Impact Of Non-pharmaceutical Interventions Research Articles

Modeling the Economic and Societal Impact of Non‐Pharmaceutical Interventions During the COVID‐19 Pandemic

Like many countries affected by the COVID‐19 pandemic, the United Kingdom (UK) has deployed a series of national and local Non‐Pharmaceutical Interventions (NPIs) in an effort to control the spread...

Transmission of SARS-CoV-2 before and after symptom onset: impact of nonpharmaceutical interventions in China

Nonpharmaceutical interventions, such as contact tracing and quarantine, have been the primary means of controlling the spread of SARS-CoV-2; however, it remains uncertain which interventions are most effective at reducing transmission at the population level. Using serial interval data from before and after the rollout of nonpharmaceutical interventions in China, we estimate that the relative frequency of presymptomatic transmission increased from 34% before the rollout to 71% afterward. The shift toward earlier transmission indicates a disproportionate reduction in transmission post-symptom onset. We estimate that, following the rollout of nonpharmaceutical interventions, transmission post-symptom onset was reduced by 82% whereas presymptomatic transmission decreased by only 16%. The observation that only one-third of transmission was presymptomatic at baseline, combined with the finding that NPIs reduced presymptomatic transmission by less than 20%, suggests that the overall impact of NPIs was driven in large part by reductions in transmission following symptom onset. This implies that interventions which limit opportunities for transmission in the later stages of infection, such as contact tracing and isolation, are particularly important for control of SARS-CoV-2. Interventions which specifically reduce opportunities for presymptomatic transmission, such as quarantine of asymptomatic contacts, are likely to have smaller, but non-negligible, effects on overall transmission.

Impact of nonpharmaceutical interventions on laboratory detections of influenza A and B in Canada.

The first coronavirus disease 2019 (COVID-19) case was reported in Canada on January 25, 2020. In response to the imminent outbreak, many provincial and territorial health authorities implemented nonpharmaceutical public health measures to curb the spread of disease. "Social distancing" measures included restrictions on group gatherings; cancellation of sports, cultural and religious events and gatherings; recommended physical distancing between people; school and daycare closures; reductions in non-essential services; and closures of businesses. To evaluate the impact of the combined nonpharmaceutical interventions imposed in March 2020 on influenza A and B epidemiology by comparing national laboratory surveillance data from the intervention period with 9-year historical influenza season control data. We obtained epidemiologic data on laboratory influenza A and B detections and test volumes from the Canadian national influenza surveillance system for the epidemiologic period December 29, 2019 (epidemiologic week 1) through May 2, 2020 (epidemiologic week 18). COVID-19-related social distancing measures were implemented in Canada from epidemiologic week 10 of this period. We compared influenza A and B laboratory detections and test volumes and trends in detection during the 2019-20 influenza season with those of the previous nine influenza seasons for evidence of changes in epidemiologic trends. While influenza detections the week prior to the implementation of social distancing measures did not differ statistically from the previous nine seasons, a steep decline in positivity occurred between epidemiologic weeks 10 and 14 (March 8-April 4, 2020). Both the percent positive on week 14 (p≤0.001) and rate of decline between weeks 10 and 14 (p=0.003) were significantly different from mean historical data. The data show a dramatic decrease in influenza A and B laboratory detections concurrent with social distancing measures and nonpharmaceutical interventions in Canada. The impact of these measures on influenza transmission may be generalizable to other respiratory viral illnesses during the study period, including COVID-19.

COVID-19 Pandemic Response Simulation in a Large City: Impact of Nonpharmaceutical Interventions on Reopening Society.

As the novel coronavirus (COVID-19) pandemic continues to expand, policymakers are striving to balance the combinations of nonpharmaceutical interventions (NPIs) to keep people safe and minimize social disruptions. We developed and calibrated an agent-based simulation to model COVID-19 outbreaks in the greater Seattle area. The model simulated NPIs, including social distancing, face mask use, school closure, testing, and contact tracing with variable compliance and effectiveness to identify optimal NPI combinations that can control the spread of the virus in a large urban area. Results highlight the importance of at least 75% face mask use to relax social distancing and school closure measures while keeping infections low. It is important to relax NPIs cautiously during vaccine rollout in 2021.

