Behavior-based dependency networks between places shape urban economic resilience (preprint)

Urban economic resilience is intricately linked to how disruptions caused by pandemics, disasters, and technological shifts ripple through businesses and urban amenities. Disruptions, such as closures of non-essential businesses during the COVID-19 pandemic, not only affect those places directly but also influence how people live and move, spreading the impact on other businesses and increasing the overall economic shock. However, it is unclear how much businesses depend on each other in these situations. Leveraging large-scale human mobility data and millions of same-day visits in New York, Boston, Los Angeles, Seattle, and Dallas, we quantify dependencies between points-of-interest (POIs) encompassing businesses, stores, and amenities. Compared to places' physical proximity, dependency networks computed from human mobility exhibit significantly higher rates of long-distance connections and biases towards specific pairs of POI categories. We show that using behavior-based dependency relationships improves the predictability of business resilience during shocks, such as the COVID-19 pandemic, by around 40% compared to distance-based models. Simulating hypothetical urban shocks reveals that neglecting behavior-based dependencies can lead to a substantial underestimation of the spatial cascades of disruptions on businesses and urban amenities. Our findings underscore the importance of measuring the complex relationships woven through behavioral patterns in human mobility to foster urban economic resilience to shocks.

The unequal effects of the health-economy tradeoff during the COVID-19 pandemic (preprint)

The potential tradeoff between health outcomes and economic impact has been a major challenge in the policy making process during the COVID-19 pandemic. Epidemic-economic models designed to address this issue are either too aggregate to consider heterogeneous outcomes across socio-economic groups, or, when sufficiently fine-grained, not well grounded by empirical data. To fill this gap, we introduce a data-driven, granular, agent-based model that simulates epidemic and economic outcomes across industries, occupations, and income levels with geographic realism. The key mechanism coupling the epidemic and economic modules is the reduction in consumption demand due to fear of infection. We calibrate the model to the first wave of COVID-19 in the New York metropolitan area, showing that it reproduces key epidemic and economic statistics, and then examine counterfactual scenarios. We find that: (a) both high fear of infection and strict restrictions similarly harm the economy but reduce infections; (b) low-income workers bear the brunt of both the economic and epidemic harm; (c) closing non-customer-facing industries such as manufacturing and construction only marginally reduces the death toll while considerably increasing unemployment; and (d) delaying the start of protective measures does little to help the economy and worsens epidemic outcomes in all scenarios. We anticipate that our model will help designing effective and equitable non-pharmaceutical interventions that minimize disruptions in the face of a novel pandemic.

Behavioral changes during the pandemic worsened income diversity of urban encounters (preprint)

Diversity of physical encounters and social interactions in urban environments are known to spur economic productivity and innovation in cities, while also to foster social capital and resilience of communities. However, mobility restrictions during the pandemic have forced people to substantially reduce urban physical encounters, raising questions on the social implications of such behavioral changes. In this paper, we study how the income diversity of urban encounters have changed during different periods throughout the pandemic, using a large-scale, privacy-enhanced mobility dataset of more than one million anonymized mobile phone users in four large US cities, collected across three years spanning before and during the pandemic. We find that the diversity of urban encounters have substantially decreased (by 15% to 30%) during the pandemic and has persisted through late 2021, even though aggregated mobility metrics have recovered to pre-pandemic levels. Counterfactual analyses show that while the reduction of outside activities (higher rates of staying at home) was a major factor that contributed to decreased diversity in the early stages of the pandemic, behavioral changes including lower willingness to explore new places and changes in visitation preferences further worsened the long-term diversity of encounters. Our findings suggest that the pandemic could have long-lasting negative effects on urban income diversity, and provide implications for managing the trade-off between the stringency of COVID-19 policies and the diversity of urban encounters as we move beyond the pandemic.

