Household transmission dynamics of COVID-19 among residents of Delhi, India: a prospective case-ascertained study.

Objective: The aim of this study was to observe the secondary infection rate and transmission dynamics of COVID-19 among household contacts, and their associations with various factors across four dimensions of interaction. Methods: This was a case-ascertained study among unvaccinated household contacts of a laboratory-confirmed COVID-19 case in New Delhi between December 2020 and July 2021. For this study, 99 index cases and their 316 household contacts were interviewed and sampled (blood and oro-nasal swab) on days 1, 7, 14, and 28. Results: The secondary infection rate among unvaccinated household contacts was 44.6% (95% confidence interval (CI) 39.1-50.1). The predictors of secondary infection among individual contact levels were: being female (odds ratio (OR) 2.13), increasing age (OR 1.01), symptoms at baseline (OR 3.39), and symptoms during follow-up (OR 3.18). Among index cases, age of the primary case (OR 1.03) and symptoms during follow-up (OR 6.29) were significantly associated with secondary infection. Among household-level and contact patterns, having more rooms (OR 4.44) and taking care of the index case (OR 2.02) were significantly associated with secondary infection. Conclusion: A high secondary infection rate highlights the need to adopt strict measures and advocate COVID-19-appropriate behaviors. A targeted approach for higher-risk household contacts would efficiently limit infections among susceptible contacts.

A Year of COVID-19 Pandemic Roller-Coaster: The Malta Experience, Lessons Learnt, and the Future.

OBJECTIVE: The coronavirus disease 2019 (COVID-19) pandemic had a global impact. The study explores the various COVID-19 experiences in Malta over the past year and provides a snapshot of acute and post-acute COVID-19 symptoms, as well as national vaccination roll-out and hesitancy. METHODS: Data on medical access, lifestyle habits, acute and post-acute COVID-19 symptoms, and vaccination hesitancy was gathered through a social media survey targeting adults of Malta. COVID-19 data were gathered from the Malta Ministry of Health COVID-19 dashboard. RESULTS: Malta controlled COVID-19 spread exceptionally well initially. Since August 2020, the positivity rate, mortality, and hospital admission rates saw a fluctuating incline. From COVID-19 onset, a decrease in physical activity and an increase in body weight was reported. Most participants acquiring COVID-19 were asymptomatic but nontrivial proportion experienced post-acute symptoms. The majority opted to take the COVID-19 vaccine with only a minority expressing safety concerns. CONCLUSIONS: Malta has experienced roller coaster events over a year. The population faced elevated levels of morbidity, mortality, and economic hardship along with negative and positive risk-associated behaviors. Vaccination in combination with population adherence to social distancing, mask wearing, and personal hygiene are expected to be the beacons of hope in the coming months.

A coalitional game-based joint monitoring mechanism for combating COVID-19.

In the absence of effective treatment for COVID-19, disease prevention and control have become a top priority across the world. However, the general lack of effective cooperation between communities makes it difficult to suppress the community spread of the global pandemic; hence repeated outbreaks of COVID-19 have become the norm. To address this problem, this paper considers community cooperation in disease monitoring and designs a joint epidemic monitoring mechanism, in which adjacent communities cooperate to enhance their monitoring capability. In this work, we formulate the epidemiological monitoring process as a coalitional game. Then, we propose a Shapley value-based payoffs distribution scheme for the coalitional game. A comprehensive analytical framework is developed to evaluate the advantages and sustainability of the cooperation between communities. Experimental results show that the proposed mechanism performs much better than the conventional non-cooperative monitoring design and can greatly increase each community's payoffs.

Urban flooding events pose risks of virus spread during the novel coronavirus (COVID-19) pandemic.

