The emergence of agriculture and the evolution of sedentary societies are among the most important processes in human history. However, although archeologists and social scientists have long been studying these processes, our understanding of them is still limited. This article focuses on the Fuxin area in present-day Liaoning province in Northeast China. A systematic archeological survey we conducted in Fuxin in recent years located sites from five successive stages of the evolution of agricultural sedentary society. We used the principles of Maximal Entropy to study changes in settlement patterns during a long-term local trajectory, from the incipient steps toward a sedentary agricultural way of life to the emergence of complex societies. Based on the detailed data collected in the field, we developed a geo-statistical model based on Maximal Entropy (MaxEnt) that characterizes the locational choices of societies during different periods. This combination of high-resolution information on the location and density of archeological remains, along with a maximal entropy-based statistical model, enabled us to chart the long-term trajectory of the interactions between human societies and their natural environment and to better understand the different stages of the transition to developed sedentary agricultural society. The transition to agriculture  was arguably the most significant ‘revolution’ in human history ,2. This In this paper, we are focusing on the analysis of data from northern China, the second oldest center of plants and animal domestication in the Old World and a region where the transition to agriculture led to the emergence of unique civilizations and the development of complex societies and states . We focuThe Maximal Entropy modeling approach was developed and applied to the social sciences beginning in the 1970s, if not earlier. It has been widely used in human geography and urban planning to model spatial interactions . SimilarThe archeological locational (predictive) model is a tool that helps to assess the probability of sites being present in different locations in the landscape ,24,25. AIn a previous paper , we compOur archeological research in the Fuxin area of western Liaoning Province , was a cIn this paper, we use the data collected during two seasons of systematic regional survey (2012 and 2013), where we were able to cover some 104 sq·km. During the survey, team members walked close to one another (20 m·spaces between one person and the next) and were instructed to walk slowly and pay careful attention even to small and very sparse concentrations of artifacts. We paid special attention to stone artifacts, which have not been identified and systematically collected in other surveys in China. Survey conditions in the Fuxin area are close to ideal, as the area occupied by modern villages and other man-made features comprises less than 10% of the entire region. Ground visibility during the time of year that the Fuxin survey was conducted (March and April) were excellent: the ground in the fields and orchards was completely exposed, allowing for extraordinary visibility of archeological artifacts. Consequently, we were able to identify and document many sites that were previously unknown. Locating sites inside the forested areas was more difficult, but using intensive sampling methods we were able to identify and rigorously assess the density of prehistoric human occupation in this ecologic niche as well . When the survey group encountered ancient artifacts or features, their location was recorded using GPS technology, and a sample of the artifacts was collected to identify their date and density on the ground. Rather than “sites” which may be more vaguely defined or include several collections from different periods, we used those “collections” as the primary analytical unit. The GIS analysis discussed below is based on the location, dimension, artifact dates, and the density of artifacts recorded for each “collection”. All together, we recorded 1152 collections, some containing evidence of occupation during more than one period. The basic information related to the five periods addressed by this paper are presented in The periods of occupation included in this study represent the trajectory from the beginning of sedentary lifeway in the Fuxin region, through the advancement of complex agricultural societies, to the development of more densely populated polities (chiefdoms?) and larger central sites (cities?). It started in the Xiaohexi period (c. 6000–5700 BCE), during which small scale sites with minimal investment in permanent structure may represent the incipient settling down of groups of complex hunter-gatherer societies . During The Fuxin regional archeological study focused on processes of socio-political and economic change. An important issue we address in the present article is how those processes are reflected in the interaction between human communities and their environment. More specifically, we want to know how people choose where to locate their community (village) and how this choice changed in accordance with changes in the social and economic organization. Among the environmental factors that are relevant to such an analysis are limiting conditions, such as steep slopes and exposed terrains, as well as attractive resources, such as water, arable land, and natural sources of food and raw materials. We know that significant climatic changes occurred during the 5000 years period addressed by this paper: the climate improved dramatically (more rain and warmer temperatures) around 5800 BCE, more or less coinciding with the beginning of the Xinglongwa period, and it worsened during the late 4th millennium BCE, more or less coinciding with the decline of the Hongshan society . While tOur initial working hypothesis was that, with the progression of time, collection units (and their clusters or ‘sites’) will become more closely associated with environmental elements that are crucial for agriculture and its intensification—elements such as arable and fertile land, stable water resources and good exposure to sunlight. Thus, we expected that Xiaohexi collections would be the least associated with such parameters and more closely associated with conditions that are good for hunting and gathering, and possibly also with locations with an abundance of stones suited to the production of stone tools. We expected the subsequent period of Xinglongwa, and even more so, the Zhaobaogou and Hongshan collections, to be closely associated with environmental niches best suited for agriculture. The Lower Xiajiadian collections of the Bronze Age are also expected to be associated with such environments but, because some of the sites are very large (an aggregate of many collectionsunits) and are fortified, other considerations such as the selection of large areas that are easily defendable, might affect the overall picture. Analyzing the location of collection units found by our survey using geo-statistical models that are based on Maximal Entropy enabled us not only to test those simple hypotheses, but to flesh out more fully the complex decision making processes and the factors involved in such decisions made by the prehistoric populations.In previous work, we used the results of the Fuxin Survey to predict the potential location of sites in a wider neighboring area. The geographical framework of the current model overlaps with the Fuxin survey area, aiming to use the model as an interpretative tool for the characterization of the ecological niches typical for each period and an examination of changes over time. The model area is 104 sq·km. It was divided into 10,400 cells, each measuring 100 × 100 m. The model’s purpose is to predict, for each such cell, the relative probability of collection unite presence, based on the different environmental parameters of each cell. Because the location of the sites (represented here by the collection units) is known definitively from the results of the systematic survey, the model results are used to explain the preferred ecological niches of each period. The dependent variable in the model is an archeological collection unit of a specific period, while the independent variables are various environmental datasets that were determined based on the geographic information available for the survey region and on our observations during the survey itself  included pottery shards dating to different stages of the Neolithic period and an additional 105 collection units (60.8 hectare.) were dated to the first phase of the Bronze Age (the LXJD period), . For eacThe environmental dataset: The relevant environmental variables included in the models were selected based on well-established hypotheses regarding the type of factors which affected human decision making in northeast China ,35,42. Thttps://search.asf.alaska.edu/#/). This imaging contains elevation data, which enabled us to create raster layers of slopes, aspect, and local terrain (land curvature). These variables are fairly common in archeological predictive models, based on the logical assumption that people tended to live in moderate areas [Topographic variables: slope, aspect and land curvature: We used 12.5 m satellite imagery from the Japanese Aerospace Exploration Agency (JAXA) available from the ASF DAAC datasets . Modern land uses: Because of the advent of agriculture and cultivation techniques, the current use of the land does not precisely reflect the way it was used in the past. Nonetheless, current land use can be employed as a heuristic device that suggests a proxy, even if its nature is not precisely known, to the productivity of the land and thus, to its attractiveness for humans. Here we used a Euclidean distance function of the present-day land uses: agriculture fields, forests, and modern settlements. We produced vector layers based on QuickBird orthophoto  for modern built areas, crops areas, and forests. Then we calculated the Euclidean distance of each survey collection unit in each layer, using the GIS “near” function. 