Social network analysis of COVID-19 transmission in Karnataka, India

COVID-19 Mortality in the Delta Wave in India: A Hospital-Based Study From Ramanagara District, Karnataka

INTRODUCTION

Coronavirus 19 (COVID-19) disease spread rapidly over the world since its inception in December 2019 in Wuhan, China. India too was crippled by the burden of high caseloads and deaths. The first death caused by COVID-19 in Karnataka was reported on March 13, 2020. There is a plethora of information on the descriptive statistics, epidemiology, and management of COVID-19 cases. However, there has not been an in-depth and extensive exploration of COVID-19 mortality data in terms of published research from India. The study area was a 300 bedded tertiary care center in Ramnagara district, Karnataka. During the second wave, 150 beds were dedicated to COVID-19 cases referred from government centers. This study was carried out to assess the in-hospital mortality at this institute during the second wave. The expected outcome of this study was to shed light on co-morbidities associated with mortality, the age and sex distribution in mortality, and any other significant factors influencing mortality due to COVID-19.

METHODOLOGY

A hospital-based, retrospective, and observational-analytical study was carried out during April-August 2021, the second wave of COVID-19. The data included all deaths recorded in-hospital during the dedicated COVID-19 referral center status. Data were collected from case sheets and mortality audit forms that included basic demographics, symptoms, co-morbidities, admission pathway, transfer to ICU, oxygen therapy, ventilator requirement, duration of hospitalization, laboratory profile, and management modalities prior to death. Data were compiled into Microsoft Excel and were analyzed with JASP software (open source). Data were interpreted in terms of frequencies, averages with standard deviation, and bivariate and multivariate analysis.

RESULTS

 We analyzed mortality audits of 91 adult patients and one neonate. The male-to-female ratio was 1.67:1 (> 60% male), with an average age of 53.4 years (standard deviation 15.4 years). Most of the patients fell in the age range of 36 to 65 years (65%). The average duration was 5.6 days (range 0-35 days). The most common symptom was fever (84, 92.31%), followed by breathlessness (77, 84.62%) and fatigue (65, 71.43%). Only 10 had a positive contact history and only one patient reported travel to a containment zone. The source of infection was indeterminate in the majority of cases. Diabetes mellitus and hypertension were the commonest associated comorbidities. Almost three-quarters of the patients were tachypneic at admission and nearly 90% had low levels which included 43 patients with critically low SpO2. The inflammatory indicators, such as WBC count, CRP, and d-dimer, were raised in many patients (WBC count raised in 40% and d-dimer, CRP raised in > 50% of cases). A striking 83% of the patients had hyperglycemia. The most common immediate cause of death pertained to the respiratory system (ARDS, refractory hypoxia, respiratory) in more than half of the patients.

CONCLUSION

 This study reported the clinical and laboratory characteristics of 91 adult COVID-19 mortality cases at a teaching hospital at the peak of the Delta wave in Karnataka. While inflammatory indicators such as WBC count, CRP, and d-dimer were raised in many patients, our most remarkable finding was the high frequency of hyperglycemia. The findings of our study would contribute to enhancing the understanding of the clinical correlates and progression of COVID-19.

A Survey on COVID-19 Data Analysis Using AI, IoT, and Social Media

Coronaviruses are a well-established and deadly group of viruses that cause illness in both humans and animals. The novel type of this virus group, named COVID-19, was firstly reported in December 2019, and, with the passage of time, coronavirus has spread to almost all parts of the world. Coronavirus has been the cause of millions of deaths around the world. Furthermore, many countries are struggling with COVID-19 and have experimented with various kinds of vaccines to eliminate the deadly virus and its variants. This survey deals with COVID-19 data analysis and its impact on human social life. Data analysis and information related to coronavirus can greatly help scientists and governments in controlling the spread and symptoms of the deadly coronavirus. In this survey, we cover many areas of discussion related to COVID-19 data analysis, such as how artificial intelligence, along with machine learning, deep learning, and IoT, have worked together to fight against COVID-19. We also discuss artificial intelligence and IoT techniques used to forecast, detect, and diagnose patients of the novel coronavirus. Moreover, this survey also describes how fake news, doctored results, and conspiracy theories were spread over social media sites, such as Twitter, by applying various social network analysis and sentimental analysis techniques. A comprehensive comparative analysis of existing techniques has also been conducted. In the end, the Discussion section presents different data analysis techniques, provides future directions for research, and suggests general guidelines for handling coronavirus, as well as changing work and life conditions.

