Cooperative Attention-Based Learning between Diverse Data Sources

Cooperative attention provides a new method to study how epidemic diseases are spread. It is derived from the social data with the help of survey data. Cooperative attention enables the detection possible anomalies in an event by formulating the spread variable, which determines the disease spread rate decision score. This work proposes a determination spread variable using a disease spread model and cooperative learning. It is a four-stage model that determines answers by identifying semantic cooperation using the spread model to identify events, infection factors, location spread, and change in spread rate. The proposed model analyses the spread of COVID-19 throughout the United States using a new approach by defining data cooperation using the dynamic variable of the spread rate and the optimal cooperative strategy. Game theory is used to define cooperative strategy and to analyze the dynamic variable determined with the help of a control algorithm. Our analysis successfully identifies the spread rate of disease from social data with an accuracy of 67% and can dynamically optimize the decision model using a control algorithm with a complexity of order O(n(2)).

The Effect of the COVID-19 Pandemic on Anxiety and Depression in Adolescents in the Military Population

Purpose of Study: Over the course of the COVID-19 pandemic, the medical field has witnessed increasing rates of mental health challenges. Several global and national studies demonstrated an increasing prevalence of anxiety and depression in our children and adolescence, for which the AAP, AACAP, and CHA made a declaration of national emergency in mental health. However, there is paucity of data on adolescents in military families, a population with unique stressors, medical access, and home environment. Given the preliminary studies showing that early life stressors may alter the effect of the pandemic on adolescent mental health, this study hopes to look at the initial effect of the COVID-19 pandemic on anxiety and depression in this unique population. Methods Used: This study is a retrospective chart review of 188 13-23 year olds seen for regular annual visits in both the year prior to March 2020 and the first year since March 2020. In this way, these patients serve as their own internal control. Mental health screeners are a routine part of these visits in this population. As such, the PHQ-9 and GAD-7 scores were collected to assess for anxiety and depression, respectively, in each of these time periods. Further covariates that were collected and analyzed include gender, age, coexisting mental health conditions, and BMI percentile. Summary of Results: There was no statistically significant difference in PHQ-9 and GAD-7 scores within this population in the first year of the COVID pandemic compared to the year prior. Furthermore, the overall prevalence of anxiety and depression saw only a minor increase (8% to 10% and 10 to 12%, respectively) as compared to national and global studies. Even when subdividing the population based upon gender, age, coexisting mental health conditions, and BMI percentile, there was still no significant difference seen in these two times periods. The only relevant statistical difference note were higher scores for anxiety and depression scoring in female compared to males. Conclusion(s): The dramatic increase in adolescent anxiety and depression that previous studies have demonstrated may not be an accurate reflection of the military population, as demonstrated by this study. Whether because of differing exposure to stressors, health care access, or household and community structures, the military adolescent population did not show as significant of an effect of the COVID-19 pandemic on their anxiety and depression. Limitations to our study include a selection towards patient's that come in for annual visits, who are likely to be a healthier subgroup. Further investigation is merited to see if anxiety and depression rates did change as the pandemic has further progressed and, if not, what protective factors may have prevented this.

The impact of COVID-19 on the international energy trade network centrality and community structures

Global trade including energy trade is expected to suffer a significant contraction as a result of the COVID-19 pandemic. In this paper, we try to measure the impact of the COVID-19 pandemic on the national status and international energy trade patterns. We apply the networks theory to quantify the dynamic process of the international energy trade network in the year 2018–2020, deriving the centrality to capture both national economic status and its topologic characteristics. Under the COVID-19, multilateral energy trade was blocked, thereby some resource-exporting countries show a downward rank of the centrality, and the opposite situation is in higher levels of economic development. By using the community detection method, we also found that new small communities detached from communities that formed before the COVID-19, but geographical related patterns of international energy trade network communities were not affected by the COVID-19 pandemic.

