Ethnic homophily affects vaccine prioritization strategies

People are more likely to interact with other people of their ethnicity--a phenomenon known as ethnic homophily. In the United States, people of color are known to hold proportionately more high-contact jobs and are thus more at risk of virus infection. At the same time, these ethnic groups are on average younger than the rest of the population. This gives rise to interesting disease dynamics and non-trivial trade-offs that should be taken into consideration when developing prioritization strategies for future mass vaccine roll-outs. Here, we study the spread of COVID-19 through the U.S. population, stratified by age, ethnicity, and occupation, using a detailed, previously-developed compartmental disease model. Based on historic data from the U.S. mass COVID-19 vaccine roll-out that began in December 2020, we show, (i) how ethnic homophily affects the choice of optimal vaccine allocation strategy, (ii) that, notwithstanding potential ethical concerns, differentiating by ethnicity in these strategies can improve outcomes (e.g., fewer deaths), and (iii) that the most likely social context in the United States is very different from the standard assumptions made by models which do not account for ethnicity and this difference affects which allocation strategy is optimal. HighlightsO_LIA social mixing model accounting for ethnic homophily and variable job-related risk level is developed. C_LIO_LIA scenario that differs strongly from standard homogeneous mixing assumptions best matches U.S. ethnicity-specific death and case counts. C_LIO_LITwo trade-offs are explored: Should (i) old or young, and (ii) people of color or White and Asian people first receive COVID-19 vaccines? C_LIO_LIExhaustive simulation of a compartmental disease model identifies the optimal allocation strategy for different demographic groups. C_LIO_LIOptimal strategies depend on the underlying mixing pattern and strategies that differentiate vaccine access by ethnicity outperform others. C_LI

Time series analysis and predicting COVID-19 affected patients by ARIMA model using machine learning.

The spread of a respiratory syndrome known as Coronavirus Disease 2019 (COVID-19) quickly took on pandemic proportions, affecting over 192 countries. An emergency of the health system was obligated for the response to this epidemic. Although containment measures in China reduced new cases by more than 90 %, the levels of reduction were not the same in other countries. So, the question that arises is: what the world will see this pandemic, and how many patients can be affected? The response would be helpful and supportive of the authority and the community to prepare for the coming days. In this study, the Autoregressive Integrated Moving Average (ARIMA) model was employed to analyze the temporal dynamics of the worldwide spread of COVID-19 in the time window from January 22, 2020 to April 7, 2020. The cumulative number of confirmed Covid-19-affected patients forecasted over the three months was between 9,189,262 - 14,906,483 worldwide. This prediction value of Covid 19-affected patients will be valid only if the situation remains unchanged, and the epidemic spreads according to the previous nature worldwide in these three months.

Incorporating the mutational landscape of SARS-COV-2 variants and case-dependent vaccination rates into epidemic models

Coronavirus Disease (COVID-19), which began as a small outbreak in Wuhan, China in December 2019, became a global pandemic within months due to its high transmissibility. In the absence of pharmaceutical treatment, various non-pharmaceutical interventions (NPIs) to contain the spread of COVID-19 brought the entire world to a halt. After almost a year of seemingly returning to normalcy with the worlds quickest vaccine development, the emergence of more infectious and vaccine resistant coronavirus variants is bringing the situation back to where it was a year ago. In the light of this new situation, we conducted a study to portray the possible scenarios based on the three key factors : impact of interventions (pharmaceutical and NPIs), vaccination rate, and vaccine efficacy. In our study, we assessed two of the most crucial factors, transmissibility and vaccination rate, in order to reduce the spreading of COVID-19 in a simple but effective manner. In order to incorporate the time-varying mutational landscape of COVID-19 variants, we estimated a weighted transmissibility composed of the proportion of existing strains that naturally vary over time. Additionally, we consider time varying vaccination rates based on the number of daily new cases. Our method for calculating the vaccination rate from past active cases is an effective approach in forecasting probable future scenarios as it actively tracks peoples attitudes toward immunization as active cases change. Our simulations show that if a large number of individuals cannot be vaccinated by ensuring high efficacy in a short period of time, adopting NPIs is the best approach to manage disease transmission with the emergence of new vaccine breakthrough and more infectious variants.

