scholarly journals Importance of suppression and mitigation measures in managing COVID-19 outbreaks

2020 ◽  
Author(s):  
Michael E. Hochberg
Keyword(s):  
Control Strategy ◽  
Reproduction Number ◽  
Herd Immunity ◽  
Mitigation Measures ◽  
Reproductive Number ◽  
Short Term ◽  
Community Size ◽  
Epidemic Curve ◽  
Total Community ◽  
Step Control

AbstractI employ a simple mathematical model of an epidemic process to evaluate how four basic quantities: the reproduction number , the numbers of sensitive (S) and infectious individuals (I), and total community size (N) affect strategies to control COVID-19. Numerical simulations show that strict suppression measures at the beginning of an epidemic can create low infectious numbers, which thereafter can be managed by mitigation measures over longer periods to flatten the epidemic curve. The stronger the suppression measure, the faster it achieves the low numbers of infections that are conducive to subsequent management. Our results on short-term strategies point to either a two-step control strategy, following failed mitigation, that begins with suppression of the reproduction number, , below 1.0, followed by renewed mitigation measures that manage the epidemic by maintaining at approximately 1.0, or should suppression not be feasible, the progressive lowering of the effective reproductive number, , below 1.0. The objectives of the full sequence of measures observed in a number of countries, and likely to see in the longer term, can be symbolically represented as: . We discuss the predictions of this analysis and how it fits into longer-term sequences of measures, including misconceptions about ‘flattening the curve’ and how the herd immunity concept can be used to ‘leverage’ immunity.

scholarly journals Countries should aim to lower the reproduction number ℛ close to 1.0 for the short-term mitigation of COVID-19 outbreaks

2020 ◽  
Author(s):  
Michael E. Hochberg
Keyword(s):  
Mathematical Model ◽  
Clinical Outcomes ◽  
Time Scales ◽  
Reproduction Number ◽  
Mitigation Measures ◽  
Reproductive Number ◽  
Short Term ◽  
Early Stages ◽  
Reproduction Numbers ◽  
Short Time

AbstractThe COVID-19 pandemic is still in its early stages and given the speed and magnitude of local outbreaks it is urgent to understand how mitigation measures translate into changes in key epidemiological and clinical outcomes. Here, we employ a mathematical model to explore the short-term consequences of lowering the reproduction number ℛ0 and delaying measures on total infections and fatalities. The positive implications of mitigation generally accrue as these measures are adopted early, with the most striking effects seen when the reproductive number is lowered to a level ℛC≈1.0. As the delay in adopting measures exceeds approximately the half-way point to the peak of an outbreak, the effects of lowering ℛ0 markedly decrease. Aiming for reproduction numbers close to 1.0 can substantially reduce fatality probabilities over short time scales, particularly for larger populations. We conclude that research is urgently needed on how mitigation measures impact ℛ0 and how these can be optimized so as to achieve ℛC≈1.0 whilst supporting individual freedoms, society and the economy.

scholarly journals Evaluating Apple Inc Mobility Trend Data Related to the COVID-19 Outbreak in Japan: Statistical Analysis

10.2196/20335 ◽  
2021 ◽  
Vol 7 (2) ◽  
pp. e20335
Author(s):  
Junko Kurita ◽  
Yoshiyuki Sugishita ◽  
Tamie Sugawara ◽  
Yasushi Ohkusa
Keyword(s):  
Statistical Analysis ◽  
Local Governments ◽  
Critical Level ◽  
Polynomial Function ◽  
Reproduction Number ◽  
Herd Immunity ◽  
Short Term ◽  
Mobility Data ◽  
Trend Data ◽  
Short Term Prediction

