Barometers are among the oldest engineered sensors. Historically, they have been primarily used either as environmental sensors to measure the atmospheric pressure for weather forecasts or as altimeters for aircrafts. With the advent of microelectromechanical system (MEMS)-based barometers and their systematic embedding in smartphones and wearable devices, a vast breadth of new applications for the use of barometers has emerged. For instance, it is now possible to use barometers in conjunction with other sensors to track and identify a wide range of human activity classes. However, the effectiveness of barometers in the growing field of human activity recognition critically hinges on our understanding of the numerous factors affecting the atmospheric pressure, as well as on the properties of the sensor itself—sensitivity, accuracy, variability, etc. This review article thoroughly details all these factors and presents a comprehensive report of the numerous studies dealing with one or more of these factors in the particular framework of human activity tracking and recognition. In addition, we specifically collected some experimental data to illustrate the effects of these factors, which we observed to be in good agreement with the findings in the literature. We conclude this review with some suggestions on some possible future uses of barometric sensors for the specific purpose of tracking human activities. Barometers have been around for a very long time. While the air was thought to be weightless till the early 1640s, this changed when the Italian physicist and mathematician Evangelista Torricelli showed that a column of air exerts a significant force that can be measured by the amount of liquid displaced by the pressing air. This led to the discovery that “air has weight” and the invention of a measurement device that quantifies the atmospheric pressure . The SI-For the first two centuries after the invention of the barometer, this device was constructed using glass tubes filled with liquids, such as water or mercury . In 1844Historically, barometers were used for weather forecasting and, thus, chiefly as environmental sensors. As a measurement device of ambient pressure, barometers have recently been used to measure evapotranspiration (transfer of water from land to atmosphere) in a given environment, for improving motor vehicle engine efficiency by modifying the air–fuel mixture, and for counting steps based on the slight disturbances in air pressure during body movements . Thanks In particular, the ubiquity of MEMS barometers in smartphones and other wearable devices makes them natural candidates as data sources for the study of human activities and for the field of human activity (and movement) recognition (HAR) . BroadlyUntil the 1990s, it seems that there is almost no reference in the literature to the use of barometers in tracking human activities, the most commonly used sensors being inertial measurement units (IMU)—comprising the accelerometer and gyroscope, along with the magnetometer. For instance, smartphones are equipped with accelerometers, whose signals are widely used to recognize most activity classes . The sitIt is, however, important to note that our ability to properly leverage the potential of barometers for HAR purposes critically hinges on our understanding of the physical properties of the atmospheric pressure. Indeed, the measure of the ambient pressure by a barometer is influenced by the static and dynamic properties of its environment . Its effIn order to systematically discuss the issues related to the use of barometric pressure to track human activities, we have collected and organized a series of scientific articles to understand the potential and limitations of such an endeavor. The current review is non-exhaustive but intentionally limited to the use of barometric sensors for the most common classes of human activity. This choice is justified by the wide range of applications offered and the fact that barometers now pervade many mobile devices . The applications explored are also primarily limited to the recent developments in using MEMS-based barometers for consumer goods/electronics. It is worth stressing that this review does not address the vast breadth of machine learning (ML) or other advanced classifiers used to interpret the sensed data or to identify a given activity with a given accuracy. However, the details provided in this review are most useful for the further development and design of effective ML/classifier strategies.This review is organized as follows: Three components are key to studying and understanding human activities, namely (i) motion tracking, (ii) recognizing the type of activity, and (iii) analyzing the obtained patterns. Barometers are part of the suite of sensors used in each of these steps. As such, the characteristics of the data collection process are similar to those for other types of sensors. They include the characteristics of sensors, their placement and orientation, the sampling frequency, and the environmental conditions. The type of application  and the method for recording the ground truth and annotating a part of the data also have to be carefully designed.Data collection and annotation are indeed critical to the effectiveness of subsequent stages of classification—e.g., data pre-processing, feature engineering and identification—in the overall workflow of sensed data associated with human activity recognition. Similarly, an informed decision on the data collection procedure depends on the specific problem under study and on the particular class of human activity being investigated. Depending on the sensor characteristics and the class to be recognized, data collection methods must be tailored to shed light on the phenomena under investigation for better accuracy and performance. The success of such data-processing activity not only depends on acquiring the data, but also on being able to effectively process it and extract meaningful features and patterns. In this section, we successively discuss the key elements of data collection to track human activities: the commonly used sensors and sensor suites, the placements and orientation of sensors and their effects, the sampling frequency for tracking and detecting the activity classes of interest, the types of activity classes related to human movements, the nature of data in terms of the levels of realism , the class imbalance issues, the diversity in sampling targets (users), and, lastly, the challenges in annotation techniques.Smartphones and watches are by far the most common and straightforward way to collect data in a natural setting. Nonetheless, some research groups studying particular behaviors and phenomena required a custom-built wearable sensor design for both laboratory testing as well as for operations in real-life conditions. For instance, three-axis accelerometers are the most widely used sensors ,11, follThe placement and orientation of the sensor might influence the characteristics of the captured signal, thereby affecting the recognition accuracy: Indeed, the training data might then fail to account for all the possible variations, often resulting in a sparse feature space. Numerous studies have focused on ways to alleviate this particular effect, and have provided solutions that range from collecting diverse data to independent features not affected by such parameters ,23,24. CThe temporal resolution of the data is directly related to the sampling frequency of the sensor used. For the vast majority of sensors used to carry out human activity recognition, the sampling frequency typically ranges from 10 to 100 Hz, with the rate going as high as 512 Hz. It is commonly reported that the characteristic frequencies of most human activities are below 10 Hz, and therefore, the optimal sampling rate—based on the Shannon–Nyquist theorem—is 20 Hz . Khan etLara and Labrador  thoroughThe data used to study human activity can be broadly grouped under two extreme conditions: (1) natural and (2) laboratory. While the former concerns data collected during real-life activities of people in a given city or country, and might thus be affected by complex environmental factors and all kinds of unknown conditions, the latter corresponds to activities performed in fully controlled environments with known conditions. Several groups have collected their own datasets to fit the specific needs and requirements of their studies ,38. ManyThe problem of class imbalance in datasets—the fact that some activities are more prevalent than others and are, therefore, over-represented—is a recurrent condition found in both public and private datasets and known to affect recognition accuracy ,52,53. FThe physical characteristics of the sampled users/carriers directly affect the measured human activity patterns; thus, they are an important aspect of the data collection process for HAR. Data collection methods can contribute to the generalization in recognition models by allowing diverse characteristics or parameters to be incorporated into the training dataset. These characteristics should be representative of the final dataset to which the model is applied. One of the important parameters is the user themself, whose diverse physical characteristics can be challenging to integrate or model . StudiesStikic et al.  note thaA practical, privacy-sensitive, and unobtrusive annotation method for studies using barometers to track humans over long durations is manual labeling of the data. Unlike the signal sensed by accelerometers or other inertial sensors, barometric pressure can indeed be less complex to interpret. However, this process comes with several challenges, one of which is the lack of complete understanding of all the factors that influence barometric pressure. This important issue is addressed in detail in For the sake of studying human activity recognition, the barometer has been primarily used to measure changes in altitude (or elevation). The scale of the altitude changes varies from a fall to vertical displacements like moving uphill, climbing a deck of stairs, riding an elevator, etc. In recent applications, the patterns of the time series of barometric signals have been shown to be a good indicator of the underlying activities, such as walking, being idle, and using transportation. The rate of pressure change can help to identify the mode of vertical transport and determine the vertical velocity of air vehicles. Barometers are widely embedded in wearable devices and used for vertical transport detection ,68,69,70One of the earliest works on using barometers to classify vertical movement is due to Sagawa et al.  and dateMEMS barometers are miniature sensors (<0.1 mm) manufactured by prominent