A Digital Twins Model for Analyzing and Simulating Cold Chain Risks

Due to the outbreak of COVID-19, increasing attention has been paid to designing a cold chain logistics mechanism to ensure the quality of vaccine delivery. In this study, a cold chain digital twins-based risk analysis model is constructed to handle and monitor the vaccine delivery process with a high level of reliability and traceability. The model integrates the Internet of Things (IoT) and digital twins to acquire data on environmental conditions and shipment movements and connect physical cold chain logistics to the digital world. Through the simulation of cold chain logistics in a virtual environment, the risk levels relating to physical operations at a certain forecast horizon can be predicted beforehand, to prevent a 'broken' cold chain. The result of this investigation will reshape the cold chain in the digital age, benefit society in terms of sustainability and environmental impact, and hence contribute to the development of cold chain logistics in Hong Kong. © 2023 IEEE.

Acute macular neuroretinopathy following COVID- 19 infection

Purpose: We aim to highlight a case of COVID-19 associated acute macular neuroretinopathy (AMN), an outer retinopathy known to occur following febrile and flu-like illnesses. Method(s): Case report. Result(s): A 23-year-old Caucasian female presented with a 10-day history of sudden onset bilateral paracentral scotomata which developed following confirmed SARSCoV- 2 infection. The patient's medical history was significant for polycystic ovary syndrome and use of the combined oral contraceptive pill (OCP). Fundoscopic examination revealed multiple reddish-brown, wedge shaped lesions in both eyes perifoveally. Optical coherence tomography of these lesions demonstrated areas of ellipsoid zone disruption associated with overlying hyperreflectivity of outer retinal layers. The lesions were hyporeflective on infrared imaging. The location of lesions at the macula correlated nearly identically with Amsler grid recordings as illustrated by the patient. As a result, the patient was diagnosed with AMN. Other inflammatory, infective and hypercoaguable causes of outer retinopathy were excluded. While OCP use is a risk factor for AMN, the aetiology was determined to be associated with COVID-19, given the onset of visual symptoms closely following the onset of acute febrile illness. Conclusion(s): We report a rare case of AMN occurring in the context of recently diagnosed COVID-19, on the background of OCP use. More broadly, this case report aims to contribute to the broader literature regarding COVID- 19 associated ocular sequelae. Clinicians should consider asking about recent COVID-19 infection when encountering patients presenting with symptoms of AMN.

A forecasting analytics model for assessing forecast error in e-fulfilment performance

Purpose>Demand forecast methodologies have been studied extensively to improve operations in e-commerce. However, every forecast inevitably contains errors, and this may result in a disproportionate impact on operations, particularly in the dynamic nature of fulfilling orders in e-commerce. This paper aims to quantify the impact that forecast error in order demand has on order picking, the most costly and complex operations in e-order fulfilment, in order to enhance the application of the demand forecast in an e-fulfilment centre.Design/methodology/approach>The paper presents a Gaussian regression based mathematical method that translates the error of forecast accuracy in order demand to the performance fluctuations in e-order fulfilment. In addition, the impact under distinct order picking methodologies, namely order batching and wave picking. As described.Findings>A structured model is developed to evaluate the impact of demand forecast error in order picking performance. The findings in terms of global results and local distribution have important implications for organizational decision-making in both long-term strategic planning and short-term daily workforce planning.Originality/value>Earlier research examined demand forecasting methodologies in warehouse operations. And order picking and examining the impact of error in demand forecasting on order picking operations has been identified as a research gap. This paper contributes to closing this research gap by presenting a mathematical model that quantifies impact of demand forecast error into fluctuations in order picking performance.

Epidemiologic Assessement of Pediatric Inflammatory Bowel Disease Presentation in Nyc during Covid-19 Suggests Increased Ibd Presentation

SARS-nCoV2 may have increased capacity to generate autoimmune disease;multiple reports suggest increased risk of Type 1 Diabetes, and case reports suggest other autoimmune linkages. Inflammatory Bowel Disease (IBD) pathogenesis appears to be a mix of genetic susceptibility, microbial populations, and immune triggers such as infections. Given the perceived role of infection in pathogenesis, decreased incidence of all infections during the pandemic secondary to non-pharmaceutical interventions should decrease IBD incidence rates. The aim of this study was to evaluate the association between the Covid-19 pandemic and IBD presentation in NYC using data from new diagnoses at a consortium of institutions. Using EMR systems all diagnoses at 4 collaborating institutions were retrieved from 2015-2021. We fit time series model (ARIMA) to the quarterly number of cases of each disease for January 2016-March 2020 and forecast the subsequent 21 months. We not only did not observe a decline in pediatric IBD secondary to absent viral illness but noted a statistically significant increase in Crohn's Disease approximately 6 months after the initial 2020 COVID wave in NYC, and trends suggesting increases overall in IBD diagnoses above the existing trend towards increased disease presentation that pre-dated the pandemic. We also note suggestive but not significant trends towards overall increases in UC diagnostic rate. This data suggests that there may be a linkage between SARS-nCoV2 infection rates and subsequent pediatric IBD presentation, warranting further evaluation in the aftermath of the Omicron wave. (Figure Presented).

