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BackgroundThroughout the COVID-19 pandemic, the SARS-CoV-2 virus has continued to evolve, with new variants outcompeting existing variants and often leading to different dynamics of disease spread. MethodsIn this paper, we performed a retrospective analysis using longitudinal sequencing data to characterize differences in the speed, calendar timing, and magnitude of 13 SARS-CoV-2 variant waves/transitions for 215 countries and sub-country regions, between October 2020 and October 2022. We then clustered geographic locations in terms of their variant behavior across all Omicron variants, allowing us to identify groups of locations exhibiting similar variant transitions. Finally, we explored relationships between heterogeneity in these variant waves and time-varying factors, including vaccination status of the population, governmental policy, and the number of variants in simultaneous competition. FindingsThis work demonstrates associations between the behavior of an emerging variant and the number of co-circulating variants as well as the demographic context of the population. We also observed an association between high vaccination rates and variant transition dynamics prior to the Mu and Delta variant transitions. InterpretationThese results suggest the behavior of an emergent variant may be sensitive to the immunologic and demographic context of its location. Additionally, this work represents the most comprehensive characterization of variant transitions globally to date. FundingLaboratory Directed Research and Development (LDRD), Los Alamos National Laboratory Research in contextO_ST_ABSEvidence before this studyC_ST_ABSSARS-CoV-2 variants with a selective advantage are continuing to emerge, resulting in variant transitions that can give rise to new waves in global COVID-19 cases and changing dynamics of disease spread. While variant transitions have been well studied individually, more work is needed to better understand how variant transitions have occurred in the past and how properties of these transitions may relate to vaccination rates, natural immunity, and population demographics. Added value of this studyOur retrospective study integrates metadata based on 12.8 million SARS-CoV-2 sequences available through the Global Initiative on Sharing All Influenza Data (GISAID) with clinical and demographic data to characterize heterogeneity in variant waves/transitions across the globe throughout the COVID-19 pandemic. We demonstrate that properties of the variant transitions (e.g., speed, timing, and magnitude of the transition) are associated with vaccination rates, prior COVID-19 cases, and the number of co-circulating variants in competition. Implications of all the available evidenceOur results indicate that there is substantial heterogeneity in how an emerging variant may compete with other viral variants across locations, and suggest that each locations contemporaneous immunologic landscape may play a role in these interactions.
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BackgroundObservational studies have identified patients with cancer as a potential subgroup of individuals at elevated risk of severe SARS-CoV-2 (COVID-19) disease and mortality. Early studies showed an increased risk of COVID-19 mortality for cancer patients, but it is not well understood how this association varies by cancer site, cancer treatment, and vaccination status. MethodsUsing electronic health record data from an academic medical center, we identified 259,893 individuals who were tested for or diagnosed with COVID-19 from March 10, 2020, to February 2, 2022. Of these, 41,218 tested positive for COVID-19 of whom 10,266 had a past or current cancer diagnosis. We conducted Firth-corrected, covariate-adjusted logistic regression to assess the association of cancer status, cancer type, and cancer treatment with four COVID-19 outcomes: hospitalization, intensive care unit (ICU) admission, mortality, and a composite "severe COVID-19" outcome which is the union of the first three outcomes. We examine the effect of the timing of cancer diagnosis and treatment relative to COVID diagnosis, and the effect of vaccination. ResultsCancer status was associated with higher rates of severe COVID-19 infection [OR (95% CI): 1.18 (1.08, 1.29)], hospitalization [OR (95% CI): 1.18 (1.06, 1.28)], and mortality [OR (95% CI): 1.22 (1.00, 1.48)]. These associations were driven by patients whose most recent initial cancer diagnosis was within the past three years. Chemotherapy receipt was positively associated with all four COVID-19 outcomes (e.g., severe COVID [OR (95% CI): 1.96 (1.73, 2.22)], while receipt of either radiation or surgery alone were not associated with worse COVID-19 outcomes. Among cancer types, hematologic malignancies [OR (95% CI): 1.62 (1.39, 1.88)] and lung cancer [OR (95% CI): 1.81 (1.34, 2.43)] were significantly associated with higher odds of hospitalization. Hematologic malignancies were associated with ICU admission [OR (95% CI): 1.49 (1.11, 1.97)] and mortality [OR (95% CI): 1.57 (1.15, 2.11)], while melanoma and breast cancer were not associated with worse COVID-19 outcomes. Vaccinations were found to reduce the frequency of occurrence for the four COVID-19 outcomes across cancer status but those with cancer continued to have elevated risk of severe COVID [cancer OR (95% CI) among those fully vaccinated: 1.69 (1.10, 2.62)] relative to those without cancer even among vaccinated. ConclusionOur study provides insight to the relationship between cancer diagnosis, treatment, cancer type, vaccination, and COVID-19 outcomes. Our results indicate that it is plausible that specific diagnoses (e.g., hematologic malignancies, lung cancer) and treatments (e.g., chemotherapy) are associated with worse COVID-19 outcomes. Vaccines significantly reduce the risk of severe COVID-19 outcomes in individuals with cancer and those without, but cancer patients are still at higher risk of breakthrough infections and more severe COVID outcomes even after vaccination. These findings provide actionable insights for risk identification and targeted treatment and prevention strategies.
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Testing for active SARS-CoV-2 infections is key to controlling the spread of the virus and preventing severe disease. A central public health challenge is defining test allocation strategies in the presence of limited resources. Inthis paper, we provide a mathematical framework for defining anoptimal strategy for allocating viral tests. The framework accounts for imperfect test results, selective testing in certain high-risk patient populations, practical constraints in terms of budget and/or total number of available tests, and the purpose of testing. Our method is not only useful for detecting infected cases, but can also be used for long-time surveillance to monitor for new outbreaks, which will be especially important during ongoing vaccine distribution across the world. In our proposed approach, tests can be allocated across population strata defined by symptom severity and other patient characteristics, allowing the test allocation plan to prioritize higher risk patient populations. We illustrate our framework using historical data from the initial wave of the COVID-19 outbreak in New York City. We extend our proposed method to address the challenge of allocating two different types of tests with different costs and accuracy (for example, the expensive but more accurate RT-PCR test versus the cheap but less accurate rapid antigen test), administered under budget constraints. We show how this latter framework can be useful to reopening of college campuses where university administrators are challenged with finite resources for community surveillance. We provide a R Shiny web application allowing users to explore test allocation strategies across a variety of pandemic scenarios. This work can serve as a useful tool for guiding public health decision-making at a community level and adapting to different stages of an epidemic, and it has broader relevance beyond the COVID-19 outbreak.
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The false negative rate of the diagnostic RT-PCR test for SARS-CoV-2 has been reported to be substantially high. Due to limited availability of testing, only a non-random subset of the population can get tested. Hence, the reported test counts are subject to a large degree of selection bias. We consider an extension of the Susceptible-Exposed-Infected-Removed (SEIR) model under both selection bias and misclassification. We derive closed form expression for the basic reproduction number under such data anomalies using the next generation matrix method. We conduct extensive simulation studies to quantify the effect of misclassification and selection on the resultant estimation and prediction of future case counts. Finally we apply the methods to reported case-death-recovery count data from India, a nation with more than 5 million cases reported over the last seven months. We show that correcting for misclassification and selection can lead to more accurate prediction of case-counts (and death counts) using the observed data as a beta tester. The model also provides an estimate of undetected infections and thus an under-reporting factor. For India, the estimated under-reporting factor for cases is around 21 and for deaths is around 6. We develop an R-package (SEIRfansy) for broader dissemination of the methods.