Rice Coronavirus Research

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https://hdl.handle.net/1911/108371

Works related to coronaviruses that are authored by members of the Rice community.

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A trivalent protein-based pan-Betacoronavirus vaccine elicits cross-neutralizing antibodies against a panel of coronavirus pseudoviruses
(Springer Nature, 2024) Thimmiraju, Syamala Rani; Adhikari, Rakesh; Redd, JeAnna R.; Villar, Maria Jose; Lee, Jungsoon; Liu, Zhuyun; Chen, Yi-Lin; Sharma, Suman; Kaur, Amandeep; Uzcategui, Nestor L.; Ronca, Shannon E.; Chen, Wen-Hsiang; Kimata, Jason T.; Zhan, Bin; Strych, Ulrich; Bottazzi, Maria Elena; Hotez, Peter J.; Pollet, Jeroen
The development of broad-spectrum coronavirus vaccines is essential to prepare for future respiratory virus pandemics. We demonstrated broad neutralization by a trivalent subunit vaccine, formulating the receptor-binding domains of SARS-CoV, MERS-CoV, and SARS-CoV-2 XBB.1.5 with Alum and CpG55.2. Vaccinated mice produced cross-neutralizing antibodies against all three human Betacoronaviruses and others currently exclusive to bats, indicating the epitope preservation of the individual antigens during co-formulation and the potential for epitope broadening.
Crykey: Rapid identification of SARS-CoV-2 cryptic mutations in wastewater
(Springer Nature, 2024) Liu, Yunxi; Sapoval, Nicolae; Gallego-García, Pilar; Tomás, Laura; Posada, David; Treangen, Todd J.; Stadler, Lauren B.
Wastewater surveillance for SARS-CoV-2 provides early warnings of emerging variants of concerns and can be used to screen for novel cryptic linked-read mutations, which are co-occurring single nucleotide mutations that are rare, or entirely missing, in existing SARS-CoV-2 databases. While previous approaches have focused on specific regions of the SARS-CoV-2 genome, there is a need for computational tools capable of efficiently tracking cryptic mutations across the entire genome and investigating their potential origin. We present Crykey, a tool for rapidly identifying rare linked-read mutations across the genome of SARS-CoV-2. We evaluated the utility of Crykey on over 3,000 wastewater and over 22,000 clinical samples; our findings are three-fold: i) we identify hundreds of cryptic mutations that cover the entire SARS-CoV-2 genome, ii) we track the presence of these cryptic mutations across multiple wastewater treatment plants and over three years of sampling in Houston, and iii) we find a handful of cryptic mutations in wastewater mirror cryptic mutations in clinical samples and investigate their potential to represent real cryptic lineages. In summary, Crykey enables large-scale detection of cryptic mutations in wastewater that represent potential circulating cryptic lineages, serving as a new computational tool for wastewater surveillance of SARS-CoV-2.
COVID-19 pandemic effects on neonatal inpatient admissions and mortality: interrupted time series analysis of facilities implementing NEST360 in Kenya, Malawi, Nigeria, and Tanzania
(Springer Nature, 2024) Malla, Lucas; Ohuma, Eric O.; Shabani, Josephine; Ngwala, Samuel; Dosunmu, Olabisi; Wainaina, John; Aluvaala, Jalemba; Kassim, Irabi; Cross, James H.; Salim, Nahya; Zimba, Evelyn; Ezeaka, Chinyere; Penzias, Rebecca E.; Gathara, David; Tillya, Robert; Chiume, Msandeni; Odedere, Opeyemi; Lufesi, Norman; Kawaza, Kondwani; Irimu, Grace; Tongo, Olukemi; Murless-Collins, Sarah; Bohne, Christine; Richards-Kortum, Rebecca; Oden, Maria; Lawn, Joy E.; Rice360 Institute for Global Health Technologies
The emergence of COVID-19 precipitated containment policies (e.g., lockdowns, school closures, etc.). These policies disrupted healthcare, potentially eroding gains for Sustainable Development Goals including for neonatal mortality. Our analysis aimed to evaluate indirect effects of COVID-19 containment policies on neonatal admissions and mortality in 67 neonatal units across Kenya, Malawi, Nigeria, and Tanzania between January 2019 and December 2021.
