Evaluation of vaccine rollout strategies for emerging infectious diseases: A model-based approach including protection attitudes.

Vaccine allocation strategies become crucial during vaccine shortages, especially in the face of potential outbreaks of new infectious diseases, as witnessed during the COVID-19 pandemic. To address this, a specialized compartmental model is created, which simulates an emerging infectious disease similar to COVID-19. This model divides the population into different age groups and is used to compare various vaccine prioritisation approaches, aiming to minimize the total number of fatalities. The model is an improvement upon previous ones as it incorporates essential behavioural factors and is adapted to account for the protective effects of vaccination against both disease infection and transmission. It takes into account human behaviors such as mask-wearing and social distancing by utilizing specific parameters related to self-protection, awareness levels, and the frequency of daily person-to-person interactions within each age group. Furthermore, a novel method for dynamic vaccine prioritisation was introduced in this study. This approach is model-independent and relies on the dynamic R number. It is the first time such a method has been developed, offering a decision-making approach that is not tied to any specific model. This innovation provides a flexible and adaptable strategy for determining vaccine priorities based on real-time data and the current state of the outbreak. Our findings reveal crucial insights into vaccine allocation strategies. When the daily rollout rates are fast (0.75% or higher) and children are eligible for vaccination, prioritising groups with high daily person-to-person interactions can lead to substantial reductions in total fatalities (up to approximately 40% lower). On the other hand, if rollout rates are slower and overall vaccination coverage is high, focusing on vaccinating elders emerges as the most effective strategy, resulting in up to approximately 10% fewer fatalities. However, the scenario changes significantly when children are not eligible for vaccination, as they constitute a highly interactive population group. In this case, the differences between priority strategies become smaller. With fast daily rollout rates, prioritisation based on interactions achieves only a 7% reduction in total fatalities, while a slower rollout with vaccination of elders first leads to an approximately 11% reduction in fatalities compared to the scenario where children are eligible for vaccination. The impact of behavioural parameters is equally critical. When the self-protection levels exercised by the population are low, it significantly affects the optimal vaccine prioritisation strategy to be followed, making it essential to consider behavioural factors in decision-making.

Optimal evaluation of energy yield and driving force in microbial metabolic pathway variants.

This work presents a methodology to evaluate the bioenergetic feasibility of alternative metabolic pathways for a given microbial conversion, optimising their energy yield and driving forces as a function of the concentration of metabolic intermediates. The tool, based on thermodynamic principles and multi-objective optimisation, accounts for pathway variants in terms of different electron carriers, as well as energy conservation (proton translocating) reactions within the pathway. The method also accommodates other constraints, some of them non-linear, such as the balance of conserved moieties. The approach involves the transformation of the maximum energy yield problem into a multi-objective mixed-integer linear optimisation problem which is then subsequently solved using the epsilon-constraint method, highlighting the trade-off between yield and rate in metabolic reactions. The methodology is applied to analyse several pathway alternatives occurring during propionate oxidation in anaerobic fermentation processes, as well as to the reverse TCA cycle pathway occurring during autotrophic microbial CO2 fixation. The results obtained using the developed methodology match previously reported literature and bring about insights into the studied pathways.

Model-based evaluation of the COVID-19 epidemiological impact on international visitors during Expo 2020.

The impact of the COVID-19 pandemic on large events has been substantial. In this work, an evaluation of the potential impact of international arrivals due to Expo 2020 in terms of potential COVID-19 infections from October 1st, 2021, until the end of April 2022 in the United Arab Emirates is presented. Our simulation results indicate that: (i) the vaccination status of the visitors appears to have a small impact on cases, this is expected as the small numbers of temporary visitors with respect to the total population contribute little to the herd immunity status; and (ii) the number of infected arrivals is the major factor of impact potentially causing a surge in cases countrywide with the subsequent hospitalisations and fatalities. These results indicate that the prevention of infected arrivals should take all precedence priority to mitigate the impact of international visitors with their vaccination status being of less relevance.

Modelling bioelectrochemical denitrification in absence of electron donors for groundwater treatment.

