Nowcasting and Forecasting the Spreading of Novel Coronavirus 2019-nCoV and Its Association with Weather Variables in 30 Chinese Provinces: A Case Study

Author(s):  
Nadia AL-Rousan ◽  
Hazem Al-Najjar
2020 ◽  
Vol 35 (9) ◽  
pp. 2675-2679 ◽  
Author(s):  
Katharine Lawrence ◽  
Kathleen Hanley ◽  
Jennifer Adams ◽  
Daniel J Sartori ◽  
Richard Greene ◽  
...  

2021 ◽  
Vol 10 (2) ◽  
pp. 257-274
Author(s):  
Nofrida Panjaitan ◽  
Joko Adianto

The spread of pneumonia cases caused by a new type of Coronavirus (Novel Coronavirus) SARS Cov-2 was established by WHO called Coronavirus Disease 2019 (COVID-19) on February 2020 and designated as a pandemic on March 11, 2020. However, an interesting phenomenon arises, despite the high number of COVID-19 spread in Jakarta. That is as of April 10, 2020, 50 out of 267 urban villages in Jakarta are declared free of COVID-19 (not infected with the virus) and one of them is the most populous village in Jakarta called the most densely populated urban village in Southeast Asia, namely Kalianyar in Tambora Sub District, West Jakarta.This study aims to find out how does Kalianyar combat the spread of COVID-19, recalling that considering the nature of the virus transmission, Kalianyar has high potential to be a vulnerable zone. The research was conducted through a qualitative analysis on a case study of Kalianyar aiming to examine the deep explanation and understanding of distinctive implementation and to obtain the lesson learned from the implementation of Fournier's idea seeing how the common process occurs. The common process occurring within social organization in Kalianyar shows that there are relational and reciprocal relationships resulting from each activity related to the three axes as suggested by Fournier.


2020 ◽  
Vol 12 (20) ◽  
pp. 8755
Author(s):  
Hsiu-Chin Hsieh ◽  
Xuan-Huynh Nguyen ◽  
Tien-Chin Wang ◽  
Jen-Yao Lee

Due to its unpredictability, the novel coronavirus (COVID-19) pandemic has changed the global business climate and commercial management practices in unprecedented ways. As a direct result of the pandemic, the hospitality and tourism sectors have shut down, and business failure rates have occurred exponentially. The franchise hospitality industry has experienced significant impact and challenged a basic understanding of knowledge management (KM) implementation in the face of the COVID-19 outbreak. A strategic KM implementation practice can not only guide a large-scale operation, but also adjust an organization’s performance and competitiveness. The purpose of this study is to examine the influential criteria of success through effective KM implementation and to predict the probability of successful KM in a post-pandemic era. The conceptual framework for KM applies an analytic hierarchical prediction model reliant upon consistent fuzzy preference relations to assist the franchise hospitality sector’s consciousness of the influential criteria. An empirical case study is used to apply pairwise comparisons used to determine the priority weights and two possible outcomes. The case study will assist franchise organizations to analyze whether or not to implement KM, interdict application, or adopt revised actions. This assistance will enhance the success possibility of KM implementation within such a crisis environment. This study uses a case setting by assessing 15 franchises hospitality experts’ opinions in Taiwan relevant to KM implementation.


2020 ◽  
Vol 14 (1) ◽  
Author(s):  
Tayebeh Rashidian ◽  
Nasibeh Sharifi ◽  
Azita Fathnezhad-Kazemi ◽  
Fatemeh Mirzamrajani ◽  
Sajad Nourollahi ◽  
...  

Abstract Introduction A novel coronavirus named severe acute respiratory syndrome coronavirus 2, was first reported in Wuhan, China, in December 2019. The virus, known as COVID-19, is recognized as a potentially life-threatening disease by causing severe respiratory disease. Since this virus has not previously been detected in humans, there is a paucity of information regarding its effects on humans. In addition, only limited or no information exists about its impact during pregnancy. Case presentation In the present case study, we report the death of a neonate born to a 32-year-old mother with coronavirus disease 2019 in Ilam, Iran, with Kurdish ethnicity. We report the infection and death of a neonate in Iran with a chest X-ray (CXR) marked abnormality 2 hours after birth demonstrating coronavirus disease 2019 disease. The neonate was born by elective cesarean section, the fetal health was assessed using fetal heart rate and a non-stress test before the birth, and there was no evidence of fetal distress. All the above-mentioned facts and radiographic abnormalities suggested that coronavirus disease 2019 is involved. Conclusions In this case study, we report the death of a neonate born to a mother with coronavirus disease 2019, 11 hours after birth. There is a paucity of data on the vertical transmission and the adverse maternal-fetal consequences of this disease, so vertical transmission from mother to child remains to be confirmed.


