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The COVID-19 pandemic has generated an enormous amount of data, providing a unique opportunity for modeling and analysis. In this paper, we present a data-informed approach for building stochastic compartmental models that is grounded in the Markovian processes underlying these models. Our initial data analyses reveal that the SIRD model -- susceptiple (S), infected (I), recovered (R), and death (D) -- is not consistent with the data. In particular, the transition times expressed in the dataset do not obey exponential distributions, implying that there exist unmodeled (hidden) states. We make use of the available epidemiological data to inform the location of these hidden states, allowing us to develop an augmented compartmental model which includes states for hospitalization (H) and end of infectious viral shedding (V). Using the proposed model, we characterize delay distributions analytically and match model parameters to empirical quantities in the data to obtain a good model fit. Insights from an epidemiological perspective are presented, as well as their implications for mitigation and control strategies.

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In the U.S., spray irrigation is the most common method used in agriculture and supplementing with animal wastewater has the potential to reduce water demands. However, this could expose individuals to respiratory pathogens such as Legionella pneumophila and nontuberculosis Mycobacteria (NTM). Disinfection with methods like anaerobic digestion is an option but can increase concentrations of cytotoxic ammonia (personal communication). Our study aimed to model the annual risks of infection from these bacterial pathogens and the air concentrations of ammonia and determine if anaerobically digesting this wastewater is a safe option. Air dispersion modeling, conducted in AERMOD, generated air concentrations of water during the irrigation season (May-September) for the years 2013-2018. These values fed into the quantitative microbial risk assessments for the bacteria and allowed calculation of ammonia air concentrations. The outputs of these models were compared to the safety thresholds of 10 −4 infections/year and 0.5 mg/m 3 , respectively, to determine their potential for negative health outcomes. It was determined that infection from NTM was not a concern for individuals near active spray irrigators, but that infection with L. pneumophila could be a concern, with a maximum predicted annual risk of infection of 3.5 × 10 −3 infections/year and 25.2% of parameter combinations exceeding the established threshold. Ammonia posed a minor risk, with 1.5% of parameter combinations surpassing the risk threshold of 0.5 mg/m 3 . These findings suggest that animal wastewater should be anaerobically digested prior to use in irrigation to remove harmful pathogens.

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