Microbial electrochemical technologies (METs) have become a widely studied technology in recent years due to the need for sustainable biotechnologies. The scope of this work is the development of a mechanistic biokinetic model, based on first principles and a robust thermodynamic basis, to provide a theoretical accurate description of a MET system that would treat water contaminated with nitrate, the most common aquifer water pollutant, in absence of external electron donors. The model aims at describing the complex processes occurring including the competition between bioelectroactive and non-bioelectroactive reactions as well as the dynamics and kinetics of multiple bioelectrochemical reactions (both in series and in parallel) taking place in the same electrode. The bioelectrochemical denitrification of groundwater was then evaluated using the model as a case study. The evaluation focused on theoretical removal rates and energy expenditure, as well as the effect of key design parameters on the system's performance. The model successfully described how changes in the applied voltage and/or hydraulic retention time may impact the performance in terms of removal rate and effluent quality. The theoretical results also predict that the impact of electrode area is potentially more significant on the energy efficiency rather than on the effluent quality.

Modelling the impact of interventions on the progress of the COVID-19 outbreak including age segregation.

In this work, a SEIR-type mathematical model of the COVID-19 outbreak was developed that describes individuals in compartments by infection stage and age group. The model assumes a close well-mixed community with no migrations. Infection rates and clinical and epidemiological information govern the transitions between stages of the disease. The impact of specific interventions (including the availability of critical care) on the outbreak time course, the number of cases and the outcome of fatalities were evaluated. Data available from the COVID-19 outbreak from Spain as of mid-May 2020 was used. Key findings in our model simulation results indicate that (i) universal social isolation measures appear effective in reducing total fatalities only if they are strict and the number of daily interpersonal contacts is reduced to very low numbers; (ii) selective isolation of only the elderly (at higher fatality risk) appears almost as effective as universal isolation in reducing total fatalities but at a possible lower economic and social impact; (iii) an increase in the number of critical care capacity directly avoids fatalities; (iv) the use of personal protective equipment (PPE) appears to be effective to dramatically reduce total fatalities when adopted extensively and to a high degree; (v) extensive random testing of the population for more complete infection recognition (accompanied by subsequent self-isolation of infected aware individuals) can dramatically reduce the total fatalities only above a high percentage threshold that may not be practically feasible.

Comprehensive Bioenergetic Evaluation of Microbial Pathway Variants in Syntrophic Propionate Oxidation.

In this work, a systematic methodology was developed (based on known biochemistry, physiology, and bioenergetics) for the automated feasibility evaluation and net ATP yield quantification of large sets of pathway variants. Possible pathway variants differ in their intermediate metabolites, in which electron carriers are involved, in which steps are consuming/producing ATP, and in which steps are coupled to (and to how many) proton (or its equivalent) translocations. A pathway variant is deemed feasible, under a given set of physiological and environmental conditions, only if all pathway reaction steps have nonpositive Gibbs energy changes and if all the metabolite concentrations remain within an acceptable physiological range (10-6 to 10-2 M). The complete understanding of syntrophic propionate oxidation remains elusive due to uncertainties in pathways and the mechanisms for interspecies electron transfer (IET). Several million combinations of pathway variants and parameters/conditions were evaluated for propionate oxidation, providing unprecedented mechanistic insight into its biochemical and bioenergetic landscape. Our results show that, under a scenario of optimum environmental conditions for propionate oxidation, the Smithella pathway yields the most ATP and the methylmalonyl-coenzyme A (CoA) pathways can generate sufficient ATP for growth only under a cyclical pathway configuration with pyruvate. The results under conditions typical of methanogenic environments show that propionate oxidation via the lactate and via the hydroxypropionyl-CoA pathways yield the most ATP. IET between propionate oxidizers and methanogens can proceed either by dissolved hydrogen via the Smithella pathway or by different mechanisms (e.g., formate or direct IET) if other pathways are used.IMPORTANCE In this work, an original methodology was developed that quantifies bioenergetically and physiologically feasible net ATP yields for large numbers of microbial metabolic pathways and their variants under different conditions. All variants are evaluated, which ensures global optimality in finding the pathway variant(s) leading to the highest ATP yield. The methodology is designed to be especially relevant to hypothesize on which microbial pathway variants should be most favored in microbial ecosystems under high selective pressure for efficient metabolic energy conservation. Syntrophic microbial oxidation of propionate to acetate has an extremely small quantity of available energy and requires an extremely high metabolic efficiency to sustain life. Our results bring mechanistic insights into the optimum pathway variants, other metabolic bottlenecks, and the impact of environmental conditions on the ATP yields. Additionally, our results conclude that, as previously reported, under specific conditions, IET mechanisms other than hydrogen must exist to simultaneously sustain the growth of both propionate oxidizers and hydrogenotrophic methanogens.

