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2.
Disaster Med Public Health Prep ; : 1-5, 2021 Apr 20.
Article in English | MEDLINE | ID: covidwho-1707669

ABSTRACT

A research initiative was launched during the initial coronavirus disease (COVID-19) outbreak by 3 New York metropolitan area institutions. Collaborators recruited community members and patients from previous research studies to examine COVID-19 experiences and mental health symptoms through self-report surveys. The current report descriptively presents findings from the initial survey characterized by both community and clinical cohorts, and discusses challenges encountered with rapid implementation. The clinical cohort exhibited higher rates of symptoms of mental health difficulties (depression, anxiety, and posttraumatic stress disorder [PTSD]) as compared to the community cohort. COVID-19 positivity rates were similar among both groups and lower than the national average. While both groups reported low rates of job loss, community members reported higher rates of financial difficulty resulting from the pandemic. Findings indicate the need for further collaborative research on the mental health impact of COVID-19.

3.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-317388

ABSTRACT

The aim of this ecological study was to assess the area-level relationship between cumulative death rate for COVID-19 and historic influenza vaccination uptake in the New York City population. Predictors of COVID-19 death included self-reported flu vaccination in 2018, as well as four CDC-defined risk factors of severe COVID-19 infection available at the ecological level, which were diabetes, asthma, BMI 30-100 (mg/k2) and hypertension, in addition to race and age (65+ years). Zip codes with a higher prevalence of influenza vaccination had lower rates of COVID-19 mortality, inciting the need to further explore the relationship between influenza vaccination uptake and COVID-19 mortality at the individual level.Funding Statement: This research received no external funding.Declaration of Interests: The authors declare that they have no competing interests.Ethics Approval Statement: Not applicable.

4.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-321880

ABSTRACT

Background: Clinical biomarkers that accurately predict mortality are needed for the effective management of patients with severe COVID-19 illness. Here, we determine whether changes in D-dimer levels after anticoagulation are predictive of in-hospital mortality.Methods: Adult patients hospitalized for severe COVID-19 who received therapeutic anticoagulation for thromboprophylaxis were identified from a large COVID-19 database of the Mount Sinai Health System in New York City. We studied the ability post-anticoagulant D-dimer levels to predict in-hospital mortality, while taking into consideration 65 other clinically important covariates including patient demographics, comorbidities, vital signs and laboratory tests at baseline.Findings: 1835 adult patients with PCR-confirmed COVID-19 who received therapeutic anticoagulation during hospitalization were included. Overall, 26% of patients died in the hospital. Significantly different in-hospital mortality rates were observed in patient groups based on the mean D-dimer levels and its trend following anticoagulation initiation: 49% for the high mean-increase trend (HI) group;27% for the high-decrease (HD) group;21% for the low-increase (LI) group;and 9% for the low-decrease (LD) group (p<0·001). Using penalized logistic regression models to simultaneously analyze 67 clinical variables, the HI (adjusted odds ratios [ORadj]: 6·58, 95% CI 3·81-11·16), LI (ORadj: 4·06, 95% CI 2·23-7·38) and HD (ORadj: 2·37;95% CI 1·37-4·09) post-anticoagulant D-dimer groups had the highest odds for in-hospital mortality when compared to the LD group.Interpretation: D-dimer levels and its trend following anticoagulation are highly predictive of in-hospital mortality and may help guide resource allocation and identify candidates for studies of emerging treatments for severe COVID-19.Funding: NoneDeclaration of Interests: Authors have no competing interests to declare.Ethics Approval Statement: The Icahn School of Medicine at Mount Sinai Institutional Review Boardconsidered the study exempt.