companies, such as Bosch ,81,82, S±1.2 Pa ,85, with [±12 Pa . It is wBarometers are constrained by their resolution and sampling frequency, which limit precision and accuracy, and the use of their data is further constrained by the presence of noise. Some studies have used filtering and signal modeling to overcome the latter problem. For instance, moving average filters over a given time window is widely used for that purpose ,84,86, fThe time series data of barometric pressure are generally converted into statistical ,92, specdp) ,79, whicThe choice of the classifier depends on the application at hand. It also depends on the range of pressure variations and durations under consideration. Some studies have only considered indoor pressure profiles of individuals ,68, whilk-Means clustering [Barometric pressure is more straightforward than inertial sensors in conveying sensed information due to its fairly direct reading, which greatly simplifies the use of classifiers. The most widely used classifiers are decision trees ,82,83,87ustering , Bayesiaustering ,100,101,ustering ,102, andustering  have alsustering , while fustering .Liu et al.  comparedSome applications do not require any classifiers. For instance, state estimation, like altitude and vertical velocity, can be determined using a Kálmán filter or one of its many variants ,104,105.Very few studies dedicated to tracking human activity and/or movement solely rely on the use of a barometer, despite the fact that their potential for applications is promising. The selected applications of using only a barometer for HAR are summarized in Barometers have been used to measure altitude as a stand-alone measuring instrument for a very long time . The surSimilarly to accelerometer data, the signal pattern encoded in a barometer output carries sufficient information to recognize a range of human activities. Ghimire et al.  observedIn climate-controlled buildings, changes in barometric pressure can be detected during indoor-to-outdoor transitions. Wu et al. , moreoveApplications that use only barometer data are more common in the studies concerned with floor localization and recognition of VDA. In the absence of location sensors, the challenge with floor localization is to have a reference pressure and associate it with the data measured by the considered wearable devices at the moment they enter a given building. To obtain such a reference pressure, Li  recommenThe challenge for recognizing VDA using only barometer data is that the sensor data should ideally be free from all factors that affect barometric pressure other than altitude. This can be guaranteed if the sensor data are collected from a controlled environment where no other factors that can be mistaken for VDA occur. As shown by Bollemeyer et al. , GhimireIn summary, in a specific environment and considering selected modes of activity, the recognition performance of VDA using barometer can be very high. More generally, in any given environment, barometers are shown to perform far better or similarly to other sensors when detecting VDA, and represent the only viable way to extract the magnitude of vertical displacement. Vanini et al.  report tFew studies have employed a barometer as the sole sensor in their application. It is usually integrated with other sensors, like inertial sensors ,82, enviTo summarize and categorize the vast breadth of applications of barometers for HAR with multi-sensors, we have gathered some selected studies into In most classical HAR analyses, the classification accuracy in detecting VDA based on sensory data without pressure is usually low ; this isAccelerometers are still the predominantly used sensors in HAR, and have been widely used as stand-alone sensors in recognizing many activities of daily living. They complement barometer-based recognition algorithms in detecting ambulatory movements such as walking, and their use also helps distinguish stair-climbing from other modes of vertical transportation, like using an elevator and escalator ,101—and For indoor localization and navigation applications, obtaining a reference pressure to calibrate all mobile sensors is critical for the system to work. This is more easily obtained in multi-sensor applications. Pipelidis et al.  used ligBarometers are known to speed up the GPS localization through their altitude estimation . ConversAtmospheric pressure is the force applied per unit area by a column of air above a specified area . It is cFrom a fundamental point of view, these factors include the atmosphere’s fluid properties, such as density, moisture content, temperature, and motion. Speaking more generally, we can attribute the change in local barometric pressure to more abstract factors, such as climate and weather ,117,118,In order to understand and demonstrate the effects of the numerous factors that may affect the barometric pressures, we collected a series of experimental data using a custom-made device embedded with a barometer sensor (BMP280). The device was designed specifically for national-scale human activity data collection and analysis . In addiThe atmospheric pressure distribution on the planet is caused by the differential heating of the sun at different latitudes, which varies from the tropics to the polar regions . The EarThe pressure distribution creates isobaric contours—i.e., the points along which atmospheric pressure is constant . By a coIt is very important to note that all weather patterns observed are caused by very small changes in pressure—i.e., the maximum change in the sea-level atmospheric pressure never exceeds 5% of the absolute atmospheric pressure . A gentlSimilarly to surface heating, the upper part of the atmosphere is heated differently over the period of a day. The resulting diurnal temperature variations give rise to a diurnal pressure cycle as illustrated in Ho et al. analyzed pressure data from 2309 U.S. cities and observed that these variations are less than 100 Pa per hour for 99% of the time , while LAmbient pressure can be controlled in an enclosed space using mechanical systems like heating, ventilation, and air-conditioning (HVAC) systems . AbsolutLstiburek et al.  list theMuralidharan et al.  observedEven if HVAC systems and the built environment have little effect on HAR, the transition between indoors and outdoors has been shown to produce noticeable pressure changes. Wu et al.  found thVehicles like cars and buses are in quasi-equilibrium with their environment due to the vents and ducts allowing the airflow in. This means that the barometric pressure inside a vehicle is very close to the exterior pressure. Note that the term ‘exterior’ denotes the air pressure in the immediate vicinity of the vehicle and not the ambient pressure far from it, which could be drastically different. This is due to the fact that ambient pressure is increased by the vehicle’s motion near its surface where the dynamic pressure increases and the static pressure falls. The stagnation pressure on the surface caused by stopping the airflow near the surface produces a pressure distribution across the vehicle. This, in turn, creates pressure fluctuations inside the vehicle whenever its motion changes. However, this effect is transient, and a quasi-equilibrium is reached quickly between interior and exterior. In order to demonstrate the effect of air velocity on the barometric pressure, we collected the data with five custom-built devices   describe sensor data collection processes to track human activities, both in general and more specifically in the case of barometers, (2) discuss the use of the barometer in human activity recognition and list its applications, and (3) understand the many factors that affect barometric pressure. This section reviews the important findings of this endeavor, explores the challenges for each application category, and recommends future research directions.In In The studies listed in The various factors that influence barometric pressure have been used by many studies to make better modeling decisions to accurately identify the activity class of interest. The steps to understand these factors have recently led to creative use of the barometric pressure measurements to identify some classes of activities that are traditionally detected using other sensors. To review the challenges and recommend future directions for different applications of barometer data, we categorized the studies that track human activities into the following groups—(1) human mobility: tracking movement of people, (2) health monitoring: tracking elderly, patients, or healthy people for health-related activities, (3) vehicle tracking: tracking movement of people riding vehicles such as cars, buses, trains, etc., and (4) building monitoring: monitoring the movement of people through the changes in building environments. A taxonomy of barometer applications to track human activities is shown in Human mobility: The subtle changes in barometric pressure during body movements are used to detect walking, understand gait patterns, and count steps . Similar(±12 Pa) . Hence, In the field of indoor navigation and positioning ,71,72,73Understanding human mobility has far-reaching applications in urban planning and economic development . While tHealth monitoring: Currently available wearable devices are increasingly designed to track health-related activities. In healthy populations, physical exercises like running, walking, playing, and the rest time between them are estimated with the help of a suite of sensors, including barometers. Barometers have been known to improve the tracking of physical activity, resulting in a better estimation of energy expenditure ,97,112. Vehicle tracking: As mentioned in One important application of tracking vehicles is to locate and track the users in lieu of using GPS ,77  wearable devices and smartphones. The vast breadth of applications listed in this review underscores the tremendous potential for use in extracting barometric pressure data on a massive scale. Some attempts with limited scope are reported in Here, we reviewed and discussed a series of previous studies on using barometers in the tracking and recognition of human activity and movement patterns. Although numerous factors may affect the barometric pressure, the potential of using barometers as sensors for tracking human activity is enormous and worth the anticipation. While most previous studies focused on short-term, indoor, and small-scale human activities, currently, there is a large research gap in the utilization of barometers for a study on a large spatial scale  and on a long temporal scale for human activity tracking, recognition, and analysis, specifically concerning the detection of vertical movements.


