scholarly journals Estimating the burden of SARS-CoV-2 in France

2020 ◽  
Author(s):  
Henrik Salje ◽  
Cécile Tran Kiem ◽  
Noémie Lefrancq ◽  
Noémie Courtejoie ◽  
Paolo Bosetti ◽  
...  
Keyword(s):  
Intensive Care ◽  
Control Measures ◽  
Reproductive Number ◽  
Infected Population ◽  
Population Immunity ◽  
Current Population ◽  
The Impact ◽  
Second Wave ◽  
Death Data

AbstractFrance has been heavily affected by the SARS-CoV-2 epidemic and went into lockdown on the 17th March 2020. Using models applied to hospital and death data, we estimate the impact of the lockdown and current population immunity. We find 2.6% of infected individuals are hospitalized and 0.53% die, ranging from 0.001% in those <20y to 8.3% in those >80y. Across all ages, men are more likely to be hospitalized, enter intensive care, and die than women. The lockdown reduced the reproductive number from 3.3 to 0.5 (84% reduction). By 11 May, when interventions are scheduled to be eased, we project 3.7 million (range: 2.3-6.7) people, 5.7% of the population, will have been infected. Population immunity appears insufficient to avoid a second wave if all control measures are released at the end of the lockdown.

scholarly journals Estimating the burden of SARS-CoV-2 in France

Science ◽  
2020 ◽  
Vol 369 (6500) ◽  
pp. 208-211 ◽  
Author(s):  
Henrik Salje ◽  
Cécile Tran Kiem ◽  
Noémie Lefrancq ◽  
Noémie Courtejoie ◽  
Paolo Bosetti ◽  
...  
Keyword(s):  
Intensive Care ◽  
Severe Acute Respiratory Syndrome ◽  
Credible Interval ◽  
Control Measures ◽  
Reproductive Number ◽  
Infected Population ◽  
Population Immunity ◽  
The Impact ◽  
Second Wave ◽  
Death Data

France has been heavily affected by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic and went into lockdown on 17 March 2020. Using models applied to hospital and death data, we estimate the impact of the lockdown and current population immunity. We find that 2.9% of infected individuals are hospitalized and 0.5% of those infected die (95% credible interval: 0.3 to 0.9%), ranging from 0.001% in those under 20 years of age to 8.3% in those 80 years of age or older. Across all ages, men are more likely to be hospitalized, enter intensive care, and die than women. The lockdown reduced the reproductive number from 2.90 to 0.67 (77% reduction). By 11 May 2020, when interventions are scheduled to be eased, we project that 3.5 million people (range: 2.1 million to 6.0 million), or 5.3% of the population (range: 3.3 to 9.3%), will have been infected. Population immunity appears to be insufficient to avoid a second wave if all control measures are released at the end of the lockdown.

scholarly journals Initial Assessment of the Impact of the Emergency State Lockdown Measures on the 1st Wave of the COVID-19 Epidemic in Portugal

2020 ◽  
Vol 33 (11) ◽  
pp. 733
Author(s):  
Vasco Ricoca Peixoto ◽  
André Vieira ◽  
Pedro Aguiar ◽  
Carlos Carvalho ◽  
Daniel Rhys Thomas ◽  
...  
Keyword(s):  
Intensive Care ◽  
Arima Model ◽  
Control Measures ◽  
Contact Tracing ◽  
Initial Assessment ◽  
Early Action ◽  
Containment Measures ◽  
Prevention And Control Measures ◽  
Care Capacity ◽  
The Impact

