Peru has been severely affected by the COVID-19 pandemic. By January 2022, Peru had surpassed 200 000 COVID-19 deaths, constituting the highest death rate per capita worldwide. Peru has had several limitations during the pandemic: insufficient testing access, limited contact tracing, a strained medical infrastructure, and many economic hurdles. These limitations hindered the gathering of accurate information about infected individuals with spatial resolution in real time, a critical aspect of effectively controlling the pandemic. Wastewater monitoring for SARS-CoV-2 RNA offered a promising alternative for providing needed population-wide information to complement health care indicators. In this study, we demonstrate the feasibility and value of implementing a decentralized SARS-CoV-2 RNA wastewater monitoring system to assess the spatiotemporal distribution of COVID-19 in three major cities in Peru: Lima, Callao, and Arequipa. Our data on viral loads showed the same trends as health indicators such as incidence and mortality. Furthermore, we were able to identify hot spots of contagion within the surveyed urban areas to guide the efforts of health authorities. Viral decay in the sewage network of the cities studied was found to be negligible (<2%). Overall, our results support wastewater monitoring for SARS-CoV-2 as a valuable and cost-effective tool for monitoring the COVID-19 pandemic in the Peruvian context.

No abstract

We extend and analyze a new class of estimators for the variance parameter of a steady-state simulation output process. These estimators are based on "folded" versions of the standardized time series (STS) of the process, and are analogous to the area and Cramér-von Mises estimators calculated from the original STS. In fact, one can apply the folding mechanism more than once to produce an entire class of estimators, all of which reuse the same underlying data stream. We show that these folded estimators share many of the same properties as their nonfolded counterparts, with the added bonus that they are often nearly independent of the nonfolded versions. In particular, we derive the asymptotic distributional properties of the various estimators as the run length increases, as well as their bias, variance, and mean squared error. We also study linear combinations of these estimators, and we show that such combinations yield estimators with lower variance than their constituents. Finally, we consider the consequences of batching, and we see that the batched versions of the new estimators compare favorably to benchmark estimators such as the nonoverlapping batch means estimator.

No abstract

No abstract