Effectiveness Of Non-pharmaceutical Interventions Research Articles

Modeling the evolution of SARS-CoV-2 under non-pharmaceutical interventions and testing.

Background and objectivesSocial and behavioral non-pharmaceutical interventions (NPIs), such as mask-wearing, social distancing and travel restrictions, as well as diagnostic tests, have been broadly implemented in response to the COVID-19 pandemic. Epidemiological models and data analysis affirm that wide adoption of NPIs helps to control the pandemic. However, SARS-CoV-2 has extensively demonstrated its ability to evolve. Therefore, it is crucial to examine how NPIs may affect the evolution of the virus. Such evolution could have important effects on the spread and impact of the pandemic.MethodologyWe used evo-epidemiological models to examine the effect of NPIs and testing on two evolutionary trajectories for SARS-CoV-2: attenuation and test evasion.ResultsOur results show that when stronger measures are taken, selection may act to reduce disease severity. Additionally, the timely application of NPIs could significantly affect the competition between viral strains, favoring the milder strain. Furthermore, a higher testing rate can select for a test-evasive viral strain, even if that strain is less infectious than the detectable competing strain. Importantly, if a less detectable strain evolves, epidemiological metrics such as confirmed daily cases may distort our assessment of the pandemic.Conclusions and implicationsOur results highlight the important implications NPIs can have on the evolution of SARS-CoV-2.Lay SummaryWe used evo-epidemiological models to examine the effect of non-pharmaceutical interventions and testing on two evolutionary trajectories for SARS-CoV-2: attenuation and test evasion. Our results show that when stronger measures are taken, selection may act to reduce disease severity.

Impact of lockdown and non-pharmaceutical interventions on the epidemiology of Legionnaires' disease.

Background and aim.In order to cope with the requirements of COVID-19 pandemic and prevent overwhelming of the healthcare systems, during 2020 social distancing measures were proposed, and generalized lockdown. Aim of our study is to ascertain whether non-pharmaceutical intervention did have any impact on the epidemiology of Legionnaires’ disease (LD), a respiratory infectious disease without interhuman spreading.Methods.Official national reports from 4 index countries in European Union (i.e. Germany, France, Italy, and Spain) were retrieved. The study included all cases notified during 2020 COVID-19 outbreak, versus the cases referred during the same period in 2019. Subgroup analyses for hospital-associated and travel-associated LD cases, as well as for lethality estimates were performed.Results.A sustained drop for incidence rate was confirmed, at EU-level (3.5 per 100,000 vs. 5.3 per 100,000) as well as in the national estimates. The decrease was particularly evident in Italy (RR 0.880, 95%CI 0.839 to 0.905), despite a transient surge in notification rates during the month of June, 2020. Subgroup analyses demonstrated a fall in travel-associated cases (-66.8% at EU level), while hospital-associated cases decreased in absolute number when compared to 2019, but where substantially stable in terms when compared to EU estimates. While Case Fatality Ratio increased in comparison with 2019, no significant trend was similarly identified in comparison to EU estimates.Conclusions.Lockdown measures have impacted on the epidemiology of LD in Europe during 2020, but some heterogeneities were identified both across the assessed countries, and the various subgroup. Even though the absolute number of total cases did substantially decrease in 2020 compared to 2019, the effect of non-pharmaceutical interventions was mostly indirect, through a reduced interaction of individuals with environments at potentially high-risk for human infections (e.g. hospitals, accommodation sites, etc.). (www.actabiomedica.it)

The Susceptibility of School Contacts of SARS‐CoV‐2 and the Effectiveness of Non-Pharmaceutical Interventions in School Environments: A Meta-Analysis

The Susceptibility of School Contacts of SARS‐CoV‐2 and the Effectiveness of Non-Pharmaceutical Interventions in School Environments: A Meta-Analysis

Effect of non-pharmaceutical interventions in the early phase of the COVID-19 epidemic in Saudi Arabia

Effect of non-pharmaceutical interventions in the early phase of the COVID-19 epidemic in Saudi Arabia

Evaluating the impacts of non-pharmaceutical interventions on the transmission dynamics of COVID-19 in Canada based on mobile network.

