Modeling the effect of exposure notification and non-pharmaceutical interventions on COVID-19 transmission in Washington state.
Ontology highlight
ABSTRACT:
SUBMITTER: Abueg M
PROVIDER: S-EPMC7955120 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:This paper describes a data-driven simulation study that explores the relative impact of several low-cost and practical non-pharmaceutical interventions on the spread of COVID-19 in an outpatient hospital dialysis unit. The interventions considered include: (i) voluntary self-isolation of healthcare personnel (HCPs) with symptoms; (ii) a program of active syndromic surveillance and compulsory isolation of HCPs; (iii) the use of masks or respirators by patients and HCPs; (iv) improved social distancing among HCPs; (v) increased physical separation of dialysis stations; and (vi) patient isolation combined with preemptive isolation of exposed HCPs. Our simulations show that under conditions that existed prior to the COVID-19 outbreak, extremely high rates of COVID-19 infection can result in a dialysis unit. In simulations under worst-case modeling assumptions, a combination of relatively inexpensive interventions such as requiring surgical masks for everyone, encouraging social distancing between healthcare professionals (HCPs), slightly increasing the physical distance between dialysis stations, and-once the first symptomatic patient is detected-isolating that patient, replacing the HCP having had the most exposure to that patient, and relatively short-term use of N95 respirators by other HCPs can lead to a substantial reduction in both the attack rate and the likelihood of any spread beyond patient zero. For example, in a scenario with R0 = 3.0, 60% presymptomatic viral shedding, and a dialysis patient being the infection source, the attack rate falls from 87.8% at baseline to 34.6% with this intervention bundle. Furthermore, the likelihood of having no additional infections increases from 6.2% at baseline to 32.4% with this intervention bundle.
| S-EPMC8291695 | biostudies-literature
Project description:Governments around the globe use non-pharmaceutical interventions (NPIs) to curb the spread of coronavirus disease 2019 (COVID-19) cases. Making decisions under uncertainty, they all face the same temporal paradox: estimating the impact of NPIs before they have been implemented. Due to the limited variance of empirical cases, researchers could so far not disentangle effects of individual NPIs or their impact on different demographic groups. In this paper, we utilize large-scale agent-based simulations in combination with Susceptible-Exposed-Infectious-Recovered (SEIR) models to investigate the spread of COVID-19 for some of the most affected federal states in Germany. In contrast to other studies, we sample agents from a representative survey. Including more realistic demographic attributes that influence agents’ behavior yields accurate predictions of COVID-19 transmissions and allows us to investigate counterfactual what-if scenarios. Results show that quarantining infected people and exploiting industry-specific home office capacities are the most effective NPIs. Disentangling education-related NPIs reveals that each considered institution (kindergarten, school, university) has rather small effects on its own, yet, that combined openings would result in large increases in COVID-19 cases. Representative survey-characteristics of agents also allow us to estimate NPIs’ effects on different age groups. For instance, re-opening schools would cause comparatively few infections among the risk-group of people older than 60 years.
| S-EPMC8553158 | biostudies-literature
Project description:Governments worldwide have rapidly deployed non-pharmaceutical interventions (NPIs) to mitigate the COVID-19 pandemic. However, the effect of these individual NPI measures across space and time has yet to be sufficiently assessed, especially with the increase of policy fatigue and the urge for NPI relaxation in the vaccination era. Using the decay ratio in the suppression of COVID-19 infections and multi-source big data, we investigated the changing performance of different NPIs across waves from global and regional levels (in 133 countries) to national and subnational (in the United States of America [USA]) scales before the implementation of mass vaccination. The synergistic effectiveness of all NPIs for reducing COVID-19 infections declined along waves, from 95.4% in the first wave to 56.0% in the third wave recently at the global level and similarly from 83.3% to 58.7% at the USA national level, while it had fluctuating performance across waves on regional and subnational scales. Regardless of geographical scale, gathering restrictions and facial coverings played significant roles in epidemic mitigation before the vaccine rollout. Our findings have important implications for continued tailoring and implementation of NPI strategies, together with vaccination, to mitigate future COVID-19 waves, caused by new variants, and other emerging respiratory infectious diseases.