Behavioral changes during the pandemic worsened income diversity of urban encounters (preprint)

Diversity of physical encounters and social interactions in urban environments are known to spur economic productivity and innovation in cities, while also to foster social capital and resilience of communities. However, mobility restrictions during the pandemic have forced people to substantially reduce urban physical encounters, raising questions on the social implications of such behavioral changes. In this paper, we study how the income diversity of urban encounters have changed during different periods throughout the pandemic, using a large-scale, privacy-enhanced mobility dataset of more than one million anonymized mobile phone users in four large US cities, collected across three years spanning before and during the pandemic. We find that the diversity of urban encounters have substantially decreased (by 15% to 30%) during the pandemic and has persisted through late 2021, even though aggregated mobility metrics have recovered to pre-pandemic levels. Counterfactual analyses show that while the reduction of outside activities (higher rates of staying at home) was a major factor that contributed to decreased diversity in the early stages of the pandemic, behavioral changes including lower willingness to explore new places and changes in visitation preferences further worsened the long-term diversity of encounters. Our findings suggest that the pandemic could have long-lasting negative effects on urban income diversity, and provide implications for managing the trade-off between the stringency of COVID-19 policies and the diversity of urban encounters as we move beyond the pandemic.

Modelling conformational state dynamics and its role on infection for SARS-CoV-2 Spike protein variants (preprint)

The COVID-19 pandemic has greatly affected us all, from individuals to the world economy. Whereas great advances have been achieved in record time, a lot remains to be learned about the infection mechanisms of its causative agent, the SARS-CoV-2 coronavirus. The Spike protein interacts with the human angiotensin converting enzyme 2 receptor as part of the viral entry mechanism. To do so, the receptor binding domain (RBD) of Spike needs to be in an open state conformation. Here we utilise coarse-grained normal mode analyses to model the dynamics of the SARS-CoV-2 Spike protein and the transition probabilities between open and closed conformations for the wild type, the D614G mutant as well other variants isolated experimentally. We proceed to perform several possible in silico single mutations of Spike, 17081 in total, to determine positions and specific Spike mutations that may affect the occupancy of the open and closed states. We estimate transition probabilities between the open and closed states from the calculated normal modes. Transition probabilities are employed in a Markov model to determine conformational state occupancies. Our results correctly model a shift in occupancy of the more infectious D614G strain towards higher occupancy of the open state via an increase of flexibility of the closed state and concomitant decrease of flexibility of the open state. Our results also suggest that the N501Y mutation recently observed, drastically increases the occupancy of the open state. We utilize global vibrational entropy differences to select candidate single point mutations that affect the flexibility of the open and closed states and confirm that these lead to shifts in occupancies for the most critical mutations. Among those, we observe a number of mutations on Glycine residues (404, 416, 504) and G252 in particular accepting a number of mutations. Other residues include K417, D467 and N501. This is, to our knowledge, the first use of normal mode analysis to model conformational state transitions and the effect of mutations thereon. The specific mutations of Spike identified here, while still requiring experimental validation, may guide future studies to increase our understanding of SARS-CoV-2 infection mechanisms as well as guide public health in their surveillance efforts.

Quantifying the importance and location of SARS-CoV-2 transmission events in large metropolitan areas (preprint)

Detailed characterization of SARS-CoV-2 transmission across different settings can help design less disruptive interventions. We used real-time, privacy-enhanced mobility data in the New York City and Seattle metropolitan areas to build a detailed agent-based model of SARS-CoV-2 infection to estimate the where, when, and magnitude of transmission events during the pandemics first wave. We estimate that only 18% of individuals produce most infections (80%), with about 10% of events that can be considered super-spreading events (SSEs). Although mass-gatherings present an important risk for SSEs, we estimate that the bulk of transmission occurred in smaller events in settings like workplaces, grocery stores, or food venues. The places most important for transmission change during the pandemic and are different across cities, signaling the large underlying behavioral component underneath them. Our modeling complements case studies and epidemiological data and indicates that real-time tracking of transmission events could help evaluate and define targeted mitigation policies.

