Scenario-Based Messages on Social Media Motivate COVID-19 Information Seeking.

Communicating information about health risks empowers individuals to make informed decisions. To identify effective communication strategies, we manipulated the specificity, self-relevance, and emotional framing of messages designed to motivate information seeking about COVID-19 exposure risk. In Study 1 (N=221,829), we conducted a large-scale social media field study. Using Facebook advertisements, we targeted users by age and political attitudes. Episodic specificity drove engagement: Advertisements that contextualized risk in specific scenarios produced the highest click-through rates, across all demographic groups. In Study 2, we replicated and extended our findings in an online experiment (N=4,233). Message specificity (but not self-relevance or emotional valence) drove interest in learning about COVID-19 risks. Across both studies, we found that older adults and liberals were more interested in learning about COVID-19 risks. However, message specificity increased engagement across demographic groups. Overall, evoking specific scenarios motivated information seeking about COVID-19, facilitating risk communication to a broad audience.

Disentangling top-down drivers of mortality underlying diel population dynamics of Prochlorococcus in the North Pacific Subtropical Gyre.

Photosynthesis fuels primary production at the base of marine food webs. Yet, in many surface ocean ecosystems, diel-driven primary production is tightly coupled to daily loss. This tight coupling raises the question: which top-down drivers predominate in maintaining persistently stable picocyanobacterial populations over longer time scales? Motivated by high-frequency surface water measurements taken in the North Pacific Subtropical Gyre (NPSG), we developed multitrophic models to investigate bottom-up and top-down mechanisms underlying the balanced control of Prochlorococcus populations. We find that incorporating photosynthetic growth with viral- and predator-induced mortality is sufficient to recapitulate daily oscillations of Prochlorococcus abundances with baseline community abundances. In doing so, we infer that grazers in this environment function as the predominant top-down factor despite high standing viral particle densities. The model-data fits also reveal the ecological relevance of light-dependent viral traits and non-canonical factors to cellular loss. Finally, we leverage sensitivity analyses to demonstrate how variation in life history traits across distinct oceanic contexts, including variation in viral adsorption and grazer clearance rates, can transform the quantitative and even qualitative importance of top-down controls in shaping Prochlorococcus population dynamics.

Communicating COVID-19 exposure risk with an interactive website counteracts risk misestimation.

During the COVID-19 pandemic, individuals depended on risk information to make decisions about everyday behaviors and public policy. Here, we assessed whether an interactive website influenced individuals' risk tolerance to support public health goals. We collected data from 11,169 unique users who engaged with the online COVID-19 Event Risk Tool (https://covid19risk.biosci.gatech.edu/) between 9/22/21 and 1/22/22. The website featured interactive elements, including a dynamic risk map, survey questions, and a risk quiz with accuracy feedback. After learning about the risk of COVID-19 exposure, participants reported being less willing to participate in events that could spread COVID-19, especially for high-risk large events. We also uncovered a bias in risk estimation: Participants tended to overestimate the risk of small events but underestimate the risk of large events. Importantly, even participants who voluntarily sought information about COVID risks tended to misestimate exposure risk, demonstrating the need for intervention. Participants from liberal-leaning counties were more likely to use the website tools and more responsive to feedback about risk misestimation, indicating that political partisanship influences how individuals seek and engage with COVID-19 information. Lastly, we explored temporal dynamics and found that user engagement and risk estimation fluctuated over the course of the Omicron variant outbreak. Overall, we report an effective large-scale method for communicating viral exposure risk; our findings are relevant to broader research on risk communication, epidemiological modeling, and risky decision-making.

Reasons for Receiving or Not Receiving Bivalent COVID-19 Booster Vaccinations Among Adults - United States, November 1-December 10, 2022.