Background In Japan, as a countermeasure against the COVID-19 outbreak, both the national and local governments issued voluntary restrictions against going out from residences at the end of March 2020 in preference to the lockdowns instituted in European and North American countries. The effect of such measures can be studied with mobility data, such as data which is generated by counting the number of requests made to Apple Maps for directions in select countries/regions, sub-regions, and cities. Objective We investigate the associations of mobility data provided by Apple Inc and an estimate an an effective reproduction number R(t). Methods We regressed R(t) on a polynomial function of daily Apple data, estimated using the whole period, and analyzed subperiods delimited by March 10, 2020. Results In the estimation results, R(t) was 1.72 when voluntary restrictions against going out ceased and mobility reverted to a normal level. However, the critical level of reducing R(t) to <1 was obtained at 89.3% of normal mobility. Conclusions We demonstrated that Apple mobility data are useful for short-term prediction of R(t). The results indicate that the number of trips should decrease by 10% until herd immunity is achieved and that higher voluntary restrictions against going out might not be necessary for avoiding a re-emergence of the outbreak.

scholarly journals COVID-19 immunization threshold(s): an analysis

2021 ◽  
Author(s):  
Luis Alfredo Bautista Balbás ◽  
Mario Gil Conesa ◽  
Blanca Bautista Balbás ◽  
Ainhoa Alcaide Jiménez ◽  
Gil Rodríguez Caravaca
Keyword(s):  
Reproduction Number ◽  
Herd Immunity ◽  
Significant Proportion ◽  
Health Programs ◽  
Vaccine Research ◽  
Short Term ◽  
Complex Issue ◽  
Public Health Programs ◽  
Similar Equation ◽  
Pharmaceutical Interventions

2AbstractAs COVID-19 vaccine research efforts seem to be yielding the first tangible results, the proportion of individuals needed to reap the benefits of herd immunity is a key element from a Public Health programs perspective.This magnitude, termed the critical immunization threshold (q), can be obtained from the classical SIR model equilibrium equation, equaling (1 − 1/R0)/ ϵ, where R0 is the basic reproduction number and ϵ is the vaccine efficacy. When a significant proportion of the population is already immune, this becomes (n − 1/R0)/ ϵ, where n is the proportion of non-immune individuals. A similar equation can be obtained for short-term immunization thresholds(qt), which are dependent on Rt.qs for most countries are between 60-75% of the population. Current qt for most countries are between 20-40%.Therefore, the combination of gradual vaccination and other non-pharmaceutical interventions will mark the transition to the herd immunity, providing that the later turns out to be a feasible objective. Nevertheless, immunization through vaccination is a complex issue and many challenges might appear.

scholarly journals Dynamics of the COVID-19 epidemic in Ireland under mitigation

2021 ◽  
Author(s):  
Bernard Cazelles ◽  
Benjamin Nguyen Van Yen ◽  
Clara Champagne ◽  
Catherine Comiskey
Keyword(s):  
Herd Immunity ◽  
Virus Transmission ◽  
Reporting Rate ◽  
Mitigation Measures ◽  
Seroprevalence Rate ◽  
The European Union ◽  
Epidemic Curve ◽  
Lack Of Information ◽  
Time Varying Parameters ◽  
Brownian Process

Abstract Background: In Ireland and across the European Union, cases of COVID-19 continue to rise with recent increases in reported cases following a period of stability. Public health interventions continue in their attempts to control the epidemic in spite of a lack of information on the scale of silent transmission. Methods: To tackle this challenge and the non-stationary aspect of the epidemic we used a modified SEIR stochastic model with time-varying parameters, following Brownian process. This model is coupled with Bayesian inference (PMCMC) for parameter estimation and used mainly confirmed reported hospitalized cases. Results: Mitigation measures provided an 80% reduction in transmission between March and May 2020. By end of October our estimated seroprevalence rate was 1.1% (95% CI: 0.5%–2.8%) far from herd immunity. We estimated that the proportion of asymptomatic transmission was approximately 40% but with large uncertainty (95% CI: 14%–73%). Finally we demonstrate that the available observed confirmed cases are not reliable for any analysis owing to the fact that their reporting rate has greatly evolved. Conclusion: We provide the first estimations of the dynamics of the COVID-19 epidemic in Ireland and its key parameters. We also quantify the effects of mitigation measures on the virus transmission before, during and after mitigation. Our results demonstrate that Ireland has significantly reduced transmission by employing mitigation measures, physical distancing and lockdown. This has to date avoided the saturation of healthcare infrastructures, flattened the epidemic curve and likely reduced mortality. However, as mitigation measures change silent transmission remain an ongoing challenge.