Online trend estimation and detection of trend deviations in sub-sewershed time series of SARS-CoV-2 RNA measured in wastewater
(Springer Nature, 2024) Ensor, Katherine B.; Schedler, Julia C.; Sun, Thomas; Schneider, Rebecca; Mulenga, Anthony; Wu, Jingjing; Stadler, Lauren B.; Hopkins, Loren
Wastewater surveillance has proven a cost-effective key public health tool to understand a wide range of community health diseases and has been a strong source of information on community levels and spread for health departments throughout the SARS- CoV-2 pandemic. Studies spanning the globe demonstrate the strong association between virus levels observed in wastewater and quality clinical case information of the population served by the sewershed. Few of these studies incorporate the temporal dependence present in sampling over time, which can lead to estimation issues which in turn impact conclusions. We contribute to the literature for this important public health science by putting forward time series methods coupled with statistical process control that (1) capture the evolving trend of a disease in the population; (2) separate the uncertainty in the population disease trend from the uncertainty due to sampling and measurement; and (3) support comparison of sub-sewershed population disease dynamics with those of the population represented by the larger downstream treatment plant. Our statistical methods incorporate the fact that measurements are over time, ensuring correct statistical conclusions. We provide a retrospective example of how sub-sewersheds virus levels compare to the upstream wastewater treatment plant virus levels. An on-line algorithm supports real-time statistical assessment of deviations of virus level in a population represented by a sub-sewershed to the virus level in the corresponding larger downstream wastewater treatment plant. This information supports public health decisions by spotlighting segments of the population where outbreaks may be occurring.
Exploring the Relation between Contextual Social Determinants of Health and COVID-19 Occurrence and Hospitalization
(MDPI, 2024) Chen, Aokun; Zhao, Yunpeng; Zheng, Yi; Hu, Hui; Hu, Xia; Fishe, Jennifer N.; Hogan, William R.; Shenkman, Elizabeth A.; Guo, Yi; Bian, Jiang
It is prudent to take a unified approach to exploring how contextual social determinants of health (SDoH) relate to COVID-19 occurrence and outcomes. Poor geographically represented data and a small number of contextual SDoH examined in most previous research studies have left a knowledge gap in the relationships between contextual SDoH and COVID-19 outcomes. In this study, we linked 199 contextual SDoH factors covering 11 domains of social and built environments with electronic health records (EHRs) from a large clinical research network (CRN) in the National Patient-Centered Clinical Research Network (PCORnet) to explore the relation between contextual SDoH and COVID-19 occurrence and hospitalization. We identified 15,890 COVID-19 patients and 63,560 matched non-COVID-19 patients in Florida between January 2020 and May 2021. We adopted a two-phase multiple linear regression approach modified from that in the exposome-wide association (ExWAS) study. After removing the highly correlated SDoH variables, 86 contextual SDoH variables were included in the data analysis. Adjusting for race, ethnicity, and comorbidities, we found six contextual SDoH variables (i.e., hospital available beds and utilization, percent of vacant property, number of golf courses, and percent of minority) related to the occurrence of COVID-19, and three variables (i.e., farmers market, low access, and religion) related to the hospitalization of COVID-19. To our best knowledge, this is the first study to explore the relationship between contextual SDoH and COVID-19 occurrence and hospitalization using EHRs in a major PCORnet CRN. As an exploratory study, the causal effect of SDoH on COVID-19 outcomes will be evaluated in future studies.
Fear of missing out and depressive symptoms during the COVID-19 pandemic
(Wiley, 2023) LeRoy, Angie S.; Lai, Vincent D.; Tsay-Jones, Arya; Fagundes, Christopher P.