Evaluation of the United States COVID-19 Vaccine Allocation Strategy

BackgroundAnticipating an initial shortage of vaccines for COVID-19, the Centers for Disease Control (CDC) in the United States developed priority vaccine allocations for specific demographic groups in the population. This study evaluates the performance of the CDC vaccine allocation strategy with respect to multiple potentially competing vaccination goals (minimizing mortality, cases, infections, and years of life lost (YLL)), under the same framework as the CDC allocation: four priority vaccination groups and population demographics stratified by age, comorbidities, occupation and living condition (congested or non-congested). MethodsWe developed a compartmental disease model that incorporates key elements of the current pandemic including age-varying susceptibility to infection, age-varying clinical fraction, an active case-count dependent social distancing level, and time-varying infectivity (accounting for the emergence of more infectious virus strains). Under this model, the CDC allocation strategy is compared to all other possibly optimal allocations that stagger vaccine roll-out in up to four phases (17.5 million strategies). ResultsThe CDC allocation strategy performed well in all vaccination goals but never optimally. Under the developed model, the CDC allocation deviated from the optimal allocations by small amounts, with 0.19% more deaths, 4.0% more cases, 4.07% more infections, and 0.97% higher YLL, than the respective optimal strategies. The CDC decision to not prioritize the vaccination of individuals under the age of 16 was optimal, as was the prioritization of health-care workers and other essential workers over non-essential workers. Finally, a higher prioritization of individuals with comorbidities in all age groups improved outcomes compared to the CDC allocation. InterpretationThe developed approach can be used to inform the design of future vaccine allocation strategies in the United States, or adapted for use by other countries seeking to optimize the effectiveness of their vaccine allocation strategies. FundingThe authors received no funding for this work. Research in contextO_ST_ABSEvidence before this studyC_ST_ABSThe Centers for Disease Control and Prevention (CDC) prioritized population groups for vaccination based on available scientific evidence, the feasibility of different implementation strategies, and ethical considerations. We searched PubMed using the query "(((COVID) AND (vaccin*)) AND (model)) AND ((priorit*) OR alloc*)" up to June 15, 2021, with no date or language restrictions. The search identified 190 articles, of which 15 used predictive models to evaluate the efficacy of vaccine allocation strategies in achieving vaccination campaign goals such as reducing mortality or incidence. All studies compared only a small number of specific, expertise-based allocations. Most studies stratified the population by age, while some considered additional characteristics such as occupation or comorbidity status, but none took into account all characteristics included in the CDC vaccine prioritizations. Added value of this studyWe developed a compartmental disease model that takes into account several important components of the COVID-19 pandemic, and stratifies the U.S. population by all characteristics included in the CDC vaccine prioritization recommendations. In a novel global optimization approach, we compared the CDC recommendations to all potentially optimal strategies (17.5 million strategies) that also stagger the vaccine roll-out in four phases. The CDC allocation strategy performed well in all considered outcome measures, but never optimally; a higher prioritization of individuals with comorbidities in all age groups improved outcomes. The CDC decision to initially not vaccinate children, as well as the prioritization of health-care workers and other essential workers over non-essential workers proved optimal under all outcome measures. Implications of all the available evidenceOur study identifies and compares the optimal vaccine allocation strategies for several competing vaccination goals. The developed global optimization approach can be used to inform the design of future vaccine allocation strategies in the United States and elsewhere.

Comprehensive annotations of the mutational spectra of SARS-CoV-2 spike protein: a fast and accurate pipeline

In order to explore nonsynonymous mutations and deletions in the spike (S) protein of SARS-CoV-2, we comprehensively analyzed 35,750 complete S protein gene sequences from across six continents and five climate zones around the world, as documented in the GISAID database as of June 24th, 2020. Through a custom Python-based pipeline for analyzing mutations, we identified 27,801 (77.77 % of spike sequences) mutated strains compared to Wuhan-Hu-1 strain. 84.40% of these strains had only single amino-acid (aa) substitution mutations, but an outlier strain from Bosnia and Herzegovina (EPI_ISL_463893) was found to possess six aa substitutions. The D614G variant of the major G clade was found to be predominant across circulating strains in all climates. We also identified 988 unique aa substitution mutations distributed across 660 positions within the spike protein, with eleven sites showing high variability - these sites had four types of aa variations at each position. Besides, 17 in-frame deletions at four major regions (three in N-terminal domain and one just downstream of the RBD) may have possible impact on attenuation. Moreover, the mutational frequency differed significantly (p= 0.003, Kruskal-Wallis test) among the SARS-CoV-2 strains worldwide. This study presents a fast and accurate pipeline for identifying nonsynonymous mutations and deletions from large dataset for any particular protein coding sequence and presents this S protein data as representative analysis. By using separate multi-sequence alignment with MAFFT, removing ambiguous sequences and in-frame stop codons, and utilizing pairwise alignment, this method can derive nonsynonymus mutations (Reference:Position:Strain). We believe this will aid in the surveillance of any proteins encoded by SARS-CoV-2, and will prove to be crucial in tracking the ever-increasing variation of many other divergent RNA viruses in the future.