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1. A prospective cohort study of preconception COVID-19 vaccination and miscarriage

   https://doi.org/10.1093/humrep/dead211  

   Yland, Jennifer J. ; Wesselink, Amelia K. ; Regan, Annette K. ; Hatch, Elizabeth E. ; Rothman, Kenneth J. ; Savitz, David A. ; Wang, Tanran R. ; Huybrechts, Krista F. ; Hernández-Díaz, Sonia ; Eisenberg, Michael L. ; et al ( October 2023 , Human Reproduction)

   To what extent is preconception maternal or paternal coronavirus disease 2019 (COVID-19) vaccination associated with miscarriage incidence?

   COVID-19 vaccination in either partner at any time before conception is not associated with an increased rate of miscarriage.

   Several observational studies have evaluated the safety of COVID-19 vaccination during pregnancy and found no association with miscarriage, though no study prospectively evaluated the risk of early miscarriage (gestational weeks [GW] <8) in relation to COVID-19 vaccination. Moreover, no study has evaluated the role of preconception vaccination in both male and female partners.

   An Internet-based, prospective preconception cohort study of couples residing in the USA and Canada. We analyzed data from 1815 female participants who conceived during December 2020–November 2022, including 1570 couples with data on male partner vaccination.

   Eligible female participants were aged 21–45 years and were trying to conceive without use of fertility treatment at enrollment. Female participants completed questionnaires at baseline, every 8 weeks until pregnancy, and during early and late pregnancy; they could also invite their male partners to complete a baseline questionnaire. We collected data on COVID-19 vaccination (brand and date of doses), history of SARS-CoV-2 infection (yes/no and date of positive test), potential confounders (demographic, reproductive, and lifestyle characteristics), and pregnancy status on all questionnaires. Vaccination status was categorized as never (0 doses before conception), ever (≥1 dose before conception), having a full primary sequence before conception, and completing the full primary sequence ≤3 months before conception. These categories were not mutually exclusive. Participants were followed up from their first positive pregnancy test until miscarriage or a censoring event (induced abortion, ectopic pregnancy, loss to follow-up, 20 weeks’ gestation), whichever occurred first. We estimated incidence rate ratios (IRRs) for miscarriage and corresponding 95% CIs using Cox proportional hazards models with GW as the time scale. We used propensity score fine stratification weights to adjust for confounding.

   Among 1815 eligible female participants, 75% had received at least one dose of a COVID-19 vaccine by the time of conception. Almost one-quarter of pregnancies resulted in miscarriage, and 75% of miscarriages occurred <8 weeks’ gestation. The propensity score-weighted IRR comparing female participants who received at least one dose any time before conception versus those who had not been vaccinated was 0.85 (95% CI: 0.63, 1.14). COVID-19 vaccination was not associated with increased risk of either early miscarriage (GW: <8) or late miscarriage (GW: 8–19). There was no indication of an increased risk of miscarriage associated with male partner vaccination (IRR = 0.90; 95% CI: 0.56, 1.44).

   The present study relied on self-reported vaccination status and infection history. Thus, there may be some non-differential misclassification of exposure status. While misclassification of miscarriage is also possible, the preconception cohort design and high prevalence of home pregnancy testing in this cohort reduced the potential for under-ascertainment of miscarriage. As in all observational studies, residual or unmeasured confounding is possible.

   This is the first study to evaluate prospectively the relation between preconception COVID-19 vaccination in both partners and miscarriage, with more complete ascertainment of early miscarriages than earlier studies of vaccination. The findings are informative for individuals planning a pregnancy and their healthcare providers.

