| 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.The household offers an ideal setting to study the transmission dynamics of viral respiratory pathogens (1–5) and, during periods of severe epidemics, to intervene and reduce the number of infections (6). Therefore, it is also the ideal setting in which to conduct trials of interventions designed to reduce infectivity and susceptibility. The known-index trial design has been used to measure the efficacy of different types of intervention in recent years, including nonpharmaceutical interventions (7–9), antivirals (10), and vaccines (11–13). In these studies, symptomatic individuals are recruited at a health care facility and asked if they—and potentially other members of their household—may want to participate in the trial. If the index agrees, biological samples are taken at that time in the clinic. Follow-ups normally occur in the household, with the first visit as soon after the recruitment of the index as possible. If other members of the household agree to participate, samples are taken at regular intervals after that first follow-up from the index and additional participating household members. Biological samples used in these studies include nasal or throat swabs, nasopharyngeal aspirates, and blood samples. Many different assays can be conducted on the samples (depending to some extent on the sample handling protocol), for example, rapid tests (14), RT-PCR (7, 15), and B-cell assays (16). Participants may also be asked to record symptoms in a diary or to report them over the phone.The primary outcome measure for these trials is the household secondary attack rate (hSAR) (sometimes called secondary infection risk). The hSAR is most commonly defined as the proportion of nonindex household members who become cases, according to prespecified criteria, during the period of the study. Cases are usually defined in terms of either symptoms or virological outcome (e.g., PCR-confirmed infection), or sometimes both (7). Although significant reductions in hSAR between study arms are indicative of an effect, the amplitude of differences in hSAR can be difficult to interpret, partly because the statistic itself is dependent on the assays used and on the precise follow-up protocol. For example, criteria based on symptoms may fail to capture asymptomatic infections, and RT-PCR tests are sensitive to the frequency and timing of sampling. Also, the observed value of the hSAR in any specific household must be sensitive to the number of household members who participate, the precise timing of follow-up samples, and the pattern of any dropout.Previous studies have analyzed the transmission dynamics of influenza in households by using household models and symptomatic data (3, 17), and also symptomatic data in conjunction with RT-PCR laboratory results (18). We defined a stochastic household transmission model, building on these works, that described the effect of interventions in reducing the daily hazard of infection, and estimated parameters of the model using Markov chain Monte Carlo (McMC) techniques (see Materials and Methods and SI Text). |