Adaptive Group Testing on Networks with Community Structure: The Stochastic Block Model

Group testing was conceived during World War II to identify soldiers infected with syphilis using as few tests as possible, and it has attracted renewed interest during the COVID-19 pandemic. A long-standing assumption in the probabilistic variant of the group testing problem is that individuals are infected by the disease independently. However, this assumption rarely holds in practice, as diseases often spread through interactions between individuals and therefore cause infections to be correlated. Inspired by characteristics of COVID-19 and other infectious diseases, we introduce an infection model over networks which generalizes the traditional i.i.d. model from probabilistic group testing. Under this model, we ask whether knowledge of the network structure can be leveraged to perform group testing more efficiently, focusing specifically on community-structured graphs drawn from the stochastic block model. We prove that a simple community-aware algorithm outperforms the baseline binary splitting algorithm when the model parameters are conducive to “strong community structure.”Moreover, our novel lower bounds imply that the community-aware algorithm is order-optimal in certain parameter regimes. We extend our bounds to the noisy setting and support our results with numerical experiments. IEEE

The research progress, hotspots, challenges and outlooks of solid-phase denitrification process.

Nitrogen pollution is one of the main reasons for water eutrophication. The difficulty of nitrogen removal in low-carbon wastewater poses a huge potential threat to the ecological environment and human health. As a clean biological nitrogen removal process, solid-phase denitrification (SPD) was proposed for long-term operation of low-carbon wastewater. In this paper, the progress, hotspots, and challenges of the SPD process based on different solid carbon sources (SCSs) are reviewed. Compared with synthetic SCS and natural SCS, blended SCSs have more application potential and have achieved pilot-scale application. Differences in SCSs will lead to changes in the enrichment of hydrolytic microorganisms and hydrolytic genes, which indirectly affect denitrification performance. Moreover, the denitrification performance of the SPD process is also affected by the physical and chemical properties of SCSs, pH of wastewater, hydraulic retention time, filling ratio, and temperature. In addition, the strengthening of the SPD process is an inevitable trend. The strengthening measures including SCSs modification and coupled electrochemical technology are regarded as the current research hotspots. It is worth noting that the outbreak of the COVID-19 epidemic has led to the increase of disinfection by-products and antibiotics in wastewater, which makes the SPD process face challenges. Finally, this review proposes prospects to provide a theoretical basis for promoting the efficient application of the SPD process and coping with the challenge of the COVID-19 epidemic.

The Effect of Bean Seed Treatment with Entomopathogenic Fungus Metarhizium robertsii on Soil Microarthropods (Acari, Collembola).

The treatment of agricultural crops with entomopathogenic fungi may disturb the structure of soil microarthropod communities, which can have an adverse impact on soil fertility and, ultimately, on the yield. The effect of the treatment of broad bean (Vicia faba L.) seeds, with the entomopathogenic fungus Metarhizium robertsii, on the abundance and community structure of soil microarthropods in the rhizosphere was assessed in different phases of plant vegetation in a two-year experiment. Under the conditions of the gradually decreased abundance of M. robertsii both in the soil cenoses and in the plants during summer, no adverse effect was revealed of the bean seed treatment, with the entomopathogenic fungus, on the abundance of soil microarthropods (Acari: Mesostigmata, Prostigmata, Oribatida and Astigmata; Collembola) and the structure of their communities. Similar results were obtained in the analysis, taking into account the positive colonization of plants. Some changes in the microarthropod community structure were explained primarily by the spatial heterogeneity of the field, the hydrothermal regime, and the features of the microarthropod life cycles. The results indicate the possibility of using dressing seeds with conidial suspension for plant inoculation with entomopathogenic fungus (at least M. robertsii) as a potentially safe plant protection method for non-target soil microarthropods.

Connecting the Dots: Public Health, Clinical, and Community Connections to Improve Contraception Access

The Association of State and Territorial Health Officials Increasing Access to Contraception Learning Community project, conducted in collaboration with the Centers for Disease Control and Prevention's Division of Reproductive Health, implemented a series of learning communities with 27 multidisciplinary teams (from 26 states and one territory) between 2014 and 2018 to improve access to the full range of contraceptive methods. The Association of State and Territorial Health Officials, the National Association for County and City Health Officials, and the National Association of Community Health Workers will partner to build the community health workforce through collaboration with community-based organizations.8 In this next phase of "life with COVID," the expanded community workforce has an opportunity to pivot to addressing other public health priorities such as contraception access with trusted frontline workers in communities serving as a link between health and social services. During the COVID-19 pandemic, public health, clinical, and community organizations have been leading data collection activities to better understand the digital literacy and telehealth experiences of patients and providers and have been working closely with providers to expand services and the capacity of communities to deliver them.9 Examples include public health efforts to accomplish digital inclusion and telehealth equity assessments, the creation of "heat maps" identifying barriers and access points for unavailable specialty services,10 and training and employment of community members in places such as libraries as digital navigators to support telehealth services.11 Early in 2020, the Office of Population Affairs of the US Department of Health and Human Services authorized telehealth as an option for Title X family planning clinics across the country and announced $35 million in grants for the Title X program to support telehealth as a means of sustaining access to contraceptive health services.12 Including contraception access within such endeavors can enhance access to services, support clinical reach, and build capacity within communities.

