scholarly journals Modelling the impact of tuberculosis preventive therapy: the importance of disease progression assumptions

2019 ◽  
Author(s):  
Tom Sumner ◽  
Richard G. White
Keyword(s):  
Disease Model ◽  
Parametric Uncertainty ◽  
Preventive Therapy ◽  
Transmission Model ◽  
Number Needed To Treat ◽  
Model Structure ◽  
Risk Of Progression ◽  
Baseline Incidence ◽  
Modelling Studies ◽  
The Impact

AbstractBackgroundFollowing infection with Mycobacterium tuberculosis (M.tb) individuals may rapidly develop tuberculosis (TB) disease or enter “latent” infection state with a low risk of progression to disease. The mechanisms underlying this process are incompletely known. Mathematical models use a variety of structures and parameterisations to represent this progression from infection with M.tb to disease. This structural and parametric uncertainty may affect the predicted impact of interventions leading to incorrect conclusions and decision making.MethodsWe used a simple dynamic transmission model to explore the effect of uncertainty in model structure and parameterisation on the predicted impact of scaling up preventive therapy. We compared three commonly used model structures and used parameter values from two different data sources. Models 1 and 2 are equally consistent with observations of the time from infection to disease. Model 3, produces a worse fit to the data, but is widely used in published modelling studies. We simulated treatment of 5% of all M.tb infected individuals per year in a population of 10,000 and calculated the reduction in TB incidence and number needed to treat to avert one TB case over 10 years.ResultsThe predicted impact of the preventive therapy intervention depended on both the model structure and the parameterisation of that structure. For example, at a baseline annual TB incidence of 500/100,000, the impact ranged from 11% to 27% and the number needed to treat to avert one TB case varied between 38 and 124. The relative importance of structure and parameters varied depending on the baseline incidence of TB.DiscussionOur analysis shows that the choice of model structure and the parameterisation can influence the predicted impact of interventions. Modelling studies should consider incorporating structural uncertainty in their analysis. Not doing so may lead to incorrect conclusions on the impact of interventions.

scholarly journals The predicted impact of tuberculosis preventive therapy: the importance of disease progression assumptions

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Tom Sumner ◽  
Richard G. White
Keyword(s):  
Decision Making ◽  
Latent Infection ◽  
Preventive Therapy ◽  
Data Sources ◽  
Number Needed To Treat ◽  
Model Structure ◽  
Relative Importance ◽  
Structural Uncertainty ◽  
Risk Of Progression ◽  
Model Structures

Abstract Background Following infection with Mycobacterium tuberculosis (M.tb), individuals may rapidly develop tuberculosis (TB) disease or enter a “latent” infection state with a low risk of progression to disease. Mathematical models use a variety of structures and parameterisations to represent this process. The effect of these different assumptions on the predicted impact of TB interventions has not been assessed. Methods We explored how the assumptions made about progression from infection to disease affect the predicted impact of TB preventive therapy. We compared the predictions using three commonly used model structures, and parameters derived from two different data sources. Results The predicted impact of preventive therapy depended on both the model structure and parameterisation. At a baseline annual TB incidence of 500/100,000, there was a greater than 2.5-fold difference in the predicted reduction in incidence due to preventive therapy (ranging from 6 to 16%), and the number needed to treat to avert one TB case varied between 67 and 157. The relative importance of structure and parameters depended on baseline TB incidence and assumptions about the efficacy of preventive therapy, with the choice of structure becoming more important at higher incidence. Conclusions The assumptions use to represent progression to disease in models are likely to influence the predicted impact of preventive therapy and other TB interventions. Modelling estimates of TB preventive therapy should consider routinely incorporating structural uncertainty, particularly in higher burden settings. Not doing so may lead to inaccurate and over confident conclusions, and sub-optimal evidence for decision making.

scholarly journals Model uncertainty and decision making: Predicting the Impact of COVID-19 Using the CovidSim Epidemiological Code

2020 ◽  
Author(s):  
Wouter Edeling ◽  
Arabnejad Hamid ◽  
Robert Sinclair ◽  
Diana Suleimenova ◽  
Krishnakumar Gopalakrishnan ◽  
...  
Keyword(s):  
Parametric Uncertainty ◽  
Model Structure ◽  
Validation Data ◽  
Government Interventions ◽  
Rapid Spread ◽  
Imperfect Knowledge ◽  
Model Structure Uncertainty ◽  
Scenario Uncertainty ◽  
The Uk ◽  
The Impact

