The ability to learn and perform a sequence of movements is a key component of voluntary motor behavior. During the learning of sequential movements, individuals go through distinct stages of performance improvement. For instance, sequential movements are initially learned relatively fast and later learned more slowly. Over multiple sessions of repetitive practice, performance of the sequential movements can be further improved to the expert level and maintained as a motor skill. How the brain binds elementary movements together into a meaningful action has been a topic of much interest. Studies in human and non-human primates have shown that a brain-wide distributed network is active during the learning and performance of skilled sequential movements. The current challenge is to identify a unique contribution of each area to the complex process of learning and maintenance of skilled sequential movements. Here, I bring together the recent progress in the field to discuss the distinct roles of cortical motor areas in this process. The production of sequential movements is a fundamental aspect of voluntary behavior. Many of our daily actions, such as playing a musical instrument, handwriting, typing, etc., depend on attaining a high level of skill in the performance of sequential movements. The performance of sequential movements can be acquired and improved to the expert level through extensive practice . Such pevs. internal cues), memory , movement outcome , and movement flow . It was suggested that spatial and non-spatial sequences may be learned and controlled by different cortical circuits , supplementary motor area (SMA), dorsal premotor cortex (PMd), primary motor cortex (M1), primary somatosensory cortex, superior parietal lobule, thalamus, basal ganglia, and the cerebellum, subserves the learning and performance of skilled sequential movements e.g., . Studiescircuits . Furthercircuits .In this review, I will focus on the roles of the SMA, PMd, and M1 in skilled sequential movements, i.e., those acquired through repetitive practice and internally generated from long-term memory. I will especially focus on the spatial sequence tasks as this type of task was used in non-human primate studies after extensive practice . The curAn important characteristic of learning skilled sequential movements is that individuals seem to go through several learning stages . An imprThe progress in the learning of sequential movements is associated with a shift in functional MRI (fMRI) activation from the anterior regions to the posterior regions of the brain . The chaA agonist), the inactivation disrupted the monkey’s performance of sequences of arm movements guided by memory, leaving the execution of simpler, single movements unaffected  and the pre-SMA . Human paffected .On the other hand, non-human primate studies using spatial sequence tasks suggested that spatial and non-spatial sequences may be learned and controlled by different cortical circuits . Even thNevertheless, an inactivation study using spatial sequences provided results different from the inactivation study using non-spatial sequences. Hikosaka’s group trained monkeys to learn a spatial sequence of reaching movements to targets . In theiTaken these findings together, the pre-SMA seems to be more critically involved in the cognitive aspects of acquiring a novel sequence of movements compared to the SMA , 2006. TThe dorsal premotor cortex (PMd) has been regarded as the area for the visual guidance of motor behavior in many studies . MoreoveMoreover, human imaging studies consistently reported the activity of the PMd during the performance of sequential movements . The stuTaken together, the results suggest that, although the PMd neurons are active during both visually guided and internally generated sequential movements, the PMd plays an important role in the internal generation of sequential movements. The inactivation results demonstrated that the PMd is involved in guiding sequential movements based on internal instructions after practice. With practice on sequential movements, the animal could learn arbitrary motor–motor associations of elements in the sequence and perform the practiced sequence in a seamless and predictive manner. Therefore, one possible interpretation is that the PMd inactivation disrupted the arbitrary motor–motor associations in the same way as lesions of the premotor cortex disrupt an animal’s performance of arbitrary sensorimotor associations . This isThe primary motor cortex (M1) controls muscle activity through its projections to the spinal cord, and its contribution to patterning muscle activity has been extensively studied . GrowingThe view that M1 is reorganized after extensive practice on sequential movements has also been supported by studies focused on the anatomical and functional changes of musicians’ M1. The volume of M1 is reported to be larger in professional musicians compared to that in amateurs or non-musicians . The motRecent fMRI studies suggested that M1’s contribution to structured and higher-order aspects of sequential movements may be limited when the training duration was short . In the Neurophysiological studies in non-human primates showed that the neural activity in M1 is modulated by the sequence components. When a monkey performs sequential movements, the neural activity of M1 neurons reflects aspects of the sequential movements . The effM1 is critical for implementing motor output, so that it has been challenging to test its involvement in the acquisition or the maintenance of motor sequences. Lesions or inactivation of M1 will abolish the motor commands to the spinal cord that generates muscle activity. A few studies reported that, when M1 was inactivated, subjects made more errors in the performance of trained sequential movements compared with that before the inactivation . HoweverDe novo protein synthesis, during or shortly after the initial training, is shown to be essential in the consolidation of long-term memory  increase within the rodent’s motor cortex and remain elevated in the plateau phase of the learning curve relative to control animals , and the SMA of monkeys were precisely studied by Dum and Strick’s group . The anaIn fact, a subset of neurons in each premotor area exhibits activity for relatively simple movements as M1 neurons do . FurtherThe