Epidemic Modeling using Hybrid of Time-varying SIRD, Particle Swarm Optimization, and Deep Learning (preprint)

Epidemiological models are best suitable to model an epidemic if the spread pattern is stationary. To deal with non-stationary patterns and multiple waves of an epidemic, we develop a hybrid model encompassing epidemic modeling, particle swarm optimization, and deep learning. The model mainly caters to three objectives for better prediction: 1. Periodic estimation of the model parameters. 2. Incorporating impact of all the aspects using data fitting and parameter optimization 3. Deep learning based prediction of the model parameters. In our model, we use a system of ordinary differential equations (ODEs) for Susceptible-Infected-Recovered-Dead (SIRD) epidemic modeling, Particle Swarm Optimization (PSO) for model parameter optimization, and stacked-LSTM for forecasting the model parameters. Initial or one time estimation of model parameters is not able to model multiple waves of an epidemic. So, we estimate the model parameters periodically (weekly). We use PSO to identify the optimum values of the model parameters. We next train the stacked-LSTM on the optimized parameters, and perform forecasting of the model parameters for upcoming four weeks. Further, we fed the LSTM forecasted parameters into the SIRD model to forecast the number of COVID-19 cases. We evaluate the model for highly affected three countries namely; the USA, India, and the UK. The proposed hybrid model is able to deal with multiple waves, and has outperformed existing methods on all the three datasets.

Approaching epidemiological dynamics of COVID-19 with physics-informed neural networks (preprint)

A physics-informed neural network (PINN) embedded with the susceptible-infected-removed (SIR) model is devised to understand the temporal evolution dynamics of infectious diseases. Firstly, the effectiveness of this approach is demonstrated on synthetic data as generated from the numerical solution of the susceptible-asymptomatic-infected-recovered-dead (SAIRD) model. Then, the method is applied to COVID-19 data reported for Germany and shows that it can accurately identify and predict virus spread trends. The results indicate that an incomplete physics-informed model can approach more complicated dynamics efficiently. Thus, the present work demonstrates the high potential of using machine learning methods, e.g., PINNs, to study and predict epidemic dynamics in combination with compartmental models.

Brain death due to neuroaspergillosis in a patient with COVID-19.

Co-infections between SARS-CoV-2 and other pathogens is an important consideration for the treatment of patients with COVID-19. Aspergillus infections are part of this consideration since they present high morbidity and mortality. We present the case of a patient with COVID-19 and Aspergillus Fumigatus coinfection that evolves with brain death due to multiple heterogeneous lesions in the brain, which after a post-mortem biopsy found pathological lesions compatible with Aspergillus.

Lung transplantation from controlled donation after circulatory death using simultaneous abdominal normothermic regional perfusion: A single center experience.

Despite the benefits of abdominal normothermic regional perfusion (A-NRP) for abdominal grafts in controlled donation after circulatory death (cDCD), there is limited information on the effect of A-NRP on the quality of the cDCD lungs. We aimed to study the effect of A-NRP in lungs obtained from cDCD and its impact on recipients´ outcomes. This is a study comparing outcomes of lung transplants (LT) from cDCD donors (September 2014 to December 2021) obtained using A-NRP as the abdominal preservation method. As controls, all lung recipients transplanted from donors after brain death (DBD) were considered. The primary outcomes were lung recipient 3-month, 1-year, and 5-year survival. A total of 269 LT were performed (60 cDCD and 209 DBD). There was no difference in survival at 3 months (98.3% cDCD vs. 93.7% DBD), 1 year (90.9% vs. 87.2%), and 5 years (68.7% vs. 69%). LT from the cDCD group had a higher rate of primary graft dysfunction grade 3 at 72 h (10% vs. 3.4%; p <  .001). This is the largest experience ever reported with the use of A-NRP combined with lung retrieval in cDCD donors. This combined method is safe for lung grafts presenting short-term survival outcomes equivalent to those transplanted through DBD.

Organ Procurement From Donors After Brain Death During the COVID-19 Pandemic: Experience of a High Prevalence Country.

