Monitoring non-pharmaceutical public health interventions during the COVID-19 pandemic.
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ABSTRACT:
SUBMITTER: Shen Y
PROVIDER: S-EPMC8385050 | biostudies-literature | 2021 Aug
REPOSITORIES: biostudies-literature
Publications
Monitoring non-pharmaceutical public health interventions during the COVID-19 pandemic.
Shen Yannan Y Powell Guido G Ganser Iris I Zheng Qulu Q Grundy Chris C Okhmatovskaia Anya A Buckeridge David L DL
Scientific data 20210824 1
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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:During the outbreak of the COVID-19 pandemic, Non-Pharmaceutical and Pharmaceutical treatments were alternative strategies for governments to intervene. Though many of these intervention methods proved to be effective to stop the spread of COVID-19, i.e., lockdown and curfew, they also posed risk to the economy; in such a scenario, an analysis on how to strike a balance becomes urgent. Our research leverages the mobility big data from the University of Maryland COVID-19 Impact Analysis Platform and employs the Generalized Additive Model (GAM), to understand how the social demographic variables, NPTs (Non-Pharmaceutical Treatments) and PTs (Pharmaceutical Treatments) affect the New Death Rate (NDR) at county-level. We also portray the mutual and interactive effects of NPTs and PTs on NDR. Our results show that there exists a specific usage rate of PTs where its marginal effect starts to suppress the NDR growth, and this specific rate can be reduced through implementing the NPTs.
| S-EPMC8504736 | biostudies-literature
Project description:Non-Pharmaceutical Public Health Interventions (NPHIs) have been used by different countries to control the spread of the COVID-19. Despite available evidence regarding the effectiveness of NPHSs, there is still no consensus about how policymakers can trust these results. Studies on the effectiveness of NPHSs are single studies conducted in specific communities. Therefore, they cannot individually prove if these interventions have been effective in reducing the spread of the infection and its adverse health outcomes. In this systematic review, we aimed to examine the effects of NPHIs on the COVID-19 case growth rate, death growth rate, Intensive Care Unit (ICU) admission, and reproduction number in countries, where NPHIs have been implemented. We searched relevant electronic databases, including Medline (via PubMed), Scopus, CINAHL, Web of Science, etc. from late December 2019 to February 1, 2021. The key terms were primarily drawn from Medical Subject Heading (MeSh and Emtree), literature review, and opinions of experts. Peer-reviewed quasi-experimental studies were included in the review. The PROSPERO registration number is CRD42020186855. Interventions were NPHIs categorized as lockdown, stay-at-home orders, social distancing, and other interventions (mask-wearing, contact tracing, and school closure). We used PRISMA 2020 guidance for abstracting the data and used Cochrane Effective Practice and Organization of Practice (EPOC) Risk of Bias Tool for quality appraisal of the studies. Hartung-Knapp-Sidik-Jonkman random-effects model was performed. Main outcomes included COVID-19 case growth rate (percentage daily changes), COVID-19 mortality growth rate (percentage daily changes), COVID-19 ICU admission (percentage daily changes), and COVID-19 reproduction number changes. Our search strategies in major databases yielded 12,523 results, which decreased to 7,540 articles after eliminating duplicates. Finally, 35 articles qualified to be included in the systematic review among which 23 studies were included in the meta-analysis. Although studies were from both low-income and high-income countries, the majority of them were from the United States (13 studies) and China (five studies). Results of the meta-analysis showed that adoption of NPHIs has resulted in a 4.68% (95% CI, -6.94 to -2.78) decrease in daily case growth rates, 4.8% (95 CI, -8.34 to -1.40) decrease in daily death growth rates, 1.90 (95% CI, -2.23 to -1.58) decrease in the COVID-19 reproduction number, and 16.5% (95% CI, -19.68 to -13.32) decrease in COVID-19 daily ICU admission. A few studies showed that, early enforcement of lockdown, when the incidence rate is not high, contributed to a shorter duration of lockdown and a lower increase of the case growth rate in the post-lockdown era. The majority of NPHIs had positive effects on restraining the COVID-19 spread. With the problems that remain regarding universal access to vaccines and their effectiveness and considering the drastic impact of the nationwide lockdown and other harsh restrictions on the economy and people's life, such interventions should be mitigated by adopting other NPHIs such as mass mask-wearing, patient/suspected case isolation strategies, and contact tracing. Studies need to address the impact of NPHIs on the population's other health problems than COVID-19.