During the early stages of the COVID-19 pandemic, governments issued public health safety measures (e.g., “stay-at-home” ordinances), leaving many people “missing out” on integral social aspects of their own lives. The fear of missing out, popularly shortened as, “FoMO,” is a felt sense of unease one experiences when they perceive they may be missing out on rewarding and/or enjoyable experiences. Among 76 participants (ages M = 69.36, SD = 5.34), who were at risk for hospitalization or death if infected with COVID-19, we found that FoMO was associated with depressive symptoms at Time 1, even when controlling for perceived stress, loneliness, and fear of COVID-19. However, FoMO did not predict future depressive symptoms, about 1 week later, when controlling for Time 1 depressive symptoms. These findings provide further evidence that FoMO is associated with depressive symptoms in a short period of time even when accounting for other powerful social factors such as loneliness. Future research should explore the potential causal relationships between FoMO and depression, especially those that may establish temporal precedence.
A Recombinant Protein XBB.1.5 RBD/Alum/CpG Vaccine Elicits High Neutralizing Antibody Titers against Omicron Subvariants of SARS-CoV-2
(2023) Thimmiraju, Syamala Rani; Adhikari, Rakesh; Villar, Maria Jose; Lee, Jungsoon; Liu, Zhuyun; Kundu, Rakhi; Chen, Yi-Lin; Sharma, Suman; Ghei, Karm; Keegan, Brian; Versteeg, Leroy; Gillespie, Portia M.; Ciciriello, Allan; Islam, Nelufa Y.; Poveda, Cristina; Uzcategui, Nestor; Chen, Wen-Hsiang; Kimata, Jason T.; Zhan, Bin; Strych, Ulrich; Bottazzi, Maria Elena; Hotez, Peter J.; Pollet, Jeroen
(1) Background: We previously reported the development of a recombinant protein SARS-CoV-2 vaccine, consisting of the receptor-binding domain (RBD) of the SARS-CoV-2 spike protein, adjuvanted with aluminum hydroxide (alum) and CpG oligonucleotides. In mice and non-human primates, our wild-type (WT) RBD vaccine induced high neutralizing antibody titers against the WT isolate of the virus, and, with partners in India and Indonesia, it was later developed into two closely resembling human vaccines, Corbevax and Indovac. Here, we describe the development and characterization of a next-generation vaccine adapted to the recently emerging XBB variants of SARS-CoV-2. (2) Methods: We conducted preclinical studies in mice using a novel yeast-produced SARS-CoV-2 XBB.1.5 RBD subunit vaccine candidate formulated with alum and CpG. We examined the neutralization profile of sera obtained from mice vaccinated twice intramuscularly at a 21-day interval with the XBB.1.5-based RBD vaccine, against WT, Beta, Delta, BA.4, BQ.1.1, BA.2.75.2, XBB.1.16, XBB.1.5, and EG.5.1 SARS-CoV-2 pseudoviruses. (3) Results: The XBB.1.5 RBD/CpG/alum vaccine elicited a robust antibody response in mice. Furthermore, the serum from vaccinated mice demonstrated potent neutralization against the XBB.1.5 pseudovirus as well as several other Omicron pseudoviruses. However, regardless of the high antibody cross-reactivity with ELISA, the anti-XBB.1.5 RBD antigen serum showed low neutralizing titers against the WT and Delta virus variants. (4) Conclusions: Whereas we observed modest cross-neutralization against Omicron subvariants with the sera from mice vaccinated with the WT RBD/CpG/Alum vaccine or with the BA.4/5-based vaccine, the sera raised against the XBB.1.5 RBD showed robust cross-neutralization. These findings underscore the imminent opportunity for an updated vaccine formulation utilizing the XBB.1.5 RBD antigen.
Public Health Interventions Guided by Houston’s Wastewater Surveillance Program During the COVID-19 Pandemic
(Sage, 2023) Hopkins, Loren; Ensor, Katherine B.; Stadler, Lauren; Johnson, Catherine D.; Schneider, Rebecca; Domakonda, Kaavya; McCarthy, James J.; Septimus, Edward J.; Persse, David; Williams, Stephen L.
Since the start of the COVID-19 pandemic, wastewater surveillance has emerged as a powerful tool used by public health authorities to track SARS-CoV-2 infections in communities. In May 2020, the Houston Health Department began working with a coalition of municipal and academic partners to develop a wastewater monitoring and reporting system for the city of Houston, Texas. Data collected from the system are integrated with other COVID-19 surveillance data and communicated through different channels to local authorities and the general public. This information is used to shape policies and inform actions to mitigate and prevent the spread of COVID-19 at municipal, institutional, and individual levels. Based on the success of this monitoring and reporting system to drive public health protection efforts, the wastewater surveillance program is likely to become a standard part of the public health toolkit for responding to infectious diseases and, potentially, other disease-causing outbreaks.
