scholarly journals The effect of non-pharmaceutical interventions on COVID-19 cases, deaths and demand for hospital services in the UK: a modelling study

2020 ◽  
Author(s):  
Nicholas G. Davies ◽  
Adam J. Kucharski ◽  
Rosalind M. Eggo ◽  
Amy Gimma ◽  
W. John Edmunds ◽  
...  
Keyword(s):  
Intensive Care ◽  
Healthcare System ◽  
Control Measures ◽  
Transmission Model ◽  
School Closures ◽  
Social Distancing ◽  
Age Structured ◽  
The Uk ◽  
The Impact ◽  
Pharmaceutical Interventions

AbstractBackgroundNon-pharmaceutical interventions have been implemented to reduce transmission of SARS-CoV-2 in the UK. Projecting the size of an unmitigated epidemic and the potential effect of different control measures has been critical to support evidence-based policymaking during the early stages of the epidemic.MethodsWe used a stochastic age-structured transmission model to explore a range of intervention scenarios, including the introduction of school closures, social distancing, shielding of elderly groups, self-isolation of symptomatic cases, and extreme “lockdown”-type restrictions. We simulated different durations of interventions and triggers for introduction, as well as combinations of interventions. For each scenario, we projected estimated new cases over time, patients requiring inpatient and critical care (intensive care unit, ICU) treatment, and deaths.FindingsWe found that mitigation measures aimed at reducing transmission would likely have decreased the reproduction number, but not sufficiently to prevent ICU demand from exceeding NHS availability. To keep ICU bed demand below capacity in the model, more extreme restrictions were necessary. In a scenario where “lockdown”-type interventions were put in place to reduce transmission, these interventions would need to be in place for a large proportion of the coming year in order to prevent healthcare demand exceeding availability.InterpretationThe characteristics of SARS-CoV-2 mean that extreme measures are likely required to bring the epidemic under control and to prevent very large numbers of deaths and an excess of demand on hospital beds, especially those in ICUs.Research in ContextEvidence before this studyAs countries have moved from early containment efforts to planning for the introduction of large-scale non-pharmaceutical interventions to control COVID-19 outbreaks, epidemic modelling studies have explored the potential for extensive social distancing measures to curb transmission. However, it remains unclear how different combinations of interventions, timings, and triggers for the introduction and lifting of control measures may affect the impact of the epidemic on health services, and what the range of uncertainty associated with these estimates would be.Added value of this studyUsing a stochastic, age-structured epidemic model, we explored how eight different intervention scenarios could influence the number of new cases and deaths, as well as intensive care beds required over the projected course of the epidemic. We also assessed the potential impact of local versus national targeting of interventions, reduction in leisure events, impact of increased childcare by grandparents, and timing of triggers for different control measures. We simulated multiple realisations for each scenario to reflect uncertainty in possible epidemic trajectories.Implications of all the available evidenceOur results support early modelling findings, and subsequent empirical observations, that in the absence of control measures, a COVID-19 epidemic could quickly overwhelm a healthcare system. We found that even a combination of moderate interventions – such as school closures, shielding of older groups and self-isolation – would be unlikely to prevent an epidemic that would far exceed available ICU capacity in the UK. Intermittent periods of more intensive lockdown-type measures are predicted to be effective for preventing the healthcare system from being overwhelmed.

How did Autistic Children, and their Parents, Experience School Transition during the COVID-19 Pandemic?

2021 ◽  
Author(s):  
Aimee Code ◽  
Umar Toseeb ◽  
Kathryn Asbury ◽  
Laura Fox
Keyword(s):  
School Transition ◽  
School Closure ◽  
School Transitions ◽  
Autistic Children ◽  
Structured Interviews ◽  
School Closures ◽  
Social Distancing ◽  
New School ◽  
The Uk ◽  
The Impact

Due to the COVID-19 pandemic and resultant school closures, social distancing measures, and restrictions placed on routine activities, the start of the academic year in September 2020 was a unique time for those transitioning to a new school. This study aimed to explore the experiences of parents who supported autistic children making a school transition in 2020, and to examine what impact parents perceived the COVID-19 pandemic had on their child’s school transition. Emphasis was placed on identifying facilitating factors that had benefitted school transitions, and barriers, which had negatively impacted these experiences. Semi-structured interviews were carried out with 13 parents of autistic children in the UK. Reflexive thematic analysis was carried out to identify themes in interview data. Parents reported a variety of experiences, and factors that were perceived as facilitatory to some were observed to be barriers by others. For some parents, the COVID-19 pandemic negatively impacted aspects of school transitions. For example, school closure in March 2020, being unable to visit their child’s new school, and social distancing measures were discussed as being barriers to an easy transition. However, other parents identified these factors as being facilitatory for their child or reported that these circumstances created opportunities to approach the school transition in a unique, improved manner. This paper sheds light on the heterogeneity of experiences and perceptions of parents of autistic children, and highlights the need to examine the impact of COVID-19 on school transitions, including practices which may be advantageous to retain.

