scholarly journals Discontinuous epidemic transition due to limited testing

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Davide Scarselli ◽  
Nazmi Burak Budanur ◽  
Marc Timme ◽  
Björn Hof
Keyword(s):  
Exponential Growth ◽  
Growth Period ◽  
High Impact ◽  
Contact Tracing ◽  
Epidemic Dynamics ◽  
Slowing Down ◽  
Containment Measures ◽  
Second Wave ◽  
Limited Testing ◽  
Pharmaceutical Interventions

AbstractHigh impact epidemics constitute one of the largest threats humanity is facing in the 21st century. In the absence of pharmaceutical interventions, physical distancing together with testing, contact tracing and quarantining are crucial in slowing down epidemic dynamics. Yet, here we show that if testing capacities are limited, containment may fail dramatically because such combined countermeasures drastically change the rules of the epidemic transition: Instead of continuous, the response to countermeasures becomes discontinuous. Rather than following the conventional exponential growth, the outbreak that is initially strongly suppressed eventually accelerates and scales faster than exponential during an explosive growth period. As a consequence, containment measures either suffice to stop the outbreak at low total case numbers or fail catastrophically if marginally too weak, thus implying large uncertainties in reliably estimating overall epidemic dynamics, both during initial phases and during second wave scenarios.

scholarly journals Understanding the Spreading Patterns of COVID-19 in UK and Its Impact on Exit Strategies

2020 ◽  
Author(s):  
Maziar Nekovee
Keyword(s):  
Exponential Growth ◽  
Slow Growth ◽  
Contact Tracing ◽  
Mixing Models ◽  
Epidemic Spreading ◽  
Seir Model ◽  
Infected Population ◽  
Exit Strategies ◽  
Spatial Epidemic ◽  
Second Wave

Prior to lockdown the spread of COVID-19 in UK is found to be exponential, with an exponent α=0.207 In case of COVID-19 this spreading patterns is quantitatively better described with mobility-driven SIR-SEIR model [2] rather than the homogenous mixing models Lockdown has dramatically slowed down the spread of COVID-19 in UK, and even more significantly has changed the growth in the total number of infected from exponential to quadratic. This significant change is due a transition from a mobility-driven epidemic spreading to a spatial epidemic which is dominated by slow growth of spatially isolated clusters of infected population. Our results strongly indicated that, to avoid a return to exponential growth of COVID-19 (also known as “second wave”) mobility restrictions should not be prematurely lifted. Instead mobility should be kept restricted while new measures, such as wearing mask and contact tracing, get implemented in order to allow a safe exit from lockdown.

scholarly journals COVID-19 in Slovenia, from a Success Story to Disaster: What Lessons Can Be Learned?

Life ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 1045
Author(s):  
Nina Ružić Gorenjec ◽  
Nataša Kejžar ◽  
Damjan Manevski ◽  
Maja Pohar Perme ◽  
Bor Vratanar ◽  
...  
Keyword(s):  
Bayesian Model ◽  
Contact Tracing ◽  
Success Story ◽  
Mobility Data ◽  
Entire Duration ◽  
Proposed Model ◽  
Second Wave ◽  
Late Stages ◽  
Similar Stage ◽  
Pharmaceutical Interventions

During the first wave of the COVID-19 pandemic in spring 2020, Slovenia was among the least affected countries, but the situation became drastically worse during the second wave in autumn 2020 with high numbers of deaths per number of inhabitants, ranking Slovenia among the most affected countries. This was true even though strict non-pharmaceutical interventions (NPIs) to control the progression of the epidemic were being enforced. Using a semi-parametric Bayesian model developed for the purpose of this study, we explore if and how the changes in mobility, their timing and the activation of contact tracing can explain the differences in the epidemic progression of the two waves. To fit the model, we use data on daily numbers of deaths, patients in hospitals, intensive care units, etc., and allow transmission intensity to be affected by contact tracing and mobility (data obtained from Google Mobility Reports). Our results imply that though there is some heterogeneity not explained by mobility levels and contact tracing, implementing interventions at a similar stage as in the first wave would keep the death toll and the health system burden low in the second wave as well. On the other hand, sticking to the same timeline of interventions as observed in the second wave and focusing on enforcing a higher decrease in mobility would not be as beneficial. According to our model, the ‘dance’ strategy, i.e., first allowing the numbers to rise and then implementing strict interventions to make them drop again, has been played at too-late stages of the epidemic. In contrast, a 15–20% reduction of mobility compared to pre-COVID level, if started at the beginning and maintained for the entire duration of the second wave and coupled with contact tracing, could suffice to control the epidemic. A very important factor in this result is the presence of contact tracing; without it, the reduction in mobility needs to be substantially larger. The flexibility of our proposed model allows similar analyses to be conducted for other regions even with slightly different data sources for the progression of the epidemic; the extension to more than two waves is straightforward. The model could help policymakers worldwide to make better decisions in terms of the timing and severity of the adopted NPIs.