2 cell/pixel, which can basically be presented as a gradual color map of continuous values. The probability values were then projected as a binary map (high probability / low probability) using a threshold value determined according to model performance. For the threshold of high/low probability, we chose the actual probability value for each period model which is able to “predict” 85% of the collection units in the dataset. This threshold and the use of a binary map rather the actual model’s values, is widely accepted in archeological locational modeling [The study area was divided to 10,400 squares of one hectare (map pixels of 100 × 100 m) each. The MaxEnt algorithm calculates relative probability for each 100modeling ,43. HoweIn our case, we already know where sites exist from the systematic survey. In this sense, we are using the model as explanatory tool, which helps us to understand and interpret the locational preferences of different periods, as well as identify a general pattern of such preferences. The model results (areas with high probability) can be interpreted as habitats, or the ecological niches that are most suitable for each of the cultural phases under investigation. The areas returning a high probability are those that are most suitable for settlement sites, based on the analysis of our observations (collection units) and their environmental properties (background variables).There are a number of widely accepted approaches to examining the effectiveness of a given model and its performance. The most common approach requires that the model should predict at least 80–85% of the collection units located in about 33% of the land defined as “high probability” areas. The smaller the predicted area is (33% or less), and the higher the percentage of collection units located in it (80% percent or more), the more effective the model is considered to be ,45. We uWe used three indicators to assess how well the model discriminates between unsuitable and suitable areas. These indicators are the AUC, Test Gain, and the ‘cover area’. The AUC score is a common statistic tool to assess and compare models . It reprWe are fully aware of the procedure of cross-validation based on independent samples of both training and testing datasets ,23,43. HBecause the purpose of this study is to explain the environmental considerations of groups at different stages of permanent settlement and agriculture, we undertook further analysis to show which environmental variables are most relevant to the model during each period. In order to understand the importance of each variable, we used the jackknife method . After rUsing the MaxEnt model, five successive periods were examined. The location of collection units was analyzed in relation to eight environmental categories. The categories include: Distance from the main rivers (River Dist.); the degree of slope (Slope); the aspect of the slope (Aspect); elevation above sea level (Elevation); Land Curvature, a GIS function which calculates the shape of the slope (this is most useful for identifying areas with rapid change in a slope or aspect) ; distancThe results of each period model are summarized in In addition, the most influential environmental parameters for each period were analyzed in order to test our hypothesis regarding the evolution of sedentism and cultivation through the Neolithic and how such socio-economic changes are reflected by both ecological niche / habitat and the different environmental variables. For the XLW and LXJD periods, we ran the model with only 0.25 observations, in order to study the effect of the sample size. No significant effect was found. XHX, is the earliest settlement period discovered in the study area, and the first phase in the transformation to a sedentary society. The model was built on the basis of 15 collections units dated to this phase. The model results for this period comprise 19% of the study area, and reflect the typical preferred ecological niches (or habitats) of this culture . In order to create samples of similar size, 25% of these (a subset of 18 collection units) were randomly sampled, where the results are an average of four model runs. The model results for this period are slightly less clear cut, which probably reflects the greater environmental variation of site locations. The percentage of land cover for the XLW model is 26%, the AUC is 0.82 and test gain is 0.85 . The mosFor the ZBG period, the survey identified remains in only 24 collection units. The model results during this period cover 14%, of the research area . For all the periods we tested, this is the most effective model. It probably indicates a small variation among the location preferences of habitation sites. A look at the map  shows a The survey located 15 collection units from the HS period. The model results cover 15% of the study area , we used a four-subsets sampling approach with a random selection of 25% of the collections. The model results here are less clear than for the previous periods: The model land cover comprises 37% of the study area, while the AUC measure is only 0.8 . These rIn Entropy is a yardstick for disorder and uniformity in a given space. Nature has a tendency to disorder and to random processes, unless some energy is deliberately invested in the opposite direction. This also applies to human location models. We expect uniform dispersal of human settlements in all periods, unless there are forces that distort this random processes. In ancient periods we can assume that there were many limitations, as well as attractions, that altered the random distribution of settlements. As time passes, different constraints and opportunities evolve and change along with the development of human technology and culture. Such changes are reflected in the choice of settlement locations.Alongside the important insights, it provides regarding the trajectory of socio-economic change in Northeast China, our study also generates important methodological contributions. One key problem in locational modeling in archaeology is the inability to perform a stable model analysis on a small dataset of observations, a situation which is quite common for archeological projects. This is one reason why locational modeling of different sub-periods, or site types is quite rare, as well as why their results are often insufficient. In this study, we demonstrate that a combination of high-resolution accurate survey data and the use of MaxEnt analysis, can provide meaningful results even when working with small datasets. The results of this paper highlight the benefits of an Entropy-based model over previous statistical approaches in archeology (such as logistic regression). As we demonstrated, it is useful not only as a predictive model, for which such techniques are often used  , but alsThree of the periods we analyzed in this study consist of only 15 to 20 collection units (independent observations), but their analysis resulted in the creation of strong models. The model thus created helped to characterize the way in which the major environmental parameters affected the location of prehistoric communities in each period, as well as the transformation over time in these location strategies. From an anthropological perspective, the locational model facilitates a systematic analysis of change in locational preferences over time, and sheds light on human strategy and decision making. Overall, the results support our initial hypotheses about the nature of each phase in the local trajectory, but using the model allowed us to gain a better and more nuanced understanding. At the incipient phase of the trajectory, the Xiaohexi period, the main environmental parameters that affected site locations were the aspect of slopes (preference for southward facing slopes) and proximity to areas which are currently forested. As discussed above, these elevated and rocky areas were not suitable for agriculture, but provided good resources for hunting-gathering, including sources of raw materials for the production of stone tools. Our analysis suggests that the location preferences of these prehistoric societies changed with the transition to the more advance phases of the Neolithic period. Although southward facing slopes (aspect) were favored in all periods, as can be expected from societies in northern latitudes, the later Neolithic societies show a clear tendency to locate their sites near areas that are presently used for agriculture and areas close to the main rivers. In contrast to the Xiaohexi, the location of sites dated to the Xinglongwa, Zhaobaogou, and Hongshan periods is what we would expect from societies whose economic focus is on cultivation. As discussed above, our analysis of the location of Bronze Age (LXJD) collections was less successful than that of the earlier periods. The model itself is far less effective (land cover of 37% versus 14–26% in different phases of the Neolithic), and none of the variables contribute significantly to the model . Although the model by itself does not sufficiently explain the location strategies of the Bronze Age societies, it does clearly indicate a change from the strategies of the Neolithic societies that previously inhabited the same area. It suggests that Bronze Age societies may have had different considerations, which are not well-reflected in our environmental proxies; nonetheless, a more detailed locational analysis of this period is beyond the scope of the present paper.


In December 2019, the world was disrupted by the news of a new strain of virus known as Novel Corona virus, taking lives of many in China. Wuhan, the capital of Central China’s Hubei province is said to be the place where the outbreak started. The city went on lockdown as the disease spread rapidly. After the lockdown, most countries like India and Bangladesh airlifted their citizens who were studying in Wuhan. Similarly, Nepal also has many youth studying medicine in Wuhan. Pleas for help from the students reached the government. This was the first encounter of such experience for Nepal government. With the help of Ministry of Health, Ministry of foreign affair, Health Emergency Organizing committee, Epidemiology and Disease Control Division, Nepal Army Hospital, Nepal Police Hospital, Waste Management team, Nepal Ambulance service, Tribhuwan international Airport Authorities and Royal Airlines the government of Nepal planned, organized and successfully brought back all the 175 students on 15 th February 2019 from Wuhan, China. The present article aims to share the experience, the challenges faced and recommendations for future similar cases. An outbreak of corona virus in Wuhan, China devastated the whole world in December 2019. Ban of travel in and out of China was discouraged and terror of the disease spread through countries. As Wuhan went down on a lockdown and panic-stricken citizens stayed in their houses waiting for the nightmare to be over, Nepal had another major issue to tackle.Since past two decades’ students from Nepal have been going to China for further studies especially Bachelor in Medicine and Surgery(MBBS). The five-year course with various scholarships and other states of art technology -based education has been a lucrative offer for the Nepalese youth. Nepalese students fock in hundreds every year for a degree to various parts of China but this year they had no idea of what was in store for them. As the disease spread rapidly, countries including Sri Lanka, India, Malaysia airlifted their students from danger. But a low-income country like Nepal was under pressure and scrutinized for not being able to do the same.Pleas for help through phone calls, social media and emails started to arrive as the students were under lockdown in their dorms with limited food and water supply. Worried parents back in Nepal started asking the government to bring back their children home, to safety. As the number of death tolls kept rising and the fear of the new strain of virus spread Nepalese students waited helplessly.Back in Nepal, an urgent meeting was held in Singha Durbar, Kathmandu. A team of experts from Nepal Army Hospital, Nepal Police Hospital, Epidemiology and Disease Control Division, Health Emergency Organizing Committee met with the Prime Minister’s officials. Within the next couple of hours, a work plan was created to bring the students home. It wouldn’t be a simple pickup drop off plan. It had to be meticulously planned to look at all details and Nepal had never experienced this kind of situation and this would be completely new challenge for the concerned authorities.Over the next few days, a lot of planning was discussed until a final plan was made. Next work division was done with team leaders for each phase. There would be four phases: Evacuation, Screening, Isolation and Quarantine.For the first step, evacuation guidelines from the CDC and WHO were revised. With limited resources, manpower and technology it was difficult to maintain the universal guideline. Based on many literature reviews and consultations with experts a basic ground rule was made which was minimum contamination. The second was consultation with the airline crew and the aeronautical engineers as aerodynamics and inflight instructions played a major role in the 4-hour flight from Wuhan to China. It was decided to have minimum technical and medical staffs to maintain minimum touch technique to avoid contamination in the plane.An urgent meeting was held in the PM office. The objective was the early evacuation of the students studying in Wuhan, China. The students had to be brought home safely then isolated and quarantined. This was a completely new task, there were no department or a body of government who handled these kinds of situations. It was stressful as time was running out and we didn’t know the magnitude of the disease very well. Many questions arose, what if the students were already infected, what if they were carriers of the disease, what if we bring the disease home, what if the medical or aeroplane crew catches the virus, will doctors that we assign be ready to get on that plane, who will risk their lives and more importantly are we putting everyone at risk. After major discussions and consultations, a decision was made, a list of names was put on the whiteboard. 