scholarly journals Predictions of COVID-19 dynamics in the UK: Short-term forecasting and analysis of potential exit strategies

2021 ◽  
Vol 17 (1) ◽  
pp. e1008619
Author(s):  
Matt J. Keeling ◽  
Edward M. Hill ◽  
Erin E. Gorsich ◽  
Bridget Penman ◽  
Glen Guyver-Fletcher ◽  
...  
Keyword(s):  
Critical Care ◽  
Health Service ◽  
Age Groups ◽  
Transmission Model ◽  
Bed Occupancy ◽  
Time Data ◽  
Social Distancing ◽  
Number Of Patients ◽  
The Uk ◽  
The Impact

Efforts to suppress transmission of SARS-CoV-2 in the UK have seen non-pharmaceutical interventions being invoked. The most severe measures to date include all restaurants, pubs and cafes being ordered to close on 20th March, followed by a “stay at home” order on the 23rd March and the closure of all non-essential retail outlets for an indefinite period. Government agencies are presently analysing how best to develop an exit strategy from these measures and to determine how the epidemic may progress once measures are lifted. Mathematical models are currently providing short and long term forecasts regarding the future course of the COVID-19 outbreak in the UK to support evidence-based policymaking. We present a deterministic, age-structured transmission model that uses real-time data on confirmed cases requiring hospital care and mortality to provide up-to-date predictions on epidemic spread in ten regions of the UK. The model captures a range of age-dependent heterogeneities, reduced transmission from asymptomatic infections and produces a good fit to the key epidemic features over time. We simulated a suite of scenarios to assess the impact of differing approaches to relaxing social distancing measures from 7th May 2020 on the estimated number of patients requiring inpatient and critical care treatment, and deaths. With regard to future epidemic outcomes, we investigated the impact of reducing compliance, ongoing shielding of elder age groups, reapplying stringent social distancing measures using region based triggers and the role of asymptomatic transmission. We find that significant relaxation of social distancing measures from 7th May onwards can lead to a rapid resurgence of COVID-19 disease and the health system being quickly overwhelmed by a sizeable, second epidemic wave. In all considered age-shielding based strategies, we projected serious demand on critical care resources during the course of the pandemic. The reintroduction and release of strict measures on a regional basis, based on ICU bed occupancy, results in a long epidemic tail, until the second half of 2021, but ensures that the health service is protected by reintroducing social distancing measures for all individuals in a region when required. Our work confirms the effectiveness of stringent non-pharmaceutical measures in March 2020 to suppress the epidemic. It also provides strong evidence to support the need for a cautious, measured approach to relaxation of lockdown measures, to protect the most vulnerable members of society and support the health service through subduing demand on hospital beds, in particular bed occupancy in intensive care units.

scholarly journals The effect of control measures on COVID-19 transmission in South Korea

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0249262
Author(s):  
Taeyong Lee ◽  
Hee-Dae Kwon ◽  
Jeehyun Lee
Keyword(s):  
Transmission Rate ◽  
School Closure ◽  
Control Measures ◽  
After School ◽  
Epidemic Threshold ◽  
Social Distancing ◽  
Control And Prevention ◽  
Age Structured ◽  
Net Reproduction ◽  
The Impact

Countries around the world have taken control measures to mitigate the spread of COVID-19, including Korea. Social distancing is considered an essential strategy to reduce transmission in the absence of vaccination or treatment. While interventions have been successful in controlling COVID-19 in Korea, maintaining the current restrictions incurs great social costs. Thus, it is important to analyze the impact of different polices on the spread of the epidemic. To model the COVID-19 outbreak, we use an extended age-structured SEIR model with quarantine and isolation compartments. The model is calibrated to age-specific cumulative confirmed cases provided by the Korea Disease Control and Prevention Agency (KDCA). Four control measures—school closure, social distancing, quarantine, and isolation—are investigated. Because the infectiousness of the exposed has been controversial, we study two major scenarios, considering contributions to infection of the exposed, the quarantined, and the isolated. Assuming the transmission rate would increase more than 1.7 times after the end of social distancing, a second outbreak is expected in the first scenario. The epidemic threshold for increase of contacts between teenagers after school reopening is 3.3 times, which brings the net reproduction number to 1. The threshold values are higher in the second scenario. If the average time taken until isolation and quarantine reduces from three days to two, cumulative cases are reduced by 60% and 47% in the first scenario, respectively. Meanwhile, the reduction is 33% and 41%, respectively, for rapid isolation and quarantine in the second scenario. Without social distancing, a second wave is possible, irrespective of whether we assume risk of infection by the exposed. In the non-infectivity of the exposed scenario, early detection and isolation are significantly more effective than quarantine. Furthermore, quarantining the exposed is as important as isolating the infectious when we assume that the exposed also contribute to infection.

