India's pragmatic vaccination strategy against COVID-19: a mathematical modelling-based analysis

Learning from the COVID-19 pandemic: A systematic review of mathematical vaccine prioritization models

As the world becomes ever more connected, the chance of pandemics increases as well. The recent COVID-19 pandemic and the concurrent global mass vaccine roll-out provides an ideal setting to learn from and refine our understanding of infectious disease models for better future preparedness. In this review, we systematically analyze and categorize mathematical models that have been developed to design optimal vaccine prioritization strategies of an initially limited vaccine. As older individuals are disproportionately affected by COVID-19, the focus is on models that take age explicitly into account. The lower mobility and activity level of older individuals gives rise to non-trivial trade-offs. Secondary research questions concern the optimal time interval between vaccine doses and spatial vaccine distribution. This review showcases the effect of various modeling assumptions on model outcomes. A solid understanding of these relationships yields better infectious disease models and thus public health decisions during the next pandemic.

Prioritising older individuals for COVID-19 booster vaccination leads to optimal public health outcomes in a range of socio-economic settings

The rapid development of vaccines against SARS-CoV-2 altered the course of the COVID-19 pandemic. In most countries, vaccinations were initially targeted at high-risk populations, including older individuals and healthcare workers. Now, despite substantial infection- and vaccine-induced immunity in host populations worldwide, waning immunity and the emergence of novel variants continue to cause significant waves of infection and disease. Policy makers must determine how to deploy booster vaccinations, particularly when constraints in vaccine supply, delivery and cost mean that booster vaccines cannot be administered to everyone. A key question is therefore whether older individuals should again be prioritised for vaccination, or whether alternative strategies (e.g. offering booster vaccines to the individuals who have most contacts with others and therefore drive infection) can instead offer indirect protection to older individuals. Here, we use mathematical modelling to address this question, considering SARS-CoV-2 transmission in a range of countries with different socio-economic backgrounds. We show that the population structures of different countries can have a pronounced effect on the impact of booster vaccination, even when identical booster vaccination targeting strategies are adopted. However, under the assumed transmission model, prioritising older individuals for booster vaccination consistently leads to the most favourable public health outcomes in every setting considered. This remains true for a range of assumptions about booster vaccine supply and timing, and for different assumed policy objectives of booster vaccination.

Learning from the COVID-19 pandemic: a systematic review of mathematical vaccine prioritization models

As the world becomes ever more connected, the chance of pandemics increases as well. The recent COVID-19 pandemic and the concurrent global mass vaccine roll-out provides an ideal setting to learn from and refine our understanding of infectious disease models for better future preparedness. In this review, we systematically analyze and categorize mathematical models that have been developed to design optimal vaccine prioritization strategies of an initially limited vaccine. As older individuals are disproportionately affected by COVID-19, the focus is on models that take age explicitly into account. The lower mobility and activity level of older individuals gives rise to non-trivial trade-offs. Secondary research questions concern the optimal time interval between vaccine doses and spatial vaccine distribution. This review showcases the effect of various modeling assumptions on model outcomes. A solid understanding of these relationships yields better infectious disease models and thus public health decisions during the next pandemic.

Predictive models for health outcomes due to SARS-CoV-2, including the effect of vaccination: a systematic review

BACKGROUND

The interaction between modelers and policymakers is becoming more common due to the increase in computing speed seen in recent decades. The recent pandemic caused by the SARS-CoV-2 virus was no exception. Thus, this study aims to identify and assess epidemiological mathematical models of SARS-CoV-2 applied to real-world data, including immunization for coronavirus 2019 (COVID-19).

METHODOLOGY

PubMed, JSTOR, medRxiv, LILACS, EconLit, and other databases were searched for studies employing epidemiological mathematical models of SARS-CoV-2 applied to real-world data. We summarized the information qualitatively, and each article included was assessed for bias risk using the Joanna Briggs Institute (JBI) and PROBAST checklist tool. The PROSPERO registration number is CRD42022344542.

FINDINGS

In total, 5646 articles were retrieved, of which 411 were included. Most of the information was published in 2021. The countries with the highest number of studies were the United States, Canada, China, and the United Kingdom; no studies were found in low-income countries. The SEIR model (susceptible, exposed, infectious, and recovered) was the most frequently used approach, followed by agent-based modeling. Moreover, the most commonly used software were R, Matlab, and Python, with the most recurring health outcomes being death and recovery. According to the JBI assessment, 61.4% of articles were considered to have a low risk of bias.

INTERPRETATION

The utilization of mathematical models increased following the onset of the SARS-CoV-2 pandemic. Stakeholders have begun to incorporate these analytical tools more extensively into public policy, enabling the construction of various scenarios for public health. This contribution adds value to informed decision-making. Therefore, understanding their advancements, strengths, and limitations is essential.