| S-EPMC8666325 | 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:The imposition and lifting of non-pharmaceutical interventions (NPIs) to avert the COVID-19 pandemic have gained popularity worldwide and will continue to be enforced until herd immunity is achieved. We developed a linear regression model to ascertain the nexus between the time-varying reproduction number averaged over a time window of six days (Rts) and seven NPIs: contact tracing, quarantine efforts, social distancing and health checks, hand hygiene, wearing of facemasks, lockdown and isolation, and health-related supports. Our analysis suggests that the second wave that emerged in Sri Lanka in early October 2020 continued despite numerous NPIs. The model indicates that the most effective single NPI was lockdown and isolation. Conversely, the least effective individual NPIs were hand hygiene and wearing of facemasks. The model also demonstrates that to mitigate the second wave to a satisfactory level (Rts<1), the best single NPI was the contact tracing with stringent imposition (% of improvement of Rts was 69.43 against the base case). By contrast, the best combination of two NPIs was the lockdown & isolation with health-related supports (% of improvement was 31.92 against the base case). As such, many health authorities worldwide can use this model to successfully strategize the imposition and lifting of NPIs for averting the COVID-19 pandemic.
| S-EPMC8261138 | biostudies-literature
Project description:INTRODUCTION:Implementing non-pharmaceutical interventions (NPIs) protect the public from COVID-19. However, the impact of NPIs has been inconsistent and remains unclear. This study, therefore, aims to measure the impact of major NPIs (social distancing, social isolation and quarantine) on reducing COVID-19 transmission. METHODS AND ANALYSIS:We will conduct a systematic review and meta-analysis research of both randomised and non-randomised controlled trials. We will undertake a systematic search of: MEDLINE, Embase, Allied & Complementary Medicine, COVID-19 Research, WHO database on COVID-19, ClinicalTrails.Gov for clinical trials on COVID-19, Cochrane Resources on Coronavirus (COVID-19), Oxford COVID-19 Evidence Service and Google Scholar for published and unpublished literatures on COVID-19 including preprint engines such as medRxiv, bioRxiv, Litcovid and SSRN for unpublished studies on COVID-19 and will be reported in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses. Outcomes of interest for impact analysis will include the reduction of COVID-19 transmission, avoiding crowds and restricting movement, isolating ill and psychological impacts. The Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols checklist has been used for this protocol. For quality of included studies, we will use the Cochrane Collaboration's tool for assessing risk of bias for randomised controlled trials and the Newcastle-Ottawa Scale for observational studies. The Grading of Recommendations Assessment, Development and Evaluation approach will grade the certainty of the evidence for all outcome measures across studies. Random-effects model for meta-analysis will measure the effect size of NPIs or the strengths of relationships. For quantitative data, risk ratio or OR, absolute risk difference (for dichotomous outcome data), or mean difference or standardised mean difference (for continuous data) and their 95% CIs will be calculated. Where statistical pooling is not possible, a narrative synthesis will be conducted for the included studies. To assess the heterogeneity of effects, I2 together with the observed effects will be evaluated to provide the true effects in the analysis. ETHICS AND DISSEMINATION:Formal ethical approval from an institutional review board or research ethics committee is not required as primary data will not be collected. The final results of this study will be published in an open-access peer-reviewed journal, and abstract will be presented at suitable national/international conferences or workshops. We will also share important information with public health authorities as well as with the WHO. In addition, we may post the submitted manuscript under review to medRxiv, or other relevant preprint servers. TRIAL REGISTRATION NUMBER:CRD42020207338.
| S-EPMC7582337 | biostudies-literature
Project description:Epidemics caused by microbial organisms are part of the natural phenomena of increasing biological complexity. The heterogeneity and constant variability of hosts, in terms of age, immunological status, family structure, lifestyle, work activities, social and leisure habits, daily division of time and other demographic characteristics make it extremely difficult to predict the evolution of epidemics. Such prediction is, however, critical for implementing intervention measures in due time and with appropriate intensity. General conclusions should be precluded, given that local parameters dominate the flow of local epidemics. Membrane computing models allows us to reproduce the objects (viruses and hosts) and their interactions (stochastic but also with defined probabilities) with an unprecedented level of detail. Our LOIMOS model helps reproduce the demographics and social aspects of a hypothetical town of 10 320 inhabitants in an average European country where COVID-19 is imported from the outside. The above-mentioned characteristics of hosts and their lifestyle are minutely considered. For the data in the Hospital and the ICU we took advantage of the observations at the Nursery Intensive Care Unit of the Consortium University General Hospital, Valencia, Spain (included as author). The dynamics of the epidemics are reproduced and include the effects on viral transmission of innate and acquired immunity at various ages. The model predicts the consequences of delaying the adoption of non-pharmaceutical interventions (between 15 and 45 days after the first reported cases) and the effect of those interventions on infection and mortality rates (reducing transmission by 20, 50 and 80%) in immunological response groups. The lockdown for the elderly population as a single intervention appears to be effective. This modeling exercise exemplifies the application of membrane computing for designing appropriate multilateral interventions in epidemic situations.
