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Inferring influenza dynamics and control in households.

ABSTRACT: Household-based interventions are the mainstay of public health policy against epidemic respiratory pathogens when vaccination is not available. Although the efficacy of these interventions has traditionally been measured by their ability to reduce the proportion of household contacts who exhibit symptoms [household secondary attack rate (hSAR)], this metric is difficult to interpret and makes only partial use of data collected by modern field studies. Here, we use Bayesian transmission model inference to analyze jointly both symptom reporting and viral shedding data from a three-armed study of influenza interventions. The reduction in hazard of infection in the increased hand hygiene intervention arm was 37.0% [8.3%, 57.8%], whereas the equivalent reduction in the other intervention arm was 27.2% [-0.46%, 52.3%] (increased hand hygiene and face masks). By imputing the presence and timing of unobserved infection, we estimated that only 61.7% [43.1%, 76.9%] of infections met the case criteria and were thus detected by the study design. An assessment of interventions using inferred infections produced more intuitively consistent attack rates when households were stratified by the speed of intervention, compared with the crude hSAR. Compared with adults, children were 2.29 [1.66, 3.23] times as infectious and 3.36 [2.31, 4.82] times as susceptible. The mean generation time was 3.39 d [3.06, 3.70]. Laboratory confirmation of infections by RT-PCR was only able to detect 79.6% [76.5%, 83.0%] of symptomatic infections, even at the peak of shedding. Our results highlight the potential use of robust inference with well-designed mechanistic transmission models to improve the design of intervention studies.

SUBMITTER: Lau MS

PROVIDER: S-EPMC4517268 | biostudies-other | 2015 Jul

REPOSITORIES: biostudies-other

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Project description:Seroprevalence survey is the most practical method for accurately estimating infection attack rate (IAR) in an epidemic such as influenza. These studies typically entail selecting an arbitrary titer threshold for seropositivity (e.g. microneutralization [MN] 1?40) and assuming the probability of seropositivity given infection (infection-seropositivity probability, ISP) is 100% or similar to that among clinical cases. We hypothesize that such conventions are not necessarily robust because different thresholds may result in different IAR estimates and serologic responses of clinical cases may not be representative. To illustrate our hypothesis, we used an age-structured transmission model to fully characterize the transmission dynamics and seroprevalence rises of 2009 influenza pandemic A/H1N1 (pdmH1N1) during its first wave in Hong Kong. We estimated that while 99% of pdmH1N1 infections became MN1?20 seropositive, only 72%, 62%, 58% and 34% of infections among age 3-12, 13-19, 20-29, 30-59 became MN1?40 seropositive, which was much lower than the 90%-100% observed among clinical cases. The fitted model was consistent with prevailing consensus on pdmH1N1 transmission characteristics (e.g. initial reproductive number of 1.28 and mean generation time of 2.4 days which were within the consensus range), hence our ISP estimates were consistent with the transmission dynamics and temporal buildup of population-level immunity. IAR estimates in influenza seroprevalence studies are sensitive to seropositivity thresholds and ISP adjustments which in current practice are mostly chosen based on conventions instead of systematic criteria. Our results thus highlighted the need for reexamining conventional practice to develop standards for analyzing influenza serologic data (e.g. real-time assessment of bias in ISP adjustments by evaluating the consistency of IAR across multiple thresholds and with mixture models), especially in the context of pandemics when robustness and comparability of IAR estimates are most needed for informing situational awareness and risk assessment. The same principles are broadly applicable for seroprevalence studies of other infectious disease outbreaks.

Project description:BackgroundMass drug administration (MDA) is part of the current trachoma control strategy, but it can be costly and results in many uninfected individuals receiving treatment. Here we explore whether alternative, targeted approaches are effective antibiotic-sparing strategies.Methodology/principal findingsWe analysed data on the prevalence of ocular infection with Chlamydia trachomatis and of active trachoma disease among 4,436 individuals from two communities in The Gambia (West Africa) and two communities in Tanzania (East Africa). An age- and household-structured mathematical model of transmission was fitted to these data using maximum likelihood. The presence of active inflammatory disease as a marker of infection in a household was, in general, significantly more sensitive (between 79% [95%CI: 60%-92%] and 86% [71%-95%] across the four communities) than as a marker of infection in an individual (24% [16%-33%]-66% [56%-76%]). Model simulations, under the best fit models for each community, showed that targeting treatment to households has the potential to be as effective as and significantly more cost-effective than mass treatment when antibiotics are not donated. The cost (2007US$) per incident infection averted ranged from 1.5 to 3.1 for MDA, from 1.0 to 1.7 for household-targeted treatment assuming equivalent coverage, and from 0.4 to 1.7 if household visits increased treatment coverage to 100% in selected households. Assuming antibiotics were donated, MDA was predicted to be more cost-effective unless opportunity costs incurred by individuals collecting antibiotics were included or household visits improved treatment uptake. Limiting MDA to children was not as effective in reducing infection as the other aforementioned distribution strategies.Conclusions/significanceOur model suggests that targeting antibiotics to households with active trachoma has the potential to be a cost-effective trachoma control measure, but further work is required to assess if costs can be reduced and to what extent the approach can increase the treatment coverage of infected individuals compared to MDA in different settings.

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Inferring influenza dynamics and control in households.

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