Modelling COVID-19 growth cases of provinces in java Island by modified spatial weight matrix GSTAR through railroad passenger's mobility

Analysis of factors in community willingness to manage floods in East Java during the pandemic

Urbanisation is an important aspect of environmental management and requires special attention, as half of the world's population lives in urban areas. As the urban population grows, this poses a number of challenges, particularly related to environmental issues and floods. Indonesia, a developing nation and the fourth most populous globally, with over 264 million inhabitants, faces significant environmental and flood concerns, notably exacerbated by the coronavirus disease 2019 (COVID-19) pandemic. These issues are poised to exert a profound and enduring influence on the populace. East Java province, one of the densely populated provinces in Indonesia, experiences complex environmental problems, floods, and air pollution. Therefore, this research identified the factors that influenced the community's willingness to participate in environmental conservation and flood management in East Java province during the COVID-19 pandemic. This research employed a mixed-method approach, combined qualitative and quantitative methods, and utilised the triangular relationship theory. The result showed six predominant factors within the population of East Java that significantly affected the environmental conditions and the frequency of flood events in the area.

Contribution

Floods in the city during the pandemic caused concern for those affected by the disaster and the implementation of activities adjusted government policies. For sustainability, the urban environment in Indonesia is working hard to anticipate flooding in cities. Apart from that, the government, private sector, community leaders, and the media also play an important role.

An innovative ensemble model based on deep learning for predicting COVID-19 infection

Nowadays, global public health crises are occurring more frequently, and accurate prediction of these diseases can reduce the burden on the healthcare system. Taking COVID-19 as an example, accurate prediction of infection can assist experts in effectively allocating medical resources and diagnosing diseases. Currently, scholars worldwide use single model approaches or epidemiology models more often to predict the outbreak trend of COVID-19, resulting in poor prediction accuracy. Although a few studies have employed ensemble models, there is still room for improvement in their performance. In addition, there are only a few models that use the laboratory results of patients to predict COVID-19 infection. To address these issues, research efforts should focus on improving disease prediction performance and expanding the use of medical disease prediction models. In this paper, we propose an innovative deep learning model Whale Optimization Convolutional Neural Networks (CNN), Long-Short Term Memory (LSTM) and Artificial Neural Network (ANN) called WOCLSA which incorporates three models ANN, CNN and LSTM. The WOCLSA model utilizes the Whale Optimization Algorithm to optimize the neuron number, dropout and batch size parameters in the integrated model of ANN, CNN and LSTM, thereby finding the global optimal solution parameters. WOCLSA employs 18 patient indicators as predictors, and compares its results with three other ensemble deep learning models. All models were validated with train-test split approaches. We evaluate and compare our proposed model and other models using accuracy, F1 score, recall, AUC and precision metrics. Through many studies and tests, our results show that our prediction models can identify patients with COVID-19 infection at the AUC of 91%, 91%, and 93% respectively. Other prediction results achieve a respectable accuracy of 92.82%, 92.79%, and 91.66% respectively, f1-score of 93.41%, 92.79%, and 92.33% respectively, precision of 93.41%, 92.79%, and 92.33% respectively, recall of 93.41%, 92.79%, and 92.33% respectively. All of these exceed 91%, surpassing those of comparable models. The execution time of WOCLSA is also an advantage. Therefore, the WOCLSA ensemble model can be used to assist in verifying laboratory research results and predict and to judge various diseases in public health events.

Predicting the distribution of COVID-19 through CGAN-Taking Macau as an example

Machine learning (ML) is an innovative method that is widely used in data prediction. Predicting the COVID-19 distribution using ML is essential for urban security risk assessment and governance. This study uses conditional generative adversarial network (CGAN) to construct a method to predict the COVID-19 hotspot distribution through urban texture and business formats and establishes a relationship between urban elements and COVID-19 so that machines can automatically predict the epidemic hotspots in cities. Taking Macau as an example, this method is used to determine the correlation between the urban texture and business hotspots of Macau and the new epidemic hotspot clusters. Different types of samples afforded different epidemic prediction accuracies. The results show the following: (1) CGAN can accurately predict the distribution area of COVID-19, and the accuracy can exceed 70%. (2) The results of predicting the COVID-19 distribution through urban texture and POI data of hospitals and stations are the best, with an accuracy of more than 60% in experiments in different regions of Macau. (3) The proposed method can also predict other areas in the city that may be at risk of COVID-19 and help urban epidemic prevention and control.

Data analytics during pandemics: a transportation and location planning perspective

The recent COVID-19 pandemic once again showed the value of harnessing reliable and timely data in fighting the disease. Obtained from multiple sources via different collection streams, an immense amount of data is processed to understand and predict the future state of the disease. Apart from predicting the spatio-temporal dynamics, it is used to foresee the changes in human mobility patterns and travel behaviors and understand the mobility and spread speed relationship. During this period, data-driven analytic approaches and Operations Research tools are widely used by scholars to prescribe emerging transportation and location planning problems to guide policy-makers in making effective decisions. In this study, we provide a review of studies which tackle transportation and location problems during the COVID-19 pandemic with a focus on data analytics. We discuss the major data collecting streams utilized during the pandemic era, highlight the importance of rapid and reliable data sharing, and give an overview of the challenges and limitations on the use of data.

The Improvement of Community Knowledge, Attitudes and Practices After Covid-19 Socialization

This study determined community knowledge, attitudes, and practices after COVID-19 socialization in Rambipuji and Suci Village, Jember District, East Java, Indonesia. This study used the analytic observational design study. As many as 40 people were given socialization about COVID-19 by gathering and online. The questionnaires were completed in two parts included before-socialization and after-socialization. The questionnaire data were analyzed descriptively by calculating frequency, percentage, and inferential statistics by t-test, Spearman correlation, and chi-square test. The data analysis used IBM SPSS version 23 software. The mean and standard deviation of percentage of knowledge pre-test, knowledge post-test, high attitude, moderate attitude, low attitude, very high practice, high practice, sufficient practice, and low practice were 58.33 ± 30.97, 77.70 ± 22.52, 62.5±21.7, 31±22.2, 6.5±17.1, 65±13.8, 32.3±13.8, 1.25±2.4, 1.25±1.8, respectively. Knowledge pre-test and post-test correlated 0.819 (p=0.001) and a t-test with p=0.003. Attitudes and practices correlated with p=0.001. Socialization of COVID-19 was useful to improve the community knowledge, attitudes, and practices in Rambipuji and Suci Village, Jember District, East Java, Indonesia that could prevent the transmission and inhibit the spread of the COVID-19 pandemic in Indonesia. Furthermore, continuous encouragement of COVID-19 socialization in wide areas was recommended.