Abstract The severe acute respiratory syndrome coronavirus 2 (SARS-CoV2) virus has rapidly spread worldwide since December 2019, and early modelling work of this pandemic has assisted in identifying effective government interventions. The UK government relied in part on the CovidSim model developed by the MRC Centre for Global Infectious Disease Analysis at Imperial College London, to model various non-pharmaceutical intervention strategies, and guide its government policy in seeking to deal with the rapid spread of the COVID-19 pandemic during March and April 2020. CovidSim is subject to different sources of uncertainty, namely parametric uncertainty in the inputs, model structure uncertainty (i.e., missing epidemiological processes) and scenario uncertainty, which relates to uncertainty in the set of conditions under which the model is applied. We have undertaken an extensive parametric sensitivity analysis and uncertainty quantification of the current CovidSim code. From the over 900 parameters that are provided as input to CovidSim, we identified a key subset of 19 parameters to which the code output is most sensitive. We find that the uncertainty in the code is substantial, in the sense that imperfect knowledge in these inputs will be magnified to the outputs, up to the extent of ca. 300%. Most of this uncertainty can be traced back to the sensitivity of three parameters. Compounding this, the model can display significant bias with respect to observed data, such that the output variance does not capture this validation data with high probability. We conclude that quantifying the parametric input uncertainty is not sufficient, and that the effect of model structure and scenario uncertainty cannot be ignored when validating the model in a probabilistic sense.

scholarly journals Model uncertainty and decision making: Predicting the Impact of COVID-19 Using the CovidSim Epidemiological Code

2020 ◽  
Author(s):  
Wouter Edeling ◽  
Arabnejad Hamid ◽  
Robert Sinclair ◽  
Diana Suleimenova ◽  
Krishnakumar Gopalakrishnan ◽  
...  
Keyword(s):  
Parametric Uncertainty ◽  
Model Structure ◽  
Validation Data ◽  
Government Interventions ◽  
Rapid Spread ◽  
Imperfect Knowledge ◽  
Model Structure Uncertainty ◽  
Scenario Uncertainty ◽  
The Uk ◽  
The Impact

Abstract The severe acute respiratory syndrome coronavirus 2 (SARS-CoV2) virus has rapidly spread worldwide since December 2019, and early modelling work of this pandemic has assisted in identifying effective government interventions. The UK government relied in part on the CovidSim model developed by the MRC Centre for Global Infectious Disease Analysis at Imperial College London, to model various non-pharmaceutical intervention strategies, and guide its government policy in seeking to deal with the rapid spread of the COVID-19 pandemic during March and April 2020. CovidSim is subject to different sources of uncertainty, namely parametric uncertainty in the inputs, model structure uncertainty (i.e. missing epidemiological processes) and scenario uncertainty, which relates to uncertainty in the set of conditions under which the model is applied. We have undertaken an extensive parametric sensitivity analysis and uncertainty quantification of the current CovidSim code. From the over 900 parameters that are provided as input to CovidSim, we identified a key subset of 20 parameters to which the code output is most sensitive. We find that the uncertainty in the code is substantial, in the sense that imperfect knowledge in these inputs will be magnified to the outputs, up to the extent of ca. 300%. Most of this uncertainty can be traced back to the sensitivity of three parameters. Compounding this, the model can display significant bias with respect to observed data, such that the output variance does not capture this validation data with high probability. We conclude that quantifying the parametric input uncertainty is not sufficient, and that the effect of model structure and scenario uncertainty cannot be ignored when validating the model in a probabilistic sense.

