Leveraging Contact Network Information in Clustered Randomized Studies of Contagion Processes.

In a randomized study, leveraging covariates related to the outcome (e.g. disease status) may produce less variable estimates of the effect of exposure. For contagion processes operating on a contact network, transmission can only occur through ties that connect affected and unaffected individuals; the outcome of such a process is known to depend intimately on the structure of the network. In this paper, we investigate the use of contact network features as efficiency covariates in exposure effect estimation. Using augmented generalized estimating equations (GEE), we estimate how gains in efficiency depend on the network structure and spread of the contagious agent or behavior. We apply this approach to simulated randomized trials using a stochastic compartmental contagion model on a collection of model-based contact networks and compare the bias, power, and variance of the estimated exposure effects using an assortment of network covariate adjustment strategies. We also demonstrate the use of network-augmented GEEs on a clustered randomized trial evaluating the effects of wastewater monitoring on COVID-19 cases in residential buildings at the the University of California San Diego.

Rapidly adapted community health strategies to prevent treatment interruption and improve COVID-19 detection for Syrian refugees and the host population with hypertension and diabetes in Jordan.

BACKGROUND: We evaluated community health volunteer (CHV) strategies to prevent non-communicable disease (NCD) care disruption and promote coronavirus disease 2019 (COVID-19) detection among Syrian refugees and vulnerable Jordanians, as the pandemic started. METHODS: Alongside medication delivery, CHVs called patients monthly to assess stockouts and adherence, provide self-management and psychosocial support, and screen and refer for complications and COVID-19 testing. Cohort analysis was undertaken of stockouts, adherence, complications and suspected COVID-19. Multivariable models of disease control assessed predictors and non-inferiority of the strategy pre-/post-initiation. Cost-efficiency and patient/staff interviews assessed implementation. RESULTS: Overall, 1119 patients were monitored over 8 mo. The mean monthly proportion of stockouts was 4.9%. The monthly proportion non-adherent (past 5/30 d) remained below 5%; 204 (18.1%) patients had complications, with 63 requiring secondary care. Mean systolic blood pressure and random blood glucose remained stable. For hypertensive disease control, age 41-65 y (OR 0.46, 95% CI 0.2 to 0.78) and with diabetes (OR 0.73, 95% CI 0.54 to 0.98) had decreased odds, and with baseline control had increased odds (OR 3.08, 95% CI 2.31 to 4.13). Cumulative suspected COVID-19 incidence (2.3/1000 population) was suggestive of ongoing transmission. While cost-efficient (108 US${\$}$/patient/year), funding secondary care was challenging. CONCLUSIONS: During multiple crises, CHVs prevented care disruption and reinforced COVID-19 detection.

Defining Key Performance Indicators for the California COVID-19 Exposure Notification System (CA Notify).

OBJECTIVES: Toward common methods for system monitoring and evaluation, we proposed a key performance indicator framework and discussed lessons learned while implementing a statewide exposure notification (EN) system in California during the COVID-19 epidemic. MATERIALS AND METHODS: California deployed the Google Apple Exposure Notification framework, branded CA Notify, on December 10, 2020, to supplement traditional COVID-19 contact tracing programs. For system evaluation, we defined 6 key performance indicators: adoption, retention, sharing of unique codes, identification of potential contacts, behavior change, and impact. We aggregated and analyzed data from December 10, 2020, to July 1, 2021, in compliance with the CA Notify privacy policy. RESULTS: We estimated CA Notify adoption at nearly 11 million smartphone activations during the study period. Among 1 654 201 CA Notify users who received a positive test result for SARS-CoV-2, 446 634 (27%) shared their unique code, leading to ENs for other CA Notify users who were in close proximity to the SARS-CoV-2-positive individual. We identified at least 122 970 CA Notify users as contacts through this process. Contact identification occurred a median of 4 days after symptom onset or specimen collection date of the user who received a positive test result for SARS-CoV-2. PRACTICE IMPLICATIONS: Smartphone-based EN systems are promising new tools to supplement traditional contact tracing and public health interventions, particularly when efficient scaling is not feasible for other approaches. Methods to collect and interpret appropriate measures of system performance must be refined while maintaining trust and privacy.

Network methods and design of randomized trials: Application to investigation of COVID-19 vaccination boosters.