4 teams were instantly created. Surprisingly energy flowed in, and more than expected volunteers from Nepal Army and Nepal Police offered their service.After the division of medical teams, work distribution was done. There would be evacuation, isolation, screening and quarantine. The responsibilities were each then assigned. Nepal Police DySP Dr.Bibek was in charge of the evacuation of students from Wuhan, Dr.Bikal Shrestha from Nepal Army was in charge of the screening process, and Dr.Naveen Phuyal from Nepal Army was in incharge of isolation and quarantine. Each team leader got their team ready and a short training was conducted for each phase.Another vital stakeholder in this operation was the waste management team. The waste produced in the plane, during the flight and later during the stay in quarantine had to be managed and disposed of properly. We lacked proper equipment and faced major setbacks. The plastic water bottles couldn’t be placed in autoclave machine with the caps on, each individual had to take the cap off before disposal, plastic bags where each individual disposed their food boxes couldn’t also be placed in the autoclave machine hence after much discussion a brown paper bag was decided instead of a plastic bag. There were so many things to consider, so many information to be gathered, and everything had to be meticulously planned, not missing any minor detail.In a low-income country like Nepal, the team did not expect to have everything in need and expected to very much adjust. But to everyone’s surprise, the logistic team was very supportive and provided almost everything that was needed. A budget was set and everything needed and that was available was provided. During this time, disposable masks were not easily available, and yet the teams were provided masks that were collected over days. The personal protection equipment provided was up to date. Waste disposal machines were brought immediately when it was realized the ones available couldn’t handle the tons of waste that were going to be created. 5 new mobile phones were bought and distributed among team leaders with sim cards and internet packages for the collection of photos of self-declaration forms (SDF). As the SDFs would be disposed of the photos would be sent to dedicated g-mail and Whatsapp for decoding and saved in case for contact tracing of the passengers. The mobile phones were also then placed in autoclaved and disposed of. 5 infra-red non touch thermometers were also provided by the logistic team on a short notice for the monitoring of temperature of the passengers. The 5 infra-red thermometers were also disposed of properly after its work was complete.Training team (EDCD) for all medical as well as other team regarding PPE, personal. A class was held for the airline crew team at TIA. Everything from aerodynamics to medical procedure was discussed.Shortly Nepal Ambulance team was also recruited and trained for the emergency. Ambulance system was made available for 24 hours. Transport Team formed of bus drivers only to maintain minimum contact. Disinfection process of airplane, bus and ambulance was planned by chemical Kala 1.4. The transport vehicles were also in quarantine and not in use as per protocol.National laboratory capacity was strengthened to prepare for huge population arriving from Wuhan, and in case of an epidemic. As soon as the quarantine protocol was finalized, the search for an isolated place that could house all 175 students as the quarantine area started. After a thorough search and lots of meetings Kharipatti, Bhaktapur was finalized as the quarantine area. It was the perfect location as it was easily accessible but still not inhabited densely, the buildings were almost empty which helped as minimum furniture was planned to avoid contamination, the building already had running water and electricity, the enclosed area was spacious which would help the students to feel comfortable and not in a confinement. Once the location was fixed the planning for the stay started as in dedicated Isolation beds were allocated as the first step.Medical Information Update team was added to the whole team to update about the status of the spread of COVID-19 and its new change and information. Risk communication was being planned simultaneously. Inter-sectorial authorise the connection and dedicated contact numbers were there, communication was strong between each team.Possible challenges of weather variation, in our case rain, instead of one plane there could be two planes to adjust the students, if any students developed symptoms inflight and just everything possible that might occur a Plan B was prepared and discussed.The draft formulation was revised and presented to the authorities.There were two phases in resource mobilization:1. Planning2. ImplementationFinancial resource planning: Fund was provided by the government, hence there was no hassles to mobilize the funds. Situation analysis budget outcome in case if not sufficient while implementing the plan, a contingency plan was formed for the specific.Human resources: Every team consisted of Nepal Army and Nepal Police personnel. Since these were bodies of discipline and following chain of command it was easier to mobilize. Prompt action could be taken and punctuality was highly maintained. The job description of the team was well defined.Local resource utilization: In case of shortage of any equipment from personal protective equipment, disinfectants local producers were allocated and were also sought to manufacture at a large level. For isolation, Patan Hospital, Sukraraj Tropical and Infectious disease Hospital and other satellite hospital were considered. These hospitals were prior informed and alerted.Formulation of guideline principles for action plan development were formed and presented. Continuous monitoring and evaluation of the action plan in an interval of 3days, any changes seen in evaluation, the strategy was modified.Lesson learnt: All planning, implemented steps, procedures were documented safely for future endeavours. In case of similar situations in future, these documented plans and implementations could provide information and guide in future challenges.The greatest advantage the team had, was proactiveness. It felt surreal to see each and everyone in the team was so full of energy and thinking proactively. It was an indescribable feeling that everyone felt during work. It is not sure if this is patriotism or passion for work or just anything but what the team felt was also witnessed by every single person who came to work at the site. Intersectional coordination was performed smoothly with dedicated contact details 24 hours.For every strategy/situation/situation and its possible failure, a plan B was formed for backup.Another vital role was played by the media. In order to avoid fake news, misconception about COVID-19 and its transmission, about the work being planned or anything related, a clear and strong communication with the media was very necessary. While communicating with the media it was very necessary to explain the ongoing situation in order to prevent unwanted fear escalating among the general public. Many controversary had arisen amidst the planning regarding the quarantine site and it needed to be cleared with information and not forcefully. We were working for the wellbeing of the public and in no way trying to harm them, and this message had to reach the mass loud and clear.Possible loopholes were early discussed and managed. Role of each allocated team and personnel were defined and responsibilities timely checked. Review of regulatory requirements were done time and again. Mock drills were performed with airline crew, medical team and the transport team. The entry point, the exit point, the use of back door in case of symptomatic passenger, the minimum contamination practice and many other were included.It may seem like very minor details and one would assume that it would be easily followed but on the ground, in reality, we were very far from it. One single mistake and this could lead to unimaginable chaos. The stakes were very high but the preparedness was even stronger. This team left no stone unturned and took into consideration every minute detail and information. This was a preparation Nepal had never witnessed before with the best of the country’s best involved.Evacuation step was divided into further 5 steps:Tribhuwan International Airport (TIA) management before the flight (At Kathmandu)On the way to Wuhan flight management (Kathmandu to Wuhan)Wuhan Airport management (At Wuhan)Wuhan to Kathmandu flight management (Wuhan to Kathmandu)After landing in TIA management. (At Kathmandu)Kathmandu to WuhanTribhuwan International Airport management before flight:1 (SDF) by Air Medical Team (AMT)Fill up of Self Declaration FormTemperature measurement of all team membersDebriefng of the medical evacuation by AMT commanderPlacement of food, drinking water, mask, sanitizer, rubbish bag, clear bag, pen, SDF, necessary information leafet on their respected seatRe-examine the checklist meticulously2. During Flight:Inspection of the aircraft diligently by the commanderStorage area, AMT and passenger seats and toilet allocationWuhanCommunication with the ground staff for Personal Protection Equipment (PPE) preparation before boarding of passengerEmptying bowel and bladder and use of adult diaper before PPE2Passengers were asked to board the plane 20 persons at a time, maintaining one-meter distance while boarding.3. At Wuhan Airport:Cabin crew announcements:To be seated in the exact seat allocation.All required materials are on their seats, to minimize movements around the plane.Use mask for whole flight duration.Announcements to check the leaflets (for information regarding hand rubbing technique to use sanitizer and handwashing technique) were made frequently .Toilets on the plane were allocated for passengers with symptoms and without