The COVID-19 outbreak has caused two waves and spread to more than 90% of Canada’s provinces since it was first reported more than a year ago. During the COVID-19 epidemic, Canadian provinces have implemented many Non-Pharmaceutical Interventions (NPIs). However, the spread of the COVID-19 epidemic continues due to the complex dynamics of human mobility. We develop a meta-population network model to study the transmission dynamics of COVID-19. The model takes into account the heterogeneity of mitigation strategies in different provinces of Canada, such as the timing of implementing NPIs, the human mobility in retail and recreation, grocery and pharmacy, parks, transit stations, workplaces, and residences due to work and recreation. To determine which activity is most closely related to the dynamics of COVID-19, we use the cross-correlation analysis to find that the positive correlation is the highest between the mobility data of parks and the weekly number of confirmed COVID-19 from February 15 to December 13, 2020. The average effective reproduction numbers in nine Canadian provinces are all greater than one during the time period, and NPIs have little impact on the dynamics of COVID-19 epidemics in Ontario and Saskatchewan. After November 20, 2020, the average infection probability in Alberta became the highest since the start of the COVID-19 epidemic in Canada. We also observe that human activities around residences do not contribute much to the spread of the COVID-19 epidemic. The simulation results indicate that social distancing and constricting human mobility is effective in mitigating COVID-19 transmission in Canada. Our findings can provide guidance for public health authorities in projecting the effectiveness of future NPIs.

COVID-19 reproduction number and non-pharmaceutical interventions in Lithuania

Currently the world is threatened by a global COVID-19 pandemic and it has induced crisis creating a lot of disruptions in the healthcare system, social life and economy. In this article we present the analysis of COVID-19 situation in Lithuania and it's municipalities taking into consideration the effect of non-pharmaceutical interventions on the reproduction number. We have analysed the period from 20/03/2020 to 20/06/2021 covering two quarantines applied in Lithuania. We calculated the reproduction number using the incidence data provided by State Data Governance Information System, while the information for applied non-pharmaceutical interventions was extracted from Oxford COVID-19 Government Response Tracker and the COVID-19 website of Government of the Republic of Lithuania. The positive effect of applied non-pharmaceutical interventions on reproduction number was observed when internal movement ban was applied in 16/12/2020 during the second quarantine in Lithuania.

Estimating local outbreak risks and the effects of non-pharmaceutical interventions in age-structured populations: SARS-CoV-2 as a case study

During the COVID-19 pandemic, non-pharmaceutical interventions (NPIs) including school closures, workplace closures and social distancing policies have been employed worldwide to reduce transmission and prevent local outbreaks. However, transmission and the effectiveness of NPIs depend strongly on age-related factors including heterogeneities in contact patterns and pathophysiology. Here, using SARS-CoV-2 as a case study, we develop a branching process model for assessing the risk that an infectious case arriving in a new location will initiate a local outbreak, accounting for the age distribution of the host population. We show that the risk of a local outbreak depends on the age of the index case, and we explore the effects of NPIs targeting individuals of different ages. Social distancing policies that reduce contacts outside of schools and workplaces and target individuals of all ages are predicted to reduce local outbreak risks substantially, whereas school closures have a more limited impact. In the scenarios considered here, when different NPIs are used in combination the risk of local outbreaks can be eliminated. We also show that heightened surveillance of infectious individuals reduces the level of NPIs required to prevent local outbreaks, particularly if enhanced surveillance of symptomatic cases is combined with efforts to find and isolate nonsymptomatic infected individuals. Our results reflect real-world experience of the COVID-19 pandemic, during which combinations of intense NPIs have reduced transmission and the risk of local outbreaks. The general modelling framework that we present can be used to estimate local outbreak risks during future epidemics of a range of pathogens, accounting fully for age-related factors.

An agent based model for assessing transmission dynamics and health systems burden for COVID-19

<span>Coronavirus disease of 2019 </span><span>(COVID-19) pandemic has caused over <br /> 230 million infections with more than 4 million deaths worldwide. Researches have been using various mathematical and simulation techniques to estimate the future trends of the pandemic to help the policymakers and healthcare fraternity. Agent-based models (ABM) could provide accurate projections than the compartmental models that have been largely used. The present study involves a simulation of ABM using a synthetic population from India to analyze the effects of interventions on the spread of the disease. A disease model with various states representing the possible progression of the disease was developed and simulated using AnyLogic. The results indicated that imposing stricter non-pharmaceutical interventions (NPI) lowered the peak values of infections, the proportion of critical patients, and the deceased. Stricter interventions offer a larger time window for the healthcare fraternity to enhance preparedness. The findings of this research could act as a start-point to understand the benefits of ABM-based models for projecting infectious diseases and analyzing the effects of NPI imposed.</span>

The effectiveness of Non-pharmaceutical interventions in reducing the COVID-19 contagion in the UK, an observational and modelling study.