   This work was supported by the Eunice Kennedy Shriver National Institute of Child Health and Human Development, the National Institute of Health [R01-HD086742 (PI: L.A.W.); R01-HD105863S1 (PI: L.A.W. and M.L.E.)], the National Institute of Allergy and Infectious Diseases (R03-AI154544; PI: A.K.R.), and the National Science Foundation (NSF-1914792; PI: L.A.W.). The funders had no role in the study design, data collection, analysis and interpretation of data, writing of the report, or the decision to submit the paper for publication. L.A.W. is a fibroid consultant for AbbVie, Inc. She also receives in-kind donations from Swiss Precision Diagnostics (Clearblue home pregnancy tests) and Kindara.com (fertility apps). M.L.E. received consulting fees from Ro, Hannah, Dadi, VSeat, and Underdog, holds stock in Ro, Hannah, Dadi, and Underdog, is a past president of SSMR, and is a board member of SMRU. K.F.H. reports being an investigator on grants to her institution from UCB and Takeda, unrelated to this study. S.H.-D. reports being an investigator on grants to her institution from Takeda, unrelated to this study, and a methods consultant for UCB and Roche for unrelated drugs. The authors report no other relationships or activities that could appear to have influenced the submitted work.

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2. A Prospective Cohort Study of COVID-19 Vaccination, SARS-CoV-2 Infection, and Fertility

   https://doi.org/10.1093/aje/kwac011  

   Wesselink, Amelia K ; Hatch, Elizabeth E ; Rothman, Kenneth J ; Wang, Tanran R ; Willis, Mary D ; Yland, Jennifer ; Crowe, Holly M ; Geller, Ruth J ; Willis, Sydney K ; Perkins, Rebecca B ; et al ( January 2022 , American Journal of Epidemiology)

   Abstract Some reproductive-aged individuals remain unvaccinated against coronavirus disease 2019 (COVID-19) because of concerns about potential adverse effects on fertility. Using data from an internet-based preconception cohort study, we examined the associations of COVID-19 vaccination and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection with fertility among couples trying to conceive spontaneously. We enrolled 2,126 self-identified female participants aged 21–45 year residing in the United States or Canada during December 2020–September 2021 and followed them through November 2021. Participants completed questionnaires every 8 weeks on sociodemographics, lifestyle, medical factors, and partner information. We fit proportional probabilities regression models to estimate associations between self-reported COVID-19 vaccination and SARS-CoV-2 infection in both partners with fecundability (i.e., the per-cycle probability of conception), adjusting for potential confounders. COVID-19 vaccination was not appreciably associated with fecundability in either partner (female fecundability ratio (FR) = 1.08, 95% confidence interval (CI): 0.95, 1.23; male FR = 0.95, 95% CI: 0.83, 1.10). Female SARS-CoV-2 infection was not strongly associated with fecundability (FR = 1.07, 95% CI: 0.87, 1.31). Male infection was associated with a transient reduction in fecundability (for infection within 60 days, FR = 0.82, 95% CI: 0.47, 1.45; for infection after 60 days, FR = 1.16, 95% CI: 0.92, 1.47). These findings indicate that male SARS-CoV-2 infection may be associated with a short-term decline in fertility and that COVID-19 vaccination does not impair fertility in either partner. 

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3. Modeling and tracking Covid-19 cases using Big Data analytics on HPCC system platform

   https://doi.org/10.1186/s40537-021-00423-z  

   Villanustre, Flavio ; Chala, Arjuna ; Dev, Roger ; Xu, Lili ; LexisNexis, Jesse Shaw ; Furht, Borko ; Khoshgoftaar, Taghi ( December 2021 , Journal of Big Data)

