Efficacy Of Non-pharmaceutical Interventions Research Articles

A six-compartment model for COVID-19 with transmission dynamics and public health strategies.

The global crisis of the COVID-19 pandemic has highlighted the need for mathematical models to inform public health strategies. The present study introduces a novel six-compartment epidemiological model that uniquely incorporates a higher isolation rate for unreported symptomatic cases of COVID-19 compared to reported cases, aiming to enhance prediction accuracy and address the challenge of initial underreporting. Additionally, we employ optimal control theory to assess the cost-effectiveness of interventions and adapt these strategies to specific epidemiological scenarios, such as varying transmission rates and the presence of asymptomatic carriers. By applying this model to COVID-19 data from India (30 January 2020 to 24 November 2020), chosen to capture the initial outbreak and subsequent waves, we calculate a basic reproduction number of 2.147, indicating the high transmissibility of the virus during this period in India. A sensitivity analysis reveals the critical impact of detection rates and isolation measures on disease progression, showing the robustness of our model in estimating the basic reproduction number. Through optimal control simulations, we demonstrate that increasing isolation rates for unreported cases and enhancing detection reduces the spread of COVID-19. Furthermore, our cost-effectiveness analysis establishes that a combined strategy of isolation and treatment is both more effective and economically viable. This research offers novel insights into the efficacy of non-pharmaceutical interventions, providing a tool for strategizing public health interventions and advancing our understanding of infectious disease dynamics.

Modeling COVID-19 spread and non-pharmaceutical interventions in South Africa: A stochastic approach

The COVID-19 pandemic has exerted a pervasive worldwide influence, including on South Africa, which stands out as one of the African nations most substantially impacted. As of March 20, 2021, the nation has encountered a multitude of obstacles in its efforts to contain the virus’s transmission. Conventional approaches to forecasting the transmission of contagious illnesses frequently prove inadequate when it comes to comprehending the intricate dynamics of COVID-19, specifically with regard to stochastic disruptions and the efficacy of non-pharmaceutical interventions. The deficiency in predictive modeling has impeded the capacity to effectively evaluate and execute measures to reduce the spread of the virus. The principal objective of this study is to construct a novel stochastic model, denoted as SEIHR, that can effectively forecast the transmission of COVID-19 in South Africa while also assessing the efficacy of diverse non-pharmacological interventions. By conducting an extensive data analysis spanning from April 17, 2020, to September 13, 2021, this research endeavors to provide a comprehensive and nuanced comprehension of the dynamics of the epidemic, with a particular focus on its early surges. Using the idea of stochastic Lyapunov function theory, this study aims to find out if there is and is not a solution in the given model. It also wants to find out what conditions must be met in order to get rid of diseases when there are random disturbances, such as white noise. Using this model, the efficacy of interventions such as lockdowns in preventing the spread of the virus during the epidemic will also be critically evaluated. The primary objective is to furnish policymakers and health authorities with invaluable insights and tools that will enable them to more effectively manage and alleviate the COVID-19 crisis in South Africa and comparable settings.

COVID-19 Mask Usage and Social Distancing in Social Media Images: Large-scale Deep Learning Analysis.

BackgroundThe adoption of nonpharmaceutical interventions and their surveillance are critical for detecting and stopping possible transmission routes of COVID-19. A study of the effects of these interventions can help shape public health decisions. The efficacy of nonpharmaceutical interventions can be affected by public behaviors in events, such as protests. We examined mask use and mask fit in the United States, from social media images, especially during the Black Lives Matter (BLM) protests, representing the first large-scale public gatherings in the pandemic.ObjectiveThis study assessed the use and fit of face masks and social distancing in the United States and events of large physical gatherings through public social media images from 6 cities and BLM protests.MethodsWe collected and analyzed 2.04 million public social media images from New York City, Dallas, Seattle, New Orleans, Boston, and Minneapolis between February 1, 2020, and May 31, 2020. We evaluated correlations between online mask usage trends and COVID-19 cases. We looked for significant changes in mask use patterns and group posting around important policy decisions. For BLM protests, we analyzed 195,452 posts from New York and Minneapolis from May 25, 2020, to July 15, 2020. We looked at differences in adopting the preventive measures in the BLM protests through the mask fit score.ResultsThe average percentage of group pictures dropped from 8.05% to 4.65% after the lockdown week. New York City, Dallas, Seattle, New Orleans, Boston, and Minneapolis observed increases of 5.0%, 7.4%, 7.4%, 6.5%, 5.6%, and 7.1%, respectively, in mask use between February 2020 and May 2020. Boston and Minneapolis observed significant increases of 3.0% and 7.4%, respectively, in mask use after the mask mandates. Differences of 6.2% and 8.3% were found in group pictures between BLM posts and non-BLM posts for New York City and Minneapolis, respectively. In contrast, the differences in the percentage of masked faces in group pictures between BLM and non-BLM posts were 29.0% and 20.1% for New York City and Minneapolis, respectively. Across protests, 35% of individuals wore a mask with a fit score greater than 80%.ConclusionsThe study found a significant drop in group posting when the stay-at-home laws were applied and a significant increase in mask use for 2 of 3 cities where masks were mandated. Although a positive trend toward mask use and social distancing was observed, a high percentage of posts showed disregard for the guidelines. BLM-related posts captured the lack of seriousness to safety measures, with a high percentage of group pictures and low mask fit scores. Thus, the methodology provides a directional indication of how government policies can be indirectly monitored through social media.