A rapid, low-cost, and highly sensitive SARS-CoV-2 diagnostic based on whole-genome sequencing
(Public Library of Science, 2023) Adastra, Per A.; Durand, Neva C.; Mitra, Namita; Pulido, Saul Godinez; Mahajan, Ragini; Blackburn, Alyssa; Colaric, Zane L.; Theisen, Joshua W. M.; Weisz, David; Dudchenko, Olga; Gnirke, Andreas; Rao, Suhas S. P.; Kaur, Parwinder; Aiden, Erez Lieberman; Aiden, Aviva Presser; Center for Theoretical Biological Physics
Early detection of SARS-CoV-2 infection is key to managing the current global pandemic, as evidence shows the virus is most contagious on or before symptom onset. Here, we introduce a low-cost, high-throughput method for diagnosing and studying SARS-CoV-2 infection. Dubbed Pathogen-Oriented Low-Cost Assembly & Re-Sequencing (POLAR), this method amplifies the entirety of the SARS-CoV-2 genome. This contrasts with typical RT-PCR-based diagnostic tests, which amplify only a few loci. To achieve this goal, we combine a SARS-CoV-2 enrichment method developed by the ARTIC Network (https://artic.network/) with short-read DNA sequencing and de novo genome assembly. Using this method, we can reliably (>95% accuracy) detect SARS-CoV-2 at a concentration of 84 genome equivalents per milliliter (GE/mL). The vast majority of diagnostic methods meeting our analytical criteria that are currently authorized for use by the United States Food and Drug Administration with the Coronavirus Disease 2019 (COVID-19) Emergency Use Authorization require higher concentrations of the virus to achieve this degree of sensitivity and specificity. In addition, we can reliably assemble the SARS-CoV-2 genome in the sample, often with no gaps and perfect accuracy given sufficient viral load. The genotypic data in these genome assemblies enable the more effective analysis of disease spread than is possible with an ordinary binary diagnostic. These data can also help identify vaccine and drug targets. Finally, we show that the diagnoses obtained using POLAR of positive and negative clinical nasal mid-turbinate swab samples 100% match those obtained in a clinical diagnostic lab using the Center for Disease Control’s 2019-Novel Coronavirus test. Using POLAR, a single person can manually process 192 samples over an 8-hour experiment at the cost of ~$36 per patient (as of December 7th, 2022), enabling a 24-hour turnaround with sequencing and data analysis time. We anticipate that further testing and refinement will allow greater sensitivity using this approach.
Stratification of Pediatric COVID-19 Cases Using Inflammatory Biomarker Profiling and Machine Learning
(MDPI, 2023) Subramanian, Devika; Vittala, Aadith; Chen, Xinpu; Julien, Christopher; Acosta, Sebastian; Rusin, Craig; Allen, Carl; Rider, Nicholas; Starosolski, Zbigniew; Annapragada, Ananth; Devaraj, Sridevi
While pediatric COVID-19 is rarely severe, a small fraction of children infected with SARS-CoV-2 go on to develop multisystem inflammatory syndrome (MIS-C), with substantial morbidity. An objective method with high specificity and high sensitivity to identify current or imminent MIS-C in children infected with SARS-CoV-2 is highly desirable. The aim was to learn about an interpretable novel cytokine/chemokine assay panel providing such an objective classification. This retrospective study was conducted on four groups of pediatric patients seen at multiple sites of Texas Children’s Hospital, Houston, TX who consented to provide blood samples to our COVID-19 Biorepository. Standard laboratory markers of inflammation and a novel cytokine/chemokine array were measured in blood samples of all patients. Group 1 consisted of 72 COVID-19, 70 MIS-C and 63 uninfected control patients seen between May 2020 and January 2021 and predominantly infected with pre-alpha variants. Group 2 consisted of 29 COVID-19 and 43 MIS-C patients seen between January and May 2021 infected predominantly with the alpha variant. Group 3 consisted of 30 COVID-19 and 32 MIS-C patients seen between August and October 2021 infected with alpha and/or delta variants. Group 4 consisted of 20 COVID-19 and 46 MIS-C patients seen between October 