Genebanks play an important role in the long-term conservation of plant genetic resources and are complementary to the conservation of diversity in farmers’ fields and in nature. In this context, documentation plays a critical role. Without well-structured documentation, it is not possible to make statements about the value of a resource, especially with regard to its potential for breeding and research. In particular, comprehensive information management is a prerequisite for the further development of genebank collections. This requires detailed information about the composition of a collection, thus allowing statements about which species and/or regions of origin are under-represented. This task is of strategic importance, especially due to the threats to crop plants and their wild relatives caused by advancing climate change. Both the actual conservation management and the fulfilment of legal obligations depend on information. Hence, documentation units have been established in almost all genebanks worldwide. They all face the challenge that knowledge about genebank accessions must be permanently managed and passed on across generations. International standards such as Multi-Crop Passport Descriptors (MCPD) have been established for the exchange of data between genebanks, and allow the operation of international information systems, such as the World Information and Early Warning System on Plant Genetic Resources for Food and Agriculture (WIEWS), the European Search Catalogue for Plant Genetic Resources (EURISCO) or Genesys. For many centuries, humans have been taking advantage of the plant world and adapting it to their needs. The resulting diversity of useful plants is the main source of food for humans and animals . In addiGenebanks play an important role in the long-term conservation of these plant genetic resources . They coIn this context, documentation plays a crucial role ,10. ImagThis article aims to provide an overview of the relevant systems and structures of plant genetic resources documentation in genebanks. The development of documentation, the current situation and international cooperation as well as the specification of data for long-term and sustainable conservation management are described. In addition, the current needs for integrative information management based on several requirements for future genebank documentation are described.For the long-term conservation and use of plant genetic resources, it is necessary to document a large amount of information at various levels, especially for the identification and characterisation of accessions.http://www.fao.org/cgrfa/).Basic information on plant genetic resources is contained in the passport data. They serve in particular to identify the material and contain information such as the accession number, the scientific name and information on the origin and acquisition of the material . Ideally, these data are following the standard of the Multi-Crop Passport Descriptors (MCPD) ,14, whicThe geographical origin of a genebank accession  can provide information on possible adaptations to biotic/abiotic stress factors. Such data should be complemented by information on the type of material . Other important data include phenotypic characterisation of the individual accession, including morphological and agronomical traits.This basic information helps to identify the individual samples of the genetic resource and to estimate its value, especially its potential for breeding, but also for research.Genebanks have been existing for many decades. This long period of time implies that the conditions at genebanks have been and continue to change. On the one hand, changes result from technological progress. This means that both the type of storage and the data itself must be adapted. On the other hand, social, political and economic changes occur, which lead to organisational changes in genebanks. This means that the description and use of plant genetic resources are also subject to constant change and may result in different identifiers for an accession over the time. In addition, the exchange between genebanks is common in order to establish conservation security through targeted safety duplication and to complement collections in the individual countries. Moreover, material is supplied to researchers and breeders, but also to other users.Before the introduction of information systems spanning several collections, a local, sometimes temporary, unambiguous identification of an accession was sufficient. However, changing identifiers lead to chains of identifiers of an accession and make it more difficult to trace transferred material. Even within a collection, unambiguity could not always be guaranteed, for example, in the case of multiple accession numbers for the same material in the course of time. Identical identifiers in aggregating information systems also pose a problem . This leMoreover, it is an indispensable task of the documentation units of the genebanks to map the historically used identifiers of the accessions to these new, unique identifiers.What is needed to recognise which parts of a collection are over- or under-represented? First and foremost, the composition of a collection naturally depends on its exact purpose. In addition, as comprehensive information as possible on individual accessions is a further essential prerequisite for the further development of genebank collections. With the help of the botanical determinations of accessions, a genebank collection can be examined to determine the extent to which the gene pool of a genus is completely represented or which species/subspecies are missing. In addition, the within species/subspecies diversity should be adequately represented. By adding geographical information, it is possible to specify more precisely from which regions material is missing and should be collected. Such detailed information on the composition of a collection is necessary to perform gap analyses ,22,23. IThe comparison of data on accessions also allows statements to be made about potential duplicates within a collection. Nevertheless, this task is not trivial. A reliable statement can only be made by the joint analysis of phenotypic, genotypic and passport data in combination with comparative cultivations ,26.https://www.ecpgr.cgiar.org/aegis/) [By analysing combined data from different genebanks, it is also possible to check which accessions are maintained in other collections and, if necessary, could be obtained from there ,28. Such/aegis/) ,29. AEGIhttps://www.cbd.int/), which entered into force in 1993, and its supplementary agreement, the Nagoya Protocol of 2014 . To this end, it is essential to document the origin of genebank accessions, as well as the time of inclusion in a collection and existing collecting permits.There are international agreements governing the conservation and sustainable use of plant genetic resources as well as access and benefit sharing (ABS), in particular, the Convention on Biological Diversity  came into force in 2004 and the signatory states committed themselves to conserve, characterise and evaluate plant genetic resources and to ensure their sustainable use. The main component of the Treaty is the Multilateral System of Access and Benefit Sharing (MLS), which regulates facilitated access to plant genetic resources that have been included in the MLS and the equitable sharing of the resulting benefits. A Standard Material Transfer Agreement  was created for this purpose. The reporting obligations attached to the SMTA are usually part of the tasks of the documentation units of the genebanks.In accordance with the CBD, the International Treaty on Plant Genetic Resources for Food and Agriculture  . Dependihttps://www.croptrust.org/our-work/svalbard-global-seed-vault/) in Svalbard was introduced as an additional safety backup system [https://www.seedvault.no/news/withdrawal-of-icarda-aleppo-seeds-accomplished/). However, such a mechanism has not yet been established for the associated data. It also does not make sense to save data as a black box, as is possible with the physical material. In the case of loss of material, information about where safety duplicates are located, but also who did receive this material , are the keys to finding the material again. Unfortunately, this information is primarily in the genebank, which could be lost. Usually only the basic passport data are shared along with safety duplicates, in some cases not even this. In addition, not all genebanks have a strategy and the ability to secure data which goes beyond this. Similar to the idea of the Global Seed Vault, a global data safe or corresponding, connected repositories would be one way to meet this challenge. Of course, this must not result in losing reference to the physical material. The International Nucleotide Sequence Database Collaboration  shows that such an approach can work [https://www.ncbi.nlm.nih.gov/Genbank/), the DNA Data Bank of Japan  and the European Nucleotide Archive .How is it generally attempted to protect plant genetic resources in genebanks from loss? A fundamental idea of genebanks is the protection against losses of accessions or entire collections, e.g., in case of war or catastrophe. The FAO Genebank Standards  recommenp system ,32,33. Tcan work ,35. The The main intention of genebanks is to conserve collections of plant genetic resources for posterity. This means that the documentation of the material must also be ensured across generations. In order to meet the resulting requirements, documentation units have been established in almost all genebanks worldwide. They all face the challenge that the knowledge about the genebank material must be continuously managed, supplemented and passed on across technical and personnel generations. The knowledge must not be tied to individual persons alone. Some genebanks are many decades old, which means that there have been several changes of personnel during this period and possibly also one or more predecessor institutions. Ideally, however, there should still be complete documentation of all accessions of plant genetic resources acquired, maintained and distributed, covering the entire period since the foundation of the genebank.Historically, the documentation structures and methods of the management of genebanks in the first decades were influenced by experiences from botanical gardens and focused on breeding activities. Accordingly, there was a collection-oriented documentation of the origins, often in the form of collecting mission reports, and a practice-oriented documentation of the collections as inventory books and cultivation documentation. The taxonomic