Since the first report in December 2019, the novel coronavirus (COVID-19) has spread to most parts of the world, with over 21.5 million people infected and nearly 768,000 deaths to date. Evidence suggests that transmission of the virus is primarily through respiratory droplets and contact routes, and airborne carriers such as atmospheric particulates and aerosols have also been proposed as important vectors for the environmental transmission of COVID-19. Sewage and human excreta have long been recognized as potential routes for transmitting human pathogens. The causative agent of the COVID-19 pandemic, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has been detected in human feces and urine, where it could remain viable for days and show infectivity. Urban flooding, a common threat in summer caused by heavy rainfalls, is frequently reported in urban communities along with sewage overflows. With summer already underway and economy re-opening in many parts of the world, urban flooding and the often-accompanied sewage overflows could jeopardize previous mitigation efforts by posing renewed risks of virus spread in affected areas and communities. In this article, we present the up-to-date evidence and discussions on sewage-associated transmission of COVID-19, and highlighted the roles of sewage overflow and sewage-contaminated aerosols in two publicized events of community outbreaks. Further, we collected evidence in real-life environments to demonstrate the shortcuts of exposure to overflowed sewage and non-dispersed human excreta during a local urban flooding event. Given that communities serviced by combined sewer systems are particularly prone to such risks, local municipalities could prioritize wastewater infrastructure upgrades and consider combined sewer separations to minimize the risks of pathogen transmission via sewage overflows during epidemics.

Longitudinal and Long-Term Wastewater Surveillance for COVID-19: Infection Dynamics and Zoning of Urban Community.

Wastewater-based epidemiology (WBE) is emerging as a potential approach to study the infection dynamics of SARS-CoV-2 at a community level. Periodic sewage surveillance can act as an indicative tool to predict the early surge of pandemic within the community and understand the dynamics of infection and, thereby, facilitates for proper healthcare management. In this study, we performed a long-term epidemiological surveillance to assess the SARS-CoV-2 spread in domestic sewage over one year (July 2020 to August 2021) by adopting longitudinal sampling to represent a selected community (~2.5 lakhs population). Results indicated temporal dynamics in the viral load. A consistent amount of viral load was observed during the months from July 2020 to November 2020, suggesting a higher spread of the viral infection among the community, followed by a decrease in the subsequent two months (December 2020 and January 2021). A marginal increase was observed during February 2021, hinting at the onset of the second wave (from March 2021) that reached it speak in April 2021. Dynamics of the community infection rates were calculated based on the viral gene copies to assess the severity of COVID-19 spread. With the ability to predict the infection spread, longitudinal WBE studies also offer the prospect of zoning specific areas based on the infection rates. Zoning of the selected community based on the infection rates assists health management to plan and manage the infection in an effective way. WBE promotes clinical inspection with simultaneous disease detection and management, in addition to an advance warning signal to anticipate outbreaks, with respect to the slated community/zones, to tackle, prepare for and manage the pandemic.

Advancing deep learning for supply chain optimization of covid-19 vaccination in rural communities

Covid19 is a global pandemic that brought lots of disruptions in day-to-day life, affected economies, closed millions of businesses, and took a lot of precious lives. Along with social distancing and wearing masks, the effective way to eradicate the virus is to administer vaccines. To prevent the spread of disease and avoid deaths, it is essential to prioritize vaccine distribution. At the request of CDC, National Academies of Science, Engineering and Medicine published the Framework for fair distribution of COVID-19 Vaccine. This paper focuses on studying the rate of vaccination in urban and rural communities and identifying gaps in the Covid19 vaccine supply chain using data science. Demand forecasting using deep learning is proposed for planning vaccine allocation and distribution. Deep learning refers to multilayer neural networks that can learn extremely complex patterns using hidden layers between inputs and outputs. Long Short-Term Memory neural networks will be used to forecast vaccine demand. © 2021 IEEE.

The Impact of United Kingdom and Malaysia's Inherent Health Systems on Their COVID-19 Responses: A Comparison of Containment Strategies.