scholarly journals Impact of non-pharmaceutical interventions on the COVID-19 epidemic: A modelling study

2020 ◽  
Vol 8 ◽  
pp. 205031212097946
Author(s):  
Salah Al Awaidy ◽  
Ozayr Mahomed
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Disease Transmission ◽  
Large Scale ◽  
Public Health Intervention ◽  
Transmission Model ◽  
Early Implementation ◽  
The Government ◽  
The Impact ◽  
Pharmaceutical Interventions

Objective: This study aimed to assess the impact of non-pharmaceutical interventions on the COVID-19 epidemic in Oman. Methods: Data were retrieved from published national surveillance data between 24 February and 30 June 2020. To show the impact of the Government introduced public health intervention early in the epidemic, we used a simple disease-transmission model equation of the 2019-n CoV epidemic. Results: From all confirmed cases, the rates of intensive care unit admission were 4.56% (1824). We estimated an R0 of 3.11 with no intervention would result in nearly the entire population of Oman being infected within 65 days. A reduction of the R0 to 1.51 provided an estimated 89,056 confirmed cases, with 167 deaths or 0.4% mortality by June 30 with a requirement of 4052 intensive care unit beds. The current scenario (24 February to 30 June 2020) indicates an R0 of 1.41, resulting in 40,070 confirmed COVID-19 cases, 176 deaths and 69% of confirmed cases recovered. Conclusion: In early implementation of non-pharmaceutical interventions, an intensive lockdown has had a profound impact on the mitigation of a large-scale COVID-19 outbreak in Oman.

scholarly journals The impact of school reopening on the spread of COVID-19 in England

2021 ◽  
Vol 376 (1829) ◽  
pp. 20200261
Author(s):  
Matt J. Keeling ◽  
Michael J. Tildesley ◽  
Benjamin D. Atkins ◽  
Bridget Penman ◽  
Emma Southall ◽  
...  
Keyword(s):  
Secondary Schools ◽  
Primary Schools ◽  
Reproduction Number ◽  
Health Impact ◽  
Transmission Model ◽  
Older Children ◽  
Developmental Needs ◽  
Social Distancing ◽  
The Uk ◽  
The Impact

By mid-May 2020, cases of COVID-19 in the UK had been declining for over a month; a multi-phase emergence from lockdown was planned, including a scheduled partial reopening of schools on 1 June 2020. Although evidence suggests that children generally display mild symptoms, the size of the school-age population means the total impact of reopening schools is unclear. Here, we present work from mid-May 2020 that focused on the imminent opening of schools and consider what these results imply for future policy. We compared eight strategies for reopening primary and secondary schools in England. Modifying a transmission model fitted to UK SARS-CoV-2 data, we assessed how reopening schools affects contact patterns, anticipated secondary infections and the relative change in the reproduction number, R . We determined the associated public health impact and its sensitivity to changes in social distancing within the wider community. We predicted that reopening schools with half-sized classes or focused on younger children was unlikely to push R above one. Older children generally have more social contacts, so reopening secondary schools results in more cases than reopening primary schools, while reopening both could have pushed R above one in some regions. Reductions in community social distancing were found to outweigh and exacerbate any impacts of reopening. In particular, opening schools when the reproduction number R is already above one generates the largest increase in cases. Our work indicates that while any school reopening will result in increased mixing and infection amongst children and the wider population, reopening schools alone in June 2020 was unlikely to push R above one. Ultimately, reopening decisions are a difficult trade-off between epidemiological consequences and the emotional, educational and developmental needs of children. Into the future, there are difficult questions about what controls can be instigated such that schools can remain open if cases increase. This article is part of the theme issue ‘Modelling that shaped the early COVID-19 pandemic response in the UK’.