2021 andJanuary 2022 infected with delta and/or omicron variants. Group 1 was used to train an L1-regularized logistic regression model which was tested using five-fold cross validation, and then separately validated against the remaining naïve groups. The area under receiver operating curve (AUROC) and F1-score were used to quantify the performance of the cytokine/chemokine assay-based classifier. Standard laboratory markers predict MIS-C with a five-fold cross-validated AUROC of 0.86 ± 0.05 and an F1 score of 0.78 ± 0.07, while the cytokine/chemokine panel predicted MIS-C with a five-fold cross-validated AUROC of 0.95 ± 0.02 and an F1 score of 0.91 ± 0.04, with only sixteen of the forty-five cytokines/chemokines sufficient to achieve this performance. Tested on Group 2 the cytokine/chemokine panel yielded AUROC = 0.98 and F1 = 0.93, on Group 3 it yielded AUROC = 0.89 and F1 = 0.89, and on Group 4 AUROC = 0.99 and F1 = 0.97. Adding standard laboratory markers to the cytokine/chemokine panel did not improve performance. A top-10 subset of these 16 cytokines achieves equivalent performance on the validation data sets. Our findings demonstrate that a sixteen-cytokine/chemokine panel as well as the top ten subset provides a highly sensitive, and specific method to identify MIS-C in patients infected with SARS-CoV-2 of all the major variants identified to date.
Mechanisms of SARS-CoV-2-induced Encephalopathy and Encephalitis in COVID-19 Cases
(Sage, 2023) Vengalil, Aaron; Nizamutdinov, Damir; Su, Matthew; Huang, Jason H.
The SARS-CoV-2 virus caused an unprecedented pandemic around the globe, infecting 36.5 million people and causing the death of over 1 million in the United States of America alone. COVID-19 patients demonstrated respiratory symptoms, cardiovascular complications, and neurologic symptoms, which in most severe cases included encephalopathy and encephalitis. Hypoxia and the uncontrolled proliferation of cytokines are commonly recognized to cause encephalopathy, while the retrograde trans-synaptic spread of the virus is thought to cause encephalitis in SARS-CoV-2-induced pathogenesis. Although recent research revealed some mechanisms explaining the development of neurologic symptoms, it still remains unclear whether interactions between these mechanisms exist. This review focuses on the discussion and analysis of previously reported hypotheses of SARS-CoV-2-induced encephalopathy and encephalitis and looks into possible overlaps between the pathogenesis of both neurological outcomes of the disease. Promising therapeutic approaches to prevent and treat SARS-CoV-2-induced neurological complications are also covered. More studies are needed to further investigate the dominant mechanism of pathogenesis for developing more effective preventative measures in COVID-19 cases with the neurologic presentation.
Balancing economic and epidemiological interventions in the early stages of pathogen emergence
(AAAS, 2023) Dobson, Andy; Ricci, Cristiano; Boucekkine, Raouf; Gozzi, Fausto; Fabbri, Giorgio; Loch-Temzelides, Ted; Pascual, Mercedes; Baker Institute for Public Policy
The global pandemic of COVID-19 has underlined the need for more coordinated responses to emergent pathogens. These responses need to balance epidemic control in ways that concomitantly minimize hospitalizations and economic damages. We develop a hybrid economic-epidemiological modeling framework that allows us to examine the interaction between economic and health impacts over the first period of pathogen emergence when lockdown, testing, and isolation are the only means of containing the epidemic. This operational mathematical setting allows us to determine the optimal policy interventions under a variety of scenarios that might prevail in the first period of a large-scale epidemic outbreak. Combining testing with isolation emerges as a more effective policy than lockdowns, substantially reducing deaths and the number of infected hosts, at lower economic cost. If a lockdown is put in place early in the course of the epidemic, it always dominates the “laissez-faire” policy of doing nothing.