A Social Network Analysis Approach to Evaluate the Relationship Between the Mobility Network Metrics and the COVID-19 Outbreak

The emergence of the new coronavirus in late 2019 further highlighted the human need for solutions to explore various aspects of deadly pandemics. Providing these solutions will enable humans to be more prepared for dealing with possible future pandemics. In addition, it helps governments implement strategies to tackle and control infectious diseases similar to COVID-19 faster than ever before. In this article, we used the social network analysis (SNA) method to identify high-risk areas of the new coronavirus in Iran. First, we developed the mobility network through the transfer of passengers (edges) between the provinces (nodes) of Iran and then evaluated the in-degree and page rank centralities of the network. Next, we developed 2 Poisson regression (PR) models to predict high-risk areas of the disease in different populations (moderator) using the mobility network centralities (independent variables) and the number of patients (dependent variable). The P-value of .001 for both prediction models confirmed a meaningful interaction between our variables. Besides, the PR models revealed that in higher populations, with the increase of network centralities, the number of patients increases at a higher rate than in lower populations, and vice versa. In conclusion, our method helps governments impose more restrictions on high-risk areas to handle the COVID-19 outbreak and provides a viable solution for accelerating operations against future pandemics similar to the coronavirus.

An analytical tool to support public policies and isolation barriers against SARS-CoV-2 based on mobility patterns and socio-economic aspects

It is crucial to develop spatiotemporal analysis tools to mitigate risks during a pandemic. Many dashboards encountered in the literature do not consider how the geolocation characteristics and travel patterns may influence the spread of the virus. This work brings an interactive tool that is capable of crossing information about mobility patterns, geolocation characteristics and epidemiologic variables. To do so, our system uses a mobility network, generated through anonymized mobile location data, which enables the division of a region into representative clusters. The clusters' aggregated socioeconomic, and epidemiologic indicators can be analyzed through multiple coordinated views. The proposal is to enable users to understand how different locations commute citizens, monitor risk over time, and understand what locations need more assistance, considering different layers of visualization, such as clusters and individual locations. The main novelty is the interactive way to construct the mobility network that defines the social distancing level and the way that risks are managed, since many different geolocation characteristics can be considered and visualized, such as socioeconomic indicators of a location, the economic importance of a set of locations, and the connection of important neighborhoods of a city with other cities. The proposed tool was built and verified by experts assembled to give scientific recommendations to the city administration of Recife, the capital city of Pernambuco. Our analysis shows how a policymaker could use the tool to evaluate different isolation scenarios considering the trade-off between economic activity and contamination risk, where the practical insights can also be used to tighten and relax mitigation measures in other phases of a pandemic.

Assessing efficacy of instructor based orientation to donning doffing protocols and modifications to doffing area infrastructure in reducing SARS-CoV-2 infection among Doctors assigned to COVID-19 patient care

Background

We assess the efficacy of orientation programmes for doctors of proper donning, doffing techniques for personal protective equipment (PPE) and safe practices inside the COVID-19 hospital in reducing the COVID-19 infection rate among doctors.

Methods

A total of 767 resident doctors and 197 faculty visits on weekly rotation were recorded over a six month period. Doctors were guided through orientation sessions before their entry into the COVID-19 hospital from 1 August 2020.The infection rate among doctors was used to study the efficacy of the programme. McNemars Chi-square test was used to compare the infection rate in the two groups before and after orientation sessions were commenced.

Discussion

A statistically significant reduction in SARS-CoV-2 infection was seen among resident doctors after orientation programmes and infrastructure modification (3% vs 7.4%, p=0.03). Twenty-eight of 32 (87.5%) doctors who tested positive developed asymptomatic to mild infection. The infection rate was 3.65% and 2.1% among residents and faculty respectively. There was no mortality recorded.

Conclusion

Orientation programme for healthcare workers for PPE donning and doffing protocols with practical demonstration and trial of PPE usage can significantly reduce COVID-19infection. Such sessions should be mandatory for all workers on deputation in designated area for Infectious Diseases and in pandemic situations.

Cluster-based analysis of COVID-19 cases using self-organizing map neural network and K-means methods to improve medical decision-making

In this study, we utilized unsupervised machine learning techniques to examine the relationship between different symptoms in cases who died of COVID-19 and cases who recovered from it. First, our data was cleared of redundancies, and the ten most important variables were selected using a filter-based technique (extra-tree classifier). Next, we calculated the Silhouette, Davis Boldin (DB), and the mean intra-cluster distance measures to select the optimal number of clusters, then clustered the data using both the K-means and hierarchical clustering based on Self Organizing Map (SOM) neural network. Our results revealed that patients who died of COVID-19 had high mean values in different symptoms, but not all patients with this characteristic necessarily died. Besides, our result indicated that the patient's age is directly related to the hospital duration, and elderly patients are more likely to be assigned to the intensive care unit (ICU). However, the patient's sex has the same distribution in different groups and does not correlate with other symptoms. In conclusion, our results confirmed past studies. Also, this research helps physicians improve medical services by considering other important factors for treating different groups of COVID-19 patients.