performance of sequential movements can be improved to the expert level and maintained as a motor skill through extensive practice. Functional imaging studies in humans show that a brain-wide network subserves the performance of skilled sequential movements. Interventional studies in non-human primates advanced our understanding of its neural basis. The results of interventional studies suggested that each motor area in the network makes a distinct contribution to skilled sequential movements. The SMA is involved in the temporal organization of multiple non-spatial movements into a sequence and the execution of the sequential actions. Its role in spatial sequences is still debatable and needs to be further investigated. The PMd may act as a key structure for the learning of sequential movements by contributing to the selection of appropriate responses. Specifically, the PMd may be critical for the acquisition and maintenance of arbitrary motor–motor associations. In M1, the neural activity, metabolic activity, and structural organization were shown to be modified by extensive practice on sequential movements. M1’s involvement in sequential movements after extensive practice was verified by an interventional study using an inhibitor for protein synthesis. These studies suggest that the PMd functions as a major source of input to M1 to guide the performance of internally generated sequences. Together, the PMd and M1 may be parts of the key structures for the learning and maintenance of internally generated sequential movements. The involvements of these areas along the dimensions of time  and sequence category  need to be further explored in future experiments.The author confirms being the sole contributor of this work and has approved it for publication.The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Introduction: Crewmembers of the “Royal Netherlands Sea Rescue Institution” (KNRM) lifeboats must wear heavy survival suits with integrated lifejackets. This and the challenging environment onboard  might influence Basic Life Support (BLS) performance. The primary objective of this study was to assess the impact of the protective gear on single-rescuer BLS-quality.Material and Methods: Sixty-five active KNRM crewmembers who had recently undergone a BLS-refresher course were randomized to wear either their protective gear (n = 32) or their civilian clothes  and performed five 2-min sessions of single rescuer BLS on a mannequin on dry land. BLS-quality was assessed according to Dutch and European Resuscitation guidelines. A between group analysis (Mann-Whitney U) and a repeated within group analysis of both groups (Friedman test) were performed.Results: There were no major demographic differences between the groups. The protective gear did not significant impair BLS-quality. It was also not associated with a significant increase in the perceived exertion of BLS . Compression depth, compression frequency, the percentage of correct compression depth and of not leaning on the thorax, and ventilation volumes in both groups were suboptimal when evaluated according to the BLS-guidelines.Conclusions: The protective gear worn by KNRM lifeboat-crewmembers does not have a significant influence on BLS-quality under controlled study conditions. The impact and significance on outcome in real life situations needs to be studied further. This study provides valuable input for optimizing the BLS-skills of lifeboat crewmembers. Lifeboat-crews play an important role in the chain of survival for drowning victims as well as non-drowning victims in cardiac arrest –4. EarlyBefore a lifeboat-crew can initiate BLS, they must first reach the victim. It can take longer to reach victims in the water than victims on dry land, since transport over water is slower than on land, especially when the weather conditions are bad . Once thThe “Royal Netherlands Sea Rescue Institution” (KNRM) is a maritime search and rescue organization that has provided help and assistance free of charge on open waters 24 h/day in all weather conditions since 1824 , but at present there is a lack of scientific literature on this important topic for the drowning researchers community . With thIdentifying specific parameters for improvement can guide the optimization of the educational curriculum of lifeboat-crews of all maritime rescue organizations and as part of the KNRM quality management in particular. Such parameters might also lead to consideration of specialized modifications to the standard BLS-protocols for lifeboat-crews and might provide valuable input for templates like the Utstein Style For Drowning (USFD) .The objective of this study was to investigate impact of the protective gear worn by KNRM lifeboat-crewmembers, on the quality of single-rescuer BLS-performance.The study was a randomized controlled trial performed in a simulation setting on land. The local ethics committee of the University Medical Center Groningen waived the requirement for a full consideration of the study on the basis that it does not fall within the remit of the Dutch law on medical research (Wet Medisch Onderzoek) (METc-reference number 2018/166). The study was registered with the Netherlands Trial Register (NL7324) according to ICMJE-requirements. Participants provided written informed consent prior to participation in this randomized controlled trial.The study participants were serving KNRM lifeboat-crewmembers. Via the KNRM headquarters the secretaries of 9 lifeboat-stations were sent an invitational e-mail explaining the study and requesting the participation of all crew-members. In total the crews of eight lifeboat-stations were willing to participate, after which a more detailed letter was sent to them to inform the individual subjects of the purpose and conduct of the study.In order to be eligible to participate in this study, a subject had to be an active KNRM lifeboat-crewmember (including trainees/aspirants) of 18 years or older, provide written informed consent and have passed a BLS refresher course prior to the measurements of this study to eliminate differences of skill deterioration after training as much as possible . Due to KNRM members who were not part of a lifeboat-crew , were (possibly) pregnant, or had physical disabilities were excluded from participation.Before the start of the measurements, the