scholarly journals The effectiveness of Non-pharmaceutical interventions in reducing the COVID-19 contagion in the UK, an observational and modelling study

PLoS ONE ◽  
2021 ◽  
Vol 16 (11) ◽  
pp. e0260364
Author(s):  
Giorgos Galanis ◽  
Corrado Di Guilmi ◽  
David L. Bennett ◽  
Georgios Baskozos
Keyword(s):  
Reproduction Number ◽  
Herd Immunity ◽  
Government Policies ◽  
Epidemiological Models ◽  
Seir Model ◽  
Epidemic Curve ◽  
Behavioural Insights ◽  
Herd Immunity Effect ◽  
The Uk ◽  
Pharmaceutical Interventions

Epidemiological models used to inform government policies aimed to reduce the contagion of COVID-19, assume that the reproduction number is reduced through Non-Pharmaceutical Interventions (NPIs) leading to physical distancing. Available data in the UK show an increase in physical distancing before the NPIs were implemented and a fall soon after implementation. We aimed to estimate the effect of people’s behaviour on the epidemic curve and the effect of NPIs taking into account this behavioural component. We have estimated the effects of confirmed daily cases on physical distancing and we used this insight to design a behavioural SEIR model (BeSEIR), simulated different scenaria regarding NPIs and compared the results to the standard SEIR. Taking into account behavioural insights improves the description of the contagion dynamics of the epidemic significantly. The BeSEIR predictions regarding the number of infections without NPIs were several orders of magnitude less than the SEIR. However, the BeSEIR prediction showed that early measures would still have an important influence in the reduction of infections. The BeSEIR model shows that even with no intervention the percentage of the cumulative infections within a year will not be enough for the epidemic to resolve due to a herd immunity effect. On the other hand, a standard SEIR model significantly overestimates the effectiveness of measures. Without taking into account the behavioural component, the epidemic is predicted to be resolved much sooner than when taking it into account and the effectiveness of measures are significantly overestimated.

scholarly journals Dynamics of the COVID-19 epidemic in Ireland under mitigation

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Bernard Cazelles ◽  
Benjamin Nguyen-Van-Yen ◽  
Clara Champagne ◽  
Catherine Comiskey
Keyword(s):  
Transmission Rate ◽  
Vaccination Campaign ◽  
Virus Transmission ◽  
Reporting Rate ◽  
Mitigation Measures ◽  
Third Wave ◽  
The European Union ◽  
Hospital Data ◽  
Prevalence Survey ◽  
Epidemic Curve

