Development of the India COVID-19 vaccine tracker.

COVID-19 was declared a pandemic by the World Health Organization (WHO) on March 11, 2020. Since then, efforts were initiated to develop safe and effective vaccines. Till date, 11 vaccines have been included in the WHO's emergency use list. The emergence and spread of variant strains of SARS-CoV-2 has altered the disease transmission dynamics, thus creating a need for continuously monitoring the real-world effectiveness of various vaccines and assessing their overall impact on disease control. To achieve this goal, the Indian Council of Medical Research (ICMR) along with the Ministry of Health and Family Welfare, Government of India, took the lead to develop the India COVID-19 Vaccination Tracker by synergizing three different public health databases: National COVID-19 testing database, CoWIN vaccination database and the COVID-19 India portal. A Vaccine Data Analytics Committee (VDAC) was constituted to advise on various modalities of the proposed tracker. The VDAC reviewed the data related to COVID-19 testing, vaccination and patient outcomes available in the three databases and selected relevant data points for inclusion in the tracker, following which databases were integrated, using common identifiers, wherever feasible. Multiple data filters were applied to retrieve information of all individuals ≥18 yr who died after the acquisition of COVID-19 infection with or without vaccination, irrespective of the time between vaccination and test positivity. Vaccine effectiveness (VE) against the reduction of mortality and hospitalizations was initially assessed. As compared to the hospitalization data, mortality reporting was found to be much better in terms of correctness and completeness. Therefore, hospitalization data were not considered for analysis and presentation in the vaccine tracker. The vaccine tracker thus depicts VE against mortality, calculated by a cohort approach using person-time analysis. Incidence of COVID-19 deaths among one- and two-dose vaccine recipients was compared with that among unvaccinated groups, to estimate the rate ratios (RRs). VE was estimated as 96.6 and 97.5 per cent, with one and two doses of the vaccines, respectively, during the period of reporting. The India COVID-19 Vaccination Tracker was officially launched on September 9, 2021. The high VE against mortality, as demonstrated by the tracker, has helped aid in allaying vaccine hesitancy, augmenting and maintaining the momentum of India's COVID-19 vaccination drive.

Timescales modulate optimal lysis-lysogeny decision switches and near-term phage reproduction.

Temperate phage can initiate lysis or lysogeny after infecting a bacterial host. The genetic switch between lysis and lysogeny is mediated by phage regulatory genes as well as host and environmental factors. Recently, a new class of decision switches was identified in phage of the SPbeta group, mediated by the extracellular release of small, phage-encoded peptides termed arbitrium. Arbitrium peptides can be taken up by bacteria prior to infection, modulating the decision switch in the event of a subsequent phage infection. Increasing the concentration of arbitrium increases the chance that a phage infection will lead to lysogeny, rather than lysis. Although prior work has centered on the molecular mechanisms of arbitrium-induced switching, here we focus on how selective pressures impact the benefits of plasticity in switching responses. In this work, we examine the possible advantages of near-term adaptation of communication-based decision switches used by the SPbeta-like group. We combine a nonlinear population model with a control-theoretic approach to evaluate the relationship between a putative phage reaction norm (i.e. the probability of lysogeny as a function of arbitrium) and the extent of phage reproduction at a near-term time horizon. We measure phage reproduction in terms of a cellular-level metric previously shown to enable comparisons of near-term phage fitness across a continuum from lysis to latency. We show the adaptive potential of communication-based lysis-lysogeny responses and find that optimal switching between lysis and lysogeny increases the near-term phage reproduction compared to fixed responses, further supporting both molecular- and model-based analyses of the putative benefits of this class of decision switches. We further find that plastic responses are robust to the inclusion of cellular-level stochasticity, variation in life history traits, and variation in resource availability. These findings provide further support to explore the long-term evolution of plastic decision systems mediated by extracellular decision-signaling molecules and the feedback between phage reaction norms and ecological context.

Vaccine stockpile sharing for selfish objectives.

The COVAX program aims to provide global equitable access to life-saving vaccines. Despite calls for increased sharing, vaccine protectionism has limited progress towards vaccine sharing goals. For example, as of April 2022 only ~20% of the population in Africa had received at least one COVID-19 vaccine dose. Here we use a two-nation coupled epidemic model to evaluate optimal vaccine-sharing policies given a selfish objective: in which countries with vaccine stockpiles aim to minimize fatalities in their own population. Computational analysis of a suite of simulated epidemics reveal that it is often optimal for a donor country to share a significant fraction of its vaccine stockpile with a recipient country that has no vaccine stockpile. Sharing a vaccine stockpile reduces the intensity of outbreaks in the recipient, in turn reducing travel-associated incidence in the donor. This effect is intensified as vaccination rates in a donor country decrease and epidemic coupling between countries increases. Critically, vaccine sharing by a donor significantly reduces transmission and fatalities in the recipient. Moreover, the same computational framework reveals the potential use of hybrid sharing policies that have a negligible effect on fatalities in the donor compared to the optimal policy while significantly reducing fatalities in the recipient. Altogether, these findings provide a self-interested rationale for countries to consider sharing part of their vaccine stockpiles.

Geo-prioritization framework for COVID-19 vaccine allocation in India.