Suppression of miR-155 attenuates lung cytokine storm induced by SARS-CoV-2 infection in human ACE2-transgenic mice (preprint)

Coronavirus disease 2019 (COVID-19) is a recent global pandemic. It is a deadly human viral disease, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), with a high rate of infection, morbidity and mortality. Therefore, there is a great urgency to develop new therapies to control, treat and prevent this disease. Endogenous microRNAs (miRNAs, miRs) of the viral host are key molecules in preventing viral entry and replication, and building an antiviral cellular defense. Here, we have analyzed the role of miR-155, one of the most powerful drivers of host antiviral responses including immune and inflammatory responses, in the pathogenicity of SARS-CoV-2 infection. Subsequently, we have analyzed the potency of anti-miR-155 therapy in a COVID-19 mouse model (mice transgenic for human angiotensin I- converting enzyme 2 receptor (tg-mice hACE2)). We report for the first time that miR-155 expression is elevated in COVID-19 patients. Further, our data indicate that the viral load as well as miR-155 levels are higher in male relative to female patients. Moreover, we find that the delivery of anti-miR-155 to SARS-CoV-2-infected tg-mice hACE2 effectively suppresses miR-155 expression, and leads to improved survival and clinical scores. Importantly, anti-miR-155-treated tg-mice hACE2 infected with SARS-CoV-2 not only exhibit reduced levels of pro-inflammatory cytokines, but also have increased anti-viral and anti-inflammatory cytokine responses in the lungs. Thus, our study suggests anti-miR-155 as a novel therapy for mitigating the lung cytokine storm induced by SARS-CoV-2 infection.

Molecular diversity analysis of the spike glycoprotein (S) gene from Hong Kong - China (preprint)

In this work, 37 haplotypes of spike glycoprotein of SARS-CoV-2 from Hong Kong, China, were used. All sequences were publicly available on the Platform of the National Center for Biotechnology Information (NCBI) and were analyzed for their Molecular Variance (AMOVA), haplotypic diversity, mismatch, demographic and spatial expansion, molecular diversity and time of evolutionary divergence. The results suggested that there was a low diversity among haplotypes, with very low numbers of transitions, transversions, indels-type mutations and with total absence of population expansion perceived in the neutrality tests. The estimators used in this study supported the uniformity among all the results found and confirm the evolutionary conservation of the gene, as well as its protein product, a fact that stimulates the use of therapies based on neutralizing antibodies, such as vaccines based on protein S.

Ruling the Roost: Avian Species Reclaim Urban Habitat During India's COVID Lockdown (preprint)

As we retreated to our dwellings in the "anthropause" of spring 2020, did other species return to our urban centres? We leverage an increase in balcony birdwatching, a million eBird entries, and difference-in-difference techniques to test if avian species richness rose during Indias COVID lockdown. We find that birdwatchers in Indias 20 most populous cities observed 8-17% more species during the lockdown. Most additional observations occurred after a two-week lag, signaling greater abundance instead of improved detection. More frequent appearances of at-risk, rare, and common species were recorded, implying that making our cities more wildlife friendly can protect threatened species in addition to urban specialists. Our contributions are: 1) to isolate and estimate a causal impact of reducing human activity on avian diversity, 2) to improve the external validity of this literature in rapidly urbanizing bio-diverse developing countries, and, 3) to illustrate a method separating abundance from detection in observational avian surveys.

The rapid, massive infection of the scientific literature and authors by COVID-19 (preprint)

