Projection of COVID-19 Jakarta using CHIME Technology (Covid-19 Hospital Impact Model for Epidemics) Application

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.

Dynamic evaluation of regional economic resilience under major public emergencies: based on an improved dynamic evaluation model of grey incidence projection-fuzzy matter element

The sudden outbreak of COVID-19 in 2020 causes great impact on the economic development of all countries and even the whole world. Under the background of major public emergencies, a timely dynamic evaluation of regional economic resilience can provide an objective basis for economic regulation and control behavior. Based on the existing evaluation model, an improved dynamic evaluation model of grey incidence projection- fuzzy matter element is proposed in this study. The improved model is a universal evaluation model that can be used in different contexts. This model method can, not only limited to analyzing economic resilience, but also be applied to other different contexts. The evaluation indexes are selected (from the market, industry, investment, foreign trade and finance) to construct an evaluation index system of regional economic resilience under major public emergencies. The improved dynamic evaluation model of grey incidence projection- fuzzy matter element is applied to evaluate economic resilience of Hubei province (with its neighboring areas) in context of COVID-19. At the same time, the relative validity of the model is tested based on the empirical evaluation results.

Three-way investment decisions during the epidemic with Choquet-based bi-projection method

The outbreak of epidemic has had a big impact on the investment market of China. Facing the turbulence in the investment market, many enterprises find it difficult to judge the development prospects of investment projects and make the right investment decisions. The three-way decisions offer a novel study perspective to solve this problem. Then the developed model is applied to select the investment projects. Firstly, some relevant attributes of the project are described with the double hierarchy hesitant fuzzy linguistic term sets. And a double hierarchy hesitant fuzzy linguistic information system is constructed for each project. Secondly, the weights of attributes are determined with the Choquet integral method. And the closeness degree calculated by Choquet-based bi-projection method is taken as the conditional probability that the project will be profitable. Next, considering the influence of the bounded rationality of decision makers, the threshold parameters are calculated based on prospect theory. Finally, the decision results about investment projects during four stages are deduced based on the principle of maximum-utility, which demonstrates the practicability and effectiveness of the proposed model.

Is the road to hell paved with good intentions? An empirical analysis of budgetary follow-up in the EU

We study the one-year-ahead budgetary projections from the Stability and Convergence Programmes (SCPs) of EU Member States since the start of the Economic and Monetary Union (EMU) until the start of the coronavirus crisis. First, we consider errors of the general government's headline budget balance, which we then split into expenditure and revenue errors. Next, we split the latter two into "base”, "growth” and "denominator” effects. We find that the most important explanatory variable is the GDP growth error: more optimism in GDP growth projections produces more optimistic budgetary projections. This effect goes beyond a mechanical denominator effect on spending and revenues as shares of GDP;it also works through the numerator of these ratios. Our findings may call for delegating the construction of output projections to adequately equipped national independent fiscal institutions. Finally, we explore how independent fiscal institutions shape projection errors. Those with high media impact producing or assessing the macroeconomic forecast appear to lead to actual budgetary improvement relative to projections.

Does digital economy development reduce carbon emission intensity?

Carbon emissions from human activities are the main cause of climate warming. Under the background of economic and social digital transformation, accurately assessing the carbon emission reduction effect of the development of the digital economy is of great significance for countries to deal with climate warming in the post-COVID-19 era. This paper constructs a dynamic evaluation model of orthogonal projection to measure the level of digital economy development at the provincial level in China from 2007 to 2019. On this basis, the panel fixed effects model and mediation model are used to empirically test the impact of digital economy development on carbon emission intensity and its mechanism. The results indicate that: (1) The development of China's digital economy is unbalanced among regions, showing a geospatial pattern of decreasing from east to west. (2) China's carbon emission intensity has a trend of decreasing year by year, and there are geospatial differences of "high in the west and low in the east” and "high in the north and low in the south.” (3) The digital economy development can effectively reduce regional carbon emission intensity through industrial structure optimization effect and resource allocation effect, and the industrial structure optimization effect can suppress carbon emission intensity more obviously. (4) The development of digital economy in different regions has different degrees of reducing carbon emission intensity. The development of digital economy in the eastern region has a stronger inhibitory effect on carbon emission intensity than that in the middle and western regions, and the development of digital economy in economically developed regions can suppress carbon emission intensity more. This paper provides enlightenment for policy makers to deal with climate warming. Copyright © 2023 Lyu, Zhang and Wang.

