Randomized controlled trials (RCTs), which evaluate hypotheses in specific contexts, are often considered the gold standard of evidence for infectious disease interventions, but their results cannot immediately generalize to other contexts. Mechanistic models are one approach to generalizing findings between contexts, but infectious disease transmission models are not immediately suited for analyzing RCTs, since they often rely on time-series surveillance data that is rarely collected by RCTs. We developed a modeling framework to explain the main outcome of an infectious disease RCT—relative risk—and applied it to a water, sanitation, and hygiene (WASH) RCT. This model can generalize the RCT results to other contexts and conditions. We developed this compartmental modeling framework to account for key WASH RCT factors: i) transmission across multiple environmental pathways, ii) multiple interventions applied individually and in combination, iii) adherence to interventions or preexisting conditions, and iv) the impact of individuals not enrolled in the study. We employed a hybrid sampling-importance resampling and estimation framework to obtain posterior estimates of mechanistic parameters and their uncertainties and illustrated our model using WASH Benefits Bangladesh RCT data (n=17,187). Our model reproduced reported diarrheal prevalence in this RCT. The baseline estimate of the basic reproduction number ℛ0 for the control arm (1.15, 95% CI: 1.09, 1.27) corresponded to an endemic prevalence of 13% (95% CI: 9–21%) in the absence of intervention or preexisting WASH conditions. No single pathway was likely able to sustain transmission: pathway-specific ℛ0s for water, fomites, and all other pathways were 0.49 (95% CI: 0.07, 0.99), 0.26 (95% CI: 0.04, 0.57), and 0.40 (95% CI: 0.02, 0.88), respectively. An infectious disease modeling approach to evaluating RCTs can complement RCT analysis by providing a rigorous framework for generating data-driven hypotheses that explain trial findings, particularly unexpected null results, opening up existing data to deeper epidemiological understanding.Author summaryA randomized controlled trial (RCT) testing an intervention to reduce infectious disease transmission can provide high-quality scientific evidence about the impact of that intervention in a specific context, but the results are often difficult to generalize to other policy-relevant contexts and conditions. Infectious disease transmission models can be used to explore what might happen to disease dynamics under different conditions, but the standard use of these models is to fit to longitudinal, surveillance data, which is rarely collected by RCTs. We developed a framework to fit an infectious disease model to steady-state diarrheal prevalence data in water, sanitation, and hygiene RCTs, explicitly accounting for completeness, coverage, and compliance. Although this framework is developed with water, sanitation, and hygiene interventions for enteropathogens in mind, it could be extended to other disease contexts. By leveraging existing large-scale RCT data sets, it will be possible to better understand the underlying disease epidemiology and investigate the likely outcomes of policy-relevant scenarios. Ultimately, this work can be incorporated into decision making for public health policy and programs.

Randomized controlled trials (RCTs) evaluate hypotheses in specific contexts and are often considered the gold standard of evidence for infectious disease interventions, but their results cannot immediately generalize to other contexts (e.g., different populations, interventions, or disease burdens). Mechanistic models are one approach to generalizing findings between contexts, but infectious disease transmission models (IDTMs) are not immediately suited for analyzing RCTs, since they often rely on time-series surveillance data. We developed an IDTM framework to explain relative risk outcomes of an infectious disease RCT and applied it to a water, sanitation, and hygiene (WASH) RCT. This model can generalize the RCT results to other contexts and conditions. We developed this compartmental IDTM framework to account for key WASH RCT factors: i) transmission across multiple environmental pathways, ii) multiple interventions applied individually and in combination, iii) adherence to interventions or preexisting conditions, and iv) the impact of individuals not enrolled in the study. We employed a hybrid sampling and estimation framework to obtain posterior estimates of mechanistic parameter sets consistent with empirical outcomes. We illustrated our model using WASH Benefits Bangladesh RCT data (n = 17,187). Our model reproduced reported diarrheal prevalence in this RCT. The baseline estimate of the basic reproduction number R 0 for the control arm (1.10, 95% CrI: 1.07, 1.16) corresponded to an endemic prevalence of 9.5% (95% CrI: 7.4, 13.7%) in the absence of interventions or preexisting WASH conditions. No single pathway was likely able to sustain transmission: pathway-specific R 0 s for water, fomites, and all other pathways were 0.42 (95% CrI: 0.03, 0.97), 0.20 (95% CrI: 0.02, 0.59), and 0.48 (95% CrI: 0.02, 0.94), respectively. An IDTM approach to evaluating RCTs can complement RCT analysis by providing a rigorous framework for generating data-driven hypotheses that explain trial findings, particularly unexpected null results, opening up existing data to deeper epidemiological understanding.

Shared water facilities are widespread in resource-poor settings within low- and middle-income countries. Since gathering water is essential, shared water sites may act as an important COVID-19 transmission pathway, despite stay-at-home recommendations. This analysis explores conditions under which shared water facility utilization may influence COVID-19 transmission. We developed two SEIR transmission models to explore COVID-19 dynamics. The first describes an urban setting, where multiple water sites are shared within a community, and the second describes a rural setting, where a single water site is shared among communities. We explored COVID-19 mitigation strategies including social distancing and adding additional water sites. Increased water site availability and social distancing independently attenuate attack rate and peak outbreak size through density reduction. In combination, these conditions result in interactive risk reductions. When water sharing intensity is high, risks are high regardless of the degree of social distancing. Even moderate reductions in water sharing can enhance the effectiveness of social distancing. In rural contexts, we observe similar but weaker effects. Enforced social distancing and density reduction at shared water sites can be an effective and relatively inexpensive mitigation effort to reduce the risk of COVID-19 transmission. Building additional water sites is more expensive but can increase the effectiveness of social distancing efforts at the water sites. As respiratory pathogen outbreaks—and potentially novel pandemics—will continue, infrastructure planning should consider the health benefits associated with respiratory transmission reduction when prioritizing investments.