Rates of SARS-CoV-2 transmission and vaccination impact the fate of vaccine-resistant strains

AbstractVaccines are thought to be the best available solution for controlling the ongoing SARS-CoV-2 pandemic. However, the emergence of vaccine-resistant strains may come too rapidly for current vaccine developments to alleviate the health, economic and social consequences of the pandemic. To quantify and characterize the risk of such a scenario, we created a SIR-derived model with initial stochastic dynamics of the vaccine-resistant strain to study the probability of its emergence and establishment. Using parameters realistically resembling SARS-CoV-2 transmission, we model a wave-like pattern of the pandemic and consider the impact of the rate of vaccination and the strength of non-pharmaceutical intervention measures on the probability of emergence of a resistant strain. As expected, we found that a fast rate of vaccination decreases the probability of emergence of a resistant strain. Counterintuitively, when a relaxation of non-pharmaceutical interventions happened at a time when most individuals of the population have already been vaccinated the probability of emergence of a resistant strain was greatly increased. Consequently, we show that a period of transmission reduction close to the end of the vaccination campaign can substantially reduce the probability of resistant strain establishment. Our results suggest that policymakers and individuals should consider maintaining non-pharmaceutical interventions and transmission-reducing behaviours throughout the entire vaccination period.

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SARS-CoV-2 transmission, vaccination rate and the fate of resistant strains

10.1101/2021.02.08.21251383 ◽

2021 ◽

Cited By ~ 1

Author(s):

Simon A. Rella ◽

Yuliya A. Kulikova ◽

Emmanouil T. Dermitzakis ◽

Fyodor A. Kondrashov

Keyword(s):

Resistant Strain ◽

Stochastic Dynamics ◽

Vaccination Campaign ◽

Vaccination Rate ◽

Social Consequences ◽

Pharmaceutical Intervention ◽

Intervention Measures ◽

Transmission Reduction ◽

Resistant Strains ◽

The Impact

Vaccines are thought to be the best available solution for controlling the ongoing SARS-CoV-2 pandemic1,2. However, the emergence of vaccine-resistant strains3–6 may come too rapidly for current vaccine developments to alleviate the health, economic and social consequences of the pandemic7,8. To quantify and characterize the risk of such a scenario, we created a SIR-derived model9,10 with initial stochastic dynamics of the vaccine-resistant strain to study the probability of its emergence and establishment. Using parameters realistically resembling SARS-CoV-2 transmission, we model a wave-like pattern of the pandemic and consider the impact of the rate of vaccination and the strength of non-pharmaceutical intervention measures on the probability of emergence of a resistant strain. We found a counterintuitive result that the highest probability for the establishment of the resistant strain comes at a time of reduced non-pharmaceutical intervention measures when most individuals of the population have been vaccinated. Consequently, we show that a period of transmission reduction close to the end of the vaccination campaign can substantially reduce the probability of resistant strain establishment. Our results suggest that policymakers and individuals should consider maintaining non-pharmaceutical interventions7,11,12 throughout the entire vaccination period.

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Simulating the impact of non-pharmaceutical interventions limiting transmission in COVID-19 epidemics using a membrane computing model

microLife ◽

10.1093/femsml/uqab011 ◽

2021 ◽

Author(s):

M Campos ◽

J M Sempere ◽

J C Galán ◽

A Moya ◽

C Llorens ◽

...

Keyword(s):