Using the four-valued Rasch model in the preparation of neutrosophic form of risk maps for the spread of COVID-19 in Turkey

This paper aims to create a neutrosophic risk map of COVID-19 cases in Turkey based on the quantum decision-making model by the qudit states. First, we generate COVID-19 data for Turkey based on real-world values and an estimate of R (reproduction number). In this paper, we try to propose four-valued logic based on the square of opposition to assess the COVID-19 data in Turkey. We analyze the data in terms of the general structure of the Rasch model. Second, we find the cumulative probabilities based on real data;the lowest is 1.30 for Turkey, and the highest is 2.21 for Turkey based on the following formulas representing the possible scenarios given to determine the range and upper and lower boundaries in our cognitive scheme. Finally, we can produce a risk map based on the upper and lower boundaries of the cumulative probabilities at the end of the march of events. Additionally, scalar data are often used in the production of visuals. The descriptive analysis of scenarios that are more strongly related to cognitive and semantic aspects in the context of vectors may provide more in-depth and detailed conclusions than the descriptive analysis of scenarios that are more directly associated with scalar data. Thus we show that the analysis of COVID-19 spreads may be carried out using the four-valued vectorial form of probability functions. © 2023 Elsevier Inc. All rights reserved.

Biology-Informed Recurrent Neural Network for Pandemic Prediction Using Multimodal Data.

In the biomedical field, the time interval from infection to medical diagnosis is a random variable that obeys the log-normal distribution in general. Inspired by this biological law, we propose a novel back-projection infected-susceptible-infected-based long short-term memory (BPISI-LSTM) neural network for pandemic prediction. The multimodal data, including disease-related data and migration information, are used to model the impact of social contact on disease transmission. The proposed model not only predicts the number of confirmed cases, but also estimates the number of infected cases. We evaluate the proposed model on the COVID-19 datasets from India, Austria, and Indonesia. In terms of predicting the number of confirmed cases, our model outperforms the latest epidemiological modeling methods, such as vSIR, and intelligent algorithms, such as LSTM, for both short-term and long-term predictions, which shows the superiority of bio-inspired intelligent algorithms. In general, the use of mobility information improves the prediction accuracy of the model. Moreover, the number of infected cases in these three countries is also estimated, which is an unobservable but crucial indicator for the control of the pandemic.

Improving Robustness of Medical Image Diagnosis System by Using Multi-loss Hybrid Adversarial Function with Heuristic Projection

Medical image diagnosis system by using deep neural networks (DNN) can improve the sensitivity and speed of interpretation of chest CT for COVID-19 screening. However, DNN based medical image diagnosis is known to be influenced by the adversarial perturbations. In order to improve the robustness of medical image diagnosis system, this paper proposes an adversarial attack training method by using multi-loss hybrid adversarial function with heuristic projection. Firstly, the effective adversarial attacks which contain the noise style that can puzzle the network are created with a multi-loss hybrid adversarial function (MLAdv). Then, instead of adding these adversarial attacks to the training data directly, we consider the similarity between the original samples and adversarial attacks by using an adjacent loss during the training process, which can improve the robustness and the generalization of the network for unanticipated noise perturbations. Experiments are finished on COVID-19 dataset. The average attack success rate of this method for three DNN based medical image diagnosis systems is 63.9%, indicating that the created adversarial attack has strong attack transferability and can puzzle the network effectively. In addition, with the adversarial attack training, the augmented networks by using adversarial attacks can improve the diagnosis accuracy by 4.75%. Therefore, the augmented network based on MLAdv adversarial attacks can improve the robustness of medical image diagnosis system. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

The Projected Burden Of Primary Total Shoulder Replacement Surgery In Australia To 2035