COVID-19 vaccination strategies in settings with limited rollout capacity: a mathematical modelling case study in Sierra Leone

BACKGROUND

COVID-19 vaccine coverage in low- and middle-income countries continues to be challenging. As supplies increase, coverage is increasingly becoming determined by rollout capacity.

METHODS

We developed a deterministic compartmental model of COVID-19 transmission to explore how age-, risk-, and dose-specific vaccine prioritisation strategies can minimise severe outcomes of COVID-19 in Sierra Leone.

RESULTS

Prioritising booster doses to older adults and adults with comorbidities could reduce the incidence of severe disease by 23% and deaths by 34% compared to the use of these doses as primary doses for all adults. Providing a booster dose to pregnant women who present to antenatal care could prevent 38% of neonatal deaths associated with COVID-19 infection during pregnancy. The vaccination of children is not justified unless there is sufficient supply to not affect doses delivered to adults.

CONCLUSIONS

Our paper supports current WHO SAGE vaccine prioritisation guidelines (released January 2022). Individuals who are at the highest risk of developing severe outcomes should be prioritised, and opportunistic vaccination strategies considered in settings with limited rollout capacity.

COVID-19 preparedness and response in rural and remote areas: A scoping review

This scoping review used the Arksey and O'Malley approach to explore COVID-19 preparedness and response in rural and remote areas to identify lessons to inform future health preparedness and response planning. A search of scientific and grey literature for rural COVID-19 preparedness and responses identified 5 668 articles published between 2019 and early 2022. A total of 293 articles were included, of which 160 (54.5%) were from high income countries and 106 (36.2%) from middle income countries. Studies focused mostly on the Maintenance of Essential Health Services (63; 21.5%), Surveillance, epidemiological investigation, contact tracing and adjustment of public health and social measures (60; 20.5%), Coordination and Planning (32; 10.9%); Case Management (30; 10.2%), Social Determinants of Health (29; 10%) and Risk Communication (22; 7.5%). Rural health systems were less prepared and national COVID-19 responses were often not adequately tailored to rural areas. Promising COVID-19 responses involved local leaders and communities, were collaborative and multisectoral, and engaged local cultures. Non-pharmaceutical interventions were applied less, support for access to water and sanitation at scale was weak, and more targeted approaches to the isolation of cases and quarantine of contacts were preferable to blanket lockdowns. Rural pharmacists, community health workers and agricultural extension workers assisted in overcoming shortages of health professionals. Vaccination coverage was hindered by weaker rural health systems. Digital technology enabled better coordination, communication, and access to health services, yet for some was inaccessible. Rural livelihoods and food security were affected through disruptions to local labour markets, farm produce markets and input supply chains. Important lessons include the need for rural proofing national health preparedness and response and optimizing synergies between top-down planning with localised planning and coordination. Equity-oriented rural health systems strengthening and action on rural social determinants is essential to better prepare for and respond to future outbreaks.

Vaccination saves lives: a real-time study of patients with chronic diseases and severe COVID-19 infection

OBJECTIVES

This study aims to describe the demographic and clinical profile and ascertain the determinants of outcome among hospitalized coronavirus disease 2019 (COVID-19) adult patients enrolled in the National Clinical Registry for COVID-19 (NCRC).

METHODS

NCRC is an on-going data collection platform operational in 42 hospitals across India. Data of hospitalized COVID-19 patients enrolled in NCRC between 1st September 2020 to 26th October 2021 were examined.

RESULTS

Analysis of 29 509 hospitalized, adult COVID-19 patients [mean (SD) age: 51.1 (16.2) year; male: 18 752 (63.6%)] showed that 15 678 (53.1%) had at least one comorbidity. Among 25 715 (87.1%) symptomatic patients, fever was the commonest symptom (72.3%) followed by shortness of breath (48.9%) and dry cough (45.5%). In-hospital mortality was 14.5% (n = 3957). Adjusted odds of dying were significantly higher in age group ≥60 years, males, with diabetes, chronic kidney diseases, chronic liver disease, malignancy and tuberculosis, presenting with dyspnoea and neurological symptoms. WHO ordinal scale 4 or above at admission carried the highest odds of dying [5.6 (95% CI: 4.6-7.0)]. Patients receiving one [OR: 0.5 (95% CI: 0.4-0.7)] or two doses of anti-SARS CoV-2 vaccine [OR: 0.4 (95% CI: 0.3-0.7)] were protected from in-hospital mortality.

CONCLUSIONS

WHO ordinal scale at admission is the most important independent predictor for in-hospital death in COVID-19 patients. Anti-SARS-CoV2 vaccination provides significant protection against mortality.

