COVID-19 UPDATE: Gov. Justice, WVSSAC director discuss plans for fall sports and activities; Amjad appointed as new State Health Officer – West…

'); $("#expandable-weather-block .modal-body #mrd-alert"+ alertCount).append(""+val.title+""); // if (window.location.hostname == "www.kmov.com" || window.location.hostname == "www.kctv5.com" || window.location.hostname == "www.azfamily.com" || window.location.hostname == "www.kptv.com" || window.location.hostname == "www.fox5vegas.com" || window.location.hostname == "www.wfsb.com") { if (val.poly != "" && val.polyimg != "") { $("#expandable-weather-block .modal-body #mrd-alert"+ alertCount).append('"+val.ihtml+""); $("#expandable-weather-block .weather-index-alerts").show(); $("#expandable-weather-block .modal-body h2").css({"font-family":"'Fira Sans', sans-serif", "font-weight":"500", "padding-bottom":"10px"}); $("#expandable-weather-block .modal-body p").css({"font-size":"14px", "line-height":"24px"}); $("#expandable-weather-block .modal-body span.wxalertnum").css({"float":"left", "width":"40px", "height":"40px", "color":"#ffffff", "line-height":"40px", "background-color":"#888888", "border-radius":"40px", "text-align":"center", "margin-right":"12px"}); $("#expandable-weather-block .modal-body b").css("font-size", "18px"); $("#expandable-weather-block .modal-body li").css({"font-size":"14px", "line-height":"18px", "margin-bottom":"10px"}); $("#expandable-weather-block .modal-body ul").css({"margin-bottom":"24px"}); $("#expandable-weather-block .modal-body pre").css({"margin-bottom":"24px"}); $("#expandable-weather-block .modal-body img").css({"width":"100%", "margin-bottom":"20px", "borderWidth":"1px", "border-style":"solid", "border-color":"#aaaaaa"}); $("#expandable-weather-block .modal-body #mrd-alert"+ alertCount).css({"borderWidth":"0", "border-bottom-width":"1px", "border-style":"dashed", "border-color":"#aaaaaa", "padding-bottom":"10px", "margin-bottom":"40px"}); }); } function parseAlertJSON(json) { console.log(json); alertCount = 0; if (Object.keys(json.alerts).length > 0) { $("#mrd-wx-alerts .modal-body ").empty(); } $.each(json.alerts, function(key, val) { alertCount++; $("#mrd-wx-alerts .alert_count").text(alertCount); $("#mrd-wx-alerts .modal-body ").append(''); $("#mrd-wx-alerts .modal-body #mrd-alert"+ alertCount).append(""+val.title+""); // if (window.location.hostname == "www.kmov.com" || window.location.hostname == "www.kctv5.com" || window.location.hostname == "www.azfamily.com" || window.location.hostname == "www.kptv.com" || window.location.hostname == "www.fox5vegas.com" || window.location.hostname == "www.wfsb.com") { if (val.poly != "" && val.polyimg != "") { $("#mrd-wx-alerts .modal-body #mrd-alert"+ alertCount).append(''); } else if (val.fips != "" && val.fipsimg != "") { // $("#mrd-wx-alerts .modal-body #mrd-alert"+ alertCount).append(''); } // } //val.instr = val.instr.replace(/[W_]+/g," "); $("#mrd-wx-alerts .modal-body #mrd-alert"+ alertCount).append(val.dhtml+"

Instruction

Abstract

Although most SARS-CoV-2-infected individuals experience mild coronavirus disease 2019 (COVID-19), some patients suffer from severe COVID-19, which is accompanied by acute respiratory distress syndrome and systemic inflammation. To identify factors driving severe progression of COVID-19, we performed single-cell RNA-seq using peripheral blood mononuclear cells (PBMCs) obtained from healthy donors, patients with mild or severe COVID-19, and patients with severe influenza. Patients with COVID-19 exhibited hyper-inflammatory signatures across all types of cells among PBMCs, particularly up-regulation of the TNF/IL-1-driven inflammatory response as compared to severe influenza. In classical monocytes from patients with severe COVID-19, type I IFN response co-existed with the TNF/IL-1-driven inflammation, and this was not seen in patients with milder COVID-19. Interestingly, we documented type I IFN-driven inflammatory features in patients with severe influenza as well. Based on this, we propose that the type I IFN response plays a pivotal role in exacerbating inflammation in severe COVID-19.

Currently, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes coronavirus disease 2019 (COVID-19), is spreading globally (1, 2), and the World Health Organization (WHO) has declared it a pandemic. As of June 2, 2020, more than 6.1 million confirmed cases and more than 376,000 deaths have been reported worldwide (3).

SARS-CoV-2 infection usually results in a mild disease course with spontaneous resolution in the majority of infected individuals (4). However, some patients, particularly elderly patients develop severe COVID-19 infection that requires intensive care with mechanical ventilation (4, 5). The mortality rate for COVID-19 in Wuhan, China, is estimated to be 1.4% (5). Although this rate is lower than that of severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS), which are caused by other human pathogenic coronaviruses (6), it is much higher than that of influenza, a common respiratory viral disease requiring hospitalization and intensive care in severe cases.

In severe cases of COVID-19, a hyper-inflammatory response, also called a cytokine storm, has been observed and is suspected of causing the detrimental progression of COVID-19 (7). Circulating levels of pro-inflammatory cytokines, including TNF and IL-6, are increased in severe cases (8). Gene expression analyses have also shown that IL-1-related pro-inflammatory pathways are highly up-regulated in severe cases (9). In a murine model of SARS-CoV infection, a delayed, but considerable type I IFN (IFN-I) response promotes the accumulation of monocytes-macrophages and the production of pro-inflammatory cytokines, resulting in lethal pneumonia with vascular leakage and impaired virus-specific T-cell responses (10).

Immune dysfunction is also observed in patients with COVID-19. In severe cases, the absolute number of T cells is reduced (8, 11), and the T cells exhibit functional exhaustion with the expression of inhibitory receptors (12, 13). However, hyper-activation of T cells as reflected in the up-regulation of CD38, HLA-DR, and cytotoxic molecules was also reported in a lethal case of COVID-19 (14). Immune dysfunction in patients with severe COVID-19 has been attributed to pro-inflammatory cytokines (15).

In the present study, we performed single-cell RNA-seq (scRNA-seq) using peripheral blood mononuclear cells (PBMCs) to identify factors associated with the development of severe COVID-19 infection. By comparing COVID-19 and severe influenza, we report that the TNF/IL-1-driven inflammatory response was dominant in COVID-19 across all types of cells among PBMCs, whereas the up-regulation of various interferon-stimulated genes (ISGs) was prominent in severe influenza. When we compared the immune responses from patients with mild and severe COVID-19 infections, we found that classical monocytes from severe COVID-19 exhibit IFN-I-driven signatures in addition to TNF/IL-1-driven inflammation.

PBMCs were collected from healthy donors (n=4), hospitalized patients with severe influenza (n=5), and patients with COVID-19 of varying clinical severity, including severe, mild, and asymptomatic (n=8). PBMCs were obtained twice from three (the subject C3, C6, and C7) of the eight COVID-19 patients at different time points during hospitalization. PBMC specimens from COVID-19 patients were assigned to severe or mild COVID-19 groups according to the National Early Warning Score (NEWS; mild < 5, severe 5) evaluated on the day of whole blood sampling (16). In NEWS scoring, respiratory rate, oxygen saturation, oxygen supplement, body temperature, systolic blood pressure, heart rate, and consciousness were evaluated (16). Severe influenza was defined when hospitalization was required irrespective of NEWS score. Patients with severe influenza were enrolled from December 2015 to April 2016, prior to the emergence of COVID-19. The severe COVID-19 group was characterized by significantly lower lymphocyte count and higher serum level of C-reactive protein than the mild COVID-19 group on the day of blood sampling (Fig. S1A). Multiplex real-time PCR for N, RdRP, and E genes of SARS-CoV-2 was performed, and there was no statistical difference in Ct values for all three genes between two groups (Fig. S1B). Demographic information is provided with experimental batch of scRNA-seq in Table S1 and clinical data in Table S2 and S3.

Employing the 10X Genomics scRNA-seq platform, we analyzed a total of 59,572 cells in all patients after filtering the data with stringent high quality, yielding a mean of 6,900 UMIs per cell and detecting 1,900 genes per cell on average (Table S4). The transcriptome profiles of biological replicates (PBMC specimens in the same group) were highly reproducible (Fig. S1C), ensuring the high quality of the scRNA-seq data generated in this study.

To examine the host immune responses in a cell type-specific manner, we subjected 59,572 cells to t-distributed stochastic neighbor embedding (tSNE) based on highly variable genes using the Seurat package (17) and identified 22 different clusters unbiased by patients or experimental batches of scRNA-seq (Fig. 1A, Fig. S1D). These clusters were assigned to 13 different cell types based on well-known marker genes and two uncategorized clusters (Fig. 1B and C, and Table S5). In downstream analysis, we only focused on 11 different immune cell types, including IgG- B cell, IgG+ B cell, effector memory (EM)-like CD4+ T cell, non-EM-like CD4+ T cell, EM-like CD8+ T cell, non-EM-like CD8+ T cell, natural killer (NK) cell, classical monocyte, intermediate monocyte, non-classical monocyte, and dendritic cell (DC) after excluding platelets, red blood cells (RBCs), and two uncategorized clusters. The subject C8 (asymptomatic case) was also excluded due to a lack of replicates. In hierarchical clustering, most transcriptome profiles from the same cell type tended to cluster together, followed by disease groups, suggesting that both immune cell type and disease biology, rather than technical artifacts, are the main drivers of the variable immune transcriptome (Fig. S1E).