scholarly journals Act early, save lives: managing COVID-19 in Greece

2020 ◽  
Author(s):  
Gountas Ilias ◽  
Hillas Georgios ◽  
Souliotis Kyriakos
Keyword(s):  
Healthcare System ◽  
Study Design ◽  
Discrete Time ◽  
Transmission Model ◽  
Individual Based Model ◽  
Social Distancing ◽  
Greek Government ◽  
The Impact

AbstractObjectivesTo assess the impact of the implemented social distancing interventions (SD) in Greece.Study DesignA dynamic, discrete time, stochastic individual-based model was developed to simulate COVID-19 transmission.MethodsWe fit the transmission model to the observed trends in deaths and ICU beds use.ResultsIf Greece had not implemented the SD measures, the healthcare system would have been overwhelmed between 30 March and 4 of April. Additionally, the SD interventions averted 4360 deaths and prevent the healthcare system from overwhelmed.ConclusionsThe fast reflexes of the Greek government limit the burden of the Covid-19 outbreak.

scholarly journals Determinants of RSV epidemiology following suppression through pandemic contact restrictions

2021 ◽  
Author(s):  
Mihaly Koltai ◽  
Fabienne Krauer ◽  
David Hodgson ◽  
Edwin van Leeuwen ◽  
Marina Treskova-Schwarzbach ◽  
...  
Keyword(s):  
Mitigation Measures ◽  
Transmission Model ◽  
Temporary Increase ◽  
Previous Exposure ◽  
Dynamic Transmission Model ◽  
Contact Frequency ◽  
Age Structured ◽  
Immune Maturation ◽  
The Impact ◽  
Pharmaceutical Interventions

Introduction COVID-19 related non-pharmaceutical interventions (NPIs) led to a suppression of RSV circulation in winter 2020/21 throughout Europe and an off-season resurgence in Summer 2021 in several European countries. We explore how such temporary interruption may shape future RSV epidemiology and what factors drive the associated uncertainty. Methods We developed an age-structured dynamic transmission model to simulate pre-pandemic RSV infections and hospitalisations. We sampled parameters governing RSV seasonality, immunity acquisition and duration of post-infection immunity and retained those simulations that qualitatively fit the UK's pre-pandemic epidemiology. From Spring 2020 to Summer 2021 we assumed a 50% reduced contact frequency, returning to pre-pandemic levels from mid-May 2021. We simulated transmission forwards until 2023 and evaluated the impact of the sampled parameters on the projected trajectories of RSV hospitalisations. Results Following a lifting of contact restrictions in summer 2021 the model replicated an out-of-season resurgence of RSV. If unmitigated, paediatric RSV hospitalisation incidence in the 2021/22 season was projected to increase by 32% to 67% compared to pre-pandemic levels. The size of the increase depended most on whether infection risk was primarily determined by immunity acquired from previous exposure or general immune maturation. While infants were less affected, the increase in seasonal hospitalisation incidence exceeded 100% in 1-2 year old children and 275% in 2-5 year old children, respectively, in some simulations where immunity from previous exposure dominated. Consequently, the average age of a case increased by 1 to 5 months, most markedly if there was strong immunity acquisition from previous exposure. If immunity to infection was largely determined by age rather than previous exposure, the 2021/22 season started earlier and lasted longer but with a peak incidence lower or similar to pre-pandemic levels. For subsequent seasons, simulations suggested a quick return to pre-pandemic epidemiology, with some slight oscillating behaviour possible depending on the strength of post-exposure immunity. Conclusion COVID-19 mitigation measures stopped RSV circulation in the 2020/21 season and generated immunity debt that will likely lead to a temporary increase in RSV burden in the season following the lifting of restrictions, particularly in 1 to 5 year old children. A more accurate understanding of immunity drivers for RSV is needed to better predict the size of such an increase and plan a potential expansion of pharmaceutical and non-pharmaceutical mitigation measures.

scholarly journals The role of mathematical model in curbing COVID-19 in Nigeria

2020 ◽  
Author(s):  
Chinwendu Emilian Madubueze ◽  
Nkiru M. Akabuike ◽  
Dachollom Sambo
Keyword(s):  
Viral Disease ◽  
Control Measures ◽  
Transmission Model ◽  
Pharmacological Interventions ◽  
Social Distancing ◽  
Testing Rate ◽  
Intervention Measures ◽  
Strict Compliance ◽  
The Impact