Toward the Impact of Non-pharmaceutical Interventions and Vaccination on the COVID-19 Pandemic With Time-Dependent SEIR Model.

The outbreak of COVID-19, caused by the SARS-CoV-2 coronavirus, has been declared a pandemic by the World Health Organization (WHO) in March, 2020 and rapidly spread to over 210 countries and territories around the world. By December 24, there are over 77M cumulative confirmed cases with more than 1.72M deaths worldwide. To mathematically describe the dynamic of the COVID-19 pandemic, we propose a time-dependent SEIR model considering the incubation period. Furthermore, we take immunity, reinfection, and vaccination into account and propose the SEVIS model. Unlike the classic SIR based models with constant parameters, our dynamic models not only predicts the number of cases, but also monitors the trajectories of changing parameters, such as transmission rate, recovery rate, and the basic reproduction number. Tracking these parameters, we observe the significant decrease in the transmission rate in the U.S. after the authority announced a series of orders aiming to prevent the spread of the virus, such as closing non-essential businesses and lockdown restrictions. Months later, as restrictions being gradually lifted, we notice a new surge of infection emerges as the transmission rates show increasing trends in some states. Using our epidemiology models, people can track, timely monitor, and predict the COVID-19 pandemic with precision. To illustrate and validate our model, we use the national level data (the U.S.) and the state level data (New York and North Dakota), and the resulting relative prediction errors for the infected group and recovered group are mostly lower than 0.5%. We also simulate the long-term development of the pandemic based on our proposed models to explore when the crisis will end under certain conditions.

The impact of non-pharmaceutical interventions on SARS-CoV-2 transmission across 130 countries and territories

BackgroundNon-pharmaceutical interventions (NPIs) are used to reduce transmission of SARS coronavirus 2 (SARS-CoV-2) that causes coronavirus disease 2019 (COVID-19). However, empirical evidence of the effectiveness of specific NPIs has been inconsistent. We assessed the effectiveness of NPIs around internal containment and closure, international travel restrictions, economic measures, and health system actions on SARS-CoV-2 transmission in 130 countries and territories.MethodsWe used panel (longitudinal) regression to estimate the effectiveness of 13 categories of NPIs in reducing SARS-CoV-2 transmission using data from January to June 2020. First, we examined the temporal association between NPIs using hierarchical cluster analyses. We then regressed the time-varying reproduction number (Rt) of COVID-19 against different NPIs. We examined different model specifications to account for the temporal lag between NPIs and changes in Rt, levels of NPI intensity, time-varying changes in NPI effect, and variable selection criteria. Results were interpreted taking into account both the range of model specifications and temporal clustering of NPIs.ResultsThere was strong evidence for an association between two NPIs (school closure, internal movement restrictions) and reduced Rt. Another three NPIs (workplace closure, income support, and debt/contract relief) had strong evidence of effectiveness when ignoring their level of intensity, while two NPIs (public events cancellation, restriction on gatherings) had strong evidence of their effectiveness only when evaluating their implementation at maximum capacity (e.g. restrictions on 1000+ people gathering were not effective, restrictions on < 10 people gathering were). Evidence about the effectiveness of the remaining NPIs (stay-at-home requirements, public information campaigns, public transport closure, international travel controls, testing, contact tracing) was inconsistent and inconclusive. We found temporal clustering between many of the NPIs. Effect sizes varied depending on whether or not we included data after peak NPI intensity.ConclusionUnderstanding the impact that specific NPIs have had on SARS-CoV-2 transmission is complicated by temporal clustering, time-dependent variation in effects, and differences in NPI intensity. However, the effectiveness of school closure and internal movement restrictions appears robust across different model specifications, with some evidence that other NPIs may also be effective under particular conditions. This provides empirical evidence for the potential effectiveness of many, although not all, actions policy-makers are taking to respond to the COVID-19 pandemic.

The impact of non-pharmaceutical interventions on the prevention and control of COVID-19 in New York City.