(A) tSNE projections of 59,572 PBMCs from healthy donors (HDs) (4 samples, 17,590 cells), severe influenza (FLU) patients (5 samples, 10,519 cells), COVID-19 patients (asymptomatic: 1 sample, 4,425 cells; mild COVID-19: 4 samples, 16,742 cells; severe COVID-19: 6 samples, 10,296 cells) colored by group information. (B) Normalized expression of known marker genes on a tSNE plot. (C) tSNE plot colored by annotated cell types. EM: effector memory, NK cell: natural killer cell, DC: dendritic cell, RBC: red blood cell. (D) Proportion of cell types in each group excluding Uncategorized 1, Uncategorized 2, RBC, and Platelet. The colors indicate cell type information. (E) Boxplots showing the fold enrichment in cell type proportions from mild COVID-19 (n=4), severe COVID-19 (n=6), and FLU (n=5) patients compared to the HD group (mild COVID-19 vs. HD: n=16, severe COVID-19 vs. HD: n=24, FLU vs. HD: n=20). For the boxplots, the box represents the interquartile range (IQR) and the whiskers correspond to the highest and lowest points within 1.5IQR. Uncategorized 1 (relatively high UMIs per cells and presence of multiple marker genes), Uncategorized 2 (B cell-like and high expression of ribosomal protein genes), RBC, and Platelet were excluded. Two-sided KolmogorovSmirnov (KS) tests were conducted for each cell type between the disease and HD group. *p<0.05, **p<0.01, and ***p<0.001.

As a feature of immunological changes, we investigated the relative proportions of immune cells among PBMCs in the disease groups compared to the healthy donor group (Fig. 1D and E, and Fig. S1F). Unlike the limited changes in mild COVID-19, significant changes were observed in both influenza and severe COVID-19 across multiple cell types among PBMCs. In severe COVID-19, the proportion of classical monocytes significantly increased whereas those of DCs, non-classical monocytes, intermediate monocytes, NK cells, EM-like CD8+ T cells, and EM-like CD4+ T cells significantly decreased (Fig. 1E). In severe influenza, the proportion of classical monocytes significantly increased whereas those of DCs, non-EM-like CD4+ T cells, EM-like CD4+ T cells, IgG+ B cells, and IgG- B cells significantly decreased. We validated the proportions of immune cell subsets from scRNA-seq by flow cytometry analysis. The relative proportions of total lymphocytes, B cells, CD4+ T cells, CD8+ T cells, NK cells, and total monocytes from scRNA-seq significantly correlated with those from flow cytometry analysis (Fig. S1G).

In order to compare the effect of infection between diseases, we performed hierarchical clustering based on relative gene expression changes against the healthy donor group. Unexpectedly, all types of cells among PBMCs were clustered together according to the disease groups instead of cell-types (Fig. 2A). Further investigation of the variable genes based on K-means clustering supported COVID-19-specific up- or down-regulated gene expression patterns across all types of cells among PBMCs (Fig. S2A). These results indicate that, in COVID-19, peripheral blood immune cells may be influenced by common inflammatory mediators regardless of cell type. Despite distinct transcriptional signatures between COVID-19 and influenza, severe COVID-19 and influenza shared transcriptional signatures in all types of monocytes and DCs (black boxed region in Fig. 2A), possibly reflecting common mechanisms underlying the innate immune responses in severe influenza and severe COVID-19.

(A) Hierarchical clustering using the Pearson correlation coefficient (PCC) of a normalized transcriptome between diseases in cell type resolution (n = 33). The color intensity of the heat map indicates the PCC values. The color bars above the heat map indicate the cell type and disease group. The black box indicates the cell types that highly correlate between the severe COVID-19 and FLU groups. (B) Illustration of the enrichment p-values for the select GO biological pathways (n = 49) of differentially expressed genes (DEGs) in COVID-19 and FLU patients (left 6 columns: DEGs for COVID-19 and FLU groups compared to HD, right 2 columns: DEGs between COVID-19 and FLU groups). (C) tSNE plot of representative gene expression patterns for GBP1 (FLU specific), CREM (COVID-19 specific), and CCL3 (COVID-19/FLU common). (D) Top, dendrogram from WGCNA analysis performed using relative normalized gene expression between the COVID-19 and FLU groups for the genes belonging to the select biological pathways in (B) (n=316). Bottom, heat map of relative normalized gene expression between the COVID-19 and FLU groups. The color bar (left) indicates cell type information clustered by hierarchical clustering based on the PCC for relative normalized gene expression. Modularized gene expression patterns by WGCNA are shown together (G1, n=10; G2, n=147; G3, n=27; G4, n=17; G5, n=12; G6, n=64; G7, n=34; G8, n=5).

Next, we sought to identify relevant biological functions in disease-specific up- or down-regulated genes in terms of the GO biological pathways. First, we combined both mild and severe COVID-19 as a COVID-19 group and identified disease-specific changes in genes for each cell type compared to the healthy donor group using model-based analysis of single cell transcriptomics (MAST) (18). NFKB1, NFKB2, IRF1, and CXCR3 were specifically up-regulated in COVID-19, and CXCL10, STAT1, TLR4, and genes for class II HLA and immunoproteasome subunits were specifically up-regulated in influenza (Table S6). TNF, TGFB1, IL1B, and IFNG were commonly up-regulated. When we directly compared COVID-19 and influenza, NFKB1, NFKB2, and TNF were up-regulated in COVID-19, whereas STAT1, TLR4, and genes for immunoproteasome subunits were up-regulated in influenza. For each group of differentially expressed genes (DEGs), we identified the top 10 enriched GO biological pathways and collected them to demonstrate p-value enrichment in each group of DEGs (Fig. 2B). Both distinct and common biological functions were identified as illustrated by inflammatory response genes being highly active in both COVID-19 and influenza, but genes for transcription factors, including inflammatory factors (i.e., NFKB1/2, and STAT4) were up-regulated in COVID-19. In contrast, a limited response in genes associated with the IFN-I and -II signaling pathways, T-cell receptor pathways, and adaptive immune response was observed in COVID-19 compared to influenza. Such disease-specific gene expression patterns were exemplified at single cell resolution by GBP1 (IFN--mediated signaling pathway) being specifically up-regulated in influenza, CREM (positive regulation of transcription) being specifically up-regulated in COVID-19, and CCL3 (inflammatory response) being commonly up-regulated (Fig. 2C and Table S7).

We expanded our analysis in a cell type specific manner by conducting weighted gene correlation network analysis (WGCNA) (19) for the collected genes associated with Fig. 2B. We identified several modular expression patterns (Fig. 2D and Table S8). In the COVID-19 group, NFKB1/2, JUN, and TNF were modularized in CD8+ T and NK cells (G6 and G7 in Fig. 2D), and IL1B, NFKBID, and OSM were modularized in all types of monocytes and DCs (G3 in Fig. 2D). In the influenza group, GBP1, TAP1, STAT1, IFITM3, OAS1, IRF3, and IFNG were modularized in all types of T cells and NK cells (G2 in Fig. 2D), and CXCL10 and TLR4 were modularized in all types of monocytes and DCs (G5 and part of G6 in Fig. 2D). Consistently, the DEGs between COVID-19 and influenza were dominant in CD8+ T cells and all types of monocytes (Fig. S2B).

To uncover disease-specific transcriptional signatures in CD8+ T cells, we performed sub-clustering analysis from EM-like and non-EM-like CD8+ T cell clusters using Seurat (17). Each disease group-specifically enriched sub-clusters compared to the two other groups were identified in the non-EM-like CD8+ T cell cluster (Fig. 3A). Of the six sub-clusters from the non-EM-like CD8+ T cell cluster, cluster 1 and cluster 3 were significantly enriched in the influenza and COVID-19 groups, respectively (Fig. 3B and C, and S3A). Clusters with the high expression of PPBP, a marker of platelets, were excluded in following analysis (e.g., cluster 6 in Fig. S3A). Intriguingly, up-regulated genes in cluster 1 and cluster 3 were associated with previously defined gene sets for influenza A virus infection and SARS-CoV infection, respectively (Fig. S3B) (20). We also found that the cluster 3-specific up-regulated genes reflect activation of immune response, including CD27, RGS1, CCL5, SELL, and RGS10 (Fig. S3C and Table S9). Protein interaction network analysis of selected top 30 up-regulated genes in each cluster based on STRING v11 (21) revealed the up-regulation of PRF1, GNLY, GZMB, and GZMH in cluster 1 and the up-regulation of GZMK, GZMA, CXCR3, and CCL5 in cluster 3 (Fig. 3D, green). STAT1, TAP1, PSMB9, and PSME2, which are up-regulated preferentially by IFN-, were overexpressed only in influenza-specific cluster 1 (Fig. 3D, blue). We validated these data by intracellular staining for granzyme B and PMA/ionomycin-stimulated intracellular cytokine staining for IFN-. The percentages of granzyme B+ and IFN-+ cells among CD8+ T cells were significantly higher in the influenza group than in the COVID-19 group (Fig. S3D). Of the seven representative GO biological pathways for the pro-inflammatory and IFN responses, pathways for responses to IFN-I and -II were more associated with influenza-specific cluster 1, whereas pathways for the response to TNF or IL-1 were more prominent in COVID-19-specific cluster 3 (Fig. 3E).