Rice Fortified with Iron Given Weekly Increases Hemoglobin Levels and Reduces Anemia in Infants: a Community Intervention Trial

2013 ◽  
Vol 83 (1) ◽  
pp. 59-66 ◽  
Author(s):  
Francisco Plácido Nogueira Arcanjo ◽  
Paulo Roberto Santos ◽  
Álvaro Jorge Madeiro Leite ◽  
Francisco Sulivan Bastos Mota ◽  
Sérgio Duarte Segall
Keyword(s):  
Community Intervention ◽  
Capillary Blood ◽  
Number Needed To Treat ◽  
Intervention Trial ◽  
Day Care Centers ◽  
Control Center ◽  
Ferric Pyrophosphate ◽  
Child Day Care ◽  
Child Day Care Centers ◽  
The Impact

More than two billion people suffer from anemia worldwide, and it is estimated that more than 50 % of cases are caused by iron deficiency. In this community intervention trial, we evaluated infants aged 10 to 23 months of age (n = 171) from two public child day-care centers. Intervention lasted 18 weeks. The 50-g individual portion (uncooked) of fortified rice provided 56.4 mg of elemental iron as ferric pyrophosphate. Capillary blood samples to test for anemia were taken at baseline and at endpoint. The objective of this study was to evaluate the impact of rice fortified with iron (Ultrarice®) on hemoglobin and anemia prevalence compared with standard household rice. For the fortified rice center, baseline mean hemoglobin was 113.7 ± 9.2 g/L, and at endpoint 119.5 ± 7.7 g/L, p < 0.0001; for the standard rice center, baseline mean hemoglobin value was 113.5 ± 40.7 g/L, and at endpoint 113.6 ± 21.0, p = 0.99. Anemia prevalence for the fortified rice center was 27.8 % (20/72) at baseline, and 11.1 % (8/72) at endpoint, p = 0.012; for the control center, 47.1 % (33/70) were anemic at baseline, and 37.1 % (26/70) at the end of the study, p = 0.23. The Number Needed to Treat (NNT) was 4. In this intervention, rice fortified with iron given weekly was effective in increasing hemoglobin levels and reducing anemia in infants.

Modelling Agricultural Controls for Flooding and Soil Erosion

2018 ◽  
pp. 776-796
Author(s):  
Roger Moussa ◽  
Bruno Cheviron
Keyword(s):  
Spatial Distribution ◽  
Model Structure ◽  
Land Uses ◽  
Research Challenges ◽  
Water And Sediment ◽  
Flow Processes ◽  
Cultivation Practices ◽  
Modelling Strategies ◽  
The Impact ◽  
Significant Factors

Floods are the highest-impact natural disasters. In agricultural basins, anthropogenic features are significant factors in controlling flood and erosion. A hydrological-hydraulic-erosion diagnosis is necessary in order to choose the most relevant action zones and to make recommendations for alternative land uses and cultivation practices in order to control and reduce floods and erosion. This chapter first aims to provide an overview of the flow processes represented in the various possible choices of model structure and refinement. It then focuses on the impact of the spatial distribution and temporal variation of hydrological soil properties in farmed basins, representing their effects on the modelled water and sediment flows. Research challenges and leads are then tackled, trying to identify the conditions in which sufficient adequacy exists between site data and modelling strategies.

scholarly journals Model-based evaluation of school- and non-school-related measures to control the COVID-19 pandemic

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Ganna Rozhnova ◽  
Christiaan H. van Dorp ◽  
Patricia Bruijning-Verhagen ◽  
Martin C. J. Bootsma ◽  
Janneke H. H. M. van de Wijgert ◽  
...  
Keyword(s):  
Reproduction Number ◽  
Additional Benefit ◽  
Transmission Model ◽  
Time Points ◽  
School Based ◽  
Admission Data ◽  
Age Structured ◽  
The Impact ◽  
Pandemic Wave

AbstractThe role of school-based contacts in the epidemiology of SARS-CoV-2 is incompletely understood. We use an age-structured transmission model fitted to age-specific seroprevalence and hospital admission data to assess the effects of school-based measures at different time points during the COVID-19 pandemic in the Netherlands. Our analyses suggest that the impact of measures reducing school-based contacts depends on the remaining opportunities to reduce non-school-based contacts. If opportunities to reduce the effective reproduction number (Re) with non-school-based measures are exhausted or undesired and Re is still close to 1, the additional benefit of school-based measures may be considerable, particularly among older school children. As two examples, we demonstrate that keeping schools closed after the summer holidays in 2020, in the absence of other measures, would not have prevented the second pandemic wave in autumn 2020 but closing schools in November 2020 could have reduced Re below 1, with unchanged non-school-based contacts.

