The rapid development of vaccines against the SARS-CoV-2 virus is an unprecedented achievement. Once vaccines become mass produced, they will have to be distributed to almost the entire population to prevent deaths and permit prompt economic recovery. The necessity to vaccinate a large number of people in a short period of time, and possibly with insufficient vaccine doses to cover most, creates in itself a new challenge for governments and health authorities: which population groups (by age or other criteria) should be targeted first and what sequence must be followed, if any at all, to achieve the minimum number of fatalities? In this work, we demonstrate the importance and impact of optimally planning the priorities for vaccine deployment by population groups using a modified SEIR-type model for the COVID-19 outbreak considering age-related groups. Finding the absolute guaranteed best solution of the mathematical optimisation problem may be hard, if even possible, and would likely require intense computational resources for every possible case study scenario. In this work, several strategies are evaluated and compared, in an attempt to approach the most effective possible vaccination priority sequence in an example case study using demographic and epidemiological data from Spain. The minimum total fatalities at the end of the vaccination campaign is the objective pursued. The population groups classifications are established based on relevant differences in mortality (due to their age) and risk-related behaviour such as their number of daily person-to-person interactions. Assuming a capacity limited constant vaccination rate, vaccination distribution strategies were evaluated for different vaccine effectiveness levels and different percentages of final vaccine population coverage. Our results unambiguously show how planning vaccination by priority groups can achieve dramatic reductions in total fatalities (more than 70% in some cases) compared to no prioritisation. The results also indicate in all cases, for all vaccine effectiveness and coverage values evaluated, that the criteria for groups vaccination priority should not be those with the highest mortality but rather those the highest number of daily person-to-person interactions. Strikingly, our results show in all cases, that prioritisation of groups with the highest mortality but less social interactions, may lead to significantly larger numbers of final total fatalities, even higher as if no group priorities were established at all. The explanation, clearly displayed by the mechanistic model, is that vaccination avoids infections that reduce mortality not only from the vaccinated group itself but also from the projected secondary and subsequent infections inflicted on the rest of the population by those vaccinated in that group. Precisely this amplification effect (exponential nature of the curve) appears to cause the larger reduction in total fatalities if the groups with the most interactions are vaccinated first. The possible contradiction of these results with some published recommendations highlight the importance of conducting an open comprehensive and rigorous analysis of this problem leaving behind any subjective preconceptions.
Infectious diseases, especially when new and highly contagious, could be devastating producing epidemic outbreaks and pandemics. Predicting the outcomes of such events in relation to possible interventions is crucial for societal and healthcare planning and forecasting of resource needs. Deterministic and mechanistic models can capture the main known phenomena of epidemics while also allowing for a meaningful interpretation of results. In this work a deterministic mechanistic population balance model was developed. The model describes individuals in a population by infection stage and age group. The population is treated as in a close well mixed community with no migrations. Infection rates and clinical and epidemiological information govern the transitions between stages of the disease. The present model provides a steppingstone to build upon and its current low complexity retains accessibility to non experts and policy makers to comprehend the variables and phenomena at play.
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.
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.
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.
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