(A) tSNE plot of the non-EM-like CD8+ T cell subpopulations in all groups (left, n=6,253), COVID-19 (top right, n=2,653), FLU (middle right, n=1,452), and HD (bottom right, n=2,148) colored by cluster information. (B, C) Boxplots showing the proportion of individual sub-clusters from the non-EM-like CD8+ T cell cluster within each group (COVID-19, n=10; FLU, n=5; HD, n=4). The proportions follow normal distribution as tested by the Shapiro-Wilk normality test except the proportion of cluster 3 in the COVID-19 group (p=0.04). Cluster 1 and cluster 3 were highly enriched in the FLU and COVID-19 group, respectively. Two-sided Welchs t test p-values were 4.4E-03 between COVID-19 and FLU in cluster 1, 3.5E-02 between FLU and HD donor in cluster 1, 8.6E-03 between COVID-19 and FLU in cluster 3, and 5.8E-3 between COVID-19 and HD in cluster 3. *p<0.05, **p<0.01. (D) STRING analysis using the top 30 up-regulated genes in cluster 1 (left) and cluster 3 (right). (E) Bar plots showing enrichment p-values of eight representative GO biological pathways for pro-inflammation and interferon in cluster 1 or cluster 3-specific up-regulated genes (cluster 1, n=66; cluster 3, n=183).

We performed sub-clustering analysis from all three types of monocyte clusters to find COVID-19-specific sub-clusters. However, there was no COVID-19-specifically enriched sub-cluster (Fig. S4A and B). Next, we further focused on classical monocytes considering their crucial roles for inflammatory responses. We investigated DEGs between influenza and COVID-19 to seek COVID-19-specific transcriptional signatures in classical monocytes (Fig. 4A). Interestingly, TNF and IL1B, major genes in the inflammatory response, were identified as COVID-19-specific and commonly up-regulated genes, respectively. To better characterize the transcriptional signatures in classical monocytes, we performed K-means clustering of up-regulated genes in at least one disease group compared to the healthy donor group. We identified five different clusters of up-regulation (Fig. 4B and Table S10): genes in cluster 1 are commonly up-regulated in all disease groups, cluster 2 is influenza-specific, cluster 3 is associated with mild/severe COVID-19, cluster 4 is associated with influenza and severe COVID-19, and cluster 5 is severe COVID-19-specific.

(A) Venn diagram of differentially expressed genes (DEGs) in COVID-19 and FLU compared to HD. The representative genes are shown together. (B) K-means clustering of DEGs between all pairs of FLU, mild COVID-19, and severe COVID-19 (n=499). The color indicates the relative gene expression between the diseases and HD. The representative genes are shown together. (C) Bar plots showing the average log10(p-value) values in enrichment analysis using the perturbed genes of four different cell lines listed in L1000 LINCS for up-regulated genes in cluster 2 (C2, left) and cluster 3 (C3, right). Error bars indicate standard deviation. (D) Combined enrichment scores were compared between C2 and C3 for the gene sets of the type I IFN response (left; GSE26104) and TNF response (right; GSE2638, GSE2639). **p<0.01. Each dot indicates an individual subject. (E) Bar plots showing the average log10(p-value) values in the enrichment analysis using the perturbed genes listed of four different cell lines in L1000 LINCS for up-regulated genes in cluster 4 (C4, left) and cluster 5 (C5, right). Error bars indicate standard deviation (C and E).

We examined each cluster-specific genes by gene set enrichment analysis (GSEA) using cytokine-responsive gene sets originated from each cytokine-treated cells (LINCS L1000 ligand perturbation analysis in Enrichr) (22). COVID-19-specific cluster 3 genes were enriched by TNF/IL-1-responsive genes whereas influenza-specific cluster 2 genes were enriched by IFN-I-responsive genes in addition to TNF/IL-1-responsive genes (Fig. 4C), indicating that the IFN-I response is dominant in influenza compared to COVID-19. We confirmed this result by analyzing cluster-specific genes with cytokine-responsive gene sets originated from other sources (Fig. 4D). Unexpectedly, cluster 4 and 5 exhibited strong associations with IFN-I-responsive genes, in addition to TNF/IL-1-responsive genes (Fig. 4E), indicating that severe COVID-19 acquires IFN-I-responsive features in addition to TNF/IL-1-inflammatory features.

Next, we directly compared classical monocytes between mild and severe COVID-19. When we analyzed DEGs, severe COVID-19 was characterized by up-regulation of various ISGs, including ISG15, IFITM1/2/3, and ISG20 (Fig. 5A). Both TNF/IL-1-responsive genes and IFN-I-responsive genes were enriched in severe COVID-19-specific up-regulated genes (Fig. 5B). We measured plasma concentrations of TNF, IL-1, IL-6, IFN-, IFN-, and IL-18 in a larger cohort of COVID-19 patients. Among these cytokines, IL-6 and IL-18 were significantly increased in severe COVID-19 compared to mild COVID-19 whereas there was no difference in plasma concentrations of the other cytokines between the two groups (Fig. S5A). These results indicate that cytokine-responsive gene signatures cannot be simply explained by a few cytokines because of overlapped effects of cytokines.

(A) Volcano plot showing DEGs between mild and severe COVID-19 groups. Each dot indicates individual gene, colored by red when a gene is significant DEG. (B) Bar plot showing the average log10(p-value) values in enrichment analysis using the perturbed genes of four different cell lines listed in L1000 LINCS for up-regulated genes in the severe COVID-19 group. Error bars indicate standard deviation. (C) Trajectory analysis of classical monocytes from specimens obtained at two different time points in a single COVID-19 patient (mild: C7-2, 1,197 cells; severe: C7-1, 631 cells). The color indicates cluster information (left) or the severity of COVID-19 (right). (D) Relative expression patterns of representative genes in the trajectory analysis are plotted along the Pseudotime. The color indicates the relative gene expression calculated by Monocle 2. (E) Bar plots showing the average log10(p-value) values in the enrichment analysis using the perturbed genes of four different cell lines in L1000 LINCS for up-regulated genes in cluster 3 (left) and cluster 1 (right). Error bars indicate standard deviation. (F) Comparison of combined enrichment scores between cluster 3 and cluster 1 for the gene sets from systemic lupus erythematosus (SLE) (n=16) and rheumatoid arthritis (RA) (n=5). ***p<0.001; ns, not significant. (G) GSEA of up-regulated genes in cluster 3 (left) and cluster 1 (right) to the class 1 gene module of monocyte-derived macrophages by Park et al. (2017). NES: normalized enrichment score, FDR: false discovery rate.

To further investigate the characteristics of severe COVID-19, we performed a trajectory analysis with Monocle 2 (23) using two internally well-controlled specimens (one severe and one mild) in which both PBMC samples were collected from a single patient (the subject C7) with COVID-19. Trajectory analysis aligned classical monocytes along the disease severity with cluster 1 and cluster 3 corresponding to later and earlier Pseudotime, respectively (Fig. 5C). Representative genes in cluster 1 was enriched in the severe stage and highly associated with the both IFN-I and TNF/IL-1-associated inflammatory response (Fig. 5D, Fig. S5B, and Table S11). GSEA confirmed that both the IFN-I response and TNF/IL-1 inflammatory response were prominent in cluster 1, but not in cluster 3 (Fig. 5E). Cluster 1 exhibited a significantly higher association with a gene set from systemic lupus erythematosus, which is a representative inflammatory disease with IFN-I features, than cluster 3 (Fig. 5F, left), but was not significantly associated with a gene set from rheumatoid arthritis (Fig. 5F, right).

We obtained additional evidence of the IFN-I-potentiated TNF inflammatory response in severe COVID-19 by analyzing a gene module that is not responsive to IFN-I, but associated with TNF-induced tolerance to TLR stimulation. Park et al. previously demonstrated that TNF tolerizes TLR-induced gene expression in monocytes, though TNF itself is an inflammatory cytokine (24). They also showed that IFN-I induces a hyper-inflammatory response by abolishing the tolerance effects of TNF, and defined a gene module responsible for the IFN-I-potentiated TNF-NF-B inflammatory response as class 1 (24). This gene module was significantly enriched in cluster 1, but not in cluster 3 (Fig. 5G), which suggests that the IFN-I response may exacerbate hyper-inflammation by abolishing a negative feedback mechanism.

Finally, we validated IFN-I response and inflammatory features using bulk RNA-seq data obtained using post-mortem lung tissues from patients with lethal COVID-19 (25). Although the analysis was limited to only two patients without individual cell-type resolution, in genome browser, up-regulation of IFITM1, ISG15, and JAK3 and down-regulation of RPS18 were observed commonly in post-mortem COVID-19 lung tissues and classical monocytes of severe COVID-19 (Fig. 6A). In the analysis with cytokine-responsive gene sets, both the IFN-I response and TNF/IL-1-inflammatory response were prominent in the lung tissues (Fig. 6B). DEGs in the lung tissues were significantly associated with cluster 4, which is commonly up-regulated in both influenza and severe COVID-19, and cluster 5, which is specific to severe COVID-19 in Fig. 4B (Fig. 6C). These genes were also significantly associated with the cluster 1 identified in the trajectory analysis, but not with cluster 3 (Fig. 6D). When gene sets were defined by DEGs between mild and severe COVID-19, the DEGs in post-mortem lung tissues were significantly associated with genes up-regulated specifically in severe COVID-19 (Fig. 6E).

(A) UCSC Genome Browser snapshots of representative genes. (B) Bar plot showing the average log10(p-value) values from the enrichment analysis using the perturbed genes of four different cell lines in L1000 LINCS for up-regulated genes (n= 386) in post-mortem lung tissues compared to biopsied healthy lung tissue. Error bars indicate standard deviation. (C) GSEA of significantly up- and down-regulated genes in post-mortem lung tissues for gene sets originated from up-regulated genes in C2 (n=96), C3 (n=143), C4 (n=218), and C5 (n=30) of Fig. 4B. (D and E) GSEA of significantly up- and down-regulated genes in post-mortem lung tissues for gene sets originated from the top 200 up-regulated genes in cluster 3 (left) and cluster 1 (right) from the trajectory analysis in Fig. 5C (D), and from gene sets originated from the top 200 up-regulated genes in classical monocytes of mild (left) and severe (right) COVID-19 (E).