5.
J Med Virol ; 2021 Sep 30.
Article in English | MEDLINE | ID: covidwho-1442018

ABSTRACT

Given recent downward trends in daily rates of COVID-19 vaccinations, it is important to reassess strategies to reach those most vulnerable. The success and efficacy of vaccination campaigns for other respiratory illnesses, such as influenza, may help inform messaging around COVID-19 vaccinations. This cross-sectional study examines the individual-level factors associated with, and the spatial distribution of, predictors of COVID-19 severity, and uptake of influenza and hepatitis B (as a negative control) vaccines across NYC. Data were obtained from the 2018 Community Health Survey (CHS), including self-reported influenza and hepatitis B vaccine uptake, diabetes, asthma, hypertension, body mass index (BMI), age, race/ethnicity, educational attainment, borough, and United Hospital Fund (UHF) neighborhood of residence. A CDC-defined COVID-19 severity risk score was created with variables available in the CHS, including diabetes, asthma, hypertension, BMI ≥ 30 kg/m2 , and age ≥65 years old. After adjustment, there was a significant positive association between COVID-19 severity risk score and influenza vaccine uptake (1: ORadj = 1.49, 95% CI 1.28-1.73; 2: ORadj = 1.99; 95% CI: 1.65-2.41; 3+: ORadj = 2.89; 95% CI: 2.32-3.60, compared to 0). Hepatitis B vaccine uptake was significantly inversely associated with COVID-19 severity risk score (1: ORadj = 0.67; 95% CI: 0.57-0.79; 2: ORadj = 0.54; 95% CI: 0.44-0.66; 3+: ORadj = 0.45; 95% CI: 0.36-0.56, compared to 0). The influenza vaccination campaign template is effective at reaching those most at risk for serious COVID-19 and, if implemented, may help reach the most vulnerable that have not yet been vaccinated against COVID-19.

6.
BMC Public Health ; 21(1): 1717, 2021 09 21.
Article in English | MEDLINE | ID: covidwho-1435238

ABSTRACT

BACKGROUND: Given the interplay between race and comorbidities on COVID-19 morbidity and mortality, it is vital that testing be performed in areas of greatest need, where more severe cases are expected. The goal of this analysis is to evaluate COVID-19 testing data in NYC relative to risk factors for COVID-19 disease severity and demographic characteristics of NYC neighborhoods. METHODS: COVID-19 testing and the racial/ethnic composition of NYC Zip Code Tabulation Areas (ZCTA) were obtained from the NYC Coronavirus data repository and the American Community Survey, respectively. The prevalence of neighborhood-level risk factors for COVID-19 severity according to the Centers for Disease Control and Prevention criteria for risk of severe illness and complications from COVID-19 were used to create a ZCTA-level risk index. Poisson regressions were performed to study the ratio of total tests relative to the total ZCTA population and the proportion of positive tests relative to the total tests performed over time. RESULTS: From March 2nd-April 6th, the total tests/population (%) was positively associated with the proportion of white residents (IRRadj: 1.0003, 95% CI: 1.0003-1.0004) and the COVID risk index (IRRadj: 1.038, 95% CI: 1.029-1.046). The risk index (IRRadj: 1.017, 95% CI: 0.939-1.101) was not associated with total tests performed from April 6th-May 12th, and inversely associated from May 12th-July 6th (IRRadj: 0.862, 95% CI: 0.814-0.913). From March 2nd-April 6th the COVID risk index was not statistically associated (IRRadj: 1.010, 95% CI: 0.987-1.034) with positive tests/total tests. From April 6th-May 12th, the COVID risk index was positively associated (IRRadj: 1.031, 95% CI: 1.002-1.060), while from May 12th-July 6th, the risk index was inversely associated (IRRadj: 1.135, 95% CI: 1.042-1.237) with positivity. CONCLUSIONS: Testing in NYC has suffered from the lack of availability in high-risk populations, and was initially limited as a diagnostic tool for those with severe symptoms, which were mostly concentrated in areas where vulnerable residents live. Subsequent time periods of testing were not targeted in areas according to COVID-19 disease risk, as these areas still experience more positive tests.


Subject(s)
COVID-19 , COVID-19 Testing , Humans , New York City/epidemiology , Residence Characteristics , SARS-CoV-2
7.
J Community Health ; 47(1): 143-149, 2022 02.
Article in English | MEDLINE | ID: covidwho-1401051