Epidemiological models used to inform government policies aimed to reduce the contagion of COVID-19, assume that the reproduction number is reduced through Non-Pharmaceutical Interventions (NPIs) leading to physical distancing. Available data in the UK show an increase in physical distancing before the NPIs were implemented and a fall soon after implementation. We aimed to estimate the effect of people's behaviour on the epidemic curve and the effect of NPIs taking into account this behavioural component. We have estimated the effects of confirmed daily cases on physical distancing and we used this insight to design a behavioural SEIR model (BeSEIR), simulated different scenaria regarding NPIs and compared the results to the standard SEIR. Taking into account behavioural insights improves the description of the contagion dynamics of the epidemic significantly. The BeSEIR predictions regarding the number of infections without NPIs were several orders of magnitude less than the SEIR. However, the BeSEIR prediction showed that early measures would still have an important influence in the reduction of infections. The BeSEIR model shows that even with no intervention the percentage of the cumulative infections within a year will not be enough for the epidemic to resolve due to a herd immunity effect. On the other hand, a standard SEIR model significantly overestimates the effectiveness of measures. Without taking into account the behavioural component, the epidemic is predicted to be resolved much sooner than when taking it into account and the effectiveness of measures are significantly overestimated.

Heterogeneity in the Effectiveness of Non-pharmaceutical Interventions During the First SARS-CoV2 Wave in the United States.

Background: Attempts to quantify effect sizes of non-pharmaceutical interventions (NPI) to control COVID-19 in the US have not accounted for heterogeneity in social or environmental factors that may influence NPI effectiveness. This study quantifies national and sub-national effect sizes of NPIs during the early months of the pandemic in the US.Methods: Daily county-level COVID-19 cases and deaths during the first wave (January 2020 through phased removal of interventions) were obtained. County-level cases, doubling times, and death rates were compared to four increasingly restrictive NPI levels. Socio-demographic, climate and mobility factors were analyzed to explain and evaluate NPI heterogeneity, with mobility used to approximate NPI compliance. Analyses were conducted separately for the US and for each Census regions (Pacific, Mountain, east/West North Central, East/West South Central, South Atlantic, Middle Atlantic and New England). A stepped-wedge cluster-randomized trial analysis was used, leveraging the phased implementation of policies.Results: Aggressive (level 4) NPIs were associated with slower COVID-19 propagation, particularly in high compliance counties. Longer duration of level 4 NPIs was associated with lower case rates (log beta −0.028, 95% CI −0.04 to −0.02) and longer doubling times (log beta 0.02, 95% CI 0.01–0.03). Effects varied by Census region, for example, level 4 effects on doubling time in Pacific states were opposite to those in Middle Atlantic and New England states. NPI heterogeneity can be explained by differential timing of policy initiation and by variable socio-demographic county characteristics that predict compliance, particularly poverty and racial/ethnic population. Climate exhibits relatively consistent relationships across Census regions, for example, higher minimum temperature and specific humidity were associated with lower doubling times and higher death rates for this period of analysis in South Central, South Atlantic, Middle Atlantic, and New England states.Conclusion and Relevance: Heterogeneity exists in both the effectiveness of NPIs across US Census regions and policy compliance. This county-level variability indicates that control strategies are best designed at community-levels where policies can be tuned based on knowledge of local disparities and compliance with public health ordinances.

Non-pharmaceutical interventions and COVID-19 cases in US summer camps: results from an American Camp Association survey

BackgroundMost camps remained closed during Summer 2020, due to concerns regarding child transmission of SARS-CoV-2 and limited information about the effectiveness of non-pharmaceutical interventions (NPIs) within child congregate settings.MethodsWe surveyed...

Modeling non-pharmaceutical interventions in the COVID-19 pandemic with survey-based simulations.

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.

International comparisons of COVID-19 case and mortality data and the effectiveness of non-pharmaceutical interventions: a plea for reconsideration.

International comparisons of the effectiveness of coronavirus disease 2019 (COVID-19) non-pharmaceutical interventions (NPIs) based on national case and mortality data are fraught with underestimated complexity. This article calls for stronger attention to just how extensive is the multifactorial nature of national case and mortality data, and argues that, unless a globally consistent benchmark of measurement can be devised, such comparisons are facile, if not misleading. This can lead to policy decisions and public support for the adoption of potentially harmful NPIs that are ineffective in combating the COVID-19 pandemic and damaging to mental health, social cohesion, human rights and economic development. The unscientific use of international comparisons of case and mortality data in public discourse, media reporting and policymaking on NPI effectiveness should be subject to greater scrutiny.