Network science methods can be useful in design, monitoring, and analysis of randomized trials for control of spread of infections. Their usefulness arises from the role of statistical network models in molecular epidemiology and in study design. Computational models, such as agent-based models that propagate disease on simulated contact networks, can be used to investigate the properties of different study designs and analysis plans. Particularly valuable is the use of these methods to assess how magnitude and detectability of intervention effects depend on both individual-level and network-level characteristics of the enrolled populations. Such investigation also provides an important approach to assessing consequences of study data being incomplete or measured with error. To address these goals, we consider two statistical network models: exponential random graph models and the more flexible congruence class models. We focus first on an historical use of these methods in design and monitoring of a cluster randomized trial in Botswana to evaluate the effect of combination HIV prevention modalities compared to standard of care on HIV incidence. We then present a framework for the design of a study of booster vaccine effects on infection with, and forward transmission of, SARS-CoV-2 variants. Motivation for the study is driven in part by guidance from the United Kingdom to base approval of booster vaccines with "strain changes" that target variants on results of neutralizing antibody tests and information about safety, but without requiring evidence of clinical efficacy. Using designs informed by our agent-based network models, we show it may be feasible to conduct a trial of novel SARS-CoV-2 vaccines in a single large campus to obtain useful information regarding vaccine efficacy against susceptibility and infectiousness. If needed, the sample size could be increased by extending the study to a small number of campuses. Novel network methods may be useful in developing pragmatic SARS-CoV-2 vaccine trials that can leverage existing infrastructure to reduce costs and hasten the development of results.

Bioactive Based Nanocarriers for the Treatment of Viral Infections and SARS-CoV-2.

Since ancient times, plants have been used for their medicinal properties. They provide us with many phytomolecules, which serve a synergistic function for human well-being. Along with anti-microbial, plants also possess anti-viral activities. In Western nations, about 50% of medicines were extracted from plants or their constituents. The spread and pandemic of viral diseases are becoming a major threat to public health and a burden on the financial prosperity of communities worldwide. In recent years, SARS-CoV-2 has made a dramatic lifestyle change. This has promoted scientists not to use synthetic anti-virals, such as protease inhibitors, nucleic acid analogs, and other anti-virals, but to study less toxic anti-viral phytomolecules. An emerging approach includes searching for eco-friendly therapeutic molecules to develop phytopharmaceuticals. This article briefly discusses numerous bioactive molecules that possess anti-viral properties, their mode of action, and possible applications in treating viral diseases, with a special focus on coronavirus and various nano-formulations used as a carrier for the delivery of phytoconstituents for improved bioavailability.

Implication of Covid-19 on Neurological Complications with Specific Emphasis on Alzheimer's and Parkinson's Disease.

It is noticeable how the novel coronavirus has spread from the Wuhan region of China to the whole world, devastating the lives of people worldwide. All the data related to the precautionary measures, diagnosis, treatment, and even the epidemiological data are being made freely accessible and reachable in a very little time as well as being rapidly published to save humankind from this pandemic. There might be neurological complications of COVID-19 and patients suffering from neurodegenerative conditions like Alzheimer's disease and Parkinson's disease might have repercussions as a result of the pandemic. In this review article, we have discussed the effect of SARS-CoV-2 viral infection on the people affected with neurodegenerative disorders such as Parkinson's and Alzheimer's. It primarily emphasizes two issues, i.e., vulnerability to infection and modifications of course of the disease concerning the clinical neurological manifestations, the advancement of the disease and novel approaches to support health care professionals in disease management, the susceptibility to these diseases, and impact on the severity of disease and management.

Evaluation of Severe Acute Respiratory Syndrome Coronavirus 2 Transmission Mitigation Strategies on a University Campus Using an Agent-Based Network Model.

Universities are faced with decisions on how to resume campus activities while mitigating severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) risk. To provide guidance for these decisions, we developed an agent-based network model of SARS-CoV-2 transmission to assess the potential impact of strategies to reduce outbreaks. The model incorporates important features related to risk at the University of California San Diego. We found that structural interventions for housing (singles only) and instructional changes (from in-person to hybrid with class size caps) can substantially reduce the basic reproduction number, but masking and social distancing are required to reduce this to at or below 1. Within a risk mitigation scenario, increased frequency of asymptomatic testing from monthly to twice weekly has minimal impact on average outbreak size (1.1-1.9), but substantially reduces the maximum outbreak size and cumulative number of cases. We conclude that an interdependent approach incorporating risk mitigation, viral detection, and public health intervention is required to mitigate risk.