Surveillance of SARS-CoV-2, rotavirus, norovirus genogroup II, and human adenovirus in wastewater as an epidemiological tool to anticipate outbreaks of COVID-19 and acute gastroenteritis in a city without a wastewater treatment plant in the Peruvian Highlands

The coronavirus disease 2019 (COVID-19) pandemic has demonstrated that Wastewater Based Epidemiology is a fast and economical alternative for monitoring severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) at the community level in high-income countries. In the present study, wastewater from a city in the Peruvian Highlands, which lacks a wastewater treatment plant, was monitored for one year to assess the relationship between the concentration of SARS-CoV-2 and the reported cases of COVID-19 in the community. Additionally, we compared the relationship between rotavirus (RV), norovirus genogroup II (NoV GGII), and human adenovirus (HAdV) with the number of reported cases of acute gastroenteritis. Before commencing the analysis of the samples, the viral recovery efficacy of three processing methods was determined in spiked wastewater with SARS-CoV-2. This evaluation demonstrated the highest recovery rate with direct analysis (72.2 %), as compared to ultrafiltration (50.8 %) and skimmed milk flocculation (5.6 %). Wastewater monitoring revealed that 72 % (36/50) of the samples tested positive for SARS-CoV-2, with direct analysis yielding the highest detection frequency and quantification of SARS-CoV-2. Furthermore, a strong correlation was observed between the concentration of SARS-CoV-2 in wastewater and the reported cases of COVID-19, mainly when we shift the concentration of SARS-CoV-2 by two weeks, which allows us to anticipate the onset of the fourth and fifth waves of the pandemic in Peru up to two weeks in advance. All samples processed using the skimmed milk flocculation method tested positive and showed high concentrations of RV, NoV GGII, and HAdV. In fact, the highest RV concentrations were detected up to four weeks before outbreaks of acute gastroenteritis reported in children under four years of age. In conclusion, the results of this study suggest that periodic wastewater monitoring is an excellent epidemiological tool for surveillance and can anticipate outbreaks of infectious diseases, such as COVID-19, in low- and middle-income countries.

REDACS: Regional emergency-driven adaptive cluster sampling for effective COVID-19 management

As COVID-19 is spreading, national agencies need to monitor and track several metrics. Since we do not have perfect testing programs on the hand, one needs to develop an advanced sampling strategies for prevalence study, control and management. Here we introduce REDACS: Regional emergency-driven adaptive cluster sampling for effective COVID-19 management and control and justify its usage for COVID-19. We show its advantages over classical massive individual testing sampling plans. We also point out how regional and spatial heterogeneity underlines proper sampling. Fundamental importance of adaptive control parameters from emergency health stations and medical frontline is outlined. Since the Northern hemisphere entered Autumn and Winter season (this paper was originally submitted in November 2020), practical illustration from spatial heterogeneity of Chile (Southern hemisphere, which already experienced COVID-19 winter outbreak peak) is underlying the importance of proper regional heterogeneity of sampling plan. We explain the regional heterogeneity by microbiological backgrounds and link it to behavior of Lyapunov exponents. We also discuss screening by antigen tests from the perspective of "on the fly" biomarker validation, i.e., during the screening.

Fractal and stochastic geometry inference for breast cancer: a case study with random fractal models and Quermass-interaction process

Fractals are models of natural processes with many applications in medicine. The recent studies in medicine show that fractals can be applied for cancer detection and the description of pathological architecture of tumors. This fact is not surprising, as due to the irregular structure, cancerous cells can be interpreted as fractals. Inspired by Sierpinski carpet, we introduce a flexible parametric model of random carpets. Randomization is introduced by usage of binomial random variables. We provide an algorithm for estimation of parameters of the model and illustrate theoretical and practical issues in generation of Sierpinski gaskets and Hausdorff measure calculations. Stochastic geometry models can also serve as models for binary cancer images. Recently, a Boolean model was applied on the 200 images of mammary cancer tissue and 200 images of mastopathic tissue. Here, we describe the Quermass-interaction process, which can handle much more variations in the cancer data, and we apply it to the images. It was found out that mastopathic tissue deviates significantly stronger from Quermass-interaction process, which describes interactions among particles, than mammary cancer tissue does. The Quermass-interaction process serves as a model describing the tissue, which structure is broken to a certain level. However, random fractal model fits well for mastopathic tissue. We provide a novel discrimination method between mastopathic and mammary cancer tissue on the basis of complex wavelet-based self-similarity measure with classification rates more than 80%. Such similarity measure relates to Hurst exponent and fractional Brownian motions. The R package FractalParameterEstimation is developed and introduced in the paper.