symptoms. (symptomatic/ asymptomatic/ crew)5 to decrease possible contamination to be followed.All recommendations as per medical protocolAsk traveler to fill up Self Declaration Form.Screening after getting off from the plane, quarantine information, separate mode of travel plans for symptomatic and asymptomatic.Passengers were asked to switch off mobile phones and “not in airplane mode” along with other devices beside medical equipment to minimize contamination (leaflets).Clean bowel and bladder before departure (in leaflets)Bring rubbish bag containing food remains or cutlery, water bottles with cap open and old mask when they get off from plane (in leaflets)Bring leaflets, SDF form, boarding pass inside clear file during screening in departure (in leaflets)Luggage managed and dropped at quarantine center.Repeated messages regarding hand rubbing and washing with minimal walking.Wuhan to Kathmandu During Flight (Return):Application of duty roster  with documentations.Monitoring of passengers for 30 minutes by each crew and medical staff Maintain register by the commander.Cabin crew and medical staffs will be under the control of Medical Commander for medical purposes as per command system.Seat allocations and its rationale:6 of passenger ensured by AMT. If possible, symptomatic should be at least 1 meterSeating started from 2 rows (2 meters) away from the exact position of the cabin crew stands during announcements to avoid contamination.The medical team were seated in the business class area and their seats were also fixed.7If any passengers developed any kind of flu symptoms they were moved to the back of the aircraft where there is negative pressure which flows the air to the back of the plane. Separation of the asymptomatic and the probable symptomatic passenger were done by 2 rows of seats again.At the back of the plane the symptomatic will be seated next to the window as the location of air suction is at this point, in contrast to the passengers seated in the middle row whose air will be circulated in the plane causing more contamination.There were 8 HEPA filters in the plane, which were checked before takeoff and found up to date. The HEPA filter, filters the air of the plane and circulate clean and filtered air back to the plane.Contact tracing was the motive behind the seat allocation of each passenger in case of future positive cases. Coding was done of the Self declaration forms as to maintain confidentiality; decoding was done on the basis of boarding pass.“Fixed Seat Allocation”If anyone is sick, should give information to ground staff and exit through back/ symptomatic door earlier than the rest of the passengers and AMT staffs on board.If no one shows any symptoms during the flight, the front door can be opened. AMT staffs will get off the plane prior to the passengers as per protocol.During disembarkation, ask passengers to come serially 4-5 people at a time maintaining 1-meter distance. Send passenger to the screening/doffing bay for screening with the “Self-Declaration form” and waste products.Hand over all documents, biohazard bags to screening team leader.After all the passengers are disembarked, report to screening station and disinfectant authority by commander. Remove PPE, dispose appropriately, perform hand hygiene by AMT member handover to screening team.AMT member departure as per protocol (doffing and screening)Lack of resources: Nepal is a low-income country where everything is limited from money to manpower. During situations as these, medical team works in scarcity and use whatever is available. There were challenges to get the latest non touch temperature measurement technique to limit contact with the passengers, we do not have latest technology to dispose biohazard waste, and the updated evidence-based information regarding similar situations.Communication: It would have been more efficient and effective if communication between management team and the people working on the ground was planned beforehand. In future a committee should be formed to handle such outbreaks where there are experts from Health Emergency Operating Centre, Home Ministry, Defence Ministry, Epidemiology and Disease Control Division team, waste management team, logistic authority team, airline crew including aeronautical engineers for information on airflow system, aircraft inbuilt system (each aircraft has a unique system that can be updated only from the manufacturer, so in order to add an audio-visual regarding the instructions to dispose water bottles and food packages, fill self-declaration form, wear the provided mask, etc. at least 10 days is required to update the aircraft system. If informed and planned accordingly this would have been possible and medical staffs and airplane crew’s contact with passengers would be minimized.An app of self-declaration form: Instead of hard copy self-declaration, if we could have developed an app for the self-declaration forms, less contact would have been possible and there would be less waste to dispose.Maintaining the standards: As it was a first encounter of such event for state, there was no standard guidelines and everything was planned based on expertise and literature review. Disinfecting the plane before and after flight, technical details that who should wear PPE, autoclave of the cutlery, disposal of the waste of the plane, manage the luggage of the passengers and how to disinfect them, technicality of quarantine procedures for passenger crew members including the cockpit member and the air medical teams, knowing the exact capacity of hub and satellite hospital for isolation, training of the PPE and waste management to AMT, screening team, ground handlers, ambulance and bus drivers and others, Proper and defined job description of the individuals.None.


The effective protection of critical infrastructure against cyber and physical security threats involves many different steps from initially the identification of risks to finally the implementation of counter measures in the infrastructure. To derive counter measures and to come to intelligent decisions facing the identified risks, the impact calculation plays a central role. The impact of a specific threat can propagate through the systems of the infrastructure and thus needs to be analysed carefully. In this paper, the role of impact propagation of cyber-physical threats for infrastructure protection is discussed, exemplified for airport systems. In the ongoing EU-H2020 project SATIE (Security of Air Transport Infrastructure of Europe) a toolkit is developed containing two tools for impact propagation, namely the Business Impact Assessment (BIA) and the Impact Propagation Simulation (IPS). Both tools are described and for a small test case the propagation of a cyber threat and the transformation into a physical threat is demonstrated in a network representation as well as an agent-based model of the airport’s systems employing the IPS. In the world of critical infrastructure, the systems involved have direct and indirect interdependencies and are vulnerable to each others threats, risks, impacts and disruptions, whether deliberate or accidental, . InterdeCyber-threats are by no means immune to this phenomenon as they can quite easily spread across networks, especially in our increasingly connected world, but also cyber-threats can even turn into physical or even safety threats . Cyber-tFor impact modeling, a correlation model is often constructed using an interconnected graph , 31, 43 Within the air transport environment, it is not difficult to imagine how risks to a cyber-attack can result in potential physical damage. In fact, a ransomware attack to a Cleveland Airport server in April of 2019 prevented the displaying of the flight information to passengers, preventing them from finding the correct gate, causing confusion, crowds, and panic among the passengers, then overburdening the airport staff, and causing flights to be grounded . There wAirports in particular are highly complex systems with a multitude of stakeholders, each of them with their own intentions and goals. Various interconnections exist between these stakeholders, and between the assets of the airports , making it difficult to assess the impact of a particular action on the whole airport and even system of connected airports. Therefore, it is vital to quickly detect threats and understand its potential impact on the rest of the complex system. This is not a novel issue, though, so there have been some attempts at aiding this understanding of propagating threats and coordinating efforts between stakeholders. The first was the airport collaborate decision making A-CDM, , 6), whi, whi6]),The lessons learned in TAM can be transferred to security research in aviation as shown by SATIE. Here it is foreseen that security practitioners and airport managers collaborate more efficiently during a crisis to achieve its mitigation. This can be done in a security operation centre (SOC), where the operators are informed about alarms and the reasons of the alerts. In a SOC emergency, procedures can be triggered simultaneously through an alerting system in order to reschedule airside/landside operations, notify first responders, cyber-security and maintenance teams towards a fast recovery. Information about impact propagation information will ultimately improve the situational awareness of the decision makers and will lead to better and faster decisions.However, the availability of additional information may also overwhelm human operators and, especially in these situations, an automated decision support has the potential to assist the quick resolution of a critical situation or even a crisis . TherefoThe mechanisms of probabilistic propagation between assets is applicable in a variety of fields, but is particularly critical in the air transport world where lives are at stake. Effective prediction of such effects allow for much more efficient and responsive countermeasures to reduce those risks at a single point which could become devastating once they propagate through the system. In this paper, impact propagation in airport infrastructure and the corresponding systems is discussed in the context of the project SATIE. Two systems of the SATIE toolkit perform this type of impact analysis, namely the Business Impact Assessment (BIA) and the Impact Propagation Simulation (IPS), which are described in Sect. SATIE proposes an architecture that combines new and existing tools in order to timely detect cyber-physical incidents, simulate their impact and deliver information to the Security and Airport Operation Centers. Live data is captured through several sensors and existing tools in the airport systems, which is then analysed through several threat prevention and detection systems. These systems will analyse data according to different perspectives and may describe events and trigger alarms. These are sent to the Security Operation Center, namely to the Correlation Engine, which will attempt to find possible correlations between the messages obtained through the different sources. Querying either the Vulnerability Management System and the Risk Assessment Platform will supply additional information about the events that generated the alerts, particularly regarding possible vulnerability exploitation and affected assets. Information is centralized in the Incident Management Portal, where a human operator can opt to aggregate different