Retraction Note: Enhanced bat algorithm for COVID-19 short-term forecasting using optimized LSTM.

[This retracts the article DOI: 10.1007/s00500-021-06075-8.].

SAA-UNet: Spatial Attention and Attention Gate UNet for COVID-19 Pneumonia Segmentation from Computed Tomography.

The disaster of the COVID-19 pandemic has claimed numerous lives and wreaked havoc on the entire world due to its transmissible nature. One of the complications of COVID-19 is pneumonia. Different radiography methods, particularly computed tomography (CT), have shown outstanding performance in effectively diagnosing pneumonia. In this paper, we propose a spatial attention and attention gate UNet model (SAA-UNet) inspired by spatial attention UNet (SA-UNet) and attention UNet (Att-UNet) to deal with the problem of infection segmentation in the lungs. The proposed method was applied to the MedSeg, Radiopaedia 9P, combination of MedSeg and Radiopaedia 9P, and Zenodo 20P datasets. The proposed method showed good infection segmentation results (two classes: infection and background) with an average Dice similarity coefficient of 0.85, 0.94, 0.91, and 0.93 and a mean intersection over union (IOU) of 0.78, 0.90, 0.86, and 0.87, respectively, on the four datasets mentioned above. Moreover, it also performed well in multi-class segmentation with average Dice similarity coefficients of 0.693, 0.89, 0.87, and 0.93 and IOU scores of 0.68, 0.87, 0.78, and 0.89 on the four datasets, respectively. Classification accuracies of more than 97% were achieved for all four datasets. The F1-scores for the MedSeg, Radiopaedia P9, combination of MedSeg and Radiopaedia P9, and Zenodo 20P datasets were 0.865, 0.943, 0.917, and 0.926, respectively, for the binary classification. For multi-class classification, accuracies of more than 96% were achieved on all four datasets. The experimental results showed that the framework proposed can effectively and efficiently segment COVID-19 infection on CT images with different contrast and utilize this to aid in diagnosing and treating pneumonia caused by COVID-19.

COVID-19-Associated Pneumatocele and Subsequent Pneumothorax.

We report a case of pneumatocele and subsequent pneumothorax, 20 days after being treated for coronavirus disease 2019 (COVID-19) and discharged. This 64-year-old patient was initially treated for COVID-19 pneumonia and pulmonary embolism (PE) over a two-week-long admission. He was discharged and then re-presented two days post-discharge with sudden exacerbation of breathlessness. Blood tests showed worsening inflammatory markers likely associated with bacterial infection, and imaging revealed multiple pneumatoceles and subsequent pneumothorax. Unfortunately, he rapidly deteriorated and passed away. This case report adds to the growing concern in the literature about the serious and life-threatening complications of COVID-19 infection and raises awareness of this rare complication.

Mapping CircRNA-miRNA-mRNA regulatory axis identifies hsa_circ_0080942 and hsa_circ_0080135 as a potential theranostic agents for SARS-CoV-2 infection.

Non-coding RNAs (ncRNAs) can control the flux of genetic information; affect RNA stability and play crucial roles in mediating epigenetic modifications. A number of studies have highlighted the potential roles of both virus-encoded and host-encoded ncRNAs in viral infections, transmission and therapeutics. However, the role of an emerging type of non-coding transcript, circular RNA (circRNA) in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has not been fully elucidated so far. Moreover, the potential pathogenic role of circRNA-miRNA-mRNA regulatory axis has not been fully explored as yet. The current study aimed to holistically map the regulatory networks driven by SARS-CoV-2 related circRNAs, miRNAs and mRNAs to uncover plausible interactions and interplay amongst them in order to explore possible therapeutic options in SARS-CoV-2 infection. Patient datasets were analyzed systematically in a unified approach to explore circRNA, miRNA, and mRNA expression profiles. CircRNA-miRNA-mRNA network was constructed based on cytokine storm related circRNAs forming a total of 165 circRNA-miRNA-mRNA pairs. This study implies the potential regulatory role of the obtained circRNA-miRNA-mRNA network and proposes that two differentially expressed circRNAs hsa_circ_0080942 and hsa_circ_0080135 might serve as a potential theranostic agents for SARS-CoV-2 infection. Collectively, the results shed light on the functional role of circRNAs as ceRNAs to sponge miRNA and regulate mRNA expression during SARS-CoV-2 infection.

