Fundamental principles of epidemic spread highlight the immediate need for large-scale serological surveys to assess the stage of the SARS-CoV-2 epidemic

AbstractThe spread of a novel pathogenic infectious agent eliciting protective immunity is typically characterised by three distinct phases: (I) an initial phase of slow accumulation of new infections (often undetectable), (II) a second phase of rapid growth in cases of infection, disease and death, and (III) an eventual slow down of transmission due to the depletion of susceptible individuals, typically leading to the termination of the (first) epidemic wave. Before the implementation of control measures (e.g. social distancing, travel bans, etc) and under the assumption that infection elicits protective immunity, epidemiological theory indicates that the ongoing epidemic of SARS-CoV-2 will conform to this pattern.Here, we calibrate a susceptible-infected-recovered (SIR) model to data on cumulative reported SARS-CoV-2 associated deaths from the United Kingdom (UK) and Italy under the assumption that such deaths are well reported events that occur only in a vulnerable fraction of the population. We focus on model solutions which take into consideration previous estimates of critical epidemiological parameters such as the basic reproduction number (R0), probability of death in the vulnerable fraction of the population, infectious period and time from infection to death, with the intention of exploring the sensitivity of the system to the actual fraction of the population vulnerable to severe disease and death.Our simulations are in agreement with other studies that the current epidemic wave in the UK and Italy in the absence of interventions should have an approximate duration of 2-3 months, with numbers of deaths lagging behind in time relative to overall infections. Importantly, the results we present here suggest the ongoing epidemics in the UK and Italy started at least a month before the first reported death and have already led to the accumulation of significant levels of herd immunity in both countries. There is an inverse relationship between the proportion currently immune and the fraction of the population vulnerable to severe disease.This relationship can be used to determine how many people will require hospitalisation (and possibly die) in the coming weeks if we are able to accurately determine current levels of herd immunity. There is thus an urgent need for investment in technologies such as virus (or viral pseudotype) neutralization assays and other robust assays which provide reliable read-outs of protective immunity, and for the provision of open access to valuable data sources such as blood banks and paired samples of acute and convalescent sera from confirmed cases of SARS-CoV-2 to validate these. Urgent development and assessment of such tests should be followed by rapid implementation at scale to provide real-time data. These data will be critical to the proper assessment of the effects of social distancing and other measures currently being adopted to slow down the case incidence and for informing future policy direction.Disclaimer(a) This material is not final and is subject to be updated any time. (b) Code used will be made available as soon as possible. (c) Contact for press enquiries: Cairbre Sugrue, [email protected], +44 (0)7502 203 769.

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Collateral impact of COVID-19: why should children continue to suffer?

European Journal of Pediatrics ◽

10.1007/s00431-021-03963-x ◽

2021 ◽

Cited By ~ 1

Author(s):

Prasad Nagakumar ◽

Ceri-Louise Chadwick ◽

Andrew Bush ◽

Atul Gupta

Keyword(s):

Young People ◽

Large Scale ◽

Hospital Admissions ◽

Severe Disease ◽

Children And Young People ◽

Collateral Effects ◽

Rsv Infection ◽

Syncytial Virus ◽

Set Up ◽

The Uk

AbstractThe COVID-19 pandemic caused by SARS-COV-2 virus fortunately resulted in few children suffering from severe disease. However, the collateral effects on the COVID-19 pandemic appear to have had significant detrimental effects on children affected and young people. There are also some positive impacts in the form of reduced prevalence of viral bronchiolitis. The new strain of SARS-COV-2 identified recently in the UK appears to have increased transmissibility to children. However, there are no large vaccine trials set up in children to evaluate safety and efficacy. In this short communication, we review the collateral effects of COVID-19 pandemic in children and young people. We highlight the need for urgent strategies to mitigate the risks to children due to the COVID-19 pandemic. What is Known:• Children and young people account for <2% of all COVID-19 hospital admissions• The collateral impact of COVID-19 pandemic on children and young people is devastating• Significant reduction in influenza and respiratory syncytial virus (RSV) infection in the southern hemisphere What is New:• The public health measures to reduce COVID-19 infection may have also resulted in near elimination of influenza and RSV infections across the globe• A COVID-19 vaccine has been licensed for adults. However, large scale vaccine studies are yet to be initiated although there is emerging evidence of the new SARS-COV-2 strain spreading more rapidly though young people.• Children and young people continue to bear the collateral effects of COVID-19 pandemic