| S-EPMC8499911 | biostudies-literature
Project description:Infectious diseases and human behavior are intertwined. On one side, our movements and interactions are the engines of transmission. On the other, the unfolding of viruses might induce changes to our daily activities. While intuitive, our understanding of such feedback loop is still limited. Before COVID-19 the literature on the subject was mainly theoretical and largely missed validation. The main issue was the lack of empirical data capturing behavioral change induced by diseases. Things have dramatically changed in 2020. Non-pharmaceutical interventions (NPIs) have been the key weapon against the SARS-CoV-2 virus and affected virtually any societal process. Travel bans, events cancellation, social distancing, curfews, and lockdowns have become unfortunately very familiar. The scale of the emergency, the ease of survey as well as crowdsourcing deployment guaranteed by the latest technology, several Data for Good programs developed by tech giants, major mobile phone providers, and other companies have allowed unprecedented access to data describing behavioral changes induced by the pandemic. Here, I review some of the vast literature written on the subject of NPIs during the COVID-19 pandemic. In doing so, I analyze 348 articles written by more than 2518 authors in the first 12 months of the emergency. While the large majority of the sample was obtained by querying PubMed, it includes also a hand-curated list. Considering the focus, and methodology I have classified the sample into seven main categories: epidemic models, surveys, comments/perspectives, papers aiming to quantify the effects of NPIs, reviews, articles using data proxies to measure NPIs, and publicly available datasets describing NPIs. I summarize the methodology, data used, findings of the articles in each category and provide an outlook highlighting future challenges as well as opportunities.
| S-EPMC7881715 | biostudies-literature
Project description:Background: Several non-pharmaceutical interventions (NPIs) have been implemented across the world to control the coronavirus disease (COVID-19) pandemic. Social distancing (SD) interventions applied so far have included school closures, remote working and quarantine. These measures have been shown to have large impacts on pandemic influenza transmission. However, there has been comparatively little examination of such measures for COVID-19. Methods: We examined the existing literature, and collated data, on implementation of NPIs to examine their effects on the COVID-19 pandemic so far. Data on NPIs were collected from official government websites as well as from media sources. Results: Measures such as travel restrictions have been implemented in multiple countries and appears to have slowed the geographic spread of COVID-19 and reduced initial case numbers. We find that, due to the relatively sparse information on the differences with and without interventions, it is difficult to quantitatively assess the efficacy of many interventions. Similarly, whilst the comparison to other pandemic diseases such as influenza can be helpful, there are key differences that could affect the efficacy of similar NPIs. Conclusions: The timely implementation of control measures is key to their success and must strike a balance between early enough application to reduce the peak of the epidemic and ensuring that they can be feasibly maintained for an appropriate duration. Such measures can have large societal impacts and they need to be appropriately justified to the population. As the pandemic of COVID-19 progresses, quantifying the impact of interventions will be a vital consideration for the appropriate use of mitigation strategies.
| S-EPMC7255913 | biostudies-literature
Project description:IntroductionDigital exposure notification (EN) approaches may offer considerable advantages over traditional contact tracing in speed, scale, efficacy, and confidentiality in pandemic control. We applied the science of learning health systems to test the effect of framing and digital means, email vs Short Message Service (SMS), on EN adoption among patients of an academic health center.MethodsWe tested three communication approaches of the Apple and Google EN system in a rapid learning cycle involving 15 000 patients pseudorandomly assigned to three groups. The patients in the first group received a 284-word email that presented EN as a tool that can help slow the spread. The patients in the second group received a 32-word SMS that described EN as a new tool to help slow the spread (SlowTheSpreadSMS). Patients in the third group received a 47-word SMS that depicted the system as a new digital tool that can empower them to protect their family and friends (EmpowerSMS). A brief four-question anonymous survey of adoption was included in a reminder message sent 2 days after the initial outreach.ResultsOne hundred and sixty people responded to the survey within 1 week: 2.33% from EmpowerSMS, 0.97% from SlowTheSpreadSMS, and 0.53% from emails; 29 (41.43%), 24 (41.38%), and 11 (34.38%) reported having adopted EN from each group, respectively. Patient reported barriers to adoption included iOS version incompatibility, privacy concerns, and low trust of government agencies or companies like Apple and Google. Patients recommended that healthcare systems play an active role in disseminating information about this tool. Patients also recommended advertising on social media and providing reassurance about privacy.ConclusionsThe EmpowerSMS resulted in relatively more survey responses. Both SMS groups had slightly higher, but not statistically significant EN adoption rates compared to email. Findings from the pilot not only informed operational decision-making in our health system but also contributed to EN rollout planning in our State.
| S-EPMC8646517 | biostudies-literature