Abstract:The promise of efficacious vaccines against SARS-CoV-2 is fulfilled and vaccination campaigns have started worldwide. However, the fight against the pandemic is far from over. Here, we propose an age-structured compartmental model to study the interplay of disease transmission, vaccines rollout, and behavioural dynamics. We investigate, via in-silico simulations, individual and societal behavioural changes, possibly induced by the start of the vaccination campaigns, and manifested as a relaxation in the adopti… Show more
“…The largest proportion of the selected studies ( N =27) investigated optimal vaccine prioritisation strategies to minimise COVID-19 cases. Of these, seven studies recommended young and middle-aged adults [ 10 , 12 , 15 , 16 , 17 , 20 , 28 ], three young adults [ 23 , 25 , 39 ], and two young adults and children [ 27 , 32 ]. One study recommended young people at the same time as another priority group (seniors at full vaccine dose and young adults at partial dose) [ 31 ].…”
Section: Resultsmentioning
confidence: 99%
“…There were also five multi-country studies which modelled LMIC settings [ 8 , 9 , 12 , 20 , 24 ] and two modelling UMIC settings [ 20 , 29 ]. The conclusions for these studies were in line with the majority conclusions for deaths and cases (except for one study which recommended prioritising both the high social contact group and seniors to minimise deaths) [ 29 ].…”
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.
“…The largest proportion of the selected studies ( N =27) investigated optimal vaccine prioritisation strategies to minimise COVID-19 cases. Of these, seven studies recommended young and middle-aged adults [ 10 , 12 , 15 , 16 , 17 , 20 , 28 ], three young adults [ 23 , 25 , 39 ], and two young adults and children [ 27 , 32 ]. One study recommended young people at the same time as another priority group (seniors at full vaccine dose and young adults at partial dose) [ 31 ].…”
Section: Resultsmentioning
confidence: 99%
“…There were also five multi-country studies which modelled LMIC settings [ 8 , 9 , 12 , 20 , 24 ] and two modelling UMIC settings [ 20 , 29 ]. The conclusions for these studies were in line with the majority conclusions for deaths and cases (except for one study which recommended prioritising both the high social contact group and seniors to minimise deaths) [ 29 ].…”
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.
“…Our M-SEIR RAMP is designed to be used by individuals either with no coding skills, or with minimal coding skills if they desire to modify some of the process descriptions incorporated into the supplied platform. It is sufficiently detailed, however, to allow the user to incorporate either supplied or user-altered versions of the following processes: (i) pathogen variant-specific shedding [ 19 ], environmental persistence [ 20 ], within-host replication [ 21 ] and mortality rates [ 22 ]; (ii) immunological waning with variant cross-immunity [ 23 , 24 ]; (iii) pathogen variant drift during transmission and within-host replication [ 25 ]; (iv) an adaptive contact rate [ 26 ]; (v) a time-dependent, uni- or multivalent vaccine rollout [ 27 , 28 ] ( figure 1 ; for mathematical details, see §2, and electronic supplementary material, appendix A). Figure 1 An overview of the processes included in our M-SEIR model (see table 1 for equation references).…”
We present methods for building a Java Runtime-Alterable-Model Platform (RAMP) of complex dynamical systems. We illustrate our methods by building a multivariant SEIR (epidemic) RAMP. Underlying our RAMP is an individual-based model that includes adaptive contact rates, pathogen genetic drift, waning and cross-immunity. Besides allowing parameter values, process descriptions and scriptable runtime drivers to be easily modified during simulations, our RAMP can used within R-Studio and other computational platforms. Process descriptions that can be runtime altered within our SEIR RAMP include pathogen variant-dependent host shedding, environmental persistence, host transmission and within-host pathogen mutation and replication. They also include adaptive social distancing and adaptive application of vaccination rates and variant-valency of vaccines. We present simulation results using parameter values and process descriptions relevant to the current COVID-19 pandemic. Our results suggest that if waning immunity outpaces vaccination rates, then vaccination rollouts may fail to contain the most transmissible variants, particularly if vaccine valencies are not adapted to deal with escape mutations. Our SEIR RAMP is designed for easy use by others. More generally, our RAMP concept facilitates construction of highly flexible complex systems models of all types, which can then be easily shared as stand-alone application programs.
“…Data analytics is also used for non-pharmaceutical interventions to handle an epidemic, such as lockdowns, social distancing, or the use of face masks. The effects of such interventions have been studied extensively to understand the possible impact on the trajectory of epidemics, including the current COVID-19 pandemic [6][7][8][9][10][11][12].…”
Epidemic models often reflect characteristic features of infectious spreading processes by coupled nonlinear differential equations considering different states of health (such as susceptible, infectious or recovered). This compartmental modelling approach, however, delivers an incomplete picture of the dynamics of epidemics, as it neglects stochastic and network effects, and the role of the measurement process, on which the estimation of epidemiological parameters and incidence values relies. In order to study the related issues, we combine established epidemiological spreading models with a measurement model of the testing process, considering the problems of false positives and false negatives as well as biased sampling. Studying a model-generated ground truth in conjunction with simulated observation processes (virtual measurements) allows one to gain insights into the fundamental limitations of purely data-driven methods when assessing the epidemic situation. We conclude that epidemic monitoring, simulation, and forecasting are wicked problems, as applying a conventional data-driven approach to a complex system with nonlinear dynamics, network effects and uncertainty can be misleading. Nevertheless, some of the errors can be corrected for, using scientific knowledge of the spreading dynamics and the measurement process. We conclude that such corrections should generally be part of epidemic monitoring, modelling and forecasting efforts.
This article is part of the theme issue ‘Data science approaches to infectious disease surveillance’.
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