scholarly journals Consideration of Gut Microbiome in Murine Models of Diseases

2021 ◽  
Vol 9 (5) ◽  
pp. 1062
Author(s):  
Chunye Zhang ◽  
Craig L. Franklin ◽  
Aaron C. Ericsson
Keyword(s):  
Animal Models ◽  
Mouse Models ◽  
Biomedical Research ◽  
Gut Microbiome ◽  
Close Association ◽  
Disease Model ◽  
Potential Contributor ◽  
Potential Factors ◽  
Models Of Disease ◽  
The Impact

The gut microbiome (GM), a complex community of bacteria, viruses, protozoa, and fungi located in the gut of humans and animals, plays significant roles in host health and disease. Animal models are widely used to investigate human diseases in biomedical research and the GM within animal models can change due to the impact of many factors, such as the vendor, husbandry, and environment. Notably, variations in GM can contribute to differences in disease model phenotypes, which can result in poor reproducibility in biomedical research. Variation in the gut microbiome can also impact the translatability of animal models. For example, standard lab mice have different pathogen exposure experiences when compared to wild or pet store mice. As humans have antigen experiences that are more similar to the latter, the use of lab mice with more simplified microbiomes may not yield optimally translatable data. Additionally, the literature describes many methods to manipulate the GM and differences between these methods can also result in differing interpretations of outcomes measures. In this review, we focus on the GM as a potential contributor to the poor reproducibility and translatability of mouse models of disease. First, we summarize the important role of GM in host disease and health through different gut–organ axes and the close association between GM and disease susceptibility through colonization resistance, immune response, and metabolic pathways. Then, we focus on the variation in the microbiome in mouse models of disease and address how this variation can potentially impact disease phenotypes and subsequently influence research reproducibility and translatability. We also discuss the variations between genetic substrains as potential factors that cause poor reproducibility via their effects on the microbiome. In addition, we discuss the utility of complex microbiomes in prospective studies and how manipulation of the GM through differing transfer methods can impact model phenotypes. Lastly, we emphasize the need to explore appropriate methods of GM characterization and manipulation.

scholarly journals Evolution of disease transmission during the COVID-19 pandemic: patterns and determinants

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jie Zhu ◽  
Blanca Gallego
Keyword(s):  
Public Health ◽  
Disease Transmission ◽  
Clustering Algorithm ◽  
Health Interventions ◽  
Transmission Model ◽  
Model Parameters ◽  
Public Health Interventions ◽  
Forecasting Accuracy ◽  
Time Lagged ◽  
The Impact

AbstractEpidemic models are being used by governments to inform public health strategies to reduce the spread of SARS-CoV-2. They simulate potential scenarios by manipulating model parameters that control processes of disease transmission and recovery. However, the validity of these parameters is challenged by the uncertainty of the impact of public health interventions on disease transmission, and the forecasting accuracy of these models is rarely investigated during an outbreak. We fitted a stochastic transmission model on reported cases, recoveries and deaths associated with SARS-CoV-2 infection across 101 countries. The dynamics of disease transmission was represented in terms of the daily effective reproduction number ($$R_t$$ R t ). The relationship between public health interventions and $$R_t$$ R t was explored, firstly using a hierarchical clustering algorithm on initial $$R_t$$ R t patterns, and secondly computing the time-lagged cross correlation among the daily number of policies implemented, $$R_t$$ R t , and daily incidence counts in subsequent months. The impact of updating $$R_t$$ R t every time a prediction is made on the forecasting accuracy of the model was investigated. We identified 5 groups of countries with distinct transmission patterns during the first 6 months of the pandemic. Early adoption of social distancing measures and a shorter gap between interventions were associated with a reduction on the duration of outbreaks. The lagged correlation analysis revealed that increased policy volume was associated with lower future $$R_t$$ R t (75 days lag), while a lower $$R_t$$ R t was associated with lower future policy volume (102 days lag). Lastly, the outbreak prediction accuracy of the model using dynamically updated $$R_t$$ R t produced an average AUROC of 0.72 (0.708, 0.723) compared to 0.56 (0.555, 0.568) when $$R_t$$ R t was kept constant. Monitoring the evolution of $$R_t$$ R t during an epidemic is an important complementary piece of information to reported daily counts, recoveries and deaths, since it provides an early signal of the efficacy of containment measures. Using updated $$R_t$$ R t values produces significantly better predictions of future outbreaks. Our results found variation in the effect of early public health interventions on the evolution of $$R_t$$ R t over time and across countries, which could not be explained solely by the timing and number of the adopted interventions.