Introduction: Portugal took early action to control the COVID-19 epidemic, initiating lockdown measures on March 16th when it recorded only 62 cases of COVID-19 per million inhabitants and reported no deaths. The Portuguese public complied quickly, reducing their overall mobility by 80%. The aim of this study was to estimate the initial impact of the lockdown in Portugal in terms of the reduction of the burden on the healthcare system.Material and Methods: We forecasted epidemic curves for: Cases, hospital inpatients (overall and in intensive care), and deaths without lockdown, assuming that the impact of containment measures would start 14 days after initial lockdown was implemented. We used exponential smoothing models for deaths, intensive care and hospitalizations and an ARIMA model for number of cases. Models were selected considering fitness to the observed data up to the 31st March 2020. We then compared observed (with intervention) and forecasted curves (without intervention).Results: Between April 1st and April 15th, there were 146 fewer deaths (-25%), 5568 fewer cases (-23%) and, as of April 15th, there were 519 fewer intensive care inpatients (-69%) than forecasted without the lockdown. On April 15th, the number of intensive care inpatients could have reached 748, three times higher than the observed value (229) if the intervention had been delayed.Discussion: If the lockdown had not been implemented in mid-March, Portugal intensive care capacity (528 beds) would have likely been breached during the first half of April. The lockdown seems to have been effective in reducing transmission of SARS-CoV-2, serious COVID-19 disease, and associated mortality, thus decreasing demand on health services.Conclusion: An early lockdown allowed time for the National Health Service to mobilize resources and acquire personal protective equipment, increase testing, contact tracing and hospital and intensive care capacity and to promote broad prevention and control measures. When lifting more stringent measures, strong surveillance and communication strategies that mobilize individual prevention efforts are necessary.

Control of Simultaneous Outbreaks of Carbapenemase-Producing Enterobacteriaceae and Extensively Drug-ResistantAcinetobacter baumanniiInfection in an Intensive Care Unit Using Interventions Promoted in the Centers for Disease Control and Prevention 2012 Carbapenemase-Resistant Enterobacteriaceae Toolkit

2014 ◽  
Vol 35 (7) ◽  
pp. 810-817 ◽  
Author(s):  
Kyle B. Enfield ◽  
Nujhat N. Huq ◽  
Megan F. Gosseling ◽  
Darla J. Low ◽  
Kevin C. Hazen ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Infection Control ◽  
Disease Control ◽  
Control Measures ◽  
Surgical Trauma ◽  
Infection Control Measures ◽  
Resistance Plasmids ◽  
Centers For Disease Control ◽  
The Impact

ObjectiveWe describe the efficacy of enhanced infection control measures, including those recommended in the Centers for Disease Control and Prevention’s 2012 carbapenem-resistant Enterobacteriaceae (CRE) toolkit, to control concurrent outbreaks of carbapenemase-producing Enterobacteriaceae (CPE) and extensively drug-resistantAcinetobacter baumannii(XDR-AB).DesignBefore-after intervention study.SettingFifteen-bed surgical trauma intensive care unit (ICU).MethodsWe investigated the impact of enhanced infection control measures in response to clusters of CPE and XDR-AB infections in an ICU from April 2009 to March 2010. Polymerase chain reaction was used to detect the presence ofblaKPCand resistance plasmids in CRE. Pulsed-field gel electrophoresis was performed to assess XDR-AB clonality. Enhanced infection-control measures were implemented in response to ongoing transmission of CPE and a new outbreak of XDR-AB. Efficacy was evaluated by comparing the incidence rate (IR) of CPE and XDR-AB before and after the implementation of these measures.ResultsThe IR of CPE for the 12 months before the implementation of enhanced measures was 7.77 cases per 1,000 patient-days, whereas the IR of XDR-AB for the 3 months before implementation was 6.79 cases per 1,000 patient-days. All examined CPE shared endemicblaKPCresistance plasmids, and 6 of the 7 XDR-AB isolates were clonal. Following institution of enhanced infection control measures, the CPE IR decreased to 1.22 cases per 1,000 patient-days (P= .001), and no more cases of XDR-AB were identified.ConclusionsUse of infection control measures described in the Centers for Disease Control and Prevention’s 2012 CRE toolkit was associated with a reduction in the IR of CPE and an interruption in XDR-AB transmission.