Nursing and Public Health Special Issue

Zauche et al. (p. S226) describe public health nursing(PHN) roles at the Centers for Disease Control and Prevention and how the nursing profession has been essential in ensuring the health and safety of our most vulnerable groups. [...]Singer et al. (p. S288) examine how spirituality may inform health and health care beliefs and behaviors among Black sex workers during COVID-19. [...]they call for meaningful partnerships between primary care, public health, and community organizations to achieve this level of cohesion. To ensure that no one is left behind, they propose solutions to the central question facing PHN educators adept in patient and individual as well as population health: "How do we improve interprofessional environmental health education to achieve effective collaboration beyond the bedside?" Harris et al. (p. S231) emphasize the urgent need to train the next cadre of nurses who are interested in public health and health policy careers to prepare them for future challenges.

METAGENOMIC INTERROGATION OF RESPIRATORY AND GUT MICROBIAL COMMUNITIES, HOST IMMUNE RESPONSE, AND RISK OF SEVERE COVID-19

Background: Human-associated microbial communities have been linked to host immune response to respiratory viral infections. Prior investigations have observed shifts in the composition of the gut or respiratory microbiome in severe COVID-19. However, there has been no comprehensive metagenomic evaluation of the interaction between lower respiratory and gut microbiomes and host immune factors in COVID-19. Methods: From April 2020 to May 2021, we prospectively enrolled 153 hospitalized patients with mild (n=12), moderate (n=65), and severe (n=76) COVID-19 infection categorized using established clinical criteria. We longitudinally collected stool (n=270) for metagenomic profiling, and in a subset, we generated comprehensive host-microbiome-molecular profiles by collecting sputum metagenomes (n=87 participants with 212 samples) and blood cytokine levels (n=109 with 181 samples) weekly until hospital discharge. We performed omnibus testing of overall gut and respiratory community structure, species-level differential abundance testing using mixed effects modeling accounting for repeated sampling, hierarchical clustering of paired gut and respiratory metagenomic profiles, and multi-omic machine learning classification of disease severity. Results: Patients with severe COVID-19 tended to be older, were more frequently male, had higher rates of overweight/obesity, and a greater mean Charlson Comorbidity Index. Patients with severe COVID-19 infection had significantly decreased stool and respiratory microbiome a-diversity irrespective of antibiotic administration. COVID severity accounted for a small proportion of variance in stool (R2=2.4%, p=0.002) and sputum (R2=4.4%, p= 0.03) profiles. Hierarchical clustering of paired gut and respiratory samples from patients with severe COVID revealed the joint expansion of oral-typical taxa typically present during systemic inflammation (i.e., increases in Streptococcus and Peptostreptococus spp. in both gut and sputum). A pro-inflammatory milieu defined by a composite elevation of circulating plasma cytokines (e.g., IL-6, TNF-a, and IL-29 among others) were linked to broad microbial excursions in community structure for both stool and sputum as measured by Bray-Curtis distances. A random forest classifier incorporating either stool or sputum taxonomic features and accounting for age, sex, body mass index, and recent antibiotic use achieved excellent classification of biospecimens from patients with severe vs. non-severe COVID patients (AUROC > 0.80). Conclusions: Alterations of the gut and respiratory microbiome were associated with differences in host immune response and COVID-19 disease severity. Further studies are needed to identify the potential role of human-associated microbial communities as a biomarker for poor patient outcomes in COVID-19 who may warrant escalated levels of care.(Figure Presented) Fig. 1. (A) Using unsupervised feature selection (species abundance > 0.001) inclusive of taxa differentially abundant by non-parametric Wilcoxon rank-sum testing (nominal p-value < 0.05), (B) we performed random forest classification using a twice-repeated 5-fold crossvalidation scheme to predict COVID-19 disease severity from shotgun metagenomic stool profiles (C) yielding an AUROC of 0.91.