Bivalent COVID-19 booster vaccines, developed to protect against both ancestral and Omicron BA.4/BA.5 variants, are recommended to increase protection against SARS-CoV-2 infection and severe disease* (1,2). However, relatively few eligible U.S. adults have received a bivalent booster dose (3), and reasons for low coverage are unclear. An opt-in Internet survey of 1,200 COVID-19-vaccinated U.S. adults was conducted to assess reasons for receiving or not receiving a bivalent booster dose. Participants could select multiple reasons from a list of suggested reasons to report why they had or had not received a bivalent booster dose. The most common reasons cited for not receiving the bivalent booster dose were lack of awareness of eligibility for vaccination (23.2%) or of vaccine availability (19.3%), and perceived immunity against infection (18.9%). After viewing information about eligibility and availability, 67.8% of participants who had not received the bivalent booster dose indicated that they planned to do so; in a follow-up survey 1 month later, 28.6% of these participants reported having received the dose. Among those who had planned to receive the booster dose but had not yet done so, 82.6% still intended to do so. Participants who had still not received the booster dose most commonly reported being too busy to get vaccinated (35.6%). To help increase bivalent booster dose coverage, health care and public health professionals should use evidence-based strategies to convey information about booster vaccination recommendations and waning immunity (4), while also working to increase convenient access.

Complex marine microbial communities partition metabolism of scarce resources over the diel cycle.

Complex assemblages of microbes in the surface ocean are responsible for approximately half of global carbon fixation. The persistence of high taxonomic diversity despite competition for a small suite of relatively homogeneously distributed nutrients, that is, 'the paradox of the plankton', represents a long-standing challenge for ecological theory. Here we find evidence consistent with temporal niche partitioning of nitrogen assimilation processes over a diel cycle in the North Pacific Subtropical Gyre. We jointly analysed transcript abundances, lipids and metabolites and discovered that a small number of diel archetypes can explain pervasive periodic dynamics. Metabolic pathway analysis of identified diel signals revealed asynchronous timing in the transcription of nitrogen uptake and assimilation genes among different microbial groups-cyanobacteria, heterotrophic bacteria and eukaryotes. This temporal niche partitioning of nitrogen uptake emerged despite synchronous transcription of photosynthesis and central carbon metabolism genes and associated macromolecular abundances. Temporal niche partitioning may be a mechanism by which microorganisms in the open ocean mitigate competition for scarce resources, supporting community coexistence.

Surveillance-to-Diagnostic Testing Program for Asymptomatic SARS-CoV-2 Infections on a Large, Urban Campus in Fall 2020.

BACKGROUND: Six months into the COVID-19 pandemic, college campuses faced uncertainty regarding the likely prevalence and spread of disease, necessitating large-scale testing to help guide policy following re-entry. METHODS: A SARS-CoV-2 testing program combining pooled saliva sample surveillance leading to diagnosis and intervention surveyed over 112,000 samples from 18,029 students, staff and faculty, as part of integrative efforts to mitigate transmission at the Georgia Institute of Technology in Fall 2020. RESULTS: Cumulatively, we confirmed 1,508 individuals diagnostically, 62% of these through the surveillance program and the remainder through diagnostic tests of symptomatic individuals administered on or off campus. The total strategy, including intensification of testing given case clusters early in the semester, was associated with reduced transmission following rapid case increases upon entry in Fall semester in August 2020, again in early November 2020, and upon re-entry for Spring semester in January 2021. During the Fall semester daily asymptomatic test positivity initially peaked at 4.1% but fell below 0.5% by mid-semester, averaging 0.84% across the Fall semester, with similar levels of control in Spring 2021. CONCLUSIONS: Owing to broad adoption by the campus community, we estimate that the program protected higher risk staff and faculty while allowing some normalization of education and research activities.

A framework for monitoring population immunity to SARS-CoV-2.