Impact of COVID-19 Vaccination on Seroprevalence of SARS-CoV-2 among the Health Care Workers in a Tertiary Care Centre, South India

Global vaccine development efforts have been accelerated in response to the devastating COVID-19 pandemic. The study aims to determine the seroprevalence of SARS-CoV-2 IgG antibodies among vaccine-naïve healthcare workers and to describe the impact of vaccination roll-out on COVID-19 antibody prevalence among the health care centers in tertiary care centers in South India. Serum samples collected from vaccinated and unvaccinated health care workers between January 2021 and April 2021were subjected to COVID-19 IgG ELISA, and adverse effects after the first and second dose of receiving the Covishield vaccine were recorded. The vaccinated group was followed for a COVID-19 breakthrough infection for a period of 6 months. Among the recruited HCW, 156 and 157 participants were from the vaccinated and unvaccinated group, respectively. The seroprevalence (COVID-19 IgG ELISA) among the vaccinated and unvaccinated Health Care Workers (HCW) was 91.7% and 38.2%, respectively, which is statistically significant. Systemic and local side-effects after Covishield vaccination occur at lower frequencies than reported in phase 3 trials. Since the COVID-19 vaccine rollout has commenced in our tertiary care hospital, seropositivity for COVID-19 IgG has risen dramatically and clearly shows trends in vaccine-induced antibodies among the health care workers.

'Imperfect but useful': pandemic response in the Global South can benefit from greater use of mathematical modelling

Mathematical modelling has been a helpful resource for planning public health responses to COVID-19. However, there is a need to improve the accessibility of models built within country contexts in the Global South. Immediately following the overwhelming 'second wave' of COVID-19 in India, we developed a user-friendly, web-based modelling simulator in partnership with the public health experts and health administrators for subnational planning. The purpose was to help policy-makers and programme officials at the state and district levels, to construct model-based scenarios for a possible third wave. Here, we describe our experiences of developing and deploying the simulator and propose the following recommendations for future such initiatives: early preparation will be the key for pandemic management planning, including establishment of networks with potential simulator users. Ideally, this preparedness should be conducted during 'peace time', and coordinated by agencies such as WHO. Second, flexible modelling frameworks will be needed, to respond rapidly to future emergencies as the precise nature of any pandemic is impossible to predict. Modelling resources will, therefore, need to be rapidly adaptable to respond as soon as a novel pathogen emerges. Third, limitations of modelling must be communicated clearly and consistently to end users. Finally, systematic mechanisms are required for monitoring the use of models in decision making, which will help in providing modelling support to those local authorities who may benefit most from it. Overall, these lessons from India can be relevant for other countries in the South-Asian-Region, to incorporate modelling resources into their pandemic preparedness planning.

Characterizing the third wave of COVID-19: An analysis from the National Clinical Registry of COVID-19

Background & objectives

Data from the National Clinical Registry for COVID-19 (NCRC) were analyzed with an aim to describe the clinical characteristics, course and outcomes of patients hospitalized with COVID-19 in the third wave of the pandemic and compare them with patients admitted earlier.

Methods

The NCRC, launched in September 2020, is a multicentre observational initiative, which provided the platform for the current investigation. Demographic, clinical, treatment and outcome data of hospitalized COVID-19 patients were captured in an electronic data portal from 38 hospitals across India. Patients enrolled during December 16, 2021 to January 17, 2022 were considered representative of the third wave of COVID-19 and compared with those registered during November 15 to December 15, 2021, representative of the tail end of the second wave.

Results

Between November 15, 2021 and January 17, 2022, 3230 patients were recruited in NCRC. Patients admitted in the third wave were significantly younger than those admitted earlier (46.7±20.5 vs. 54.6±18 yr). The patients admitted in the third wave had a lower requirement of drugs including steroids, interleukin (IL)-6 inhibitors and remdesivir as well as lower oxygen supplementation and mechanical ventilation. They had improved hospital outcomes with significantly lower in-hospital mortality (11.2 vs. 15.1%). The outcomes were better among the fully vaccinated when compared to the unvaccinated or partially vaccinated.

Interpretation & conclusions

The pattern of illness and outcomes were observed to be different in the third wave compared to the last wave. Hospitalized patients were younger with fewer comorbidities, decreased symptoms and improved outcomes, with fully vaccinated patients faring better than the unvaccinated and partially vaccinated ones.