ABSTRACT

To understand how observed COVID-19 diagnostic testing disparities across New York City (NYC) have impacted infection rates and COVID-19 spread, we examined neighborhood-level factors associated with, and the spatial distribution of, antibody test and infection rates, and compared changes over time by NYC ZIP code tabulation area (ZCTA). Data were obtained from 2019 American Community Survey 5-year estimates to create an SES index by ZCTA. Other predictors obtained from 2018 census data were the proportions of white residents, Hispanic residents and residents ≥ 65 years old. Multivariable Poisson regressions were performed to assess the rate of change for antibody testing and positivity, and to assess the independent associations with SES, race and age. Results: There was a significant association between the rate of antibody tests and SES quartiles (Q1: ßadj = 0.04, Q2: ßadj = 0.03 and Q3: ßadj = - 0.03, compared to Q4), and the proportion of residents who are white (ßadj = 0.004, p < .0001), Hispanic   (ßadj = 0.001, p < .0001), and ≥ 65 years (ßadj = 0.01, p < .0001). Total number of positive antibody tests was significantly inversely associated with SES quartile (Q1: ßadj = 0.50, Q2: ßadj = 0.48 and Q3: ßadj = 0.29, compared to Q4), and proportion of white residents (ß = - 0.001, p < .0001) and ≥ 65 years (ß = - 0.02, p < .0001), and significantly positively associated with proportion of Hispanic residents (ß = 0.003, p < .0001). There are disparities in antibody testing and positivity, reflecting disproportionate impacts and undercounts of COVID-19 infection across NYC ZCTAs. Future public health response should increase testing in these vulnerable areas to diminish infection spread.


Subject(s)
COVID-19 , Aged , COVID-19 Testing , Humans , New York City/epidemiology , SARS-CoV-2 , Socioeconomic Factors
8.
Blood ; 136(Supplement 1):41-42, 2020.
Article in English | PMC | ID: covidwho-1339048

ABSTRACT

IntroductionCoronavirus 2019 (COVID-19) has led to an unprecedented global pandemic. Given highly variable clinical presentations, readily obtainable predictive biomarkers are urgently needed to identify patients are risk of severe disease and death. Anemia is commonly present in hospitalized patients yet its prognostic potential in COVID-19 remains largely unexplored.MethodsWe evaluated all adult patients at a large tertiary health system during the month of March 2020 who were diagnosed with COVID-19, excluding patients without a complete blood count (CBC) obtained at the time of diagnosis. Anemia was defined by as a hemoglobin (hgb) <13 g/dL for males and <12 g/dL for females. Chi-squared and Wilcoxon rank sum tests were performed to assess difference in anemia, demographics, and other laboratory values according to mortality. Multivariable logistic regression was performed to assess independent predictors of outcomes including mechanical ventilation and death. An adaption of the receiver operating characteristic (ROC) curve was conducted to identify optimal cut points of hemoglobin levels that best predict mechanical ventilation and mortality.ResultsDuring the study period, 3,777 adults with COVID-19 were identified, of which 2,439 had a CBC. 861 patients (35.4%) met criteria for anemia, as shown in the Table, and they were significantly older and had a higher comorbidity burden. Mechanical ventilation was required in 531 patients (14.1%) and 629 patients (17%) were deceased. On multivariable logistic regression adjusting for age, sex, race, and Charlson comorbidity index (CCI), anemia was significantly associated with higher risk of mortality (adjusted OR [ORadj]: 1.45, 95% Confidence Interval [CI]: 1.17-1.80) and need for mechanical ventilation (ORadj: 2.03, 95% CI: 1.64-2.51).For mortality, the optimal cutoff of hgb by ROC curve was 12.5 g/dL in males and 12.1 g/dL in females. These hgb cutoff for mechanical ventilation were 13.5 g/dL for male and 11.0 g/dL for females. When applying these cutoffs to the definition of anemia, a stronger association was observed between anemia and mortality (ORadj 1.68, 95% CI: 1.22-1.93) and need for mechanical ventilation (ORadj 2.09, 95% CI: 1.68-2.61).We also performed a subgroup analysis of patients with a normal hemoglobin to assess the independent predictive value of red blood cell distribution width (RDW). Of the 1578 patients without anemia, 139 (8.8%) had an elevated RDW (>15%). On multivariable logistic regression adjusting for age, sex, race, and CCI, an elevated RDW was statistically associated with need for mechanical ventilation (ORadj: 1.74, CI: 1.12-2.69) and mortality (ORadj: 1.93, CI: 1.26-2.95).ConclusionsIn this single health system study, anemia was significantly associated with both need for mechanical ventilation and mortality in patients with COVID-19. Based on ROC analysis, the ideal cutoff for considering anemias a significant contributor to these outcomes was just below normal, suggesting that even mild anemia in the context of COVID-19 is an independent predictor of mortality and need for intubation. In addition, an elevated RDW in non-anemic patients was also independently predictive of mortality. Our analysis is limited by inability to determine the chronicity of baseline anemia. Given that a CBC is almost universally obtained on patients with COVID-19, this study identifies a readily accessible predictive biomarker to inform risk adaptive clinical care. The mechanisms by which anemia impacts outcomes in COVID-19 are likely to be multiple in nature and require further investigation.Table

