Dynamic coupling between the COVID epidemic timeline and the behavioral response to PAUSE in New York State counties

By many expert opinions, the COVID 19 epidemic is still much in its developing stages. While awaiting for effective clinical answers for the outbreak, the best approach remains that of social distancing. This raises a few crucial questions related to the extent to which social distancing measures were (1) well timed, (2) necessary and (3) efficient. By investigating correlations between epidemic measures such as daily infection rate, and social mobility measures (as reported by Apple and Google), our study aims to establish whether data identifies social distancing measures as a primary player in controlling the outbreak in New York State.

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Dynamic coupling between the COVID-19 epidemic timeline and the behavioral response to PAUSE in New York State counties

PLoS ONE ◽

10.1371/journal.pone.0255236 ◽

2021 ◽

Vol 16 (8) ◽

pp. e0255236

Author(s):

Anca Rǎdulescu ◽

Shelah Ballard ◽

Kaitlyn Gonzalez ◽

Johnathan Linton

Keyword(s):

New York ◽

Behavioral Response ◽

New York State ◽

The State ◽

Social Response ◽

Epidemic Outbreak ◽

Dynamic Coupling ◽

York State ◽

Epidemic Dynamics ◽

Epidemic Wave

Behavioral epidemiology suggests that there is a tight dynamic coupling between the timeline of an epidemic outbreak, and the social response in the affected population (with a typical course involving physical distancing between individuals, avoidance of large gatherings, wearing masks, etc). We study the bidirectional coupling between the epidemic dynamics of COVID-19 and the population social response in the state of New York, between March 1, 2020 (which marks the first confirmed positive diagnosis in the state), until June 20, 2020. This window captures the first state-wide epidemic wave, which peaked to over 11,000 confirmed cases daily in April (making New York one of the US states most severely affected by this first wave), and subsided by the start of June to a count of consistently under 1,500 confirmed cases per day (suggesting temporary state-wide control of the epidemic). In response to the surge in cases, social distancing measures were gradually introduced over two weeks in March, culminating with the PAUSE directive on March 22nd, which mandated statewide shutdown of all nonessential activity. The mandates were then gradually relaxed in stages throughout summer, based on how epidemic benchmarks were met in various New York regions. In our study, we aim to examine on one hand, whether different counties exhibited different responses to the PAUSE centralized measures depending on their epidemic situation immediately preceding PAUSE. On the other hand, we explore whether these different county-wide responses may have contributed in turn to modulating the counties’ epidemic timelines. We used the public domain to extract county-wise epidemic measures (such as cumulative and daily incidence of COVID-19), and social mobility measures for different modalities (driving, walking, public transit) and to different destinations. Our correlation analyses between the epidemic and the mobility time series found significant correlations between the size of the epidemic and the degree of mobility drop after PAUSE, as well as between the mobility comeback patterns and the epidemic recovery timeline. In line with existing literature on the role of the population behavioral response during an epidemic outbreak, our results support the potential importance of the PAUSE measures to the control of the first epidemic wave in New York State.

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COVID-19 in New York state: Effects of demographics and air quality on infection and fatality

10.1101/2021.02.22.21252262 ◽

2021 ◽

Author(s):

Sumona Mondal ◽

Chaya Chaipitakporn ◽

Vijay Kumar ◽

Bridget Wangler ◽

Supraja Gurajala ◽

...

Keyword(s):