events into incidents and query the impact simulation tools in order to know which assets could be affected by a single incident, and obtain possible mitigation strategies. Figure An ontology is developed in SATIE to describe the contexts of the exchanged messages, namely the sub-domain of incidents, impacts and assessments. The impact’s specification is directly related to the needs of the BIA and IPS, describing how the performance of assets may be affected, how assets affect each other and suggesting possible mitigation strategies, each with their own expected performances. How different events and assets may affect each other is described by the threat propagation path and the threat propagation event concepts respectively.The SATIE toolkit includes the BIA, that analyses how cyber-threats propagate to the organization assets and assesses the impact caused on the organization’s business critical services and goals. As can be seen in Fig. logic-based models [probabilistic-based models [sensitivity-based models [For impact propagation, a model-based analysis is used and can be categorized into d models , 28, 29,d models , 37, 42 d models –26, 31. d models , 37, 38,d models , 16, 43,The IPS follows a different approach than the BIA, i.e. the focus is more on system’s assets and passenger behaviour than on business processes. Furthermore, the IPS accounts for cyber and also physical threats. Even if this is not foreseen in the project, the BIA and IPS approaches could be integrated in future work. The IPS is based on two complementary modeling approaches, namely a network and an agent-based model.Network Model. The network model of IPS is based on CaESAR (Cascading Effects Simulation in urban Areas to assess and increase Resilience), a tool developed at Fraunhofer EMI [ofer EMI . This toDifferent types of threats can be modelled including ones that only attacks certain types of nodes, or threats that change or move over time , or ones that only affect nodes in a specific region. Once an adverse event is simulated, nodes are removed from the model as they are damaged. Once the threat has completed its propagation, the recovery begins with the nodes being added back to their network once the repair times have finished.Mitigation measures can also be implemented into this simulation tool which allows for resilience improvements to be made. Different measures are selected and the simulations are completed again. The effectiveness of the measures are compared utilizing the performance time curves that the tool outputs. The measures can have different effects on the different aspects of the curve, and thus effect the system’s resilience. Effects can be seen for example, on the damage sustained, or the recovery required. Depending on the goal for the resilience of the networks, the best mitigation measures can be determined. Additional models can be combined in CaESAR to determine how the threat can propagate to other networks and investigate cascading effects. This can be done with very different networks such as e.g. telecommunications, power and water networks.Agent-Based Model (ABM). To observe an infrastructure in various threat situations and to explore possible states of the system, ABM can be employed [employed . ABM hasemployed , air traemployed  and evacemployed  and respemployed , 46. In Hybrid Model. The transformation of threats from cyber to physical motivated the creation of a combined model. In the project SATIE, a hybrid model consisting of a network representation and an ABM is developed. The network threat propagation is triggered by received incidents, as described in Sect. The airport network structure for impact propagation is based on three airport systems, namely the Physical Access Control System, the FIDS and the PA System. The corresponding network representation is give in Fig. The scenario assumes an attack on the workstation, node #42, which is linked to the remote PA server via connecting components #37, #36, #35 (in this order). The PA server holds the database (DB) of pre-recorded messages to be used for announcements in the airport. Another node, the remote station, node #41, directly depends on the server and is also connected to it in the same manner . A hybrid approach consisting of a network representation of the airport and an ABM was employed to simulate the processes during the attack on the involved airport systems. The simulations presented here are a first step in the development of the hybrid model for the IPS in SATIE. The network model represents the top-down view based on assumptions about connectivity of nodes and repair times whereas the ABM enables to observe what will happen with only passenger attributes being defined, representing the bottom-up perspective. Further work involves that the network model and ABM need to be aligned in time and the communication between the tools needs to be automated. The ABM specifically is still lacking some parts of human behaviour such as e.g.. group- and panic dynamics. The network model in particular needs to be extended with further airport systems and more mitigation options need to be defined. Finally, the model will be designed to be applicable to other critical infrastructures and the respective impact analysis.Complex systems are ever-more present in our world as systems and people become even-more connected. When the functioning of these systems is instrumental in upholding and supplying our society with its necessities , then a more holistic and complex threat modelling is necessary to better predict, understand, and then mitigate its effects. The toolkit used in SATIE aims to clear the sky for future endeavours of improving cyber- and physical security around the world.


After the Fukushima Daiichi nuclear accident of 2011, interest regarding radiation safety in everyday life has increased considerably. This study investigates the general public’s current level of awareness of six warning pictograms in regard to medical and natural radiation safety utilized under ISO 7010, as per Korea industrial regulations. Namely, it tests whether survey respondents can recognize pictograms related to radiation safety according to their purpose, as their inability to do so poses a serious safety problem. The empirical analysis results regarding the awareness levels for radiation safety pictograms are as follows. First, 63.3% of the respondents were unable to correctly identify the pictograms; that is, their level of understandings of the six pictograms related to everyday radiation were low. Second, the mean score for the correct responses to the question of what the six pictograms indicated in relation to everyday radiation safety was also relatively low, with a mean score of 2.79 and a standard deviation of 1.447. The primary reasons for the low awareness and understanding levels were identified to be insufficient education related to radiation safety in schools. Additionally, it is necessary to revise and rectify current warning pictograms established by the Korea Industrial Standards and ISO 7010. This study is thus significant in that it identifies the level of understanding of the pictograms and suggests the need for improvement as a diversified effort toward improving everyday radiation safety. We are unknowingly exposed to radiation over the course of our daily lives. In general, radiation includes natural radiation from all materials on Earth and artificial radiation that humans generate for a particular purpose. Generally, artificial radiation is regulated by the Nuclear Safety Act and regulations enacted under the guidelines of the International Atomic Energy Agency (IAEA) . FurtherThe management and regulation of natural radiation can be considered meaningless, but much of the radiation the general public is exposed to is natural. Notable examples include household goods, such as the stone plates of some thermal beds, health bracelets or necklaces, wallpapers, pillows , or longHowever, there seems to be no significant change regarding the safety management of radiation emissions in everyday life. Originally, safety referred to a “comfortable state where humans are at ease with no anxiety over the possibility of bad results on their welfare without any intimidation of their existence” . HoweverMeanwhile, to ensure the safety of the general public, 300 pictograms for public facilities such as toilets, restaurants, and subways have already been established and used as the industrial standard in Korea (KS). According to Huh , a publiBased on this, the Korea Standards Service Network proposed six categories of pictograms related to facilities and five categories related to safety . Pictograms are thus used as effective complementary and alternative communication methods not only for non-disabled people, but also for people with communication disabilities ; they arIf the general public does not fully understand and recognize the meanings of pictograms related to safety, serious problems may sometimes arise, potentially posing risks to the health of the general public. This is especially true for warning pictograms concerning medical and natural radiation safety. At a time during which radiation safety is required in everyday life, the nature and use of radiation safety-related pictograms differ from those used for public facilities and events. Therefore, regulatory management is required, and more emphasis should be placed on accurate semantic transfer than on originality .It is important to investigate the level of awareness, how accurately the general public understands safety-related pictograms, and find solutions. In this study, we examine the awareness of six pictograms related to radiation safety from among the warning pictograms presented in ISO 7010, derive areas for improvement through statistical analysis, and propose the need for radiation safety education. To this end, we try to answer the following research questions: (1) What is the level of understanding and rate of correct answers for the six presented pictograms? (2) Are there statistically significant differences among genders and ages regarding the rate of correct answers? (3) Is there a statistically significant correlation between the scores, levels of understanding, and adequacy of the six presented pictograms. Lastly, (4) what is the statistical model that can predict the adequacy using the level of understanding? Finally, we present measures that can increase awareness for pictograms related to radiation safety.A pictogram is a graphic symbol which refers to the meaning of a specific concept as a means for removing language barriers . SpecifiThere are three functions of pictograms: guidance (notice), command, and symbol. The guidance function is primarily represented by arrows that provide direction, direction indicators on road signs, and guidance toward facilities. The command function leads to action and is used for various road signs, prohibition signs, and danger signs, generally demonstrating rules and actions that people must follow. The symbol function includes quality-related indicators, symbols such as the addition and subtraction symbols in mathematics, musical notes, and geographical symbols indicating the state or characteristics of land . The picUsing a prior study  that exaThe survey consisted of two main areas. The first was focused on the recognition of the radiation-related pictograms in (Question) The following