Failure of Healthcare Provision for Attention-Deficit/Hyperactivity Disorder in the United Kingdom: A Consensus Statement.

Background: Despite evidence-based national guidelines for ADHD in the United Kingdom (UK), ADHD is under-identified, under-diagnosed, and under-treated. Many seeking help for ADHD face prejudice, long waiting lists, and patchy or unavailable services, and are turning to service-user support groups and/or private healthcare for help. Methods: A group of UK experts representing clinical and healthcare providers from public and private healthcare, academia, ADHD patient groups, educational, and occupational specialists, met to discuss shortfalls in ADHD service provision in the UK. Discussions explored causes of under-diagnosis, examined biases operating across referral, diagnosis and treatment, together with recommendations for resolving these matters. Results: Cultural and structural barriers operate at all levels of the healthcare system, resulting in a de-prioritization of ADHD. Services for ADHD are insufficient in many regions, and problems with service provision have intensified as a result of the response to the COVID-19 pandemic. Research has established a range of adverse outcomes of untreated ADHD, and associated long-term personal, social, health and economic costs are high. The consensus group called for training of professionals who come into contact with people with ADHD, increased funding, commissioning and monitoring to improve service provision, and streamlined communication between health services to support better outcomes for people with ADHD. Conclusions: Evidence-based national clinical guidelines for ADHD are not being met. People with ADHD should have access to healthcare free from discrimination, and in line with their legal rights. UK Governments and clinical and regulatory bodies must act urgently on this important public health issue.

COVID-CheXNet: hybrid deep learning framework for identifying COVID-19 virus in chest X-rays images.

The outbreaks of Coronavirus (COVID-19) epidemic have increased the pressure on healthcare and medical systems worldwide. The timely diagnosis of infected patients is a critical step to limit the spread of the COVID-19 epidemic. The chest radiography imaging has shown to be an effective screening technique in diagnosing the COVID-19 epidemic. To reduce the pressure on radiologists and control of the epidemic, fast and accurate a hybrid deep learning framework for diagnosing COVID-19 virus in chest X-ray images is developed and termed as the COVID-CheXNet system. First, the contrast of the X-ray image was enhanced and the noise level was reduced using the contrast-limited adaptive histogram equalization and Butterworth bandpass filter, respectively. This was followed by fusing the results obtained from two different pre-trained deep learning models based on the incorporation of a ResNet34 and high-resolution network model trained using a large-scale dataset. Herein, the parallel architecture was considered, which provides radiologists with a high degree of confidence to discriminate between the healthy and COVID-19 infected people. The proposed COVID-CheXNet system has managed to correctly and accurately diagnose the COVID-19 patients with a detection accuracy rate of 99.99%, sensitivity of 99.98%, specificity of 100%, precision of 100%, F1-score of 99.99%, MSE of 0.011%, and RMSE of 0.012% using the weighted sum rule at the score-level. The efficiency and usefulness of the proposed COVID-CheXNet system are established along with the possibility of using it in real clinical centers for fast diagnosis and treatment supplement, with less than 2 s per image to get the prediction result.

COVID-19 and time-frequency connectedness between green and conventional financial markets

Against the backdrop of the exponentially growing trend in green finance investments and the calls for green recovery in the post-COVID world, this study presents the time-frequency connectedness between green and conventional financial markets by using the spillover models of Diebold and Yilmaz (2012) and Baruník and Křehlík (2018). Covering a sample period from January 01, 2008, to July 31, 2020, we aim to explore the dynamics of connectedness between conventional and green investments in fixed income, equity, and energy markets. Additionally, we determine the role of market-wide uncertainty in altering the connectedness structure by performing a subsample analysis for the ongoing COVID-19 pandemic crisis period. Our results show that competing energy investments are not connected, and there is only one-way spillovers from the conventional bonds in the fixed-income investments. Additionally, we observe a low (high) intergroup connectedness for conventional (green) investments. Moreover, the frequency-based analysis shows that connectedness between these competing markets is more pronounced during the short-run. The subsample analysis for the pandemic crisis period shows similar results except for the disconnection between bond markets in the short-run frequency. Our time-varying analysis shows peaks and troughs in the connectedness between climate-friendly and conventional investments that suggest different global events such as the Eurozone Debt Crisis and Shale Oil Revolution drives the association between alternate investments. Similarly, we observe an enhanced connectedness during the recent COVID-19 period, suggesting that financial stability would be a significant factor in determining the smooth transition to green investments.

