Estimating heterogeneous transmission with multiple infectives using MCMC methods

Chu, Haitao; Preziosi, Marie Pierre; Halloran, M. Elizabeth

doi:10.1002/sim.1590

Cited by 9 publications

(5 citation statements)

References 28 publications

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“…Other recent approaches for estimating VE S and VE I (e.g., Chu et al, 2004;Halloran et al, 2003) require details about the sequence of infections within each household in order to determine primary and secondary cases. In practice, it may be difficult to obtain information on time of onset or on "who infected whom.…”

Section: Discussionmentioning

confidence: 99%

“…While direct estimation of vaccine efficacy was not considered in their paper, they did propose two separate forms for including heterogeneity in infectiousness and susceptibility. Without considering transmission probability from community, Chu et al (2004) proposed a logistic regressionbased method that specifies the heterogeneous transmission probabilities among households as a function of vaccination statuses of the infective (only the primary cases) and the susceptible. Their method is based on a Bayesian approach which assumes heterogeneous transmission between and within households and allows Downloaded by [University of Tasmania] at 16:30 09 January 2015 simultaneous estimation of VE S , VE I , and VE T (VE T is the total VE, a measure combining the vaccine's effects on susceptibility and infectiousness).…”

Section: Introductionmentioning

confidence: 99%

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Estimating Vaccine Efficacy from Outbreak Size Household Data in the Presence of Heterogeneous Transmission Probabilities

Davis

Waller

Haber

2006

Journal of Biopharmaceutical Statistics

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We develop a Bayesian approach for estimating vaccine efficacy for susceptibility (VEs) and infectiousness (VEI) using outbreak size household data. Our method allows for heterogeneity in transmission probabilities due to factors that are related to individuals' characteristics, such as age, in addition to vaccination status. It also allows for between-household heterogeneity in transmission probabilities due to random effects associated with households, such as genetic or environmental effects. Using age as a potential covariate causing heterogeneity in individuals' transmission probabilities in households consisting of adults and children, we present the results of a simulation study designed to evaluate the performance of the proposed estimators of VEs and VE(I). We found that estimates of VE(I) have larger bias and variance compared to those of VEs. We also use the approach to compare two vaccination designs: one vaccinating both adults and children, the other only children. Simulations reveal that the design that vaccinates both adults and children provides better estimates of VEs. There is no obvious difference between the two designs in the performance of the estimates of VE(I). In regard to random effects between households and the scenarios considered, models that do not account for between-households heterogeneity produce fairly robust estimates even when household-level random effects are present.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Estimating Vaccine Efficacy from Outbreak Size Household Data in the Presence of Heterogeneous Transmission Probabilities

Davis

Waller

Haber

2006

Journal of Biopharmaceutical Statistics

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“…Parameters are estimated using Markov chain Monte Carlo methods. Reference: [ 26 ] Title: Mortality of a heterogeneous cohort; description and implications. Abstract: A recent model for heterogeneous mortality by Vaupel et al is shown to be based on incorrect definitions.…”

Section: Appendix 1: Abstracts From Literature Searchmentioning

confidence: 99%

Disaggregating proportional multistate lifetables by population heterogeneity to estimate intervention impacts on inequalities