Membrane Computing ◽

Immunological Response ◽

The Elderly ◽

Work Activities ◽

Intervention Measures ◽

Single Intervention ◽

The Impact ◽

Local Parameters ◽

Pharmaceutical Interventions ◽

Computing Models

ABSTRACT Epidemics caused by microbial organisms are part of the natural phenomena of increasing biological complexity. The heterogeneity and constant variability of hosts, in terms of age, immunological status, family structure, lifestyle, work activities, social and leisure habits, daily division of time, and other demographic characteristics make it extremely difficult to predict the evolution of epidemics. Such prediction is, however, critical for implementing intervention measures in due time and with appropriate intensity. General conclusions should be precluded, given that local parameters dominate the flow of local epidemics. Membrane computing models allows us to reproduce the objects (viruses, hosts) and their interactions (stochastic but also with defined probabilities) with an unprecedented level of detail. Our LOIMOS model helps reproduce the demographics and social aspects of a hypothetical town of 10,320 inhabitants in an average European country where COVID-19 is imported from the outside. The above-mentioned characteristics of hosts and their lifestyle are minutely considered. For the data in the Hospital and the ICU we took advantage of the observations at the Nursery Intensive Care Unit of the Consortium University General Hospital, Valencia, Spain (included as author). The dynamics of the epidemics are reproduced and include the effects on viral transmission of innate and acquired immunity at various ages. The model predicts the consequences of delaying the adoption of non-pharmaceutical interventions (between 15 and 45 days after the first reported cases) and the effect of those interventions on infection and mortality rates (reducing transmission by 20%, 50%, and 80%) in immunological response groups. The lockdown for the elderly population as a single intervention appears to be effective. This modelling exercise exemplifies the application of membrane computing for designing appropriate multilateral interventions in epidemic situations.

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Simulating the impact of non-pharmaceutical interventions limiting transmission in COVID-19 epidemics using a membrane computing model

10.1101/2021.07.01.21259828 ◽

2021 ◽

Author(s):

Fernando Baquero ◽

Marcelino Campos ◽

Jose-Maria Sempere ◽

Juan-Carlos Galan ◽

Andres Moya ◽

...

Keyword(s):

Membrane Computing ◽

Immunological Response ◽

The Elderly ◽

Work Activities ◽

Intervention Measures ◽

Single Intervention ◽

The Impact ◽

Local Parameters ◽

Pharmaceutical Interventions ◽

Computing Models

Epidemics caused by microbial organisms are part of the natural phenomena of increasing biological complexity. The heterogeneity and constant variability of hosts, in terms of age, immunological status, family structure, lifestyle, work activities, social and leisure habits, daily division of time, and other demographic characteristics make it extremely difficult to predict the evolution of epidemics. Such prediction is, however, critical for implementing intervention measures in due time and with appropriate intensity. General conclusions should be precluded, given that local parameters dominate the flow of local epidemics. Membrane computing models allows us to reproduce the objects (viruses, hosts) and their interactions (stochastic but also with defined probabilities) with an unprecedented level of detail. Our LOIMOS model helps reproduce the demographics and social aspects of a hypothetical town of 10,320 inhabitants in an average European country where COVID-19 is imported from the outside. The above-mentioned characteristics of hosts and their lifestyle are minutely considered. The dynamics of the epidemics are reproduced and include the effects on viral transmission of innate and acquired immunity at various ages. The model predicts the consequences of delaying the adoption of non-pharmaceutical interventions (between 15 and 45 days after the first reported cases) and the effect of those interventions on infection and mortality rates (reducing transmission by 20%, 50%, and 80%) in immunological response groups. The lockdown for the elderly population as a single intervention appears to be effective. This modelling exercise exemplifies the application of membrane computing for designing appropriate interventions in epidemic situations.

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PERCOVID: A Model to Describe COVID Percolation on a Network of Social Relationships

10.1101/2021.08.31.21262909 ◽

2021 ◽

Author(s):

Jean-Francois Mathiot ◽

Laurent Gerbaud ◽

Vincent J Breton

Keyword(s):

Social Interactions ◽

Social Relationships ◽

Vaccination Campaign ◽

Epidemiological Data ◽

Human Populations ◽

The Social ◽

A Site ◽

The Impact ◽

Pharmaceutical Interventions ◽

Typical Scenario

We develop a site-bond percolation model, called PERCOVID, in order to describe the time evolution of COVID epidemics and more generally all epidemics propagating through respiratory tract in human populations. This model is based on a network of social relationships representing interconnected households experiencing governmental non-pharmaceutical interventions. The model successfully accounts for the COVID-19 epidemiological data in metropolitan France from December 2019 up to July 2021. Our model shows the impact of lockdowns and curfews, as well as the influence of the progressive vaccination campaign in order to keep COVID-19 pandemic under the percolation threshold. We illustrate the role played by the social interactions by comparing a typical scenario for the epidemic evolution in France, Germany and Italy during the first wave from January to May 2020. We investigate finally the role played by the alpha and delta variants in the evolution of the epidemic in France till autumn 2021, paying particular attention to the essential role played by the vaccination. Our model predicts that the rise of the epidemic observed in July 2021 will not result in a fourth major epidemic wave in France.