Purpose: Lifetime risk estimates show that the use of primary total shoulder replacement (TSR) surgery in Australia has increased in recent years, but future demand for surgery has not been estimated. This study aimed to forecast the number of primary TSR procedures likely to be performed in the year 2035, and associated costs to the Australian health system. Method(s): De-identified primary TSR data for 2009-2019 were obtained from the Australian Orthopaedic Association National Joint Replacement Registry. Australian population data (by age and sex) to the year 2021 and population projections to the year 2035 (by age and sex) were obtained from the Australian Bureau of Statistics. Data on average episode of care costs were sourced from the National Hospital Cost Data Collection and private health insurer websites. Procedure rates to the year 2035 were projected according to two scenarios: Scenario 1 assumed that the rate of TSR remained constant from 2019 onwards, with consideration of anticipated population growth and ageing;Scenario 2 assumed a continued increase in the rate of surgery as seen from 2009-2019 plus anticipated population growth and ageing. For Scenario 1, age- and sex-specific rates of TSR in 2019 were calculated and applied to population projections for the years 2020-2035. For Scenario 2, negative binomial regression models (which controlled for age, sex, and year) were used to estimate TSR procedures for the years 2020-2035. For both scenarios, healthcare costs for 2035 were estimated for the projected number of TSR procedures, with average procedure costs for public and private hospitals inflated to 2035 Australian dollars using the Total Health Price Index. Result(s): The use of TSR increased by 242% in Australia from 2009 to 2019 for adults over 40 years of age (from 1,983 to 6,789 procedures). In 2019, 60% of procedures (n=4,062) were performed for females and 73% (n=4,925) were performed for people aged 60-79 years. Fifty-three per cent of procedures in 2019 (n=3,608) were performed for osteoarthritis. Under Scenario 1, the incidence of TSR is predicted to rise from 6,789 procedures in 2019 to 9,676 procedures by 2035 (a 43% increase), at an estimated cost of $AUD 317.69 million. Under Scenario 2, TSR incidence is forecast to increase to 45,295 procedures by 2035 (a 567% increase) at an estimated cost of $AUD 1.49 billion. Under this scenario, 69% of the total forecast costs (equating to $AUD 1.02 billion) relate to the private hospital sector. Conclusion(s): The use of TSR in Australia has increased substantially over a decade, which likely relates to a range of factors including improvements in prosthesis design, improved clinical outcomes for patients, greater surgeon proficiency, and expanded clinical indications for surgery. Under a conservative forecasting scenario, a 43% in the number of procedures is estimated to occur by 2035. However, under an exponential growth scenario that considers growth in TSR rates plus population growth and ageing, Australia would be facing a more than five-fold increase in TSR procedures by 2035. This would have profound implications for the healthcare budget and surgical workforce requirements. Future research is needed to model the impacts of COVID-19 on TSR provision and catch up of unmet need due to elective surgery restrictions and cancellations.Copyright © 2023

SIGGRAPH Asia 2022-course notes: "become a guardian of al wasl": Developing productions for the world s largest real-Time interactive projection experience

At the time of writing (2021-22), "Become a Guardian of Al Wasl"represented the world's largest interactive experience. Designing, producing and deploying an immersive interactive experience at the monumental scale of the Al Wasl Plaza 360° projection surface necessitated the research, prototyping and testing of proposed solutions, including systems architecture, to meet the scope and specifications of the project. Four problem spaces emerged during development;real-Time rendering, projection-mapping, redundancy, and content synchronisation. In addition, budget constraints, Covid-19, and remote deployment motivated the exploration of other innovative solutions. As there is limited research on the design and development of interactive dome experiences, this paper will present the challenges encountered in developing productions for the world's largest dome display system and in building the underlying real-Time display and support systems. © 2022 Owner/Author.

Time series analysis of animal bite cases attending anti-rabies clinic.