figure is an internationally designated warning picture (pictogram) regarding radiation safety. Please indicate each picture conveys by matching each picture with right-hand side of the image.We informed the respondents of the purpose of the study, announcing to them that confidentiality was guaranteed, that they could stop answering at any given moment without adverse consequences, and that they had the right to discontinue the study. We obtained their consent before collecting the data. The survey took around five minutes, and the respondents were paid a small monetary amount as a token of appreciation. Subsequently, statistical analysis was conducted using an IBM SPSS25 on the collected data entered into Excel, and the insincere responses were removed during preprocessing . RegardiAs is shown in The mean response to the question regarding the meanings of the six pictograms in p = 0.044). In other words, male respondents’ correct answers were statistically significantly higher compared with those of the female respondents. These results are, in part, similar to Park’s [Independent t-testing to determine whether there were statistically significant differences in the scores of the correct answers for each gender showed that the difference in the scores for the male and female respondents was statistically significant at a significance level of 5%, as shown in o Park’s  study ofA one-way analysis of variance was then conducted to determine whether there were statistically significant differences in the scores of the correct answers by age, and the results are shown in Furthermore, correlation analysis was performed to determine whether there was a statistically significant correlation between the scores for the question about the meanings of the six pictograms in p-value of 0.000 (<0.05), the model was statistically significant at a significance level of 5%, and its explanatory power was 0.333.To establish a statistical model that could predict the adequacy by using the level of understanding, a simple linear regression analysis was conducted, and the results are shown in Radiation has been present in nature and has coexisted with humanity long before humans started using it in a peaceful manner. In addition to being exposed to medical radiation during the course of treatment, individuals are also exposed to natural radiation from soil, air, food, and the cosmos in their daily lives. As such, radiation is one of the energy sources we always encounter in our living environment. Exposure to such radiation can have biological effects, such as changes, damage, and even harm, depending on the extent of the exposure. As a result, in Korea, the Nuclear Safety Law or Radiation Safety Control Law on everyday radiation have been enacted to help ensure our health and safety from radiation. As the general public is not familiar with radiation, warning signs and pictures have been developed and utilized to help ensure their safety. To find out how easily these pictograms are recognized by the general public, this study conducted an empirical analysis on the understanding level of pictograms related to everyday radiation safety, as established and used in Korean industry standards. The results were as follows.First, 63.3% of the respondents responded negatively regarding their level of understanding of the six pictograms related to everyday radiation, and only 6.0% responded positively. Furthermore, in terms of pictogram adequacy, the negative responses (55.8%) were almost five times higher than the positive ones (11.6%). Regarding their knowledge related to radiation safety in everyday life, only 42.2% chose radon (Rn) as the answer. We therefore concluded that the level of knowledge regarding the risks of radon gas is low.Second, only 42.2% of the respondents chose radon as the correct answer when inquired about their knowledge of radiation safety in their lives. As such, respondents’ knowledge about the risk of radon gas was thus deemed to be low.Third, for the question of what the pictograms related to everyday radiation safety meant, the mean response score was relatively low at 2.79 (standard deviation of 1.447). Furthermore, the rate of correctly matching the pictograms with their descriptions was relatively high for the pictograms that were relatively easy to guess or were widely used. However, for the pictograms on laser beams, biological hazards, and non-ionizing radiation, the correct response rates were lower. These results were due to the lack of formal education (schooling) and guidance related to radiation safety. This is also demonstrated by the fact that the seven standards for safety education in South Korean schools, implemented in 2016, do not contain any information on radiation safety.Fourth, for the mean comparisons among genders regarding the rate of correct answers, male respondents’ scores were statistically significantly higher than those of female respondents at a significance level of 5%. The correlation coefficient between the level of understanding and the adequacy of the presented pictogram was relatively high, being 0.577. The higher the level of understanding was, the higher the adequacy.Based on the above results, awareness of the pictograms used for radiation safety in everyday life is not high. As such, we would like to suggest ways to improve this in the future.First, education related to everyday radiation safety should be reinforced at the school level. To do so, safety training standards must include areas related to radiation safety. Second, the current warning pictograms enacted by ISO 7010 concerning the safety of daily radiation should be modified to deliver the message more clearly. Furthermore, in the case of South Korea, warnings are depicted only as pictograms. However, if these are presented along with text, the level of understanding could be further enhanced.This study is significant in that it identified the actual conditions related to radiation safety in everyday life and used pictograms to improve radiation safety awareness.


The increasing number of COVID-19 patients has increased health care professionals’ workloads, making the management of dynamic patient information in a timely and comprehensive manner difficult and sometimes impossible. Compounding this problem is a lack of health care professionals and trained medical staff to handle the increased number of patients. Although Saudi Arabia has recently improved the quality of its health services, there is still no suitable intelligent system that can help health practitioners follow the clinical guidelines and automated risk assessment and treatment plan remotely, which would allow for the effective follow-up of patients of COVID-19. The proposed system includes five sub-systems: an information management system, a knowledge-based expert system, adaptive learning, a notification and follow-up system, and a mobile tracker system. This study shows that, to control epidemics, there is a method to overcome the shortage of specialists in the management of infections in Saudi Arabia, both today and in the future. The availability of computerized clinical guidance and an up-to-date knowledge base play a role in Saudi health organizations, which may not have to constantly train their physician staff and may no longer have to rely on international experts, since the expert system can offer clinicians all the information necessary to treat their patients. COVID-19 is a new human infectious disease caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), which characteristically manifests as respiratory symptoms and is transmissible from human to human. The possible spread of COVID-19 worldwide has been described by the World Health Organization  as an international public health emergency [Clinical guidelines are standardized statements designed to assist physicians and patients in deciding on appropriate treatment in certain situations . A set oIn the 1980s, the dominant intelligent technology consisted of expert systems that aimed to simulate human expertise and provide solutions based on current knowledge . SubsequSeveral studies have been conducted using artificial intelligence techniques to assist health care workers during the COVID-19 crisis . One is Saudi Arabia Vision 2030 notes that the ability of medical facilities and hospitals should be increased and properly implemented and that the quality of health services, both therapeutic and preventive, should be improved . Indeed,The proposed system in our research can be an extension of the current apps and systems designed to enhance health care and unify actions taken for the diagnosis and management of COVID-19 patients after staying in private or public Saudi hospitals. The use of WHO clinical guidelines could facilitate access to existing standards and medical vocabularies that enable semantic and technical interoperability between hospitals.However, to the best of our knowledge, there is still no suitable intelligent system in Saudi Arabia that can help health practitioners follow the clinical guidelines and automated risk assessment and treatment plan to manage the patients and follow-up patients of COVID-19 effectively at hospitals. There is currently no option to adopt sets of new clinical rules that can be discovered automatically from stored data or to locate mobile numbers to warn healthy individuals to avoid places that have been visited by suspected cases.In this paper, we propose a prototype for COVID-19 diagnosis and management in Saudi Arabia. This design can monitor COVID-19 patients and reduce the burden on medical practitioners and the costs of health care. Clinical guidelines can be used as expert knowledge to build up an expert system. The proposed prototype can also evolve with dynamic knowledge by learning new knowledge adaptively from the integration of new clinical guidelines, with an electronic health record (EHR) and a health organizational workflow, without the aid of a knowledge engineer. In addition, risk classification scores can be integrated to distinguish patients according to the severity of risk and can provide patient triage at hospital admission ). Messaging notifications and targeted content that trigger action at times of importance can be automated.The following key issues need to be resolved: What relevant domain knowledge is essential for diagnosing and managing COVID-19? What are the appropriate technologies to develop an intelligent system with the ability to evolve diagnostic and management guidelines for COVID-19?According to the Saudi MOH , suspectThe software development life cycle (SDLC) is a framework that defines the stages involved in the development of software at each level. The proposed intelligent system for COVID-19 diagnosis and management includes five cases of users: health care workers, health care practitioners, patients, lab managers or radiologists, and healthy persons. The framework includes five sub-systems: an information management system, an expert system, adaptive learning, a notification and follow-up system, and a mobile tracker system.The system architecture is shown in An information management system (IMS) is an organizational network of people, behaviors, and devices that provides managers with the information they need . This coAn expert system allows human knowledge to be encoded into an automated system in a narrow domain . To creaThe acquisition of knowledge concerns