Empirical Analysis of the Dynamics of the COVID-19 Epidemic in Urban Embedded Social Networks

Background

Due to the continual recurrence of COVID-19 in urban areas, it is important to know more about the evolution of the epidemic within this setting to mitigate the risk of the situation getting worse. As the virus spreads through human society, the social networks of confirmed cases can provide us with crucial new insights on this question.

Methods

Based on the epidemiological reports of 235 COVID-19 cases in Nanjing, we constructed a social contact network for the epidemic. By analyzing the structure of this network, we explored the transmission characteristics of the epidemic, to provide evidence-based explanations for its transmission.

Results

In our constructed transmission network, more than half (95/165, 57.58%) of patients were found not to have transmitted the infection, with only 15 (9.10%) source patients accounting for more than a third of the contagion (60, 36.36%), suggesting that the transmission of COVID-19 varies per individuals. Patients in the 31 to 50 age group were the main source of infectious clusters, with females playing a more active role in passing on the infection. Network component analysis identified nine components with disproportionate concentrations of influential patients, accounting for 49.09% (81) of the patients and 59.09% (78) of epidemiological network contacts. Family aggregation may favor disease transmission, and parenthood is the relationship with the highest infection risk within the family cluster. In addition, some specific public places, such as chess and card parlors, were found to be notable hotspots for community infection.

Conclusion

This study presents the evolution of the urban epidemic from the perspective of individual-level and socially interactive processes. This real-world evidence can help to increase public awareness of the epidemic, formulate countermeasures, and allocate limited public health resources for urban management.

Symptom-based analysis of COVID-19 cases using supervised machine learning approaches to improve medical decision-making

The world today faces a new challenge that is unprecedented in the last 100 years. The emergence of a new coronavirus has led to a human catastrophe. Scientists in various sciences have been looking for solutions to this problem so far. In addition to general vaccination, maintaining social distance and adherence to government guidelines on safety precaution measures are the most well-known strategies to prevent COVID-19 infection. In this research, we tried to examine the symptoms of COVID-19 cases through different supervised machine learning methods. We solved the class imbalance problem using the synthetic minority over-sampling (SMOTE) method and then developed some classification models to predict the outcome of COVID-19 cases (recovery or death). Besides, we implemented a rule-based technique to identify different combinations of variables with specific ranges of their values that together affect disease severity. Our results showed that the random forest model with 95.6% accuracy, 97.1% sensitivity, 94.0% specification, 94.4% precision, 95.8% F-score, and 99.3% AUC-score outperforms state-of-the-art classification models. Finally, we identified the most significant rules that state various combinations of 6 features in certain ranges of their values lead to patients’ recovery with a confidence value of 90%. In conclusion, the classification results in this study show better performance than recent studies, and the extracted rules help physicians consider other important factors to improve health services and medical decision-making for different groups of COVID-19 patients.

Mining relationships between transmission clusters from contact tracing data: An application for investigating COVID-19 outbreak

OBJECTIVE

Contact tracing of reported infections could enable close contacts to be identified, tested, and quarantined for controlling further spread. This strategy has been well demonstrated in the surveillance and control of COVID-19 (coronavirus disease 2019) epidemics. This study aims to leverage contact tracing data to investigate the degree of spread and the formation of transmission cascades composing of multiple clusters.

MATERIALS AND METHODS

An algorithm on mining relationships between clusters for network analysis is proposed with 3 steps: horizontal edge creation, vertical edge consolidation, and graph reduction. The constructed network was then analyzed with information diffusion metrics and exponential-family random graph modeling. With categorization of clusters by exposure setting, the metrics were compared among cascades to identify associations between exposure settings and their network positions within the cascade using Mann-Whitney U test.

RESULTS

Experimental results illustrated that transmission cascades containing or seeded by daily activity clusters spread faster while those containing social activity clusters propagated farther. Cascades involving work or study environments consisted of more clusters, which had a higher transmission range and scale. Social activity clusters were more likely to be connected, whereas both residence and healthcare clusters did not preferentially link to clusters belonging to the same exposure setting.

CONCLUSIONS

The proposed algorithm could contribute to in-depth epidemiologic investigation of infectious disease transmission to support targeted nonpharmaceutical intervention policies for COVID-19 epidemic control.