Benefits of COVID-19 non-pharmaceutical interventions on the prevention of other notifiable infectious diseases

Benefits of COVID-19 non-pharmaceutical interventions on the prevention of other notifiable infectious diseases

Date of introduction and epidemiologic patterns of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in Mogadishu, Somalia: estimates from transmission modelling of satellite-based excess mortality data in 2020

Background: In countries with weak surveillance systems, confirmed coronavirus disease 2019 (COVID-19) deaths are likely to underestimate the pandemic’s death toll. Many countries also have incomplete vital registration systems, hampering excess mortality estimation. Here, we fitted a dynamic transmission model to satellite imagery data of cemeteries in Mogadishu, Somalia during 2020 to estimate the date of introduction and other epidemiologic parameters of the early spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in this low-income, crisis-affected setting. Methods: We performed Markov chain Monte Carlo (MCMC) fitting with an age-structured compartmental COVID-19 model to provide median estimates and credible intervals for the date of introduction, the basic reproduction number ( R 0 ) and the effect of non-pharmaceutical interventions (NPIs) up to August 2020. Results: Under the assumption that excess deaths in Mogadishu March-August 2020 were attributable to SARS-CoV-2 infections, we arrived at median estimates of November-December 2019 for the date of introduction and low R 0 estimates (1.4-1.7) reflecting the slow and early rise and long plateau of excess deaths. The date of introduction, the amount of external seeding, the infection fatality rate (IFR) and the effectiveness of NPIs are correlated parameters and not separately identifiable in a narrow range from deaths data. Nevertheless, to obtain introduction dates no earlier than November 2019 a higher population-wide IFR (≥0.7%) had to be assumed than obtained by applying age-specific IFRs from high-income countries to Somalia’s age structure. Conclusions: Model fitting of excess mortality data across a range of plausible values of the IFR and the amount of external seeding suggests an early SARS-CoV-2 introduction event may have occurred in Somalia in November-December 2019. Transmissibility in the first epidemic wave was estimated to be lower than in European settings. Alternatively, there was another, unidentified source of sustained excess mortality in Mogadishu from March to August 2020.

A year of living distantly: global trends in the use of stay-at-home orders over the first 12 months of the COVID-19 pandemic.

During the first year of the COVID-19 pandemic, non-pharmaceutical interventions (NPIs) were the main pillar of defence to protect human society against the virus. While a variety of modelling studies try to quantify the effects of NPIs, this paper investigates when and how national and subnational governments have taken actions. We observe longitudinal changes in the global pattern of policymaking to combat the COVID-19 pandemic, with a particular focus on stay-at-home orders. Drawing on data from the Oxford COVID-19 Government Response Tracker, we show several important trends. First, while national governments exhibited a strong alignment in policy settings initially in March and April 2020, their cross-country policy heterogeneity has grown since May 2020, although countries within global regions continue to display similarities in their approaches. Second, most governments that have implemented multiple stay-at-home orders over the course of the pandemic have become less sensitive to case levels (insofar as they implement subsequent restrictions at progressively higher case levels), apart from a small number of contrast cases which have mostly eliminated domestic community transmission. Third, pandemic policies are increasingly specific to subnational levels, and there is often significant heterogeneity with regard to policy approaches even within the same country.

Date of introduction and epidemiologic patterns of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in Mogadishu, Somalia: estimates from transmission modelling of satellite-based excess mortality data in 2020.

Background: In countries with weak surveillance systems, confirmed coronavirus disease 2019 (COVID-19) deaths are likely to underestimate the pandemic's death toll. Many countries also have incomplete vital registration systems, hampering excess mortality estimation. Here, we fitted a dynamic transmission model to satellite imagery data of cemeteries in Mogadishu, Somalia during 2020 to estimate the date of introduction and other epidemiologic parameters of the early spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in this low-income, crisis-affected setting. Methods: We performed Markov chain Monte Carlo (MCMC) fitting with an age-structured compartmental COVID-19 model to provide median estimates and credible intervals for the date of introduction, the basic reproduction number ( R 0 ) and the effect of non-pharmaceutical interventions (NPIs) up to August 2020. Results: Under the assumption that excess deaths in Mogadishu March-August 2020 were attributable to SARS-CoV-2 infections, we arrived at median estimates of November-December 2019 for the date of introduction and low R 0 estimates (1.4-1.7) reflecting the slow and early rise and long plateau of excess deaths. The date of introduction, the amount of external seeding, the infection fatality rate (IFR) and the effectiveness of NPIs are correlated parameters and not separately identifiable in a narrow range from deaths data. Nevertheless, to obtain introduction dates no earlier than November 2019 a higher population-wide IFR (≥0.7%) had to be assumed than obtained by applying age-specific IFRs from high-income countries to Somalia's age structure. Conclusions: Model fitting of excess mortality data across a range of plausible values of the IFR and the amount of external seeding suggests an early SARS-CoV-2 introduction event may have occurred in Somalia in November-December 2019. Transmissibility in the first epidemic wave was estimated to be lower than in European settings. Alternatively, there was another, unidentified source of sustained excess mortality in Mogadishu from March to August 2020.

