease.ConclusionWe check the existence of the optimal solution for the system. At the end, Using matlab we produce numerical simulations for validation of results of control variables. The results demonstrate that if there is no control (variables/interventios), 900 out 1000 susceptible individuals may be infected (exposed) in very short period. As such a circumstances no agency fighting against COVID-19 could be successful due to its limited resources.
| S-EPMC7339477 | biostudies-literature
Project description:BackgroundThe first half of 2020 has been marked as the era of COVID-19 pandemic which affected the world globally in almost every aspect of the daily life from societal to economical. To prevent the spread of COVID-19, countries have implemented diverse policies regarding Non-Pharmaceutical Intervention (NPI) measures. This is because in the first stage countries had limited knowledge about the virus and its contagiousness. Also, there was no effective medication or vaccines. This paper studies the effectiveness of the implemented policies and measures against the deaths attributed to the virus between January and May 2020.MethodsData from the European Centre for Disease Prevention and Control regarding the identified cases and deaths of COVID-19 from 48 countries have been used. Additionally, data concerning the NPI measures related policies implemented by the 48 countries and the capacity of their health care systems was collected manually from their national gazettes and official institutes. Data mining, time series analysis, pattern detection, machine learning, clustering methods and visual analytics techniques have been applied to analyze the collected data and discover possible relationships between the implemented NPIs and COVID-19 spread and mortality. Further, we recorded and analyzed the responses of the countries against COVID-19 pandemic, mainly in urban areas which are over-populated and accordingly COVID-19 has the potential to spread easier among humans.ResultsThe data mining and clustering analysis of the collected data showed that the implementation of the NPI measures before the first death case seems to be very effective in controlling the spread of the disease. In other words, delaying the implementation of the NPI measures to after the first death case has practically little effect on limiting the spread of the disease. The success of implementing the NPI measures further depends on the way each government monitored their application. Countries with stricter policing of the measures seems to be more effective in controlling the transmission of the disease.ConclusionsThe conducted comparative data mining study provides insights regarding the correlation between the early implementation of the NPI measures and controlling COVID-19 contagiousness and mortality. We reported a number of useful observations that could be very helpful to the decision makers or epidemiologists regarding the rapid implementation and monitoring of the NPI measures in case of a future wave of COVID-19 or to deal with other unknown infectious pandemics. Regardless, after the first wave of COVID-19, most countries have decided to lift the restrictions and return to normal. This has resulted in a severe second wave in some countries, a situation which requires re-evaluating the whole process and inspiring lessons for the future.
| S-EPMC8409702 | biostudies-literature