Urban Aerobiome and Effects on Human Health: A Systematic Review and Missing Evidence

Urban air pollutants are a major public health concern and include biological matters which composes about 25% of the atmospheric aerosol particles. Airborne microorganisms were traditionally characterized by culture-based methods recognizing just 1.5–15.3% of the total bacterial diversity that was evaluable by genome signature in the air environment (aerobiome). Despite the large number of exposed people, urban aerobiomes are still weakly described even if recently advanced literature has been published. This paper aims to systematically review the state of knowledge on the urban aerobiome and human health effects. A total of 24 papers that used next generation sequencing (NGS) techniques for characterization and comprised a seasonal analysis have been included. A core of Proteobacteria, Actinobacteria, Firmicutes, and Bacteroides and various factors that influenced the community structure were detected. Heterogenic methods and results were reported, for both sampling and aerobiome diversity analysis, highlighting the necessity of in-depth and homogenized assessment thus reducing the risk of bias. The aerobiome can include threats for human health, such as pathogens and resistome spreading;however, its diversity seems to be protective for human health and reduced by high levels of air pollution. Evidence of the urban aerobiome effects on human health need to be filled up quickly for urban public health purposes.

Analysis of Worldwide Time-Series Data Reveals Some Universal Patterns of Evolution of the SARS-CoV-2 Pandemic

Predicting the evolution of the current epidemic depends significantly on understanding the nature of the underlying stochastic processes. To unravel the global features of these processes, we analyse the world data of SARS-CoV-2 infection events, scrutinising two 8-month periods associated with the epidemic’s outbreak and initial immunisation phase. Based on the correlation-network mapping, K-means clustering, and multifractal time series analysis, our results reveal several universal patterns of infection dynamics, suggesting potential predominant drivers of the pandemic. More precisely, the Laplacian eigenvectors localisation has revealed robust communities of different countries and regions that break into clusters according to similar profiles of infection fluctuations. Apart from quantitative measures, the immunisation phase differs significantly from the epidemic outbreak by the countries and regions constituting each cluster. While the similarity grouping possesses some regional components, the appearance of large clusters spanning different geographic locations is persevering. Furthermore, characteristic cyclic trends are related to these clusters;they dominate large temporal fluctuations of infection evolution, which are prominent in the immunisation phase. Meanwhile, persistent fluctuations around the local trend occur in intervals smaller than 14 days. These results provide a basis for further research into the interplay between biological and social factors as the primary cause of infection cycles and a better understanding of the impact of socio-economical and environmental factors at different phases of the pandemic. Copyright © 2022 Mitrović Dankulov, Tadić and Melnik.

COVID-19 on the Navajo Nation: Experiences of Dine Families of Children with Asthma

BACKGROUND: Approximately 173,000 persons live on the Navajo Nation (NN) and 14.7% live in multi-generational households. One-third of the Nation's residents are children and 44% live in poverty. The median household income is $27,389 with 1/3 having incomes < $15,000/year. The first confirmed case of COVID-19 on the NN was identified March 17, 2020. The Navajo government took swift action to combat COVID-19 by declaring a public health state of emergency which established the Navajo Department of Health Command Operations Center, closed the government offices except for essential employees, ceased inperson classroom instruction for all schools located within the borders of the NN and issued travel restriction for governmental employees. Even with strong public health efforts, Navajo Nation saw the highest per capita infection rate in the US during May of 2020 with 2450/100,000 versus New York 2119/100,000. METHODS: The Community Asthma Program is an NHLBI funded program working to improve health outcomes for children with asthma on the NN. We sought to determine the impact of COVID- 19 on the families of children with asthma who were participating in our study. RESULTS: Sixty-six of 193 families (34%) were interviewed about their pandemic experience. The average age of the child with asthma was 13.5 (SD=3.9) and 33% were female. On average, 5.2 people lived in each house (SD=2.1). Results of the interviews are shown in the table. Our data indicate that most Diné children with asthma in our study did not contract COVID-19. However, the pandemic had a significant impact on them and their families. Many family members contracted COVID-19, some children lost family members, and half of interviewed parents reported a decline in their child's mental health. Responses suggest that Navajo families may have been less able to work remotely than the US population at large, perhaps increasing stress for families. Despite the trauma from COVID-19, families adopted strategies to cope with the pandemic. Most diligently followed health guidelines including washing hands, wearing masks, and social distancing. One in four families sought the help of a traditional healer. Many accessed medical care through telehealth and most were able to obtain asthma medications when needed. More recently, as the pandemic subsides, parents indicate that their outlook and mental health have significantly improved. CONCLUSION. Despite significant challenges, our research indicated resilience among Navajo families and we heard stories of positive community structures and relationships that are particular to the Diné culture. (Figure Presented).