Severe COVID-19 has been shown to be caused by a hyper-inflammatory response (7). Particularly, inflammatory cytokines secreted by classical monocytes and macrophages are considered to play a crucial role in severe progression of COVID-19 (26). In the current study, we confirmed the results from previous studies by showing that the TNF/IL-1 inflammatory response is dominant in COVID-19 although a small number of patients were enrolled. However, we also found that severe COVID-19 is accompanied by the IFN-I response in addition to the TNF/IL-1 response. These results indicate that the IFN-I response might contribute to the hyper-inflammatory response by potentiating TNF/IL-1-driven inflammation in severe progression of COVID-19.

In the current study, we carried out scRNA-seq using PBMCs instead of specimens from the site of infection, e.g., lung tissues or bronchoalveolar lavage (BAL) fluids. However, hierarchical clustering based on relative changes to the healthy donor group showed that all types of cells among PBMCs were clustered together according to the disease groups as shown in Fig. 2A, indicating that there is disease-specific global impact across all types of cells among PBMCs. This finding suggests that peripheral blood immune cells are influenced by common inflammatory mediators regardless of cell type. However, we could not examine granulocytes in the current study because we used PBMCs, not whole blood samples for scRNA-seq.

In transcriptome studies for cytokine responses, we often analyze cytokine-responsive genes rather than cytokine genes themselves. However, we cannot exactly specify responsible cytokine(s) from the list of up-regulated genes because of overlapped effects of cytokines. For example, up-regulation of NF-B-regulated genes can be driven by TNF, IL-1 or other cytokines, and up-regulation of IFN-responsive genes can be driven by IFN-I or other interferons. In the current study, we designated the IFN-I response because many up-regulated IFN-responsive genes were typical ISGs.

Recently, Wilk et al. also performed scRNA-seq using PBMCs from COVID-19 patients and healthy controls (27). Similar to our study, they found IFN-I-driven inflammatory signatures in monocytes from COVID-19 patients. However, they did not find substantial expression of pro-inflammatory cytokine genes such as TNF, IL6, IL1B, CCL3, CCL4 and CXCL2 in peripheral monocytes from COVID-19 patients whereas we detected the up-regulation of TNF, IL1B, CCL3, CCL4 and CXCL2 in the current study. Moreover, they found a developing neutrophil population in COVID-19 patients that was not detected in our study. These discrepant results might be due to different platforms for scRNA-seq. Wilk et al. used the Seq-Well platform whereas we used the 10X Genomics platform that is more generally used. We also note that recent scRNA-seq analyses of COVID-19 sometimes lead to unrelated or contradictory conclusions to each other despite the same platform (28, 29). Although it often occurs in unsupervised analysis of highly multi-dimensional data, more caution will be required in designing scRNA-seq analysis of COVID-19, including definition of the severity and sampling time points.

Recently, Blanco-Melo et al. examined the transcriptional response to SARS-CoV-2 in in vitro infected cells, infected ferrets, and post-mortem lung samples from lethal COVID-19 patients and reported that IFN-I and -III responses are attenuated (25). However, we noted that IFN-I signaling pathway and innate immune response genes were relatively up-regulated in post-mortem lung samples from lethal COVID-19 patients compared to SARS-CoV-2-infected ferrets in their paper. Given that SARS-CoV-2 induces only mild disease without severe progression in ferrets (30), we interpret that IFN-I response is up-regulated in severe COVID-19 (e.g., post-mortem lung samples from lethal COVID-19 patients), but not in mild COVID-19 (e.g., SARS-CoV-2-infected ferrets). Indeed, severe COVID-19-specific signatures discovered in our current study were significantly enriched in the publically available data of post mortem lung tissues from the Blanco-Melo et al.s study although the analysis was limited to only two patients without individual cell-type resolution (Fig. 6). In a recent study, Zhou et al. also found a robust IFN-I response in addition to pro-inflammatory response in BAL fluid of COVID-19 patients (31). Moreover, up-regulation of IFN-I-responsive genes has been demonstrated in SARS-CoV-2-infected intestinal organoids (32).

Although IFN-I has direct antiviral activity, their immunopathological role was also reported previously (33). In particular, the detrimental role of the IFN-I response was elegantly demonstrated in a murine model of SARS (10). In SARS-CoV-infected BALB/c mice, the IFN-I response induced the accumulation of pathogenic inflammatory monocytes-macrophages and vascular leakage, leading to death. It was proposed that a delayed, but considerable IFN-I response is critical for the development of acute respiratory distress syndrome and increased lethality during pathogenic coronavirus infection (6, 34).

Currently, the management of patients with severe COVID-19 relies on intensive care and mechanical ventilation without a specific treatment because the pathogenic mechanisms of severe COVID-19 have not yet been clearly elucidated. In the current study, we demonstrated that severe COVID-19 is characterized by TNF/IL-1-inflammatory features combined with the IFN-I response. In a murine model of SARS-CoV infection, timing of the IFN-I response is a critical factor determining outcomes of infection (6, 10). Delayed IFN-I response contributes to pathological inflammation whereas early IFN-I response controls viral replication. Therefore, we propose that anti-inflammatory strategies targeting not only inflammatory cytokines, including TNF, IL-1, and IL-6, but also pathological IFN-I response needs to be investigated for the treatment of patients with severe COVID-19.

Patients diagnosed with COVID-19 were enrolled from Asan Medical Center, Severance Hospital, and Chungbuk National University Hospital. SARS-CoV-2 RNA was detected in patients nasopharyngeal swab and sputum specimens by multiplex real-time reverse-transcriptase PCR using the Allplex 2019-nCoV Assay kit (Seegene, Seoul, Republic of Korea). In this assay, N, RdRP, and E genes of SARS-CoV-2 were amplified, and Ct values were obtained for each gene. SARS-CoV-2-specific antibodies were examined using the SARS-CoV-2 Neutralization Antibody Detection kit (GenScript, Piscataway, NJ) and were positive in all COVID-19 patients in convalescent plasma samples or the last plasma sample in a lethal case. Hospitalized patients diagnosed with influenza A virus infection by a rapid antigen test of a nasopharyngeal swab were also enrolled from Asan Medical Center and Chungbuk National University Hospital from December 2015 to April 2016, prior to the emergence of COVID-19. Patients clinical features, laboratory findings, and chest radiographs were collected from their electronic medical records at each hospital. This study protocol was reviewed and approved by the institutional review boards of all participating institutions. Written informed consent was obtained from all patients.

Peripheral blood mononuclear cells (PBMCs) were isolated from peripheral venous blood via standard Ficoll-Paque (GE Healthcare, Uppsala, Sweden) density gradient centrifugation, frozen in freezing media, and stored in liquid nitrogen until use. All samples showed a high viability of about 90% on average after thawing. Single-cell RNA-seq libraries were generated using the Chromium Single Cell 3 Library & Gel Bead Kit v3 (10X genomics, Pleasanton, CA) following the manufacturers instructions. Briefly, thousands of cells were separated into nanoliter-scale droplets. In each droplet, cDNA was generated through reverse transcription. As a result, a cell barcoding sequence and Unique Molecular Identifier (UMI) were added to each cDNA molecule. Libraries were constructed and sequenced as a depth of approximately 50,000 reads per cell using the Nextseq 550 or Novaseq 6000 platform (Illumina, San Diego, CA).

The sequenced data were de-multiplexed using mkfastq (cellranger 10X genomics, v3.0.2) to generate fastq files. After de-multiplexing, the reads were aligned to the human reference genome (GRCh38; 10x cellranger reference GRCh38 v3.0.0), feature-barcode matrices generated using the cellranger count, and then aggregated by cellranger aggr using default parameters. The following analysis was performed using Seurat R package v3.1.5 (17). After generating the feature-barcode matrix, we discarded cells that expressed <200 genes and genes not expressed in any cells. To exclude low-quality cells from our data, we filtered out the cells that express mitochondrial genes in >15% of their total gene expression as described in previous studies (29, 35, 36). Doublets were also excluded, which were dominant in the cluster Uncategorized 1. Although there was a high variability in the number of UMIs detected per cell, majority of cells (90.5%) were enriched in a reasonable range of the UMIs (1,000 - 25,000), and 59% of cells with less than 1,000 UMIs were platelet or RBC excluded in downstream analysis. In each cell, the gene expression was normalized based on the total read count and log-transformed. To align the cells originating from different samples, 2000 highly variable genes from each sample were identified by the vst method in Seurat R package v3.1.5 (17). Using the canonical correlation analysis (CCA), we found anchors and aligned the samples based on the top 15 canonical correlation vectors. The aligned samples were scaled and principal component analysis (PCA) conducted. Finally, the cells were clustered by unsupervised clustering (0.5 resolution) and visualized by tSNE using the top 15 principal components.

To identify marker genes, up-regulated genes in each cluster relative to the other clusters were selected based on the Wilcoxon rank sum test in Seurats implementation with >0.25 log fold change compared to the other clusters and a Bonferroni-adjusted p < 0.05 (Table S4). By manual inspection, among the 22 different clusters, 20 were assigned to 11 known immune cell types, RBCs which are characterized by HBA1, HBA2, and HBB, and platelets. The clusters characterized by similar marker genes were manually combined as one cell type. The two remaining clusters were assigned to Uncategorized 1 and Uncategorized 2 because they had no distinct features of known cell types. Based on the distribution of UMI counts, the cluster Uncategorized 1 was featured by relatively high UMIs per cell compared to other clusters and presence of higher expression of multiple cell type marker genes. The cluster Uncategorized 2 was featured by a B cell-like signatures and high expression of ribosomal protein genes, not recommended to be further analyzed according to the 10X platform guideline. In these aspects, RBCs, platelets, Uncategorized 1, and Uncategorized 2 were excluded in downstream analysis.