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Impact of vaccination and non-pharmaceutical interventions on SARS-CoV-2 dynamics in Switzerland

10.1101/2021.04.14.21255503 ◽

2021 ◽

Author(s):

Andrew J. Shattock ◽

Epke A. Le Rutte ◽

Robert P Duenner ◽

Swapnoleena Sen ◽

Sherrie L Kelly ◽

...

Keyword(s):

Sensitivity Analysis ◽

Vaccination Coverage ◽

Vaccination Campaign ◽

Population Level ◽

Morbidity And Mortality ◽

Transmission Model ◽

Third Wave ◽

The Impact ◽

Pharmaceutical Interventions

As vaccination coverage against SARS-CoV-2 increases amidst the emergence and spread of more infectious and potentially more deadly viral variants, decisions on timing and extent of relaxing effective, but unsustainable, non-pharmaceutical interventions (NPIs) need to be made. An individual-based transmission model of SARS-CoV-2 dynamics, OpenCOVID, was developed to compare the impact of various vaccination and NPI strategies on the COVID-19 epidemic in Switzerland. We estimate that any relaxation of NPIs in March 2021 will lead to increasing cases, hospitalisations, and deaths resulting in a "third wave" in spring and into summer 2021. However, we find a cautious phased relaxation can substantially reduce population-level morbidity and mortality. We find that faster vaccination campaign can offset the size of such a wave, allowing more flexibility for NPI to be relaxed sooner. Our sensitivity analysis revealed that model results are particularly sensitive to the infectiousness of variant B.1.1.7.

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Highlighting the impact of social relationships on the propagation of respiratory viruses using percolation theory

Scientific Reports ◽

10.1038/s41598-021-03812-9 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Jean-François Mathiot ◽

Laurent Gerbaud ◽

Vincent Breton

Keyword(s):

Social Interactions ◽

Social Relationships ◽

Vaccination Campaign ◽

Respiratory Viruses ◽

Percolation Model ◽

Human Populations ◽

A Site ◽

The Impact ◽

Epidemic Wave ◽

Pharmaceutical Interventions

AbstractWe develop a site-bond percolation model, called PERCOVID, in order to describe the time evolution of all epidemics propagating through respiratory tract or by skin contacts in human populations. This model is based on a network of social relationships representing interconnected households experiencing governmental non-pharmaceutical interventions. As a very first testing ground, we apply our model to the understanding of the dynamics of the COVID-19 pandemic in France from December 2019 up to December 2021. Our model shows the impact of lockdowns and curfews, as well as the influence of the progressive vaccination campaign in order to keep COVID-19 pandemic under the percolation threshold. We illustrate the role played by social interactions by comparing two typical scenarios with low or high strengths of social relationships as compared to France during the first wave in March 2020. We investigate finally the role played by the α and δ variants in the evolution of the epidemic in France till autumn 2021, paying particular attention to the essential role played by the vaccination. Our model predicts that the rise of the epidemic observed in July and August 2021 would not result in a new major epidemic wave in France.