New York ◽

Air Quality ◽

Infection Rate ◽

Environmental Variables ◽

Strong Association ◽

New York State ◽

Significant Risk ◽

Mitigation Strategies ◽

York State ◽

Direct Location

ABSTRACTThe coronavirus disease 2019 (COVID-19) has had a global impact that has been unevenly distributed amongst and, even within countries. Multiple demographic and environmental factors have been associated with the risk of COVID-19 spread and fatality, including age, gender, ethnicity, poverty, and air quality among others. However, specific contributions of these factors are yet to be understood. Here, we attempted to explain the variability in infection, death, and fatality rates by understanding the contributions of a few selected factors. We compared the incidence of COVID-19 in New York State (NYS) counties during the first wave of infection and analyzed how different demographic and environmental variables associate with the variation observed across the counties. We observed that the two important COVID-19 metrics of infection rates and death rates to be well correlated, and both metrics being highest in counties located near New York City, considered one of the epicenters of the infection in the US. In contrast, disease fatality was found to be highest in a different set of counties despite registering a low infection rate. To investigate this apparent discrepancy, we divided the counties into three clusters based on COVID-19 infection, death rate, or fatality, and compared the differences in the demographic and environmental variables such as ethnicity, age, population density, poverty, temperature, and air quality in each of these clusters. Furthermore, a regression model built on this data reveals PM2.5 and distance from the epicenter are significant risk factors for high infection rate, while disease fatality has a strong association with age and PM2.5. Our results demonstrate, for the NYS, distinct contributions of old age, PM2.5, ethnicity these factors to the overall COVID-19 burden and highlight the detrimental impact of poor air quality. These results could help design and direct location-specific control and mitigation strategies.

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Mathematical assessment of the impact of non-pharmaceutical interventions on curtailing the 2019 novel Coronavirus

10.1101/2020.04.15.20066480 ◽

2020 ◽

Cited By ~ 5

Author(s):

Calistus N. Ngonghala ◽

Enahoro Iboi ◽

Steffen Eikenberry ◽

Matthew Scotch ◽

Chandini Raina MacIntyre ◽

...

Keyword(s):

New York ◽

New York State ◽

Effective Control ◽

Asymptotically Stable ◽

York State ◽

Social Distancing ◽

The Us ◽

Novel Coronavirus ◽

The Impact ◽

Pharmaceutical Interventions

AbstractA pandemic of a novel Coronavirus emerged in December of 2019 (COVID-19), causing devastating public health impact across the world. In the absence of a safe and effective vaccine or antivirals, strategies for controlling and mitigating the burden of the pandemic are focused on non-pharmaceutical interventions, such as social-distancing, contact-tracing, quarantine, isolation and the use of face-masks in public. We develop a new mathematical model for assessing the population-level impact of the aforementioned control and mitigation strategies. Rigorous analysis of the model shows that the disease-free equilibrium is locally-asymptotically stable if a certain epidemiological threshold, known as the reproduction number (denoted by ℛc), is less than unity. This equilibrium is globally-asymptotically stable, for a special case of the model where quarantined-susceptible individuals do not acquire COVID-19 infection during quarantine, when ℛc is less than unity. The epidemiological consequence of this theoretical result is that, the community-wide implementation of control interventions that can bring (and maintain) ℛc to a value less than unity will lead to the effective control (or elimination) of COVID-19 in the community. Simulations of the model, using data relevant to COVID-19 transmission dynamics in the US state of New York and the entire US, show that the pandemic burden will peak in mid and late April, respectively. The worst-case scenario projections for cumulative mortality (based on baseline levels of interventions) are 105, 100 for New York state and 164, 000 for the entire US by the end of the pandemic. These numbers dramatically decreased by 80% and 64%, respectively, if adherence to strict social-distancing measures is improved and maintained until the end of May or June. The duration and timing of the relaxation or termination of the strict social-distancing measures are crucially-important in determining the future trajectory of the COVID-19 pandemic. This study shows that early termination of the strict social-distancing measures could trigger a devastating second wave with burden similar to those projected before the onset of the strict social-distance measures were implemented. The use of efficacious face-masks (such as surgical masks, with estimated efficacy ≥ 70%) in public could lead to the elimination of the pandemic if at least 70% of the residents of New York state use such masks in public consistently (nationwide, a compliance of at least 80% will be required using such masks). The use of low efficacy masks, such as cloth masks (of estimated efficacy less than 30%), could also lead to significant reduction of COVID-19 burden (albeit, they are not able to lead to elimination). Combining low efficacy masks with improved levels of the other anti-COVID-19 intervention strategies can lead to the elimination of the pandemic. This study emphasizes the important role social-distancing plays in curtailing the burden of COVID-19. Increases in the adherence level of social-distancing protocols result in dramatic reduction of the burden of the pandemic, and the timely implementation of social-distancing measures in numerous states of the US may have averted a catastrophic outcome with respect to the burden of COVID-19. Using face-masks in public (including the low efficacy cloth masks) is very useful in minimizing community transmission and burden of COVID-19, provided their coverage level is high. The masks coverage needed to eliminate COVID-19 decreases if the masks-based intervention is combined with the strict social-distancing strategy.

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