   Abstract This project is funded by the US National Science Foundation (NSF) through their NSF RAPID program under the title “Modeling Corona Spread Using Big Data Analytics.” The project is a joint effort between the Department of Computer & Electrical Engineering and Computer Science at FAU and a research group from LexisNexis Risk Solutions. The novel coronavirus Covid-19 originated in China in early December 2019 and has rapidly spread to many countries around the globe, with the number of confirmed cases increasing every day. Covid-19 is officially a pandemic. It is a novel infection with serious clinical manifestations, including death, and it has reached at least 124 countries and territories. Although the ultimate course and impact of Covid-19 are uncertain, it is not merely possible but likely that the disease will produce enough severe illness to overwhelm the worldwide health care infrastructure. Emerging viral pandemics can place extraordinary and sustained demands on public health and health systems and on providers of essential community services. Modeling the Covid-19 pandemic spread is challenging. But there are data that can be used to project resource demands. Estimates of the reproductive number (R) of SARS-CoV-2 show that at the beginning of the epidemic, each infected person spreads the virus to at least two others, on average (Emanuel et al. in N Engl J Med. 2020, Livingston and Bucher in JAMA 323(14):1335, 2020). A conservatively low estimate is that 5 % of the population could become infected within 3 months. Preliminary data from China and Italy regarding the distribution of case severity and fatality vary widely (Wu and McGoogan in JAMA 323(13):1239–42, 2020). A recent large-scale analysis from China suggests that 80 % of those infected either are asymptomatic or have mild symptoms; a finding that implies that demand for advanced medical services might apply to only 20 % of the total infected. Of patients infected with Covid-19, about 15 % have severe illness and 5 % have critical illness (Emanuel et al. in N Engl J Med. 2020). Overall, mortality ranges from 0.25 % to as high as 3.0 % (Emanuel et al. in N Engl J Med. 2020, Wilson et al. in Emerg Infect Dis 26(6):1339, 2020). Case fatality rates are much higher for vulnerable populations, such as persons over the age of 80 years (> 14 %) and those with coexisting conditions (10 % for those with cardiovascular disease and 7 % for those with diabetes) (Emanuel et al. in N Engl J Med. 2020). Overall, Covid-19 is substantially deadlier than seasonal influenza, which has a mortality of roughly 0.1 %. Public health efforts depend heavily on predicting how diseases such as those caused by Covid-19 spread across the globe. During the early days of a new outbreak, when reliable data are still scarce, researchers turn to mathematical models that can predict where people who could be infected are going and how likely they are to bring the disease with them. These computational methods use known statistical equations that calculate the probability of individuals transmitting the illness. Modern computational power allows these models to quickly incorporate multiple inputs, such as a given disease’s ability to pass from person to person and the movement patterns of potentially infected people traveling by air and land. This process sometimes involves making assumptions about unknown factors, such as an individual’s exact travel pattern. By plugging in different possible versions of each input, however, researchers can update the models as new information becomes available and compare their results to observed patterns for the illness. In this paper we describe the development a model of Corona spread by using innovative big data analytics techniques and tools. We leveraged our experience from research in modeling Ebola spread (Shaw et al. Modeling Ebola Spread and Using HPCC/KEL System. In: Big Data Technologies and Applications 2016 (pp. 347-385). Springer, Cham) to successfully model Corona spread, we will obtain new results, and help in reducing the number of Corona patients. We closely collaborated with LexisNexis, which is a leading US data analytics company and a member of our NSF I/UCRC for Advanced Knowledge Enablement. The lack of a comprehensive view and informative analysis of the status of the pandemic can also cause panic and instability within society. Our work proposes the HPCC Systems Covid-19 tracker, which provides a multi-level view of the pandemic with the informative virus spreading indicators in a timely manner. The system embeds a classical epidemiological model known as SIR and spreading indicators based on causal model. The data solution of the tracker is built on top of the Big Data processing platform HPCC Systems, from ingesting and tracking of various data sources to fast delivery of the data to the public. The HPCC Systems Covid-19 tracker presents the Covid-19 data on a daily, weekly, and cumulative basis up to global-level and down to the county-level. It also provides statistical analysis for each level such as new cases per 100,000 population. The primary analysis such as Contagion Risk and Infection State is based on causal model with a seven-day sliding window. Our work has been released as a publicly available website to the world and attracted a great volume of traffic. The project is open-sourced and available on GitHub. The system was developed on the LexisNexis HPCC Systems, which is briefly described in the paper. 

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4. Learning From the Experiences of COVID-19 Survivors: Web-Based Survey Study

   https://doi.org/10.2196/23009  

   Prioleau, Temiloluwa ( January 2021 , JMIR Formative Research)

   null (Ed.)