In the effort to control SARS-CoV-2 transmission, public health agencies in the United States and globally are aiming to increase population immunity. Immunity through vaccination and acquired following recovery from natural infection are the two means to build up population immunity, with vaccination being the safe pathway. However, measuring the contribution to population immunity from vaccination or natural infection is non-trivial. Historical COVID-19 case counts and vaccine coverage are necessary information but are not sufficient to approximate population immunity. Here, we consider the nuances of measuring each and propose an analytical framework for integrating the necessary data on cumulative vaccinations and natural infections at the state and national level. To guide vaccine roll-out and other aspects of control over the coming months, we recommend analytics that combine vaccine coverage with local (e.g. county-level) history of case reports and adjustment for waning antibodies to establish local estimates of population immunity. To do so, the strategic use of minimally-biased serology surveys integrated with vaccine administration data can improve estimates of the aggregate level of immunity to guide data-driven decisions to re-open safely and prioritize vaccination efforts.

A single-cell polony method reveals low levels of infected Prochlorococcus in oligotrophic waters despite high cyanophage abundances.

Long-term stability of picocyanobacteria in the open oceans is maintained by a balance between synchronous division and death on daily timescales. Viruses are considered a major source of microbial mortality, however, current methods to measure infection have significant methodological limitations. Here we describe a method that pairs flow-cytometric sorting with a PCR-based polony technique to simultaneously screen thousands of taxonomically resolved individual cells for intracellular virus DNA, enabling sensitive, high-throughput, and direct quantification of infection by different virus lineages. Under controlled conditions with picocyanobacteria-cyanophage models, the method detected infection throughout the lytic cycle and discriminated between varying infection levels. In North Pacific subtropical surface waters, the method revealed that only a small percentage of Prochlorococcus (0.35-1.6%) were infected, predominantly by T4-like cyanophages, and that infection oscillated 2-fold in phase with the diel cycle. This corresponds to 0.35-4.8% of Prochlorococcus mortality daily. Cyanophages were 2-4-fold more abundant than Prochlorococcus, indicating that most encounters did not result in infection and suggesting infection is mitigated via host resistance, reduced phage infectivity and inefficient adsorption. This method will enable quantification of infection for key microbial taxa across oceanic regimes and will help determine the extent that viruses shape microbial communities and ecosystem level processes.

Real-time, interactive website for US-county-level COVID-19 event risk assessment.

Large events and gatherings, particularly those taking place indoors, have been linked to multitransmission events that have accelerated the pandemic spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). To provide real-time, geolocalized risk information, we developed an interactive online dashboard that estimates the risk that at least one individual with SARS-CoV-2 is present in gatherings of different sizes in the United States. The website combines documented case reports at the county level with ascertainment bias information obtained via population-wide serological surveys to estimate real-time circulating, per-capita infection rates. These rates are updated daily as a means to visualize the risk associated with gatherings, including county maps and state-level plots. The website provides data-driven information to help individuals and policy makers make prudent decisions (for example, increasing mask-wearing compliance and avoiding larger gatherings) that could help control the spread of SARS-CoV-2, particularly in hard-hit regions.

Spread of COVID-19 through Georgia, USA. Near-term projections and impacts of social distancing via a metapopulation model.

Epidemiological forecasts of COVID-19 spread at the country and/or state level have helped shape public health interventions. However, such models leave a scale-gap between the spatial resolution of actionable information (i.e. the county or city level) and that of modeled viral spread. States and nations are not spatially homogeneous and different areas may vary in disease risk and severity. For example, COVID-19 has age-stratified risk. Similarly, ICU units, PPE and other vital equipment are not equally distributed within states. Here, we implement a county-level epidemiological framework to assess and forecast COVID-19 spread through Georgia, where 1,933 people have died from COVID-19 and 44,638 cases have been documented as of May 27, 2020. We find that county-level forecasts trained on heterogeneity due to clustered events can continue to predict epidemic spread over multi-week periods, potentially serving efforts to prepare medical resources, manage supply chains, and develop targeted public health interventions. We find that the premature removal of physical (social) distancing could lead to rapid increases in cases or the emergence of sustained plateaus of elevated fatalities.