A Systematic Review for Effective Preventive Public Education of Respiratory Infection.

The present study aimed to systematically review to find the best available evidence on the efficacy of non-pharmaceutical interventions that have been used in the community so far. Through eight electronic journal database, 9 articles met our inclusion Participants, Intervention, Control, Outcomes, and Study Design (PICOS) criteria based on medical symptoms, interventions, and improvements. In general, interventions included hand hygiene, mask use, health education such as cough etiquette, hand washing and sanitizer methods. In addition, exercise and meditation were performed to improve immunity. As a result, the number of incidents and absences related to respiratory infections were reduced, the frequency and method of handwashing improved, and there were also positive effects in knowledge, attitude/perception, and performance. We concluded that it is necessary to create an environment and systematic support so that organizations or governments can determine healthy behavior at the same time as an individual approach. Furthermore, the follow-up for evaluating the effectiveness of interventions and the monitoring period should be included during the study, consequently resulting in having an opportunity to continuously remind people about health behavior. The community provides information on various types of non-pharmaceutical intervention to maintain healthy management and lifestyles in the public.

COVID-19 Superspreading Suggests Mitigation by Social Network Modulation.

Although COVID-19 has caused severe suffering globally, the efficacy of nonpharmaceutical interventions has been greater than typical models have predicted. Meanwhile, evidence is mounting that the pandemic is characterized by superspreading. Capturing this phenomenon theoretically requires modeling at the scale of individuals. Using a mathematical model, we show that superspreading drastically enhances mitigations which reduce the overall personal contact number and that social clustering increases this effect.

Social mobilization and polarization can create volatility in COVID-19 pandemic control

During the COVID-19 pandemic, political polarization has emerged as a significant threat that inhibits coordinated action of central and local institutions reducing the efficacy of non-pharmaceutical interventions (NPIs). Yet, it is not well-understood to what extent polarization can affect grass-roots, voluntary social mobilization targeted at mitigating the pandemic spread. Here, we propose a polarized mobilization model amidst the pandemic for demonstrating the differential responses to COVID-19 as mediated by the USA’s political landscape. We use a novel dataset and models from time-critical social mobilization competitions, voting records, and a high-resolution county-wise friendship network. Our simulations show that a higher degree of polarization impedes the overall spread of mobilization and leads to a highly-heterogeneous impact among states. Our hypothetical compliance campaign to mitigate COVID-19 spread predicts grass-roots mitigation strategies’ success before the dates of actual lockdowns in identically polarized states with more than three times of success rate than oppositely polarized states. Finally, we analyze the coupling of social mobilization leading to unrest and the growth of COVID-19 infections. These findings highlight social mobilization as both a collective precautionary measure and a potential threat to countermeasures, together with a warning message that the emerging polarization can be a significant hurdle of NPIs relying on coordinated action.

Assessing the influence of climate on wintertime SARS-CoV-2 outbreaks

High susceptibility has limited the role of climate in the SARS-CoV-2 pandemic to date. However, understanding a possible future effect of climate, as susceptibility declines and the northern-hemisphere winter approaches, is an important open question. Here we use an epidemiological model, constrained by observations, to assess the sensitivity of future SARS-CoV-2 disease trajectories to local climate conditions. We find this sensitivity depends on both the susceptibility of the population and the efficacy of non-pharmaceutical interventions (NPIs) in reducing transmission. Assuming high susceptibility, more stringent NPIs may be required to minimize outbreak risk in the winter months. Our results suggest that the strength of NPIs remain the greatest determinant of future pre-vaccination outbreak size. While we find a small role for meteorological forecasts in projecting outbreak severity, reducing uncertainty in epidemiological parameters will likely have a more substantial impact on generating accurate predictions.

Ecologic association between influenza and COVID-19 mortality rates in European countries.