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Rapid Impact Analysis of B 1.1.7 Variant on the Spread of SARS-CoV-2 in North Carolina

10.1101/2021.02.07.21251291 ◽

2021 ◽

Author(s):

Michael DeWitt

Keyword(s):

North Carolina ◽

Impact Analysis ◽

Reproduction Number ◽

Population Level ◽

Vaccination Rate ◽

Vaccination Rates ◽

Current Vaccine ◽

Spread Of Infection ◽

The Impact ◽

Pharmaceutical Interventions

AbstractBackgroundSeveral cases of the B1.1.7 variant of the SARS-CoV-2 virus were identified in North Carolina first on January 23, 2021 in Mecklenburg County and later in Guilford County on January 28, 2021.[1,2] This variant has been associated with higher levels of transmissibility.[3–6] This study examines the potential impact of increased transmissibility as the B1.1.7 variant becomes more predominant given current vaccine distribution plans and existing non-pharmaceutical interventions (NPIs).MethodWe explored the anticipated impact on the effective reproduction number for North Carolina and Guilford County given the date of import of B1.1.7. The approximate growth rate in proportion of B1.1.7 observed in the United Kingdom was fit and used to establish the estimate share of B1.1.7 circulating in North Carolina. Using the nowcasted reproduction numbers, a stochastic discrete compartmental model was fit with the current vaccination rates and B1.1.7 transmissibility to estimate the impact on the effective reproduction number.ResultsWe found that the effective reproduction number for North Carolina and Guilford County may exceed one, indicating a return to accelerating spread of infection in April as the proportion of B1.1.7 increases. The effective reproduction number will likely decrease into March, then increase as the proportion of B1.1.7 increases in circulation in the population.ConclusionsExisting non-pharmaceutical interventions will need to remain in effect through the spring. Given the current vaccination rate and these interventions, it is likely that there will be an increase in SARS-CoV-2 infections. The impact of the variant will likely be heterogeneous across North Carolina given the reproduction number and volume of susceptible persons in each county at the time of introduction of the variant. Age-based vaccinations will likely reduce the overall impact on hospitalizations. This analysis underlines the need for population level genetic surveillance to confirm the proportion of variants circulating.

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COVID-19 prediction in South Africa: Understanding the unascertained cases -- the hidden part of the epidemiological iceberg

10.1101/2020.12.10.20247361 ◽

2020 ◽

Author(s):

Xuelin Gu ◽

Bhramar Mukherjee ◽

Sonali Das ◽

Jyotishka Datta

Keyword(s):

South Africa ◽

Reproduction Number ◽

Case Fatality ◽

Cumulative Number ◽

Epidemiological Models ◽

Intervention Measures ◽

Long Term Forecasting ◽

The Impact ◽

Pharmaceutical Interventions

Background: Understanding the impact of non-pharmaceutical interventions remains a critical epidemiological problem in South Africa that reported the largest number of confirmed COVID-19 cases and deaths from the African continent. Methods: In this study, we applied two existing epidemiological models, an extension of the Susceptible-Infected-Removed model (eSIR) and SAPHIRE, to fit the daily ascertained infected (and removed) cases from March 15 to July 31 in South Africa. To combine the desirable features from the two models, we further extended the eSIR model to an eSEIRD model. Results: Using the eSEIRD model, the COVID-19 transmission dynamics in South Africa was characterized by the estimated basic reproduction number (R0) at 2.10 (95%CI: [2.09,2.10]). The decrease of effective reproduction number with time implied the effectiveness of interventions. The low estimated ascertained rate was found to be 2.17% (95%CI: [2.15%, 2.19%]) in the eSEIRD model. The overall infection fatality ratio (IFR) was estimated as 0.04% (95%CI: [0.02%, 0.06%]) while the reported case fatality ratio was 4.40% (95% CI: [<0.01%, 11.81%]). As of December 31, 2020, the cumulative number of ascertained cases and total infected would reach roughly 801 thousand and 36.9 million according to the long-term forecasting. Conclusions: The dynamics based on our models suggested a decline of COVID-19 infection and that the severity of the epidemic might be largely mitigated through strict interventions. Besides providing insights on the COVID-19 dynamics in South Africa, we develop powerful forecasting tools that allow incorporating ascertained rate and IFR estimation and inquiring into the effect of intervention measures on COVID-19 spread.