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 Predicted impact of vaccination and active case finding measures to control epidemic of Coronavirus Disease 2019 (COVID-19) in a migrant-populated area in Thailand

2021 ◽  
Author(s):  
Rapeepong Suphanchaimat ◽  
Natthaprang Nittayasoot ◽  
Panithee Thammawijaya ◽  
Pard Teekasap ◽  
Kamnuan Ungchusak
Keyword(s):  
Vaccination Coverage ◽  
Basic Reproductive Number ◽  
Reproductive Number ◽  
Initial Population ◽  
Deterministic System ◽  
Single Measure ◽  
Vaccination Strategies ◽  
Epidemic Area ◽  
The Impact ◽  
Second Wave

Abstract Background: Thailand experienced the first wave of Coronavirus Disease 2019 (COVID-19) during March-May 2020 and was now facing the second wave of COVID-19 since December 2020. For the second wave, the intensity was more pronounced. The area faced the greatest hit was Samut Sakhon, a main migrant-receiving province in the country. Thus, the Thai Ministry of Public Health (MOPH) was now considering the initiation of vaccination strategies in combination with active face finding (ACF) in the epidemic area. The objective of this study was to assess the impact of various vaccination and ACF policy scenarios in terms of case reduction and deaths averted.Methods: The study obtained data mainly from the Division of Epidemiology, Department of Disease Control (DDC), MOPH. Deterministic system dynamics and compartmental models were exercised. Basic reproductive number (R0) was estimated at 3 from the beginning. Vaccine efficacy against disease transmission was assumed to be 50%. A total of 10,000 people were estimated as an initial population size.Results: The findings showed that the greater the vaccination coverage was, the smaller the size of incident and cumulative cases. Compared with no-vaccination and no-ACF scenario, the 90%-vaccination coverage combined with 90%-ACF coverage contributed a reduction of cumulative cases by 33%. The case reduction benefit would be greater when R0 was smaller (⁓53% and ⁓51% when R0 equated 2 and 1.5 respectively).Conclusion: This study reaffirmed the idea that a combination of vaccination and ACF measures contributed to favourable results in reducing the number of COVID-19 cases and deaths, relative to the implementation of only a single measure. The greater the vaccination and ACF coverage was, the greater the volume of cases could be saved. Though we demonstrated the benefit of vaccination strategies in this setting, the actual implementation needs to consider many more policy angles, such as social acceptability, cost-effectiveness and operational feasibility. Further studies that address these topics based on empirical evidence are of great value.

scholarly journals Dynamical analysis of the spread of African swine fever with the live pig price in China

2021 ◽  
Vol 18 (6) ◽  
pp. 8123-8148
Author(s):  
Yihao Huang ◽  
◽  
Jing Li ◽  
Juan Zhang ◽  
Zhen Jin ◽  
...  
Keyword(s):  
Supply And Demand ◽  
African Swine Fever ◽  
Control Measures ◽  
Basic Reproductive Number ◽  
Dynamical Analysis ◽  
Critical Conditions ◽  
Reproductive Number ◽  
Price Function ◽  
The Government ◽  
The Impact

<abstract><p>Pork makes up the highest proportion of household expenditure on meat in China and supply and demand have been basically stable in the past decade. However, the catastrophic outbreak of African swine fever (ASF) in August 2018 disrupted the balance and reduced the national herd by half within six months. The consequence was a gross lack of supply to the market and consumer demand was unable to be met. Accordingly, live pig prices rose sharply from 2019. In order to assess the influence of ASF on the price of the live pigs, we use a price function to characterize the relationship between price of the live pigs and the nation's pig stock, and then establish a time delay ASF epidemic dynamical model with the price function. By analyzing the dynamical behaviors of the model, we calculate the basic reproductive number, discuss the stability of equilibrium, and obtain the critical conditions for Hopf bifurcation. The model reasonableness is confirmed by carrying out data fitting and parameter estimation based on price data of the live pigs, the pig stock data and the outbreak data of ASF. By performing sensitivity analysis, we intuitively show the impact of ASF on the price of live pigs and the pig stocks, and assess the key factors affecting the outbreak of ASF. The conclusion is drawn that, with the control measures adopted by related government department in China, the basic reproductive number ($ R_0 = 0.6005 $) means that the ASF epidemic has been controlled. Moreover, the price of the live pig increases linearly with $ R_0 $, while the effect of the number of infected pigs on the subsequent price is non-linear related. Our findings suggest that society and the government should pay more attention to the prevention of animal disease epidemics.</p></abstract>

scholarly journals Using ICU data to improve the real-time estimation of the effective reproductive number of the COVID-19 epidemic in 9 European countries

