Effect of hybrid immunity, school reopening, and the Omicron variant on the trajectory of the COVID-19 epidemic in India: a modelling study

Mozaffer, Farhina; Cherian, Philip; Krishna, Sandeep; Wahl, Brian; Menon, Gautam I.

doi:10.1016/j.lansea.2022.100095

Cited by 9 publications

(10 citation statements)

References 30 publications

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“…Given the strong dependence of the IFR on age, we would expect mortality arising from school reopenings to impact the population far more substantially in France in comparison to India [71]. A different modelling study using a compartmental model with India-specific assumptions yields results very similar to those described here [55].…”

Section: Effects Of School Reopening On the Disease Trajectorysupporting

confidence: 63%

BharatSim: An agent-based modelling framework for India

Cherian,

Kshirsagar,

Neekhra

et al. 2023

Preprint

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BharatSim is an open-source agent-based-modelling framework for the Indian population. It can simulate populations at multiple scales, from small communities to states. BharatSim uses a synthetic population created by applying statistical methods and machine learning algorithms to survey data from multiple sources, including the Census of India, the India Human Development Survey, the National Sample Survey, and the Gridded Population of the World. This synthetic population defines individual agents with multiple attributes, among them age, gender, home and work locations, pre-existing health conditions, and socio-economic and employment status. BharatSim's domain-specific language provides a framework for the simulation of diverse models. Its computational core, coded in Scala, supports simulations of a large number of individual agents, up to 50 million. Here, we describe the design and implementation of BharatSim, using it to address three questions motivated by the COVID-19 pandemic in India: (i) When can schools be safely reopened given specified levels of hybrid immunity?, (ii) How do new variants alter disease dynamics in the background of prior infections and vaccinations? and (iii) How can the effects of varied non-pharmaceutical interventions (NPIs) be quantified for a model Indian city? Through its India-specific synthetic population, BharatSim allows disease modellers to address questions unique to this country. It should also find use in the computational social sciences, potentially providing new insights into emergent patterns in social behaviour.

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Section: Effects Of School Reopening On the Disease Trajectorysupporting

confidence: 63%

BharatSim: An agent-based modelling framework for India

Cherian,

Kshirsagar,

Neekhra

et al. 2023

Preprint

Self Cite

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“…Many of the earlier studies did not include flexible hybrid immunity arrangements and/or did not include waning immunity or multiple reinfections (e.g. [44, 45, 43]), which here we show is key to the occurrence, size and timing of the second wave. While we did not include heterologous vaccination schedules, such as was modelled in [67], our modelling framework has the ability to support these scenarios.…”

Section: Discussionmentioning

confidence: 77%

“…Modelling has been used to support decision making around the world in regards to managing COVID-19 [38, 39, 40]. It has been used extensively to compare different vaccination strategies [41, 42, 43], but many either do not include waning of immunity [44], or do not take a hybrid-immunity approach [45]. However, the inclusion of both of these factors is key in understanding the combined population-level effect of vaccination and prior exposure upon future transmission dynamics [34, 46].…”

Section: Introductionmentioning

confidence: 99%

“…Prior to the rollout of vaccination, the WPR region had low seroprevalence compared to other regions such as Europe [48]. As such, infection-derived immunity within this region is now largely a result of the recent Omicron wave, with relatively few infections caused by previous variant strains, unlike the populations with high prior Delta exposure pre-Omicron [34, 44]. Hence, our focus will be on analysing scenarios where a population’s past infection-derived immunity is due to Omicron.…”

Section: Introductionmentioning

confidence: 99%

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Modelling the impact of hybrid immunity on future COVID-19 epidemic waves