scholarly journals The effects of non-pharmaceutical interventions on SARS-CoV-2 transmission in different socioeconomic populations in Kuwait: a modeling study

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Fatima Khadadah ◽  
Abdullah A. Al-Shammari ◽  
Ahmad Alhashemi ◽  
Dari Alhuwail ◽  
Bader Al-Saif ◽  
...  
Keyword(s):  
Disease Transmission ◽  
Reproduction Number ◽  
Transmission Model ◽  
Socioeconomic Groups ◽  
Before And After ◽  
Cross Transmission ◽  
Community Transmission ◽  
The Impact ◽  
Pharmaceutical Interventions ◽  
The Basic Reproduction Number

Abstract Background Aggressive non-pharmaceutical interventions (NPIs) may reduce transmission of SARS-CoV-2. The extent to which these interventions are successful in stopping the spread have not been characterized in countries with distinct socioeconomic groups. We compared the effects of a partial lockdown on disease transmission among Kuwaitis (P1) and non-Kuwaitis (P2) living in Kuwait. Methods We fit a modified metapopulation SEIR transmission model to reported cases stratified by two groups to estimate the impact of a partial lockdown on the effective reproduction number ($$ {\mathcal{R}}_e $$ R e ). We estimated the basic reproduction number ($$ {\mathcal{R}}_0 $$ R 0 ) for the transmission in each group and simulated the potential trajectories of an outbreak from the first recorded case of community transmission until 12 days after the partial lockdown. We estimated $$ {\mathcal{R}}_e $$ R e values of both groups before and after the partial curfew, simulated the effect of these values on the epidemic curves and explored a range of cross-transmission scenarios. Results We estimate $$ {\mathcal{R}}_e $$ R e at 1·08 (95% CI: 1·00–1·26) for P1 and 2·36 (2·03–2·71) for P2. On March 22nd, $$ {\mathcal{R}}_e $$ R e for P1 and P2 are estimated at 1·19 (1·04–1·34) and 1·75 (1·26–2·11) respectively. After the partial curfew had taken effect, $$ {\mathcal{R}}_e $$ R e for P1 dropped modestly to 1·05 (0·82–1·26) but almost doubled for P2 to 2·89 (2·30–3·70). Our simulated epidemic trajectories show that the partial curfew measure greatly reduced and delayed the height of the peak in P1, yet significantly elevated and hastened the peak in P2. Modest cross-transmission between P1 and P2 greatly elevated the height of the peak in P1 and brought it forward in time closer to the peak of P2. Conclusion Our results indicate and quantify how the same lockdown intervention can accentuate disease transmission in some subpopulations while potentially controlling it in others. Any such control may further become compromised in the presence of cross-transmission between subpopulations. Future interventions and policies need to be sensitive to socioeconomic and health disparities.

Quantifying parametric uncertainty in the Rothermel model

2008 ◽  
Vol 17 (5) ◽  
pp. 638 ◽  
Author(s):  
Edwin Jimenez ◽  
M. Yousuff Hussaini ◽  
Scott Goodrick
Keyword(s):  
Variance Reduction ◽  
Input Parameter ◽  
Parametric Uncertainty ◽  
Fire Spread ◽  
The United States ◽  
Linear System Of Equations ◽  
Spread Model ◽  
Non Linear System ◽  
The Impact ◽  
Sensitivity Derivative

The purpose of the present work is to quantify parametric uncertainty in the Rothermel wildland fire spread model (implemented in software such as BehavePlus3 and FARSITE), which is undoubtedly among the most widely used fire spread models in the United States. This model consists of a non-linear system of equations that relates environmental variables (input parameter groups) such as fuel type, fuel moisture, terrain, and wind to describe the fire environment. This model predicts important fire quantities (output parameters) such as the head rate of spread, spread direction, effective wind speed, and fireline intensity. The proposed method, which we call sensitivity derivative enhanced sampling, exploits sensitivity derivative information to accelerate the convergence of the classical Monte Carlo method. Coupled with traditional variance reduction procedures, it offers up to two orders of magnitude acceleration in convergence, which implies that two orders of magnitude fewer samples are required for a given level of accuracy. Thus, it provides an efficient method to quantify the impact of input uncertainties on the output parameters.

Sign in / Sign up

Export Citation Format

Share Document