SURVEY OF NYC PEDIATRIC INSTITUTIONS SUGGESTS POSSIBLE INCREASE IN PEDIATRIC INFLAMMATORY BOWEL DISEASE DIAGNOSIS ASSOCIATED WITH PREVALENCE OF COVID INFECTION

Background: One of the myriad issues COVID-19 has generated is a concern for increased capacity to generate autoimmune disease. (Saad et al, 2020, Ehrenfeld et al 2020). Recent case reports have potentially linked new onset UC associated with recent COVID infection (Aydin et al 2020), which raises concern for potential impact of COVID-19 infection rates on pediatric IBD diagnosis rates. As non-pharmaceutical interventions massively decreased the incidence of infectious illnesses in the first year of the pandemic (Sullivan et al, 2020), IBD rates would likely be expected to stay stable or decrease. We have formed a consortium of New York City pediatric institutions aimed at characterizing this change, and here report findings from the Children's Hospital of Montefiore, Maimonides Medical Center, SUNY Downstate, and New York University. Methods: New IBD diagnoses were identified between 2016-2019, as well as new diagnoses documented between 3/2020 and 3/2021. Data was examined using a direct comparison of new diagnostic rate 3/20-3/21 to mean diagnostic rate from 2013-2019. Results: an overall secular increase in IBD diagnostic rate of approximately 5% was noted, consistent with prior findings demonstrating increased incidence of IBD annually (Ye et al, 2020). Direct comparison with mean diagnostic rate over the preceding 4 years noted a substantial increase in diagnostic rate in the pandemic year relative to previous year average, with 109 new diagnoses in our consortium compared to an average of 79. Our data demonstrates this increase is driven by the institutions in the Brooklyn and the Bronx, with a 51% increase in diagnoses (78 compared to mean of 51.5, 95% CI 10.19). NYU diagnostic rate was 31 (previous mean of 27.5, 95% CI 5.29). This aligns well with published rates of COVID-19 in these regions, with the outer boroughs averaging 14,169 cases/100k and Manhattan 10,516/100k. Discussion: Our results suggest a possible increase in IBD diagnostic rate in the outer boroughs of New York City, aligning with density of COVID-19 infections, despite surveillance data from NYC DOH demonstrating almost nonexistent pediatric influenza-like-illness. There are many possible confounding factors in this initial work with substantial further evaluation needed, but this data is suggestive of a possible capacity for COVID19 to generate new onset IBD in excess of normal infections and normal rates of presentation. Next steps will include expanding data collection to additional NYC institutions, subgroup analysis by disease type, gender, age of presentation, more detailed analysis of biomarkers, and geospatial analysis given geographic variations in COVID19 infection density.

Impact of COVID19 epidemic and public health response on pediatric IBD presentation in selected NYC institutions

Background: COVID-19 has presented the global medical establishment with innumerable problems in the last year. The disease has a varied presentation, including severe pneumonia, acute respiratory distress syndrome, flu-like symptoms, gastrointestinal disease, and multiorgan failure. Concerning for the field of pediatric gastroenterology is the significant capacity of COVID-19 to generate an immune response which may increase risk of downstream autoimmune disease (Saad et al, 2020, Ehrenfeld et al 2020). Recent case studies have noted new onset UC associated with recent COVID infection (Aydin et al 2020). Interestingly, non-pharmaceutical interventions to prevent COVID-19 have massively decreased the incidence of infectious illnesses (Sullivan et al, 2020) which, if anything, would be expected to drive a decreased incidence of IBD. We have formed a consortium of New York City pediatric institutions aimed at characterizing this change, and here report early findings from three of our member institutions, SUNY Downstate Health Sciences University, Maimonides Medical Center, and New York University. Methods: New IBD diagnoses were identified between 2013-2019, as well as new diagnoses documented between 3/2020 and 3/2021. Data was examined using a direct comparison of new diagnostic rate 3/20-3/21 to mean diagnostic rate from 2013-2019, as well as using a mixed effects epidemiologic model to assess significance of any change. Results: An overall secular increase in IBD diagnostic rate of approximately 5% was noted, consistent with prior findings demonstrating increased incidence of IBD annually (Ye et al, 2020). Direct comparison with mean diagnostic rate over the preceding 7 years noted comparable IBD diagnostic rates in the pandemic year in comparison to pre-pandemic (50 new diagnoses vs. avg 43). Mixed effects modeling suggests a 1/3rd decrease in expected IBD diagnostic rate, although this did not meet the standard for significance. Discussion: Our initial results from three institutions suggest a maintenance of IBD diagnostic rates, despite surveillance data from NYC DOH demonstrating almost nonexistent pediatric influenza-like-illness and a significant decrease in overall pediatric gastroenterology visits. There are many possible confounding factors in this initial work, but this data is suggestive of a possible capacity for COVID19 (which per NYC records infected approximately 5% of the pediatric cohort in the city) to generate new onset IBD. Next steps will include expanding data collection to additional NYC institutions, subgroup analysis by disease type, gender, age of presentation, more detailed analysis of biomarkers, and analysis by borough given geographic variations in COVID19 infection density.

Enhancing data-driven elderly appointment services in domestic care communities under COVID-19

Purpose Under the impact of Coronavirus disease 2019 (COVID-19), this paper contributes in the deployment of the Artificial Intelligence of Things (AIoT)-based system, namely AIoT-based Domestic Care Service Matching System (AIDCS), to the existing electronic health (eHealth) system so as to enhance the delivery of elderly-oriented domestic care services. Design/methodology/approach The proposed AIDCS integrates IoT and Artificial Intelligence (AI) technologies to (1) capture real-time health data of the elderly at home and (2) provide the knowledge support for decision making in the domestic care appointment service in the community. Findings A case study was conducted in a local domestic care centre which provided elderly oriented healthcare services to the elderly. By integrating IoT and AI into the service matching process of the mobile apps platform provided by the local domestic care centre, the results proved that customer satisfaction and the quality of the service delivery were improved by observing the key performance indicators of the transactions after the implementation of the AIDCS. Originality/value Following the outbreak of COVID-19, this is a new attempt to overcome the limited research done on the integration of IoT and AI techniques in the domestic care service. This study not only inherits the ability of the existing eHealth system to automatically capture and monitor the health status of the elderly in real-time but also improves the overall quality of domestic care services in term of responsiveness, effectiveness and efficiency.