To check the reproducibility of biological replicates (individuals within a same group), we calculated the Spearmans rank correlation coefficient for UMI counts that were merged according to each individual. The correlation coefficients of all individual pairs within the same group were visualized by a boxplot (COVID-19, n=45; FLU, n=10; HD, n=6).

In Fig. S1E, to investigate the similarity of the transcriptomes between cell types across diseases, we merged the UMI counts of each cell type according to healthy donor, influenza, mild COVID-19, and severe COVID-19. Next, the UMI counts for each gene were divided by the total UMI count in each cell type and multiplied by 100,000 as the normalized gene expression. Based on a median expression value >0.5, we calculated the relative changes in gene expression divided by the median value for each gene. Hierarchical clustering analysis was performed based on the PCC of the relative change in gene expression.

In Fig. 2A and Fig. S2A, to compare the highly variable gene expression among mild and severe COVID-19 and influenza relative to healthy donors, the normalized gene expression used in Fig. S1E was divided by the values in the healthy donor group. We selected the highly variable genes in terms of the top 25% standard deviation followed by log2-transformation (pseudo-count =1). In Fig. 2A, hierarchical clustering analysis was performed based on the PCCs of the selected highly variable genes. For Fig. S2A, to investigate the expression patterns of the selected highly variable genes (n=6,052), K-means clustering (k=50) was performed based on Euclidean distance. We manually ordered the clusters and visualized them as a heat map, revealing four distinct patterns: influenza-specific (n=1,046), COVID-19 specific (n=1,215), influenza/COVID-19 common (n=1,483), and cell type-specific (n=2,308).

To investigate the dynamic changes in cell type composition, we calculated the proportion of cell types in each individual. As a control, we calculated the relative variation in each cell type composition between all pairs of healthy donors. Similarly, for each disease group, we calculated the relative variation in each cell type by dividing the fraction of the cell type in individual patient by that of individual healthy donor. After log2-transformation, we conducted statistical analysis using the relative variation in composition between the control and disease groups using a two-sided KolmogorovSmirnov test.

For any two transcriptome profiles, to identify DEGs, we utilized the model-based analysis of single cell transcriptomics (MAST) algorithm in Seurats implementation based on a Bonferroni-adjusted p<0.05 and a log2 fold change > 0.25.

In Fig. 2B, the DEGs in COVID-19 and influenza compared to healthy donors or COVID-19 compared to influenza were identified at cell type resolution. All DEGs were combined according to the disease groups for further analysis. The overlapping up or down DEGs between COVID-19 and influenza compared to healthy donors were defined as Common up or Common down. The specific DEGs in COVID-19 or influenza were assigned as COVID-19 up/down or FLU up/down, respectively. In addition, COVID-19-specific up- or down-regulated genes compared to influenza were assigned as COVID-19>FLU or FLU>COVID-19, respectively. The Gene Ontology analysis was performed by DAVID. For each group of DEGs, the top 10 enriched GO biological pathways were selected, resulting in 49 unique GO biological pathways across all groups. The -log10(p-values) are shown as a heat map in Fig. 2B.

The weighted gene correlation network analysis (WGCNA) was conducted with the genes listed in the top 10 GO biological pathways of COVID-19 up, FLU up, and Common up defined in Fig. 2B. The normalized gene expression values of the genes in COVID-19 were divided by the values in influenza and log2-transformed (pseudo-count =1). We used default parameters with the exception of soft threshold =10 and networkType = signed when we constructed a topological overlap matrix. The modular gene expression patterns were defined using cutreeDynamic with a minClusterSize of 5. We visualized the modular gene expression pattern as a heat map in which the cell types were ordered according to hierarchical clustering with the default parameters of hcluster in R.

To find disease-specific subpopulations, each immune cell type was subjected to the subclustering analysis using Seurat. Briefly, the highly variable genes (n=1000) were selected based on vst and then scaled by ScaleData in Seurat with the vars.to.regress option to eliminate variation between individuals. The subpopulations were identified by FindClusters with default parameters, except resolution (non-EM-like CD8+ T cells, 0.3; classical monocytes, 0.2); the inputs were the top eight principal components (PCs) obtained from PCA of the scaled expression of the highly variable genes. The subpopulations were visualized by tSNE using the top eight PCs.

The trajectory analysis was performed with 2000 highly variable genes in classical monocytes across mild (C7-2) and severe (C7-1) COVID-19 as defined by the vst method in Seurat. The following analysis was performed using Monocle2. Briefly, the input was created from the UMI count matrix of the highly variable genes using the newCellDataSet function with default parameters, except expressionFamily = negbinomial.size. The size factors and dispersion of gene expression were estimated. The dimension of the normalized data was reduced based on DDRTree using reduceDimension with default parameters, except scaling = FALSE, which aligned the cells to the trajectory with three distinct clusters.

To determine genes that gradually changed along the trajectory, we identified the DEGs using MAST between clusters 1 and 3, which represent the severe stage and mild stage, respectively. The expression patterns of representative DEGs were visualized along the Pseudotime after correction with estimated size factors and dispersion for all genes.

In Fig. 4B, we performed K-means clustering of DEGs among all pairs of mild COVID-19, severe COVID-19, and influenza. The log2-transformed relative gene expression of DEGs compared to healthy donors was subjected to K-means clustering (k=10). Here, we used up-regulated DEGs in at least one disease group compared to the healthy donor group. We manually assigned five clusters based on gene expression patterns.

The transcriptome profiles of post-mortem lung tissues from two lethal cases of COVID-19 and biopsied heathy lung tissues from two donors were downloaded from a public database (GSE147507). The DEGs were identified using DESeq2 based on a Bonferroni-adjusted p < 0.05 and a log2 fold change > 1.

Enrichr, the web-based software for gene set enrichment analysis (GSEA) was used for LINCS L1000 ligand perturbation analysis (22), virus perturbation analysis, and disease perturbation analysis from the GEO database. Combined score was calculated as a parameter of enrichment as the log(p-value) multiplied by the z-score from the Fisher exact test. GSEA 4.0.3 software was used to conduct the GSEA when a ranked list of genes was available (Fig. 5G, Fig. 6C-E) (37). Results for IFN--responsive genes were not presented because those were considerably overlapped with IFN--responsive genes, which are typical ISGs. The normalized enrichment score and FDR-q value were calculated to present the degree and significance of enrichment.

Cryopreserved PBMCs were thawed, and dead cells were stained using the Live/Dead Fixable Cell Stain kit (Invitrogen, Carlsbad, CA). Cells were stained with fluorochrome-conjugated antibodies, including anti-CD3 (BV605; BD Biosciences), anti-CD4 (BV510; BD Biosciences), anti-CD8 (BV421; BD Biosciences), anti-CD14 (PE-Cy7; BD Biosciences), anti-CD19 (Alexa Fluor 700; BD Biosciences), and anti-CD56 (VioBright FITC; Miltenyi Biotec). For staining with anti-granzyme B (BD Biosciences), cells were permeabilized using a Foxp3 staining buffer kit (eBioscience).

For intracellular cytokine staining of IFN-, PBMCs were stimulated with phorbol 12-myristate 13-acetate (PMA, 50 ng/ml) (Sigma Aldrich) and ionomycin (1 g/ml) (Sigma Aldrich). Brefeldin A (GolgiPlug, BD Biosciences) and monesin (GolgiStop, BD Biosciences) were added 1 hour later. After another 5 hours of incubation, cells were harvested for staining with the Live/Dead Fixable Cell Stain kit, anti-CD3, anti-CD4, and anti-CD8. Following cell permeabilization, cells were further stained with anti-IFN- (Alexa Fluor 488; eBioscience).

Flow cytometry was performed on an LSR II instrument using FACSDiva software (BD Biosciences) and the data analyzed using FlowJo software (Treestar, San Carlos, CA).

Cytokines were measured in plasma samples, including IFN-, IL-18 (ELISA, R&D Systems, Minneapolis, MN), IL-1 (Cytometric bead array flex kit, BD Biosciences, San Jose, CA), TNF, IL-6, and IFN- (LEGENDplex bead-based immunoassay kit, BioLegend, San Diego, CA).

We performed the KS test to compare the distributions of two groups without assuming that the distributions follow normality. Welchs t test was conducted to compare the two distributions after confirming the normality of the distributions using the Shapiro-Wilk normality test. A Wilcoxon signed rank test was conducted to compare the differences between two groups with paired subjects. The Mann-Whitney test was performed to compare the means of two groups. Statistical analyses were performed using Prism software version 5.0 (GraphPad, La Jolla, CA). p<0.05 was considered significant.

immunology.sciencemag.org/cgi/content/full/5/49/eabd1554/DC1

Fig. S1. Clinical characteristics and assessment of the quality of scRNA-seq results.

Fig. S2. Transcriptome features of highly variable genes.

Fig. S3. Characterization of disease-specific CD8+ T-cell subpopulations.

Fig. S4. Subpopulation analysis of classical monocytes.

Fig. S5. STRING analysis of up-regulated genes in cluster 1 obtained from the trajectory analysis of classical monocytes.

Table S1. Experimental batches of scRNA-seq.

Table S2. Clinical characteristics of severe influenza patients.

Table S3. Clinical characteristics of COVID-19 patients.

Table S4. The scRNA-seq results.

Table S5. A list of marker genes for each cluster.

Table S6. A list of DEGs and associated biological pathways in Fig. 2B.

Table S7. Cell types in which the GBP1, CREM, and CCL3 were upregulated in Fig. 2C.

Table S8. A list of genes in each module obtained from WGCNA in Fig. 2D.

Table S9. A list of up-regulated genes in non-EM-like CD8+ T-cell subpopulations.