| S-EPMC8610259 | biostudies-literature
Project description:BackgroundThe widespread pandemic of novel coronavirus disease 2019 (COVID-19) poses an unprecedented global health crisis. In the United States (US), different state governments have adopted various combinations of non-pharmaceutical public health interventions (NPIs), such as non-essential business closures and gathering bans, to mitigate the epidemic from February to April, 2020. Quantitative assessment on the effectiveness of NPIs is greatly needed to assist in guiding individualized decision making for adjustment of interventions in the US and around the world. However, the impacts of these approaches remain uncertain.MethodsBased on the reported cases, the effective reproduction number (Rt) of COVID-19 epidemic for 50 states in the US was estimated. Measurements on the effectiveness of nine different NPIs were conducted by assessing risk ratios (RRs) between Rt and NPIs through a generalized linear model (GLM).ResultsDifferent NPIs were found to have led to different levels of reduction in Rt. Stay-at-home contributed approximately 51% (95% CI 46-57%), wearing (face) masks 29% (15-42%), gathering ban (more than 10 people) 19% (14-24%), non-essential business closure 16% (10-21%), declaration of emergency 13% (8-17%), interstate travel restriction 11% (5-16%), school closure 10% (7-14%), initial business closure 10% (6-14%), and gathering ban (more than 50 people) 7% (2-11%).ConclusionsThis retrospective assessment of NPIs on Rt has shown that NPIs played critical roles on epidemic control in the US in the past several months. The quantitative results could guide individualized decision making for future adjustment of NPIs in the US and other countries for COVID-19 and other similar infectious diseases.
| S-EPMC8139542 | biostudies-literature
Project description:In response to the outbreak of the coronavirus disease 2019 (Covid-19), governments worldwide have introduced multiple restriction policies, known as non-pharmaceutical interventions (NPIs). However, the relative impact of control measures and the long-term causal contribution of each NPI are still a topic of debate. We present a method to rigorously study the effectiveness of interventions on the rate of the time-varying reproduction number Rt and on human mobility, considered here as a proxy measure of policy adherence and social distancing. We frame our model using a causal inference approach to quantify the impact of five governmental interventions introduced until June 2020 to control the outbreak in 113 countries: confinement, school closure, mask wearing, cultural closure, and work restrictions. Our results indicate that mobility changes are more accurately predicted when compared to reproduction number. All NPIs, except for mask wearing, significantly affected human mobility trends. From these, schools and cultural closure mandates showed the largest effect on social distancing. We also found that closing schools, issuing face mask usage, and work-from-home mandates also caused a persistent reduction on Rt after their initiation, which was not observed with the other social distancing measures. Our results are robust and consistent across different model specifications and can shed more light on the impact of individual NPIs.
| S-EPMC9518862 | biostudies-literature
Project description:The COVID-19 pandemic has forced societies across the world to resort to social distancing to slow the spread of the SARS-CoV-2 virus. Due to the economic impacts of social distancing, there is growing desire to relax these measures. To characterize a range of possible strategies for control and to understand their consequences, we performed an optimal control analysis of a mathematical model of SARS-CoV-2 transmission. Given that the pandemic is already underway and controls have already been initiated, we calibrated our model to data from the USA and focused our analysis on optimal controls from May 2020 through December 2021. We found that a major factor that differentiates strategies that prioritize lives saved versus reduced time under control is how quickly control is relaxed once social distancing restrictions expire in May 2020. Strategies that maintain control at a high level until at least summer 2020 allow for tapering of control thereafter and minimal deaths, whereas strategies that relax control in the short term lead to fewer options for control later and a higher likelihood of exceeding hospital capacity. Our results also highlight that the potential scope for controlling COVID-19 until a vaccine is available depends on epidemiological parameters about which there is still considerable uncertainty, including the basic reproduction number and the effectiveness of social distancing. In light of those uncertainties, our results do not constitute a quantitative forecast and instead provide a qualitative portrayal of possible outcomes from alternative approaches to control.