Enabling accurate and early detection of recently emerged SARS-CoV-2 variants of concern in wastewater
(Springer Nature, 2023) Sapoval, Nicolae; Liu, Yunxi; Lou, Esther G.; Hopkins, Loren; Ensor, Katherine B.; Schneider, Rebecca; Stadler, Lauren B.; Treangen, Todd J.
As clinical testing declines, wastewater monitoring can provide crucial surveillance on the emergence of SARS-CoV-2 variant of concerns (VoCs) in communities. In this paper we present QuaID, a novel bioinformatics tool for VoC detection based on quasi-unique mutations. The benefits of QuaID are three-fold: (i) provides up to 3-week earlier VoC detection, (ii) accurate VoC detection (>95% precision on simulated benchmarks), and (iii) leverages all mutational signatures (including insertions & deletions).
SARS-CoV-2 Exposure in Norway Rats (Rattus norvegicus) from New York City
(American Society for Microbiology, 2023) Wang, Yang; Lenoch, Julianna; Kohler, Dennis; DeLiberto, Thomas J.; Tang, Cynthia Y.; Li, Tao; Tao, Yizhi Jane; Guan, Minhui; Compton, Susan; Zeiss, Caroline; Hang, Jun; Wan, Xiu-Feng
Millions of Norway rats (Rattus norvegicus) inhabit New York City (NYC), presenting the potential for transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) from humans to rats. We evaluated SARS-CoV-2 exposure among 79 rats captured from NYC during the fall of 2021. Our results showed that 13 of the 79 rats (16.5%) tested IgG- or IgM-positive, and partial SARS-CoV-2 genomes were recovered from all 4 rats that were qRT-PCR (reverse transcription-quantitative PCR)-positive. Genomic analyses suggest these viruses were associated with genetic lineage B, which was predominant in NYC in the spring of 2020 during the early pandemic period. To further investigate rat susceptibility to SARS-CoV-2 variants, we conducted a virus challenge study and showed that Alpha, Delta, and Omicron variants can cause infections in wild-type Sprague Dawley (SD) rats, including high replication levels in the upper and lower respiratory tracts and induction of both innate and adaptive immune responses. Additionally, the Delta variant resulted in the highest infectivity. In summary, our results indicate that rats are susceptible to infection with Alpha, Delta, and Omicron variants, and wild Norway rats in the NYC municipal sewer systems have been exposed to SARS-CoV-2. Our findings highlight the need for further monitoring of SARS-CoV-2 in urban rat populations and for evaluating the potential risk of secondary zoonotic transmission from these rat populations back to humans. IMPORTANCE The host tropism expansion of SARS-CoV-2 raises concern for the potential risk of reverse-zoonotic transmission of emerging variants into rodent species, including wild rat species. In this study, we present both genetic and serological evidence for SARS-CoV-2 exposure to the New York City wild rat population, and these viruses may be linked to the viruses that were circulating during the early stages of the pandemic. We also demonstrated that rats are susceptible to additional variants (i.e., Alpha, Delta, and Omicron) that have been predominant in humans and that susceptibility to infection varies by variant. Our findings highlight the reverse zoonosis of SARS-CoV-2 to urban rats and the need for further monitoring of SARS-CoV-2 in rat populations for potential secondary zoonotic transmission to humans.
Aging and Burnout for Nurses in an Acute Care Setting: The First Wave of COVID-19
(MDPI, 2023) Beier, Margaret E.; Cockerham, Mona; Branson, Sandy; Boss, Lisa
We examined the relationship between age, coping, and burnout during the peak of the COVID-19 pandemic with nurses in Texas (N = 376). Nurses were recruited through a professional association and snowball sampling methodology for the cross-sectional survey study. Framed in lifespan development theories, we expected that nurse age and experience would be positively correlated with positive coping strategies (e.g., getting emotional support from others) and negatively correlated with negative coping strategies (e.g., drinking and drug use). We also expected age to be negatively related to the emotional exhaustion and depersonalization facets of burnout and positively related to the personal accomplishment facet of burnout. Findings were largely supported in that age was positively associated with positive coping and personal accomplishment and age and experience were negatively correlated with negative coping and depersonalization. Age was not, however, associated with emotional exhaustion. Mediation models further suggest that coping explains some of the effect of age on burnout. A theoretical extension of lifespan development models into an extreme environment and practical implications for coping in these environments are discussed.