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Immunological characteristics govern the changing severity of COVID-19 during the transition to endemicity

10.1101/2020.09.03.20187856 ◽

2020 ◽

Cited By ~ 1

Author(s):

Jennie S Lavine ◽

Ottar N Bjornstad ◽

Rustom Antia

Keyword(s):

Common Cold ◽

Severe Disease ◽

Herd Immunity ◽

Epidemiological Data ◽

Control Measures ◽

Vulnerable Groups ◽

Model Framework ◽

Benign Nature ◽

The Common ◽

Human Coronaviruses

As prospects for eradicating CoV-2 dwindle, we are faced with the question of how the severity of CoV-2 disease may change in the years ahead. Will CoV-2 continue to be a pathogenic scourge that, like smallpox or measles, can be tamed only by ongoing vaccination, or will it join the ranks of mild endemic human coronaviruses (HCoVs)? Our analysis of immunological and epidemiological data on HCoVs shows that infection-blocking immunity wanes rapidly, but disease-reducing immunity is long-lived. We estimate the relevant parameters and incorporate them into a new epidemiological model framework which separates these different components of immunity. Our model recapitulates both the current severity of CoV-2 and the relatively benign nature of HCoVs; suggesting that once the endemic phase is reached, CoV-2 may be no more virulent than the common cold. The benign outcome at the endemic phase is contingent on the virus causing primary infections in children. We predict a very different outcome were a CoV like MERS (that causes severe disease in children) to become endemic. These results force us to re-evaluate control measures that rely on identifying and isolating symptomatic infections, and reconsider ideas regarding herd immunity and the use of immune individuals as shields to protect vulnerable groups.

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

10.1101/2020.04.01.20049908 ◽

2020 ◽

Cited By ~ 52

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.

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Control of a Nosocomial Outbreak of COVID-19 in a University Hospital

Open Forum Infectious Diseases ◽

10.1093/ofid/ofaa512 ◽

2020 ◽

Vol 7 (12) ◽

Author(s):

Sei Harada ◽

Shunsuke Uno ◽

Takayuki Ando ◽

Miho Iida ◽

Yaoko Takano ◽

...

Keyword(s):

High Risk ◽

Large Scale ◽

Secondary Infection ◽

Control Measures ◽

University Hospital ◽

Contact Tracing ◽

Effective Control ◽

Primary Case ◽

Pcr Screening ◽

Social Distancing

Abstract Background Nosocomial spread of coronavirus disease 2019 (COVID-19) causes clusters of infection among high-risk individuals. Controlling this spread is critical to reducing COVID-19 morbidity and mortality. We describe an outbreak of COVID-19 in Keio University Hospital, Japan, and its control and propose effective control measures. Methods When an outbreak was suspected, immediate isolation and thorough polymerase chain reaction (PCR) testing of patients and health care workers (HCWs) using an in-house system, together with extensive contact tracing and social distancing measures, were conducted. Nosocomial infections (NIs) were defined as having an onset or positive test after the fifth day of admission for patients and having high-risk contacts in our hospital for HCWs. We performed descriptive analyses for this outbreak. Results Between March 24 and April 24, 2020, 27 of 562 tested patients were confirmed positive, of whom 5 (18.5%) were suspected as NIs. For HCWs, 52 of 697 tested positive, and 40 (76.9%) were considered NIs. Among transmissions, 95.5% were suspected of having occurred during the asymptomatic period. Large-scale isolation and testing at the first sign of outbreak terminated NIs. The number of secondary cases directly generated by a single primary case found before March 31 was 1.74, compared with 0 after April 1. Only 4 of 28 primary cases generated definite secondary infection; these were all asymptomatic. Conclusions Viral shedding from asymptomatic cases played a major role in NIs. PCR screening of asymptomatic individuals helped clarify the pattern of spread. Immediate large-scale isolation, contact tracing, and social distancing measures were essential to containing outbreaks.