the process of extracting, structuring, and organizing the domain knowledge from field experts into a development program . ClinicaAccording to Peleg , the domIn hierarchical representations of data, the declarative representation of knowledge is used. The flowchart shows the knowledge derived from the clinical guidelines of COVID-19 diagnosis and management. Following an interpretation of the guidelines, a draw.io program was used to construct the rule diagram. In An expert in AI language and knowledge representation—i.e., a knowledge engineer—examines a specific problem field, determines important concepts, and makes correct and effective representations of objects and relations in the field . In FiguThe inference engine scheme consists of forward chaining. The fundamental idea of forward chaining is that, when data satisfies the premises of a rule (the if portion), the expert system asserts the conclusion of the rule (the then portion) as true . The infThe expert system is integrated into the organization workflow and, in particular, with EHRs to adapt the new knowledge. The left side of Adaptive learning includes two phases: knowledge verification and validation by human experts  and reviThe clinical history, laboratory tests, and biochemical monitoring data are used by the machine learning technique  to discoThe patients’ data stored in the EHR can be analyzed by machine learning techniques. Patient data stored in the HER include clinical, biological, and laboratory result data at diagnosis and data regarding treatment that differs from the recommended treatment by WHO. After the database reaches 200 records, the data will be inserted into the classification and regression trees (CARTs) . This apIF factor A is category 1 AND factor B is category 2. THEN outcome 1.The outcome is treatment, where factors A and B are the laboratory tests, category 1 and category 2 are the subcategories that belong to factors A and B, and the treatment is the class . Nested cross-validation is used, so the training set is separated into training and validation, comprising 70%. The rest of the records are used for the test set. The model is trained on the training set, and performance is evaluated on the test set.After the new clinical rule is formed, the rule appears on the knowledge inbox screen. The experts will be involved in the process of revising the knowledge see .A chest x-ray (CXR) is used regularly to diagnose and monitor various respiratory disorders as a cost-efficient diagnostic test and is currently used as a method for the discrimination of COVID-19. Early stage progression and severe progression are the stages of the COVID-19 chest images . The clinical rules are filtered using the biased sampling (BiSAM) similarity measure . The setThe feedback is required by the human experts after the revised knowledge is updated with a set of new rules that come from the adaptive learner. The system will offer clinicians an explanatory summary of how patients have been managed during their hospitalization and how the system understands the test results. To validate the revised knowledge, the expert should give his/her agreement. The nature and imperfect knowledge of the medical decision-making process demands special techniques for dealing with fuzzy logic, which is defined by Zadeh . The fuzIn COVID-19, health practitioners have high workloads and other attention demands that disturb the prospective memory of a provider for follow-up tasks. In the system, health practitioners are notified about abnormal test results through EHR-based alerts. This concept is demonstrated in the Alert Watch and Response Engine (AWARE) software . The sysThe system integrates Google Maps to locate the phone number of suspected cases so that places they have visited recently can be identified and notified of possible exposure to the virus. A JavaScript file contains all the mobile number data for suspected and confirmed cases after discharge and under isolation. For privacy concerns, this subproject can be conducted after getting the Saudi government’s approval to obtain mobile numbers recorded in the Tataman application and the HESN database to retrieve mobile location and service provider details.In practice, the system can be developed as a website and merged with the sub-system development process in parallel. These sub-systems can be combined in a manner that allows the first to perform its activities in parallel with the second, and the second can provide feedback and important information to the first and vice-versa for activities. The website can be integrated and implemented using HTML, C#, ASP.net, and the SQL database in Microsoft Visual Studio 2019.To demonstrate the prototype, a confirmed COVID-19 case is presented as an example: a 35-year-old man, who had a five-day history of cough, headache, and fever, after visiting his friend in Wuhan, China. Physical examination revealed a 39 °C body temperature, 134/87 mm Hg blood pressure, a pulse of 110 bpm, a breathing rate of 31 breaths per minute, and 96% oxygen saturation during the patient’s breathing. Later, the patient was administered nasopharyngeal and oropharyngeal swabs for 2019-nCoV by a real-time reverse transcriptase polymerase chain reaction (rRT-PCR) assay, and the result was positive. The patient reported a dry cough, two days of nausea and vomiting, diarrhea, and no shortness of breath. He had supportive care for symptom management. Treatment with 400/100 mg of Ritonavir every 12 h and 400 mg of ribavirin and interferon beta-1b every 12 h improved the clinical status of the patient. Laboratory testing was performed on the 3rd, 5th, 7th, 9th, and 10th days after admission, and a chest radiograph was taken on the 3rd, 5th, and 6th days in the hospital.The sub-systems prototype was designed based on the user types. The authorities for each user should be identified after logging into the website. There are five types of users in the system, and the hospital can decide who is responsible for using the system: health care workers, health care practitioners, patients, lab managers or radiologists, and healthy persons. First, health workers, such as nurses, can fill out a form that contains the patient information, the date of notification, the name of the person who completes the form, the hospital name, and whether the patient is a confirmed or suspected case or case under investigation. All the clinical information of the symptoms is required. Other details to assess the patient, such as hospitalization information, epidemiological information, contact exposure, and animal exposure, are also required. Health practitioners use an interface that displays the expert system recommendations in terms of the risk classification, the appropriate supportive care, recommended tests, and treatment recommendations for managing the case. The second interface displays the medical history, including all the tests, symptoms, and treatment from admission to discharge from the hospital, for viewing by the health care practitioner. In this interface, the alerts are displayed to catch the staff’s attention of any warnings. The third interface should be available when the health care practitioners add new treatments in managing the case. This interface shows the system evaluation of the new clinical rules and awaits the response from the experts (agree or disagree) before sending the new rules from the revised knowledge base into the validated knowledge base. Third, the patients use the interface remotely for daily follow-up of their symptoms. Fourth, health care personnel use the interface to insert any test results and chest images that are used. Finally, healthy individuals can use Google Maps to check on his/her destination—whether it is safe or it has been visited recently by suspected cases. Several benefits could result from automating tasks. Firstly, reducing manual handling could reduce the errors after increasing health awareness education, support, and medical advice. Secondly, clinical guidance compliance can result, since the laboratory tests are recommended based on the WHO guidelines and are automatically requested by the expert system. Thirdly, the automated risk classification, triage checklist, recommended clinical action, and reported patients’ symptoms during the monitoring of health status reduce workload management. Finally, the proposed automation task could control the transmission of COVID-19 by exchanging paperwork and reducing the exchange cycle. This paper proposes a prototype for the diagnosis and management of COVID-19 cases. It can view the patient’s health information, enable health practitioners to access treatment recommendations and risk classification, and recommend test lists, as well as tracking test results and symptoms quickly. Upon its completion, such a system will help in decreasing health care professionals’ workload at the hospital; reducing the costs of health care; overcoming the shortage of trained health care professionals; and establishing universal health care coverage with accessibility guidelines, ensuring fair and efficient access that leads to improved diagnosis and management of COVID-19. By promoting scientific uniformity and consistency in health decisions and daily clinical practice, an intelligent system will allow for the management of various medical information items and the continuous update of medical records. According to Kwan et al. , the majIn the context of the coronavirus (COVID-19) global pandemic crisis, we present a diagnosis and management prototype using advanced intelligent technology to ensure the safety of the citizens and residents of the Kingdom of Saudi Arabia. The expert system paradigms that have been presented are for managing hospital patients and guiding clinicians and patients based on the medical knowledge approved by the Saudi MOH and the WHO. The previous field study showed that there is a shortage of specialists in the management of infections in Saudi Arabia because existing structures are not sufficient to control epidemics, both today and in the future. COVID-19 is a new disease, and the WHO is still updating the clinical guidelines as more scientific evidence and information become available. Therefore, a main part of our prototype that will satisfy the requirements of updating the knowledge base is the use of adaptive learning. Moreover, using an automated risk assessment for each case is essential to provide the right medical service for each patient.The main advantages of the prototype consist of its ability to assist health practitioners with patients upon admission until after discharge from the hospital through controlling the spread of disease by managing the disease remotely and limiting direct contact with the patients. The availability of the computerized clinical guidelines and up-to-date knowledge base could also play an economic role; specifically, Saudi Arabia’s health organizations may not need to constantly train medical staff or to rely on foreign professionals because the expert system could provide all the information required for clinicians to handle the patients. In future work, we aim to proceed with the implementation of the proposed system. In addition, based on the same technology, in case of a need to handle any new epidemics, the expert system and its interface, as well as adaptive learning, can be used to build another system for other medical applications.


