Application of neighborhood-scale wastewater-based epidemiology in low COVID-19 incidence situations.

Wastewater-based epidemiology (WBE), an emerging approach for community-wide COVID-19 surveillance, was primarily characterized at large sewersheds such as wastewater treatment plants serving a large population. Although informed public health measures can be better implemented for a small population, WBE for neighborhood-scale sewersheds is less studied and not fully understood. This study applied WBE to seven neighborhood-scale sewersheds (average population of 1471) from January to November 2021. Community testing data showed an average of 0.004 % incidence rate in these sewersheds (97 % of monitoring periods reported two or fewer daily infections). In 92 % of sewage samples, SARS-CoV-2 N gene fragments were below the limit of quantification. We statistically determined 10-2.6 as the threshold of the SARS-CoV-2 N gene concentration normalized to pepper mild mottle virus (N/PMMOV) to alert high COVID-19 incidence rate in the studied sewershed. This threshold of N/PMMOV identified neighborhood-scale outbreaks (COVID-19 incidence rate higher than 0.2 %) with 82 % sensitivity and 51 % specificity. Importantly, neighborhood-scale WBE can discern local outbreaks that would not otherwise be identified by city-scale WBE. Our findings suggest that neighborhood-scale WBE is an effective community-wide disease surveillance tool when COVID-19 incidence is maintained at a low level.

Identification of Thresholds on Population Density for Understanding Transmission of COVID-19.

Pathways of transmission of coronavirus (COVID-19) disease in the human population are still emerging. However, empirical observations suggest that dense human settlements are the most adversely impacted, corroborating a broad consensus that human-to-human transmission is a key mechanism for the rapid spread of this disease. Here, using logistic regression techniques, estimates of threshold levels of population density were computed corresponding to the incidence (case counts) in the human population. Regions with population densities greater than 3,000 person per square mile in the United States have about 95% likelihood to report 43,380 number of average cumulative cases of COVID-19. Since case numbers of COVID-19 dynamically changed each day until 30 November 2020, ca. 4% of US counties were at 50% or higher probability to 38,232 number of COVID-19 cases. While threshold on population density is not the sole indicator for predictability of coronavirus in human population, yet it is one of the key variables on understanding and rethinking human settlement in urban landscapes.

Identification of thresholds on population density for understanding transmission of COVID‐19

Pathways of transmission of coronavirus (COVID‐19) disease in the human population are still emerging. However, empirical observations suggest that dense human settlements are the most adversely impacted, corroborating a broad consensus that human‐to‐human transmission is a key mechanism for the rapid spread of this disease. Here, using logistic regression techniques, estimates of threshold levels of population density were computed corresponding to the incidence (case counts) in the human population. Regions with population densities greater than 3000 person per square mile in the United States have about 95% likelihood to report 43380 number of average cumulative cases of COVID‐19. Since case numbers of COVID‐19 dynamically changed each day until November 30, 2020, ca. 4% of US counties were at 50% or higher probability to 38232 number of COVID‐19 cases .. While threshold on population density is not the sole indicator for predictability of coronavirus in human population, yet it is one of the key variables on understanding and rethinking human settlement in urban landscapes. Key Points Based on data from the USA, the population density of 1192 persons per square mile represented a 50% or higher probability of more than 38000 COVID‐19 cumulative cases at county scale as of November 30,2020 About 35 counties in the USA with population density greater or equal to 3000 per square mile are at very high chances (95% or higher) of more than 43000 cumulative cases at county scale as of November 30,2020 Analysis shows the vulnerability of urban towns to respiratory infectious disease

Subclinical left ventricular dysfunction in COVID-19 recovered patients using speckle tracking echocardiography (preprint)

Objectives: Myocardial injury during active coronavirus disease-2019 (COVID-19) infection is well described however, its persistence during recovery is unclear. We assessed left ventricle (LV) global longitudinal strain (GLS) using speckle tracking echocardiography (STE) in COVID-19 recovered patients and studied its correlation with various parameters. Methods: A total of 134 subjects within 30-45 days post recovery from COVID-19 infection and normal LV ejection fraction were enrolled. Routine blood investigations, inflammatory markers (on admission) and comprehensive echocardiography including STE were done for all. Results: Of the 134 subjects, 121 (90.3%) were symptomatic during COVID-19 illness and were categorized as mild: 61 (45.5%), moderate: 50 (37.3%) and severe: 10 (7.5%) COVID-19 illness. Asymptomatic COVID-19 infection was reported in 13 (9.7%) patients. Subclinical LV and right ventricle (RV) dysfunction were seen in 40 (29.9%) and 14 (10.5%) patients respectively. Impaired LVGLS was reported in 1 (7.7%), 8 (13.1%), 22 (44%) and 9 (90%) subjects with asymptomatic, mild, moderate and severe disease respectively. LVGLS was significantly lower in patients recovered from severe illness (mild: -21 ± 3.4%; moderate: -18.1 ± 6.9%; severe: -15.5 ± 3.1%; P < 0.0001). Subjects with reduced LVGLS had significantly higher interleukin-6 (P < 0.0001), C-reactive protein (P = 0.001), lactate dehydrogenase (P = 0.009) and serum ferritin (P = 0.03) levels during index admission. Conclusions: Subclinical LV dysfunction was seen in nearly a third of recovered COVID-19 patients while 10.5% had RV dysfunction. Our study suggests a need for closer follow-up among COVID-19 recovered subjects to elucidate long-term cardiovascular outcomes.