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Plankton-derived, microscopic, and macroscopic sinking aggregates constitute most of the particulate organic carbon (POC) flux in the oceans. While the flux of particulate organic matter and associated elements has been quantified at the Bermuda Atlantic Time-series Study (BATS) station for several decades, we lack an understanding of the source and composition of sinking particles, as well as the fate of predominant phytoplankton taxa. We determined the composition of individual sinking particles and their microbial communities in the upper 300 m depth at the BATS station in fall 2017 and spring 2018 by image analysis and V4 amplicon sequencing of the 16S and 18S rRNA genes. The sinking particles were primarily composed of phytodetrital aggregates, fecal aggregates, and fecal pellets. In the fall, phytodetrital aggregates were numerically dominant and drove the majority of the POC flux; however, in the spring, particle flux of all particle categories declined below 150 m, and the POC flux at 200 m shifted to one driven by fecal aggregates. The relative composition of the microbial communities associated with phytodetrital and fecal aggregates were statistically indistinguishable in both seasons, and prokaryotic taxa known to be associated with the gut microbiomes of zooplankton were indicators of the sinking particles. Our results point to the utilization and modification of sinking particles by resident midwater zooplankton populations, and to fecal pellets as the predominant mechanism transporting picophytoplankton to depth.

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India has been the latest global epicenter for COVID-19, a novel coronavirus disease that emerged in China in late 2019. We present a base mathematical model for the transmission dynamics of COVID-19 in India and its neighbor, Pakistan. The base model, which takes the form of a deterministic system of nonlinear differential equations, is parameterized using cumulative COVID-19 mortality data from each of the two countries. The model was used to assess the population-level impact of the control and mitigation strategies implemented in the two countries (notably community lockdowns, use of face masks, and social-distancing). Numerical simulations of the basic model indicate that, based on the current baseline levels of the control and mitigation strategies implemented, the pandemic trajectory in India is on a downward trend (as characterized by the reproduction number of the disease dynamics in India below, but close to, unity). This downward trend will be reversed, and India will be recording mild outbreaks (i.e., pandemic waves), if the control and mitigation strategies are relaxed from their current levels (e.g., relaxed to the extent that the associated community transmission parameters are increased by 20% or 40% from their current baseline values). Our simulations suggest that India could record up to 460,000 cumulative deaths by early September 2021 under the baseline levels of the control strategies implemented (up to 25,000 of the projected deaths could be averted if the control and mitigation measures are strengthened to the extent that the associated community transmission parameters are reduced by 20% from their baseline values). Our simulations show that the pandemic in Pakistan is much milder, with an estimated projected cumulative mortality of about 24,000 by early September 2021 under the baseline scenario. The basic model was extended to assess the impact of back-and-forth mobility between the two countries. Simulations of the resulting metapopulation model, which uses a Lagrangian mobility framework (based on residence-time spent in each country), shows that the burden of the pandemic in Pakistan increases with increasing values of the average time residents of India spend in Pakistan. In particular, it is shown that the India-to-Pakistan mobility pattern may trigger a significant fourth wave of the pandemic in Pakistan (under certain mobility scenarios and mitigation levels), with daily mortality peaking in mid-August to mid-September of 2021. It is also shown that extending the current travel restrictions by at least three months would significantly enhance the prospect of eliminating the pandemic in both countries. On the other hand, it is shown that, in addition to causing future multiple waves of the pandemic, easing the current levels of control and mitigation measures in the two countries (including travel restrictions) would result in delaying pandemic elimination in India and Pakistan to November and July 2022, respectively.

Wastewater-based surveillance (WBS) has been widely used as a public health tool to monitor SARS-CoV-2 transmission. However, epidemiological inference from WBS data remains understudied and limits its application. In this study, we have established a quantitative framework to estimate COVID-19 prevalence and predict SARS-CoV-2 transmission through integrating WBS data into an SEIR-V model. We conceptually divide the individual-level viral shedding course into exposed, infectious, and recovery phases as an analogy to the compartments in population-level SEIR model. We demonstrated that the temperature effect on viral losses in the sewer can be straightforwardly incorporated in our framework. Using WBS data from the second wave of the pandemic (Oct 02, 2020 – Jan 25, 2021) in the Great Boston area, we showed that the SEIR-V model successfully recapitulates the temporal dynamics of viral load in wastewater and predicts the true number of cases peaked earlier and higher than the number of reported cases by 16 days and 8.6 folds (R = 0.93), respectively. This work showcases a simple, yet effective method to bridge WBS and quantitative epidemiological modeling to estimate the prevalence and transmission of SARS-CoV-2 in the sewershed, which could facilitate the application of wastewater surveillance of infectious diseases for epidemiological inference and inform public health actions.