Abstract Background In Ireland and across the European Union the COVID-19 epidemic waves, driven mainly by the emergence of new variants of the SARS-CoV-2 have continued their course, despite various interventions from governments. Public health interventions continue in their attempts to control the spread as they wait for the planned significant effect of vaccination. Methods To tackle this challenge and the observed non-stationary aspect of the epidemic we used a modified SEIR stochastic model with time-varying parameters, following Brownian process. This enabled us to reconstruct the temporal evolution of the transmission rate of COVID-19 with the non-specific hypothesis that it follows a basic stochastic process constrained by the available data. This model is coupled with Bayesian inference (particle Markov Chain Monte Carlo method) for parameter estimation and utilized mainly well-documented Irish hospital data. Results In Ireland, mitigation measures provided a 78–86% reduction in transmission during the first wave between March and May 2020. For the second wave in October 2020, our reduction estimation was around 20% while it was 70% for the third wave in January 2021. This third wave was partly due to the UK variant appearing in Ireland. In June 2020 we estimated that sero-prevalence was 2.0% (95% CI: 1.2–3.5%) in complete accordance with a sero-prevalence survey. By the end of April 2021, the sero-prevalence was greater than 17% due in part to the vaccination campaign. Finally we demonstrate that the available observed confirmed cases are not reliable for analysis owing to the fact that their reporting rate has as expected greatly evolved. Conclusion We provide the first estimations of the dynamics of the COVID-19 epidemic in Ireland and its key parameters. We also quantify the effects of mitigation measures on the virus transmission during and after mitigation for the three waves. Our results demonstrate that Ireland has significantly reduced transmission by employing mitigation measures, physical distancing and lockdown. This has to date avoided the saturation of healthcare infrastructures, flattened the epidemic curve and likely reduced mortality. However, as we await for a full roll out of a vaccination programme and as new variants potentially more transmissible and/or more infectious could continue to emerge and mitigation measures change silent transmission, challenges remain.

scholarly journals Huanglongbing Model under the Control Strategy of Discontinuous Removal of Infected Trees

Symmetry ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 1164
Author(s):  
Weiwei Ling ◽  
Pinxia Wu ◽  
Xiumei Li ◽  
Liangjin Xie
Keyword(s):  
Infectious Disease ◽  
Global Stability ◽  
Control Strategy ◽  
Theoretical Basis ◽  
Dynamic Properties ◽  
Transmission Mechanism ◽  
Basic Reproductive Number ◽  
Reproductive Number ◽  
Vector Borne ◽  
Citrus Trees

By using differential equations with discontinuous right-hand sides, a dynamic model for vector-borne infectious disease under the discontinuous removal of infected trees was established after understanding the transmission mechanism of Huanglongbing (HLB) disease in citrus trees. Through calculation, the basic reproductive number of the model can be attained and the properties of the model are discussed. On this basis, the existence and global stability of the calculated equilibria are verified. Moreover, it was found that different I0 in the control strategy cannot change the dynamic properties of HLB disease. However, the lower the value of I0, the fewer HLB-infected citrus trees, which provides a theoretical basis for controlling HLB disease and reducing expenditure.

scholarly journals The Role of Risk Preferences in Responses to Messaging About COVID-19 Vaccine Take-Up

2021 ◽  
pp. 194855062199962
Author(s):  
Jennifer S. Trueblood ◽  
Abigail B. Sussman ◽  
Daniel O’Leary
Keyword(s):  
Large Scale ◽  
Risk Preferences ◽  
Herd Immunity ◽  
Disease Spread ◽  
Short Term ◽  
Policy Makers ◽  
Health Crisis ◽  
Flu Vaccine ◽  
Nationally Representative

Development of an effective COVID-19 vaccine is widely considered as one of the best paths to ending the current health crisis. While the ability to distribute a vaccine in the short-term remains uncertain, the availability of a vaccine alone will not be sufficient to stop disease spread. Instead, policy makers will need to overcome the additional hurdle of rapid widespread adoption. In a large-scale nationally representative survey ( N = 34,200), the current work identifies monetary risk preferences as a correlate of take-up of an anticipated COVID-19 vaccine. A complementary experiment ( N = 1,003) leverages this insight to create effective messaging encouraging vaccine take-up. Individual differences in risk preferences moderate responses to messaging that provides benchmarks for vaccine efficacy (by comparing it to the flu vaccine), while messaging that describes pro-social benefits of vaccination (specifically herd immunity) speeds vaccine take-up irrespective of risk preferences. Findings suggest that policy makers should consider risk preferences when targeting vaccine-related communications.

scholarly journals Herd immunity drives the epidemic fadeout of avian cholera in Arctic-nesting seabirds