Linking the trust of industrial entrepreneurs on elements of ecosystem with entrepreneurial success: Determining startup behavior as mediator and entrepreneurial strategy as moderator.

Purpose: This study aimed to apply "multi-criteria decision approach and attitude-change theory" to examine post-COVID-19 impact on entrepreneurial mindset by investigating the link between entrepreneurs social capital (trust on three elements of ecosystem i.e., experts & enterprises, media, and government) and entrepreneurial success (both individual and organizational). Specifically, this study analyzed entrepreneurs' dispositional factor (startup behavior) as an underlying mechanism to bridge trust and entrepreneurial success. Furthermore, it also analyzed entrepreneurs' situational factor (entrepreneurial strategy) as boundary condition. Design/methodology/approach: We applied time-lagged data collection from 505 industrial entrepreneurs. Survey method was used for data collection. A 7-point Likert scale was used for the respondent response. Hayes developed PROCESS models 4 and 7 were used to test the hypothesis. Findings: The direct impact of trust on three elements of the ecosystem was found significantly positive on both startup behavior and entrepreneurial success. The direct impact of startup behavior on entrepreneurial success is also significantly positive. The impact of startup behavior on indirect mediation between trust and entrepreneurial success is visibly positive. The moderated and moderated mediation impact of entrepreneurial strategy found positively significant at low and medium values. However, this study found an insignificant moderated impact at high values of entrepreneurial strategy between trust on media and startup behavior. Furthermore, this study also found insignificant moderated mediation impact at high values of entrepreneurial strategy by interacting with two elements of ecosystem (trust on media and trust on government) through startup behavior on entrepreneurial success. Originality/value: The authors suggested that startup behavior is an underlying mechanism through which industrial entrepreneurs trust achieved desired entrepreneurial success. The authors also suggested that the influencing role of "low level of entrepreneurial strategy" in comparison with "high level entrepreneurial strategy" is more helpful to achieve entrepreneurial success. Implications: This study contributed to the literature on entrepreneurial strategy for its conditional indirect moderated impact on startup behavior and moderated mediation impact on firm entrepreneurial success. It also contributed to owners of the manufacturing industry for their startup behavior as an underlying mechanism through which trust influences entrepreneurial success.

Multi-omics personalized network analyses highlight progressive disruption of central metabolism associated with COVID-19 severity.

The clinical outcome and disease severity in coronavirus disease 2019 (COVID-19) are heterogeneous, and the progression or fatality of the disease cannot be explained by a single factor like age or comorbidities. In this study, we used system-wide network-based system biology analysis using whole blood RNA sequencing, immunophenotyping by flow cytometry, plasma metabolomics, and single-cell-type metabolomics of monocytes to identify the potential determinants of COVID-19 severity at personalized and group levels. Digital cell quantification and immunophenotyping of the mononuclear phagocytes indicated a substantial role in coordinating the immune cells that mediate COVID-19 severity. Stratum-specific and personalized genome-scale metabolic modeling indicated monocarboxylate transporter family genes (e.g., SLC16A6), nucleoside transporter genes (e.g., SLC29A1), and metabolites such as α-ketoglutarate, succinate, malate, and butyrate could play a crucial role in COVID-19 severity. Metabolic perturbations targeting the central metabolic pathway (TCA cycle) can be an alternate treatment strategy in severe COVID-19.

AI bot to detect fake COVID‐19 vaccine certificate

As the world is now fighting against rampant virus COVID‐19, the development of vaccines on a large scale and making it reach millions of people to be immunised has become quintessential. So far 40.9% of the world got vaccinated. Still, there are more to get vaccinated. Those who got vaccinated have the chance of getting the vaccine certificate as proof to move, work, etc., based on their daily requirements. But others create their own forged vaccine certificate using advanced software and digital tools which will create complex problems where we cannot distinguish between real and fake vaccine certificates. Also, it will create immense pressure on the government and as well as healthcare workers as they have been trying to save people from day 1, but parallelly people who have fake vaccine certificates roam around even if they are COVID/Non‐COVID patients. So, to avoid this huge problem, this paper focuses on detecting fake vaccine certificates using a bot powered by Artificial Intelligence and neurologically powered by Deep Learning in which the following are the stages: a) Data Collection, b) Preprocessing to remove noise from the data, and convert to grayscale and normalised, c) Error level analysis, d) Texture‐based feature extraction for extracting logo, symbol and for the signature we extract Crest‐Trough parameter, and e) Classification using DenseNet201 and thereby giving the results as fake/real certificate. The evaluation of the model is taken over performance measures like accuracy, specificity, sensitivity, detection rate, recall, f1‐score, and computation time over state‐of‐art models such as SVM, RNN, VGG16, Alexnet, and CNN in which the proposed model (D201‐LBP) outperforms with an accuracy of 0.94.

