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1. Evaluation of COVID-19 vaccination strategies with a delayed second dose

   https://doi.org/10.1371/journal.pbio.3001211  

   Moghadas, Seyed M. ; Vilches, Thomas N. ; Zhang, Kevin ; Nourbakhsh, Shokoofeh ; Sah, Pratha ; Fitzpatrick, Meagan C. ; Galvani, Alison P. ( April 2021 , PLOS Biology)

   Read, Andrew Fraser (Ed.)

   Two of the Coronavirus Disease 2019 (COVID-19) vaccines currently approved in the United States require 2 doses, administered 3 to 4 weeks apart. Constraints in vaccine supply and distribution capacity, together with a deadly wave of COVID-19 from November 2020 to January 2021 and the emergence of highly contagious Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) variants, sparked a policy debate on whether to vaccinate more individuals with the first dose of available vaccines and delay the second dose or to continue with the recommended 2-dose series as tested in clinical trials. We developed an agent-based model of COVID-19 transmission to compare the impact of these 2 vaccination strategies, while varying the temporal waning of vaccine efficacy following the first dose and the level of preexisting immunity in the population. Our results show that for Moderna vaccines, a delay of at least 9 weeks could maximize vaccination program effectiveness and avert at least an additional 17.3 (95% credible interval [CrI]: 7.8–29.7) infections, 0.69 (95% CrI: 0.52–0.97) hospitalizations, and 0.34 (95% CrI: 0.25–0.44) deaths per 10,000 population compared to the recommended 4-week interval between the 2 doses. Pfizer-BioNTech vaccines also averted an additional 0.60 (95% CrI: 0.37–0.89) hospitalizations and 0.32 (95% CrI: 0.23–0.45) deaths per 10,000 population in a 9-week delayed second dose (DSD) strategy compared to the 3-week recommended schedule between doses. However, there was no clear advantage of delaying the second dose with Pfizer-BioNTech vaccines in reducing infections, unless the efficacy of the first dose did not wane over time. Our findings underscore the importance of quantifying the characteristics and durability of vaccine-induced protection after the first dose in order to determine the optimal time interval between the 2 doses. 

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2. Modeling of malaria vaccine effectiveness on disease burden and drug resistance in 42 African countries

   https://doi.org/10.1038/s43856-023-00373-y  

   Hamilton, Alisa ; Haghpanah, Fardad ; Hasso-Agopsowicz, Mateusz ; Frost, Isabel ; Lin, Gary ; Schueller, Emily ; Klein, Eili ; Laxminarayan, Ramanan ( December 2023 , Communications Medicine)

   The emergence of antimalarial drug resistance poses a major threat to effective malaria treatment and control. This study aims to inform policymakers and vaccine developers on the potential of an effective malaria vaccine in reducing drug-resistant infections.

   A compartmental model estimating cases, drug-resistant cases, and deaths averted from 2021 to 2030 with a vaccine againstPlasmodium falciparuminfection administered yearly to 1-year-olds in 42 African countries. Three vaccine efficacy (VE) scenarios and one scenario of rapidly increasing drug resistance are modeled.

   When VE is constant at 40% for 4 years and then drops to 0%, 235.7 (Uncertainty Interval [UI] 187.8–305.9) cases per 1000 children, 0.6 (UI 0.4–1.0) resistant cases per 1000, and 0.6 (UI 0.5–0.9) deaths per 1000 are averted. When VE begins at 80% and drops 20 percentage points each year, 313.9 (UI 249.8–406.6) cases per 1000, 0.9 (UI 0.6–1.3) resistant cases per 1000, and 0.9 (UI 0.6–1.2) deaths per 1000 are averted. When VE remains 40% for 10 years, 384.7 (UI 311.7–496.5) cases per 1000, 1.0 (0.7–1.6) resistant cases per 1000, and 1.1 (UI 0.8–1.5) deaths per 1000 are averted. Assuming an effective vaccine and an increase in current levels of drug resistance to 80% by 2030, 10.4 (UI 7.3–15.8) resistant cases per 1000 children are averted.

   Widespread deployment of a malaria vaccine could substantially reduce health burden in Africa. Maintaining VE longer may be more impactful than a higher initial VE that falls rapidly.

    

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3. Differential Overlap in Human and Animal Fecal Microbiomes and Resistomes in Rural versus Urban Bangladesh

   https://doi.org/10.1128/aem.00759-22  

   Swarthout, Jenna M. ; Fuhrmeister, Erica R. ; Hamzah, Latifah ; Harris, Angela R. ; Ahmed, Mir A. ; Gurley, Emily S. ; Satter, Syed M. ; Boehm, Alexandria B. ; Pickering, Amy J. ( July 2022 , Applied and Environmental Microbiology)

   Elkins, Christopher A. (Ed.)