Ecologic studies investigating COVID-19 mortality determinants, used to make predictions and design public health control measures, generally focused on population-based variable counterparts of individual-based risk factors. Influenza is not causally associated with COVID-19, but shares population-based determinants, such as similar incidence/mortality trends, transmission patterns, efficacy of non-pharmaceutical interventions, comorbidities and underdiagnosis. We investigated the ecologic association between influenza mortality rates and COVID-19 mortality rates in the European context. We considered the 3-year average influenza (2014-2016) and COVID-19 (31 May 2020) crude mortality rates in 34 countries using EUROSTAT and ECDC databases and performed correlation and regression analyses. The two variables - log transformed, showed significant Spearman's correlation ρ = 0.439 (P = 0.01), and regression coefficients, b = 0.743 (95% confidence interval, 0.272-1.214; R2 = 0.244; P = 0.003), b = 0.472 (95% confidence interval, 0.067-0.878; R2 = 0.549; P = 0.02), unadjusted and adjusted for confounders (population size and cardiovascular disease mortality), respectively. Common significant determinants of both COVID-19 and influenza mortality rates were life expectancy, influenza vaccination in the elderly (direct associations), number of hospital beds per population unit and crude cardiovascular disease mortality rate (inverse associations). This analysis suggests that influenza mortality rates were independently associated with COVID-19 mortality rates in Europe, with implications for public health preparedness, and implies preliminary undetected SARS-CoV-2 spread in Europe.

Exercise-induced bronchoconstriction update—2016

The first practice parameter on exercise-induced bronchoconstriction (EIB) was published in 2010. This updated practice parameter was prepared 5years later. In the ensuing years, there has been increased understanding of the pathogenesis of EIB and improved diagnosis of this disorder by using objective testing. At the time of this publication, observations included the following: dry powder mannitol for inhalation as a bronchial provocation test is FDA approved however not currently available in the United States; if baseline pulmonary function test results are normal to near normal (before and after bronchodilator) in a person with suspected EIB, then further testing should be performed by using standardized exercise challenge or eucapnic voluntary hyperpnea (EVH); and the efficacy of nonpharmaceutical interventions (omega-3 fatty acids) has been challenged. The workgroup preparing this practice parameter updated contemporary practice guidelines based on a current systematic literature review. The group obtained supplementary literature and consensus expert opinions when the published literature was insufficient. Asearch of the medical literature on PubMed was conducted, and search terms included pathogenesis, diagnosis, differential diagnosis, and therapy (both pharmaceutical and nonpharmaceutical) of exercise-induced bronchoconstriction or exercise-induced asthma (which is no longer a preferred term); asthma; and exercise and asthma. References assessed as relevant to the topic were evaluated to search for additional relevant references. Published clinical studies were appraised by category of evidence and used to document the strength of the recommendation. The parameter was then evaluated by Joint Task Force reviewers and then by reviewers assigned by the parent organizations, as well as the general membership. Based on this process, the parameter can be characterized as an evidence- and consensus-based document.

Reduction in the Incidence of Influenza A But Not Influenza B Associated With Use of Hand Sanitizer and Cough Hygiene in Schools

Laboratory-based evidence is lacking regarding the efficacy of nonpharmaceutical interventions (NPIs) such as alcohol-based hand sanitizer and respiratory hygiene to reduce the spread of influenza. The Pittsburgh Influenza Prevention Project was a cluster-randomized trial conducted in 10 elementary schools in Pittsburgh, PA, during the 2007 to 2008 influenza season. Children in 5 intervention schools received training in hand and respiratory hygiene, and were provided and encouraged to use hand sanitizer regularly. Children in 5 schools acted as controls. Children with influenza-like illness were tested for influenza A and B by reverse-transcriptase polymerase chain reaction. A total of 3360 children participated in this study. Using reverse-transcriptase polymerase chain reaction, 54 cases of influenza A and 50 cases of influenza B were detected. We found no significant effect of the intervention on the primary study outcome of all laboratory-confirmed influenza cases (incidence rate ratio [IRR]: 0.81; 95% confidence interval [CI]: 0.54, 1.23). However, we did find statistically significant differences in protocol-specified ancillary outcomes. Children in intervention schools had significantly fewer laboratory-confirmed influenza A infections than children in control schools, with an adjusted IRR of 0.48 (95% CI: 0.26, 0.87). Total absent episodes were also significantly lower among the intervention group than among the control group; adjusted IRR 0.74 (95% CI: 0.56, 0.97). NPIs (respiratory hygiene education and the regular use of hand sanitizer) did not reduce total laboratory-confirmed influenza. However, the interventions did reduce school total absence episodes by 26% and laboratory-confirmed influenza A infections by 52%. Our results suggest that NPIs can be an important adjunct to influenza vaccination programs to reduce the number of influenza A infections among children.