   Background There are still many unanswered questions about the novel coronavirus; however, a largely underutilized source of knowledge is the millions of people who have recovered after contracting the virus. This includes a majority of undocumented cases of COVID-19, which were classified as mild or moderate and received little to no clinical care during the course of illness. Objective This study aims to document and glean insights from the experiences of individuals with a first-hand experience in dealing with COVID-19, especially the so-called mild-to-moderate cases that self-resolved while in isolation. Methods This web-based survey study called C19 Insider Scoop recruited adult participants aged 18 years or older who reside in the United States and had tested positive for COVID-19 or antibodies. Participants were recruited through various methods, including online support groups for COVID-19 survivors, advertisement in local news outlets, as well as through professional and other networks. The main outcomes measured in this study included knowledge of contraction or transmission of the virus, symptoms, and personal experiences on the road to recovery. Results A total of 72 participants (female, n=53; male, n=19; age range: 18-73 years; mean age: 41 [SD 14] years) from 22 US states were enrolled in this study. The top known source of how people contracted SARS-CoV-2, the virus known to cause COVID-19, was through a family or household member (26/72, 35%). This was followed by essential workers contracting the virus through the workplace (13/72, 18%). Participants reported up to 27 less-documented symptoms that they experienced during their illness, such as brain or memory fog, palpitations, ear pain or discomfort, and neurological problems. In addition, 47 of 72 (65%) participants reported that their symptoms lasted longer than the commonly cited 2-week period even for mild cases of COVID-19. The mean recovery time of the study participants was 4.5 weeks, and exactly one-half of participants (50%) still experienced lingering symptoms of COVID-19 after an average of 65 days following illness onset. Additionally, 37 (51%) participants reported that they experienced stigma associated with contracting COVID-19. Conclusions This study presents preliminary findings suggesting that emphasis on family or household spread of COVID-19 may be lacking and that there is a general underestimation of the recovery time even for mild cases of illness with the virus. Although a larger study is needed to validate these results, it is important to note that as more people experience COVID-19, insights from COVID-19 survivors can enable a more informed public, pave the way for others who may be affected by the virus, and guide further research. 

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5. Experimental Susceptibility of North American Raccoons (Procyon lotor) and Striped Skunks (Mephitis mephitis) to SARS-CoV-2

   https://doi.org/10.3389/fvets.2021.715307  

   Francisco, Raquel ; Hernandez, Sonia M. ; Mead, Daniel G. ; Adcock, Kayla G. ; Burke, Sydney C. ; Nemeth, Nicole M. ; Yabsley, Michael J. ( January 2022 , Frontiers in Veterinary Science)

   Recent spillback events of SARS-CoV-2 from humans to animals has raised concerns about it becoming endemic in wildlife. A sylvatic cycle of SARS-CoV-2 could present multiple opportunities for repeated spillback into human populations and other susceptible wildlife. Based on their taxonomy and natural history, two native North American wildlife species —the striped skunk ( Mephitis mephitis ) and the raccoon ( Procyon lotor) —represent a high likelihood of susceptibility and ecological opportunity of becoming infected with SARS-CoV-2. Eight skunks and raccoons were each intranasally inoculated with one of two doses of the virus (10 3 PFU and 10 5 PFU) and housed in pairs. To evaluate direct transmission, a naïve animal was added to each inoculated pair 48 h post-inoculation. Four control animals of each species were handled like the experimental groups. At predetermined intervals, we collected nasal and rectal swabs to quantify virus shed via virus isolation and detect viral RNA via rRT-PCR and blood for serum neutralization. Lastly, animals were euthanized at staggered intervals to describe disease progression through histopathology and immunohistochemistry. No animals developed clinical disease. All intranasally inoculated animals seroconverted, suggesting both species are susceptible to SARS-CoV-2 infection. The highest titers in skunks and raccoons were 1:128 and 1:64, respectively. Low quantities of virus were isolated from 2/8 inoculated skunks for up to day 5 post-inoculation, however no virus was isolated from inoculated raccoons or direct contacts of either species. Neither species had gross lesions, but recovering mild chronic pneumonia consistent with viral insult was recorded histologically in 5/8 inoculated skunks. Unlike another SARS-CoV-2 infection trial in these species, we detected neutralizing antibodies in inoculated raccoons; thus, future wildlife serologic surveillance results must be interpreted with caution. Due to the inability to isolate virus from raccoons, the lack of evidence of direct transmission between both species, and low amount of virus shed by skunks, it seems unlikely for SARS-CoV-2 to become established in raccoon and skunk populations and for virus to spillback into humans. Continued outbreaks in non-domestic species, wild and captive, highlight that additional research on the susceptibility of SARS-CoV-2 in wildlife, especially musteloidea, and of conservation concern, is needed. 

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