Computational Modeling of Reaction-Diffusion COVID-19 Model Having Isolated Compartment

Cases of COVID-19 and its variant omicron are raised all across the world. The most lethal form and effect of COVID-19 are the omicron version, which has been reported in tens of thousands of cases daily in numerous nations. Following WHO (World health organization) records on 30 December 2021, the cases of COVID-19 were found to be maximum for which boarding individuals were found 1,524,266, active, recovered, and discharge were found to be 82,402 and 34,258,778, respectively. While there were 160,989 active cases, 33,614,434 cured cases, 456,386 total deaths, and 605,885,769 total samples tested. So far, 1,438,322,742 individuals have been vaccinated. The coronavirus or COVID-19 is inciting panic for several reasons. It is a new virus that has affected the whole world. Scientists have introduced certain ways to prevent the virus. One can lower the danger of infection by reducing the contact rate with other persons. Avoiding crowded places and social events with many people reduces the chance of one being exposed to the virus. The deadly COVID-19 spreads speedily. It is thought that the upcoming waves of this pandemic will be even more dreadful. Mathematicians have presented several mathematical models to study the pandemic and predict future dangers. The need of the hour is to restrict the mobility to control the infection from spreading. Moreover, separating affected individuals from healthy people is essential to control the infection. We consider the COVID-19 model in which the population is divided into five compartments. The present model presents the population???s diffusion effects on all susceptible, exposed, infected, isolated, and recovered compartments. The reproductive number, which has a key role in the infectious models, is discussed. The equilibrium points and their stability is presented. For numerical simulations, finite difference (FD) schemes like nonstandard finite difference (NSFD), forward in time central in space (FTCS), and Crank Nicolson (CN) schemes are implemented. Some core characteristics of schemes like stability and consistency are calculated.

COVID-19 and Connectedness between Sustainable and Islamic Equity Markets

Because of the increasing importance of and demand for ethical investment, this paper investigates the dynamics of connectedness between sustainable and Islamic investment in nineteen countries that represent developed and emerging financial markets worldwide. To this end, we apply models proposed by Diebold and Yilmaz and Barunik and Krehlik to explore the overall and frequency-based connectedness between selected ethical investments. Our results reveal evidence of a moderate to strong intra country-level connectedness between sustainable and Islamic investment and limited cross-country connectedness between ethical investments. The time-varying connectedness analysis suggests enhanced connectedness during periods of market-wide turmoil, such as the European debt crisis, the Chinese financial crisis, and the COVID-19 pandemic. Moreover, the COVID-19 subsample analysis shows an enhanced and idiosyncratic country-level and cross-country connectedness structure between ethical investments, indicating the evolving nature of the relationship between sustainable and Islamic investment.

Diversifier or more? Hedge and safe haven properties of green bonds during COVID-19.

Against the backdrop of the COVID-19 pandemic, the study explores the hedging and safe-haven potential of green bonds for conventional equity, fixed income, commodity, and forex investments. We employ the cross-quantilogram approach to understand better the dynamic relationship between two assets under different market conditions. Our full sample results reveal that the green bond index could serve as a diversifier asset for medium- and long-term equity investors. Besides, it can serve as a hedging and safe-haven instrument for currency and commodity investments. Moreover, the sub-sample analysis of the pandemic period shows a heightened short- and medium-term lead-lag association between the green bond index and conventional investment returns. However, the green bond index emerges as a significant hedging and safe-haven asset for long-term investors of conventional financial assets. Our findings offer valuable insights for long-term investors when their portfolios are comprised of conventional assets such as equities, commodities, forex, and fixed income securities. Further, our findings reveal the potential role of green bond investments in global financial recovery efforts without compromising the low-carbon transition targets.

