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 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.

scholarly journals Impact of self-imposed prevention measures and short-term government intervention on mitigating and delaying a COVID-19 epidemic

2020 ◽  
Author(s):  
Alexandra Teslya ◽  
Thi Mui Pham ◽  
Noortje G. Godijk ◽  
Mirjam E. Kretzschmar ◽  
Martin C.J. Bootsma ◽  
...  
Keyword(s):  
Attack Rate ◽  
Government Intervention ◽  
Healthcare Systems ◽  
Transmission Model ◽  
Prevention Measures ◽  
Short Term ◽  
Government Interventions ◽  
Social Distancing ◽  
Peak Number ◽  
The Impact

AbstractBackgroundWith new cases of COVID-19 surging around the world, many countries have to prepare for moving beyond the containment phase. Prediction of the effectiveness of non-case-based interventions for mitigating, delaying or preventing the epidemic is urgent, especially for countries affected by the ongoing seasonal influenza activity.MethodsWe developed a transmission model to evaluate the impact of self-imposed prevention measures (handwashing, mask-wearing, and social distancing) due to the spread of COVID-19 awareness and of short-term government-imposed social distancing on the peak number of diagnoses, attack rate and time until the peak number of diagnoses.FindingsFor fast awareness spread in the population, self-imposed measures can significantly reduce the attack rate, diminish and postpone the peak number of diagnoses. A large epidemic can be prevented if the efficacy of these measures exceeds 50%. For slow awareness spread, self-imposed measures reduce the peak number of diagnoses and attack rate but do not affect the timing of the peak. Early implementation of short-term government interventions can only delay the peak (by at most 7 months for a 3-month intervention).InterpretationHandwashing, mask-wearing and social distancing as a reaction to information dissemination about COVID-19 can be effective strategies to mitigate and delay the epidemic. We stress the importance of rapidly spreading awareness on the use of these self-imposed prevention measures in the population. Early-initiated short-term government-imposed social distancing can buy time for healthcare systems to prepare for an increasing COVID-19 burden.FundingThis research was funded by ZonMw project 91216062, One Health EJP H2020 project 773830, Aidsfonds project P-29704.Research in contextEvidence before this studyEvidence to date suggests that containment of SARS-CoV-2 using quarantine, travel restrictions, isolation of symptomatic cases, and contact tracing may need to be supplemented by other interventions. Given its rapid spread across the world and immense implications for public health, it is urgent to understand whether non-case-based interventions can mitigate, delay or even prevent a COVID-19 epidemic. One such strategy is a broader-scale contact rate reduction enforced by governments which was used during previous outbreaks, e.g., the 1918 influenza pandemic and the 2009 influenza A/H1N1 pandemic in Mexico. Alternatively, governments and media may stimulate self-imposed prevention measures (handwashing, mask-wearing, and social distancing) by generating awareness about COVID-19, especially when economic and societal consequences are taken into account. Both of these strategies may have a significant impact on the outbreak dynamics. Currently, there are no comparative studies that investigate their viability for controlling a COVID-19 epidemic.Added value of this studyUsing a transmission model parameterized with current best estimates of epidemiological parameters, we evaluated the impact of handwashing, mask-wearing, and social distancing due to COVID-19 awareness and of government-imposed social distancing on the peak number of diagnoses, attack rate, and time until the peak number of diagnoses. We show that a short-term (1-3 months) government intervention initiated early into the outbreak can only delay the peak number of diagnoses but neither alters its magnitude nor the attack rate. Our analyses also highlight the importance of spreading awareness about COVID-19 in the population, as the impact of self-imposed measures is strongly dependent on it. When awareness spreads fast, simple self-imposed measures such as handwashing are more effective than short-term government intervention. Self-imposed measures do not only diminish and postpone the peak number of diagnoses, but they can prevent a large epidemic altogether when their efficacy is sufficiently high (above 50%). Qualitatively, these results will aid public health professionals to compare and select interventions for designing effective outbreak control policies.Implications of all available evidenceOur results highlight that dissemination of evidence-based information about effective prevention measures (hand-washing, mask-wearing, and self-imposed social distancing) can be a key strategy for mitigating and postponing a COVID-19 epidemic. Government interventions (e.g., closing schools and prohibiting mass gatherings) implemented early into the epidemic and lasting for a short-time can only buy time for healthcare systems to prepare for an increasing COVID-19 burden.

scholarly journals Changes in contact patterns shape the dynamics of the COVID-19 outbreak in China