OBJECTIVES: Our aim was to compare the donation process before and after the COVID-19 pandemic in an organ procurement unit in Iran and to discuss different strategies to address the impact of the pandemic on the donation process. MATERIALS AND METHODS: All activities including donor detection, donor selection, family approach, donors characteristics, rate of organs per donor, and types of organs were compared between 2 intervals over 18 months (March 2020 to June 2021 [during the COVID19 pandemic] vs November 2018 to February 2020 [before the pandemic]). RESULTS: Before and during the COVID-19 pandemic, there were 218 and 137 brain dead donors with mean age of 42.6 ± 14.5 and 42.8 ± 15.5 years, respectively (P = .82). The prevalence of tumors leading to brain death decreased by more than half during the COVID-19 pandemic (P = .04). There was a 52% increase in cardiac death before organ retrieval during the COVID-19 pandemic, reaching 38% from 25% before COVID-19. During the 2 intervals, the number of organs per donor was 2.3 ± 1.2 and 2.2 ± 1.2 (P = .52). The rate of actual to potential donors before and during the pandemic was significantly different, with 42.16 ± 7.8% before and 29.9 ± 4.8% during the pandemic, mostly as a result of unsuitable donors. The time to obtain family consent during the COVID-19 pandemic was 35.1 ± 8.5 hours, which was a significantly longer length of time than before the pandemic (21.3 ± 12.3 hours; P = .008). CONCLUSIONS: In our organ procurement unit, which encompasses a population of 5.5 million in Tehran, Iran, the number of actual donors was reduced dramatically during the studied pandemic period. However, despite a high workload, all transplant centers and organ procurement units in Iran worked to identify and transplant the available organs to reduce wait list mortality.

Brain death in a vaccinated patient with COVID-19 infection.

We present a case of brain death in a vaccinated, immunocompromised patient who presented with COVID-19 pneumonia. Imaging was characterized by diffuse cerebral edema, pseudo-subarachnoid hemorrhage, and no antegrade flow above the terminal internal carotid arteries. To our knowledge, this is the first case report with such findings in a vaccinated patient.

SARS-CoV-2 Mortality Surveillance among Community Deaths brought to University Teaching Hospital Mortuary in Lusaka, Zambia, 2020 (preprint)

Introduction During March-December 2020, Zambia recorded 20,725 confirmed COVID-19 cases, with the first wave peaking between July and August. Of the 388 COVID-19-related deaths occurring nationwide, most occurred in the community. We report findings from COVID-19 mortality surveillance among community deaths brought to the University Teaching Hospital (UTH) mortuary in Lusaka. Methods In Zambia, when a person dies in the community, and is brought into a health facility mortuary, they are recorded as ‘brought in dead’ (BID). The UTH mortuary accepts persons BID for Lusaka District, the most populated district in Zambia. We analyzed data for persons BID at UTH during 2020. We analyzed two data sources: weekly SARS-CoV-2 test results for persons BID and monthly all-cause mortality numbers among persons BID. For all-cause mortality among persons BID, monthly deaths during 2020 that were above the upper bound of the 95% confidence interval for the historic mean (2017-2019) were considered significant. Spearman’s rank test was used to correlate the overall percent positivity in Zambia with all-cause mortality and SARS-CoV-2 testing among persons BID at UTH mortuary. Results During 2020, 7,756 persons were BID at UTH (monthly range 556-810). SARS-CoV-2 testing began in April 2020, and through December 3,131 (51.9%) of 6,022 persons BID were tested. Of these, 212 (6.8%) were SARS-CoV-2 positive with weekly percent test positivity ranging from 0-32%, with the highest positivity occurring during July 2020. There were 1,139 excess persons BID from all causes at UTH mortuary in 2020 compared to the 2017-2019 mean. The monthly number of persons BID from all causes was above the upper bound of the 95% confidence interval during June-September and December. Conclusion Increases in all-cause mortality and SARS-CoV-2 test positivity among persons BID at UTH mortuary corresponded with the first peak of the COVID-19 epidemic in June and August 2020, indicating possible increased mortality related to the COVID-19 epidemic in Zambia. Combining all-cause mortality and SARS-CoV-2 testing for persons BID provides useful information about the severity of the epidemic in Lusaka and should be implemented throughout Zambia.

Multiple organ retrieval in a brain dead left ventricular assist device donor.

Left ventricular assist device (LVAD) support in donors may contribute in preserving proper haemodynamics and systemic perfusion during organ retrieval thus decreasing the risk of multiple organ injury. This is an option to expand the current organ supply. We report on intra-abdominal organs procurement strategy in a selected LVAD recipient who suffered a fatal cerebrovascular accident at the time of COVID-19 pandemic outbreak. The liver and kidneys grafts have been successfully transplanted.

The inhibition of eIF5A hypusination by GC7, a preconditioning protocol to prevent brain death-induced renal injuries in a preclinical porcine kidney transplantation model.