Locating Multi-Sources in Social Networks With a Low Infection Rate

With the development of modern technology, numerous economic losses are incurred by various spreading phenomena. Thus, it is of great significance to identify the initial sources triggering such phenomena. The investigation of source localization in social networks has gained substantial attention and become a popular topic of study. For practical spreading phenomena on social networks, the infection rates are relatively low. Hence, a high uncertainty of spreading trace might be incurred, which further incurs the reduction of localization accuracy obtained through existed source localization methods, especially the observer-based ones. Aiming to solve the source localization problem with a low infection rate, we propose a novel localization algorithm, i.e., path-based source identification (PBSI). First, a small number of nodes are selected and designated as observers. After the propagation process triggered by sources, we can obtain a snapshot. Later, a label is assigned to represent whether a node is infected or not, and observers are supposed to record the paths through which nodes are successfully infected. Based on source centrality theory, observers make the labels flow in the direction recorded during the label iteration process, which ensures the labels of nodes in the direction of the source increase gradually. Extensive experiments indicate that the proposed PBSI can handle source localization problems for both single and multi-source scenarios with better performance than that of state-of-the-art algorithms under different propagation models.

ERIS: An Approach Based on Community Boundaries to Assess Polarization in Online Social Networks

Detection and characterization of polarization are of major interest in Social Network Analysis, especially to identify conflictual topics that animate the interactions between users. As gatekeepers of their community, users in the boundaries significantly contribute to its polarization. We propose ERIS, a formal graph approach relying on community boundaries and users’ interactions to compute two metrics: the community antagonism and the porosity of boundaries. These values assess the degree of opposition between communities and their aversion to external exposure, allowing an understanding of the overall polarization through the behaviors of the different communities. We also present an implementation based on matrix computations, freely available online. Our experiments show a significant improvement in terms of efficiency in comparison to existing solutions. Finally, we apply our proposal on real data harvested from Twitter with a case study about the vaccines and the COVID-19. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Pronounced temporal changes in soil microbial community and nitrogen transformation caused by benzalkonium chloride.

As one typical cationic disinfectant, quaternary ammonium compounds (QACs) were approved for surface disinfection in the coronavirus disease 2019 pandemic and then unintentionally or intentionally released into the surrounding environment. Concerningly, it is still unclear how the soil microbial community succession happens and the nitrogen (N) cycling processes alter when exposed to QACs. In this study, one common QAC (benzalkonium chloride (BAC) was selected as the target contaminant, and its effects on the temporal changes in soil microbial community structure and nitrogen transformation processes were determined by qPCR and 16S rRNA sequencing-based methods. The results showed that the aerobic microbial degradation of BAC in the two different soils followed first-order kinetics with a half-life (4.92 vs. 17.33 days) highly dependent on the properties of the soil. BAC activated the abundance of N fixation gene (nifH) and nitrification genes (AOA and AOB) in the soil and inhibited that of denitrification gene (narG). BAC exposure resulted in the decrease of the alpha diversity of soil microbial community and the enrichment of Crenarchaeota and Proteobacteria. This study demonstrates that BAC degradation is accompanied by changes in soil microbial community structure and N transformation capacity.