Integration of bioenergetics in the ADM1 and its impact on model predictions.

In this work, the integration of dynamic bioenergetic calculations in the IWA Anaerobic Digestion Model No. 1 (ADM1) is presented. The impact of bioenergetics on kinetics was addressed via two different approaches: a thermodynamic-based inhibition function and variable microbial growth yields based on dynamic Gibbs free energy calculations. The dynamic bioenergetic calculations indicate that the standard ADM1 predicts positive reaction rates under thermodynamically unfeasible conditions. The dissolved hydrogen inhibition approach used in ADM1 is, however, deemed as adequate, offering the trade-off of not requiring dynamic bioenergetics computation despite the need of hydrogen inhibition parameters. Simulations of the model with bioenergetics showed the low amount of energy available in butyrate and propionate oxidation, suggesting that microbial growth on these substrates must be very limited or occur via alternative mechanisms rather than dissolved hydrogen.

A compilation and bioenergetic evaluation of syntrophic microbial growth yields in anaerobic digestion.

A compilation and analysis of experimentally determined microbial growth yields for syntrophic volatile fatty acid (VFA), lactate oxidisers and methanogens in anaerobic digestion (AD) systems is presented. Only studies based on experimental determinations or sound model-to-data fitting that specifically address parameter identifiability, have been considered. The experimentally determined values are compared and discussed with estimations based on bioenergetic correlations. Only for acetoclastic methanogens the experimentally determined microbial yields appear in good consistency with bioenergetic estimations. For syntrophic microbial groups, the experimetal yield values reported appear much higher than those expected from the low amount of metabolic energy available. These large deviations imply either inaccuracy on the microbial biomass quantification methods or that the syntrophic interspecies electron transfer occurs under mechanisms, or hydrogen equivalent intermediate activities, much below those ever observed in methanogenic environments. In addition, the microbial growth yield values most widely adopted in AD model applications (those reported in the IWA Anaerobic Digestion Model No. 1 (ADM1)) are even higher than the experimental determinations from literature. It is therefore proposed that microbial growth yield values should be restricted by the maximum harvestable ATP calculated through a detailed bioenergetic pathway analysis. Model simulations with different parameter configurations for different yield sources (default ADM1, experimentally determined and bioenergetically estimated values) displayed low sensitivity of the simulations with respect to the yield values as long as the maximum specific microbial growth rate (µmax) remain the same. This suggests that model calibrations could target the accuracy of µmax maintaining the bioenergetic upper limit for microbial growth yields.

Activity corrections are required for accurate anaerobic digestion modelling.

The impact on the prediction of key process variables in anaerobic digestion (AD) when activity corrections are neglected (e.g. when ideal solution is assumed) is evaluated in this paper. The magnitude of deviations incurred in key variables was quantified using a generalised physicochemistry modelling framework that incorporates activity corrections. Deviations incurred on the intermediate and partial alkalinity ratio (a key control variable in AD) already reach values over 20% in typical AD scenarios at low ionic strengths. Deviations of moderate importance (∼5%) in free ammonia, hydrogen sulfide inhibition, as well as in the biogas composition, were observed. Those errors become very large for components involving multiple deprotonations, such as inorganic phosphorus, and their magnitude (∼40%) would impede proper precipitation modelling. A dynamic AD case simulation involving a series of overloads showed model underpredictions of the process acidification when activity corrections are neglected. This compromises control actions based on such models. Based on these results, a systematic incorporation of activity corrections in AD models is strongly recommended. This will prevent model overfitting to observations related to inaccurate physicochemistry modelling, at a marginal computational cost. Alternatives for these implementations are also discussed.