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jacintha G. B. van Dijk ◽  
Samuel A. Iverson ◽  
H. Grant Gilchrist ◽  
N. Jane Harms ◽  
Holly L. Hennin ◽  
...  
Keyword(s):  
Colony Size ◽  
Pasteurella Multocida ◽  
Herd Immunity ◽  
Reproductive Number ◽  
Somateria Mollissima ◽  
Common Eiders ◽  
Serological Status ◽  
Antibody Titers ◽  
Avian Cholera

AbstractAvian cholera, caused by the bacterium Pasteurella multocida, is a common and important infectious disease of wild birds in North America. Between 2005 and 2012, avian cholera caused annual mortality of widely varying magnitudes in Northern common eiders (Somateria mollissima borealis) breeding at the largest colony in the Canadian Arctic, Mitivik Island, Nunavut. Although herd immunity, in which a large proportion of the population acquires immunity to the disease, has been suggested to play a role in epidemic fadeout, immunological studies exploring this hypothesis have been missing. We investigated the role of three potential drivers of fadeout of avian cholera in eiders, including immunity, prevalence of infection, and colony size. Each potential driver was examined in relation to the annual real-time reproductive number (Rt) of P. multocida, previously calculated for eiders at Mitivik Island. Each year, colony size was estimated and eiders were closely monitored, and evaluated for infection and serological status. We demonstrate that acquired immunity approximated using antibody titers to P. multocida in both sexes was likely a key driver for the epidemic fadeout. This study exemplifies the importance of herd immunity in influencing the dynamics and fadeout of epidemics in a wildlife population.

scholarly journals Development of the reproduction number from coronavirus SARS-CoV-2 case data in Germany and implications for political measures

BMC Medicine ◽  
2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Sahamoddin Khailaie ◽  
Tanmay Mitra ◽  
Arnab Bandyopadhyay ◽  
Marta Schips ◽  
Pietro Mascheroni ◽  
...  
Keyword(s):  
Structural Changes ◽  
Reproduction Number ◽  
National Level ◽  
Transmission Dynamics ◽  
Federal State ◽  
Parameter Estimates ◽  
Time Varying ◽  
Short Term ◽  
Robert Koch Institute ◽  
Federal States

Abstract Background SARS-CoV-2 has induced a worldwide pandemic and subsequent non-pharmaceutical interventions (NPIs) to control the spread of the virus. As in many countries, the SARS-CoV-2 pandemic in Germany has led to a consecutive roll-out of different NPIs. As these NPIs have (largely unknown) adverse effects, targeting them precisely and monitoring their effectiveness are essential. We developed a compartmental infection dynamics model with specific features of SARS-CoV-2 that allows daily estimation of a time-varying reproduction number and published this information openly since the beginning of April 2020. Here, we present the transmission dynamics in Germany over time to understand the effect of NPIs and allow adaptive forecasts of the epidemic progression. Methods We used a data-driven estimation of the evolution of the reproduction number for viral spreading in Germany as well as in all its federal states using our model. Using parameter estimates from literature and, alternatively, with parameters derived from a fit to the initial phase of COVID-19 spread in different regions of Italy, the model was optimized to fit data from the Robert Koch Institute. Results The time-varying reproduction number (Rt) in Germany decreased to <1 in early April 2020, 2–3 weeks after the implementation of NPIs. Partial release of NPIs both nationally and on federal state level correlated with moderate increases in Rt until August 2020. Implications of state-specific Rt on other states and on national level are characterized. Retrospective evaluation of the model shows excellent agreement with the data and usage of inpatient facilities well within the healthcare limit. While short-term predictions may work for a few weeks, long-term projections are complicated by unpredictable structural changes. Conclusions The estimated fraction of immunized population by August 2020 warns of a renewed outbreak upon release of measures. A low detection rate prolongs the delay reaching a low case incidence number upon release, showing the importance of an effective testing-quarantine strategy. We show that real-time monitoring of transmission dynamics is important to evaluate the extent of the outbreak, short-term projections for the burden on the healthcare system, and their response to policy changes.

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