Phillip Cooley

Modeling targeted layered containment of an influenza pandemic in the United States

Planning a response to an outbreak of a pandemic strain of influenza is a high public health priority. Three research groups using different individual-based, stochastic simulation models have examined the consequences of intervention strategies chosen in consultation with U.S. public health workers. The first goal is to simulate the effectiveness of a set of potentially feasible intervention strategies. Combinations called targeted layered containment (TLC) of influenza antiviral treatment and prophylaxis and nonpharmaceutical interventions of quarantine, isolation, school closure, community social distancing, and workplace social distancing are considered. The second goal is to examine the robustness of the results to model assumptions. The comparisons focus on a pandemic outbreak in a population similar to that of Chicago, with ≈8.6 million people. The simulations suggest that at the expected transmissibility of a pandemic strain, timely implementation of a combination of targeted household antiviral prophylaxis, and social distancing measures could substantially lower the illness attack rate before a highly efficacious vaccine could become available. Timely initiation of measures and school closure play important roles. Because of the current lack of data on which to base such models, further field research is recommended to learn more about the sources of transmission and the effectiveness of social distancing measures in reducing influenza transmission.

FRED (A Framework for Reconstructing Epidemic Dynamics): an open-source software system for modeling infectious diseases and control strategies using census-based populations

BackgroundMathematical and computational models provide valuable tools that help public health planners to evaluate competing health interventions, especially for novel circumstances that cannot be examined through observational or controlled studies, such as pandemic influenza. The spread of diseases like influenza depends on the mixing patterns within the population, and these mixing patterns depend in part on local factors including the spatial distribution and age structure of the population, the distribution of size and composition of households, employment status and commuting patterns of adults, and the size and age structure of schools. Finally, public health planners must take into account the health behavior patterns of the population, patterns that often vary according to socioeconomic factors such as race, household income, and education levels.ResultsFRED (a Framework for Reconstructing Epidemic Dynamics) is a freely available open-source agent-based modeling system based closely on models used in previously published studies of pandemic influenza. This version of FRED uses open-access census-based synthetic populations that capture the demographic and geographic heterogeneities of the population, including realistic household, school, and workplace social networks. FRED epidemic models are currently available for every state and county in the United States, and for selected international locations.ConclusionsState and county public health planners can use FRED to explore the effects of possible influenza epidemics in specific geographic regions of interest and to help evaluate the effect of interventions such as vaccination programs and school closure policies. FRED is available under a free open source license in order to contribute to the development of better modeling tools and to encourage open discussion of modeling tools being used to evaluate public health policies. We also welcome participation by other researchers in the further development of FRED.

T-ACASI reduces bias in STD measurements: The National STD and Behavior Measurement Experiment:

Background: Although telephone surveys provide an economical method for assessing patterns of diagnosed sexually transmitted diseases (STDs) and STD-related behaviors in populations, the requirement that respondents report such information to human telephone interviewers introduces an opportunity for substantial reporting bias. Telephone computer-assisted self-interviewing (T-ACASI) surveys substitute a computer for human interviewers when asking sensitive questions. Methods: A randomized experiment was embedded in a telephone survey that drew probability samples of the populations of the United States (N = 1543) and Baltimore city (N = 744). Respondents were randomly assigned to have sensitive questions asked either by a T-ACASI computer or by a human telephone interviewer. Results: Respondents interviewed by a T-ACASI computer were more likely to report STD symptoms [dysuria, genital sores, genital discharge, and genital warts; adjusted odds ratios (ORs) = 1.5–2.8] and a diagnosis of gonococcal or chlamydial infection during the past year (adjusted ORs = 3.6 and 6.1). T-ACASI respondents with a main sex partner in the past year were more likely to report that their partner has had an STD (adjusted OR = 2.4). For some measurements, the impact of T-ACASI was strongest among younger and less-educated respondents. When sampling weights were applied to project National STD and Behavior Measurement Experiment results to the populations of the United States and Baltimore, we found that reliance on data obtained by human interviewers would underestimate the annual incidence of chlamydial and gonococcal infections in these populations by factors of 2.4 to 9.7. Conclusions: Compared with human telephone interviewers, T-ACASI surveys obtain increased reporting of STD symptoms, infections, and STD-related behaviors.

