Modelling the influence of naturally acquired immunity from subclinical infection on outbreak dynamics and persistence of rabies in domestic dogs

Gold, Susannah; Donnelly, Christl A.; Woodroffe, Rosie; Nouvellet, Pierre

doi:10.1371/journal.pntd.0009581

“…Such a young cell lining may be more resistant to a new virus, but this effect is expected to be short lived and wane fast as some older and effete cells accumulate. It is also possible that exposure to a pathogen at sub-infectious level contributes to some immunity (Gold et al 2021, Santos Rocha 2018, not comparable in magnitude to that achieved by active infection or by vaccines (Krammer 2021). Since immune response is costly, the body has evolved not to give a full strength immune response to every attack.…”

Section: Introductionmentioning

confidence: 99%

Epidemiology: Gray immunity model gives qualitatively different predictions

Watve,

Bhisikar,

Kharate

et al. 2024

J Biosci

0

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Compartmental models like the SIR model that dynamically divide the host population in categories such as susceptible, infected and immune form the mainstream of epidemiological modelling. Effectively such models treat infection and immunity as binary variables. We show here that considering immunity as a continuous variable instead of binary and incorporating factors that bring about small changes in immunity lead to qualitatively different epidemiological predictions. The small immunity effects (SIE) constitute cross immunity by other infections, small increments in immunity by sub clinical exposures and slow decay in the absence of repeated exposure. The SIE model explains many epidemiological patterns observed during the Covid-19 pandemic that are not adequately explained by conventional models. In the SIE model repeated waves are possible without the need for new variants. Peak and decline of a wave much before reaching herd immunity threshold, low level apparently stable existence of the pathogen, new surges after variable and unpredictable gaps, new surge after vaccinating majority of population are the common features of the pandemic mimicked by simulations using the SIE model. The model further shows complex interactions of different interventions that can be contextually synergistic as well as antagonistic. As a result, interventions intended to arrest the transmission are not always effective and can turn counterproductive under some conditions. Interventions that are beneficial in the short run can be potentially hazardous in the long run. In the absence of empirical estimates of many parameters, the model may not be useful to make quantitative predictions at this stage but it certainly challenges traditional wisdom and currently held beliefs behind non-pharmaceutical interventions recommended to control the epidemic. We also suggest testable predictions to differentiate between the causal logic of the SIE model against the prevalent explanations for the same observed phenomena.

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“…Such a young cell lining may be more resistant to a new virus, but this effect is expected to be short lived and wane fast as older and effete cells accumulate. It is also possible that exposure to a pathogen at sub-infectious level contributes to some immunity (Gold et al 2021, Santos Rocha 2018, not comparable in magnitude to that achieved by active infection or by vaccines (Krammer 2021). Since immune response is costly, the body has evolved not to give a full strength immune response to every attack.…”

Section: Introductionmentioning

confidence: 99%

Epidemiology: Gray Immunity Model Gives Qualitatively Different Predictions

Watve¹,

Bhisikar²,

Kharate³

et al. 2022

Preprint

0

View full text Add to dashboard Cite

Compartmental models that dynamically divide the host population in categories such as susceptible, infected and immune constitute the mainstream of epidemiological modelling. Effectively such models treat infection and immunity as binary variables. We constructed an individual based stochastic model that considers immunity as a continuous variable and incorporates factors that bring about small changes in immunity. The small immunity effects (SIE) comprise cross immunity by other infections, small increments in immunity by sub clinical exposures and slow decay in the absence of repeated exposure. The model makes qualitatively different epidemiological predictions including repeated waves without the need for new variants, dwarf peaks (peak and decline of a wave much before reaching herd immunity threshold), symmetry in the upward and downward slopes of a wave, endemic state, new surges after variable and unpredictable gaps, new surge after vaccinating majority of population. In effect the SIE model raises alternative possible causes of the universally observed dwarf and symmetric peaks and repeated surges, observed particularly well during the Covid-19 pandemic. We also suggest testable predictions to differentiate between the alternative causes for repeated waves. The model further shows complex interactions of different interventions that can be synergistic as well as antagonistic. The model suggests that interventions that are beneficial in the short run can also be hazardous in the long run.

show abstract

“…Such a young cell lining may be more resistant to a new virus, but this effect is expected to be short lived and wane fast as some older and effete cells accumulate. It is also possible that exposure to a pathogen at sub-infectious level contributes to some immunity (Gold et al 2021, Santos Rocha 2018, not comparable in magnitude to that achieved by active infection or by vaccines (Krammer 2021). Since immune response is costly, the body has evolved not to give a full strength immune response to every attack.…”

Section: Introductionmentioning

confidence: 99%

Epidemiology: Gray Immunity Model Gives Qualitatively Different Predictions

Watve¹,

Bhisikar²,

Kharate³

2021

Preprint

View full text Add to dashboard Cite

Compartmental models like the SIR model that dynamically divide the host population in categories such as susceptible, infected and immune form the mainstream of epidemiological modelling. Effectively such models treat infection and immunity as binary variables. We show here that considering immunity as a continuous variable instead of binary and incorporating factors that bring about small changes in immunity lead to qualitatively different epidemiological predictions. The small immunity effects (SIE) constitute cross immunity by other infections, small increments in immunity by sub clinical exposures and slow decay in the absence of repeated exposure. The SIE model explains many epidemiological patterns observed during the Covid-19 pandemic that are not adequately explained by conventional models. In the SIE model repeated waves are possible without the need for new variants. Peak and decline of a wave much before reaching herd immunity threshold, low level apparently stable existence of the pathogen, new surges after variable and unpredictable gaps, new surge after vaccinating majority of population are the common features of the pandemic mimicked by simulations using the SIE model. The model further shows complex interactions of different interventions that can be contextually synergistic as well as antagonistic. As a result, interventions intended to arrest the transmission are not always effective and can turn counterproductive under some conditions. Interventions that are beneficial in the short run can be potentially hazardous in the long run. In the absence of empirical estimates of many parameters, the model may not be useful to make quantitative predictions at this stage but it certainly challenges traditional wisdom and currently held beliefs behind non-pharmaceutical interventions recommended to control the epidemic. We also suggest testable predictions to differentiate between the causal logic of the SIE model against the prevalent explanations for the same observed phenomena.

show abstract

Modelling the influence of naturally acquired immunity from subclinical infection on outbreak dynamics and persistence of rabies in domestic dogs

Cited by 4 publications

References 48 publications

Epidemiology: Gray immunity model gives qualitatively different predictions

Epidemiology: Gray immunity model gives qualitatively different predictions

Epidemiology: Gray Immunity Model Gives Qualitatively Different Predictions

Epidemiology: Gray Immunity Model Gives Qualitatively Different Predictions

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