COVID-19 prediction models: a systematic literature review

As the world grapples with the problem of the coronavirus disease 2019 (COVID-19) pandemic and its devastating effects, scientific groups are working towards solutions to mitigate the effects of the virus. This paper aimed to collate information on COVID-19 prediction models. A systematic literature review is reported, based on a manual search of 1,196 papers published from January to December 2020. Various databases such as Google Scholar, Web of Science, and Scopus were searched. The search strategy was formulated and refined in terms of subject keywords, geographical purview, and time period according to a predefined protocol. Visualizations were created to present the data trends according to different parameters. The results of this systematic literature review show that the study findings are critically relevant for both healthcare managers and prediction model developers. Healthcare managers can choose the best prediction model output for their organization or process management. Meanwhile, prediction model developers and managers can identify the lacunae in their models and improve their data-driven approaches.

The role of age in the spreading of COVID-19 across a social network in Bucharest

We analyse officially procured data detailing the COVID-19 transmission in Romania's capital Bucharest between 1st August and 31st October 2020. We apply relational hyperevent models on 19,713 individuals with 13,377 infection ties to determine to what degree the disease spread is affected by age whilst controlling for other covariate and human-to-human transmission network effects. We find that positive cases are more likely to nominate alters of similar age as their sources of infection, thus providing evidence for age homophily. We also show that the relative infection risk is negatively associated with the age of peers, such that the risk of infection increases as the average age of contacts decreases. Additionally, we find that adults between the ages 35 and 44 are pivotal in the transmission of the disease to other age groups. Our results may contribute to better controlling future COVID-19 waves, and they also point to the key age groups which may be essential for vaccination given their prominent role in the transmission of the virus.

Revisiting the Effects of High-Speed Railway Transfers in the Early COVID-19 Cross-Province Transmission in Mainland China

Coronavirus disease 2019 (COVID-19) is an ongoing pandemic that was reported at the end of 2019 in Wuhan, China, and was rapidly disseminated to all provinces in around one month. The study aims to assess the changes in intercity railway passenger transport on the early spatial transmission of COVID-19 in mainland China. Examining the role of railway transport properties in disease transmission could help quantify the spatial spillover effects of large-scale travel restriction interventions. This study used daily high-speed railway schedule data to compare the differences in city-level network properties (destination arrival and transfer service) before and after the Wuhan city lockdown in the early stages of the spatial transmission of COVID-19 in mainland China. Bayesian multivariate regression was used to examine the association between structural changes in the railway origin-destination network and the incidence of COVID-19 cases. Our results show that the provinces with rising transfer activities after the Wuhan city lockdown had more confirmed COVID-19 cases, but changes in destination arrival did not have significant effects. The regions with increasing transfer activities were located in provinces neighboring Hubei in the widthwise and longitudinal directions. These results indicate that transfer activities enhance interpersonal transmission probability and could be a crucial risk factor for increasing epidemic severity after the Wuhan city lockdown. The destinations of railway passengers might not be affected by the Wuhan city lockdown, but their itinerary routes could be changed due to the replacement of an important transfer hub (Wuhan city) in the Chinese railway transportation network. As a result, transfer services in the high-speed rail network could explain why the provinces surrounded by Hubei had a higher number of confirmed COVID-19 cases than other provinces.

Density or Connectivity: What Are the Main Causes of the Spatial Proliferation of COVID-19 in Korea?

COVID-19 has sparked a debate on the vulnerability of densely populated cities. Some studies argue that high-density urban centers are more vulnerable to infectious diseases due to a higher chance of infection in crowded urban environments. Other studies, however, argue that connectivity rather than population density plays a more significant role in the spread of COVID-19. While several studies have examined the role of urban density and connectivity in Europe and the U.S., few studies have been conducted in Asian countries. This study aims to investigate the role of urban spatial structure on COVID-19 by comparing different measures of urban density and connectivity during the first eight months of the outbreak in Korea. Two measures of density were derived from the Korean census, and four measures of connectivity were computed using social network analysis of the Origin-Destination data from the 2020 Korea Transport Database. We fitted both OLS and negative binomial models to the number of confirmed COVID-19 patients and its infection rates at the county level, collected individually from regional government websites in Korea. Results show that both density and connectivity play an important role in the proliferation of the COVID-19 outbreak in Korea. However, we found that the connectivity measure, particularly a measure of network centrality, was a better indicator of COVID-19 proliferation than the density measures. Our findings imply that policies that take into account different types of connectivity between cities might be necessary to contain the outbreak in the early phase.