In March 2020, the outbreak of COVID-19 was officially declared a global pandemic by the World Health Organization. Given the novelty of the virus, and hence, lack of official guidance on effective containment strategies, individual countries opted for different containment approaches ranging from herd immunity to strict lockdown. The opposing strategies followed by the United Kingdom and its former colony, Malaysia, stand exemplary for this. Real-time polymerase chain reaction was implemented for testing in both counties. Malaysia acted with strict quarantining rules and infection surveillance. The United Kingdom followed an initially lenient, herd-immunity approach with strict lockdown only enforced weeks later. Although based on the same health-care structure historically, Malaysia developed a more unified health system compared with the United Kingdom. We suggest that this more centralized structure could be one possible explanation for why Malaysia was able to react in a more timely and efficient manner, despite its closer geographic proximity to China. We further explore how the differences in testing and quarantining strategy, as well as political situation and societal compliance could account for the discrepancy in the United Kingdom's versus Malaysia's relative success of COVID-19 containment.

Early Acceleration of COVID-19 in Areas with Larger Nursing Homes and Certificate of Need Laws.

BACKGROUND: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) outbreaks have become common in large nursing homes, placing not only residents but also staff and community members at risk for infection. However, the relationship between larger nursing homes and the community spread of SARS-CoV-2 has not yet been documented. OBJECTIVE: To examine the association between county average nursing home bed size and presence of certificate of need (CON) laws, which influence nursing home size, with county-level SARS-CoV-2 prevalence over time. DESIGN: Cross-sectional study using county-level data from March 11 through June 12, 2020. PARTICIPANTS: All US counties with at least one nursing home (n = 2,883). MAIN MEASURES: The main explanatory variables were county average nursing home bed size and presence of a CON law. The main outcome was the cumulative number of SARS-CoV-2 cases on each day of the study period adjusted for county population size and density, demographic and socioeconomic characteristics, total nursing home bed supply, other health care supply measures, epidemic stage, and census region. KEY RESULTS: By June 12, a between-county difference in average nursing home size equal to 1 bed was associated with 3.92 additional SARS-COV-2 cases (95% CI = 2.14 to 5.69; P < 0.001), on average, and counties subject to CON laws had 104.53 additional SARS-CoV-2 cases (95% CI = 7.68 to 201.38; P < 0.05), on average. Counties with larger nursing homes also demonstrated higher growth in the frequency of SARS-COV-2 throughout the study period. CONCLUSIONS: At the county level, average nursing home size and CON law presence was associated with a greater frequency of SARS-CoV-2 cases. Controlling the impact of the coronavirus 2019 pandemic may require additional resources for communities with larger nursing homes and more attention towards long-term care policies.

Demographic and Socioeconomic Characteristics of COVID-19 Patients Treated in the Emergency Department of a New York City Hospital.

Demographic and socioeconomic factors can contribute to community spread of COVID-19. The aim of this study is to describe the demographics and socioeconomic factors in relation to geolocation of COVID-19 patients who were discharged from the emergency department (ED) back into the community. This retrospective study was conducted over a 7-week period, at an urban, adult, level 1 trauma center in New York City. Demographics, socioeconomic factors, and geolocation of COVID-19 patients discharged from the ED were extracted from the electronic medical records. Patients were stratified by gender for data analysis. A total of 634 patients were included in the study, 376 (59.3%) were male and 205 (32.3%) were Hispanic White. The median age of patients was 50 years (IQR: 38, 60, Min:15, Max:96). The unemployment rate in our population was 41.2% and 75.5% reported contracting the virus via community spread. ED mortality rate was 11.8%; the majority of which were male (N = 50, 66.7%) and the median age was 70 years (IQR: 59, 82). There were 9.4% (95% CI 2.9-12.4) more Black males and 5.4% (95% CI 0.4-10.4) more males who had no insurance coverage compared to females. 26.8% (95% CI 14.5-39) more females worked in the healthcare field and 7.1% (95% CI 0.3-13.9) more were infected via primary contact compared to males. COVID-19 disproportionately affected minorities and males. Socioeconomic factors should be taken into consideration when preparing strategies for preventing the spread of the virus, especially for individuals who are expected to self-isolate.