Abell

Conway

et al. 2023

Preprint

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Since the emergence of SARS-CoV-2 (COVID-19), there have been multiple waves of infection and multiple rounds of vaccination rollouts. Both prior infection and vaccination can prevent future infection and reduce severity of outcomes, combining to form hybrid immunity against COVID-19 at the individual and population level. Here, we explore how different combinations of hybrid immunity affect the size and severity of near-future Omicron waves. To investigate the role of hybrid immunity, we use an agent-based model of COVID-19 transmission with waning immunity to simulate outbreaks in populations with varied past attack rates and past vaccine coverages, basing the demographics and past histories on the World Health Organization (WHO) Western Pacific Region (WPR). We find that if the past infection immunity is high but vaccination levels are low, then the secondary outbreak with the same variant can occur within a few months after the first outbreak; meanwhile, high vaccination levels can suppress near-term outbreaks and delay the second wave. Additionally, hybrid immunity has limited impact on future COVID-19 waves with immune-escape variants. Enhanced understanding of the interplay between infection and vaccine exposure can aid anticipation of future epidemic activity due to current and emergent variants, including the likely impact of responsive vaccine interventions.

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“…As summarized by Bates and colleagues, “the additional antigen exposure from natural infection substantially boosts the quantity, quality, and breadth of humoral immune response regardless of whether it occurs before or after vaccination” [ 67 ]. Newer models are starting to include this effect, for example by assuming that a prior infection counts as a single vaccination, hence the ‘actual’ vaccination has the effect of a booster [ 68 ]. A recent retrospective cohort study in Sweden [ 69 ] confirmed several of the numbers in the OpenCOVID model and sheds light on the effect of the synergistic effect of hybrid immunity.…”

Section: Immunity: Variants Waning Effect and Hybrid Casesmentioning

confidence: 99%

A Scoping Review of Three Dimensions for Long-Term COVID-19 Vaccination Models: Hybrid Immunity, Individual Drivers of Vaccinal Choice, and Human Errors

2022

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The virus that causes COVID-19 changes over time, occasionally leading to Variants of Interest (VOIs) and Variants of Concern (VOCs) that can behave differently with respect to detection kits, treatments, or vaccines. For instance, two vaccination doses were 61% effective against the BA.1 predominant variant, but only 24% effective when BA.2 became predominant. While doses still confer protection against severe disease outcomes, the BA.5 variant demonstrates the possibility that individuals who have received a few doses built for previous variants can still be infected with newer variants. As previous vaccines become less effective, new ones will be released to target specific variants and the whole process of vaccinating the population will restart. While previous models have detailed logistical aspects and disease progression, there are three additional key elements to model COVID-19 vaccination coverage in the long term. First, the willingness of the population to participate in regular vaccination campaigns is essential for long-term effective COVID-19 vaccination coverage. Previous research has shown that several categories of variables drive vaccination status: sociodemographic, health-related, psychological, and information-related constructs. However, the inclusion of these categories in future models raises questions about the identification of specific factors (e.g., which sociodemographic aspects?) and their operationalization (e.g., how to initialize agents with a plausible combination of factors?). While previous models separately accounted for natural- and vaccine-induced immunity, the reality is that a significant fraction of individuals will be both vaccinated and infected over the coming years. Modeling the decay in immunity with respect to new VOCs will thus need to account for hybrid immunity. Finally, models rarely assume that individuals make mistakes, even though this over-reliance on perfectly rational individuals can miss essential dynamics. Using the U.S. as a guiding example, our scoping review summarizes these aspects (vaccinal choice, immunity, and errors) through ten recommendations to support the modeling community in developing long-term COVID-19 vaccination models.

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Effect of hybrid immunity, school reopening, and the Omicron variant on the trajectory of the COVID-19 epidemic in India: a modelling study

Cited by 9 publications

References 30 publications

BharatSim: An agent-based modelling framework for India

BharatSim: An agent-based modelling framework for India

Modelling the impact of hybrid immunity on future COVID-19 epidemic waves

A Scoping Review of Three Dimensions for Long-Term COVID-19 Vaccination Models: Hybrid Immunity, Individual Drivers of Vaccinal Choice, and Human Errors

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