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“It’s Been Ugly”: A Large-Scale Qualitative Study into the Difficulties Frontline Doctors Faced across Two Waves of the COVID-19 Pandemic

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph182413067 ◽

2021 ◽

Vol 18 (24) ◽

pp. 13067

Author(s):

Sophie Harris ◽

Elizabeth Jenkinson ◽

Edward Carlton ◽

Tom Roberts ◽

Jo Daniels

Keyword(s):

Content Analysis ◽

Intensive Care ◽

Large Scale ◽

Control Measures ◽

Emotional Impact ◽

Infection Control Measures ◽

Conventional Content Analysis ◽

The Uk ◽

Insight Into

This study aimed to gain an uncensored insight into the most difficult aspects of working as a frontline doctor across successive COVID-19 pandemic waves. Data collected by the parent study (CERA) was analysed using conventional content analysis. Participants comprised frontline doctors who worked in emergency, anaesthetic, and intensive care medicine in the UK and Ireland during the COVID-19 pandemic (n = 1379). All seniority levels were represented, 42.8% of the sample were male, and 69.2% were white. Four themes were identified with nine respective categories (in parentheses): (1) I’m not a COVID hero, I’m COVID cannon fodder (exposed and unprotected, “a kick in the teeth”); (2) the relentlessness and pervasiveness of COVID (“no respite”, “shifting sands”); (3) the ugly truths of the frontline (“inhumane” care, complex team dynamics); (4) an overwhelmed system exacerbated by COVID (overstretched and under-resourced, constant changes and uncertainty, the added hinderance of infection control measures). Findings reflect the multifaceted challenges faced after successive pandemic waves; basic wellbeing needs continue to be neglected and the emotional impact is further pronounced. Steps are necessary to mitigate the repeated trauma exposure of frontline doctors as COVID-19 becomes endemic and health services attempt to recover with inevitable long-term sequelae.

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Determining the optimal strategy for reopening schools, work and society in the UK: balancing earlier opening and the impact of test and trace strategies with the risk of occurrence of a secondary COVID-19 pandemic wave

10.1101/2020.06.01.20100461 ◽

2020 ◽

Author(s):

Jasmina Panovska-Griffiths ◽

Cliff Kerr ◽

Robyn Margaret Stuart ◽

Dina Mistry ◽

Daniel Klein ◽

...

Keyword(s):