Intervention Serology and Interaction Substitution: Modeling the Role of 'Shield Immunity' in Reducing COVID-19 Epidemic Spread.

The COVID-19 pandemic has precipitated a global crisis, with more than 690,000 confirmed cases and more than 33,000 confirmed deaths globally as of March 30, 2020 [1-4]. At present two central public health control strategies have emerged: mitigation and suppression (e.g, [5]). Both strategies focus on reducing new infections by reducing interactions (and both raise questions of sustainability and long-term tactics). Complementary to those approaches, here we develop and analyze an epidemiological intervention model that leverages serological tests [6, 7] to identify and deploy recovered individuals as focal points for sustaining safer interactions via interaction substitution, i.e., to develop what we term 'shield immunity' at the population scale. Recovered individuals, in the present context, represent those who have developed protective, antibodies to SARS-CoV-2 and are no longer shedding virus [8]. The objective of a shield immunity strategy is to help sustain the interactions necessary for the functioning of essential goods and services (including but not limited to tending to the elderly [9], hospital care, schools, and food supply) while decreasing the probability of transmission during such essential interactions. We show that a shield immunity approach may significantly reduce the length and reduce the overall burden of an outbreak, and can work synergistically with social distancing. The present model highlights the value of serological testing as part of intervention strategies, in addition to its well recognized roles in estimating prevalence [10, 11] and in the potential development of plasma-based therapies [12-15].

Modeling shield immunity to reduce COVID-19 epidemic spread.

The COVID-19 pandemic has precipitated a global crisis, with more than 1,430,000 confirmed cases and more than 85,000 confirmed deaths globally as of 9 April 20201-4. Mitigation and suppression of new infections have emerged as the two predominant public health control strategies5. Both strategies focus on reducing new infections by limiting human-to-human interactions, which could be both socially and economically unsustainable in the long term. We have developed and analyzed an epidemiological intervention model that leverages serological tests6,7 to identify and deploy recovered individuals8 as focal points for sustaining safer interactions via interaction substitution, developing what we term 'shield immunity' at the population scale. The objective of a shield immunity strategy is to help to sustain the interactions necessary for the functioning of essential goods and services9 while reducing the probability of transmission. Our shield immunity approach could substantively reduce the length and reduce the overall burden of the current outbreak, and can work synergistically with social distancing. The present model highlights the value of serological testing as part of intervention strategies, in addition to its well-recognized roles in estimating prevalence10,11 and in the potential development of plasma-based therapies12-15.

An empirical model of carbon flow through marine viruses and microzooplankton grazers.

Viruses and microzooplankton grazers represent major sources of mortality for marine phytoplankton and bacteria, redirecting the flow of organic material throughout the world's oceans. Here, we investigate the use of nonlinear population models of interactions between phytoplankton, viruses and grazers as a means to quantitatively constrain the flow of carbon through marine microbial ecosystems. We augment population models with a synthesis of laboratory-based estimates of prey, predator and viral life history traits that constrain transfer efficiencies. We then apply the model framework to estimate loss rates in the California Current Ecosystem (CCE). With our empirically parameterized model, we estimate that, of the total losses mediated by viruses and microzooplankton grazing at the focal CCE site, 22 ± 3%, 46 ± 27%, 3 ± 2% and 29 ± 20% were directed to grazers, sloppy feeding (as well as excretion and respiration), viruses and viral lysate respectively. We identify opportunities to leverage ecosystem models and conventional mortality assays to further constrain the quantitative rates of critical ecosystem processes.

The Effect of Strain Level Diversity on Robust Inference of Virus-Induced Mortality of Phytoplankton.