The contagion number: How fast can a disease spread?
(National Library of Serbia, 2023) Blessley, Misty; Davila, Randy; Hale, Trevor; Pepper, Ryan
The burning number of a graph models the rate at which a disease, information, or other externality can propagate across a network. The burning number is known to be NP-hard even for a tree. Herein, we define a relative of the burning number that we coin the contagion number (CN). We aver that the CN is a better metric to model disease spread than the burning number as it only counts first time infections (i.e., constrains a node from getting the same disease/same variant/same alarm more than once). This is important because the Centers for Disease Control and Prevention report that COVID-19 reinfections are rare. This paper delineates a method to solve for the contagion number of any tree, in polynomial time, which addresses how fast a disease could spread (i.e., a worst-cast analysis) and then employs simulation to determine the average contagion number (ACN) (i.e., a most-likely analysis) of how fast a disease would spread. The latter is analyzed on scale-free graphs, which are used to model human social networks generated through a preferential attachment mechanism. With CN differing across network structures and almost identical to ACN, our findings advance disease spread understanding and reveal the importance of network structure. In a borderless world without replete resources, understanding disease spread can do much to inform public policy and managerial decision makers’ allocation decisions. Furthermore, our direct interactions with supply chain executives at two COVID-19 vaccine developers provided practical grounding on what the results suggest for achieving social welfare objectives.
The COVID-19 Challenge Now Is Getting Into Heads, Arms Will Follow
(Taylor & Francis, 2021) O’Rourke, Thomas; Iammarino, Nicholas
With the onset and rapid spread of COVID-19 without a safe and effective vaccine, initial efforts to reduce community spread focused on basic public health measures such as mask wearing, social distancing, handwashing, avoiding large gatherings, and suspected cases isolation and quarantine. Following was the development of the COVID-19 vaccination and a shift to immunize the U.S. population to achieve herd immunity and halt the pandemic. Many diverse methods to influence vaccine uptake behaviors have been implemented including increasing the number and accessibility of vaccine sites, lowering the eligible age, relaxing eligibility requirements, public education and outreach campaigns, introducing state, local and job-based incentives and, in some instances, vaccine mandates. With two-thirds of the population now vaccinated with at least one shot, additional gains will be more difficult requiring more creative approaches rooted in behavior change theories and strategies. The behaviors associated with COVID-19 are not new and “tried and true” behaviorally oriented prevention strategies created long before COVID-19 arrived can effectively be used to educate people. Health educators and professionals can play a critical role with this remaining resistant population subset and must employ behaviorally oriented messages that are factually accurate, persuasive and relevant, and culturally and linguistically appropriate.
Modeling the positive testing rate of COVID-19 in South Africa using a semi-parametric smoother for binomial data
(Frontiers, 2023) Owokotomo, Olajumoke Evangelina; Manda, Samuel; Cleasen, Jürgen; Kasim, Adetayo; Sengupta, Rudradev; Shome, Rahul; Subhra Paria, Soumya; Reddy, Tarylee; Shkedy, Ziv
Identification and isolation of COVID-19 infected persons plays a significant role in the control of COVID-19 pandemic. A country's COVID-19 positive testing rate is useful in understanding and monitoring the disease transmission and spread for the planning of intervention policy. Using publicly available data collected between March 5th, 2020 and May 31st, 2021, we proposed to estimate both the positive testing rate and its daily rate of change in South Africa with a flexible semi-parametric smoothing model for discrete data. There was a gradual increase in the positive testing rate up to a first peak rate in July, 2020, then a decrease before another peak around mid-December 2020 to mid-January 2021. The proposed semi-parametric smoothing model provides a data driven estimates for both the positive testing rate and its change. We provide an online R dashboard that can be used to estimate the positive rate in any country of interest based on publicly available data. We believe this is a useful tool for both researchers and policymakers for planning intervention and understanding the COVID-19 spread.