classification of the accessions was an important ordering criterion. Many genebanks regularly created a catalogue, a so-called Index Seminum, to make their holdings publicly accessible. Depending on the size and focus of the genebank, more or less complex index card systems and registries were established. Archives with correspondence and collecting mission reports supplemented these, but often only with weak linkage to collection management. These document holdings were difficult to search and exploit. Due to their limited storage capacity, the first electronic systems were therefore established parallel to paper documentation in order to facilitate searchability, filtering and presentation of the information. With the advancing technical development of databases and spreadsheet programs, different IT solutions were created depending on size, focus and organisational form  of the genebank, which increasingly replaced paper documentation ,38,39,40https://sesto.nordgen.org/) was developed for the joint use of the Nordic countries and was operated by the Nordic Genetic Resource Centre (NordGen) in Alnarp, Sweden. SESTO was also used by the genebanks of the Baltic countries for documentation purposes. However, even today there are still a large number of smaller collections that do not have these capabilities and manage data with simpler means, such as MS Excel lists [https://www.grin-global.org/) [Many larger genebanks have implemented in-house genebank information systems in recent years, e.g., GENIS , GBIS 4 or GBIMSel lists . For thoal.org/) , is incral.org/) . This syal.org/)  of the GIn order to provide an overview of the current situation of information management in genebanks, the authors conducted an ad hoc survey among the National Inventory Focal Points of the European Search Catalogue for Plant Genetic Resources (EURISCO) network (see below). They were asked whether they could provide information on (1) how the genebanks in their respective countries manage their data and (2) which data are managed in addition to basic passport data. From 40 persons contacted, 30 replied. Even though this survey is not representative and certainly reflects only a part of the overall situation, it provides a basic overview. A more extensive survey, aiming in particular at perspective approaches to the future development of the documentation systems in the individual countries, would be a logical consequence, but was not feasible in the context of this article.https://www.nordic-baltic-genebanks.org/gringlobal/). Thus, 28% in total use the GRIN-Global system, while only 3% apply other systems that are used in different genebanks. More than 40% use MS Access or FoxPro for data management. The most widespread use is MS Excel (59%). Thirty-four percent still document in paper form , per form .All respondents indicated that they manage passport data. This was to be expected because this is the basic information for genebanks. In addition, it was stated that more than 90% also hold phenotypic data and information on seed stocks. Sixty-nine percent also manage data on seed orders.Despite ever better IT support through genebank information systems, these remained largely isolated from each other. Since the 1980s, a start has been made on compiling data on accessions of one or more crop species obtained in genebanks in a region or even worldwide into databases, the so-called Central Crop Databases (CCDBs) . Two of http://www.fao.org/wiews/), EURISCO (http://eurisco.ecpgr.org/), Genesys (https://www.genesys-pgr.org/) or the Global Biodiversity Information Facility , which combine the information as homogeneously as possible and make it available beyond the boundaries of the individual genebank collection.Aggregating platforms and databases for a cross-genebank search for suitable accessions have also been and are being developed. MCPD and Darwin Core ,52 have While the intention of WIEWS is to provide periodic, country-driven assessments of the plant genetic resources conservation status for FAO, the European Search Catalogue for Plant Genetic Resources (EURISCO) provides detailed accession-specific information on the majority of European collections . Of the https://cgiar.org/). This system was originally developed in 1994 and was put on a new technological basis with Genesys. In addition, EURISCO as the European hub and the US GRIN system as the North American hub regularly feed their data into Genesys as well. A source of additional Australian data is the University of Queensland’s Crop Trait Mining Informatics Platform [Another approach is the Genesys information system, which has been developed since 2008 and is based on the System-wide Information Network for Genetic Resources (SINGER) system. SINGER was an integrated system for the management and exchange of data on plant genetic resources held in Consultative Group on International Agricultural Research (CGIAR) genebanks  should be mentioned, under which a large number of Central Crop Databases as well as the EURISCO system were developed. From the field of natural science collections, the Distributed System of Scientific Collections  approach should be mentioned here. DiSSCo aims to curate and make accessible the holdings of European natural science collections according to uniform criteria. This approach could provide additional impulses to advance the international networking of genebanks and their documentation.In general, networks at the regional and/or international level play an outstanding role in developing the above approaches. They contribute to bundling existing strengths and resources in order to master common challenges. In this context, the European Cooperative Programme for Plant Genetic Resources . Besides the GLIS DOI portal (https://ssl.fao.org/glis/) of the Treaty Secretariat, WIEWS, Genesys, EURISCO and GRIN-Global are major components of GLIS.In the context of international cooperation, reference should be made here once again to the International Treaty on Plant Genetic Resources for Food and Agriculture . Article 17 of the Treaty contains the vision for the development of a global information system. Since 2015, a Global Information System (GLIS) for plant genetic resources on the basis of existing information systems has been underway. The goal of the GLIS is to provide a global entry point for knowledge and information to support the conservation, management and use of plant genetic resources  developed by 193 member states of the United Nations.Successful international cooperation, also in the field of documentation, supports the fulfilment of the Sustainable Development Goals , which is a worldwide collaboration to support the creation, integration and exchange of data on plant genetic resources. A number of working groups have been established for this purpose.Finally, it is worth mentioning the international DivSeek network  and Intelligent Collections of Food Legumes Genetic Resources for European Agrofood Systems  projects.Moreover, such approaches can contribute significantly to the long-term development of traditional genebanks into biodigital resource centres. This means integrated centres which, in addition to the actual plant genetic resources, also provide a large amount of associated information from various data domains and thus enable better and more targeted access to the material ,69. AccoHowever, new challenges arise from this thoroughly gratifying development. Single-seed descent (SSD) lines must be generated for genotyping. However, depending on the type of accession, these SSD lines reflect only a selection from the original accessions. This is particularly problematic in the case of populations. Strictly speaking, the SSD lines would have to be preserved as precision collections in addition to the original accessions. The challenge for the documentation is that the data do not explicitly represent the SSD lines either. For example, it is doubtful that an SSD line that is derived from a landrace can still be called a landrace, since the character of a landrace is greatly lost through the selection of a single-seed descent. Conversely, the data obtained by genotyping an SSD line cannot be transferred to the entire accession. The same applies to phenotypic data that is not necessarily valid for both the original accession and the SSD line created from it. If, for capacity reasons, not all SSD lines can be preserved, assigning the data collected to the original accessions without comment should be avoided.In this article, an overview was given of the most important topics, systems and structures of the documentation of plant genetic resources in genebanks. Furthermore, existing challenges were described.In the context of the conservation of plant genetic resources, structured documentation of the material is essential. This includes a variety of basic information, such as origin or legal status, but also data on conservation management, such as storage quantities, germinability, etc. In addition, phenotypic and agronomic characterisation play an important role. Only through this it is possible to exploit the potential of the conserved material for research and breeding.Over the past decades, there have been significant efforts to intensify cooperation between genebanks in order to conserve plant biodiversity in the best possible way. To this end, networks have been established, such as the European Cooperative Programme for Plant Genetic Resources. Such networks have been an important basis for the development of international aggregator systems such as EURISCO or Genesys, which serve as central entry points for the search for plant genetic resources.These information systems are currently limited to passport data and phenotypic data. In terms of a sustainable use of genebank collections, it seems promising to continuously expand the interactions between the globally available information systems and to enrich the existing information on genebank accessions with additional data from other domains. In this context, a number of pilot projects have been successfully carried out in the recent past, which aimed at the molecular exploitation of genebank collections. These promising approaches must be continued in the future.In order to meet the requirements of the modern research landscape, which is characterised by constant diversification and big data, the information systems of the genebanks must be able to connect with other data sources and to make their data available in a network. The linkage with each other and with other data domains leads to new knowledge about plant genetic resources, which ultimately improves their usability and the reputation of the genebanks.