Looking back to move forward: A travel rule underlined by the current pandemic

Learning from the past - is easier said than done. In this narrative, "travel" refers to the forward movement of the society at large on the path of health and development. It is suggested that looking back and learning from the lived experiences of the past outbreaks could help generating public health insights and incorporating them in planning for a better future. In the process, a country may choose to revisit what took place in the recent past during the COVID-19 pandemic within its boundary and beyond. However, unfolding of events in the past, which is not as immediate as COVID neither too far as the flu pandemic of 1918, also has lessons to offer. Recognizably, a few alarms, that rang in the recent past and cried for mass attention towards beefed up public health preparedness, were missed. It is therefore necessary now to critically examine the past-efforts to eradicate, eliminate or control diseases such as small pox, polio, HIV, tuberculosis, leprosy, measles or malaria. Results of such evaluation could inform the future courses of actions around disease elimination science and health (DESH) and help develop better nations.

Community voices around COVID-19 vaccine in Chennai, India: A qualitative exploration during early phase of vaccine rollout

Background & objectives

Globally, vaccination is considered as an important public health strategy to mitigate the impact of the COVID-19 pandemic. The purpose of the current study was to conduct an in-depth inquiry to explore perceptions of community members around COVID-19 vaccines in the southern city of Chennai, Tamil Nadu. This was conducted during the early phase of the vaccine rollout programme in India.

Methods

A qualitative investigation was conducted between January-February 2021 through in-depth interviews. Healthcare workers, religious leaders, community influencers, local administrators and representatives of marginalized communities were included. The key informant interview guides and probes explored five domains; (i) vaccine availability, (ii) trust in COVID-19 vaccines, (iii) vaccine-related concerns, (iv) health/risk balance and (v) vaccine prioritization. Transcripted interviews were coded using a thematic approach and analyzed manually as well as with the help of ATLAS.ti 9 software.

Results

Eagerness to receive COVID-19 vaccines amongst some of the respondents was linked with freedom from fear, possible restoration of normalcy, protection of family and ability to travel and work abroad. Concerns around threat of emergence of new variants, damage caused by such viral mutants and trust in policymakers were other facilitatory influencers for vaccine uptake. On the other hand, doubts surrounding safety and fear of side effects of COVID-19 vaccine were the feeders to vaccine hesitancy. Lack of accurate information, sensational media reports and rumours exacerbated this fear and provoked anxiety among people. Apprehensions around COVID-19 vaccine in the wake of its rapid development and approval for use and reluctance to take it during the declining phase of the epidemic were identified as other inhibitory factors. Participants underlined the importance of having responsive communication strategies in place focussing on vaccine safety. Making vaccines available to people free of cost and ensuring wider access were other programmatic suggestions.

Interpretation & conclusions

In conclusion, our study findings suggest that it is essential to remain engaged with communities and execute evidence-based information dissemination strategy about the safety and efficacy of the vaccines. We identified that it is also imperative to sensitize and train media professionals on how to report side effects related to vaccines. Responsive communication strategies will thus have the potential to serve as a key public health approach pertaining to future pandemic preparedness as well as to manage the demands of clinical and public health issues in an ongoing pandemic situation.

Models of COVID-19 vaccine prioritisation: a systematic literature search and narrative review

BACKGROUND

How best to prioritise COVID-19 vaccination within and between countries has been a public health and an ethical challenge for decision-makers globally. We reviewed epidemiological and economic modelling evidence on population priority groups to minimise COVID-19 mortality, transmission, and morbidity outcomes.

METHODS

We searched the National Institute of Health iSearch COVID-19 Portfolio (a database of peer-reviewed and pre-print articles), Econlit, the Centre for Economic Policy Research, and the National Bureau of Economic Research for mathematical modelling studies evaluating the impact of prioritising COVID-19 vaccination to population target groups. The first search was conducted on March 3, 2021, and an updated search on the LMIC literature was conducted from March 3, 2021, to September 24, 2021. We narratively synthesised the main study conclusions on prioritisation and the conditions under which the conclusions changed.

RESULTS

The initial search identified 1820 studies and 36 studies met the inclusion criteria. The updated search on LMIC literature identified 7 more studies. 43 studies in total were narratively synthesised. 74% of studies described outcomes in high-income countries (single and multi-country). We found that for countries seeking to minimise deaths, prioritising vaccination of senior adults was the optimal strategy and for countries seeking to minimise cases the young were prioritised. There were several exceptions to the main conclusion, notably that reductions in deaths could be increased if groups at high risk of both transmission and death could be further identified. Findings were also sensitive to the level of vaccine coverage.

CONCLUSION

The evidence supports WHO SAGE recommendations on COVID-19 vaccine prioritisation. There is, however, an evidence gap on optimal prioritisation for low- and middle-income countries, studies that included an economic evaluation, and studies that explore prioritisation strategies if the aim is to reduce overall health burden including morbidity.