Non-Medical risk assessment of COVID-19 in parts of Central and East Java, Indonesia

The impact of the Coronavirus disease 2019 (COVID-19) pandemic varies as each country has a different capacity to stop the virus transmission and apply social distancing. A densely populated country, such as Indonesia, tends to face challenges in implementing social distancing due to population characteristics. The Indonesian government focuses on the medical aspect as this virus is new and has been deadly with a high transmission rate. Meanwhile, the non-medical risk during the pandemic is still unclear. The main objective of this study is to assess the non-medical risk at the village level in two agglomeration cities of Central Java: Greater Surakarta and Surabaya. The methodologies use a risk index, derived from the risk reduction concept. The hazard refers to the death toll, while the vulnerability relates to parameters such as disaster, social and public facilities, health facilities, economics and demography. Further, the parameters were weighted based on expert judgement derived using analytical hierarchy process (AHP). The study found that the disaster aspect had the highest weight (0.38), followed by health facilities (0.31), economics (0.17), social-public facilities (0.11) and demography (0.04). The standard deviations of those parameters were relatively low, between 0.12 and 0.25. A low vulnerability index (0.05–0.36) was observed to be dominant in both study areas. There are 11 villages in Greater Surakarta and 30 villages in Greater Surabaya with high vulnerability index. Disaster-prone areas, low economic growth, lack of health facilities and aged demographic structure significantly added to this vulnerability. Further, a high-risk index (0.67–1.00) is observed in three villages in Greater Surabaya and one village in Greater Surakarta. These villages are relatively close to the city centre and have good accessibility. Furthermore, these four villages experienced the severest impact of the pandemic because the furniture and tourism sectors were their primary industries. [ FROM AUTHOR] Copyright of Quaestiones Geographicae is the property of Sciendo and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Corporate investment and government policy during the COVID-19 crisis

We investigate the impact of the US government response to the COVID-19 pandemic, including stringent social measures and economic support packages, on corporate investment. The empirical results show that despite the overall decreased investment due to the economic impact of the pandemic, the government response to COVID-19 and economic supports have a positive effect on corporate investment after subtracting the impact of the pandemic on firm-level investment. We find that the impact of economic support packages on corporate investment is stronger than that of health containment policies. Further analyses show that the effect is weak in firms with higher levels of political risk and investment irreversibility, while being more pronounced in firms with higher technology intensity. Our findings provide fresh insights into the firms’ reaction to the government policies during the pandemic and suggest that both social measures and economic support are vital to restoring corporate investment as well as the economic recovery process.

Modeling COVID-19 Data with a Novel Extended Exponentiated Class of Distributions

The COVID-19 epidemic has affected every aspect of daily life since December 2019 and caused massive damage to the world. The coronavirus epidemic has affected more than 150 countries around the world. Many researchers have tried to develop a statistical model which can be utilized to analyze the behavior of the COVID-19 data. This article contributes to the field of probability theory by introducing a novel family of distributions, named the novel extended exponentiated class of distributions. Explicit expressions for numerous mathematical characterizations of the proposed family have been obtained with special concentration on a three-parameter submodel of the new class of distributions, named the new extended exponentiated Weibull distribution. The unknown model parameter estimates are obtained via the maximum likelihood estimation method. To assess the performance of these estimates, a comprehensive simulation study is conducted. Three different sets of COVID-19 data are used to check the applicability of the submodel case. The submodel of the new family is compared with three well-known probability distributions. Using different analytical measures, the results demonstrate that the new extended exponentiated Weibull distribution gives promising results in terms of its flexibility and offers data modeling with increasing decreasing, unimodal, and modified unimodal shapes.

An Improved COVID-19 Detection using GAN-Based Data Augmentation and Novel QuNet-Based Classification.

COVID-19 is a fatal disease caused by the SARS-CoV-2 virus that has caused around 5.3 Million deaths globally as of December 2021. The detection of this disease is a time taking process that have worsen the situation around the globe, and the disease has been identified as a world pandemic by the WHO. Deep learning-based approaches are being widely used to diagnose the COVID-19 cases, but the limitation of immensity in the publicly available dataset causes the problem of model over-fitting. Modern artificial intelligence-based techniques can be used to increase the dataset to avoid from the over-fitting problem. This research work presents the use of various deep learning models along with the state-of-the-art augmentation methods, namely, classical and generative adversarial network- (GAN-) based data augmentation. Furthermore, four existing deep convolutional networks, namely, DenseNet-121, InceptionV3, Xception, and ResNet101 have been used for the detection of the virus in X-ray images after training on augmented dataset. Additionally, we have also proposed a novel convolutional neural network (QuNet) to improve the COVID-19 detection. The comparative analysis of achieved results reflects that both QuNet and Xception achieved high accuracy with classical augmented dataset, whereas QuNet has also outperformed and delivered 90% detection accuracy with GAN-based augmented dataset.