Table S10. A list of genes included in each cluster defined by K-mean clustering of classical monocytes.

Table S11. A list of genes up-regulated in early and late Pseudotime.

This is an open-access article distributed under the terms of the Creative Commons Attribution license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

More patients are in the hospital due to COVID-19 than ever.

This, along with a recent surge in positive cases, has hospital officials on high alert.

Officials at Blessing Hospital said there's currently eight patients in the hospital due to COVID-19.

They said they're ready if that number were to continue to increase.

As hospitalizations at Blessing Hospital rise, so do efforts to keep everything under control.

"We're ready," Blessing Health System CEO and President Maureen Kahn said. "We've got the equipment, we've got the staff who are trained and ready to take care of the patients as they come into the organization."

Kahn said while each case is different, the hospital is equipped to handle it.

"We're managing these patients in the medical surgical units in the hospital in our negative pressure rooms, which give them the added protection of containing and giving them special airflow in those rooms," Kahn said.

She said they have enough beds and PPE for severe cases.

"We have plenty of ventilators, should a patient need to be put on a ventilator," Kahn said. "But now, we have none of our ventilators in use on any of these patients."

Kahn said they also have enough medication for ways to treat symptoms.

"Remember, these medications are not cures, but they help minimize the symptoms these patients may experience," Kahn said.

Health department officials said they want residents to take this more serious in order to help limit cases and hospitalizations.

"Everybody in our community, despite how they feel about masking, despite what they think about gathering together in this environment, need to be conscientious about your individual behaviors right now for a lot of reasons," Adams County Public Health Administrator Jerrod Welch said.

Should they have the need, Kahn said they are prepared to admit more patients.

"Eight is a large number, but we have plenty of capacity," Kahn said. "We probably have like 30 available beds right now."

Kahn said two of the patients are not from Adams County, but from surrounding counties.

She said they are still allowing visitors here at the hospital and still have a number of guidelines in place for them.

Kahn said if you have any symptoms or think you may have been exposed, you should get tested.

To do so, you can call the COVID-19 Community Hotline.

People who know Maria Van Kerkhove describe her as someone who has worked her whole life to be in this place, at this moment.

This place is at the core of the World Health Organizations coronavirus team, this moment is when the WHO is trying to steer the globes response to the Covid-19 pandemic. No one would expect such a job to be anything less than highly stressful, but lately, the ride has been a rocky one.

Van Kerkhove, who for months has joined Director-General Tedros Adhanom Ghebreyesus during regular press briefings, found herself in a firestorm last month after saying that people with Covid-19 who are asymptomatic very rarely transmit the infection.

advertisement

The agencys pandemic response team, which Van Kerkhove helps lead as the head of the WHOs emerging diseases unit, came under fire again this week when more than 200 scientists accused the WHO in an open letter of resisting evidence that virus-laced aerosols emitted by people infected with Covid-19 are fueling spread of the disease.

While the latter critique was aimed broadly at the WHO, Van Kerkhove was personally in the hot seat in the case of the earlier controversy. At the time, she was speaking about people who never develop symptoms. But asymptomatic is also a term sometimes used to describe people who are infected and who havent yet developed symptoms. Its been established those people can and do transmit the infection.

advertisement

Covid-19 Twitter erupted after Van Kerkhoves remarks. The following day, she and Mike Ryan, the head of the WHOs health emergencies program, turned to Facebook Live to clarify the comments.

She should have recognized thats how it would get interpreted, Ashish Jha, director of Harvards Global Health Institute, said a couple of days later. Even the next days walk-back I just dont think they clarified it well enough.

Van Kerkhove, who strives for precision in her Covid-19 messaging, is still bruised by the episode.

I was talking with my husband [recently] and I was saying Im struggling at the moment with the pushback and the second-guessing and the challenging, she told STAT in an interview. Im trying to get information out to help people. Im trying to get the information out to clarify WHOs position, which is to help people, which is to suppress transmission and to save lives.

Theres no other motivation. So, the idea that, you know, were doing things that could potentially be harming people and hurting people, its very difficult for me to rectify, Van Kerkhove said.

To that end, Van Kerkhove and others at the WHO have written hundreds of guidance documents on Covid-19 on breastfeeding while infected (encouraged when possible), on preparing hospitals for surges in patients, on controlling spread of the virus at border crossings, to name but a few.

Ryan described the incident as a storm in a teacup. When Van Kerkhove wanted to clarify her statement the following day, he joined her online. Through that whole thing she showed just immense courage and character, he said.

Ryan, of course, is hardly unbiased. Certain professionals, you almost feel like theyve spent a lifetime preparing for a role a role they didnt know they were going to have, he said in a recent interview. She feels like someone like that to me. Someone whos been subconsciously preparing for the big one.

Indeed, Van Kerkhoves awe of science germinated early on. Her twin sister, Alisa DeJoseph, with whom she grew up in upstate New York, recalls being the right brain, excelling in the arts, while Maria was the left brain, excelling in math and science.

Peter Goodfriend, who taught Van Kerkhove advanced placement biology her senior year of high school, said he once videotaped and brought into class a TV segment about The Hot Zone, Richard Prestons just-released bestseller on Ebola. He also bought and read the book, then lent it to Van Kerkhove one of those students who just stand out, as he put it.

It made an impression. Van Kerkhove, now 43, said she remembers trying to figure out the careers of the characters Preston wrote about. Some were virologists, she knew. But there was another set of professionals, doing a job she hadnt previously heard of: epidemiologists.

I thought the idea of understanding why certain people get sick, why others dont, what were those differences? That was quite fascinating. Almost detective-like, she said.

Pursuit of this newfound career path took Van Kerkhove to some of the best universities around. They were also places where degrees dont come cheap Cornell, Stanford, and the London School of Hygiene and Tropical Medicine, where she got a bachelor of biological sciences, a masters in epidemiology, and a Ph.D. in infectious disease epidemiology, respectively. I had a lot of student loans, she said.

During summer breaks at Cornell, she did field work on projects run by her professors traveling to Mexico, Venezuela, and Costa Rica. Sometimes the research involved studying the plants indigenous peoples used for medicinal purposes; one summer she was studying leaves and fruits capuchin monkeys rub on their fur. Decades later she recalls how dark the nights were, the constant chorus of frogs, the tang of freshly squeezed juice in the mornings.

After Cornell, Van Kerkhove was accepted to Stanford to do a masters degree in epidemiology, a one- or two-year program that she completed in one. She then pressed pause on her studies, moving to New York City to take a job as an epidemiologist for Exponent Health Services Practice, a consulting firm. Much of her time was spent on the issue of power line expansions and the fears of communities that electromagnetic fields emitted by them could cause cancers.

In what people who know her well would probably describe as classic Van Kerkhove behavior, she dug in, trying to learn everything she could about the subject. The experience taught her how to weigh evidence, she said, and the critical importance of risk communications one of the skills shes leaning heavily into in the Covid-19 pandemic.

What I tried to do was link the science to the concern and tried to explain, you know, what I could and alleviate some fears, she said.

With some of her student loans paid off, Van Kerkhove was ready to pursue a Ph.D. She wanted to study at an institution that focused on global health. Enter the London School of Hygiene and Tropical Medicine.

This was the mid-2000s, when bird flu the H5N1 virus was racing through Asia and beyond, decimating poultry flocks. It rarely infected people, but when it did, the outcome was more often than not fatal. About 60% of people known to have been infected with that virus died.

Van Kerkhove spent the better part of two years shuttling between London and Cambodia, where she worked with scientists at the Pasteur Institute in Phnom Penh, trying to chart the movement of poultry in a country where commercial-scale poultry production didnt exist.

The study Van Kerkhove and her Cambodian colleagues produced showed that infected poultry entering the country from China made its way through Vietnam to Cambodia through a series of middlemen. It became the subject of Van Kerkhoves Ph.D. thesis. That was a great piece of work, said Malik Peiris, a world-renowned virologist at Hong Kong University who was one of the thesis reviewers and was later a colleague on Van Kerkhoves work on MERS, a camel coronavirus.

A number of the Cambodian scientists Van Kerkhove collaborated with remain at the Pasteur Institute. Sowath Ly, who is now deputy head of the institute, said they marvel to see the scientist with whom they quizzed Cambodian villagers about bird flu sitting beside the director general of the WHO informing the world about Covid-19.

We are very proud of her, said Ly, who described Van Kerkhove as a good mentor.

Others are more reserved about the WHOs handling of the pandemic response. Jha, the Harvard expert, described the agencys communications efforts as good but not great. (Still, he credited the agency for communicating at all, noting that the Centers for Disease Control and Prevention barely briefs at all these days.)

Multiple people who have worked with Van Kerkhove talk about her laser focus and her prodigious capacity for work.

While doing postdoctoral work at Londons Imperial College under prominent mathematical modeler Neil Ferguson, she became a liaison between Fergusons group and the WHOs influenza team. Effectively, Ferguson lent Van Kerkhove to WHO; for a number of years she traveled weekly from London to Geneva to lend a badly needed hand.

She worked under Tony Mounts, a CDC infectious diseases epidemiologist who was at the time seconded to the WHO. Van Kerkhoves productivity intimidated some of his other staff, Mounts recalled, because she was so efficient that she tended to run circles around people at times.

When the 2009 influenza pandemic began, his unit tapped into that capacity, producing with her help important papers assessing the risk factors for severe H1N1 infection that is the flu strain that triggered the pandemic and estimating global mortality.

Its really stuff we couldnt have gotten done without her. We just didnt have the time or the people or the expertise on our team without her to do that, says Mounts, who is now on assignment to USAID. She just kind of buckles down and gets work done.