The eIF5A hypusination inhibitor GC7 (N1-guanyl-1,7-diaminoheptane) was shown to protect from ischemic injuries. We hypothesized that GC7 could be useful for preconditioning kidneys from donors before transplantation. Using a preclinical porcine brain death (BD) donation model, we carried out in vivo evaluation of GC7 pretreatment (3 mg/kg iv, 5 minutes after BD) at the beginning of the 4h-donor management, after which kidneys were collected and cold-stored (18h in University of Wisconsin solution) and 1 was allotransplanted. Groups were defined as following (n = 6 per group): healthy (CTL), untreated BD (Vehicle), and GC7-treated BD (Vehicle + GC7). At the end of 4h-management, GC7 treatment decreased BD-induced markers, as radical oxygen species markers. In addition, GC7 increased expression of mitochondrial protective peroxisome proliferator-activated receptor-gamma coactivator-1-alpha (PGC1α) and antioxidant proteins (superoxyde-dismutase-2, heme oxygenase-1, nuclear factor [erythroid-derived 2]-like 2 [NRF2], and sirtuins). At the end of cold storage, GC7 treatment induced an increase of NRF2 and PGC1α mRNA and a better mitochondrial integrity/homeostasis with a decrease of dynamin- related protein-1 activation and increase of mitofusin-2. Moreover, GC7 treatment significantly improved kidney outcome during 90 days follow-up after transplantation (fewer creatininemia and fibrosis). Overall, GC7 treatment was shown to be protective for kidneys against BD-induced injuries during donor management and subsequently appeared to preserve antioxidant defenses and mitochondria homeostasis; these protective effects being accompanied by a better transplantation outcome.

Deceased-Donor Organ Transplantation in India: Current Status, Challenges, and Solutions.

Tamil Nadu, Gujarat, Telangana, Maharashtra, Kerala, Chandigarh, Karnataka, National Capital Territory of Delhi, and Rajasthan are states and union territories having active deceased-donor organ transplant programs in India. Transplant data (2013-2018) have been collected by the National Organ and Tissue Transplant Organization from all states and union territories of India and submitted to the Global Observatory on Donation and Transplantation. From 2013 to 2018, 49155 transplants were reported in India, including 39000 living-donor organ recipients and 10 155 deceased-donor organ recipients. These transplants were for kidney (living donor = 32584, deceased donor = 5748), liver (living donor = 6416, deceased donor = 2967), heart (deceased donor = 895), lung (deceased donor = 459), pancreas (deceased donor = 78), and small bowel (deceased donor = 8). According to 2018 data, India was the second largest transplanting country in the world in terms of the absolute number of transplants. Here, we discuss the status, progress, challenges, and solutions for deceaseddonor organ transplantation. The plan to increase rates of organ donation in India include the following points: teamwork and focus by intensive care unit doctors; public education on organ donation using social media; professional education and family donation conversation programs for brain death declaration and donor management; organ procurement organizations; international collaboration and regular meetings and updates for organizations working in the field of organ transplantation; grief counseling and reporting of potential donation for families of recently deceased people; nonfinancial incentivization to families of potential organ donors; expert committees and standard operating protocols for use of marginal donor organs, donation after circulatory death programs, and machine perfusion; maintenance of transparency and ethics in organ donation, allocation, and transplantation as directed by governmental, nongovernmental, and intergovernmental entities; and regular audit of progress and registry data.

Brain Death Diagnosis for Potential Organ Donors During the Covid-19 Pandemic.

Formal brainstem reflex testing remains one of the most important procedures in identification and evaluation of patients who meet clinical criteria for brainstem death. Early identification of such patients is critical since willing donors may contribute to the organ donation process. During the first two waves of the coronavirus disease of 2019 (COVID-19) pandemic, organ transplantation from brainstem dead donors has declined significantly due to several reasons, including perceived increased risk of virus transmission to both physicians as well as patients as well as lack of awareness regarding donor workup in the context of the COVID-19 pandemic.

SARS-CoV-2 infects blood monocytes to activate NLRP3 and AIM2 inflammasomes, pyroptosis and cytokine release (preprint)

SARS-CoV-2 causes acute respiratory distress that can progress to multiorgan failure and death in a minority of patients. Although severe COVID-19 disease is linked to exuberant inflammation, how SARS-CoV-2 triggers inflammation is not understood. Monocytes and macrophages are sentinel immune cells in the blood and tissue, respectively, that sense invasive infection to form inflammasomes that activate caspase-1 and gasdermin D (GSDMD) pores, leading to inflammatory death (pyroptosis) and processing and release of IL-1 family cytokines, potent inflammatory mediators. Here we show that expression quantitative trait loci (eQTLs) linked to higher GSDMD expression increase the risk of severe COVID-19 disease (odds ratio, 1.3, p<0.005). We find that about 10% of blood monocytes in COVID-19 patients are infected with SARS-CoV-2. Monocyte infection depends on viral antibody opsonization and uptake of opsonized virus by the Fc receptor CD16. After uptake, SARS-CoV-2 begins to replicate in monocytes, as evidenced by detection of double-stranded RNA and subgenomic RNA and expression of a fluorescent reporter gene. However, infection is aborted, and infectious virus is not detected in infected monocyte supernatants or patient plasma. Instead, infected cells undergo inflammatory cell death (pyroptosis) mediated by activation of the NLRP3 and AIM2 inflammasomes, caspase-1 and GSDMD. Moreover, tissue-resident macrophages, but not infected epithelial cells, from COVID-19 lung autopsy specimens showed evidence of inflammasome activation. These findings taken together suggest that antibody-mediated SARS-CoV-2 infection of monocytes/macrophages triggers inflammatory cell death that aborts production of infectious virus but causes systemic inflammation that contributes to severe COVID-19 disease pathogenesis.