scholarly journals Understanding the spreading patterns of COVID-19 in UK and its impact on exit strategies

2020 ◽  
Author(s):  
Maziar Nekovee
Keyword(s):  
Health Services ◽  
Exponential Growth ◽  
Slow Growth ◽  
Contact Tracing ◽  
Mixing Models ◽  
Epidemic Spreading ◽  
Seir Model ◽  
Infected Population ◽  
Spatial Epidemic ◽  
Second Wave

Prior to lockdown the spread of COVID-19 in UK is found to be exponential, with an exponent 0.207. In case of COVID-19 this spreading behaviour is quantitatively better described with a mobility-driven SIR-SEIR model [2] rather than the homogenous mixing models. Lockdown has dramatically slowed down the spread of COVID-19 in UK, and even more significantly, has changed the growth in the total number of infected from exponential to quadratic. This significant change is due to a transition from a mobility-driven epidemic spreading to a spatial epidemic which is dominated by slow growth of spatially isolated clusters of infected population. Our results strongly indicate that, to avoid a return to exponential growth of COVID-19 (also known as second wave), mobility restrictions should not be prematurely lifted. Instead mobility should be kept restricted while new measures, such as wearing of masks and contact tracing, get implemented in order to prevent health services becoming overwhelmed due to a resurgence of exponential growth.

scholarly journals A single-agent extension of the SIR model describes the impact of mobility restrictions on the COVID-19 epidemic

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Matteo Paoluzzi ◽  
Nicoletta Gnan ◽  
Francesca Grassi ◽  
Marco Salvetti ◽  
Nicola Vanacore ◽  
...  
Keyword(s):  
Exponential Growth ◽  
Single Agent ◽  
Sir Model ◽  
Epidemic Spreading ◽  
Peak Reduction ◽  
Slowing Down ◽  
Individual Mobility ◽  
Simple Rules ◽  
The Impact ◽  
Second Wave

AbstractMobility restrictions are successfully used to contain the diffusion of epidemics. In this work we explore their effect on the epidemic growth by investigating an extension of the Susceptible-Infected-Removed (SIR) model in which individual mobility is taken into account. In the model individual agents move on a chessboard with a Lévy walk and, within each square, epidemic spreading follows the standard SIR model. These simple rules allow to reproduce the sub-exponential growth of the epidemic evolution observed during the Covid-19 epidemic waves in several countries and which cannot be captured by the standard SIR model. We show that we can tune the slowing-down of the epidemic spreading by changing the dynamics of the agents from Lévy to Brownian and we investigate how the interplay among different containment strategies mitigate the epidemic spreading. Finally we demonstrate that we can reproduce the epidemic evolution of the first and second COVID-19 waves in Italy using only 3 parameters, i.e , the infection rate, the removing rate, and the mobility in the country. We provide an estimate of the peak reduction due to imposed mobility restrictions, i. e., the so-called flattening the curve effect. Although based on few ingredients, the model captures the kinetic of the epidemic waves, returning mobility values that are consistent with a lock-down intervention during the first wave and milder limitations, associated to a weaker peak reduction, during the second wave.

scholarly journals Covasim: An agent-based model of COVID-19 dynamics and interventions

2021 ◽  
Vol 17 (7) ◽  
pp. e1009149
Author(s):  
Cliff C. Kerr ◽  
Robyn M. Stuart ◽  
Dina Mistry ◽  
Romesh G. Abeysuriya ◽  
Katherine Rosenfeld ◽  
...  
Keyword(s):  
Source Model ◽  
Ease Of Use ◽  
Asia Pacific ◽  
Contact Tracing ◽  
Local Health ◽  
Term Care ◽  
Loss To Follow Up ◽  
Agent Based ◽  
Epidemic Dynamics ◽  
Pharmaceutical Interventions

The COVID-19 pandemic has created an urgent need for models that can project epidemic trends, explore intervention scenarios, and estimate resource needs. Here we describe the methodology of Covasim (COVID-19 Agent-based Simulator), an open-source model developed to help address these questions. Covasim includes country-specific demographic information on age structure and population size; realistic transmission networks in different social layers, including households, schools, workplaces, long-term care facilities, and communities; age-specific disease outcomes; and intrahost viral dynamics, including viral-load-based transmissibility. Covasim also supports an extensive set of interventions, including non-pharmaceutical interventions, such as physical distancing and protective equipment; pharmaceutical interventions, including vaccination; and testing interventions, such as symptomatic and asymptomatic testing, isolation, contact tracing, and quarantine. These interventions can incorporate the effects of delays, loss-to-follow-up, micro-targeting, and other factors. Implemented in pure Python, Covasim has been designed with equal emphasis on performance, ease of use, and flexibility: realistic and highly customized scenarios can be run on a standard laptop in under a minute. In collaboration with local health agencies and policymakers, Covasim has already been applied to examine epidemic dynamics and inform policy decisions in more than a dozen countries in Africa, Asia-Pacific, Europe, and North America.