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What pushed Israel out of herd immunity? Modeling COVID-19 spread of Delta and Waning immunity

10.1101/2021.09.12.21263451 ◽

2021 ◽

Author(s):

Hilla De-Leon ◽

Dvir Aran

Keyword(s):

Dynamic Model ◽

Vaccination Campaign ◽

Herd Immunity ◽

Aggregate Data ◽

Spatial Dynamic ◽

Waning Immunity ◽

The Impact ◽

Pharmaceutical Interventions

Following a successful vaccination campaign at the beginning of 2021 in Israel, where approximately 60% of the population were vaccinated with an mRNA BNT162b2 vaccine, it seemed that Israel had crossed the herd immunity threshold (HIT). Nonetheless, Israel has seen a steady rise in COVID-19 morbidity since June 2021, reaching over 1,000 cases per million by August. This outbreak is attributed to several events that came together: the temporal decline of the vaccine's efficacy (VE); lower efficacy of the vaccine against the current Delta (B.1.617.2) variant; highly infectiousness of Delta; and temporary halt of mandated NPIs (non-pharmaceutical interventions) or any combination of the above. Using a novel spatial-dynamic model and recent aggregate data from Israel, we examine the extent of the impact of the Delta variant on morbidity and whether it can solely explain the outbreak. We conclude that both Delta infectiousness and waning immunity could have been able to push Israel above the HIT independently, and thus, to mitigate the outbreak effective NPIs are required. Our analysis cautions countries that once vaccines' will wane a highly infectious spread is expected, and therefore, the expected decline in the vaccine's effectiveness in those countries should be accompanied by another vaccination campaign and effective NPIs

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Assessing the impact of non-pharmaceutical interventions on SARS-CoV-2 transmission in Switzerland

10.1101/2020.05.04.20090639 ◽

2020 ◽

Cited By ~ 1

Author(s):

Joseph. C. Lemaitre ◽

Javier Perez-Saez ◽

Andrew S. Azman ◽

Andrea Rinaldo ◽

Jacques Fellay

Keyword(s):

Disease Transmission ◽

Large Scale ◽

Transmission Model ◽

Natural Immunity ◽

Close Monitoring ◽

Mobility Patterns ◽

Individual Level ◽

Transmission Reduction ◽

The Impact ◽

Pharmaceutical Interventions

AbstractFollowing the rapid dissemination of COVID-19 cases in Switzerland, large-scale non-pharmaceutical interventions (NPIs) were implemented by the cantons and the federal government between February 28 and March 20. Estimates of the impact of these interventions on SARS-CoV-2 transmission are critical for decision making in this and future outbreaks. We here aim to assess the impact of these NPIs on disease transmission by estimating changes in the basic reproduction number (R0) at national and cantonal levels in relation to the timing of these NPIs. We estimate the time-varying R0 nationally and in twelve cantons by fitting a stochastic transmission model explicitly simulating within hospital dynamics. We use individual-level data of >1,000 hospitalized patients in Switzerland and public daily reports of hospitalizations and deaths. We estimate the national R0 was 3.15 (95% CI: 2.13-3.76) at the start of the epidemic. Starting from around March 6, we find a strong reduction in R0 with a 85% median decrease (95% quantile range, QR: 83%-90%) to a value of 0.44 (95% QR: 0.27-0.65) in the period of March 29-April 5. At the cantonal-level R0 decreased over the course of the epidemic between 71% and 94%. We found that reductions in R0 were synchronous with changes in mobility patterns as estimated through smartphone activity, which started before the official implementation of NPIs. We found that most of the reduction of transmission is due to behavioural changes as opposed to natural immunity, the latter accounting for only about 3% of the total reduction in effective transmission. As Switzerland considers relaxing some of the restrictions of social mixing, current estimates of R0 well below one are promising. However most of inferred transmission reduction was due to behaviour change (<3% due to natural immunity buildup), with an estimated 97% (95% QR: 96.6%-97.2%) of the Swiss population still susceptible to SARS-CoV-2 as of April 24. These results warrant a cautious relaxation of social distance practices and close monitoring of changes in both the basic and effective reproduction numbers.

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