The emergence of coronavirus disease 2019 (COVID-19) has infected more than 62 million people worldwide. Control responses varied across countries with different outcomes in terms of epidemic size and social disruption. This study presents an age-specific susceptible-exposed-infected-recovery-death model that considers the unique characteristics of COVID-19 to examine the effectiveness of various non-pharmaceutical interventions (NPIs) in New York City (NYC). Numerical experiments from our model show that the control policies implemented in NYC reduced the number of infections by 72% [interquartile range (IQR) 53-95] and the number of deceased cases by 76% (IQR 58-96) by the end of 2020. Among all the NPIs, social distancing for the entire population and protection for the elderly in public facilities is the most effective control measure in reducing severe infections and deceased cases. School closure policy may not work as effectively as one might expect in terms of reducing the number of deceased cases. Our simulation results provide novel insights into the city-specific implementation of NPIs with minimal social disruption considering the locations and population characteristics.

The Impact of Non-Pharmaceutical Interventions on the Second Wave of COVID-19: Insights from an Artificial Intelligence-Based, Cross-Country Study

Objective: To determine the relative influences of various non-pharmaceutical interventions (NPIs) put in place during the first wave of COVID-19 across countries on the spreading rate of COVID-19 during the second wave, taking into account national-level climatic, environmental, clinical, health, economic, pollution, social, and demographic factors. Methods: We first estimate the growth of the first and second wave across countries by fitting a logistic model to reported daily case numbers, up to the first and second epidemic peaks. Using the growth rate, we estimate the basic and effective reproduction number (second wave) Re across countries. Next, we use Random Forest, an “off-the-shelf” machine learning algorithm, to study the association between the growth rate of the second wave of COVID-19 and NPIs as well as pre-existing country characteristics (climatic, environmental, clinical, health, economic, pollution, social, and demographic factors). Lastly, we compare the growth rate of the first and second waves of COVID-19. Findings: Our findings reveal that the mean R0 and Re were respectively 2.02 (S.D 1.09) and 1.07 (S.D. 0.41). R0 has the highest value in Israel (R0 = 6.93) and lowest in Senegal (R0 = 1.13) whereas Re (second wave) had the highest value in Mexico ( Re = 3.08) and lowest in Bangladesh (Re = 1.07). The top three factors associated with the growth of the second wave are body mass index, the number of days that the government sets restrictions on requiring facial coverings outside the home at all times regardless of location or presence of other people in some areas, and restrictions on gatherings of 10 people or less. We found a statistically significant difference between the means of the first and second waves. Conclusion: Artificial intelligence techniques can enable scholars as well as public health decision- and policy-makers to estimate the effectiveness of public health policies and mitigation strategies to counteract the toll of the outbreak in terms of infections and deaths, enforcing and implementing “smart” interventions, which are as efficacious as drastic and stringent ones.

The impact of non-pharmaceutical interventions, demographic, social, and climatic factors on the initial growth rate of COVID-19: A cross-country study

On March 11, 2020 the World Health Organization announced that the COVID-19 disease developed into a global pandemic. In the present paper, we aimed at analysing how the implementation of Non-Pharmaceutical Interventions (NPI) as well as climatic, social, and demographic variables affected the initial growth rate of COVID-19. In more detail, we aimed at identifying and assessing all the predictors in a whole picture of the COVID-19 outbreak and the effectiveness of the response of the countries to the pandemic. It can be expected, indeed, that there is a subtle and complex interplay among the various parameters. As such, we estimated the initial growth rate of COVID-19 for countries across the globe, and used a multiple linear regression model to study the association between the initial growth rate and NPI as well as pre-existing country characteristics (climatic, social and demographic variables measured before the current epidemic began). We obtained a mean initial growth rate of 0.120 (SD 0.076), in the range 0.023–0.315. Ten (8 pre-existing country characteristics and 2 NPI) out of 29 factors considered (21 pre-existing country characteristics and 8 NPI) were associated with the initial growth of COVID-19. Population in urban agglomerations of more than 1 million, PM2.5 air pollution mean annual exposure, life expectancy, hospital beds available, urban population, Global Health Security detection index and restrictions on international movement had the most significant effects on the initial growth of COVID-19. Based on available data and the results we obtained, NPI put in place by governments around the world alone may not have had a significant impact on the initial growth of COVID-19. Only restrictions on international movements had a relative significance with respect to the initial growth rate, whereas demographic, climatic, and social variables seemed to play a greater role in the initial growth rate of COVID-19.