Up until now, countries have adopted a 'isolate-test-treat-trace' strategy to contain the COVID-19 pandemic. The next critical intervention in the fight against COVID-19 will be effective delivery of safe and efficacious vaccines. Various countries such as the USA, the UK, Canada, Israel, etc., have started administering vaccines to priority population groups. India is gearing up its critical components of the vaccine delivery system to effectively deliver vaccines across the country and has prioritized certain population groups to whom the vaccine will be administered. Considering India's ambitious target to vaccinate close to 300 million people in the first phase of the vaccination drive with limited initial supply (which will be ramped up gradually), it is critical for stakeholders at all the levels - national, state and district - to understand the estimated need for vaccines across geographies based on the vulnerable population and disease epidemiology with the objective of preventing maximum number of future infections from the disease. This paper aims to describe a comprehensive geo-prioritization framework based on existing prevalence of COVID-19, high-risk co-morbidities, and demographic analysis to identify states/districts that could be most in need of the COVID-19 vaccines.

Disease-dependent interaction policies to support health and economic outcomes during the COVID-19 epidemic.

Lockdowns and stay-at-home orders have partially mitigated the spread of Covid-19. However, en masse mitigation has come with substantial socioeconomic costs. In this paper, we demonstrate how individualized policies based on disease status can reduce transmission risk while minimizing impacts on economic outcomes. We design feedback control policies informed by optimal control solutions to modulate interaction rates of individuals based on the epidemic state. We identify personalized interaction rates such that recovered/immune individuals elevate their interactions and susceptible individuals remain at home before returning to pre-lockdown levels. As we show, feedback control policies can yield similar population-wide infection rates to total shutdown but with significantly lower economic costs and with greater robustness to uncertainty compared to optimal control policies. Our analysis shows that test-driven improvements in isolation efficiency of infectious individuals can inform disease-dependent interaction policies that mitigate transmission while enhancing the return of individuals to pre-pandemic economic activity.

Disease-dependent interaction policies to support health and economic outcomes during the COVID-19 epidemic.

Lockdowns and stay-at-home orders have partially mitigated the spread of Covid-19. However, the indiscriminate nature of mitigation - applying to all individuals irrespective of disease status - has come with substantial socioeconomic costs. Here, we explore how to leverage the increasing reliability and scale of both molecular and serological tests to balance transmission risks with economic costs involved in responding to Covid-19 epidemics. First, we introduce an optimal control approach that identifies personalized interaction rates according to an individual's test status; such that infected individuals isolate, recovered individuals can elevate their interactions, and activity of susceptible individuals varies over time. Critically, the extent to which susceptible individuals can return to work depends strongly on isolation efficiency. As we show, optimal control policies can yield mitigation policies with similar infection rates to total shutdown but lower socioeconomic costs. However, optimal control policies can be fragile given mis-specification of parameters or mis-estimation of the current disease state. Hence, we leverage insights from the optimal control solutions and propose a feedback control approach based on monitoring of the epidemic state. We utilize genetic algorithms to identify a 'switching' policy such that susceptible individuals (both PCR and serological test negative) return to work after lockdowns insofar as recovered fraction is much higher than the circulating infected prevalence. This feedback control policy exhibits similar performance results to optimal control, but with greater robustness to uncertainty. Overall, our analysis shows that test-driven improvements in isolation efficiency of infectious individuals can inform disease-dependent interaction policies that mitigate transmission while enhancing the return of individuals to pre-pandemic economic activity.

Intervention Serology and Interaction Substitution: Modeling the Role of 'Shield Immunity' in Reducing COVID-19 Epidemic Spread.

The COVID-19 pandemic has precipitated a global crisis, with more than 690,000 confirmed cases and more than 33,000 confirmed deaths globally as of March 30, 2020 [1-4]. At present two central public health control strategies have emerged: mitigation and suppression (e.g, [5]). Both strategies focus on reducing new infections by reducing interactions (and both raise questions of sustainability and long-term tactics). Complementary to those approaches, here we develop and analyze an epidemiological intervention model that leverages serological tests [6, 7] to identify and deploy recovered individuals as focal points for sustaining safer interactions via interaction substitution, i.e., to develop what we term 'shield immunity' at the population scale. Recovered individuals, in the present context, represent those who have developed protective, antibodies to SARS-CoV-2 and are no longer shedding virus [8]. The objective of a shield immunity strategy is to help sustain the interactions necessary for the functioning of essential goods and services (including but not limited to tending to the elderly [9], hospital care, schools, and food supply) while decreasing the probability of transmission during such essential interactions. We show that a shield immunity approach may significantly reduce the length and reduce the overall burden of an outbreak, and can work synergistically with social distancing. The present model highlights the value of serological testing as part of intervention strategies, in addition to its well recognized roles in estimating prevalence [10, 11] and in the potential development of plasma-based therapies [12-15].

Modeling shield immunity to reduce COVID-19 epidemic spread.

The COVID-19 pandemic has precipitated a global crisis, with more than 1,430,000 confirmed cases and more than 85,000 confirmed deaths globally as of 9 April 20201-4. Mitigation and suppression of new infections have emerged as the two predominant public health control strategies5. Both strategies focus on reducing new infections by limiting human-to-human interactions, which could be both socially and economically unsustainable in the long term. We have developed and analyzed an epidemiological intervention model that leverages serological tests6,7 to identify and deploy recovered individuals8 as focal points for sustaining safer interactions via interaction substitution, developing what we term 'shield immunity' at the population scale. The objective of a shield immunity strategy is to help to sustain the interactions necessary for the functioning of essential goods and services9 while reducing the probability of transmission. Our shield immunity approach could substantively reduce the length and reduce the overall burden of the current outbreak, and can work synergistically with social distancing. The present model highlights the value of serological testing as part of intervention strategies, in addition to its well-recognized roles in estimating prevalence10,11 and in the potential development of plasma-based therapies12-15.