Infection and lysis of phytoplankton by viruses affects population dynamics and nutrient cycles within oceanic microbial communities. However, estimating the quantitative rates of viral-induced lysis remains challenging in situ. The modified dilution method is the most commonly utilized empirical approach to estimate virus-induced killing rates of phytoplankton. The lysis rate estimates of the modified dilution method are based on models which assume virus-host interactions can be represented by a single virus and a single host population with homogeneous life-history traits. Here, using modeling approaches, we examine the robustness of the modified dilution method in multi-strain, complex communities. We assume that strains differ in their life history traits, including growth rates (of hosts) and lysis rates (by viruses). We show that trait differences affect resulting experimental dynamics such that lysis rates measured using the modified dilution method may be driven by the fastest replicating strains; which are not necessarily the most abundant in situ. We discuss the implications of using the modified dilution method and alternative dilution-based approaches for estimating viral-induced lysis rates in marine microbial communities.

Disentangling niche competition from grazing mortality in phytoplankton dilution experiments.

The dilution method is the principal tool used to infer in situ microzooplankton grazing rates. However, grazing is the only mortality process considered in the theoretical model underlying the interpretation of dilution method experiments. Here we evaluate the robustness of mortality estimates inferred from dilution experiments when there is concurrent niche competition amongst phytoplankton. Using a combination of mathematical analysis and numerical simulations, we find that grazing rates may be overestimated-the degree of overestimation is related to the importance of niche competition relative to microzooplankton grazing. In response, we propose a conceptual method to disentangle the effects of niche competition and grazing by diluting out microzooplankton, but not phytoplankton. Our theoretical results suggest this revised "Z-dilution" method can robustly infer grazing mortality, regardless of the dominant phytoplankton mortality driver in our system. Further, we show it is possible to independently estimate both grazing mortality and niche competition if the classical and Z-dilution methods can be used in tandem. We discuss the significance of these results for quantifying phytoplankton mortality rates; and the feasibility of implementing the Z-dilution method in practice, whether in model systems or in complex communities with overlap in the size distributions of phytoplankton and microzooplankton.

An Innovative Rapid Method for Analysis of 10 Organophosphorus Pesticide Residues in Wheat by HS-SPME-GC-FPD/MSD.

The rapid detection of pesticide residues in wheat has become a top food security priority. A solvent-free headspace solid-phase microextraction (HS-SPME) has been evaluated for rapid screening of organophosphorus pesticide (OPP) residues in wheat with high sensitivity. Individual wheat samples (1.7 g), spiked with 10 OPPs, were placed in a 4 mL sealed amber glass vial and heated at 60°C for 45 min. During this time, the OPP residues were extracted with a 50 µm/30 µm divinylbenzene (DVB)/carboxen (CAR)/plasma desorption mass spectroscopy polydimethylsiloxane (PDMS) fiber from the headspace above the sample. The fiber was then removed and injected into the GC injection port at 250°C for desorption of the extracted chemicals. The multiple residues were identified by a GC mass spectrometer detector (GC-MSD) and quantified with a GC flame photometric detector (GC-FPD). Seven spiked levels of 10 OPPs on wheat were analyzed. The GC responses for a 50 µm/30 µm DVB/CAR/PDMS fiber increased with increasing spiking levels, yielding significant (R(2) > 0.98) linear regressions. The lowest LODs of the multiple pesticide standards were evaluated under the conditions of the validation study in a range of levels from 0 (control) to 100 ng of pesticide residue per g of wheat that separated on a low-polar GC capillary column (Agilent DB-35UI). The results of the HS-SPME method were compared with the QuEChERS AOAC 2007.01 method and they showed several advantages over the latter. These included improved sensitivity, selectivity, and simplicity.

Improved community detection in weighted bipartite networks.