2020 ◽  
Author(s):  
Samuel Hurtado ◽  
David Tinajero
Keyword(s):  
Intensive Care ◽  
Healthcare System ◽  
Time Estimation ◽  
European Countries ◽  
Reproductive Number ◽  
Mass Gatherings ◽  
Number Of Patients ◽  
Response Team ◽  
Using Data ◽  
The Impact

1.SummaryWe replicate a recent study by the Imperial College COVID-19 Response Team (Flaxman et al, 2020) that estimates both the effective reproductive number, Rt, of the current COVID-19 epidemic in 11 European countries, and the impact of different nonpharmaceutical interventions that have been implemented to try to contain the epidemic, including case isolation, the closure of schools and universities, banning of mass gatherings and/or public events, and most recently, widescale social distancing including local and national lockdowns. The main indicator they use for measuring the evolution of the epidemic is the daily number of deaths by COVID-19 in each country, which is a better statistic than the number of identified cases because it doesn’t depend so much on the testing strategy that is in place in each country at each moment in time.We improve on their estimation by using data from the number of patients in intensive care, which provides two advantages over the number of deaths: first, it can be used to construct a signal with less bias: as the healthcare system of a country reaches saturation, the mortality rate would be expected to increase, which would bias the estimates of Rt and of the impact of measures implemented to contain the epidemic; and second, it is a signal with less lag, as the time from onset of symptoms to ICU admission is shorter than the time from onset to death (on average, 7.5 days shorter). The intensive care signal we use is not just the number of people in ICU, as this would also be biased if the healthcare system has reached saturation (in this case, biased downwards, as admissions are no longer possible when all units are in use). Instead, we estimate the daily demand of intensive care, as the sum of two components: the part that is satisfied (new ICU admissions) and the part that is not (which results in excess mortality).Thanks to the advantages of this ICU signal in terms of timeliness and bias, we find that most of the countries in the study have already reached Rt<1 with 95% confidence (Italy, Spain, Austria, Denmark, France, Norway and Switzerland, but not Belgium or Sweden), whereas the original methodology of Flaxman et al (2020), even with updated data, would only find Rt<1 with 95% confidence for Italy and Switzerland.

scholarly journals The effect of the COVID-19 pandemic on the epidemiology of positive blood cultures in Swiss intensive care units: a nationwide surveillance study

Critical Care ◽  
2021 ◽  
Vol 25 (1) ◽  
Author(s):  
Lauro Damonti ◽  
Andreas Kronenberg ◽  
Jonas Marschall ◽  
Philipp Jent ◽  
Rami Sommerstein ◽  
...  
Keyword(s):  
Intensive Care ◽  
Blood Culture ◽  
Intensive Care Units ◽  
Bloodstream Infections ◽  
Incidence Rates ◽  
Blood Cultures ◽  
Nationwide Surveillance ◽  
The Impact ◽  
Second Wave ◽  
Interim Period

Abstract Background Evidence about the impact of the pandemic of COVID-19 on the incidence rates of blood cultures contaminations and bloodstream infections in intensive care units (ICUs) remains scant. The objective of this study was to investigate the nationwide epidemiology of positive blood cultures drawn in ICUs during the first two pandemic waves of COVID-19 in Switzerland. Methods We analyzed data on positive blood cultures among ICU patients, prospectively collected through a nationwide surveillance system (ANRESIS), from March 30, 2020, to May 31, 2021, a 14-month timeframe that included a first wave of COVID-19, which affected the French and Italian-speaking regions, an interim period (summer 2020) and a second wave that affected the entire country. We used the number of ICU patient-days provided by the Swiss Federal Office of Public Health as denominator to calculate incidence rates of blood culture contaminations and bloodstream infections (ICU-BSI). Incidence rate ratios comparing the interim period with the second wave were determined by segmented Poisson regression models. Results A total of 1099 blood culture contaminations and 1616 ICU-BSIs were identified in 52 ICUs during the study. Overall, more episodes of blood culture contaminations and ICU-BSI were observed during the pandemic waves, compared to the interim period. The proportions of blood culture contaminations and ICU-BSI were positively associated with the ICU occupancy rate, which was higher during the COVID-19 waves. During the more representative second wave (versus interim period), we observed an increased incidence of blood culture contaminations (IRR 1.57, 95% CI 1.16–2.12) and ICU-BSI (IRR 1.20, 95% CI 1.03–1.39). Conclusions An increase in blood culture contaminations and ICU-BSIs was observed during the second COVID-19 pandemic wave, especially in months when the ICU burden of COVID-19 patients was high.