Investment in vaccine product development should be guided by up-to-date and transparent global burden of disease estimates, which are also fundamental to policy recommendation and vaccine introduction decisions. For low- and middle-income countries (LMICs), vaccine prioritization is primarily driven by the number of deaths caused by different pathogens. Enteric diseases are known to be a major cause of death in LMICs. The two main modelling groups providing mortality estimates for enteric diseases are the Institute for Health Metrics and Evaluation (IHME) at the University of Washington, Seattle and the Maternal Child Epidemiology Estimation (MCEE) group, led by Johns Hopkins Bloomberg School of Public Health. Whilst previous global diarrhoea mortality estimates for under five-year-olds from these two groups were closely aligned, more recent estimates for 2016 have diverged, particularly with respect to numbers of deaths attributable to different enteric pathogens. This has impacted prioritization and investment decisions for vaccines in the development pipeline.The mission of the Product Development for Vaccines Advisory Committee (PDVAC) at the World Health Organisation (WHO) is to accelerate product development of vaccines and technologies that are urgently needed and ensure they are appropriately targeted for use in LMICs. At their 2018 meeting, PDVAC recommended the formation of an independent working group of subject matter experts to explore the reasons for the difference between the IHME and MCEE estimates, and to assess the respective strengths and limitations of the estimation approaches adopted, including a review of the data on which the estimates are based.Here, we report on the proceedings and recommendations from a consultation with the working group of experts, the IHME and MCEE modelling groups, and other key stakeholders. We briefly review the methodological approaches of both groups and provide a series of proposals for investigating the drivers for the differences in enteric disease burden estimates. Cryptosporidium and enterotoxigenic Escherichia coli (ETEC) also contribute significantly to overall burden Today, diarrhoeal diseases with the highest burden in under five-year olds are considered to be rotavirus, The estimated global burden of disease, and in-particular the mortality attributable to each pathogen, impacts priority setting for investment in vaccine research and development (R&D), as well as policy recommendations for introduction of new vaccines into immunization programmes. It is important, therefore, that the methodology used to derive the estimates is well understood, and accepted, by global stakeholders, including product developers and policy-makers. Two modelling groups, IHME and MCEE, have generated estimates of mortality due to the different causes of diarrhoea. Historically, the global diarrhoea mortality estimates for children under 5 years of age (U5s) from both groups have been similar with broad and overlapping confidence intervals The enteric burden of disease working group (WG) was established and convened in 2018. The initial focus of the WG has been on the aetiology of diarrhoeal deaths in children U5. Morbidity and long-term sequelae, though constituting a significant proportion of the burden from enteric pathogens in this age group, have not yet been considered by the WG, nor has mortality in older age groups. However, these factors need to be included in the assessment of the global burden of various diarrhoeal pathogens and the potential public health value that a vaccine could offer.This report summarizes the findings from the first meeting of the WG.2(I)Identify areas of commonality and divergence across methodologies and assumptions.(II)Develop recommendations and identify areas for further work that may inform future iterations of the diarrhoeal aetiology-specific mortality assessments for U5s.(III)Increase the transparency and understanding of how aetiology estimates are derived.The IHME GBD and the MCEE models were reviewed with the objectives to:Shigella and ETEC as examples to show the divergence in modelled estimates between the groups. Reference will be made to two large landmark epidemiology studies, namely the Global Enteric Multicentre Study of Diarrheal Disease (GEMS) Box 1The Global Enteric Multicentre Study of Diarrheal Disease (GEMS)The GEMS study was a 3-year, prospective, age-stratified, case-control study to estimate the population burden and microbiologic aetiology of acute moderate-to-severe diarrhoea (MSD). Children aged 0–59 months seeking care for diarrhoea as outpatients or inpatients at health care centres (cases) were compared with non-diarrhoeal community controls. In addition, cases were followed up after 2 months to study short-term mortality after an episode of MSD. The research was carried out in seven field sites in Southern Asia and sub-Saharan Africa, using qPCR as a diagnostic tool in cases and controls to detect evidence of different pathogens in stool samples and these results were used to estimate the fraction of moderate-to-severe diarrhoea attributable to each pathogen.The Aetiology, Risk Factors, and Interactions of Enteric Infections and Malnutrition and the Consequences for Child Health and Development (MAL-ED)The MAL-ED study was a birth cohort study at eight sites in South America, sub-Saharan Africa, and Asia. It used a prospective longitudinal design to assess diarrhoea, subclinical enteropathogen carriage and undernutrition up to the age of 2 years in a community setting. Among the factors evaluated were enteric infections (with or without diarrhoea) and other indicators including micronutrient levels, diet, socioeconomic status, gut function and environment. The study examined these factors, their inter-relationships and overall impact on health outcomes. Because participants were selected in the community, rather than from among hospital patients, few children with severe, dehydrating diarrhoea were observed.The sections below outline the main methodological approaches of the two modelling groups, highlighting similarities and differences. We use 3Both the IHME and MCEE modelling groups employ a step-wise process for generating under-five all-cause and pathogen-specific diarrhoea mortality estimates. The first stage is to generate estimates for all-cause mortality in U5s. This is then disaggregated into the percentage of U5 deaths due to diarrhoea, and then, estimates for deaths attributed to individual pathogens are generated . Table 13.1For the 2017 iteration, the IHME model uses IHME-generated estimates for all demographic inputs. The MCEE model uses United Nations Inter-agency Group for Child Mortality Estimation (UN IGME) modelled estimates, generated using UN Population Division demographic data of population, fertility, migration and mortality by age, sex, location and time, from national censuses and specialised surveys The second output is an estimate for the percentage of U5 deaths due to diarrhoea. Both IHME and MCEE use vital registration, verbal autopsy and surveillance system data to inform the estimate of percentage of U5 deaths due to diarrhoea. However, there are differences in the data used by each of the groups, with respect to inclusion and exclusion criteria and access to unpublished data. The MCEE group have more stringent inclusion and exclusion criteria for the threshold completeness of vital registration data: MCEE only accepts studies with >80% data complete whereas IHME includes studies with > 60% data completeness.To generate diarrhoeal-specific estimates, IHME redistributes deaths that cannot be atrtibuted to a pathogen to specific causes of death (like diarrhoea) and uses an internal model called the Cause of Death Ensemble model (CODEm), a Bayesian hierarchical model to generate, by location, age group (four age groups under 5 years), sex, and year, estimates of the percentage of deaths due to diarrhoea. The procedures used by both groups to generate the percentage of U5 diarrheal deaths due to different pathogens, which was the major focus of the workshop. Differences in methodological approaches between groups at this stage are most likely to contribute to the divergence seen in pathogen-specific mortality estimates (aetiology). In 44.1Due to a lack of representative datasets in which deaths due to specific diarrhoeal pathogens have been identified, the initial step in the process of attributing diarrhoeal deaths to specific pathogens is to identify a proxy measure. Both groups use the proportion of diarrheal episodes in which a particular pathogen is detected as the proxy measure; however, IHME use data from both hospital inpatient and outpatient studies and MCEE uses only data from hospital inpatient studies, assuming that hospitalization is a more accurate proxy for mortality and that aetiologic fractions differ between inpatient and outpatient studies. Both groups only use studies which were conducted for 12 or more months. Studies which did not stratify data from inpatients, outpatients and the community were excluded by MCEE. Facility-based studies of both inpatients and outpatients are thus only included if inpatient-specific proportions are reported or raw data are available to allow subset analysis with the exception of the GEMS and MAL-ED studies, for which unpublished stratified data were obtained. Both IHME and MCEE include only diarrhoea proportion data from studies with a minimum of 100 samples tested and conducted for longer than one year, and not restricted to specific subpopulations. The proportion positive in hospitalised and ‘severe’ episodes are tracked, and all data meeting inclusion criteria is used in the models 4.1.1The approach of IHME is to use all available data with limited restrictions, whilst MCEE have stringent inclusion and exclusion criteria for data. The inclusion of ‘severe’ cases as well as hospitalised cases allows both inpatient and outpatient data to be used for IHME’s model estimates. However, there is variation between studies in what may constitute a ‘severe’ case (i.e. the case definition), and the interpretation of this, which may limit data quality. The IHME approach uses more data compared to MCEE, although there is likely to be a large proportion of studies that were included by both modelling groups. A comparative analysis of the criteria for data inclusion, data sources and data quality of the modelling groups could help to optimize approaches for the next iterations of models, ensuring that future diarrhoeal disease models abide to a high and consistent standard of data quality.4.2Both groups use systematic literature reviews that date back to 1985 (IHME) or 1990 (MCEE), to extract pathogen prevalence from studies meeting their inclusion and exclusion criteria. This includes, where available, data from scientific literature on different aetiologies, hospital data, population survey data, and data from unpublished sources, including detailed data for the GEMS and MAL-ED studies and more recent cohort studies.4.2.1The validity of using studies dating back to the 1980's by both groups, in terms of robustness and reproducibility of diagnostic methods compared to more recent studies, is highlighted as a potential limitation. The improvement over time in culture-based and other diagnostic techniques as well as likely shifts in the aetiology of diarrhoeal disease, due to initiatives such as Water, Sanitation and Hygiene (WASH), may suggest that, for contemporary estimates, a greater weighting should be placed on more recent studies 4.34.3.1The final step in the generation of estimates for individual pathogen mortality is data adjustment and processing. In the case of IHME, epidemiological data