9.
BMC Public Health ; 21(1): 1452, 2021 07 24.
Article in English | MEDLINE | ID: covidwho-1322931

ABSTRACT

BACKGROUND: New York City (NYC) was the epicenter of the COVID-19 pandemic, and is home to underserved populations with higher prevalence of chronic conditions that put them in danger of more serious infection. Little is known about how the presence of chronic risk factors correlates with mortality at the population level. Here we determine the relationship between these factors and COVD-19 mortality in NYC. METHODS: A cross-sectional study of mortality data obtained from the NYC Coronavirus data repository (03/02/2020-07/06/2020) and the prevalence of neighborhood-level risk factors for COVID-19 severity was performed. A risk index was created based on the CDC criteria for risk of severe illness and complications from COVID-19, and stepwise linear regression was implemented to predict the COVID-19 mortality rate across NYC zip code tabulation areas (ZCTAs) utilizing the risk index, median age, socioeconomic status index, and the racial and Hispanic composition at the ZCTA-level as predictors. RESULTS: The COVID-19 death rate per 100,000 persons significantly decreased with the increasing proportion of white residents (ßadj = - 0.91, SE = 0.31, p = 0.0037), while the increasing proportion of Hispanic residents (ßadj = 0.90, SE = 0.38, p = 0.0200), median age (ßadj = 3.45, SE = 1.74, p = 0.0489), and COVID-19 severity risk index (ßadj = 5.84, SE = 0.82, p <  0.001) were statistically significantly positively associated with death rates. CONCLUSIONS: Disparities in COVID-19 mortality exist across NYC and these vulnerable areas require increased attention, including repeated and widespread testing, to minimize the threat of serious illness and mortality.


Subject(s)
COVID-19 , Pandemics , Cross-Sectional Studies , Humans , New York City/epidemiology , SARS-CoV-2 , Socioeconomic Factors
10.
ERJ Open Res ; 7(3)2021 Jul.
Article in English | MEDLINE | ID: covidwho-1299322

ABSTRACT

Clinical biomarkers that accurately predict mortality are needed for the effective management of patients with severe coronavirus disease 2019 (COVID-19) illness. In this study, we determine whether changes in D-dimer levels after anticoagulation are independently predictive of in-hospital mortality. Adult patients hospitalised for severe COVID-19 who received therapeutic anticoagulation for thromboprophylaxis were identified from a large COVID-19 database of the Mount Sinai Health System in New York City (NY, USA). We studied the ability of post-anticoagulant D-dimer levels to predict in-hospital mortality, while taking into consideration 65 other clinically important covariates including patient demographics, comorbidities, vital signs and several laboratory tests. 1835 adult patients with PCR-confirmed COVID-19 who received therapeutic anticoagulation during hospitalisation were included. Overall, 26% of patients died in the hospital. Significantly different in-hospital mortality rates were observed in patient groups based on mean D-dimer levels and trend following anticoagulation: 49% for the high mean-increase trend group; 27% for the high-decrease group; 21% for the low-increase group; and 9% for the low-decrease group (p<0.001). Using penalised logistic regression models to simultaneously analyse 67 clinical variables, the high increase (adjusted odds ratios (ORadj): 6.58, 95% CI 3.81-11.16), low increase (ORadj: 4.06, 95% CI 2.23-7.38) and high decrease (ORadj: 2.37; 95% CI 1.37-4.09) D-dimer groups (reference: low decrease group) had the highest odds for in-hospital mortality among all clinical features. Changes in D-dimer levels and trend following anticoagulation are highly predictive of in-hospital mortality and may help guide resource allocation and future studies of emerging treatments for severe COVID-19.