Science ◽  
2020 ◽  
Vol 368 (6498) ◽  
pp. 1481-1486 ◽  
Author(s):  
Juanjuan Zhang ◽  
Maria Litvinova ◽  
Yuxia Liang ◽  
Yan Wang ◽  
Wei Wang ◽  
...  
Keyword(s):  
Confidence Interval ◽  
Odds Ratio ◽  
School Closure ◽  
Hunan Province ◽  
Contact Tracing ◽  
Transmission Model ◽  
Social Distancing ◽  
Susceptibility To Infection ◽  
Contact Patterns ◽  
The Impact

Intense nonpharmaceutical interventions were put in place in China to stop transmission of the novel coronavirus disease 2019 (COVID-19). As transmission intensifies in other countries, the interplay between age, contact patterns, social distancing, susceptibility to infection, and COVID-19 dynamics remains unclear. To answer these questions, we analyze contact survey data for Wuhan and Shanghai before and during the outbreak and contact-tracing information from Hunan province. Daily contacts were reduced seven- to eightfold during the COVID-19 social distancing period, with most interactions restricted to the household. We find that children 0 to 14 years of age are less susceptible to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection than adults 15 to 64 years of age (odds ratio 0.34, 95% confidence interval 0.24 to 0.49), whereas individuals more than 65 years of age are more susceptible to infection (odds ratio 1.47, 95% confidence interval 1.12 to 1.92). Based on these data, we built a transmission model to study the impact of social distancing and school closure on transmission. We find that social distancing alone, as implemented in China during the outbreak, is sufficient to control COVID-19. Although proactive school closures cannot interrupt transmission on their own, they can reduce peak incidence by 40 to 60% and delay the epidemic.

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 Effectiveness of Second Wave COVID-19 Response Strategies in Australia

2020 ◽  
Author(s):  
George Milne ◽  
Simon Xie ◽  
Dana Poklepovich ◽  
Dan O’Halloran ◽  
Matthew Yap ◽  
...  
Keyword(s):  
Health Care Facilities ◽  
Mitigation Strategies ◽  
Transmission Model ◽  
Alternative Response ◽  
Social Distancing ◽  
Response Strategies ◽  
Department Of Health ◽  
Response Measures ◽  
The Impact ◽  
Second Wave

AbstractBackgroundThere is a significant challenge in responding to second waves of COVID-19 cases, with governments being hesitant in introducing hard lockdown measures given the resulting economic impact. In addition, rising case numbers reflect an increase in coronavirus transmission some time previously, so timing of response measures is highly important. Australia experienced a second wave from June 2020 onwards, confined to greater Melbourne, with initial social distancing measures failing to reduce rapidly increasing case numbers. We conducted a detailed analysis of this outbreak, together with an evaluation of the effectiveness of alternative response strategies, to provide guidance to countries experiencing second waves of SARS-Cov-2 transmission.MethodAn individual-based transmission model was used to 1) describe a second-wave COVID-19 epidemic in Australia; 2) evaluate the impact of lockdown strategies used; and 3) evaluate effectiveness of alternative mitigation strategies. The model was calibrated using daily diagnosed case data prior to lockdown. Specific social distancing interventions were modelled by adjusting person-to-person contacts in mixing locations.ResultsModelling earlier activation of lockdown measures are predicted to reduce total case numbers by more than 50%. Epidemic peaks and duration of the second wave were also shown to reduce. Our results suggest that activating lockdown measures when second-wave case numbers first indicated exponential growth, would have been highly effective in reducing COVID-19 cases. The model was shown to realistically predict the epidemic growth rate under the social distancing measures applied, validating the methods applied.ConclusionsThe timing of social distancing activation is shown to be critical to their effectiveness. Data showing exponential rise in cases, doubling every 7-10 days, can be used to trigger early lockdown measures. Such measures are shown to be necessary to reduce daily and total case numbers, and the consequential health burden, so preventing health care facilities being overwhelmed. Early control of second wave resurgence potentially permits strict lockdown measures to be eased earlier.All authors have seen and approved the manuscript. Research funding from Department of Health, Western Australia and Department of Health, Queensland is acknowledged. The authors confirm that these organisations had no influence on the submitted work, nor are there any competing interests.

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 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 Isolation and contact tracing can tip the scale to containment of COVID-19 in populations with social distancing

2020 ◽  
Author(s):  
Mirjam E. Kretzschmar ◽  
Ganna Rozhnova ◽  
Michiel van Boven
Keyword(s):  
Reproduction Number ◽  
Added Value ◽  
Contact Tracing ◽  
Transmission Model ◽  
Critical Threshold ◽  
Strong Impact ◽  
Social Distancing ◽  
Household Contacts ◽  
Novel Coronavirus ◽  
The Impact

