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Building resource constraints and feasibility considerations in mathematical models for infectious disease: A systematic literature review.

ABSTRACT: Priority setting for infectious disease control is increasingly concerned with physical input constraints and other real-world restrictions on implementation and on the decision process. These health system constraints determine the 'feasibility' of interventions and hence impact. However, considering them within mathematical models places additional demands on model structure and relies on data availability. This review aims to provide an overview of published methods for considering constraints in mathematical models of infectious disease. We systematically searched the literature to identify studies employing dynamic transmission models to assess interventions in any infectious disease and geographical area that included non-financial constraints to implementation. Information was extracted on the types of constraints considered and how these were identified and characterised, as well as on the model structures and techniques for incorporating the constraints. A total of 36 studies were retained for analysis. While most dynamic transmission models identified were deterministic compartmental models, stochastic models and agent-based simulations were also successfully used for assessing the effects of non-financial constraints on priority setting. Studies aimed to assess reductions in intervention coverage (and programme costs) as a result of constraints preventing successful roll-out and scale-up, and/or to calculate costs and resources needed to relax these constraints and achieve desired coverage levels. We identified three approaches for incorporating constraints within the analyses: (i) estimation within the disease transmission model; (ii) linking disease transmission and health system models; (iii) optimising under constraints (other than the budget). The review highlighted the viability of expanding model-based priority setting to consider health system constraints. We show strengths and limitations in current approaches to identify and quantify locally-relevant constraints, ranging from simple assumptions to structured elicitation and operational models. Overall, there is a clear need for transparency in the way feasibility is defined as a decision criteria for its systematic operationalisation within models.

SUBMITTER: Bozzani FM

PROVIDER: S-EPMC8207450 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundThe availability of genome sequences for many organisms enabled the reconstruction of several genome-scale metabolic network models. Currently, significant efforts are put into the automated reconstruction of such models. For this, several computational tools have been developed that particularly assist in identifying and compiling the organism-specific lists of metabolic reactions. In contrast, the last step of the model reconstruction process, which is the definition of the thermodynamic constraints in terms of reaction directionalities, still needs to be done manually. No computational method exists that allows for an automated and systematic assignment of reaction directions in genome-scale models.ResultsWe present an algorithm that - based on thermodynamics, network topology and heuristic rules - automatically assigns reaction directions in metabolic models such that the reaction network is thermodynamically feasible with respect to the production of energy equivalents. It first exploits all available experimentally derived Gibbs energies of formation to identify irreversible reactions. As these thermodynamic data are not available for all metabolites, in a next step, further reaction directions are assigned on the basis of network topology considerations and thermodynamics-based heuristic rules. Briefly, the algorithm identifies reaction subsets from the metabolic network that are able to convert low-energy co-substrates into their high-energy counterparts and thus net produce energy. Our algorithm aims at disabling such thermodynamically infeasible cyclic operation of reaction subnetworks by assigning reaction directions based on a set of thermodynamics-derived heuristic rules. We demonstrate our algorithm on a genome-scale metabolic model of E. coli. The introduced systematic direction assignment yielded 130 irreversible reactions (out of 920 total reactions), which corresponds to about 70% of all irreversible reactions that are required to disable thermodynamically infeasible energy production.ConclusionAlthough not being fully comprehensive, our algorithm for systematic reaction direction assignment could define a significant number of irreversible reactions automatically with low computational effort. We envision that the presented algorithm is a valuable part of a computational framework that assists the automated reconstruction of genome-scale metabolic models.

Project description:INTRODUCTION:Timely notification of infectious diseases is crucial for prompt response by public health services. Adequate notification systems facilitate timely notification. A systematic literature review was performed to assess outcomes of studies on notification timeliness and to determine which aspects of notification systems are associated with timely notification. METHODOLOGY:Articles reviewing timeliness of notifications published between 2000 and 2017 were searched in Pubmed and Scopus. Using a standardized notification chain, timeliness of reporting system for each article was defined as either sufficient (≥ 80% notifications in time), partly sufficient (≥ 50-80%), or insufficient (< 50%) according to the article's predefined timeframe, a standardized timeframe for all articles, and a disease specific timeframe. Electronic notification systems were compared with conventional methods (postal mail, fax, telephone, email) and mobile phone reporting. RESULTS:48 articles were identified. In almost one third of the studies with a predefined timeframe (39), timeliness of notification systems was either sufficient or insufficient (11/39, 28% and 12/39, 31% resp.). Applying the standardized timeframe (45 studies) revealed similar outcomes (13/45, 29%, sufficient notification timeframe, vs 15/45, 33%, insufficient). The disease specific timeframe was not met by any study. Systems involving reporting by laboratories most often complied sufficiently with predefined or standardized timeframes. Outcomes were not related to electronic, conventional notification systems or mobile phone reporting. Electronic systems were faster in comparative studies (10/13); this hardly resulted in sufficient timeliness, neither according to predefined nor to standardized timeframes. CONCLUSION:A minority of notification systems meets either predefined, standardized or disease specific timeframes. Systems including laboratory reporting are associated with timely notification. Electronic systems reduce reporting delay, but implementation needs considerable effort to comply with notification timeframes. During outbreak threats, patient, doctors and laboratory testing delays need to be reduced to achieve timely detection and notification. Public health authorities should incorporate procedures for this in their preparedness plans.

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Building resource constraints and feasibility considerations in mathematical models for infectious disease: A systematic literature review.

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