ImportanceCOVID-19 is a major global crisis and the scientific community has been mobilized to deal with this crisis. ObjectiveTo estimate the extent to which the scientific workforce in different fields has been engaged publishing papers relative to the COVID-19 pandemic. Design, setting, and participantsWe evaluated Scopus (data cut, December 1, 2020) for all indexed published papers and preprints relevant to COVID-19. We mapped this COVID-19 literature in terms of its authors across 174 subfields of science according to the Science Metrix classification. We also evaluated the extent to which the most influential scientists across science (based on a composite citation indicator) had published COVID-19-related research. Finally, we assessed the features of authors who published the highest number of COVID-19 publications and of those with the highest impact in the COVID-19 field based on the composite citation indicator limited to COVID-19 publications. Main outcomes and measuresPublishing scientists (authors) and their published papers and citation impact. Results84,180 indexed publications were relevant to COVID-19 including 322,279 unique authors. The highest rates of COVID-19 publications were seen for authors classified in Public Health and in Clinical Medicine, where 11.3% (6,388/56,516) and 11.1% (92,570/833,060) of authors, respectively, had published on COVID-19. Almost all (173/174) subfields (except for Automobile Design & Engineering) had some authors publishing on COVID-19. Among active scientists at the top 2% of citation impact, 15,803 (13.3%) had published on COVID-19 in their publications in the first 11 months of 2020. The rates were the highest in the fields of Clinical Medicine (27.7%) and Public Health (26.8%). In 83 of the 174 subfields of science, at least one in ten active, influential authors in that field had authored something on COVID-19. 65 authors had already at least 30 (and up to 133) COVID-19 publications each. Among the 300 authors with the highest composite citation indicator for COVID-19 publications, 26 were journalists or editors publishing news stories or editorials in prestigious journals; most common countries for the remaining were China (n=77), USA (n=66), UK (n=27), and Italy (n=20). Conclusions and relevanceThe scientific literature and publishing scientists have been rapidly and massively infected by COVID-19 creating opportunities and challenges. There is evidence for hyper-prolific productivity.

Effect of COVID-19 response policies on walking behavior in US cities (preprint)

The COVID-19 pandemic has caused mass disruption to our daily lives. Mobility restrictions implemented to reduce the spread of COVID-19 have impacted walking behavior, but the magnitude and spatio-temporal aspects of these changes have yet to be explored. Walking is the most common form of physical activity and non-motorized transport, and so has an important role in our health and economy. Understanding how COVID-19 response measures have affected walking behavior of populations and distinct subgroups is paramount to help devise strategies to prevent the potential health and societal impacts of declining walking levels. In this study, we integrated mobility data from mobile devices and area-level data to study the walking patterns of 1.62 million anonymous users in 10 metropolitan areas in the United States (US). The data covers the period from mid-February 2020 (pre-lockdown) to late June 2020 (easing of lockdown restrictions). We detected when users were walking, measured distance walked and time of the walk, and classified each walk as recreational or utilitarian. Our results revealed dramatic declines in walking, especially utilitarian walking, while recreational walking has recovered and even surpassed the levels before the pandemic. However, our findings demonstrated important social patterns, widening existing inequalities in walking behavior across socio-demographic groups. COVID-19 response measures had a larger impact on walking behavior for those from low-income areas, of low education, and high use of public transportation. Provision of equal opportunities to support walking could be key to opening up our society and the economy.

Modeling the impact of social distancing, testing, contact tracing and household quarantine on second-wave scenarios of the COVID-19 epidemic (preprint)

The new coronavirus disease 2019 (COVID-19) has required the implementation of severe mobility restrictions and social distancing measures worldwide. While these measures have been proven effective in abating the epidemic in several countries, it is important to estimate the effectiveness of testing and tracing strategies to avoid a potential second wave of the COVID-19 epidemic. We integrate highly detailed (anonymized, privacy-enhanced) mobility data from mobile devices, with census and demographic data to build a detailed agent-based model to describe the transmission dynamics of SARS-CoV-2 in the Boston metropolitan area. We find that enforcing strict social distancing followed by a policy based on a robust level of testing, contact-tracing and household quarantine, could keep the disease at a level that does not exceed the capacity of the health care system. Assuming the identification of 50% of the symptomatic infections, and the tracing of 40% of their contacts and households, which corresponds to about 9% of individuals quarantined, the ensuing reduction in transmission allows the reopening of economic activities while attaining a manageable impact on the health care system. Our results show that a response system based on enhanced testing and contact tracing can play a major role in relaxing social distancing interventions in the absence of herd immunity against SARS-CoV-2.