11.
J Community Health ; 46(6): 1177-1182, 2021 Dec.
Article in English | MEDLINE | ID: covidwho-1242807

ABSTRACT

This study aimed to assess the coronavirus disease (COVID-19) school-related information New York City residents sought through the 311 Call Center. July to November inquiries were downloaded from the NYC Open Data website for 2018-2020. Calls were categorized as related to "Schools", "Access", "Food", "Hospitals", "Transportation", and "Unemployment". Overall call types, and among school-related calls, detailed call types, were compared over the years, using chi-squared tests. School-related inquiries increased by 71% from 2018 to 2020. During 2020, the most common (49%, n = 22,471) call description was "Coronavirus and Schools", encompassing calls about learning options, safety, and resources. Spikes in these calls corresponded to official announcements, including those about Fall reopening plans (August 31: n = 678; September 1: n = 624) and schedules and staffing (September 16th: n = 1043; September 17th: n = 713), and after the start of in-person learning (September 21: n = 680). This study demonstrates that as government officials updated NYC schooling plans for Fall 2020, there were increased concerns among NYC residents. Future COVID-19 schooling changes need to be conveyed clearly and disseminated effectively in order to avoid confusion about NYC's pandemic learning strategy and to address health and safety concerns.


Subject(s)
COVID-19 , Humans , New York City/epidemiology , Pandemics , SARS-CoV-2 , Schools
12.
EJHaem ; 2021 May 06.
Article in English | MEDLINE | ID: covidwho-1222636

ABSTRACT

The coronavirus disease 2019 (COVID-19) caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has led to an unprecedented international health crisis. COVID-19 clinical presentations cover a wide range from asymptomatic to severe illness and death. Given the limited therapeutic resources and unexpected clinical features of the disease, readily accessible predictive biomarkers are urgently needed to improve patient care and management. We asked the degree to which anemia may influence the outcome of patients with COVID-19. To this end, we identified 3777 patients who were positively diagnosed with COVID-19 between March 1 and April 1 2020 in New York City. We evaluated 2,562 patients with available red blood cell, hemoglobin, and related laboratory values. Multivariable cox proportional hazards regression showed that anemia was a significant independent predictor of mortality (hazard ratio (HR): 1.26, 95% Confidence Interval [CI]: 1.06-1.51), independent of age, sex, and comorbidities. There was a direct correlation between the degree of anemia and the risk of mortality when hemoglobin was treated as a continuous variable (HRadj 1.05; [CI]: 1.01-1.09). The hemoglobin level that was maximally predictive of mortality, was 11.5 g/dL in males and 11.8 g/dL in females. These findings identify a routinely measured biomarker that is predictive of disease outcomes and will aid in refining clinical care algorithms and optimize resource allocation. Mechanisms of impacts of anemia on COVID-19 outcome are likely to be multiple in nature and require further investigation.

17.
JNCI Cancer Spectr ; 5(1): Pkaa085, 2021 02.
Article in English | MEDLINE | ID: covidwho-900441

ABSTRACT

Background: Complications in cancer patients with coronavirus disease 2019 (COVID-19) have not been examined. This analysis aimed to compare characteristics of COVID-19 patients with and without cancer and assess whether cancer is associated with COVID-19 morbidity or mortality. Methods: COVID-19-positive patients with an inpatient or emergency encounter at the Mount Sinai Health System between March 1, 2020, and May 27, 2020, were included and compared across cancer status on demographics and clinical characteristics. Multivariable logistic regressions were used to model the associations of cancer with sepsis, venous thromboembolism, acute kidney injury, intensive care unit admission, and all-cause mortality. Results: There were 5556 COVID-19-positive patients included, 421 (7.6%) with cancer (325 solid, 96 nonsolid). Those with cancer were statistically significantly older, more likely to be non-Hispanic Black and to be admitted to the hospital during their encounter, and had more comorbidities than noncancer COVID-19 patients. Cancer patients were statistically significantly more likely to develop sepsis (adjusted odds ratio [ORadj] = 1.31, 95% confidence interval [CI] = 1.06 to 1.61) and venous thromboembolism (ORadj = 1.77, 95% CI = 1.01 to 3.09); there was no statistically significant difference in acute kidney injury (ORadj = 1.10, 95% CI = 0.87 to 1.39), intensive care unit admissions (ORadj = 1.04, 95% CI = 0.80 to 1.34), or mortality (ORadj = 1.02, 95% CI = 0.81 to 1.29). Conclusions: COVID-19 patients with cancer may have a higher risk for adverse outcomes. Although there was no statistically significant difference in mortality, COVID-19 patients with cancer have statistically significantly higher risk of thromboembolism and sepsis. Further research is warranted into the potential effects of cancer treatments on inflammatory and immune responses to COVID-19 and on the efficacy of anticoagulant therapy in these patients.