scholarly journals Using contact data to model the impact of contact tracing and physical distancing to control the SARS-CoV-2 outbreak in Kenya

2020 ◽  
Vol 5 ◽  
pp. 212
Author(s):  
Moritz Wagner ◽  
Ivy K. Kombe ◽  
Moses Chapa Kiti ◽  
Rabia Aziza ◽  
Edwine Barasa ◽  
...  
Keyword(s):  
Process Model ◽  
Contact Tracing ◽  
Short Delay ◽  
Household Contacts ◽  
Age Related ◽  
Containment Measures ◽  
Contact Data ◽  
General Physical ◽  
The Impact ◽  
Pharmaceutical Interventions

Background: Across the African continent, other than South Africa, COVID-19 cases have remained relatively low. Nevertheless, in Kenya, despite early implementation of containment measures and restrictions, cases have consistently been increasing. Contact tracing forms one of the key strategies in Kenya, but may become infeasible as the caseload grows. Here we explore different contact tracing strategies by distinguishing between household and non-household contacts and how these may be combined with other non-pharmaceutical interventions. Methods: We extend a previously developed branching process model for contact tracing to include realistic contact data from Kenya. Using the contact data, we generate a synthetic population of individuals and their contacts categorised by age and household membership. We simulate the initial spread of SARS-CoV-2 through this population and look at the effectiveness of a number of non-pharmaceutical interventions with a particular focus on different contact tracing strategies and the potential effort involved in these. Results: General physical distancing and avoiding large group gatherings combined with contact tracing, where all contacts are isolated immediately, can be effective in slowing down the outbreak, but were, under our base assumptions, not enough to control it without implementing extreme stay at home policies. Under optimistic assumptions with a highly overdispersed R0 and a short delay from symptom onset to isolation, control was possible with less stringent physical distancing and by isolating household contacts only. Conclusions: Without strong physical distancing measures, controlling the spread of SARS-CoV-2 is difficult. With limited resources, physical distancing combined with the isolation of households of detected cases can form a moderately effective strategy, and control is possible under optimistic assumptions. More data are needed to understand transmission in Kenya, in particular by studying the settings that lead to larger transmission events, which may allow for more targeted responses, and collection of representative age-related contact data.

scholarly journals Vaccination and SARS-CoV-2 variants: how much containment is still needed? A quantitative assessment.

2021 ◽  
Author(s):  
Giulia Giordano ◽  
Marta Colaneri ◽  
Alessandro Di Filippo ◽  
Franco Blanchini ◽  
Paolo Bolzern ◽  
...  
Keyword(s):  
Healthcare System ◽  
Quantitative Assessment ◽  
Vaccination Campaign ◽  
Contact Tracing ◽  
Transmission Rates ◽  
Italian Case ◽  
Containment Measures ◽  
Vaccination Campaigns ◽  
Pharmaceutical Interventions

Abstract Despite the progress in medical care, combined population-wide interventions (such as physical distancing, testing and contact tracing) are still crucial to manage the SARS-CoV-2 pandemic, aggravated by the emergence of new highly transmissible variants. We combine the compartmental SIDARTHE model, predicting the course of COVID-19 infections, with a new data-based model that projects new cases onto casualties and healthcare system costs. Based on the Italian case study, we outline several scenarios: mass vaccination campaigns with different paces, different transmission rates due to new variants, and different enforced countermeasures, including the alternation of opening and closure phases. Our results demonstrate that non-pharmaceutical interventions (NPIs) have a higher impact on the epidemic evolution than vaccination, which advocates for the need to keep containment measures in place throughout the vaccination campaign. We also show that, if intermittent open-close strategies are adopted, deaths and healthcare system costs can be drastically reduced, without any aggravation of socioeconomic losses, as long as one has the foresight to start with a closing phase rather than an opening one.

scholarly journals A Hybrid Modeling Technique of Epidemic Outbreaks with Application to COVID-19 Dynamics in West Africa

Biology ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 365
Author(s):  
Chénangnon Frédéric Tovissodé ◽  
Jonas Têlé Doumatè ◽  
Romain Glèlè Kakaï
Keyword(s):  
West Africa ◽  
Growth Curve ◽  
Exponential Growth ◽  
Latency Period ◽  
Growth Curves ◽  
Exponential Growth Phase ◽  
Model Framework ◽  
Logistic Regression Models ◽  
Containment Measures ◽  
Reporting Data