AbstractBackgroundNovel coronavirus (SARS-CoV-2) has extended its range of transmission in all parts of the world, with substantial variation in rates of transmission and severity of associated disease. Many countries have implemented social distancing as a measure to control further spread.MethodsWe evaluate whether and under which conditions containment or slowing down COVID-19 epidemics are possible by isolation and contact tracing in settings with various levels of social distancing. We use a stochastic transmission model in which every person generates novel infections according to a probability distribution that is affected by the incubation period distribution (time from infection to symptoms), distribution of the latent period (time from infection to onset of infectiousness), and overall transmissibility. The model distinguishes between close contacts (e.g., within a household) and other contacts in the population. Social distancing affects the number of contacts outside but not within the household.FindingsThe proportion of asymptomatic or unascertained cases has a strong impact on the controllability of the disease. If the proportion of asymptomatic infections is larger than 30%, contact tracing and isolation cannot achieve containment for an R0 of 2.5. Achieving containment by social distancing requires a reduction of numbers of non-household contacts by around 90%. Depending on the realized level of contact reduction, tracing and isolation of only household contacts, or of household and non-household contacts are necessary to reduce the effective reproduction number to below 1. A combination of social distancing with isolation and contact tracing leads to synergistic effects that increase the prospect of containment.InterpretationIsolation and contact tracing can be an effective means to slow down epidemics, but only if the majority of cases are ascertained. In a situation with social distancing, contact tracing can act synergistically and tip the scale towards containment, and can therefore be a tool for controlling COVID-19 epidemics as part of an exit strategy from current lockdown measures.FundingThis research was partly funded by ZonMw project number 91216062.Research in contextEvidence before this studyAs of 8 April 2020, the novel coronavirus (SARS-CoV-2) has spread to more than 170 countries and has caused near 90,000 deaths of COVID-19 worldwide. In the absence of effective medicines and vaccines, the preventive measures are limited to social distancing, isolation of confirmed and suspected cases, and identification and quarantining of their contacts. Evidence suggests that a substantial portion of transmission may occur before the onset of symptoms and before cases can be isolated, and that many cases remain unascertained. This has potentially important implications for the prospect of containment by combinations of these measures.Added value of this studyUsing a stochastic transmission model armed with current best estimates of epidemiological parameters, we evaluated under which conditions containment could be achieved with combinations of social distancing, isolation and contact tracing. We investigated the level of social distancing needed for containment, and how an additional implementation of isolation and contact tracing may likely help to in reducing the effective reproduction number to below 1, the critical threshold. We analyzed what proportion of household and non-household contacts need to be isolated effectively to achieve containment depending on the level of social distancing in the population. We estimated the impact of combinations of these measures on epidemic growth rate and doubling time for the number of infections. We find that under realistic assumptions on the level of social distancing, additional isolation and contact tracing are needed for stopping the epidemic. Whether quarantining only household contacts is sufficient, depends on levels of social distancing and timeliness of tracing and isolation.Implications of all the available evidenceOur analyses based on best understanding of the epidemiology of COVID-19, highlight that if social distancing is not complete, isolation and contact tracing at least of household contacts can help to delay and lower the epidemic peak. High levels of timely contact tracing of household and non-household contacts may be sufficient to control the epidemic.

scholarly journals A stochastic, individual-based model for the evaluation of the impact of non-pharmacological interventions on COVID-19 transmission in Slovakia

2020 ◽  
Author(s):  
Miroslav Gasparek ◽  
Michal Racko ◽  
Michal Dubovsky
Keyword(s):  
Population Distribution ◽  
Pharmacological Interventions ◽  
Individual Based Model ◽  
Social Distancing ◽  
Proposed Model ◽  
Population Reduction ◽  
The Social ◽  
Age Dependent ◽  
The Impact ◽  
Effect Of Age

The COVID-19 pandemic represents one of the most significant healthcare challenges that humanity faces. We developed a stochastic, individual-based model of transmission of COVID-19 in Slovakia. The proposed model is based on current clinical knowledge of the disease and takes into account the age structure of the population, distribution of the population into the households, interactions within the municipalities, and interaction among the individuals travelling between municipalities. Furthermore, the model incorporates the effect of age-dependent severity of COVID-19 and realistic trajectories of patients through the healthcare system. We assess the impact of the governmental non-pharmacological interventions, such as population-wide social distancing, social distancing within specific subsets of population, reduction of travel between the municipalities, and self-quarantining of the infected individuals. We also evaluate the impact of relaxing of strict restrictions, efficacy of the simple state feedback-based restrictions in controlling the outbreak, and the effect of superspreaders on the disease dynamics. Our simulations show that non-pharmacological interventions reduce the number of infected individuals and the number of fatalities, especially when the social distancing of particularly susceptible subgroups of the population is employed along with case isolation.

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

2020 ◽  
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

AbstractBackgroundEfforts 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.MethodsWe 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.FindingsWe 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.DiscussionOur 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.

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