DYNAMICS OF CHILDHOOD RESPIRATORY INFECTIONS DURING THE COVID-19 PANDEMIC: THE EFFECT OF QUARANTINE АND BEYOND

Monitoring epidemic processes and the dynamics of the spread of infectious diseases is essential for predicting their distribution and effective planning in healthcare. The importance of studying seasonal trends in the spread of respiratory viral infections and the specific effects of non-pharmaceutical interventions in nationwide scales and the use of available vaccines stand out even more in the context of the coronavirus disease-19 (COVID-19) pandemic. Even if the dynamics of pediatric respiratory viral infections show some variation at the national and local levels, depending on health regulation, respiratory viral pathogens follow a typical pattern of incidence. Therefore, we hypothesize that anticipated reduction of the incidence of common respiratory viral infections would undoubtedly exert positive effects, such as ease of burdening healthcare that combates the COVID-19 pandemic. However, we suspect a shift in familiar seasonal characteristics of common respiratory viral infections. We also speculate that strict long-term limitations of the natural spread of respiratory viral infections can lead to the development of hard-to-predict epidemiological outliers. Additionally, the tricky balance between humanity’s natural impulse to return to normalcy and control the new and still dynamically evolving infection could lead to new threats from old and well-known pathogens. Finally, we hypothesize that the absence of regular influenza virus circulation may lead to a high mismatch rate and a significant reduction in flu vaccine efficacy.

Sustainable targeted interventions to mitigate the COVID-19 pandemic: A big data-driven modeling study in Hong Kong.

Nonpharmaceutical interventions (NPIs) for contact suppression have been widely used worldwide, which impose harmful burdens on the well-being of populations and the local economy. The evaluation of alternative NPIs is needed to confront the pandemic with less disruption. By harnessing human mobility data, we develop an agent-based model that can evaluate the efficacies of NPIs with individualized mobility simulations. Based on the model, we propose data-driven targeted interventions to mitigate the COVID-19 pandemic in Hong Kong without city-wide NPIs. We develop a data-driven agent-based model for 7.55×106 Hong Kong residents to evaluate the efficacies of various NPIs in the first 80 days of the initial outbreak. The entire territory of Hong Kong has been split into 4905 500×500m2 grids. The model can simulate detailed agent interactions based on the demographics data, public facilities and functional buildings, transportation systems, and travel patterns. The general daily human mobility patterns are adopted from Google's Community Mobility Report. The scenario without any NPIs is set as the baseline. By simulating the epidemic progression and human movement at the individual level, we propose model-driven targeted interventions which focus on the surgical testing and quarantine of only a small portion of regions instead of enforcing NPIs in the whole city. The effectiveness of common NPIs and the proposed targeted interventions are evaluated by 100 extensive simulations. The proposed model can inform targeted interventions, which are able to effectively contain the COVID-19 outbreak with much lower disruption of the city. It represents a promising approach to sustainable NPIs to help us revive the economy of the city and the world.

Coronavirus Disease 2019 on the Heels of Ebola Virus Disease in West Africa.

This study utilized modeling and simulation to examine the effectiveness of current and potential future COVID-19 response interventions in the West African countries of Guinea, Liberia, and Sierra Leone. A comparison between simulations can highlight which interventions could have an effect on the pandemic in these countries. An extended compartmental model was used to run simulations incorporating multiple vaccination strategies and non-pharmaceutical interventions (NPIs). In addition to the customary categories of susceptible, exposed, infected, and recovered (SEIR) compartments, this COVID-19 model incorporated early and late disease states, isolation, treatment, and death. Lessons learned from the 2014–2016 Ebola virus disease outbreak—especially the optimization of each country’s resource allocation—were incorporated in the presented models. For each country, models were calibrated to an estimated number of infections based on actual reported cases and deaths. Simulations were run to test the potential future effects of vaccination and NPIs. Multiple levels of vaccination were considered, based on announced vaccine allocation plans and notional scenarios. Increased vaccination combined with NPI mitigation strategies resulted in thousands of fewer COVID-19 infections in each country. This study demonstrates the importance of increased vaccinations. The levels of vaccination in this study would require substantial increases in vaccination supplies obtained through national purchases or international aid. While this study does not aim to develop a model that predicts the future, it can provide useful information for decision-makers in low- and middle-income nations. Such information can be used to prioritize and optimize limited available resources for targeted interventions that will have the greatest impact on COVID-19 pandemic response.