Mizdrak

Wilson

Davies

et al. 2022

Popul Health Metrics

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Background Simulation models can be used to quantify the projected health impact of interventions. Quantifying heterogeneity in these impacts, for example by socioeconomic status, is important to understand impacts on health inequalities. We aim to disaggregate one type of Markov macro-simulation model, the proportional multistate lifetable, ensuring that under business-as-usual (BAU) the sum of deaths across disaggregated strata in each time step returns the same as the initial non-disaggregated model. We then demonstrate the application by deprivation quintiles for New Zealand (NZ), for: hypothetical interventions (50% lower all-cause mortality, 50% lower coronary heart disease mortality) and a dietary intervention to substitute 59% of sodium with potassium chloride in the food supply. Methods We developed a disaggregation algorithm that iteratively rescales mortality, incidence and case-fatality rates by time-step of the model to ensure correct total population counts were retained at each step. To demonstrate the algorithm on deprivation quintiles in NZ, we used the following inputs: overall (non-disaggregated) all-cause mortality & morbidity rates, coronary heart disease incidence & case fatality rates; stroke incidence & case fatality rates. We also obtained rate ratios by deprivation for these same measures. Given all-cause and cause-specific mortality rates by deprivation quintile, we derived values for the incidence, case fatality and mortality rates for each quintile, ensuring rate ratios across quintiles and the total population mortality and morbidity rates were returned when averaged across groups. The three interventions were then run on top of these scaled BAU scenarios. Results The algorithm exactly disaggregated populations by strata in BAU. The intervention scenario life years and health adjusted life years (HALYs) gained differed slightly when summed over the deprivation quintile compared to the aggregated model, due to the stratified model (appropriately) allowing for differential background mortality rates by strata. Modest differences in health gains (HALYs) resulted from rescaling of sub-population mortality and incidence rates to ensure consistency with the aggregate population. Conclusion Policy makers ideally need to know the effect of population interventions estimated both overall, and by socioeconomic and other strata. We demonstrate a method and provide code to do this routinely within proportional multistate lifetable simulation models and similar Markov models.

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“…Parameters are estimated using Markov chain Monte Carlo methods. Reference: (25) Title: Mortality of a heterogeneous cohort; description and implications . Abstract: A recent model for heterogeneous mortality by Vaupel et al is shown to be based on incorrect definitions. An alternative formulation is presented.…”

Section: Abstracts From Literature Searchmentioning

confidence: 99%

Disaggregating proportional multistate lifetables by population heterogeneity to estimate intervention impacts on inequalities

Andersen

Mizdrak

Wilson

et al. 2021

Preprint

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BackgroundSimulation models can be used to quantify the projected health impact of interventions. Quantifying heterogeneity in these impacts, for example by socioeconomic status, is important to understand impacts on health inequalities.We aim to disaggregate one type of Markov macro-simulation model, the proportional multistate lifetable, ensuring that under business-as-usual (BAU) the sum of deaths across disaggregated strata in each time step returns the same as the initial non-disaggregated model. We then demonstrate the application by deprivation quintiles for New Zealand (NZ), for: hypothetical interventions (50% lower all-cause mortality, 50% lower coronary heart disease mortality) and a dietary intervention to substitute 59% of sodium with potassium chloride in the food supply.MethodsWe developed a disaggregation algorithm that iteratively rescales mortality, incidence and case fatality rates by time-step of the model to ensure correct total population counts were retained at each step.To demonstrate the algorithm on deprivation quintiles in NZ, we used the following inputs: overall (non-disaggregated) all-cause mortality &morbidity rates, coronary heart disease incidence &case fatality rates; stroke incidence &case fatality rates. We also obtained rate ratios by deprivation for these same measures. Given all-cause and cause-specific mortality rates by deprivation quintile, we derived values for the incidence, case fatality and mortality rates for each quintile, ensuring rate ratios across quintiles and the total population mortality and morbidity rates were returned when averaged across groups.The three interventions were then run on top of these scaled BAU scenarios.ResultsThe algorithm exactly disaggregated populations by strata in BAU. The intervention scenario life years and health adjusted life years (HALYs) gained differed slightly when summed over the deprivation quintile compared to the aggregated model, due to the stratified model (appropriately) allowing for differential background mortality rates by strata. Modest differences in health gains (health adjusted life years) resulted from rescaling of sub-population mortality and incidence rates to ensure consistency with the aggregate population.ConclusionPolicy makers ideally need to know the effect of population interventions estimated both overall, and by socioeconomic and other strata. We demonstrate a method and provide code to do this routinely within proportional multistate lifetable simulation models and similar Markov models.

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Estimating heterogeneous transmission with multiple infectives using MCMC methods

Cited by 9 publications

References 28 publications

Estimating Vaccine Efficacy from Outbreak Size Household Data in the Presence of Heterogeneous Transmission Probabilities

Estimating Vaccine Efficacy from Outbreak Size Household Data in the Presence of Heterogeneous Transmission Probabilities

Disaggregating proportional multistate lifetables by population heterogeneity to estimate intervention impacts on inequalities

Disaggregating proportional multistate lifetables by population heterogeneity to estimate intervention impacts on inequalities

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