   ABSTRACT Low- and middle-income countries (LMICs) bear the largest mortality burden of antibiotic-resistant infections. Small-scale animal production and free-roaming domestic animals are common in many LMICs, yet data on zoonotic exchange of gut bacteria and antibiotic resistance genes (ARGs) in low-income communities are sparse. Differences between rural and urban communities with regard to population density, antibiotic use, and cohabitation with animals likely influence the frequency of transmission of gut bacterial communities and ARGs between humans and animals. Here, we determined the similarity in gut microbiomes, using 16S rRNA gene amplicon sequencing, and resistomes, using long-read metagenomics, between humans, chickens, and goats in a rural community compared to an urban community in Bangladesh. Gut microbiomes were more similar between humans and chickens in the rural (where cohabitation is more common) than the urban community, but there was no difference for humans and goats in the rural versus the urban community. Human and goat resistomes were more similar in the urban community, and ARG abundance was higher in urban animals than rural animals. We identified substantial overlap of ARG alleles in humans and animals in both settings. Humans and chickens had more overlapping ARG alleles than humans and goats. All fecal hosts from the urban community and rural humans carried ARGs on chromosomal contigs classified as potentially pathogenic bacteria, including Escherichia coli , Campylobacter jejuni , Clostridioides difficile , and Klebsiella pneumoniae . These findings provide insight into the breadth of ARGs circulating within human and animal populations in a rural compared to urban community in Bangladesh. IMPORTANCE While the development of antibiotic resistance in animal gut microbiomes and subsequent transmission to humans has been demonstrated in intensive farming environments and high-income countries, evidence of zoonotic exchange of antibiotic resistance in LMIC communities is lacking. This research provides genomic evidence of overlap of antibiotic resistance genes between humans and animals, especially in urban communities, and highlights chickens as important reservoirs of antibiotic resistance. Chicken and human gut microbiomes were more similar in rural Bangladesh, where cohabitation is more common. Incorporation of long-read metagenomics enabled characterization of bacterial hosts of resistance genes, which has not been possible in previous culture-independent studies using only short-read sequencing. These findings highlight the importance of developing strategies for combatting antibiotic resistance that account for chickens being reservoirs of ARGs in community environments, especially in urban areas. 

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4. The Impact of Vaccination on Coronavirus Disease 2019 (COVID-19) Outbreaks in the United States

   https://doi.org/10.1093/cid/ciab079  

   Moghadas, Seyed M ; Vilches, Thomas N ; Zhang, Kevin ; Wells, Chad R ; Shoukat, Affan ; Singer, Burton H ; Meyers, Lauren Ancel ; Neuzil, Kathleen M ; Langley, Joanne M ; Fitzpatrick, Meagan C ; et al ( January 2021 , Clinical Infectious Diseases)

   null (Ed.)

   Abstract Background Global vaccine development efforts have been accelerated in response to the devastating coronavirus disease 2019 (COVID-19) pandemic. We evaluated the impact of a 2-dose COVID-19 vaccination campaign on reducing incidence, hospitalizations, and deaths in the United States. Methods We developed an agent-based model of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission and parameterized it with US demographics and age-specific COVID-19 outcomes. Healthcare workers and high-risk individuals were prioritized for vaccination, whereas children under 18 years of age were not vaccinated. We considered a vaccine efficacy of 95% against disease following 2 doses administered 21 days apart achieving 40% vaccine coverage of the overall population within 284 days. We varied vaccine efficacy against infection and specified 10% preexisting population immunity for the base-case scenario. The model was calibrated to an effective reproduction number of 1.2, accounting for current nonpharmaceutical interventions in the United States. Results Vaccination reduced the overall attack rate to 4.6% (95% credible interval [CrI]: 4.3%–5.0%) from 9.0% (95% CrI: 8.4%–9.4%) without vaccination, over 300 days. The highest relative reduction (54%–62%) was observed among individuals aged 65 and older. Vaccination markedly reduced adverse outcomes, with non-intensive care unit (ICU) hospitalizations, ICU hospitalizations, and deaths decreasing by 63.5% (95% CrI: 60.3%–66.7%), 65.6% (95% CrI: 62.2%–68.6%), and 69.3% (95% CrI: 65.5%–73.1%), respectively, across the same period. Conclusions Our results indicate that vaccination can have a substantial impact on mitigating COVID-19 outbreaks, even with limited protection against infection. However, continued compliance with nonpharmaceutical interventions is essential to achieve this impact. 

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5. Investigating associations between COVID-19 mortality and population-level health and socioeconomic indicators in the United States: A modeling study

   https://doi.org/10.1371/journal.pmed.1003693  

   Kandula, Sasikiran ; Shaman, Jeffrey ( July 2021 , PLOS Medicine)

   Turner, Richard (Ed.)

   Background With the availability of multiple Coronavirus Disease 2019 (COVID-19) vaccines and the predicted shortages in supply for the near future, it is necessary to allocate vaccines in a manner that minimizes severe outcomes, particularly deaths. To date, vaccination strategies in the United States have focused on individual characteristics such as age and occupation. Here, we assess the utility of population-level health and socioeconomic indicators as additional criteria for geographical allocation of vaccines. Methods and findings County-level estimates of 14 indicators associated with COVID-19 mortality were extracted from public data sources. Effect estimates of the individual indicators were calculated with univariate models. Presence of spatial autocorrelation was established using Moran’s I statistic. Spatial simultaneous autoregressive (SAR) models that account for spatial autocorrelation in response and predictors were used to assess (i) the proportion of variance in county-level COVID-19 mortality that can explained by identified health/socioeconomic indicators (R 2 ); and (ii) effect estimates of each predictor. Adjusting for case rates, the selected indicators individually explain 24%–29% of the variability in mortality. Prevalence of chronic kidney disease and proportion of population residing in nursing homes have the highest R 2 . Mortality is estimated to increase by 43 per thousand residents (95% CI: 37–49; p < 0.001) with a 1% increase in the prevalence of chronic kidney disease and by 39 deaths per thousand (95% CI: 34–44; p < 0.001) with 1% increase in population living in nursing homes. SAR models using multiple health/socioeconomic indicators explain 43% of the variability in COVID-19 mortality in US counties, adjusting for case rates. R 2 was found to be not sensitive to the choice of SAR model form. Study limitations include the use of mortality rates that are not age standardized, a spatial adjacency matrix that does not capture human flows among counties, and insufficient accounting for interaction among predictors. Conclusions Significant spatial autocorrelation exists in COVID-19 mortality in the US, and population health/socioeconomic indicators account for a considerable variability in county-level mortality. In the context of vaccine rollout in the US and globally, national and subnational estimates of burden of disease could inform optimal geographical allocation of vaccines. 

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