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Spatial heterogeneity can lead to substantial local variations in COVID-19 timing and severity.

ABSTRACT: Standard epidemiological models for COVID-19 employ variants of compartment (SIR or susceptible-infectious-recovered) models at local scales, implicitly assuming spatially uniform local mixing. Here, we examine the effect of employing more geographically detailed diffusion models based on known spatial features of interpersonal networks, most particularly the presence of a long-tailed but monotone decline in the probability of interaction with distance, on disease diffusion. Based on simulations of unrestricted COVID-19 diffusion in 19 US cities, we conclude that heterogeneity in population distribution can have large impacts on local pandemic timing and severity, even when aggregate behavior at larger scales mirrors a classic SIR-like pattern. Impacts observed include severe local outbreaks with long lag time relative to the aggregate infection curve, and the presence of numerous areas whose disease trajectories correlate poorly with those of neighboring areas. A simple catchment model for hospital demand illustrates potential implications for health care utilization, with substantial disparities in the timing and extremity of impacts even without distancing interventions. Likewise, analysis of social exposure to others who are morbid or deceased shows considerable variation in how the epidemic can appear to individuals on the ground, potentially affecting risk assessment and compliance with mitigation measures. These results demonstrate the potential for spatial network structure to generate highly nonuniform diffusion behavior even at the scale of cities, and suggest the importance of incorporating such structure when designing models to inform health care planning, predict community outcomes, or identify potential disparities.

SUBMITTER: Thomas LJ

PROVIDER: S-EPMC7533653 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

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Spatial heterogeneity can lead to substantial local variations in COVID-19 timing and severity.

Thomas Loring J LJ Huang Peng P Yin Fan F Luo Xiaoshuang Iris XI Almquist Zack W ZW Hipp John R JR Butts Carter T CT

Proceedings of the National Academy of Sciences of the United States of America 20200910 39

Standard epidemiological models for COVID-19 employ variants of compartment (SIR or susceptible-infectious-recovered) models at local scales, implicitly assuming spatially uniform local mixing. Here, we examine the effect of employing more geographically detailed diffusion models based on known spatial features of interpersonal networks, most particularly the presence of a long-tailed but monotone decline in the probability of interaction with distance, on disease diffusion. Based on simulations ...[more]

PMID: 32913057

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Project description:Using RNA-seq and high-resolution mass spectrometry we performed a comprehensive systems analysis on 128 plasma and leukocyte samples from hospitalized patients with or without COVID-19 (n=102 and 26 respectively) and with differing degrees of disease severity. We generated abundance measurements for over 17,000 transcripts, proteins, metabolites, and lipids and compiled them with clinical data into a curated relational database. This resource offers the unique opportunity to perform systems analysis and cross-ome correlations to both molecules and patient outcomes. In total 219 molecular features were mapped with high significance to COVID-19 status and severity, including those involved in processes such as complement system activation, dysregulated lipid transport, and B cell activation. In one example, we detected a trio of covarying molecules – citrate, plasmenyl-phosphatidylcholines, and gelsolin (GSN) – that offer both pathophysiological insight and potential novel therapeutic targets. Further, our data revealed in some cases, and supported in others, that several biological processes were dysregulated in COVID-19 patients including vessel damage, platelet activation and degranulation, blood coagulation, and acute phase response. For example, we observed that the coagulation-related protein, cellular fibronectin (cFN), was highly increased within COVID-19 patients and provide new evidence that the upregulated proteoform stems from endothelial cells, consistent with endothelial injury as a major activator of the coagulation cascade. The abundance of prothrombin, which is cleaved to form thrombin during clotting, was significantly reduced and correlated with severity and might help to explain the hyper coagulative environment of SARS-CoV-2 infection. From transcriptomic analysis of leukocytes, we concluded that COVID-19 patients with acute respiratory distress syndrome (ARDS) demonstrated a phenotype that overlapped with, but was distinct from, that found in patients with non-COVID-19-ARDS. To aid in the global efforts toward elucidation of disease pathophysiology and therapeutic development, we created a web-based tool with interactive visualizations allowing for easy navigation of this systems-level compendium of biomolecule abundance in relation to COVID-19 status and severity. Finally, we leveraged these multi-omic data to predict COVID-19 patient outcomes with machine learning, which highlighted the predictive power of these expansive molecular measurements beyond the standardized clinical estimate of 10-year survival Charlson score.

Project description:BackgroundCOVID-19 during pregnancy can have serious effects on pregnancy outcomes. The placenta acts as an infection barrier to the fetus and may mediate adverse outcomes. Increased frequency of maternal vascular malperfusion has been detected in the placentas of patients with COVID-19 compared with controls, but little is known about how the timing and severity of infection affect placental pathology.ObjectiveThis study aimed to examine the effects of SARS-CoV-2 infection on placental pathology, specifically whether the timing and severity of COVID-19 affect pathologic findings and associations with perinatal outcomes.Study designThis was a descriptive retrospective cohort study of pregnant people diagnosed with COVID-19 who delivered between April 2020 and September 2021 at 3 university hospitals. Demographic, placental, delivery, and neonatal outcomes were collected through medical record review. The timing of SARS-CoV-2 infection was noted, and the severity of COVID-19 was categorized on the basis of the National Institutes of Health guidelines. The placentas of all patients with positive nasopharyngeal reverse transcription-polymerase chain reaction COVID-19 testing were sent for gross and microscopic histopathologic examinations at the time of delivery. Nonblinded pathologists categorized histopathologic lesions according to the Amsterdam criteria. Univariate linear regression and chi-square analyses were used to assess how the timing and severity of SARS-CoV-2 infection affected placental pathologic findings.ResultsThis study included 131 pregnant patients and 138 placentas, with most patients delivered at the University of California, Los Angeles (n=65), followed by the University of California, San Francisco (n=38) and Zuckerberg San Francisco General Hospital (n=28). Most patients were diagnosed with COVID-19 in the third trimester of pregnancy (69%), and most infections were mild (60%). There was no specific placental pathologic feature based on the timing or severity of COVID-19. There was a higher frequency of placental features associated with response to infection in the placentas from infections before 20 weeks of gestation than that from infections after 20 weeks of gestation (P=.001). There was no difference in maternal vascular malperfusion by the timing of infection; however, features of severe maternal vascular malperfusion were only found in the placentas of patients with SARS-CoV-2 infection in the second and third trimesters of pregnancy, not in the placentas of patients with COVID-19 in the first trimester of pregnancy.ConclusionPlacentas from patients with COVID-19 showed no specific pathologic feature, regardless of the timing or severity of the disease. There was a higher proportion of placentas from patients with COVID-19-positive tests in earlier gestations with evidence of placental infection-associated features. Future studies should focus on understanding how these placental features in SARS-CoV-2 infections go on to affect pregnancy outcomes.

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Spatial heterogeneity can lead to substantial local variations in COVID-19 timing and severity.

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Spatial heterogeneity can lead to substantial local variations in COVID-19 timing and severity.

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