Large Scale ◽

Reproduction Number ◽

Epidemiological Data ◽

Contact Tracing ◽

Cumulative Number ◽

Social Distancing ◽

Uk Government ◽

The Uk ◽

The Impact ◽

Pandemic Wave

Background In order to slow down the spread of SARS-CoV-2, the virus causing the COVID-19 pandemic, the UK government has imposed strict physical distancing (lockdown) measures including school 'dismissals' since 23 March 2020. As evidence is emerging that these measures may have slowed the spread of the pandemic, it is important to assess the impact of any changes in strategy, including scenarios for school reopening and broader relaxation of social distancing. This work uses an individual-based model to predict the impact of a suite of possible strategies to reopen schools in the UK, including that currently proposed by the UK government. Methods We use Covasim, a stochastic agent-based model for transmission of COVID-19, calibrated to the UK epidemic. The model describes individuals' contact networks stratified as household, school, work and community layers, and uses demographic and epidemiological data from the UK. We simulate a range of different school reopening strategies with a society-wide relaxation of lockdown measures and in the presence of different non-pharmaceutical interventions, to estimate the number of new infections, cumulative cases and deaths, as well as the effective reproduction number with different strategies. To account for uncertainties within the stochastic simulation, we also simulated different levels of infectiousness of children and young adults under 20 years old compared to older ages. Findings We found that with increased levels of testing of people (between 25% and 72% of symptomatic people tested at some point during an active COVID-19 infection depending on scenarios) and effective contact-tracing and isolation for infected individuals, an epidemic rebound may be prevented across all reopening scenarios, with the effective reproduction number (R) remaining below one and the cumulative number of new infections and deaths significantly lower than they would be if testing did not increase. If UK schools reopen in phases from June 2020, prevention of a second wave would require testing 51% of symptomatic infections, tracing of 40% of their contacts, and isolation of symptomatic and diagnosed cases. However, without such measures, reopening of schools together with gradual relaxing of the lockdown measures are likely to induce a secondary pandemic wave, as are other scenarios for reopening. When infectiousness of <20 year olds was varied from 100% to 50% of that of older ages, our findings remained unchanged. Interpretation To prevent a secondary COVID-19 wave, relaxation of social distancing including reopening schools in the UK must be implemented alongside an active large-scale population-wide testing of symptomatic individuals and effective tracing of their contacts, followed by isolation of symptomatic and diagnosed individuals. Such combined measures have a greater likelihood of controlling the transmission of SARS-CoV-2 and preventing a large number of COVID-19 deaths than reopening schools and society with the current level of implementation of testing and isolation of infected individuals.

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Impact of Lockdown in India: A Case Study on Karnataka with International Model

Kesmas National Public Health Journal ◽

10.21109/kesmas.v15i2.3978 ◽

2020 ◽

Vol 15 (2) ◽

Author(s):

Stelvin Sebastian ◽

Aby Paul ◽

Joel Joby ◽

Sanjo Saijan ◽

Jeeva Joseph ◽

...

Keyword(s):

Mortality Rate ◽

Recovery Rate ◽

World Health ◽

Second Phase ◽

Social Distancing ◽

Epidemic Curve ◽

The United Kingdom ◽

Two Phases ◽

Health Organization ◽

The Uk

Coronavirus disease 2019 (COVID-19) was declared an epidemic and a global health emergency by the World Health Organization (WHO), prompting various countries to implement early and stringent social distancing protocols through lockdown, to flatten the epidemic curve. The objective of our present study was to assess the impacts and effectiveness of the lockdown protocol in Karnataka and Punjab, compared with the implementation of this method in Australia and the United Kingdom (UK). This study involved the collection of data from different authorized databases, in two phases. The first phase included the time starting with the first-reported index case through the 14th day after the declaration of lockdown, for each country. The second phase involved the data collected between the 15th day through the 28th day of the lockdown. The highest doubling rate for cases was observed in Australia, followed by Karnataka and Punjab, whereas the lowest was observed in the UK. Comparisons of the numbers of the samples tested, the mortality rate, and the recovery rate between Karnataka and Punjab, after the implementation of lockdown, revealed a better recovery rate and lower mortality rate in Karnataka than in Punjab. Our study revealed that the implementation of social distancing and lockdown reduced the transmission of the coronavirus and the number of cases reported. However, the effectiveness of lockdown varied among locations, due to demographic and physiological differences.

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“Herd Immunity” Controlled and Planned versus Uncontrolled and Random, in the strategy to fight against COVID 19: An option with Live Pathogen Virus Vaccine (Preprint)

10.2196/preprints.22381 ◽

2020 ◽

Author(s):

Eduardo Rivas Ruiz

Keyword(s):