extracted from the literature, hospital data, and population survey data are analysed using their DisMod tool  to estimate the proportion of diarrhoeal cases that are caused by different organisms. In order to adjust for the different pathogen detection methods used, GEMS and MAL-ED data are used to calculate the sensitivity and specificity of laboratory diagnostic tests, such as culture and immunoassay, relative to qPCR, for each pathogen and a qPCR adjustment factor is applied to the pathogen-specific proportions after the model is run. A major short-coming of diarrheal disease studies conducted prior to GEMS has been the failure to perform comprehensive assessment of major enteric pathogens, due to demanding technical approaches, in settings with high burdens, often lacking the necessary laboratories and diagnostics The PAF is applied to calculate deaths due to diarrheal aetiologies by location, year, age, and sex. The schematic in Vibro cholerae and Clostridium difficile, the PAF is calculated from the proportion of diarrhoea cases that test positive for each aetiology and the OR of having diarrhoea if the pathogen is detected. Since diarrhoea can be caused by co-infections with multiple pathogens, PAFs can overlap and therefore add up to more than 1. The purpose of using ORs is to address the issue of subclinical carriage, namely that enteric pathogens are frequently detected in the absence of diarrhoea. For each pathogen, PAFs and uncertainty intervals are multiplied by the diarrhoea mortality envelopes to estimate age-, sex-, location- and year-specific pathogen deaths.For all pathogen aetiologies except 4.3.2MCEE takes a different approach. They do not adjust studies and data where pathogens are detected using non-qPCR detection methods to match with those detected by qPCR. However, MCEE uses pathogen positivity according to GEMS qPCR to define pathogenicity. Organisms with <2 site- and age-specific attributable fractions that were different to 1 (not associated with causing diarrhoea) in the GEMS qPCR re-analysis 4.3.3Samples from the GEMS study have recently been reanalysed using qPCR methods rather than the culture-based and other microbiological methods that were used in the original study. This has led to a re-estimation of the percentage of diarrhoea attributable to a named pathogen, with 89% of all episodes attributed to a pathogen, compared to 52% in the original GEMS analysis 4.44.4.1Currently, only IHME uses ORs to calculate the pathogen-specific mortality and population attributable risk, whilst MCEE use the pathogen prevalence . Both groups consider evidence from multiple pathogen studies, thereby addressing the issue of mixed aetiologies. This gives rise to the issue of subclinical carriage, where a pathogen may be present but is not responsible for symptoms. This approach relies heavily on the assumption that the ratio of the prevalence of a pathogen in cases compared to controls is a clear indicator of pathogenicity. This assumption may be too conservative, especially in high incidence settings IHME does not constrain the PAFs to add up to 1, and therefore they assume that in instances where multiple pathogens are detected, diarrhoea would still occur even if one of the pathogens was removed. MCEE also considers mortality attribution may be a result of multiple pathogens, but they do constrain the pathogen prevalence envelope to add up to 1 minus the proportion of diarrhoea without a known cause . Burden estimates could be significantly inflated, if subclinical carriage  is not considered, by way of calculating attributable fractions. In addition to this there may be variation in pathogen load needed to cause infection between sites, i.e. variations in the probability of clinical symptoms caused by a certain pathogen in different settings, which would lead to a restriction in the generalizability of assumptions. It therefore remains unclear how often ‘true’ aetiologic mixed infections occur. Evidence from qPCR shows high variation in durations of carriage of subclinical infections, and there is also evidence to suggest infections could be sequential rather than mixed 4.4.2A strength of the current approach of both groups is the consideration of sub-clinical infections, but there is a lack of knowledge on how to interpret the information and estimates of the burden of sub-clinical infections which are not generated by either group. The presence of sub-clinical infection may vary geographically for individual pathogens, such that the occurrence of one pathogen may cause symptoms in one setting but not in another. A further limitation is lack of knowledge on whether co-infections enhance the probability of clinical symptoms and how different pathogens interact and impact aetiology in the presence of others. This is potentially further complicated by geographical variations and a lack of understanding on whether infections are ‘mixed’ or ‘sequential’. For IHME, these combined factors may result in heterogeneity of ORs that complicates the use of a single ‘global’ median OR for each pathogen to calculate PAFs, currently adopted by IHME. A potential solution, to strengthen the approach adopted by IHME, may be to generate ORs across similar geographical regions and population archetypes, based on the proportion of pathogens excreted in diarrheal and non-diarrheal stools. However, the sparsity of data on pathogen prevalence that is currently available from healthy controls limits this option, and additional control samples from geographical areas would need to be generated. This is also a limitation for the MCEE group, who considers that mortality may be attributed to multiple pathogens, with pathogen prevalence informing aetiology. Additional information on pathogen prevalence in healthy controls, would likewise help to also strengthen MCEE’s approach.4.54.5.1In order, to produce global estimates for individual pathogen estimates, IHME extrapolate the ORs derived from children under 5 years of age from Africa and Asia (GEMS) to all estimates, including adults and in developed countries. Prevalence of each pathogen is estimated for both ages and locations where data do not currently exist, and models are continually updated. Whilst in many instances, sub-regional data may be able to predict country-level burden attribution with a high degree of confidence, it is possible that outliers, where data quality is poor or is not reflective of sub-regional estimates, may result in inaccurate estimates for some countries. MCEE use a global median to estimate pathogen-specific proportions for regions with missing data. This is reliant on the data being a good proxy for other regional estimates, and it is not clear if this is always the case.4.5.2Both modelling approaches assume that the distribution of pathogen aetiology as assessed via each group’s accepted proxy measure directly reflects the distribution of mortality, such that detection of every pathogen in a hospitalized or severely ill child is assumed to be equally likely to cause death. MCEE assume that case fatality ratios are the same across all pathogens based on the prevalence estimate they generate when they scale pathogen-specific proportions. The same implicit assumption is made by IHME, when they generate PAFs. If indeed, there is variation between pathogens, this will affect the distribution of pathogen aetiology in patients who die from diarrhoea and may also be dependent on geography, socio-economic status and population. More evidence is needed to explore variations in case-fatality rates for individual pathogens. This is challenging because the majority of data on mortality is obtained in hospital settings and therefore may bias estimates, given that untreated severe diarrhoea within the community, particularly in LMICs, is more likely to result in death, in most cases. Data collected from study sites is also extrapolated at country-level, which may not necessarily be representative. Whilst this is a limitation, to address this issue of generalisability would be particularly difficult.5(1)How differences in data inputs impact the distribution of U5 aetiology estimates within the mortality envelope.There are both strengths and limitations to the methods presented by both IHME and MCEE groups, as outlined above. We believe that a key outcome of the consultation should be to initially focus on understanding the main reasons for variations in aetiology estimates within the published diarrhoea mortality envelope for U5s. Three key thematic areas were proposed for future investigation.Identifying the drivers for differences between the modelling outputs produced by IHME and MCEE will be a key step in helping to quantify and assess the source of differences in estimates. Whilst much of the data used by both groups are expected to be common and comparable, there are important differences in the criteria that determine which data are included. An improved understanding of factors that are the strongest determinants of the model estimates is vital to better understanding their accuracy and robustness, since these estimates drive decision-making with respect to vaccine development and introduction. The impact or trade-off of utilising a small number of high-quality studies (as MCEE do) versus a larger number of studies of varying quality (as IHME do), to generate global estimates needs to be investigated. Additionally, the validity of using historical studies that use less sensitive diagnostic techniques, weighted equally within the data analysis, needs reviewing.i.Conduct a systematic comparison of studies and data included in both models, in an effort to understand how data selection criteria and access to data impacts on the quality and quantity of studies used.ii.Agree on a standardised dataset to directly compare how differences in model structure and associated assumptions (rather than input data) impact estimates.iii.Conduct a data grading review to improve understanding of the quality and standard of data used by each group, followed by a model sensitivity analysis to explore the impact of inclusion and exclusion criteria on model estimates.(2)How extrapolation of odds ratios from 7 GEMS sites in Africa and Asia impacts global aetiology estimates.To investigate these steps, we propose the following strategies, to be refined in an iterative manner as greater understanding of the processes evolve:i.Conduct a systematic review to identify studies that tested for the presence of key diarrheal pathogens among non-diarrheal controls and older age groups.ii.Use the findings to carry out a careful and comprehensive statistical approach using network meta-analysis to assess variability in odds ratios within and across regions and settings, built through consensus with IHME and MCEE.