| S-EPMC7473596 | biostudies-literature
Project description:BackgroundIn an influenza pandemic, the benefit of vaccines and antiviral medications will be constrained by limitations on supplies and effectiveness. Non-pharmaceutical public health interventions will therefore be vital in curtailing disease spread. However, the most comprehensive assessments of the literature to date recognize the generally poor quality of evidence on which to base non-pharmaceutical pandemic planning decisions. In light of the need to prepare for a possible pandemic despite concerns about the poor quality of the literature, combining available evidence with expert opinion about the relative merits of non-pharmaceutical interventions for pandemic influenza may lead to a more informed and widely accepted set of recommendations. We evaluated the evidence base for non-pharmaceutical public health interventions. Then, based on the collective evidence, we identified a set of recommendations for and against interventions that are specific to both the setting in which an intervention may be used and the pandemic phase, and which can be used by policymakers to prepare for a pandemic until scientific evidence can definitively respond to planners' needs.MethodsBuilding on reviews of past pandemics and recent historical inquiries, we evaluated the relative merits of non-pharmaceutical interventions by combining available evidence from the literature with qualitative and quantitative expert opinion. Specifically, we reviewed the recent scientific literature regarding the prevention of human-to-human transmission of pandemic influenza, convened a meeting of experts from multiple disciplines, and elicited expert recommendation about the use of non-pharmaceutical public health interventions in a variety of settings (healthcare facilities; community-based institutions; private households) and pandemic phases (no pandemic; no US pandemic; early localized US pandemic; advanced US pandemic).ResultsThe literature contained a dearth of evidence on the efficacy or effectiveness of most non-pharmaceutical interventions for influenza. In an effort to inform decision-making in the absence of strong scientific evidence, the experts ultimately endorsed hand hygiene and respiratory etiquette, surveillance and case reporting, and rapid viral diagnosis in all settings and during all pandemic phases. They also encouraged patient and provider use of masks and other personal protective equipment as well as voluntary self-isolation of patients during all pandemic phases. Other non-pharmaceutical interventions including mask-use and other personal protective equipment for the general public, school and workplace closures early in an epidemic, and mandatory travel restrictions were rejected as likely to be ineffective, infeasible, or unacceptable to the public.ConclusionThe demand for scientific evidence on non-pharmaceutical public health interventions for influenza is pervasive, and present policy recommendations must rely heavily on expert judgment. In the absence of a definitive science base, our assessment of the evidence identified areas for further investigation as well as non-pharmaceutical public health interventions that experts believe are likely to be beneficial, feasible and widely acceptable in an influenza pandemic.
| S-EPMC2040158 | biostudies-literature
Project description: Not available
| S-BSST1055 | biostudies-other
Project description:BackgroundDuring the COVID-19 pandemic, atypical respiratory syncytial virus (RSV) circulation patterns emerged, with the occurrence of RSV activity outside the typical winter season. This study investigates the impact of COVID-19 and associated non-pharmaceutical interventions (NPIs) on RSV seasonality.MethodsThe onset, offset and peak of RSV epidemics from 2018 to 2022 across 12 European countries were determined using the 3% positivity threshold method. A multilevel longitudinal logit regression model for proportions assessed the associations between five NPIs (school closures, mask use, workplace measures, public gathering restrictions and closure of public spaces) and RSV, utilising RSV surveillance data, two NPI databases (ECDC-JRC and Oxford) and COVID-19 surveillance data.ResultsBefore 2020, consistent RSV seasonality patterns were observed, but the seasonal increase of RSV-positive cases in winter remained absent during the COVID-19 pandemic (2020-2022). Analysis revealed inconsistent associations between individual NPIs and RSV. The associations differed depending on the data source used (ECDC-JRC or Oxford), not only in magnitude but also in the direction of the coefficients. Public gathering restrictions and closure of public spaces exhibited significant negative associations with RSV incidence. However, this was only observed when using surveillance data for the entire epidemiological year and not when only examining weeks with increased RSV activity.ConclusionsThis study highlights the need for standardised international data collection and procedures for infectious disease modelling, as varying NPI implementations, NPI registration and RSV surveillance across countries complicate the understanding of RSV dynamics during the pandemic. Caution is recommended when interpreting the effects of NPIs on RSV circulation.
| S-EPMC11750802 | biostudies-literature
Project description:BackgroundAcross Europe, countries have responded to the COVID-19 pandemic with a combination of non-pharmaceutical interventions and vaccination. Evaluating the effectiveness of such interventions is of particular relevance to policy-makers.MethodsWe leverage almost three years of available data across 38 European countries to evaluate the effectiveness of governmental responses in controlling the pandemic. We developed a Bayesian hierarchical model that flexibly relates daily COVID-19 incidence to past levels of vaccination and non-pharmaceutical interventions as summarised in the Stringency Index. Specifically, we use a distributed lag approach to temporally weight past intervention values, a tensor-product smooth to capture non-linearities and interactions between both types of interventions, and a hierarchical approach to parsimoniously address heterogeneity across countries.ResultsWe identify a pronounced negative association between daily incidence and the strength of non-pharmaceutical interventions, along with substantial heterogeneity in effectiveness among European countries. Similarly, we observe a strong but more consistent negative association with vaccination levels. Our results show that non-linear interactions shape the effectiveness of interventions, with non-pharmaceutical interventions becoming less effective under high vaccination levels. Finally, our results indicate that the effects of interventions on daily incidence are most pronounced at a lag of 14 days after being in place.ConclusionsOur Bayesian hierarchical modelling approach reveals clear negative and lagged effects of non-pharmaceutical interventions and vaccination on confirmed COVID-19 cases across European countries.
| S-EPMC11391057 | biostudies-literature