Real-world complex networks are composed of non-random quantitative interactions. Identifying communities of nodes that tend to interact more with each other than the network as a whole is a key research focus across multiple disciplines, yet many community detection algorithms only use information about the presence or absence of interactions between nodes. Weighted modularity is a potential method for evaluating the quality of community partitions in quantitative networks. In this framework, the optimal community partition of a network can be found by searching for the partition that maximizes modularity. Attempting to find the partition that maximizes modularity is a computationally hard problem requiring the use of algorithms. QuanBiMo is an algorithm that has been proposed to maximize weighted modularity in bipartite networks. This paper introduces two new algorithms, LPAwb+ and DIRTLPAwb+, for maximizing weighted modularity in bipartite networks. LPAwb+ and DIRTLPAwb+ robustly identify partitions with high modularity scores. DIRTLPAwb+ consistently matched or outperformed QuanBiMo, while the speed of LPAwb+ makes it an attractive choice for detecting the modularity of larger networks. Searching for modules using weighted data (rather than binary data) provides a different and potentially insightful method for evaluating network partitions.

Phosphine resistance in Australian Cryptolestes species (Coleoptera: Laemophloeidae): perspectives from mitochondrial DNA cytochrome oxidase I analysis.

BACKGROUND: The flat grain beetle (FGB) species Cryptolestes ferrugineus, C. pusillus, C. pusilloides and C. turcicus are major stored-product pests worldwide, of which the first three are present in Australia. C. ferrugineus is also a species with high phosphine resistance status in various countries. Morphological identification of Cryptolestes species is difficult and represents an additional barrier to effective management of phosphine resistance in FGBs. RESULT: Mitochondrial DNA cytochrome oxidase I (mtDNA COI) gene characterisation enabled differentiation of the four major FGB pest species through direct sequence comparison, and enabled the development of a PCR-RFLP method for rapid species differentiation. We detected two mtDNA haplotypes (Cunk-01, 02) present at low frequencies with an average nucleotide divergence rate of 0.079 ± 0.011 (SE) from C. pusillus. This nucleotide divergence rate is similar to that between C. ferrugineus and C. pusilloides (0.088 ± 0.012). Male and female genitalia morphologies of the Cunk-02 individuals indicated they were consistent with C. pusillus, yet DNA sequence analyses suggested species-level divergence. The mtDNA COI gene of phosphine-bioassayed, lab-reared F1 generation survivors supported the presence of strong phosphine resistance in C. ferrugineus, but unexpectedly also in C. pusilloides and C. pusillus F1 survivors. CONCLUSION: We demonstrated the utility of molecular DNA techniques for differentiating closely related insect species, and its usefulness in assisting the management of pest insect species. The likely presence of a cryptic C. pusillus species in Australia and the possible development of strong phosphine resistance in Australian FGB pest species require further investigation. © 2014 Society of Chemical Industry.

Analysis of whole genome sequencing for the Escherichia coli O157:H7 typing phages.

BACKGROUND: Shiga toxin producing Escherichia coli O157 can cause severe bloody diarrhea and haemolytic uraemic syndrome. Phage typing of E. coli O157 facilitates public health surveillance and outbreak investigations, certain phage types are more likely to occupy specific niches and are associated with specific age groups and disease severity. The aim of this study was to analyse the genome sequences of 16 (fourteen T4 and two T7) E. coli O157 typing phages and to determine the genes responsible for the subtle differences in phage type profiles. RESULTS: The typing phages were sequenced using paired-end Illumina sequencing at The Genome Analysis Centre and the Animal Health and Veterinary Laboratories Agency and bioinformatics programs including Velvet, Brig and Easyfig were used to analyse them. A two-way Euclidian cluster analysis highlighted the associations between groups of phage types and typing phages. The analysis showed that the T7 typing phages (9 and 10) differed by only three genes and that the T4 typing phages formed three distinct groups of similar genomic sequences: Group 1 (1, 8, 11, 12 and 15, 16), Group 2 (3, 6, 7 and 13) and Group 3 (2, 4, 5 and 14). The E. coli O157 phage typing scheme exhibited a significantly modular network linked to the genetic similarity of each group showing that these groups are specialised to infect a subset of phage types. CONCLUSION: Sequencing the typing phage has enabled us to identify the variable genes within each group and to determine how this corresponds to changes in phage type.