(3)Heterogeneity in case fatality ratios (CFRs) for different pathogens.There were concerns regarding potential bias resulting from the use of GEMS data to determine global median ORs or pathogens, given substantial variation in the rates of subclinical pathogen carriage across ages and settings. Concerns were further raised regarding the direct use of odds ratios exclusively from seven GEMS sites and extrapolation of this data regionally and to older age groups . There was a desire to improve our understanding of the relationship between the presence of pathogen and diarrhoea by utilising additional data from controls to provide more geographically representative ORs which can be consistently used in future modelling analysis, for both IHME, MCEE and other groups. Recommendations included:i.Carry out a systematic literature review to identify the CFR for selected enteric pathogens. The review should aim to include both hospital and non-hospital data .There is a strong recommendation to assess potential variability in CFRs, particularly in-light of the high number of co-infections and sub-clinical carriage reported in study participants in low- and middle-income countries, and to assess the relationship between the OR of disease and risk of death. This approach will need to account for the different approaches taken by each of the modelling groups, since neither group uses CFR in its current methodology, to ensure the process provides useful information that can be adapted and applied by both MCEE and IHME in the future. A limitation is the potential scarcity of data available for non-hospitalised (community) estimates of death , which will likely introduce bias since treatment within a hospital setting is likely to significantly increase chances of survival. Additionally, mortality may be affected by the rate of onset of illness and time to death (which may affect the probability that a child with diarrhoea reaches a facility), and this may vary depending on the pathogen. Factors such as economic status and geography are also important. To address this issue, there was a recommendation to:6The global mortality due to diarrhoeal disease is declining, and for some pathogens, such as ETEC, the current (GBD 2017 for the year 2016) IHME burden of disease estimate is now considered too low to warrant prioritization of vaccine development by some stakeholders. However, given the variation between mortality estimates from different groups, the methodology and robustness of each estimate needs to be carefully understood and investigated. For this reason, WHO’s PDVAC recommended the formation of an independent working group to evaluate these aspects. As we have discussed here, we identified several elements of the methodology that we suggest, be further investigated.The proposed systematic review to extract data from healthy controls will help to further explore the relationship between the presence of a pathogen and disease. It is expected there will be a degree of geographical variation within the results, to complement the current GEMS data, which assumes homogeneity across sites for the odds ratios. This additional information may help to better explain geographical differences in the susceptibility of individuals and populations to certain pathogens, and thus provide improved information on burden.The results from the second systematic review will explore the likelihood of mortality given the presence of disease, known as the case fatality ratio, for individual pathogens. The current strategy employed by both modelling groups is to assume equal likelihood of death regardless of the aetiology. This is likely to over-estimate the risk of mortality for some pathogens and under-estimate the risk for others. A clearer understanding of the variation in case fatality ratio will be of great importance in informing vaccine development, particularly for pathogens which are associated with a higher risk of mortality.7The modelling approaches employed by both the IHME and MCEE groups have historically provided well-regarded results that have guided policy recommendations. As new data, diagnostic and surveillance techniques emerge, the incorporation of new evidence is essential to ensure more reliable estimates. However, the interpretation and use of new evidence should be subject to a high level of scrutiny, and where appropriate, new guidelines developed for its use. In addition, it will be important to address gaps within the data that give rise to inconsistencies and less reliable information. Emerging evidence generated through studies, such as The Child Health and Mortality Prevention Surveillance (CHAMPS) study, designed to track definitive causes of child mortality in sites throughout Asia and sub-Saharan Africa through minimally invasive tissue sampling, will help to provide validation on key measures such as the diarrhoeal mortality proxy. More generally, steps can be taken to assess the quality of data employed by modelling groups, and systematic reviews can be undertaken to address other key gaps in the evidence.Future modelling work, should also assess the longer-term sequalae of diarrhea There is a clear case for ensuring diarrhoeal pathogen mortality estimates are constantly reviewed and updated, and adequate resources are made available to ensure model comparison exercises, since these are a core component of vaccine priority evaluations. This work will serve as an example of the importance of critically assessing data quality in modelling exercises, whilst identifying and addressing crucial gaps in the evidence which must be addressed.The proposed work aims to provide a thorough assessment of the current data and approaches used by IHME and MCEE groups. Our hope is that this can include a sensitivity analysis of the model input data to improve understanding of key input variables, potentially leading to improved guidance for data use, and enhanced interpretation of model results to guide decision-making. Whilst the activities outlined above help to better define which pathogens should be prioritized for vaccine development, future work should include assessment of data and model to quantify the impact of longer-term sequelae. Collectively, robust data and burden models are imperative for building the evidence-base to inform prioritization and policy decisions.810.13039/100000865Bill & Melinda Gates Foundation for funding support (OPP1135836). The views, findings, and conclusions contained within are those of the authors and should not be construed to represent the positions or policies of the Bill & Melinda Gates Foundation, the US Department of Defense, or the World Health Organisation. We are very grateful to all participants at the meeting.We gratefully acknowledge funding from HJ Prudden and B Giersing contributed significantly to the development of text and content, produced the main figures and tables and carried out extensive editing. RE Black, C Troeger, and RC Reiner contributed to the development of the methodology and details of the overview (3) section of the paper, as well as additional edits to the main manuscript.M Hasso-Agopsowicz, R Breiman, M Jit, G Kang, L Lamberti, CF Lanata, BA Lopman, W Ndifon, VE Pitzer, J Platts-Mills, M Riddle, P Smith, and R Hutubessy, as members of the WHO Burden of Disease Working Group, all contributed to multiple rounds of editing of the document, the production of figures and the table, and the overall structure of the manuscript. We declare that all authors have reviewed and approved the final article.


In order to the significance of lessons learned from the natural disasters for health care systems particularly in developing and under-developed countries, the main purpose of this study was to identify challenges and limitations in light of the earthquake experience in Kermanshah Province.The present study was conducted in 2019 as a qualitative research using content analysis method. In this regard, 19 key informants were selected using snowball sampling. To enhance the accuracy of the study, the four validation criteria for qualitative studies in data coding developed by Guba and Lincoln including credibility, transferability, dependability and confirmability were used. Data was analyzed applying Graneheim and Lundman (2004) approach for analyzing the qualitative content of an interview text.Analysis of the data led to the identification of 2 main themes, 5 sub- themes and 17 main categories. The first main theme was health system oriented challenges containing challenges of medication supply and preparation, structural challenges, challenges in crisis-scene management and challenges of service delivery and the second main theme was non-health system oriented challenges including social and psychosocial challenges.According to the results, along with health system oriented challenges with the inter-sectoral or intra-sectoral nature, the non-health system oriented challenges the same as social, cultural and psychological factors can be considered as the major challenges of Iran’s healthcare system in the face of crises. This complicated context can shed the light to policy makers that not only attention to the medicine and medical equipment supply chain, manpower preparation and service delivery system can be considered as an emergency, but also careful attention to the structural challenges and crisis-scene management should be planned and considered as a priority. Besides, the policy makers and the local managers should try to plan and act in a contingent situation according to the social and cultural characteristics of the region and the psychological condition and the mental needs of the people. All through history, human life and property has been continuously confronted with unpredicted events and sometimes frightening and dangerous disasters such as earthquakes, floods, storms, and wars . Such acEvidence in this respect shows that healthcare systems need to have the distinguishing features of flexibility, strategic resource allocation, as well as strengthened health structures to deal with crises and natural disasters, considering the fact that the quality of such accidents demands flexibility, preparedness, and planning in advance because of its unpredictability . The heaAn efficient healthcare system can take action properly in critical situations with respect to complexities of patients’ medical conditions during crises; however, it can be really difficult to do planning and manage due to staffing and infrastructure constraints as well as extents of such events .On November 25, 2018, Kermanshah Province in western Iran was hit by a 7.3 magnitude earthquake, and about 700 people lost their lives and more than 10 thousand were injured . The maiThe present study was conducted in 2019 as a qualitative research using content analysis method. In this study, semi-structured interviews aimed at explaining challenges facing Iran’s healthcare system in times of natural disasters were performed with senior members of Kermanshah Province Crisis Committee, emergency wards, Medical Supply Units, Food and Drug Administration executives of Red Crescent Society, as well as hospital heads involved in crisis, and financial managers. The interviewees were selected using snowball sampling method. Accordingly, the head of the Red Crescent Society and his deputy managers were first interviewed, and then they were requested to identify people who were experts in this field. The study participants were selected out of individuals well-informed about the crisis and how to cope with it, and also willing to share their information. At this stage, the interviews were done in-person at participants’ workplace according to their opinions and prior arrangements. The interviews were made by one of the researchers (OS). At the beginning of the interviews, general explanations were given about the study and its objectives and confidentiality of information was orally ensured. Moreover, all the individuals signed written consent forms and they were allowed to withdraw from the study at any stage of the study. The interviews lasted at least 50 min and they were conducted by one of the researchers. The given interviews were also recorded upon obtaining participants’ consent and transcribed verbatim just after they were completed. They also continued until saturation, after 19 interviews. To prepare the semi-structured interview guide consisting of 18 items, the related literature was reviewed and it was approved by two experts at Red Crescent Society. Moreover, face validity of the interview guide was assessed through conducting three initial interviews with the participants and some minor changes were done for better understanding of the questions. The content of these three interviews did not include in the final analysis.To enhance the accuracy of the study, the four validation criteria for qualitative studies in data coding developed by Guba and Lincoln including credibility, transferability, dependability, and confirmability were used . In ordeThe suggested approach of Graneheim and Lundman (2004) for analyzing the qualitative content  of an inManifest analysis of the data led to the identification of 62 final codes (Appendix). After continuing the analysis process via both manifest and latent analyzing, 17 categories were appeared that were merged and synthesized into 5 sub-themes and 2 main themes (Table1). According to Table The first theme recognized is health system oriented challenges. These kind of challenges are directly and without intermediators related to the health system. Although the nature of these factors are the same, some of them have the inter-sectoral and some have the intra-sectoral relationships. According to this some clarification of each of these sub-themes are as follows.It is necessary to make predictions before the crisis for medications, equipment, and supplies in different regions for specific periods in terms of population, size of the region, as well as geographical extent influenced by a crisis” (P10).