Wastewater surveillance of SARS-CoV-2 and influenza in preK-12 schools shows school, community, and citywide infections
(Elsevier, 2023) Wolken, Madeline; Sun, Thomas; McCall, Camille; Schneider, Rebecca; Caton, Kelsey; Hundley, Courtney; Hopkins, Loren; Ensor, Katherine; Domakonda, Kaavya; Prashant, Kalvapalle; Persse, David; Williams, Stephen; Stadler, Lauren B.
Wastewater surveillance is a passive and efficient way to monitor the spread of infectious diseases in large populations and high transmission areas such as preK-12 schools. Infections caused by respiratory viruses in school-aged children are likely underreported, particularly because many children may be asymptomatic or mildly symptomatic. Wastewater monitoring of SARS-CoV-2 has been studied extensively and primarily by sampling at centralized wastewater treatment plants, and there are limited studies on SARS-CoV-2 in preK-12 school wastewater. Similarly, wastewater detections of influenza have only been reported in wastewater treatment plant and university manhole samples. Here, we present the results of a 17-month wastewater monitoring program for SARS-CoV-2 (n = 2176 samples) and influenza A and B (n = 1217 samples) in 51 preK-12 schools. We show that school wastewater concentrations of SARS-CoV-2 RNA were strongly associated with COVID-19 cases in schools and community positivity rates, and that influenza detections in school wastewater were significantly associated with citywide influenza diagnosis rates. Results were communicated back to schools and local communities to enable mitigation strategies to stop the spread, and direct resources such as testing and vaccination clinics. This study demonstrates that school wastewater surveillance is reflective of local infections at several population levels and plays a crucial role in the detection and mitigation of outbreaks.
Analysis of bronchoalveolar lavage fluid metatranscriptomes among patients with COVID-19 disease
(Springer Nature, 2022) Jochum, Michael; Lee, Michael D.; Curry, Kristen; Zaksas, Victoria; Vitalis, Elizabeth; Treangen, Todd; Aagaard, Kjersti; Ternus, Krista L.
To better understand the potential relationship between COVID-19 disease and hologenome microbial community dynamics and functional profiles, we conducted a multivariate taxonomic and functional microbiome comparison of publicly available human bronchoalveolar lavage fluid (BALF) metatranscriptome samples amongst COVID-19 (n = 32), community acquired pneumonia (CAP) (n = 25), and uninfected samples (n = 29). We then performed a stratified analysis based on mortality amongst the COVID-19 cohort with known outcomes of deceased (n = 10) versus survived (n = 15). Our overarching hypothesis was that there are detectable and functionally significant relationships between BALF microbial metatranscriptomes and the severity of COVID-19 disease onset and progression. We observed 34 functionally discriminant gene ontology (GO) terms in COVID-19 disease compared to the CAP and uninfected cohorts, and 21 GO terms functionally discriminant to COVID-19 mortality (q < 0.05). GO terms enriched in the COVID-19 disease cohort included hydrolase activity, and significant GO terms under the parental terms of biological regulation, viral process, and interspecies interaction between organisms. Notable GO terms associated with COVID-19 mortality included nucleobase-containing compound biosynthetic process, organonitrogen compound catabolic process, pyrimidine-containing compound biosynthetic process, and DNA recombination, RNA binding, magnesium and zinc ion binding, oxidoreductase activity, and endopeptidase activity. A Dirichlet multinomial mixtures clustering analysis resulted in a best model fit using three distinct clusters that were significantly associated with COVID-19 disease and mortality. We additionally observed discriminant taxonomic differences associated with COVID-19 disease and mortality in the genus Sphingomonas, belonging to the Sphingomonadacae family, Variovorax, belonging to the Comamonadaceae family, and in the class Bacteroidia, belonging to the order Bacteroidales. To our knowledge, this is the first study to evaluate significant differences in taxonomic and functional signatures between BALF metatranscriptomes from COVID-19, CAP, and uninfected cohorts, as well as associating these taxa and microbial gene functions with COVID-19 mortality. Collectively, while this data does not speak to causality nor directionality of the association, it does demonstrate a significant relationship between the human microbiome and COVID-19. The results from this study have rendered testable hypotheses that warrant further investigation to better understand the causality and directionality of host–microbiome–pathogen interactions.

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