Background: Rabies is a disease transmitted mostly through animal bites in humans, and seasonal variation in animal bites has been reported by different studies. There has been no study in India using time series analysis for studying monthly variation in animal bite cases. Aim: (a) To find out long-term trends and monthly variations in new animal bite cases. (b) To make projections for new animal bite cases. (c) To find out the difference between actual and projected new animal bite cases following the COVID-19 pandemic. Methodology: A retrolective, record-based study was conducted in a tertiary care facility, Jaipur, and data of new Category II and Category III animal bite cases were taken from January 2007 to December 2021. A multiplicative model was used for conducting time series analysis. The projected monthly number of cases was estimated using the line of best fit based on the least square method. Result: An increasing trend in the annual number of animal bite cases was observed from 7,982 in 2007 to 10,134 in 2019. The monthly index was lowest for the months July to November (0.88 to 0.95), peaked in January (1.14), remained higher from January to June, and fell in July (0.95). The monthly number of new animal bite cases from April 2020 to December 2021 was significantly lower than the projected number (P-value < 0.001). Conclusion: Because the monthly index of animal bite cases was high from January onward, information education and communication (IEC) activities should be increased in the preceding months (November onward) for making people aware of immediate care to be provided to animal bite cases and seek prompt medical care.

Multi-camera BEV video-surveillance system for efficient monitoring of social distancing.

The current sanitary emergency situation caused by COVID-19 has increased the interest in controlling the flow of people in indoor infrastructures, to ensure compliance with the established security measures. Top view camera-based solutions have proven to be an effective and non-invasive approach to accomplish this task. Nevertheless, current solutions suffer from scalability problems: they cover limited range areas to avoid dealing with occlusions and only work with single camera scenarios. To overcome these problems, we present an efficient and scalable people flow monitoring system that relies on three main pillars: an optimized top view human detection neural network based on YOLO-V4, capable of working with data from cameras at different heights; a multi-camera 3D detection projection and fusion procedure, which uses the camera calibration parameters for an accurate real-world positioning; and a tracking algorithm which jointly processes the 3D detections coming from all the cameras, allowing the traceability of individuals across the entire infrastructure. The conducted experiments show that the proposed system generates robust performance indicators and that it is suitable for real-time applications to control sanitary measures in large infrastructures. Furthermore, the proposed projection approach achieves an average positioning error below 0.2 meters, with an improvement of more than 4 times compared to other methods.

Comprehensive analysis of the novel omicron receptor AXL in cancers.

The SARS-CoV-2 is constantly mutating, and the new coronavirus such as Omicron has spread to many countries around the world. Anexelekto (AXL) is a transmembrane protein with biological functions such as promoting cell growth, migration, aggregation, metastasis and adhesion, and plays an important role in cancers and coronavirus disease 2019 (COVID-19). Unlike angiotensin-converting enzyme 2 (ACE2), AXL was highly expressed in respiratory system cells. In this study, we verified the AXL expression in cancer and normal tissues and found AXL expression was strongly correlated with cancer prognosis, tumor mutation burden (TMB), the microsatellite instability (MSI) in most tumor types. Immune infiltration analysis also demonstrated that there was an inextricable link between AXL expression and immune scores in cancer patients, especially in BLCA, BRCA and CESC. The NK-cells, plasmacytoid dendritic cells, myeloid dendritic cells, as one of the important components of the tumor microenvironment, were highly expressed AXL. In addition, AXL-related tumor neoantigens were identified and might provide the novel potential targets for tumor vaccines or SARS-Cov-2 vaccines research in cancer patients.

Spread and seasonality of COVID-19 pandemic confirmed cases in sub-Saharan Africa: experience from Democratic Republic of Congo, Nigeria, Senegal, and Uganda.

BACKGROUND: The COVID-19 pandemic has impacted the world negatively with huge health and socioeconomic consequences. This study estimated the seasonality, trajectory, and projection of COVID-19 cases to understand the dynamics of the disease spread and inform response interventions. METHOD: Descriptive analysis of daily confirmed COVID-19 cases from January 2020 to 12th March 2022 was conducted in four purposefully selected sub-Saharan African countries (Nigeria, Democratic Republic of Congo (DRC), Senegal, and Uganda). We extrapolated the COVID-19 data from (2020 to 2022) to 2023 using a trigonometric time series model. A decomposition time series method was used to examine the seasonality in the data. RESULTS: Nigeria had the highest rate of spread (ß) of COVID-19 (ß = 381.2) while DRC had the least rate (ß = 119.4). DRC, Uganda, and Senegal had a similar pattern of COVID-19 spread from the onset through December 2020. The average doubling time in COVID-19 case count was highest in Uganda (148 days) and least in Nigeria (83 days). A seasonal variation was found in the COVID-19 data for all four countries but the timing of the cases showed some variations across countries. More cases are expected in the 1st (January-March) and 3rd (July-September) quarters of the year in Nigeria and Senegal, and in the 2nd (April-June) and 3rd (October-December) quarters in DRC and Uganda. CONCLUSION: Our findings show a seasonality that may warrant consideration for COVID-19 periodic interventions in the peak seasons in the preparedness and response strategies.

