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Patch dynamics modeling framework from pathogens' perspective: Unified and standardized approach for complicated epidemic systems.

ABSTRACT: Mathematical models are powerful tools to investigate, simulate, and evaluate potential interventions for infectious diseases dynamics. Much effort has focused on the Susceptible-Infected-Recovered (SIR)-type compartment models. These models consider host populations and measure change of each compartment. In this study, we propose an alternative patch dynamic modeling framework from pathogens' perspective. Each patch, the basic module of this modeling framework, has four standard mechanisms of pathogen population size change: birth (replication), death, inflow, and outflow. This framework naturally distinguishes between-host transmission process (inflow and outflow) and within-host infection process (replication) during the entire transmission-infection cycle. We demonstrate that the SIR-type model is actually a special cross-sectional and discretized case of our patch dynamics model in pathogens' viewpoint. In addition, this patch dynamics modeling framework is also an agent-based model from hosts' perspective by incorporating individual host's specific traits. We provide an operational standard to formulate this modular-designed patch dynamics model. Model parameterization is feasible with a wide range of sources, including genomics data, surveillance data, electronic health record, and from other emerging technologies such as multiomics. We then provide two proof-of-concept case studies to tackle some of the existing challenges of SIR-type models: sexually transmitted disease and healthcare acquired infections. This patch dynamics modeling framework not only provides theoretical explanations to known phenomena, but also generates novel insights of disease dynamics from a more holistic viewpoint. It is also able to simulate and handle more complicated scenarios across biological scales such as the current COVID-19 pandemic.

SUBMITTER: Chen S

PROVIDER: S-EPMC7561140 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Patch dynamics modeling framework from pathogens' perspective: Unified and standardized approach for complicated epidemic systems.

Chen Shi S Owolabi Yakubu Y Li Ang A Lo Eugenia E Robinson Patrick P Janies Daniel D Lee Chihoon C Dulin Michael M

PloS one 20201015 10

Mathematical models are powerful tools to investigate, simulate, and evaluate potential interventions for infectious diseases dynamics. Much effort has focused on the Susceptible-Infected-Recovered (SIR)-type compartment models. These models consider host populations and measure change of each compartment. In this study, we propose an alternative patch dynamic modeling framework from pathogens' perspective. Each patch, the basic module of this modeling framework, has four standard mechanisms of ...[more]

PMID: 33057348

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Project description:BackgroundSimple phenomenological growth models can be useful for estimating transmission parameters and forecasting epidemic trajectories. However, most existing phenomenological growth models only support single-peak outbreak dynamics whereas real epidemics often display more complex transmission trajectories.MethodsWe develop and apply a novel sub-epidemic modeling framework that supports a diversity of epidemic trajectories including stable incidence patterns with sustained or damped oscillations to better understand and forecast epidemic outbreaks. We describe how to forecast an epidemic based on the premise that the observed coarse-scale incidence can be decomposed into overlapping sub-epidemics at finer scales. We evaluate our modeling framework using three outbreak datasets: Severe Acute Respiratory Syndrome (SARS) in Singapore, plague in Madagascar, and the ongoing Ebola outbreak in the Democratic Republic of Congo (DRC) and four performance metrics.ResultsThe sub-epidemic wave model outperforms simpler growth models in short-term forecasts based on performance metrics that account for the uncertainty of the predictions namely the mean interval score (MIS) and the coverage of the 95% prediction interval. For example, we demonstrate how the sub-epidemic wave model successfully captures the 2-peak pattern of the SARS outbreak in Singapore. Moreover, in short-term sequential forecasts, the sub-epidemic model was able to forecast the second surge in case incidence for this outbreak, which was not possible using the simple growth models. Furthermore, our findings support the view that the national incidence curve of the Ebola epidemic in DRC follows a stable incidence pattern with periodic behavior that can be decomposed into overlapping sub-epidemics.ConclusionsOur findings highlight how overlapping sub-epidemics can capture complex epidemic dynamics, including oscillatory behavior in the trajectory of the epidemic wave. This observation has significant implications for interpreting apparent noise in incidence data where the oscillations could be dismissed as a result of overdispersion, rather than an intrinsic part of the epidemic dynamics. Unless the oscillations are appropriately modeled, they could also give a false positive, or negative, impression of the impact from public health interventions. These preliminary results using sub-epidemic models can help guide future efforts to better understand the heterogenous spatial and social factors shaping sub-epidemic patterns for other infectious diseases.

Project description:The roles of corticotrophin-releasing factor (CRF), opioid peptides, leptin and ghrelin in anorexia nervosa (AN) were discussed in this paper. CRF is the key mediator of the hypothalamo-pituitary-adrenal (HPA) axis and also acts at various other parts of the brain, such as the limbic system and the peripheral nervous system. CRF action is mediated through the CRF1 and CRF2 receptors, with both HPA axis-dependent and HPA axis-independent actions, where the latter shows nil involvement of the autonomic nervous system. CRF1 receptors mediate both the HPA axis-dependent and independent pathways through CRF, while the CRF2 receptors exclusively mediate the HPA axis-independent pathways through urocortin. Opioid peptides are involved in the adaptation and regulation of energy intake and utilization through reward-related behavior. Opioids play a role in the addictive component of AN, as described by the "auto-addiction opioids theory". Their interactions have demonstrated the psychological aspect of AN and have shown to prevent the functioning of the physiological homeostasis. Important opioids involved are ?-lipotropin, ?-endorphin and dynorphin, which interact with both µ and ? opioids receptors to regulate reward-mediated behavior and describe the higher incidence of AN seen in females. Moreover, ghrelin is known as the "hunger" hormone and helps stimulate growth hormone (GH) and hepatic insulin-like-growth-factor-1(IGF-1), maintaining anabolism and preserving a lean body mass. In AN, high levels of GH due to GH resistance along with low levels of IGF-1 are observed. Leptin plays a role in suppressing appetite through the inhibition of neuropeptide Y gene. Moreover, the CRF, opioid, leptin and ghrelin mechanisms operate collectively at the HPA axis and express the physiological and psychological components of AN. Fear conditioning is an intricate learning process occurring at the level of the hippocampus, amygdala, lateral septum and the dorsal raphe by involving three distinct pathways, the HPA axis-independent pathway, hypercortisolemia and ghrelin. Opioids mediate CRF through noradrenergic stimulation in association with the locus coeruleus. Furthermore, CRF's inhibitory effect on gonadotropin releasing hormone can be further explained by the direct relationship seen between CRF and opioids. Low levels of gonadotropin have been demonstrated in AN where only estrogen has shown to mediate energy intake. In addition, estrogen is involved in regulating µ receptor concentrations, but in turn both CRF and opioids regulate estrogen. Moreover, opioids and leptin are both an effect of AN, while many studies have demonstrated a causal relationship between CRF and anorexic behavior. Moreover, leptin, estrogen and ghrelin play a role as predictors of survival in starvation. Since both leptin and estrogen are associated with higher levels of bone marrow fat they represent a longer survival than those who favor the ghrelin pathway. Future studies should consider cohort studies involving prepubertal males and females with high CRF. This would help prevent the extrapolation of results from studies on mice and draw more meaningful conclusions in humans. Studies should also consider these mechanisms in post-AN patients, as well as look into what predisposes certain individuals to develop AN. Finally, due to its complex pathogenesis the treatment of AN should focus on both the pharmacological and behavioral perspectives.

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Patch dynamics modeling framework from pathogens' perspective: Unified and standardized approach for complicated epidemic systems.

Publications

Patch dynamics modeling framework from pathogens' perspective: Unified and standardized approach for complicated epidemic systems.

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