The global effort to eradicate poliovirus relies heavily on the oral poliovirus vaccine (OPV), yet the environmental shedding of vaccine-derived poliovirus (VDPV) into surface waters remains a critical, often overlooked, secondary hazard. In regions with poor sanitation and limited infrastructure, downstream communities may be unknowingly exposed to infectious viruses shed by upstream vaccinated populations. This study aimed to develop a quantitative microbial risk assessment (QMRA) framework to characterize these downstream infection risks and evaluate the efficacy of water treatment as a mitigation strategy. We utilized an in-silico model to simulate viral transport between two hypothetical under-vaccinated rural communities of varying sizes. Infection risks were estimated using two distinct approaches for calculating viral loading: a traditional mechanistic method based on individual shedding rates and a novel regression approach integrated with wastewater-based epidemiology (WBE) data. A Monte Carlo simulation (10,000 iterations) was employed to characterize uncertainties in pathogen occurrence, environmental decay, and within-host dose-response. Our results indicate that classical mechanistic models tend to overestimate viral loading and subsequent community risk, particularly in smaller populations. The QMRA estimated mean probabilities of infection as high as for large communities consuming untreated source water. However, sensitivity analysis identified water treatment as the most critical factor in risk reduction; standard filtration processes were shown to decrease the mean probability of infection by up to 99%. This research demonstrates that watershed dynamics and community placement are essential considerations for poliovirus risk management during vaccination campaigns. By transitioning toward WBE-based regression for risk estimation, public health officials can achieve more accurate environmental surveillance. Ultimately, our findings highlight the urgent need for accessible water filtration and integrated watershed modeling to protect vulnerable populations from silent viral circulation.