Subject(s)
COVID-19/complications , Hospitalization/statistics & numerical data , Intensive Care Units/statistics & numerical data , Neoplasms/complications , Acute Kidney Injury/complications , Aged , COVID-19/epidemiology , COVID-19/virology , Female , Hospital Mortality , Humans , Logistic Models , Male , Middle Aged , Multivariate Analysis , Neoplasms/mortality , Pandemics , Retrospective Studies , Risk Factors , SARS-CoV-2/physiology , Venous Thromboembolism/complications
18.
Clinical Cancer Research ; 26(18), 2020.
Article in English | Web of Science | ID: covidwho-839337
19.
Am J Prev Med ; 59(3): 326-332, 2020 09.
Article in English | MEDLINE | ID: covidwho-614429

ABSTRACT

INTRODUCTION: Existing socioeconomic and racial disparities in healthcare access in New York City have likely impacted the public health response to COVID-19. An ecological study was performed to determine the spatial distribution of COVID-19 testing by ZIP code Tabulation Area and investigate if testing was associated with race or SES. METHODS: Data were obtained from the New York City coronavirus data repository and 2018 American Community Survey 5-year estimates. A combined index of SES was created using principal component analysis and incorporated household income, gross rent, poverty, education, working class status, unemployment, and occupants per room. Multivariable Poisson regressions were performed to predict the number of total tests and the ratio of positive tests to total tests performed, using the SES index, racial composition, and Hispanic composition as predictors. RESULTS: The number of total tests significantly increased with the increasing proportion of white residents (ß=0.004, SE=0.001, p=0.0032) but not with increasing Hispanic composition or SES index score. The ratio of positive tests to total tests significantly decreased with the increasing proportion of white residents in the ZIP code Tabulation Area (ß= -0.003, SE=0.000 6, p<0.001) and with increasing SES index score (ß= -0.001 6, SE=0.0007, p=0.0159). CONCLUSIONS: In New York City, COVID-19 testing has not been proportional to need; existing socioeconomic and racial disparities in healthcare access have likely impacted public health response. There is urgent need for widespread testing and public health outreach for the most vulnerable communities in New York City.


Subject(s)
Clinical Laboratory Techniques , Coronavirus Infections/epidemiology , Health Services Accessibility , Healthcare Disparities , Pneumonia, Viral/epidemiology , COVID-19 , COVID-19 Testing , Coronavirus Infections/diagnosis , Coronavirus Infections/ethnology , /statistics & numerical data , Humans , New York City/epidemiology , Pandemics , Pneumonia, Viral/diagnosis , Pneumonia, Viral/ethnology , Poverty , Socioeconomic Factors , /statistics & numerical data
20.
J Public Health (Oxf) ; 42(3): 448-450, 2020 Aug 18.
Article in English | MEDLINE | ID: covidwho-599278

ABSTRACT

In the midst of widespread community transmission of coronavirus disease 2019 (COVID-19) in New York, residents have sought information about COVID-19. We analyzed trends in New York State (NYS) and New York City (NYC) data to quantify the extent of COVID-19-related queries. Data on the number of 311 calls in NYC, Google Trend data on the search term 'Coronavirus' and information about trends in COVID-19 cases in NYS and the USA were compiled from multiple sources. There were 1228 994 total calls to 311 between 22 January 2020 and 22 April 2020, with 50 845 calls specific to COVID-19 in the study period. The proportion of 311 calls related to COVID-19 increased over time, while the 'interest over time' of the search term 'Coronavirus' has exponentially increased since the end of February 2020. It is vital that public health officials provide clear and up-to-date information about protective measures and crucial communications to respond to information-seeking behavior across NYC.


Subject(s)
Coronavirus Infections/epidemiology , Information Seeking Behavior , Pneumonia, Viral/epidemiology , Public Health/statistics & numerical data , Public Health/trends , Betacoronavirus , COVID-19 , Forecasting , Humans , New York , New York City/epidemiology , Pandemics , Population Surveillance , SARS-CoV-2
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