scholarly journals A nonlinear programming approach for estimation of transmission parameters in childhood infectious disease using a continuous time model

2012 ◽  
Vol 9 (73) ◽  
pp. 1983-1997 ◽  
Author(s):  
Daniel P. Word ◽  
Derek A. T. Cummings ◽  
Donald S. Burke ◽  
Sopon Iamsirithaworn ◽  
Carl D. Laird
Keyword(s):  
Infectious Disease ◽  
Nonlinear Programming ◽  
Differential Equations ◽  
Large Scale ◽  
Efficient Estimation ◽  
Programming Approach ◽  
Transmission Parameters ◽  
Disease Case ◽  
Primal Dual ◽  
Case Data

Mathematical models can enhance our understanding of childhood infectious disease dynamics, but these models depend on appropriate parameter values that are often unknown and must be estimated from disease case data. In this paper, we develop a framework for efficient estimation of childhood infectious disease models with seasonal transmission parameters using continuous differential equations containing model and measurement noise. The problem is formulated using the simultaneous approach where all state variables are discretized, and the discretized differential equations are included as constraints, giving a large-scale algebraic nonlinear programming problem that is solved using a nonlinear primal–dual interior-point solver. The technique is demonstrated using measles case data from three different locations having different school holiday schedules, and our estimates of the seasonality of the transmission parameter show strong correlation to school term holidays. Our approach gives dramatic efficiency gains, showing a 40–400-fold reduction in solution time over other published methods. While our approach has an increased susceptibility to bias over techniques that integrate over the entire unknown state-space, a detailed simulation study shows no evidence of bias. Furthermore, the computational efficiency of our approach allows for investigation of a large model space compared with more computationally intensive approaches.

scholarly journals Health inequities in influenza transmission and surveillance

2020 ◽  
Author(s):  
Casey M. Zipfel ◽  
Shweta Bansal
Keyword(s):  
Public Health ◽  
Infectious Disease ◽  
Health Disparities ◽  
Disease Burden ◽  
Large Scale ◽  
Health Inequities ◽  
Low Ses ◽  
Healthcare Data ◽  
Use Of Resources ◽  
Disease Case

AbstractMotivationThe lower an individual’s socio-economic position, the higher their risk of poor health in low-, middle-, and high-income settings alike. As health inequities grow, it is imperative that we develop an empirically-driven mechanistic understanding of the determinants of health disparities, and capture disease burden in at-risk populations to prevent exacerbation of disparities. Past work has been limited in data or scope and has thus fallen short of generating generalizable insights.Approach & ResultsHere, we integrate empirical data from observational studies and large-scale healthcare data with models to characterize the dynamics and spatial heterogeneity of health disparities in an infectious disease case study: influenza. We find that variation in social, behavioral, and physiological determinants exacerbates influenza epidemics, and that low SES individuals disproportionately bear the burden of infection. We also identify geographical hotspots of disproportionate influenza burden in low SES populations, and find that these differences are most predicted by variation in healthcare utilization and susceptibility.ConclusionThe negative association between health and socio-economic prosperity has a long history in the epidemiological literature. Addressing health inequities in respiratory infectious disease burden is an important step towards social justice in public health, and ignoring them promises to pose a serious threat to the entire population. Our results highlight that the effect of overlapping behavioral social, and physiological factors is synergistic and that reducing this intersectionality can significantly reduce inequities. Additionally, health disparities are expressed geographically, as targeting public health efforts spatially may be an efficient use of resources to abate inequities.

scholarly journals Health inequities in influenza transmission and surveillance

2021 ◽  
Vol 17 (3) ◽  
pp. e1008642
Author(s):  
Casey M. Zipfel ◽  
Vittoria Colizza ◽  
Shweta Bansal
Keyword(s):  
Public Health ◽  
Infectious Disease ◽  
Health Disparities ◽  
Disease Burden ◽  
Large Scale ◽  
Health Inequities ◽  
Influenza Surveillance ◽  
Sickness Absenteeism ◽  
Use Of Resources ◽  
Disease Case

The lower an individual’s socioeconomic position, the higher their risk of poor health in low-, middle-, and high-income settings alike. As health inequities grow, it is imperative that we develop an empirically-driven mechanistic understanding of the determinants of health disparities, and capture disease burden in at-risk populations to prevent exacerbation of disparities. Past work has been limited in data or scope and has thus fallen short of generalizable insights. Here, we integrate empirical data from observational studies and large-scale healthcare data with models to characterize the dynamics and spatial heterogeneity of health disparities in an infectious disease case study: influenza. We find that variation in social and healthcare-based determinants exacerbates influenza epidemics, and that low socioeconomic status (SES) individuals disproportionately bear the burden of infection. We also identify geographical hotspots of influenza burden in low SES populations, much of which is overlooked in traditional influenza surveillance, and find that these differences are most predicted by variation in susceptibility and access to sickness absenteeism. Our results highlight that the effect of overlapping factors is synergistic and that reducing this intersectionality can significantly reduce inequities. Additionally, health disparities are expressed geographically, and targeting public health efforts spatially may be an efficient use of resources to abate inequities. The association between health and socioeconomic prosperity has a long history in the epidemiological literature; addressing health inequities in respiratory-transmitted infectious disease burden is an important step towards social justice in public health, and ignoring them promises to pose a serious threat.

scholarly journals A Mass-Temperature Decoupled Discretization Strategy for Large-Scale Molecular-Level Kinetic Model

2021 ◽  
Author(s):  
Zhengyu Chen ◽  
Dong Guan ◽  
Xiaojie Zhang ◽  
Ying Zhang ◽  
Suoqi Zhao ◽  
...  
Keyword(s):  
Kinetic Model ◽  
Differential Equations ◽  
Heat Balance ◽  
Large Scale ◽  
Molecular Level ◽  
Complex Mixture ◽  
Estimation Methods ◽  
Computational Time ◽  
Node Number ◽  
Mass Temperature

The molecular conversion of complex mixture involves a large number of species and reactions. The corresponding kinetic model is consist of a series of ordinary differential equations (ODEs) with severe stiffness, leading to an exponentially growing computational time. To reduce the computational time, we proposed a mass-temperature decoupled discretization strategy for a large-scale molecular-level kinetic model. The method separates the mass balance and heat balance calculations in the rigorous adiabatic reactor model and divided the reactor into several isothermal segments. After discretization, the differential equations for heat balance can be replaced by algebraic equations between nodes. We used a molecular-level diesel hydrotreating kinetic model as the case to validate the proposed method. We investigated the effects of temperature estimation methods and node number on the accuracy of the model. A good agreement between the discretization model and rigorous model was observed while the computational time was significantly shortened

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