The evacuation of the population from flood-affected regions is a non-structural measure to mitigate flood hazards. Shelters used for this purpose usually accommodate a large number of flood evacuees ...for a temporary period. Floods during pandemic result in a compound hazard. Evacuations under such situations are difficult to plan as social distancing is nearly impossible in the highly crowded shelters. This results in a multi-objective problem with conflicting objectives of maximizing the number of evacuees from flood-prone regions and minimizing the number of infections at the end of the shelter's stay. To the best of our knowledge, such a problem is yet to be explored in literature. Here we develop a simulation-optimization framework, where multiple objectives are handled with a max-min approach. The simulation model consists of an extended Susceptible Exposed Infectious Recovered Susceptible (SEIRS) model.We apply the proposed model to the flood-prone Jagatsinghpur district in the state of Odisha, India. We find that the proposed approach can provide an estimate of people required to be evacuated from individual flood-prone villages to reduce flood hazards during the pandemic. At the same time, this does not result in an uncontrolled number of new infections. The proposed approach can generalize to different regions and can provide a framework to stakeholders to manage conflicting objectives in disaster management planning and to handle compound hazards.
The dense social contact networks and high mobility in congested urban areas facilitate the rapid transmission of infectious diseases. Typical mechanistic epidemiological models are either based on ...uniform mixing with ad-hoc contact processes or need real-time or archived population mobility data to simulate the social networks. However, the rapid and global transmission of the novel coronavirus (SARS-CoV-2) has led to unprecedented lockdowns at global and regional scales, leaving the archived datasets to limited use. While it is often hypothesized that population density is a significant driver in disease propagation, the disparate disease trajectories and infection rates exhibited by the different cities with comparable densities require a high-resolution description of the disease and its drivers. In this study, we explore the impact of the creation of containment zones on travel patterns within the city. Further, we use a dynamical network-based infectious disease model to understand the key drivers of disease spread at sub-kilometer scales demonstrated in the city of Ahmedabad, India, which has been classified as a SARS-CoV-2 hotspot. We find that in addition to the contact network and population density, road connectivity patterns and ease of transit are strongly correlated with the rate of transmission of the disease. Given the limited access to real-time traffic data during lockdowns, we generate road connectivity networks using open-source imageries and travel patterns from open-source surveys and government reports. Within the proposed framework, we then analyze the relative merits of social distancing, enforced lockdowns, and enhanced testing and quarantining mitigating the disease spread.
Network structures in a wide array of systems such as social networks, transportation, power and water distribution infrastructures, and biological and ecological systems can exhibit critical ...thresholds or tipping points beyond which there are disproportionate losses in the system functionality. There is growing concern over tipping points and failure tolerance of such systems as tipping points can lead to an abrupt loss of intended functionality and possibly non-recoverable states. While attack tolerance of networked systems has been intensively studied for the disruptions originating from a single point of failure, there have been instances where real-world systems are subject to simultaneous or sudden onset of concurrent disruption at multiple locations. Using open-source data from the United States Airspace Airport network and Indian Railways Network, and random networks as prototype class of systems, we study their responses to synthetic attack strategies of varying sizes. For both types of networks, we observe the presence of warning regions, which serve as a precursor to the tipping point. Further, we observe the statistically significant relationships between network robustness and size of simultaneous distribution, which generalizes to the networks with different topological attributes for random failures and targeted attacks. We show that our approach can determine the entire robustness characteristics of networks of disparate architecture subject to disruptions of varying sizes. Our approach can serve as a paradigm to understand the tipping point in real-world systems, and the principle can be extended to other disciplines to address critical issues of risk management and resilience.
Designing effective recovery strategies for damaged networked systems is critical to the resilience of built, human and natural systems. However, progress has been limited by the inability to bring ...together distinct philosophies, such as complex network topology through centrality measures and network flow optimization through entropy measures. Network centrality-based metrics are relatively more intuitive and computationally efficient while optimization-based approaches are more amenable to dynamic adjustments. Here we show, with case studies in real-world transportation systems, that the two distinct network philosophies can be blended to form a hybrid recovery strategy that is more effective than either, with the relative performance depending on aggregate network attributes. Direct applications include disaster management and climate adaptation sciences, where recovery of lifeline networks can save lives and economies.
The structure, interdependence, and fragility of systems ranging from power grids and transportation to ecology, climate, biology and even human communities and the Internet, have been examined ...through network science. While the response to perturbations has been quantified, recovery strategies for perturbed networks have usually been either discussed conceptually or through anecdotal case studies. Here we develop a network science-based quantitative methods framework for measuring, comparing and interpreting hazard responses and as well as recovery strategies. The framework, motivated by the recently proposed temporal resilience paradigm, is demonstrated with the Indian Railways Network. The methods are demonstrated through the resilience of the network to natural or human-induced hazards and electric grid failure. Simulations inspired by the 2004 Indian Ocean Tsunami and the 2012 North Indian blackout as well as a cyber-physical attack scenario. Multiple metrics are used to generate various recovery strategies, which are simply sequences in which system components should be recovered after a disruption. Quantitative evaluation of recovery strategies suggests that faster and more resource-effective recovery is possible through network centrality measures. Case studies based on two historical events, specifically the 2004 Indian Ocean tsunami and the 2012 North Indian blackout, and a simulated cyber-physical attack scenario, provides means for interpreting the relative performance of various recovery strategies. Quantitative evaluation of recovery strategies suggests that faster and more resource-effective restoration is possible through network centrality measures, even though the specific strategy may be different for sub-networks or for the partial recovery.