Viral Load ◽

Health Services ◽

Virus Vaccine ◽

Herd Immunity ◽

Control Measures ◽

Health Consequences ◽

Economic Activities ◽

Social Distancing ◽

The Impact

UNSTRUCTURED Abstract This study proposes a feasible strategy for the fight against COVID 19. The strategy aims to reduce the risks of “herd immunity” (or collective immunity) that can occur in an uncontrolled and random way, which has arisen in view of the current pandemic-related situation. This type of strategy would be useful in reactivation of economic activities and lifting of measures of social distancing and quarantine; however, these activities could lead governments to promote misguided decisions with negative health consequences, new spikes in infections, collapse of health services, and re-implementation of control measures and social distancing. Thus, when analyzing the consequences of this strategy, we consider that this concept of herd immunity should be designed differently so that the results are different from the above-mentioned negative consequence. We propose the development of a live pathogen virus vaccine (LPV) with a low viral load that meets the required criteria and allows us to apply this vaccine in conjunction with a herd immunity for generating the necessary immunity, reducing the impact and consequences on health and economies, and reducing the risks in general although this does not mean that they do not exist as they are presented with other types of vaccines, to achieve a return to gradually normality.

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Gaussian Parameters Correlate with the Spread of COVID-19 Pandemic: The Italian Case

Applied Sciences ◽

10.3390/app11136119 ◽

2021 ◽

Vol 11 (13) ◽

pp. 6119

Author(s):

Carmelo Corsaro ◽

Alessandro Sturniolo ◽

Enza Fazio

Keyword(s):

Growth Rate ◽

Logistic Model ◽

Large Scale ◽

Herd Immunity ◽

Control Measures ◽

Cumulative Distribution ◽

Time Interval ◽

Gaussian Functions ◽

Italian Case ◽

Conversion Rates

Until today, numerous models have been formulated to predict the spreading of Covid-19. Among them, the actively discussed susceptible-infected-removed (SIR) model is one of the most reliable. Unfortunately, many factors (i.e., social behaviors) can influence the outcomes as well as the occurrence of multiple contributions corresponding to multiple waves. Therefore, for a reliable evaluation of the conversion rates, data need to be continuously updated and analyzed. In this work, we propose a model using Gaussian functions, coming from the solution of an ordinary differential equation representing a logistic model, able to describe the growth rate of infected, deceased and recovered people in Italy. We correlate the Gaussian parameters with the number of people affected by COVID-19 as a function of the large-scale anti-contagion control measures strength, and also of vaccines effects adopted to reach herd immunity. The superposition of gaussian curves allow modeling the growth rate of the total cases, deceased and recovered people and reproducing the corresponding cumulative distribution and probability density functions. Moreover, we try to predict a time interval in which all people will be infected or vaccinated (with at least one dose) and/or the time end of pandemic in Italy when all people have been infected or vaccinated with two doses.

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A prognostic dynamic model applicable to infectious diseases providing easily visualized guides: a case study of COVID-19 in the UK

Scientific Reports ◽

10.1038/s41598-021-87882-9 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Yuxuan Zhang ◽

Chen Gong ◽

Dawei Li ◽

Zhi-Wei Wang ◽

Shengda D. Pu ◽

...

Keyword(s):

Infectious Diseases ◽

Disease Control ◽

Control Strategies ◽

Herd Immunity ◽

Iterative Algorithms ◽

Vaccination Strategy ◽

Control Measures ◽

Transmission Model ◽

Government Interventions ◽

The Uk

AbstractA reasonable prediction of infectious diseases’ transmission process under different disease control strategies is an important reference point for policy makers. Here we established a dynamic transmission model via Python and realized comprehensive regulation of disease control measures. We classified government interventions into three categories and introduced three parameters as descriptions for the key points in disease control, these being intraregional growth rate, interregional communication rate, and detection rate of infectors. Our simulation predicts the infection by COVID-19 in the UK would be out of control in 73 days without any interventions; at the same time, herd immunity acquisition will begin from the epicentre. After we introduced government interventions, a single intervention is effective in disease control but at huge expense, while combined interventions would be more efficient, among which, enhancing detection number is crucial in the control strategy for COVID-19. In addition, we calculated requirements for the most effective vaccination strategy based on infection numbers in a real situation. Our model was programmed with iterative algorithms, and visualized via cellular automata; it can be applied to similar epidemics in other regions if the basic parameters are inputted, and is able to synthetically mimic the effect of multiple factors in infectious disease control.

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