The widely used logistic model for epidemic case reporting data may be either restrictive or unrealistic in presence of containment measures when implemented after an epidemic outbreak. For flexibility in epidemic case reporting data modeling, we combined an exponential growth curve for the early epidemic phase with a flexible growth curve to account for the potential change in growth pattern after implementation of containment measures. We also fitted logistic regression models to recoveries and deaths from the confirmed positive cases. In addition, the growth curves were integrated into a SIQR (Susceptible, Infective, Quarantined, Recovered) model framework to provide an overview on the modeled epidemic wave. We focused on the estimation of: (1) the delay between the appearance of the first infectious case in the population and the outbreak (“epidemic latency period”); (2) the duration of the exponential growth phase; (3) the basic and the time-varying reproduction numbers; and (4) the peaks (time and size) in confirmed positive cases, active cases and new infections. The application of this approach to COVID-19 data from West Africa allowed discussion on the effectiveness of some containment measures implemented across the region.

scholarly journals Assessing Interventions against Coronavirus Disease 2019 (COVID-19) in Osaka, Japan: A Modeling Study

2021 ◽  
Vol 10 (6) ◽  
pp. 1256
Author(s):  
Ko Nakajo ◽  
Hiroshi Nishiura
Keyword(s):  
Model Comparison ◽  
Reproduction Number ◽  
State Of Emergency ◽  
Illness Onset ◽  
Epidemic Dynamics ◽  
Osaka Prefecture ◽  
Time Of Infection ◽  
Single Intervention ◽  
Second Wave ◽  
Combined Interventions

Estimation of the effective reproduction number, R(t), of coronavirus disease (COVID-19) in real-time is a continuing challenge. R(t) reflects the epidemic dynamics based on readily available illness onset data, and is useful for the planning and implementation of public health and social measures. In the present study, we proposed a method for computing the R(t) of COVID-19, and applied this method to the epidemic in Osaka prefecture from February to September 2020. We estimated R(t) as a function of the time of infection using the date of illness onset. The epidemic in Osaka came under control around 2 April during the first wave, and 26 July during the second wave. R(t) did not decline drastically following any single intervention. However, when multiple interventions were combined, the relative reductions in R(t) during the first and second waves were 70% and 51%, respectively. Although the second wave was brought under control without declaring a state of emergency, our model comparison indicated that relying on a single intervention would not be sufficient to reduce R(t) < 1. The outcome of the COVID-19 pandemic continues to rely on political leadership to swiftly design and implement combined interventions capable of broadly and appropriately reducing contacts.

scholarly journals Uncovering two phases of early intercontinental COVID-19 transmission dynamics

2020 ◽  
Vol 27 (8) ◽  
Author(s):  
Jing Yang ◽  
Juan Li ◽  
Shengjie Lai ◽  
Corrine W Ruktanonchai ◽  
Weijia Xing ◽  
...  
Keyword(s):  
Phylogenetic Analyses ◽  
International Travel ◽  
Contact Tracing ◽  
Genetic Sequencing ◽  
Response Strategies ◽  
The World ◽  
Containment Measures ◽  
The Usa ◽  
Community Transmission ◽  
Two Phases

Abstract Background The COVID-19 pandemic has posed an ongoing global crisis, but how the virus spread across the world remains poorly understood. This is of vital importance for informing current and future pandemic response strategies. Methods We performed two independent analyses, travel network-based epidemiological modelling and Bayesian phylogeographic inference, to investigate the intercontinental spread of COVID-19. Results Both approaches revealed two distinct phases of COVID-19 spread by the end of March 2020. In the first phase, COVID-19 largely circulated in China during mid-to-late January 2020 and was interrupted by containment measures in China. In the second and predominant phase extending from late February to mid-March, unrestricted movements between countries outside of China facilitated intercontinental spread, with Europe as a major source. Phylogenetic analyses also revealed that the dominant strains circulating in the USA were introduced from Europe. However, stringent restrictions on international travel across the world since late March have substantially reduced intercontinental transmission. Conclusions Our analyses highlight that heterogeneities in international travel have shaped the spatiotemporal characteristics of the pandemic. Unrestricted travel caused a large number of COVID-19 exportations from Europe to other continents between late February and mid-March, which facilitated the COVID-19 pandemic. Targeted restrictions on international travel from countries with widespread community transmission, together with improved capacity in testing, genetic sequencing and contact tracing, can inform timely strategies for mitigating and containing ongoing and future waves of COVID-19 pandemic.

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