Haemophilus influenzae type b (Hib), hepatitis B, varicella, and human papillomavirus (HPV). The estimates accounted for mortality and morbidity trends observed for these infections prior to mass vaccination and the impact of advances in standard of living and health care. The estimates also considered populations with and without known factors leading to an elevated risk of permanent injury from infection. Mass vaccination prevented an estimated 20 million infections and 12,000 deaths and permanent disabilities in 2014, including 10,800 deaths and permanent disabilities in persons at elevated risk. Though 9000 of the estimated prevented deaths were from liver cirrhosis and cancer, mass vaccination programs have not, at this point, shown empirical impacts on the prevalence of those conditions. Future studies can refine these estimates, assess the impact of adjusting estimation assumptions, and consider additional risk factors that lead to heightened risk of permanent harm from infection.Since the late 1940s, mass vaccination programs in the USA have contributed to the significantly reduced morbidity and mortality of infectious diseases. To assist the evaluation of the benefits of mass vaccination programs, the number of individuals who would have suffered death or permanent disability in the USA in 2014, had mass vaccination never been implemented, was estimated for measles, mumps, rubella, tetanus, diphtheria, pertussis, polio,  To measure the benefit of a mass vaccination program targeting an infectious disease, it is useful to assess what the risk of death or permanent injury would be from the disease in the absence of the mass vaccination program. There is an abundance of medical literature detailing the risks associated with infectious diseases; however, the information is scattered through dozens of sources that are often lengthy and consider only a narrow scope of the risks involved. For example, some sources describe the symptoms of a disease without specifying how many patients fully recover ; other sWe calculated rates of deaths and disabilities from infectious diseases, had mass vaccination never been implemented, using data principally from reports of the Centers for Disease Control and Prevention (CDC), complemented by reports from other federal entities such as the US Bureau of the Census and the US Public Health Service. We relied on data recorded in scientific journals  in cases when data from government sources were unavailable or incomplete. For example, although the impact of risk factors for many diseases is considered in CDC data, there are instances when measurements of risk factor relationships to outcomes are not provided. Other examples of information that is not always available in government records include estimates of the number of unreported cases and of permanent disability from certain diseases. In addition, when US data for measurements were unavailable, we relied on data from other developed countries.Because we researched and gathered data for our estimates in 2016, when the latest available CDC mortality data were from 2014, we projected our estimates to 2014. When calculating the rates of deaths and disabilities corresponding to an infectious disease, we considered the trend in deaths and disabilities during a range of years just prior to the licensing of a vaccine or at the start of a nationwide mass vaccination program targeting the disease. This range of years is referred to as the “reference years.” The duration of each range was chosen by using the number of years between relative peaks of incidence of each disease.Haemophilus influenzae type b (Hib), or varicella in 2014. Therefore, we counted all estimated cases projected to 2014 in the absence of mass vaccination as preventable.Though <100% of the population is vaccinated and vaccines are <100% effective, mass vaccination programs have contributed to the significantly reduced morbidity and mortality of infectious diseases. There were fewer than two dozen deaths from diphtheria, tetanus, pertussis, polio, measles, mumps, rubella, We employed certain assumptions in our estimates. For example, we presumed that each vaccine neither reduced nor enhanced vulnerability to the incidence or outcomes of diseases that were not targeted by the vaccine. Throughout our text, we strive to be explicit whenever an assumption was made.The rates of deaths or disabilities corresponding to various infectious diseases were computed using a denominator of 307 million individuals <80 years of age in 2014. We chose that age group because the life expectancy in 2014 was 79 years. In instances where age-specific counts of cases of infection were not available, case counts from the entire US population (319 million) were used. Moreover, we considered the broader population <80 years of age rather than the population of children, both because mass vaccination programs are intended to provide lifetime immunity to infection and because protecting against infection averts late complications of infection. For example, the polio mass vaccination program affected not only permanent injury from polio in children but also permanent injury from post-polio syndrome in adults.To simplify and reduce the length of the report, we omitted analyses of certain mass vaccination programs that had marginal or unclear impacts on mortality. We excluded rotavirus and hepatitis A because each of those infections caused fewer than 100 annual pre-vaccine deaths ,6. We exWhen accounting for risk factors leading to an elevated risk of permanent injury from an infection, we included only those factors that were observed in a high fraction of cases of permanent injury from the infection. To simplify the report, risk factors that were present in a small fraction of such cases were excluded if accounting for those factors resulted in a rate of permanent injury that lay within the 95% confidence interval of the rate computed without those factors .It was estimated that 20 million infections and 12,000 deaths and permanent disabilities may have occurred in 2014 in the absence of mass vaccination, with 10,800 deaths and disabilities among individuals who have conditions or behaviors that would put them at higher risk of such outcomes and 1200 deaths and disabilities among persons without those conditions or behaviors. The following is a discussion of each disease. Recall that the “reference years” refer to the time period before the introduction of the corresponding mass vaccination program. Using data recorded during these years, we derived estimates of the expected number of deaths and permanent disabilities from each disease had mass vaccination not been introduced.During the reference years of 1959–1962, before the introduction of mass vaccination, there were four million annual measles cases ] + [15% × 1%]; Using the government tracking of cases of hepatitis B during the reference years of 1988–1990, before the introduction of mass vaccination, we calculated 190,000 cases for 2014 in the absence of mass vaccination , includiCDC analyses of hepatitis B have shown that permanent disability in hepatitis B survivors is very rare . HoweverIndividuals at high risk of exposure are more likely to contract hepatitis B . Of the During the reference years of 1991–1994, before the introduction of mass vaccination, there were four million annual varicella cases  were found to comprise 90%  of all the estimated cases of prevented death and permanent disability, with the remaining 1200 cases in persons at normal risk (or with risk factors excluded from this report). It is possible that the high risk conditions described in this report might expose individuals to permanent harm from other causes. More research in this arena would be useful.Pre-vaccine declines in mortality rates recorded for measles, tetanus, diphtheria, and pertussis were not unique to those infections. In the early 20th century, significant declines in mortality rates were recorded for numerous infectious diseases that were not targeted by mass vaccination programs, such as those for tuberculosis, syphilis, typhoid fever, and dysentery . The humMass vaccination programs are best known for preventing deaths and permanent disabilities that occur a relatively short time after infection. However, 75%  of the estimated cases of death and permanent disability prevented in this report would be from conditions occurring much later in life—liver cirrhosis and cancer. Though hepatitis B and HPV are the causes of these conditions, the hepatitis B and HPV mass vaccination programs have not, at this point, shown empirical impacts on the prevalence of liver cirrhosis and cancer. In spite of the significant reduction in acute cases of hepatitis B, the prevalence of chronic hepatitis B has remained practically unchanged since 1976 . As for This report had other limitations. The accuracy of our estimates depended on the quality of the available evidence concerning the risks and effects of the diseases, which could have been imperfect. In many instances, we projected to 2014 from statistics recorded decades earlier. The accuracy of such projections could have been affected by changes in the organisms targeted by vaccines, changes in host resilience, or changes in health care practices deviating from pre-vaccine trends, among other factors. In addition, inaccuracies can propagate from one estimate to another when an estimate is used to derive the other. For example, the case fatality rate of a disease is sometimes used to estimate the total number of cases of that disease based on its estimated number of fatalities. Another limitation was related to the potential aggregate impact of risk factors that were not considered in generating our estimates, either because they are  less commonly implicated or are undiscovered or inadequately studied. The comprehensive consideration of such factors might shift more of the vaccine-averted deaths and disability to the high-risk category. The same limitations might apply to the long term effects of some of the diseases. It is also possible that relevant information was missed in our literature review. We sought to convey the challenges surrounding some of the available data in the discussion of each disease and have tried to be explicit about assumptions made. We explained our choices in relation to the application of pre-vaccine trends and in gauging expected disease outcomes as a function of whether an individual is at higher risk. Furthermore, this study only estimated the number of deaths and permanent disabilities prevented by mass vaccination programs. It did not consider similar outcomes that may be caused by these programs.Despite these limitations, we believe this report employed the best processes among the available data and studies for estimating the numbers of deaths and permanent disabilities that would have occurred (here estimated for 2014) in the absence of mass vaccination programs. Though other studies have presented estimates of the benefits of mass vaccination programs, they have not accounted for disease risk factors, cases of nonfatal permanent disability, pre-vaccine trends in mortality, post-vaccine improvements in factors tied to disease outcomes resulting in improved case fatality rates in unvaccinated populations , and adjustments of pre-vaccine estimates using data recorded after vaccine licensure ,4,5. FurDespite the decline in mortality rates of infectious diseases recorded since the late 19th century, the data in this report indicate that mass vaccination programs may still have prevented 20 million infections and 12,000 deaths and permanent disabilities in 2014. In addition, mass vaccination programs have reduced the burden on health services, hospitals, intensive care, and the economy caused by the diseases they target. Put another way, measuring the number of deaths and cases of permanent disability prevented by mass vaccination programs is not the only way to measure the benefit of those programs, as those benefits can also be measured by other outcomes such as hospitalizations or the economic burden associated with a disease. However, those outcomes may be more greatly influenced by a range of factors beyond the impact of the vaccine and the disease, such as shifts in approaches to and costs of hospitalization over time. Nonetheless, because such outcomes are generally a function of the morbidity and mortality of the disease, the data in this report might also be useful in generating estimates for those outcomes.We believe this report provides a useful reference for the effect of mass vaccination programs on the most serious complications of the diseases they target. Future studies can seek to further refine these estimates, use these estimates in risk-benefit analyses, and assess how adjusting assumptions influences effect estimates.