Analysis of non-pharmaceutical interventions and their impacts on COVID-19 in Kerala.
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ABSTRACT:
SUBMITTER: Goult E
PROVIDER: S-EPMC8755744 | biostudies-literature | 2022 Jan
REPOSITORIES: biostudies-literature
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Analysis of non-pharmaceutical interventions and their impacts on COVID-19 in Kerala.
Goult Elizabeth E Sathyendranath Shubha S Kovač Žarko Ž Kong Christina Eunjin CE Stipanović Petar P Abdulaziz Anas A Menon Nandini N George Grinson G Platt Trevor T
Scientific reports 20220112 1
In the absence of an effective vaccine or drug therapy, non-pharmaceutical interventions are the only option for control of the outbreak of the coronavirus disease 2019, a pandemic with global implications. Each of the over 200 countries affected has followed its own path in dealing with the crisis, making it difficult to evaluate the effectiveness of measures implemented, either individually, or collectively. In this paper we analyse the case of the south Indian state of Kerala, which received ...[more]
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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
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| S-EPMC8251211 | biostudies-literature
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| S-EPMC7339477 | 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:Worldwide, governments imposed non-pharmaceutical interventions (NPIs) during the COVID-19 pandemic to contain the pandemic more effectively. We examined the effectiveness of individual NPIs in the United States during the first wave of the pandemic. Three types of analyses were performed. First, a prototypical Bayesian hierarchical model was employed to gauge the effectiveness of five NPIs and they are gathering restriction, restaurant capacity restriction, business closure, school closure, and stay-at-home order in the 42 states with over 100 deaths by the end of the wave. Second, we examined the effectiveness of the face mask mandate, the sixth and most controversial NPI by counterfactual modeling, which is a variant of the prototypical Bayesian hierarchical model allowing us to answer the question of what if the state had imposed the mandate or not. The third analysis used an advanced Bayesian hierarchical model to evaluate the effectiveness of all six NPIs in all 50 states and the District of Columbia, and thereby provide a full-scale estimation of the effectiveness of NPIs and the relative effectiveness of each NPI in the entire United States. Our results have enhanced the collective knowledge on the general effectiveness of NPIs in arresting the spread of COVID-19.
| S-EPMC11399256 | 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.
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Project description:The information set from which individuals make their decision on vaccination includes signals from trusted agents, such as governments, community leaders and the media. By implementing restrictions, or by relaxing them, governments can provide a signal about the underlying risk of the pandemic and indirectly affect vaccination take-up. Rather than focusing on measures specifically designed to increase vaccine acceptance, this paper studies how governments' non-pharmaceutical policy responses to the pandemic can modify the degree of preventive health behavior, including vaccination. To do so, we use repeated waves of a global survey on COVID-19 Beliefs, Behaviors and Norms covering 18 countries from October 2020 to March 2021. Controlling for the usual determinants, we explore how individuals' willingness to get vaccinated is affected by changes in government restriction measures (as measured by the Oxford Stringency Index). This relationship is mediated by individual characteristics, social norms (social pressure to conform with what most people do), and trust in government institutions. Our results point to a complex picture as the implementation of restrictions is associated with increased acceptance in some contexts and decreased acceptance in others. The stringency of government restrictions has significant positive correlations with vaccine acceptance in contexts of weak social norms of vaccine acceptance and lower trust in government. In countries or communities with tighter social norms and high trust in health authorities, vaccine acceptance is high but less sensitive to changes in policies. These results suggest that the effect of government policy stringency is stronger among individuals who report lower trust and weaker social norms of vaccine acceptance.
| S-EPMC9840231 | biostudies-literature
Project description:BackgroundNon-pharmaceutical interventions (NPIs) are mitigation strategies used to reduce the spread of transmissible diseases. The relative effectiveness of specific NPIs remains uncertain.MethodsWe used state-level Coronavirus disease 2019 (COVID-19) case and mortality data between January 19, 2020 and March 7, 2021 to model NPI policy effectiveness. Empirically derived breakpoints in case and mortality velocities were used to identify periods of stable, decreasing, or increasing COVID-19 burden. The associations between NPI adoption and subsequent decreases in case or death velocities were estimated using generalized linear models accounting for weekly variability shared across states. State-level NPI policies included: stay at home order, indoor public gathering ban (mild >10 or severe ≤10), indoor restaurant dining ban, and public mask mandate.Results28,602,830 cases and 511,899 deaths were recorded. The odds of a decrease in COVID-19 case velocity were significantly elevated for stay at home (OR 2.02, 95% CI 1.63-2.52), indoor dining ban (OR 1.62, 95% CI 1.25-2.10), public mask mandate (OR 2.18, 95% CI 1.47-3.23), and severe gathering ban (OR 1.68, 95% CI 1.31-2.16). In mutually adjusted models, odds remained elevated for stay at home (AOR 1.47, 95% CI 1.04-2.07) and public mask mandate (AOR = 2.27, 95% CI 1.51-3.41). Stay at home (OR 2.00, 95% CI 1.53-2.62; AOR 1.89, 95% CI 1.25-2.87) was also associated with greater likelihood of decrease in death velocity in unadjusted and adjusted models.ConclusionsNPIs employed in the U.S. during the COVID-19 pandemic, most significantly stay at home orders, were associated with decreased COVID-19 burden.
| S-EPMC8491859 | biostudies-literature
Project description:On 11 March 2020, the World Health Organization (WHO) declared coronavirus disease 2019 (COVID-19) a pandemic1. The strategies based on non-pharmaceutical interventions that were used to contain the outbreak in China appear to be effective2, but quantitative research is still needed to assess the efficacy of non-pharmaceutical interventions and their timings3. Here, using epidemiological data on COVID-19 and anonymized data on human movement4,5, we develop a modelling framework that uses daily travel networks to simulate different outbreak and intervention scenarios across China. We estimate that there were a total of 114,325 cases of COVID-19 (interquartile range 76,776-164,576) in mainland China as of 29 February 2020. Without non-pharmaceutical interventions, we predict that the number of cases would have been 67-fold higher (interquartile range 44-94-fold) by 29 February 2020, and we find that the effectiveness of different interventions varied. We estimate that early detection and isolation of cases prevented more infections than did travel restrictions and contact reductions, but that a combination of non-pharmaceutical interventions achieved the strongest and most rapid effect. According to our model, the lifting of travel restrictions from 17 February 2020 does not lead to an increase in cases across China if social distancing interventions can be maintained, even at a limited level of an on average 25% reduction in contact between individuals that continues until late April. These findings improve our understanding of the effects of non-pharmaceutical interventions on COVID-19, and will inform response efforts across the world.
| S-EPMC7116778 | biostudies-literature