The role of mathematical models in controlling infectious diseases cannot be overemphasized. COVID-19 is a viral disease that is caused by Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2) which has no approved vaccine. The available control measures are non-pharmacological interventions like wearing face masks, social distancing, and lockdown which are being advocated for by the WHO. This work assesses the impact of non-pharmaceutical control measures (social distancing and use of face-masks) and mass testing on the spread of COVID-19 in Nigeria. A community-based transmission model for COVID-19 in Nigeria is formulated with observing social distancing, wearing face masks in public and mass testing. The model is parameterized using Nigeria data on COVID-19 in Nigeria. The basic reproduction number is found to be less than unity( R_0<1) when the compliance with intervention measures is moderate (50%≤α<70%) and the testing rate per day is moderate (0.5≤σ_2<0.7) or when the compliance with intervention measures is strict (α≥70%) and the testing rate per day is poor (σ_2=0.3). This implies that Nigeria will be able to halt the spread of COVID-19 under these two conditions. However, it will be easier to enforce strict compliance with intervention measures in the presence of poor testing rate due to the limited availability of testing facilities and manpower in Nigeria. Hence, this study advocates that Nigerian governments (Federal and States) should aim at achieving a testing rate of at least 0.3 per day while ensuring that all the citizens strictly comply with wearing face masks and observing social distancing in public.

scholarly journals Accounting for underreporting in mathematical modelling of transmission and control of COVID-19 in Iran

2020 ◽  
Author(s):  
Meead Saberi ◽  
Homayoun Hamedmoghadam ◽  
Kaveh Madani ◽  
Helen M. Dolk ◽  
Andrei S. Morgan ◽  
...  
Keyword(s):  
Reproduction Number ◽  
Estimation Method ◽  
Case Fatality ◽  
Transmission Model ◽  
School Closures ◽  
The Public ◽  
Case Fatality Ratio ◽  
And Control ◽  
The Impact ◽  
Pharmaceutical Interventions

SUMMARYBackgroundIran has been the hardest hit country by the outbreak of SARS-CoV-2 in the Middle East with 74,877 confirmed cases and 4,683 deaths as of 15 April 2020. With a relatively high case fatality ratio and limited testing capacity, the number of confirmed cases reported is suspected to suffer from significant under-reporting. Therefore, understanding the transmission dynamics of COVID-19 and assessing the effectiveness of the interventions that have taken place in Iran while accounting for the uncertain level of underreporting is of critical importance. We use a mathematical epidemic model utilizing official confirmed data and estimates of underreporting to understand how transmission in Iran has been changing between February and April 2020.MethodsWe developed a compartmental transmission model to estimate the effective reproduction number and its fluctuations since the beginning of the outbreak in Iran. We associate the variations in the effective reproduction number with a timeline of interventions and national events. The estimation method also accounts for the underreporting due to low case ascertainment by estimating the percentage of symptomatic cases using delay-adjusted case fatality ratio based on the distribution of the delay from hospitalization-to-death.FindingsOur estimates of the effective reproduction number ranged from 0.66 to 1.73 between February and April 2020, with a median of 1.16. We estimate a reduction in the effective reproduction number during this period, from 1.73 (95% CI 1.60 – 1.87) on 1 March 2020 to 0.69 (95% CI 0.68-0.70) on 15 April 2020, due to various non-pharmaceutical interventions including school closures, a ban on public gatherings including sports and religious events, and full or partial closure of non-essential businesses. Based on these estimates and given that a near complete containment is no longer feasible, it is likely that the outbreak may continue until the end of the 2020 if the current level of physical distancing and interventions continue and no effective vaccination or therapeutic are developed and made widely available.InterpretationThe series of non-pharmaceutical interventions and the public compliance that took place in Iran are found to be effective in slowing down the speed of the spread of COVID-19 within the studied time period. However, we argue that if the impact of underreporting is overlooked, the estimated transmission and control dynamics could mislead the public health decisions, policy makers, and general public especially in the earlier stages of the outbreak.FundingNil.

scholarly journals Adoption and impact of non-pharmaceutical interventions for COVID-19

2020 ◽  
Vol 5 ◽  
pp. 59 ◽  
Author(s):  
Natsuko Imai ◽  
Katy A.M. Gaythorpe ◽  
Sam Abbott ◽  
Sangeeta Bhatia ◽  
Sabine van Elsland ◽  
...  
Keyword(s):  
Mitigation Strategies ◽  
Control Measures ◽  
School Closures ◽  
Geographic Spread ◽  
Travel Restrictions ◽  
Influenza Transmission ◽  
Societal Impacts ◽  
Government Websites ◽  
The Impact ◽  
Pharmaceutical Interventions

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.

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