Lessons for the Opioid Crisis-Integrating Social Determinants of Health Into Clinical Care

Overdose deaths accelerated with the emergence of COVID-19, and this acceleration was fastest among Black, Latinx, and Native Americans, whose overdose rates had already increased before COVID-19.1,2 COVID-19 led to limits on access to medications for opioid use disorder and harm-reduction services, exacerbating low treatment and retention rates,3-5 in the face of toxic drug supplies laced with high-potency synthetic opioids.6 Disproportionate deaths from substance use disorders (SUDs) and from COVID-19 among low-income people marginalized by race, ethnicity, and migrant status have similar upstream causes of exposure, including unstable and crowded housing, high-risk employment or unemployment, and high levels of policing and incarceration, combined with low levels of access to health care and preventive measures. Punitive drug law enforcement discourages help seeking and treatment and leads to unstable drug supplies that are contaminated with fentanyl and other high-potency synthetic opioids that heighten overdose risk.10 Incarcerated people are at an elevated risk of drug overdose in the weeks following release,11 and communities with high incarceration rates have higher mortality.12 Drug courts disproportionately cite low-income people of color for infractions, leading to imprisonment rather than treatment.13 Economic precarity and unstable housing disrupt the social networks that sustain health and prevent overdose.14 Urban planners often displace residents of Black and Latinx neighborhoods, leaving them exposed to narcotic trade and HIV.15 The child welfare system disproportionately removes low-income Black, Latinx, and Indigenous children from families affected by SUDs, and children raised in foster care are at high risk for SUDs.16,17 Therefore, reducing SUD-related deaths and disability requires the redress of discriminatory public policies. Studies of integration of buprenorphine maintenance with organized healing sessions, fishing, hunting, and community gardening in Canadian First Nations communities have shown high rates oftreatment retention (74%) at 18 months,22 and healing sessions combined with buprenorphine have had high levels oftreatment participation, community-level reductions in criminal charges and child protection measures, increased school attendance, and increased flu vaccination.23 Faith-Based Organizations as Partners Imani Breakthrough is a culturally informed approach based on a partnership of Yale University Department of Psychiatry clinicians with Black and Latinx churches. CONCLUSIONS Clinicians can use their symbolic capital to advocate policies that address SDOH and collaborate with community organizations and nonhealth sectors to identify and act on institutional barriers to their patients' health, such as through a structural competency approach.25 Health systems must engage communities, destigmatize SUD, and link to social services with locally controlled, adaptable funds akin to the Ryan White CARE Act to build community-based infrastructure: accessible, trusted services including in cultural, faith-based, and harm-reduction organizations as well as local businesses such as pharmacies.

Deciphering COVID-19-Associated Effects on Hospital MRSA Transmission and Social Networks