scholarly journals Modelling the impact of lockdown easing measures on cumulative COVID-19 cases and deaths in England

2020 ◽  
Author(s):  
H Ziauddeen ◽  
N Subramaniam ◽  
D Gurdasani
Keyword(s):  
Bayesian Model ◽  
Added Value ◽  
Reproductive Number ◽  
Baseline Scenario ◽  
Community Transmission ◽  
Uk Government ◽  
The Uk ◽  
Incident Cases ◽  
The Impact ◽  
Death Data

AbstractBackgroundAs countries begin to ease the lockdown measures instituted to control the COVID-19 pandemic, there is a risk of a resurgence of the pandemic. The UK started easing lockdown in England when levels of community transmission remained high, which could have a major impact on case numbers and deaths. Using a Bayesian model we assessed the potential impacts of successive lockdown easing measures in England, focussing on scenarios where the reproductive number (R) remains ≤1 in line with the UK government’s stated aim.MethodsWe developed a Bayesian model to infer incident cases and R in England, from incident death data from the Office of National Statistics. We then used this to forecast excess cases and deaths in multiple plausible scenarios in which R increases at one or more time points, compared to a baseline scenario where R remains unchanged by the easing of lockdown.FindingsThe model inferred an R of 0.81 on the 13th May when England first started easing lockdown. In the most conservative scenario where R increases to 0.85 as lockdown was eased further on 1st June and then remained constant, the model predicts an excess 400 (95% CI 34-1988) deaths and 56,019 (95% CI 4768-278,083) cumulative cases over 90 days. In the scenario with maximal increases in R (but staying ≤1) with successive easing of lockdown, the model predicts 1,946 (95% CI 165-9,667) excess cumulative deaths and 351,460 (95% CI 29,894-1,747,026) excess cases.InterpretationWhen levels of transmission are high, even small changes in R with easing of lockdown can have significant impacts on expected cases and deaths, even if R remains ≤1. This will have a major impact on tracing systems and health care services in England. This model can be updated with incoming death data to refine predictions over time.FundingNone.Research in contextEvidence before this studyThe impact of social distancing and lockdown measures on controlling the COVID-19 pandemic has been studied extensively over the last few months. However there has been little examination of the likely impact of easing lockdown measures in a staged manner as is being currently carried out in England, UK. We searched PubMed, medRxiv, bioRxiv, arXiv, and Wellcome Open Research for peer-reviewed articles, preprints, and research reports using the terms “COVID-19”, “United Kingdom” and “lockdown” for research examining these impacts, but found no relevant research that could inform the impact of phased easing of lockdown within England, UK.Added value of this studyDecisions around timing of easing lockdown need to be informed by current scientific evidence. In this context, this study provides urgently needed information about the potential impact of lockdown easing at this point within the COVID-19 pandemic in England. Using an epidemiological approach with Bayesian inference, we specifically assess several plausible scenarios of increase in R from baseline as a result of easing lockdown measures at levels of current community transmission, even when the R is maintained ≤1, which is the stated aim of the UK government. We provide a comparison of these scenarios, with a baseline scenario where R remains constant, as well as against elimination strategies, where transmission is aggressively suppressed to the lowest level possible. As our code is publicly available, these methods can be easily applied to accruing data, and to any number of scenarios to better understand the implications for public health policy.Implications of all the available evidenceEasing lockdown at a point of relatively high community transmission within the UK would lead to substantial excesses of deaths, and cases, even if R is maintained at ≤1. As expected, these increases are more marked, when R rises above 1, which is a distinct possibility, given recent estimates of R by a UK government advisory group.1 Our findings suggest that an elimination strategy would be more appropriate at this point, to allow suppression of community transmission to a point where easing of lockdown would not have the same impact, as with current transmission, and would likely not overwhelm systems of test, trace and isolate, and health services within England.

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