In 2015 she was hired by the Institut Pasteur in Paris to create a network of rapid outbreak response teams throughout the famed organizations 33 branches worldwide. Van Kerkhove speaks well of the experience, but friends say she didnt get the support she needed to make the goal a reality. Two years later the WHO was looking for someone to head its coronavirus work. It was a job she wanted, and back to Geneva she went.

Around Christmastime last year, Van Kerkhove was in North Carolina with her husband, Neil, and their two children. They were visiting family when she got a phone call that changed the tenor of the vacation. A mysterious virus spreading in China, she was told. A couple of days later, she was en route to Geneva again.

The work has been nonstop since.

Van Kerkhove was part of the WHOs nine-day mission to China in February to study the new disease and Chinas response to it. After her return to Geneva, some staff at WHO headquarters contracted Covid-19. Fearful shed bring the virus home to her family, Van Kerkhove decided to quarantine herself when she was at home.

For at least two months, she didnt touch her children: Cole, now 9 , and Miro, who is 18 months old.

She often left for work before they were up, arriving home after they were in bed. When she was home, she sequestered herself in a room a technique many frontline health workers have used in this pandemic. She would talk to her children through windows. It was awful. Awful! she shuddered.

Cole, who had initially been excited his mother was trying to help the world respond to a crisis, became convinced shed die from the new disease when she went to China. Miro thought his mother was playing a game of hide and seek, and would run after her whenever he saw her.

I would laugh in front of him and then come into the bedroom and cry because it was just a horrible, horrible thing, she said. Eventually the rate of new infections in Geneva started dropping, schools reopened, and there were no recent cases among WHO staff.

There was one day that I came home and I was on front lawn, and the baby just ran up to me and I just grabbed him. I just couldnt do it anymore, Van Kerkhove said.

She credits her husband for being incredibly supportive, but acknowledges 2020 has been a slog.

Its difficult for all of us. I havent been home a lot in six months, she said.

Will I get COVID-19 doing this? Here's how risky normal activities are  KOMO News

In January, vaccine researchers lined up on the starting blocks, waiting to hear a pistol. That shot came on January 10, when scientists in China announced the complete genetic makeup of the novel coronavirus. With that information in hand, the headlong race toward a vaccine began.

As the virus, now known as SARS-CoV-2, began to spread like wildfire around the globe, researchers sprinted to catch up with treatments and vaccines. Now, six months later, there is still no cure and no preventative for the disease caused by the virus, COVID-19, though there are glimmers of hope. Studies show that two drugs can help treat the sick: The antiviral remdesivir shortens recovery times (SN: 4/29/20) and a steroid called dexamethasone reduces deaths among people hospitalized with COVID-19 who need help breathing (SN: 6/16/20).

But the finish line in this race remains a safe and effective vaccine. With nearly 180 vaccine candidates now being tested in lab dishes, animals and even already in humans, that end may be in sight. Some experts predict that a vaccine may be available for emergency use for the general public by the end of the year even before it receives expedited U.S. Food and Drug Administration approval.

Velocity might come at the expense of safety and efficacy, some experts worry. And that could stymie efforts to convince enough people to get the vaccine in order to build the herd immunity needed to end the pandemic.

Were calling for transparency of data, says Esther Krofah, executive director of FasterCures, a Washington, D.C.-based nonprofit. We want things to accelerate meaningfully in a way that does not compromise safety or the science, but we need to see the data, she says.

Traditionally, vaccines are made from weakened or killed viruses, or virus fragments. But producing large amounts of vaccine that way can take years, because such vaccines must be made in cells (SN: 7/7/20), which often arent easy to grow in large quantities.

Getting an early good look at the coronaviruss genetic makeup created a shortcut. It let scientists quickly harness the viruss genetic information to make copies of a crucial piece of SARS-CoV-2 that can be used as the basis for vaccines.

That piece is known as the spike protein. It studs the viruss surface, forming its halo and allowing the virus to latch onto and enter human cells. Because the spike protein is on the outside of the virus, its also an easy target for antibodies to recognize.

Researchers have copied the SARS-CoV-2 version of instructions for making the spike protein into RNA or DNA, or synthesized the protein itself, in order to create vaccines of various types (see sidebar). Once the vaccine is delivered into the body, the immune system makes antibodies that recognize the virus and block it from getting into cells, either preventing infection or helping people avoid serious illness.

Using this approach, drugmakers have set speed records in devising vaccines and beginning clinical trials. FasterCures, which is part of the Milken Institute think tank, is tracking 179 vaccine candidates, most of which are still being tested in lab dishes and animals. But nearly 20 have already begun testing in people.

Some front-runners have emerged, leading the pack in a neck-and-neck race. Some have been propelled by an effort by the U.S. federal government, called Operation Warp Speed, which has picked a handful of vaccine candidates to fast-track.

First out of the starting gate was one developed by Moderna, a Cambridge, Mass.based biotech company. It inoculated the first volunteer with its candidate vaccine on March 16, just 63 days after the viruss genetic makeup was revealed. The company has since reported preliminary safety data, and some evidence that its vaccine stimulates the immune system to produce antibodies against the coronavirus (SN: 5/18/20).

That company and several others now have vaccines entering Phase III clinical trials. Moderna and the National Institute of Allergy and Infectious Diseases, in Bethesda, Md., will begin inoculating 30,000 volunteers with either the vaccine or a placebo in July to test the vaccines efficacy in large numbers of people.

Modernas vaccine requires two doses; a prime and a boost. That means it will take 28 days to get any individual person vaccinated, NIAID director Anthony Fauci said June 26 during a Milken Institute webinar. It will take weeks and months to give the full set of shots to all those people. Then it will take time to determine whether more people in the placebo group get COVID-19 than those in the vaccine group a sign that the vaccine works. Those results could come in late fall or early winter.

NIAID launched a clinical trials network July 8 to recruit volunteers at sites across the United States for phase III testing of vaccines and antibodies to prevent COVID-19. Modernas vaccine will be the first in line for testing.

Some researchers propose accelerating clinical trials even further by trying controversial challenge trials, in which vaccinated volunteers are intentionally exposed to the coronavirus (SN: 5/27/20). None of those studies have gotten the green light yet.

Three other global drug and vaccine companies have announced plans to launch similarly sized trials this summer: Johnson & Johnson; AstraZeneca, working with the University of Oxford; and Pfizer Inc., which has teamed up with the German company BioNTech. Like Moderna, all are part of Operation Warp Speed, or will be joining it.

Usually, Phase III trials are about determining efficacy. But the rush to get through earlier stages designed to make sure a drug doesnt cause harm means that scientists also will be keeping a keen eye on safety, Fauci said. Researchers will be watching, in particular, for any suggestion that antibodies generated by the vaccine might enhance infection.

That can happen when antibodies stimulated by the vaccine dont fully neutralize the virus and can aid it getting into cells and replicating, or because the vaccine alters immune cell responses in unhelpful ways. Vaccines against MERS and SARS coronaviruses made infections with the real virus worse in some animal studies.

Such enhanced infections are a worry for any unproven vaccine candidate, but some experimental vaccines in the works may be more concerning than others, says Peter Pitts, president of the Center for Medicine in the Public Interest, a nonprofit research and education organization headquartered in New York City.

For instance, China-based CanSino Biologics Inc. has developed a hybrid virus vaccine: Its made by putting the coronavirus spike protein into a common cold virus called adenovirus 5. That virus can infect humans but has been altered so that it can no longer replicate.

In a small study, reported June 13 in the Lancet, CanSinos vaccine triggered antibody production against the spike protein. But many volunteers already had preexisting antibodies to the adenovirus, raising concerns that that could weaken their response to the vaccine. A weakened response might make an infection worse when people encounter the real coronavirus, Pitts says.

Thats of particular concern because CanSino said in a June 29 statement to the Hong Kong stock exchange that its vaccine was approved by the Chinese government for temporary use by the Chinese military. Thats essentially turning soldiers into guinea pigs, Pitts says.

The type of antibodies stimulated by the vaccine will be important in determining whether the vaccine protects against disease or makes things worse, Yale University immunologists Akiko Iwasaki and Yexin Yang, warned April 21 in Nature Reviews Immunology. Some types of antibodies have been associated with more severe COVID-19.

And it will be important to monitor the ratio of neutralizing antibodies and non-neutralizing antibodies, as well as activity of other immune cells triggered by the vaccines, an international working group of scientists recommended in a conference report in the June 26 Vaccine.

Public health officials will also be tracking side effects closely. As big as the vaccine trials may be, we cant be sure that there arent rare side effects, Anne Schuchat, principal deputy director of the Centers for Disease Control and Prevention, said June 29 during a question-and-answer session with the Journal of the American Medical Association. Thats why even when we get enough to vaccinate large numbers, were going to need to be following it.

In 1976 for instance, it turned out that Guillain-Barr syndrome, a rare neurological condition in which the immune system attacks parts of the nervous system, was a rare side effect of the swine flu influenza vaccine. That didnt become obvious until the vaccine had already been rolled out to 45 million people in the United States.

Scientists and journalists share a core belief in questioning, observing and verifying to reach the truth. Science News reports on crucial research and discovery across science disciplines. We need your financial support to make it happen every contribution makes a difference.

Early on, it was unclear whether scientists could devise a vaccine against the coronavirus at all. Its now a question of when rather than if well have a vaccine.

But some researchers have expressed concern that rushing clinical trials might lead federal regulators to approve a vaccine based on its ability to trigger antibody production alone. Its still unclear how well antibodies protect against reinfection with the coronavirus and how long any such immunity may last (SN: 4/28/20). The measure of whether the vaccine works should be its ability to protect against illness, not antibody production, Fauci said.

I really want to make sure that we dont have a vaccine thats distributed among the American people unless we know its safe and we know it is effective, he said. Not that we think it might be effective, but that we know its effective.