Impact of Nonpharmaceutical Interventions on the Incidence of Respiratory Infections During the Coronavirus Disease 2019 (COVID-19) Outbreak in Korea: A Nationwide Surveillance Study.

Background Many countries have implemented non-pharmaceutical interventions (NPIs) to slow the spread of coronavirus disease 2019 (COVID-19). We aimed to determine whether NPIs led to the decline in the incidences of respiratory infections.Methods We conducted a retrospective, ecological study using a nationwide notifiable diseases database and a respiratory virus sample surveillance collected from January 2016 through July 2020 in the Republic of Korea. Intervention period was defined as February–July 2020, when the government implemented NPIs nationwide. Observed incidences in the intervention period were compared to the predicted incidences by autoregressive integrated moving average model and the 4-year mean cumulative incidences (CuIs) in the same months of the pre-intervention period.Results Five infectious diseases met the inclusion criteria: chickenpox, mumps, invasive pneumococcal disease, scarlet fever, and pertussis. The incidences of chickenpox and mumps during the intervention period were significantly lower than the prediction model. The CuIs of chickenpox and mumps were 36.4% (95% CI, 23.9–76.3) and 63.4% (95% CI, 48.0–93.3) of the predicted values. Subgroup analysis showed that the decrease in the incidence was universal for chickenpox, while mumps showed a marginal reduction among those aged <18 years, but not in adults. The incidence of respiratory viruses was significantly lower than both the predicted incidence (19.5%; 95% CI, 11.8–55.4%) and the 4-year mean CuIs in the pre-intervention period (24.5%; P<0.001).Conclusions The implementation of NPIs was associated with a significant reduction in the incidences of several respiratory infections in Korea.

Mathematical modeling of COVID-19: Impact of non-pharmaceutical interventions in India.

In the absence of effective vaccine/antiviral strategies for reducing the burden of the coronavirus disease 2019 (COVID-19) pandemic in India, the main focus has been on basic non-pharmaceutical interventions (NPIs), such as nationwide lockdown (travel restrictions and the closure of schools, shopping malls, and worshipping and other gathering places), quarantining of exposed individuals, and isolation of infected individuals. In the present study, we propose a compartmental epidemic model incorporating quarantine and isolation compartments to (i) describe the current transmission patterns of COVID-19 in India, (ii) assess the impact of currently implemented NPIs, and (iii) predict the future course of the pandemic with various scenarios of NPIs in India. For R0<1, the system has a globally asymptotically stable disease free equilibrium, while for R0>1, the system has one unstable disease free equilibrium and a unique locally stable endemic equilibrium. By using the method of least squares and the best fit curve, we estimate the model parameters to calibrate the model with daily new confirmed cases and cumulative confirmed cases in India for the period from May 1, 2020 to June 25, 2020. Our result shows that the implementation of an almost perfect isolation in India and 33.33% increment in contact-tracing on June 26, 2020 may reduce the number of cumulative confirmed cases of COVID-19 in India by around 53.8% at the end of July 2020. Nationwide lockdown with high efficiency can diminish COVID-19 cases drastically, but combined NPIs may accomplish the strongest and most rapid impact on the spreading of COVID-19 in India.

Impact of non-pharmaceutical interventions for reducing transmission of COVID-19: a systematic review and meta-analysis protocol

IntroductionImplementing 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...