“With reference to the strategic drug storage, a participant had also said that:In connection with single-use items such as stents, balloons, IUDs, and pacemakers as strategic commodities, we are totally dependent on other countries. So, we have stored them before crises and I think this can greatly help us.” (P9).“Maintaining and promoting supply chain was correspondingly one of the key points deliberated. In this respect, one of the interviewees had reiterated that:If the representatives of the largest medical supply companies were not accessible, we would face lots of troubles distributing resources in the region. Different companies at the provincial level helped a lot during recent earthquakes” (P13).“One of the most important challenges of the healthcare system in times of earthquake was associated with supplying and preparing medications. The main categories in this domain were estimation of reasonable needs, strategic drug storage, supply-chain maintenance and promotion, and domestic production. In this regard, one of the study participants had stated that:If there was a systematic management headquarter dividing work, defining crisis management structure, and assuming responsibilities for crisis management and its subordinates such as rescue, healthcare management, accommodation, relocation, feeding, and crisis management as a whole, everyone could do their jobs properly” (P10).“Another challenge identified in this study was structural challenges with the main categories of crisis management structure, planning, and coordination with stakeholders. Accordingly, the participants had accentuated the significance of defining the structure of crisis in healthcare system as well as identifying the role of governmental and non-governmental organizations in delivering services in times of crisis. In this regard, one of the participants had stated that:On the subject of medical consumables required at the night when the earthquake struck the region, we sent some items just an hour later with the contribution of some companies” (P14).“Or, elsewhere, an interviewee had added that:We always give emphasis to human safety first and then work, but there was all the safety left. I went there in the early hours. There were many hospital personnel working with no gloves and they were operating and stitching under inappropriate conditions. They were also working in the sun and covered with dust” (P17).“Reflecting on challenges of crisis-scene management, the main categories of crisis financing, human resources, medical equipment, and information management were identified. In this respect, the study participants had assumed that:… the only hospital in the city had been completely demolished and all the patients were merely being examined in the middle of the yard. There was no place where the medical staff could offer healthcare services. On the other hand, we were obliged to follow some standards for setting up a field hospital, that should be located in a known and accessible place to the public, so we launched a mobile medical system and a field hospital” (P6).“Somewhere else, another interviewee had said that:Our main problem was that we did not have helicopters with night-vision equipment. At the same time as the earthquake happened at 9 o’clock, we only had land transport that night but no air transport” (P3).“Or, another participant had reiterated that:Health packs are very helpful in times of crises, but disasters are characterized by different conditions and extents that can have effects on the design of such kits. We thus prepared basic health items including toothpastes and toothbrushes, towels, sanitary napkins, as well as mother-child packs, which could be of big help” (P2).“One other challenge in times of crisis was service delivery to the injured. Unpredictability of crisis could also make the given process challenging. The main categories of medical infrastructure, healthcare services during crisis, and pre-hospital emergency were thus ascertained. The participants’ opinions were also as follows:The second theme explored in the present study, was the non-health system oriented challenges. These kind of challenges have the general nature depend on the social and cultural characteristics of the region and also the psychological condition after disaster. Although these challenges are not driven from the health system directly; the performance of the health system can affect them a lot and ignoring them can lead to great challenges along with those directly related to the health system.In times of crisis, we do not usually confront with severe mental problems, but they generally occur after it. So, we recruited a psychologist in our visits and we also practiced mental health screening” (P16).“And, elsewhere, one of the interviewees had said that:Perhaps, we and the organizations that were in charge of helping the injured in previous crises could not do well, so we lost public trust” (P18).“And, finally one of the interviewees had noted that:People used to trust in governmental agencies easily, but unfortunately they lost it in the earthquake striking Sarpol-e Zahab County. It may be due to the performance of the organizations or even gossips and news about what had happened in previous crises” (P5).“Psychosocial- Social crises among the injured and their families were one of the challenges making crisis management difficult in this natural disaster in this study. This challenge was identified with the main categories of trust-building, cultural conditions of the region, and psychosocial interventions. Accordingly, the participants raised the need for a community mental health team in the region to assess the situation in coordination with other units affiliated to the Ministry of Health and Medical Education as well as other relevant organizations to design and implement appropriate interventions, aimed at promoting public mental health and preventing mental disorders caused by disasters. For example, the study participants had stated that:This study was aimed at exploring the Iranian health system challenges during disasters. Applying the four main criteria of Guba and Lincoln  that wasAccording to the present results the challenges related to service delivery, crisis scene management and medical supply and preparation are among the inter-sectoral challenges related to the health system.Among challenges of medication supply and preparation, maintaining and promoting supply chain as well as improving performance especially in times of crisis are of utmost importance. Similarly, evidence suggests that supply-chain maintenance and promotion is in a highly weak position as crises and disasters occur . One of In the next subtheme of inter-sector challenges related to the health system, we can consider challenges in crisis scene management. Human resources are the main issue to consider in this regard. Proper planning and employee training for crisis are thus two key elements that can prepare healthcare systems to act in response to crisis-induced problems . EvidencThe third subtheme of inter-sector challenges related to the health system was service delivery. In this regard, the present results have shown that one of the major challenges was lack of preparedness and proper planning by organizations involved in earthquakes. The shortage of strategic drugs and anesthetics, especially under the influence of economic sanctions, was another challenge recognized in this study. McLean and Whang (2019) have mentioned that economic sanctions can have a negative impact on the country under sanction’s overall budget and the budget allocating to the preparedness for disasters .Other results of the study have shown that, structural challenges are among the intra-sectoral challenges related to the health system. Coordination between governmental and non-governmental organizations was one of the key features of crisis management structure in this category, as there is strong evidence that cooperating with stakeholders and sharing resources such as equipment as well as inter-governmental and non-governmental experiences can improve performance, cost effectiveness, and timely service delivery . PlanninAccording to the other results of the study, non-related health system challenges the same as social, cultural and psychological factors are considered among the other challenges of Iranian health system during disasters. First of all, evidence shows that social vulnerability during disasters as a complicated and multidisciplinary concept, can be influenced by many factors the same as the aging structure of the community, the economic status, individual characteristics and cultural variables . In thisAnother challenges raised in this category was the issue of trust-building and social capital. Evidence shows that crisis management focuses more on infrastructure and equipment such as medical facilities and vaccines, and less on social infrastructure such as trust, education, crisis knowledge, and social capital . Since cIn sum, although different studies try to introduce their frame works for disaster management, it seems that in spite of the similarities of the models the same as considering prevention and preparedness before disasters, response phase during the disasters and recovery, relief and rehabilitation right after the previous phase  this topResults of this study revealed that, along with health system oriented challenges that can have the inter-sectoral or intra-sectoral nature, the non-health system oriented challenges the same as social, cultural and psychological factors can be considered as the major challenges of Iran’s healthcare system in the face of crises. This complicated context can shed the light to policy makers that not only attention to the medicine and medical equipment supply chain, manpower preparation and service delivery system can be considered as an emergency, but also careful attention to the structural challenges and crisis-scene management should be planned and considered as a priority. Besides, the policy makers and the local managers should try to plan and act in a contingent situation according to the social and cultural characteristics of the region and the psychological condition and the mental needs of the people.Additional file 1. The final codes of the Iranian challenges during natural disasters.