Modelling, Simulations and Analysis of the First and Second COVID-19 Epidemics in Beijing (preprint)

To date, over 182 million people on infected with COVID-19. It causes more 3.9 millions deaths. This paper introduces a symptomatic-asymptomatic-recoverer-dead differential equation model (SARDDE). It gives the conditions of the asymptotical stability on the disease-free equilibrium of SARDDE. It proposes the necessary conditions of disease spreading for the SARDDE. Based on the reported data of the first and the second COVID-19 epidemics in Beijing and simulations, it determines the parameters of SARDDE, respectively. Numerical simulations of SARDDE describe well the outcomes of current symptomatic and asymptomatic individuals, recovered symptomatic and asymptomatic individuals, and died individuals, respectively. The numerical simulations suggest that both symptomatic and asymptomatic individuals cause lesser asymptomatic spread than symptomatic spread; the blocking rates of about 80% and 97.5% to the symptomatic individuals cannot prevent the spread of the first and second COVID19 epidemics in Beijing, respectively. Virtual simulations suggest that the strict prevention and control strategies implemented by Beijing government are not only very effective but also completely necessary. The numerical simulations suggest also that using the data from the beginning to the day after about 14 -- 17 days at the turning point can estimate well the following outcomes of the two COVID-19 academics, respectively. It is expected that the research can provide better understanding, explaining, and dominating for epidemic spreads, prevention and control measures.

Mobility decisions, economic dynamics and epidemic (preprint)

In this paper we propose a theoretical model including a susceptible-infected-recovered-dead (SIRD) model of epidemic in a dynamic macroeconomic general equilibrium framework with agents' mobility. The latter affect both their income (and consumption) and their probability of infecting and of being infected. Strategic complementarities among individual mobility choices drive the evolution of aggregate economic activity, while infection externalities caused by individual mobility affect disease diffusion. Rational expectations of forward looking agents on the dynamics of aggregate mobility and epidemic determine individual mobility decisions. The model allows to evaluate alternative scenarios of mobility restrictions, especially policies dependent on the state of epidemic. We prove the existence of an equilibrium and provide a recursive construction method for finding equilibrium(a), which also guides our numerical investigations. We calibrate the model by using Italian experience on COVID-19 epidemic in the period February 2020 - May 2021. We discuss how our economic SIRD (ESIRD) model produces a substantially different dynamics of economy and epidemic with respect to a SIRD model with constant agents' mobility. Finally, by numerical explorations we illustrate how the model can be used to design an efficient policy of state-of-epidemic-dependent mobility restrictions, which mitigates the epidemic peaks stressing health system, and allows for trading-off the economic losses due to reduced mobility with the lower death rate due to the lower spread of epidemic.

Modelling, Simulations and Analysis ofthe First and Second COVID-19 Epidemics in Beijing (preprint)

To date, over 130 million people on infected with COVID-19. It causes more 2.8 millions deaths. This paper introduces a symptomatic-asymptomatic-recoverer-dead differential equation model (SARDDE). It gives the conditions of the asymptotical stability on the disease-free equilibrium of SARDDE. It proposes the necessary conditions of disease spreading for the SARDDE. Based on the reported data of the first and the second COVID-19 epidemics in Beijing and simulations, it determines the parameters of SARDDE, respectively. Numerical simulations of SARDDE describe well the outcomes of current symptomatic and asymptomatic individuals, recovered symptomatic and asymptomatic individuals, and died individuals, respectively. The numerical simulations suggest that both symptomatic and asymptomatic individuals cause lesser asymptomatic spread than symptomatic spread; blocking rate of about 90% cannot prevent the spread of the COVID19 epidemic in Beijing; the strict prevention and control strategies implemented by Beijing government is not only very effective but also completely necessary. The numerical simulations suggest also that using the data from the beginning to the day after about two weeks at the turning point can estimate well or approximately the following outcomes of the two COVID-19 academics, respectively. It is expected that the research can provide better understanding, explaining, and dominating forepidemic spreads, prevention and control measures.