Background. The COVID-19 pandemic was associated with a significant (28%) reduction of methicillin-resistant Staphylococcus aureus (MRSA) acquisition at UVA Hospital (P=0.016). This "natural experiment" allowed us to analyze 3 key mechanisms by which the pandemic may have influenced nosocomial transmission: 1) enhanced infection control measures (i.e., barrier precautions and hand hygiene), 2) patient-level risk factors, and 3) networks of healthcare personnel (HCP)-mediated contacts. Hospital MRSA acquisition was defined as a new clinical or surveillance positive in patients with prior unknown or negative MRSA status occurring >72h after admission. 10 month time periods pre- (5/6/2019 to 2/23/2020) and post-COVID-19 (5/4/2020 to 2/28/2021) were chosen to mitigate the effects of seasonality. A 6-week wash-in period was utilized coinciding with the onset of several major hospital-wide infection control measures (opening of 2 special pathogen units with universal contact/airborne precautions on 4/1/21 and 5/1/21, universal mask 4/10/21 and eye protection 4/20/20 policies instituted along with staff education efforts including the importance of standard precautions). Box and whisker plots depict quartile ranges, median (dotted line), and mean values. Mean MRSA acquisition rates pre- (0.92 events per 1,000 patient days) significantly declined post-COVD-19 (to 0.66;P=0.016). Independent-samples t tests were used (2-tailed) for statistical comparisons except for variables without a normal distribution (Shorr Scores), for which a Mann-Whitney U test was used. Methods. Census-adjusted hospital-acquired MRSA acquisition events were analyzed over 10 months pre- (5/6/2019 to 2/23/2020) and post-COVD-19 (5/4/2020 to 2/28/2021), with a 6-week wash-in period coinciding with hospital-wide intensification of infection control measures (e.g., universal masking). HCP hand hygiene compliance rates were examined to reflect adherence to infection control practices. To examine impacts of non-infection control measures on MRSA transmission, we analyzed pre/post-COVD-19 differences in individual risk profiles for MRSA acquisition as well as a broad suite of properties of the hospital social network using person-location and person-person interactions inferred from the electronic medical record. Figure 2. Social Network Construction We constructed a contact network of hospitalized patients and staff at University of Virginia Hospital to analyze the properties of both person-location and person-person networks and their changes pre- and post-COVID-19. Colocation data (inferred from shared patient rooms and healthcare personnel (HCP)-patient interactions recorded in the electronic health record, e.g., medication administration) were used to construct contact networks, with nodes representing patients and HCP, and edges representing contacts. The above schematic shows how the temporal networks are inferred. In the figure, circles represent patients and the small filled squares represent HCP, while the larger rectangles represent patient rooms. The first room is a shared room with two patients. At each time step, co-location is inferred from the EMR data, which specifies interactions between HCP and patients. This can be represented as the temporal network (t) at the bottom. Results. Hand hygiene compliance significantly improved post-COVD-19, in parallel with other infection control measures. Patient Shorr Scores (an index of individual MRSA risk) were statistically similar pre-/post-COVD-19. Analysis of various network properties demonstrated no trends to suggest a reduced outbreak threshold post-COVD-19. Figure 3. Hand Hygiene Compliance Rates Analysis of hospital-wide hand hygiene auditing data (anonymous auditors deployed to various units across UVA Hospital with an average 1,710 observations per month (range 340 - 7,187)) demonstrated a statistically significant (6%) improvement in average monthly hand hygiene compliance (86.9% pre- versus 93.1% post-COVD-19;P=0.008). Figure 4. Individual MRSA Risk Factors We calculated the Shorr Score (a validated tool to estimate individual risk for MRSA carriage in hospitalized patients;Shorr et al. Arch Intern Med. 2008;168(20):2205-10) for patients using data from the electronic health record to test the hypothesis that individual risk factors in aggregate did not change significantly in the post-COVD-19 period to explain changes in MRSA acquisition. Values for this score ranged from 0 to 10 with the following criteria: recent hospitalization (4), nursing home residence (3), hemodialysis (2), ICU admission (1). Pictured are frequency distributions of Shorr scores in the pre-COVID-19 and post-COVID-19 periods. The Mann-Whitney effect size (E), 0.53 (P=0.51), indicated that pre- and post-COVD-19 distributions were very similar. We analyzed three major types of network properties for this analysis: (1) Node properties of the pre- and post-COVID-19 networks consisted of all the edges in the pre- and post-COVID-19 periods, respectively. We considered a number of standard properties used in social network analysis to quantify opportunities for patient-patient transmission: degree centrality (links held by each node), betweenness centrality (times each node acts as the shortest 'bridge' between two other nodes), closeness centrality (how close each node is to other nodes in network), Eigenvector centrality (node's relative influence on the network), and clustering coefficient (degree to which nodes cluster together) in the first five panels (left to right, top to bottom);(Newman, Networks: An Introduction, 2010). Each panel shows the frequency distributions of these properties. These properties generally did not have a normal distribution and therefore we used a Mann Whitney U test on random subsets of nodes in these networks to compare pre- and post-COVID properties. The mean effect size (E) and P-values are shown for each metric in parenthesis. We concluded that all of these pre- versus post-COVID-19 network properties were statistically similar. (2) Properties of the ego networks (networks induced by each node and its 'one-hop' neighbors). We considered density (average number of neighbors for each node;higher density generally favors lower outbreak threshold) and degree centrality (number of links held by each node) of ego networks (middle right and bottom left panels). The mean effect size and p-values using the Mann Whitney test are shown in parenthesis;there were no statistically significant differences in these properties in the pre- and post-COVID networks. (3) Aggregate properties of the weekly networks, consisting of all the interactions within a week. We considered modularity (measure of how the community structure differs from a random network;higher modularity means a stronger community structure and lower likelihood of transmission) and density (average number of neighbors each node;higher density generally favors lower outbreak threshold) of the weekly networks (bottom middle and bottom right panels). The modularity in the post-COVID weekly networks was slightly lower (i.e., it has a weaker community structure, and the network is more well mixed), while density was slightly higher, the differences of which were statistically significant;a caveat is that these are relatively small datasets (about 40 weeks). These differences (higher density, and better connectivity) both increase the risk of transmission in the post-COVID networks. In summary, the post-COVID networks either have similar properties as the pre-COVID networks, or had changes which are unlikely to have played a role in reducing MRSA transmission. Conclusion. A significant reduction in post-COVD-19 MRSA transmission may have been an unintended positive effect of enhanced infection control measures, particularly hand hygiene and increased mask use. A modest (11.6%) post-COVD-19 reduction in surveillance testing may have also played a role. Despite pandemic-related cohorting and census fluctuations, most network properties were not significantly different post-COVID-19, except for aggregate density and modularity which varied in a directio that instead favored transmission;therefore, HCP-based networks did not play a significant role in reducing MRSA transmission. Multivariate modeling to isolate relative contributions of these factors is underway. Figure 6. Surveillance Testing and Clinical Culturing Post-COVD-19, there was a modest (11.6%) but statistically significant reduction in surveillance PCR testing (42.4 mean tests per 1,000 patient days pre- versus 37.5 post-COVD-19;P<0.002). There was not a statistically significant difference in rates of clinical cultures sent (2.48 cultures per 1,000 patient days pre- versus 2.23 post-COVD-19;P=0.288).