Systematic evaluation of emergency management capacity for rural public health emergencies

Rural areas' emergency response capacities are generally weaker when compared to tier one cities and this can have an adverse effect on residents' livelihood and health. Evaluation of rural emergency management is of great significance for improving the rural emergency management capacity. This paper innovatively constructs an evaluation system for the emergency management capabilities with the rural public health emergencies, which includes four dimensions: emergency subject, mechanism, resources and concept. A Projection Pursuit model for objectively processing high-dimensional is constructed, and data from 2010 to 2020 in the rural areas of Xiantao City are selected as samples for empirical research. The results show that: (1) Each dimension of emergency management of public health emergencies contributes more than 20% to the ability. Compared with the other three dimensions, contribution of the emergency concept accounted for the lowest proportion, which was 21.69%, and indicates that this dimension is the key factor restricting the improvement of the emergency management capabilities. (2) From 2010 to 2019, the average annual growth rate of comprehensive emergency management capacity in the rural areas of Xiantao City was 14.9%, and by 2020, the rural emergency management capacity, impacted by the COVID-19 epidemic, grew very rapidly with an annual growth rate of 33.8%. (3) The development of an effective rural emergency management capacity system is not sufficient and unbalanced, which leads to the "barrel effect.” This study can provide theoretical guidelines for the evaluation of rural emergency management capabilities, and provide methodological support for similar research in other regions. © 2022 Elsevier Ltd

Biserial targeted feature projection based radial kernel regressive deep belief neural learning for covid-19 prediction.

Coronavirus disease 2019 (COVID-19) is a highly infectious viral disease caused by the novel SARS-CoV-2 virus. Different prediction techniques have been developed to predict the coronavirus disease's existence in patients. However, the accurate prediction was not improved and time consumption was not minimized. In order to address these existing problems, a novel technique called Biserial Targeted Feature Projection-based Radial Kernel Regressive Deep Belief Neural Learning (BTFP-RKRDBNL) is introduced to perform accurate disease prediction with lesser time consumption. The BTFP-RKRDBNL techniques perform disease prediction with the help of different layers such as two visible layers namely input and layer and two hidden layers. Initially, the features and data are collected from the dataset and transmitted to the input layer. The Point Biserial Correlative Target feature projection is used to select relevant features and other irrelevant features are removed with minimizing the disease prediction time. Then the relevant features are sent to the hidden layer 2. Next, Radial Kernel Regression is applied to analyze the training features and testing disease features to identify the disease with higher accuracy and a lesser false positive rate. Experimental analysis is planned to measure the prediction accuracy, sensitivity, and specificity, and prediction time for different numbers of patients. The result illustrates that the method increases the prediction accuracy, sensitivity, and specificity by 10, 6, and 21% and reduces the prediction time by 10% as compared to state-of-the-art works.

The impacts of oil price volatility on financial stress: Is the COVID-19 period different?

This study analyses the effects of oil price volatility on financial stress with various measures for a large panel of countries. The study places a special focus on comparing the pattern of these effects during the Great Recession period and the COVID-19 recession period. Using the local projection approach, the paper finds that oil price volatility has a positive and persistent effect on financial stress. However, the magnitude and the degree of persistency of oil price volatility impacts on financial stress are much greater for the Great Recession period than for the COVID-19 recession period. A possible explanation for this result would be that COVID-19 is better thought of as a "natural disaster” in which companies under stress were not being mismanaged. Another explanation would be that active intervention by the government through monetary and fiscal channels reduces the sensitivity of financial instability to oil price volatility during the COVID-19 period.