New Enterprise Creation Process: Pakistan's Entrepreneurs' Perspective

Despite a history of 30 years of research in the field of new enterprise development process, there is no pattern in this process. We researched new enterprise development process in the hospitality industry (café', bakeries, and restaurants) of Pakistan. Using qualitative research, based on semi-structured interviews from the entrepreneurs, we developed four typologies of the entrepreneurs. Typologies, based on the ownership structure and the level of experience the entrepreneurs have, helped us to explain variation in their behavior with respect to some of the avenues of new enterprise development process including motivation behind initiation of new business, role of leap of faith vs. market research, entrepreneurs' sources of inspiration and the extent of strategic thinking in their operations. Our research offers few theoretical and methodological contributions in addition to few of the ways forward for the aspiring entrepreneurs and the policymakers.

Sentiment analysis and topic modeling of Indonesian public conversation about COVID-19 epidemics on Twitter

The World Health Organization (WHO) declared the COVID-19 outbreak has resulted in more than six million confirmed cases and more than 371.000 deaths globally on June 1, 2020. The incident sparked a flood of scientific research to help society deal with the virus both inside and outside the medical domain. Research related to public health analysis and public conversations about the spread of COVID-19 on social media is one of the highlights of researchers in the world. People can analyze information from social media as supporting data about public health. Analyzing public conversations will help the relevant authorities understand public opinion and information gaps between them and the public, helping them develop appropriate emergency response strategies to address existing problems in the community during the pandemic and provide information on the population’s emotions in different contexts. However, research related to the analysis of public health and public conversations was so far conducted only through supervised analysis of textual data. In this study we aim to analyze specifically the sentiment and topic modeling of Indonesian public conversations about the COVID-19 on Twitter using the NLP technique. We applied some methods to analyze the sentiment to obtain the best classification method. In this study the topic modeling was carried out unsupervised using Latent Dirichlet Allocation (LDA). The results of this study reveal that the most frequently discussed topic related to the COVID-19 pandemic is economic issues.

Manajemen komunikasi bencana pemerintah desa dalam penanganan COVID-19

The complexity of the COVID-19 disaster problem requires a mature communication arrangement or management in an effort to handle it, so that it can be carried out in a directed and integrated manner. The purpose of the study is to explain how disaster communication management is carried out by the village government in handling Covid-19. The method used in this research is descriptive qualitative. The sampling technique used was purposive sampling, while the data collection technique used in-depth interview and observation techniques. The results of this study indicate the importance of coordination and communication between parties related to disaster communication management. Through an alternative model approach to disaster communication management which includes aspects of planning, organizing, implementing and evaluating. Good relationships between both the village government and community members, can create effective disaster communication management so that disaster risks that may arise can be reduced or even avoided. Keywords: Management, Disaster Communication, COVID-19 Kompleksitas dari permasalahan bencana COVID-19 memerlukan suatu penataan atau manajemen komunikasi yang matang dalam upaya penanganannya, sehingga dapat dilaksanakan secara terarah dan terpadu. Tujuan penelitian adalah menjelaskan bagaimana manajemen komunikasi bencana yang dilakukan oleh pemerintah desa dalam penanganan COVID-19. Metode yang digunakan dalam penelitian ini adalah deskriptif kualitatif. Teknik penentuan sampel yang dengan menggunakan purposive sampling, sedangkan teknik pengambilan data menggunakan teknik wawancara mendalam (in depth interview) dan observasi. Hasil dari penelitian ini menunjukkan pentingnya koordinasi dan komunikasi antar pihak terkait manajemen komunikasi bencana. Melalui pendekatan model alternatif manajemen komunikasi bencana meliputi aspek perencanaan, pengorganisasian, pelaksanaan dan evaluasi. Hubungan baik antara pemerintah desa dan warga masyarakat, dapat menciptakan manajemen komunikasi bencana yang efektif sehingga resiko bencana yang mungkin muncul dapat dikurangi bahkan dihindari. Kata kunci: Manajemen, Komunikasi Bencana, COVID-19