Author(s):  
Nesrine Ben Yahia ◽  
Mohamed Dhiaeddine Kandara ◽  
Narjes Bellamine Ben Saoud

Abstract Due to the continuous spread of the novel coronavirus (COVID-19) worldwide, it is urgent to develop accurate decision-aided methods to support healthcare policymakers to control and early detect COVID-19 outbreak especially in the data science era. In this context, our main goal is to build a generic and accurate method that can predict daily conrmed cases which helps stake-holders to make and review their epidemic response plans. This method takes advantage of the complementarity of DNN (Deep Neuronal Networks), LSTM (Long Short Term Memory) and CNN (Convolutional Neuronal Networks) where their forecasted values represent the inputs of stacked ensemble meta-learners that will generate the nal outbreak predictions. To the best of our knowledge, this is the rst time that deep ensemble learning is used to deal with this issue. The proposed method is validated on three experimental scenarios, Tunisia case study, China case study and the third one is based on China data and models to predict Tunisia COVID-19 outbreak. Experiment results indicate that, compared with individual learners, the stacked-DNN meta-learner, whose input are forecasted values of DNN, LSTM and CNN, achieved the best accurate results in terms of accuracy as well as RMSE for the three scenarios. In conclusion, our ndings demonstrate that i) deep ensemble learning may be used as an accurate decision support tool for improving COVID-19 outbreak forecasting, ii) it is possible to reuse China learners and meat-learners to make prediction of the epidemic trend for other countries when preventive and control measures are comparable.


2020 ◽  
Author(s):  
Mitra Feldman ◽  
Lieven Vernaeve ◽  
James Tibenderana ◽  
Leo Braack ◽  
Mark Debackere ◽  
...  

Abstract Background Impressive progress in reducing malaria trends combined with the 2018 report of no malaria related deaths for the first time, puts Cambodia well on track to reaching its malaria elimination goals. However, the novel coronavirus SARS-CoV-2 (COVID-19) pandemic presents a potential challenge to this goal. The path towards malaria elimination is dependent on sustained interventions to prevent rapid resurgence, which can quickly set back any gains achieved. Methods Mobile Malaria Workers (MMWs) need to have a strong understanding of the local geography and, most importantly, build and maintain trust among the communities they serve. To achieve this, Malaria Consortium uses a peer-to-peer approach for the MMWs and ensures the same level of trust operates between the MMWs and Malaria Consortium. Malaria Consortium’s policy during COVID-19 has been to follow national guidelines while continuing to support community-based malaria services via the MMWs / mobile malaria posts (MPs) with as minimal disruption as possible. A risk assessment was carried out by Malaria Consortium, with a mitigation plan quickly developed and implemented, to ensure MMWs were able to continue providing services without putting themselves or their patients at risk. Results Malaria Consortium ensured the MMW/ mobile MP program is built on trust, relevance to, and connection with the communities being served. An overall decline in malaria testing was reported from Health Centres and VMWs among all three provinces in March and April, not seen in previous years and possibly attributable to fear of COVID-19. However, Malaria Consortium supported MMWs have not reported any such decline in the utilization of their services and attribute this to the trust they have among the communities. Conclusion Malaria Consortium has effectively demonstrated care and solidarity with and among the MMWs and communities being served. This has ensured a high level of trust, and therefore willingness among the MMWs and communities to continue providing and utilising malaria services as usual despite the fear of COVID-19. Building trust among rural communities builds resilience and ensures uninterrupted and effective malaria elimination activities can continue even during a potential extraneous disruptive force, such as the Covid-19 pandemic.


2021 ◽  
Vol 56 (3) ◽  
pp. 72-82
Author(s):  
Farrah Anuar ◽  
Norzila Othman ◽  
Wahid Ali Hamood Altowayti ◽  
Nurina Fitriani

Novel coronavirus pneumonia (COVID-19) was first detected in Wuhan, China, soon its rapid escalation in global and become global spread of infection including Malaysia. The principal cases in Malaysia were distinguished on 25th January 2020. The number of cases keeps on ascending since March 2020 until Malaysia has the highest number of cases in Southeast Asia. Therefore, to respond and control the COVID-19 pandemic in the country, the 2020 Movement Control Order (MCO) is implemented. The MCO actualizes a movement of reasonable steps to control advance flare-ups of COVID-19 within the country, counting closing down all organization and private premises aside from those related with "fundamental administrations" and several parts with government permission, and travel disallowances on all outsiders entering Malaysia and on Malaysians taking off the country. Correspondingly, this MCO has brought about the decrease of air contamination as the number of engine vehicles and the activity of businesses is suspended. This study aims to determine the concentrations of particulate matter 2.5 at the selected monitoring stations in UTHM and to correlate environmental factor which is wind speed and wind direction with the Particulate Matter 2.5. This study uses PM2.5 fixations to explain the air pollution before to MCO, stages I, II, III, and IV, where the contingent MCO is actualized. Such outcomes relate to the air contamination list as it was discovered that the PM2.5 fixations showed a high decrease of up to -74 μg/m3 during Phase IV MCO.


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