OCR for TIFF Compressed Document Images Directly in Compressed Domain Using Text segmentation and Hidden Markov Model

In today's technological era, document images play an important and integral part in our day to day life, and specifically with the surge of Covid-19, digitally scanned documents have become key source of communication, thus avoiding any sort of infection through physical contact. Storage and transmission of scanned document images is a very memory intensive task, hence compression techniques are being used to reduce the image size before archival and transmission. To extract information or to operate on the compressed images, we have two ways of doing it. The first way is to decompress the image and operate on it and subsequently compress it again for the efficiency of storage and transmission. The other way is to use the characteristics of the underlying compression algorithm to directly process the images in their compressed form without involving decompression and re-compression. In this paper, we propose a novel idea of developing an OCR for CCITT (The International Telegraph and Telephone Consultative Committee) compressed machine printed TIFF document images directly in the compressed domain. After segmenting text regions into lines and words, HMM is applied for recognition using three coding modes of CCITT-horizontal, vertical and the pass mode. Experimental results show that OCR on pass modes give a promising results. © 2022 IEEE.

Classification of COVID-19 from community-acquired pneumonia: Boosting the performance with capsule network and maximum intensity projection image of CT scans.

BACKGROUND: The coronavirus disease 2019 (COVID-19) and community-acquired pneumonia (CAP) present a high degree of similarity in chest computed tomography (CT) images. Therefore, a procedure for accurately and automatically distinguishing between them is crucial. METHODS: A deep learning method for distinguishing COVID-19 from CAP is developed using maximum intensity projection (MIP) images from CT scans. LinkNet is employed for lung segmentation of chest CT images. MIP images are produced by superposing the maximum gray of intrapulmonary CT values. The MIP images are input into a capsule network for patient-level pred iction and diagnosis of COVID-19. The network is trained using 333 CT scans (168 COVID-19/165 CAP) and validated on three external datasets containing 3581 CT scans (2110 COVID-19/1471 CAP). RESULTS: LinkNet achieves the highest Dice coefficient of 0.983 for lung segmentation. For the classification of COVID-19 and CAP, the capsule network with the DenseNet-121 feature extractor outperforms ResNet-50 and Inception-V3, achieving an accuracy of 0.970 on the training dataset. Without MIP or the capsule network, the accuracy decreases to 0.857 and 0.818, respectively. Accuracy scores of 0.961, 0.997, and 0.949 are achieved on the external validation datasets. The proposed method has higher or comparable sensitivity compared with ten state-of-the-art methods. CONCLUSIONS: The proposed method illustrates the feasibility of applying MIP images from CT scans to distinguish COVID-19 from CAP using capsule networks. MIP images provide conspicuous benefits when exploiting deep learning to detect COVID-19 lesions from CT scans and the capsule network improves COVID-19 diagnosis.

Systematic evaluation of emergency management capacity for rural public health emergencies

Rural areas' emergency response capacities are generally weaker when compared to tier one cities and this can have an adverse effect on residents' livelihood and health. Evaluation of rural emergency management is of great significance for improving the rural emergency management capacity. This paper innovatively constructs an evaluation system for the emergency management capabilities with the rural public health emergencies, which includes four dimensions: emergency subject, mechanism, resources and concept. A Projection Pursuit model for objectively processing high-dimensional is constructed, and data from 2010 to 2020 in the rural areas of Xiantao City are selected as samples for empirical research. The results show that: (1) Each dimension of emergency management of public health emergencies contributes more than 20% to the ability. Compared with the other three dimensions, contribution of the emergency concept accounted for the lowest proportion, which was 21.69%, and indicates that this dimension is the key factor restricting the improvement of the emergency management capabilities. (2) From 2010 to 2019, the average annual growth rate of comprehensive emergency management capacity in the rural areas of Xiantao City was 14.9%, and by 2020, the rural emergency management capacity, impacted by the COVID-19 epidemic, grew very rapidly with an annual growth rate of 33.8%. (3) The development of an effective rural emergency management capacity system is not sufficient and unbalanced, which leads to the "barrel effect.” This study can provide theoretical guidelines for the evaluation of rural emergency management capabilities, and provide methodological support for similar research in other regions.