Dynamic contrast-enhanced (DCE) lymphangiography is a novel imaging technique with a potential role in suspected cases of lymphatic leaks. A 15-year-old male with a post operative chylous leak and an ...8-year-old male who developed chylous ascites secondary to disseminated tuberculosis are presented. Both children underwent MR lymphangiography. Contribution The role of DCE-MR lymphangiography in cases of chylous ascites to help guide appropriate management.
This study attempts to generate preliminary data regarding post-COVID pulmonary fungal infections, namely, COVID-19-associated pulmonary aspergillosis (CAPA), COVID-19-associated pulmonary ...mucormycosis (CAPM), and mixed infections from the Himalayas and compares the micro-radio-clinical profile and outcomes of the affected patients.
A retrospective data analysis was conducted, where clinical profiles, microbiological and radiological reports, and outcomes of
= 16 patients of post-COVID pulmonary infections were compared.
Of
= 16 patients,
= 7 had CAPA (
= 5
s,
= 1
1
),
= 5 CAPM (Rhizopus arrhizus)
and
= 4 with mixed infections (
3 infected with
and
spp. and
= 1 with
and
). Thick-walled cavitary lesions, air-fluid levels, and multiple centrilobular nodules were some of the common radiological findings reported among these patients.
The immuno-compromised state following COVID-19 infection and treatment might be responsible for the progression of regular exposure to the dense Himalayan vegetation into an invasive pulmonary fungal infection. Suspecting post-COVID pulmonary fungal infection is necessary for primary care physicians to ensure timely referral to higher centers. Mixed pulmonary fungal infections (coinfection with
spp. and
spp.) are also emerging as important sequelae of COVID-19.
Abstract
Poster session 2, September 22, 2022, 12:30 PM - 1:30 PM
Objective
The study aims to generate preliminary data about post-COVID pulmonary fungal infections in the Himalayas and analyze ...patients’ micro-radio-clinical profiles and outcomes.
Methodology
We conducted a retrospective study at a tertiary care teaching hospital in the Himalayas to generate preliminary post-COVID pulmonary fungal infection data. Sputum, Endotracheal Tube (ET), and Bronchoalveolar lavage (BAL) samples of patients sent to the Mycology laboratory were subjected to KOH mount and aerobic inoculation on Sabouraud dextrose agar plates at 37°C. The patients’ symptoms, diagnosis, clinical-radiological profile, and outcome were collected from the hospital database.
Results
Among n = 16 cases of post-COVID pulmonary fungal infections aged 53 +/- 13.38 years, n = 7 (43.75%) had Pulmonary Aspergillosis (n = 5 A. fumigatus, n = 1 A. flavus, n = 1 A. niger), n = 5 (31.25%) had Pulmonary Mucormycosis (Rhizopus arrhizus), and n = 4 (25%) had mixed infection. In 2 of 4 mixed infection patients, R. arrhizus was identified on KOH microscopy and A. fumigatus on SDA Agar. Both A. fumigatus and R. arrhizus were identified on KOH Microscopy of the third patient, while only A. fumigatus was cultivated on his SDA Agar. Aspergillus flavus and R. arrhizus were isolated simultaneously from the sample of the last patient, but only R. arrhizus was identified on KOH Microscopy.
Clinical symptoms were similar among Pulmonary Aspergillosis and Mucormycosis patients, but hemoptysis was reported only among Pulmonary Aspergillosis patients. Pre-existing co-morbid end-organ damage, AKI, CKD, CLD, COPD, and CAD was more common among Pulmonary Mucormycosis patients and rare among Pulmonary Aspergillosis patients. Treatment requirements and clinical outcomes of patients infected with either mold were similar. The clinical profile of mixed infection patients was notably different from the others. All the patients were males, none complained of chest pain or expectoration, and none had a history of PTB, AKI, CKD, CLD, COPD, or CAD. Only 2 (50%) mixed infection patients needed supplemental high flow oxygen, unlike all (100%) patients diagnosed with single mold infection. None of the mixed infection patients required steroids. Moreover, none of the mixed infection patients died, unlike 60% mortality in cases of single-species infections.
On radiological investigation, n = 6 had typical thick-walled cavitary lesions with air-fluid levels and multiple centrilobular nodules giving a tree in bud appearance, of which n = 4 had bilateral lung involvement, and n = 2 had only one lung involved. n = 1 patient had a well-circumscribed lung abscess.
Conclusion
COVID patients from the Himalayas had a higher prevalence of invasive pulmonary fungal infections, probably due to the dense surrounding vegetation. The immuno-compromised state following COVID-19 infection/treatment might be responsible for the progression of regular exposure to invasive pulmonary infection.