So far though, companies are measuring success by the antibody. For instance, INOVIO, a biotechnology company based in Plymouth Meeting, Pa., announced June 30 that 94 percent of participants in a small safety trial made antibodies against the coronavirus. The data, delivered via news release like that from numerous other companies rushing to show progress, had not been peer-reviewed and other details about the companys DNA-based vaccine were sparse.

Despite still having much to prove, companies are gearing up manufacturing without knowing if their product will ever reach the market. By the end of the year, companies promise they can have hundreds of millions of doses. We keep saying, Are you sure? And they keep saying yes, Fauci said. Thats pretty impressive if they can do it.

For instance, if everything goes right, a vaccine in testing now from Pfizer might be available as soon as October, Pfizer chairman and chief executive Albert Bourla said during the Milken Institute session. If we are lucky, and the product works and we do not have significant bumps on our way to manufacturing, he said, the company expects to be able to make 1 billion doses by early next year.

Pfizer released preliminary data on the safety of one of four vaccine candidates it is evaluating July 1 at medRxiv.org. In the small study of 45 people, no severe side effects were noted. Vaccination produced neutralizing antibodies at levels 1.8 to 2.8 times levels found in blood plasma from people who had recovered from COVID-19, researchers reported.

Novavax Inc., a Gaithersburg, Md.-based biotechnology company, announced July 7 that it was being award $1.6 billion from Operation Warp Speed to conduct phase III trials and to deliver 100 million doses of its vaccine as early as the end of the year.

If manufacturers can deliver a vaccine as promised, there could be another big hurdle: Theres no guarantee people will line up for shots. About a quarter of Americans said in recent polls that they would definitely or probably not get a coronavirus vaccine if one were available. Thats a pending public health crisis, Pitts says.

Krofah agrees. We need to think about the post-pandemic world in the midst of all of this, she says. We need to start building that public trust now. Tackling issues of vaccine hesitancy shouldnt be left until a vaccine is available, she says.

Whether with vaccines or treatments, we need to expedite, but not rush, Pitts says. Theres a perception that therapeutics or vaccines will be approved willy-nilly because of politics, and thats a dangerous misperception. The FDA laid out guidelines, including an accelerated approval process, on June 30 that should ensure any approved vaccines work, he says.

There is good news for those who are eagerly awaiting vaccines, Krofah and Pitts say: There wont be just one winner in the race. Instead, there may be multiple options to choose from. Thats not a luxury; it may be a necessity. Multiple vaccines may be needed to protect different segments of the population, Krofah says. For instance, elderly people may need a vaccine that prods the immune system harder to make antibodies, and children may need different vaccines than adults do.

Whats more, long-term investments in development will be needed so that vaccines can be altered if the virus mutates. We need to stay the front and not declare victory once a vaccine has been approved for emergency use, she says.

For now, vaccine makers are moving both as quickly and as carefully as possible, Bourla said. I am aware that right now that billions of people, millions of businesses, hundreds of governments are investing their hope for a solution in a handful of pharma companies.

Headlines and summaries of the latest Science News articles, delivered to your inbox

Dr. Anthony Fauci, the nation's leading infectious disease expert, said Friday the development of a coronavirus vaccine must be for the benefit of all countries, calling it a "responsibility to the entire planet."

That responsibility is "not just to the individual country thats making the vaccine," he said during a virtual presentation at the COVID-19 Conference.

Full coverage of the coronavirus outbreak

Because of this, he continued, the companies that the U.S. government is working with are "already in discussion to start gearing up to make hundreds of millions of doses." Some companies are promising to have a billion doses of a vaccine within a year or so, he added.

There were likely to be several types of vaccines for the virus, which work in different ways.

Let our news meet your inbox. The news and stories that matters, delivered weekday mornings.

Fauci, the director of the National Institute of Allergy and Infectious Diseases, likened the development of these different vaccines to taking "multiple shots on goal" in hockey. There are "some that you could get off quickly and ramp up quickly, some that have more experience, and some that we know are tried and true."

An mRNA vaccine was one that researchers were able to get off the ground quickly, he said. Moderna, a Cambridge, Massachusetts-based company, started the first clinical trial in the United States with its mRNA-based vaccine in March. This type of vaccine uses genetic material to teach cells how to defend against the coronavirus.

Another type of vaccine, which uses the virus' protein to teach cells to fight it, took longer to develop but was not any less important, he said.

Download the NBC News app for full coverage of the coronavirus outbreak

The COVID-19 Conference brought together experts from around the world to discuss the latest science on the disease that's infected more than 12 million people globally and caused more than 550,000 deaths.

During the same session, Fauci pointed out the challenges of containing the spread of the virus from asymptomatic individuals.

"The situation that were facing in the U.S. is significant and serious in that we have community spread in areas where many of these individuals are without symptoms," he said. "That is complicating our task."

Dr. Deborah Birx, the White House Coronavirus Task Force coordinator, who also spoke at Friday's session, noted the importance of finding asymptomatic cases.

"This is something that has been done so extraordinarily well in HIV, where you have to find the asymptomatic individuals to stop community spread," she said. "Same principle in this respiratory disease."

Follow NBC HEALTH on Twitter & Facebook.

Sara G. Miller is the health editor for NBC News, Health & Medical Unit.

Maryland man treated with experimental COVID-19 vaccine

A Maryland man who was one of the first people to be vaccinated with an experimental COVID-19 vaccine through Pfizer/Biontech says the vaccine may be working.

COLLEGE PARK, Md. (FOX 5 DC) - A Maryland man who was one of the first people to be vaccinated with an experimental COVID-19 vaccine through Pfizer/Biontech says the vaccine may be working.

FOX 5first interviewed David Rach, a graduate immunology student, back in May.

A University of Maryland's School of Medicine spokesperson is now telling FOX 5that Phase 1 and 2 early results from the first roughly 40 individuals in this multi-site study show some signs of promise and some signs of an immune response. This means that the patients have generated antibodies that stop the virus from being able to affect cells.

RELATED:Volunteers infected with COVID-19 in human challenge study

"Going into the trial, I wasn't certain the vaccine would be effective at producing an immune response, because we were the first people being tested with the vaccine... At the same time, just because I have antibodies doesn't mean you are protected against the virus, So i'm monitoring my symptoms every day," said Rach.

It's also important to remember that this was a randomized blind trial meaning that some participants received a placebo saline solution and some actually got the vaccine. The participants don't know which one they received, but based on his blood tests, his symptoms and the fact that he's a scientist in training, Rach says he's confident he got the vaccine.

Researchers are also comparing the immune response of those vaccinated with patients who have recovered from covid in the hospitals... and are seeing some promising results there as well.

As for safety, Rach says the research shows some fever in participants with the low dose of the vaccine, but no serious symptoms.

Download the FOX 5 DC News App for Local Breaking News and Weather

Another important factor with a trial, is that the participant is surrounded by the virus. Rach says he's trying to get out more.

"I'm starting to move around, going to and from work... I still do my own grocery runs- and I'm still social distancing so I can do my own part," said Rach.

This is one of many vaccine trials underway. In July, the University of Maryland School of Medicine is expected to take part in a phase 3 trial for another covid 19 vaccine developed by Moderna. The focus will be to target the most vulnerable communities impacted by coronavirus-- the Latino and African American communities and older adults with underlying health issues.

For the first few months researchers will follow Rach closely and check in on him every 6, 12 months to see how long the antibodies last.

If the trial proves to be successful, Pfizer promises to produce 100 million doses before the end of 2020 and more than a billion doses next year

Your coronavirus questions answered

Coronavirus: Symptoms, testing and how to prepare amid growing COVID-19 outbreak

LIVE: Interactive map tracks global spread of COVID-19

How to help during the coronavirus pandemic

Despite the race to replenish the domestic needle and syringe supply, about 400 shipping containers of syringes have left the U.S. for countries including Germany, Colombia, Australia, Brazil and Italy this year, according to Panjiva Inc., a service that independently tracks global trade. Thats the same, on average, as syringe exports over the past five years.

Text size

The pandemic has affected every aspect of the economy. If there is no vaccine in the foreseeable future, the overwhelming majority of the workforce will require more safety protections, job flexibility, and better health care.

Only 25% of U.S. workers are employed in industries where they can work from home, says Marissa Baker, assistant professor of occupational health sciences at the University of Washington. The other 75% of workersin industries such as retail, health care, and transportation, where working from home isnt an optionwill need added protections to stay safe.

At a minimum, they will need more personal protective equipment, and training on how to use it properly. People who interact with large numbers of people, such as bus drivers, may even need to wear N95 masks, which provide a greater level of protection than cloth or paper masks.

Another challenge will be ensuring that everyone who interacts with workers also wears a mask. Baker says employers may need to hire a person trained to deal with customer confrontations to stand at the door to enforce mask wearing.

Essential workers will also need paid time off if they get sick or need to take care of children or other family members. Ideally, says Baker, these workers need a way to work flexibly while still maintaining their job and benefits.

The 25% of the workforce who dont have to go to an office, store, factory, or other physical location could benefit from a new working-at-home economy, says Nicholas Bloom, professor of economics at Stanford Graduate School of Businessalthough some studies have shown that working from home is linked to a decrease in productivity and an increase in inequality. Educated, higher-paid employees who can afford to work from home will develop skills and advance their careers, but those who cant will be left behind, says Bloom.

Still, as the pandemic wears on, both the at-home workers and those making commutes will face a significant mental health crisis, Baker predicts. People who cant work from home but who arent doing essential work will face job displacement and job insecurity, which Baker says is linked to depression.

Even those who can work through the pandemic are likely to face mental health stress from trying to balance their work and home life, especially parents if schools remain closed. Greater access to affordable health care, including mental health care, will be necessary for workers, says Baker.

Email: editors@barrons.com