ABSTRACT
After the coronavirus outbreak, the disease known as COVID-19 has been infecting millions of people, and the number of deaths is pilling up to hundreds of thousands. In Indonesia, especially Jakarta, some of the deaths are caused by pandemic-related surges that strain hospital capacity. Besides, people had many obstacles in this pandemic condition because of the lack of knowledge about COVID-19. On that matter, several models emerged worldwide to help inform public decision making in this pandemic situation. With today's technological advances the CHIME (COVID-19 Hospital Impact Model for Epidemics) application is designed to assist hospitals and public health officials with understanding hospital capacity needs as they relate to the COVID pandemic. This paper aims to help inform public health decision making regarding the transmission of COVID-19 in Jakarta using CHIME. This work uses Jakarta COVID-19 data from November 24th, 2021 and its accumulation from 14 days before (November 10th, 2021) to predict the course of COVID-19 in 30 days. With ArcGIS Pro and ArcGIS Experience, this work successfully made a map that uses CHIME to inform about peak demand of each city in DKI Jakarta and the daily new admissions and hospitalization graph. In addition, a Jakarta COVID-19 dashboard is also made to inform more about the transmission of COVID-19. © 2022 IEEE.
ABSTRACT
Activity restrictions implemented to control the spread of the COVID-19 virus imposed a significant effect on the air quality of cities across the world. The initiative of the World Meteorological Organisation/Global Atmospheric Watch for studying effects of the 2020 COVID-19 lockdowns on air quality has produced two sets of analysis results for cities across the world, based on observational data and modelling, respectively. The modelling study aims to evaluate the modelling tools in a regime involving significant changes of activity, and at the same provide insights on the effect of selectively reduced emissions on the chemistry and composition of urban pollutants. For most of the cities, a reduction on NOx average concentrations between 11% and 70% was calculated for the lockdown period, while PM10 was reduced by 8% up to 35% in a good agreement with measured reductions observed during the 2020 lockdown period compared to the corresponding period of 2019. Taking advantage of an operational Air Quality Modelling System, which is in continuous application in the cities of Thessaloniki and Nicosia, the contribution of sectoral emissions and the role of meteorology over the observed concentration reductions was assessed. The study reveals that in both cities, observed reductions of urban PM2.5, PM10 and NOx concentration patterns can be mainly attributed to the corresponding emissions reductions in the transport and heating sectors, while O3 is strongly affected by titration near the city centre. At the same time, meteorological patterns appear to strongly influence and even mask these effects in terms of daily averages, while the impact of imposed large-scale boundary conditions on the modelling results can also be significant. © British Crown Copyright (2022)
ABSTRACT
The human coronavirus OC43 (HCoV-OC43) is one of the most common causes of common cold but can lead to fatal pneumonia in children and elderly. However, the available animal models of HCoV-OC43 did not show respiratory symptoms that are insufficient to assist in screening antiviral agents for respiratory diseases. In this study, we adapted the HCoV-OC43 VR-1558 strain by serial passage in suckling C57BL/6 mice and the resulting mouse-adapted virus at passage 9 (P9) contained 8 coding mutations in polyprotein 1ab, spike (S) protein, and nucleocapsid (N) protein. Pups infected with the P9 virus significantly lost body weight and died within 5 dpi. In cerebral and pulmonary tissues, the P9 virus replication induced the production of G-CSF, IFN-γ, IL-6, CXCL1, MCP-1, MIP-1α, RANTES, IP-10, MIP-1ß, and TNF-α, as well as pathological alterations including reduction of neuronal cells and typical symptoms of viral pneumonia. We found that the treatment of arbidol hydrochloride (ARB) or Qingwenjiere Mixture (QJM) efficiently improved the symptoms and decreased n gene expression, inflammatory response, and pathological changes. Furthermore, treating with QJM or ARB raised the P9-infected mice's survival rate within a 15 day observation period. These findings suggested that the new mouse-adapted HCoV-OC43 model is applicable and reproducible for antiviral studies of HCoV-OC43.
ABSTRACT
The positive role of clinical decision support systems based on clinical guidelines in reducing medical errors and improving patient outcomes has been widely recognized. However, the knowledge in clinical guidelines is usually hard-coded into clinical decision support systems, making it difficult for these systems to adapt to the rapid changes of clinical guidelines. Knowledge being hard-coded into the system also means that the system is a black box, and doctors cannot understand the decision-making logic behind the system. These reasons make it difficult for clinical decision support systems to be applied on a large scale. This paper proposes a flexible clinical decision support model, which contains two key parts, namely the knowledge authoring environment and the knowledge execution environment. The transition of knowledge from hard-coded to flexible editing is illustrated in the COVID-19 case. This flexible method will be applied to more complex clinical problems in the future. © 2021 IEEE.
ABSTRACT
Noise poses challenge to nonlinear Hammerstein-Wiener (HW) subsystem model application, because HW subsystem need large number of parameter interactions. However, flexibility, soft computing, and automatic adjustment to dynamic observation for best model fitting make it potential for forecasting nonlinear data. In this article, we adopted improved HW inference from Levenberg-Marquardt optimization algorithm to optimize HW subsystem and to select best model parameters. Therefore, the adopted model is tested on COVID-19 confirmed reported cases, to estimate transmission rate of COVID-19 virus for period from 15th March 2020 to 29th April 2020. Model validation is carried out on small dataset, which outperforms some existing models. The adopted model is further evaluated using statistical metrics and reported best accuracy of 0.127 and 0.998 for Mean Absolute percentage error (MAPE) and coefficient of determination (R2) respectively, with best model complexity of 1.86. The obtained results are promising enough in predicting spread of COVID-19 virus and may inspire as guidance to relax lockdown restriction policies. © 2022, IAENG International Journal of Applied Mathematics. All Rights Reserved.
ABSTRACT
The worldwide pandemic of coronavirus disease 2019 (COVID-19) has become an unprecedented challenge to global public health. With the intensification of the COVID-19 epidemic, the development of vaccines and therapeutic drugs against the etiological agent severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is also widespread. To prove the effectiveness and safety of these preventive vaccines and therapeutic drugs, available animal models that faithfully recapitulate clinical hallmarks of COVID-19 are urgently needed. Currently, animal models including mice, golden hamsters, ferrets, nonhuman primates, and other susceptible animals have been involved in the study of COVID-19. Moreover, 117 vaccine candidates have entered clinical trials after the primary evaluation in animal models, of which inactivated vaccines, subunit vaccines, virus-vectored vaccines, and messenger ribonucleic acid (mRNA) vaccines are promising vaccine candidates. In this review, we summarize the landscape of animal models for COVID-19 vaccine evaluation and advanced vaccines with an efficacy range from about 50% to more than 95%. In addition, we point out future directions for COVID-19 animal models and vaccine development, aiming at providing valuable information and accelerating the breakthroughs confronting SARS-CoV-2.