Predicting Demand for Shared E-Scooter Using Community Structure and Deep Learning Method

The shared e-scooter is a popular and user-convenient mode of transportation, owing to the free-floating manner of its service. The free-floating service has the advantage of offering pick-up and drop-off anywhere, but has the disadvantage of being unavailable at the desired time and place because it is spread across the service area. To improve the level of service, relocation strategies for shared e-scooters are needed, and it is important to predict the demand for their use within a given area. Therefore, this study aimed to develop a demand prediction model for the use of shared e-scooters. The temporal scope was selected as October 2020, when the demand for e-scooter use was the highest in 2020, and the spatial scope was selected as Seocho and Gangnam, where shared e-scooter services were first introduced and most frequently used in Seoul, Korea. The spatial unit for the analysis was set as a 200 m square grid, and the hourly demand for each grid was aggregated based on e-scooter trip data. Prior to predicting the demand, the spatial area was clustered into five communities using the community structure method. The demand prediction model was developed based on long short-term memory (LSTM) and the prediction results according to the activation function were compared. As a result, the model employing the exponential linear unit (ELU) and the hyperbolic tangent (tanh) as the activation function produced good predictions regarding peak time demands and off-peak demands, respectively. This study presents a methodology for the efficient analysis of the wider spatial area of e-scooters.

COVID-19 and face masks - To use or not to use! (Special Issue: The epidemiologist and the pandemic.)

COVID-19 has evolved into a pandemic in quick time and being a droplet infection, it was quickly understood that prevention is the key. People started to use all types of masks and there was a panic as stocks started running out. Health care workers must use a triple layered surgical mask and those exposed to aerosol generating procedures must use an N 95 mask and these should be kept reserved for them, especially in a resource limited setting. Though initial advice from experts to the general public was not to use a mask in community settings unless they are sick or taking care of someone sick, the advice had to be later modified. Though CDC Atlanta currently advices everyone with no symptoms to wear cloth masks in the community, WHO opines there is no clear evidence to advise for or against mask use in the community. However, WHO encourages countries advising community mask use as it can generate useful evidence. Along with mask use, practicing all other preventive measures such as hand washing, cough etiquette, social distancing, quarantine and isolation are of utmost importance, without which, using surgical masks or even N95 masks, will not be much effective in the community setting.

Community-distributed compartmental models

A framework that allows the incorporation of community structure into epidemiological compartmental models has been developed. The models resulting from this process are compartmental models as well, which are related to the base models. This work includes an existence and uniqueness theorem, showing that, under certain conditions on the mobility, epidemiological models in which f(t,X) is continuous in time and Lipschitz continuous on the compartments induce unique community models;and a homogeneous mixing limit, showing that under high mobility conditions the base model is recovered in the global population. Applications of the SIR model and the impact of the community structure on the estimation of their effective parameters are discussed in detail. An open computational implementation of this framework is available to the scientific community. It allows modeling community distribution using mobility data, as shown with Spain data during the 2020 state of alarm.