Applications of predictive modelling early in the COVID-19 epidemic

Applications of predictive modelling early in the COVID-19 epidemic

Awareness and Impact of Non-pharmaceutical Interventions During Coronavirus Disease 2019 Pandemic in Renal Transplant Recipients

The concerns generated by coronavirus disease 2019 (COVID-19) pandemic are having profound impact on solid organ transplantation (SOT). Non-pharmaceutical interventions (NPI) are currently the only measures available to contain COVID-19 in the general population and in more vulnerable recipients of any organ transplant.In this cross-sectional case control study from a patient survey undertaken in 2 transplant centers (TxC) in the Kingdom of Saudi Arabia and Italy, we aimed to appraise awareness of the NPI implemented by respective these governments. We have also evaluated the impact of COVID-19 on our kidney transplant (KT) recipients and a control group of kidney living donors (KLD).In our series, there were zero cases of COVID-19 among 111 KT recipients and 70 KLD of the control group. Demography, transplant type, immunosuppression regimes, and, importantly, the different COVID-19 prevalence in the 2 regions of the TxC did not appear to influence incidence of COVID-19 in our KT recipients.The absence of COVID-19 cases in our series was unexpected. Our findings suggest that awareness of NPI is associated with a successful containment of COVID-19 in vulnerable, immunosuppressed KT recipients.

Analytics and Prediction Modeling During the COVID-19 Pandemic

Analytics and Prediction Modeling During the COVID-19 Pandemic

The effects of physical distancing on population mobility during the COVID-19 pandemic in the UK

The effects of physical distancing on population mobility during the COVID-19 pandemic in the UK

COVID-19 and Long-term Planning for Procedure-based Specialties During Extended Mitigation and Suppression Strategies

COVID-19 and Long-term Planning for Procedure-based Specialties During Extended Mitigation and Suppression Strategies

RETRACTED: COVID-19 and potential global mortality - Revisited

This article has been retracted: please see Elsevier Policy on Article Withdrawal ( https://www.elsevier.com/about/our-business/policies/article-withdrawal ). This article has been retracted at the request of the author of the author. This article was based on very early data and reports from the World Health Organization and Ferguson et al from which the article drew imprecise conclusions. We now have a far better, albeit still incomplete, understanding of COVID-19. The anticipated mortality will fortunately be far less than estimated in the paper itself. References World Health Organisation. Coronavirus disease 2019. WHO Report 41. 01 March 2020. Ferguson NM, Laydon D, Nedjati-Gilani G, Imai N, Ainslie K, Baguelin M, Bhatia S, Boonyasiri A, Cucunubá Z, Cuomo-Dannenburg G, Dighe A. Impact of non-pharmaceutical interventions (NPIs) to reduce COVID-19 mortality and healthcare demand. March 2020, Imperial College.

The Impact of Non-Pharmaceutical Interventions on Unemployment During a Pandemic

We use high-frequency Google search data, combined with data on the announcement dates of non-pharmaceutical interventions (NPIs) during the COVID-19 pandemic in U.S. states, to isolate the direct impact of state-level NPIs on own-state unemployment in an event study framework. Exploiting the differential timing of the introduction of restaurant and bar limitations, non-essential business closures, stay-at-home orders, large-gatherings bans, school closures, and emergency declarations, we analyze how Google searches for claiming unemployment insurance varied from day to day and across states. We describe a set of assumptions under which proxy outcomes can be used to estimate a causal parameter of interest when data on the outcome of interest are limited. Using this method, we quantify the share of overall growth in unemployment during the COVID-19 pandemic that was directly due to each of these state-level NPIs. We find that between March 14 and 28, restaurant and bar limitations and non-essential business closures can explain 6.0% and 6.4% of UI claims respectively, while the other NPIs did not directly increase own-state UI claims.

The Medium Run Impact of Non Pharmaceutical Interventions. Evidence from the 1918 Flu in US Cities

This paper discusses the impact of Non Pharmaceutical Interventions (NPIs) used during the 1918 pandemic on local death rates and city growth. First, I estimate the impact of Non Pharmaceutical Interventions (NPIs) as school closures and social distancing on the mortality rate on the medium run in 43 US cities. I show that if the death rate is lower in 1918 it becomes larger during the two next years, probably because the herd immunity is lower, reducing the net benefits of the policy. Second I investigate the impact of NPIs on cities dynamics. I don’t find any significant association between city’s growth and the implementation of NPIs. In a nutshell the results suggest that if NPIs might lower the fatality rate during the pic of the pandemic, their health benefits are fragile on the medium run as cities remain more sensitive to diseases in the following years. This suggests that the optimal policy responses to pandemics should also account for herd immunity.