School closure as an influenza mitigation strategy: how variations in legal authority and plan criteria can alter the impact

BackgroundStates’ pandemic influenza plans and school closure statutes are intended to guide state and local officials, but most faced a great deal of uncertainty during the 2009 influenza H1N1 epidemic. Questions remained about whether, when, and for how long to close schools and about which agencies and officials had legal authority over school closures.MethodsThis study began with analysis of states’ school-closure statutes and pandemic influenza plans to identify the variations among them. An agent-based model of one state was used to represent as constants a population’s demographics, commuting patterns, work and school attendance, and community mixing patterns while repeated simulations explored the effects of variations in school closure authority, duration, closure thresholds, and reopening criteria.ResultsThe results show no basis on which to justify statewide rather than school-specific or community-specific authority for school closures. Nor do these simulations offer evidence to require school closures promptly at the earliest stage of an epidemic. More important are criteria based on monitoring of local case incidence and on authority to sustain closure periods sufficiently to achieve epidemic mitigation.ConclusionsThis agent-based simulation suggests several ways to improve statutes and influenza plans. First, school closure should remain available to state and local authorities as an influenza mitigation strategy. Second, influenza plans need not necessarily specify the threshold for school closures but should clearly define provisions for early and ongoing local monitoring. Finally, school closure authority may be exercised at the statewide or local level, so long as decisions are informed by monitoring incidence in local communities and schools.

Inconsistencies between human genetic cytolocations and those derived using genomic sequence

One result of the publishing of the human genome sequence is the ability to define objects through their position on the consensus sequence. While this has simplified the process of creating order maps for genes on a chromosome, it has created discrepancies between the published cytolocations of human genes, as presented through genetic references, and those locations derived computationally from the genomic sequence. For the 6,830 records with HUGO gene symbols shared between the online version of Mendelian Inheritance in Man and Ensembl, 18% of the records have a discrepancy of at least one cytogenetic band between the datasets. Discordance between data sets at this frequency would have a significant impact on the utility of datasets created by the amalgamation of numerous biological databases.

Analysis of a biomarker for Wegener's granulomatosis.

This molecular epidemiology study integrated questionnaire and genotype information to examine a disease susceptibility hypothesis. The study was based on a previously reported association demonstrated between a single nucleotide polymorphism (SNP) identified as A‐564G within the promoter of the proteinase‐3 gene (PRTN3) and the autoimmune disease Wegeners granulomatosis (WG). To further examine the strength of this association, we employed a family‐based design in which the inheritance of alternate alleles could be ascertained from the parents of affected and unaffected progeny. Genotype information for the study participants was derived from DNA samples from participants who collected buccal cells using a harvesting method that was non‐invasive and self‐administered. A brief questionnaire captured demographic data on the participants, the family relationships between participants, and the prevalence of autoimmune disease among family members. Samples were obtained on 132 individuals representing 43 WG cases and 89 unaffected controls. Thirty‐four nuclear families containing at least one unaffected sibling or parent of a WG case were represented in this sample. We found no evidence for an association between A‐564G and the likelihood of a WG diagnosis. We examined five additional SNPs and a sixth SNP haplotype within the PRTN3 promoter region in a family‐based association analysis and found no evidence that mutations within PRTN3 are associated with WG diagnosis.

Estimating HIV/AIDS incidence: Adjusting case reports for changes in definition

The purpose of this paper is to assess different procedures used in the modeling of the AIDS epidemic that account for changes in the definition of AIDS. Specifically, we assess three methods that adjust for the effect of definition changes on the modeling of HIV reconstructions. Our results suggest that how we account for definition changes is a critical influence on the severity and the shape of the estimated HIV/AIDS epidemic. Also, the scope of the recent (1993) definition change is sufficiently broad to limit approaches based on adjusting to a single, consistent definition. The most realistic approach appears to be one based on a dynamic disease progression model that accounts for AIDS definition changes via changes in disease incubation.

A linked risk group model for investigating the spread of HIV

This paper describes a model that simulates the spread of HIV and progression to AIDS. The model is based on classical models of disease transmission. It consists of six linked risk groups and tracks the numbers of infectives, AIDS cases, AIDS related deaths, and other deaths of infected persons in each risk group. Parametric functions are used to represent risk-group-specific and time-dependent average contact rates. Contacts are needle sharing, sexual contacts, or blood product transfers. An important feature of the model is that the contact rate parameters are estimated by minimizing differences between AIDS incidence and reported AIDS cases adjusted for undercounting biases. This feature results in an HIV epidemic curve that is analogous to one estimated by backcalculation models but whose dynamics are determined by simulating disease transmission. The model exhibits characteristics of both the disease transmission and the backcalculation approaches, i.e., the model: *reconstructs the historical behavior patterns of the different risk groups, *includes separate effects of treatment and changes in average contact rates, *accounts for other mortality risks for persons infected with HIV, *calculates short-term projections of AIDS incidence, HIV incidence, and HIV prevalence, *calculates cumulative HIV infections (the quantity calculated by backcalculation approaches) and HIV prevalence (the quantity measured by seroprevalence and sentinel surveys). This latter feature permits the validation of the estimates generated by two distinct approaches. We demonstrate the use of the model with an application to U.S. AIDS data through 1991.