subjects received a further verbal explanation of the study and were asked to sign an informed consent form. Subsequently block randomization was performed  to create two groups. The participants in the study group wore protective gear: a waterproof survival-suit with a sealed collar, arm-cuffs and boots, of only modestly flexibility with an insulating inner part and an integrated life jacket. When dry, they weigh between 11 and 13 Kg, depending on the size, and between 13 and 16 Kg when wet. . Participants in the control group wore only their normal civilian clothes . All volThe standard number of crewmembers on a lifeboat depends on the type of lifeboat and ranges from 3 to 6. The coxswain steers the lifeboat but needs a navigator to bring the vessel to the correct location. This means that in the worst-case scenario when only three crewmembers are on board a lifeboat during a resuscitation, only two crewmembers are available to perform BLS, and they do so in turns usually lasting 2 min each. This scenario was simulated during the study. The measurements were conducted on land to eliminate potential confounds caused by differences in wave and weather conditions causing different boat movements.The individual participants in both groups performed single-rescuer BLS on a resuscitation-mannequin with electronic data collection  during five sessions of 2 min. All participants participated in the study at their own lifeboat station (indoors). As per their usual training, mouth-to-mask ventilations were provided, but via a standard airway filter device attached to the mask. After every 2-min session of BLS the participants had a 2-min break during which they were asked to complete the Borg's rating scale ranging from 6 (no exertion) to 20 , which is a validated tool to describe perceived exertion . During After their fifth BLS-session, all volunteers were given the opportunity to provide additional written comments on how realistic they found the performance of BLS during this study to be.Quality of BLS was judged by measuring compliance and performance relative to published standards as follows: mean compression depth 50-60mm, mean compression frequency 100–120/min, percentage compressions for which compression depth was correct: 100%, percentage of not leaning on the thorax after compressions  100%, percentage time during which hand placement on the thorax was correct: 100%, ventilation frequency <10/min, mean ventilation volume of recue breathing 500–600 ml; hands off time (time of no-BLS) as short as possible , 24. TheWe also assessed the differences between the groups regarding demographic factors including Body Mass Index (BMI), and physical fitness  score), function and experience on lifeboat, and prior BLS-experience on the quality of BLS. In addition, we assessed if the perceived exertion of the rescuers after each BLS session were congruent with the quality of BLS. Finally we sought to assess the subjective opinions of rescuers on the influence of protective gear on their performance of BLS, and on how realistic they thought the simulation was.t test. Non-normally distributed data are summarized as median (IQR) per group, and tested for statistical significance between groups using the Mann-Whitney U test. The Friedman test (a non-parametric repeated measurement test) was used to analyze differences in the repeated measurements within both groups. We considered a p ≤ 0.05 to be statistically significant.The study data was entered in a database and subsequently checked by two persons (AMV and MMS), after which statistical analysis was performed using SPSS Statistics version 23 . We used the Kolmogorov-Smirnov test to assess the distribution of the data. Normally distributed data are summarized as mean (SD) per group, and were tested for statistical significance between groups using the students As far as we know, there are no published data on the influence of the wearing of protective gear that lifeboat-crewmembers wear with respect to the quality of BLS. This led us to choose a pragmatic approach in which the number of crewmembers per lifeboat-station , potential drop-outs and feasibility considerations (discussed with KNRM representative) were taken into account. It was not possible to include all KNRM lifeboat-crewmembers, because of the additional time costs related to the study. On this basis it was concluded that the maximum number of participants that could be included without jeopardizing the successful conclusion of the study, was 60 .n = 32) and the control group (n = 33) were similar. The majority of participants in both groups were male and the median ages of participants in the control and study group were 39 and 41 years, respectively.Between October 23, 2018 and July 18, 2019 in total 71 volunteers from eight lifeboat-stations participated in the study. The data of 6 crewmembers were excluded from analysis . As seenp = 0.049) there were no significant differences.The medians and IQRs of the primary study parameters per epoch, as well as the medians of all epochs combined, are shown in post hoc pairwise analyses of the findings of different epochs. A significant within-group difference over time of the parameters “ventilation frequency” (increase) and “percentage of correct compression depth” (decrease) was only detected for one group  (The Friedman test demonstrated a significant within-group difference over time for both groups for the parameters “mean compression depth” (decrease), “mean compression frequency” (increase), “volume of rescue breathing” (increase) and the “Borg's rating scale” (increase). Given the small sample size, and the small changes, we did not perform ctively) –4.In total six volunteers  provided comments regarding the warmth of the protective gear. In both groups the majority of the volunteers expected that the protective gear would have a negative influence on BLS quality. Both groups considered the study measurements realistic, or had a neutral opinion about it.Overall, the results of this study do not demonstrate a significant negative influence of the protective gear worn by KNRM lifeboat-crewmembers, on the quality of BLS-performance. The results also do not show a significant increase in the perceived exertion as measured on the Borg's Rating scale. However, for both groups compression depth, compression frequency, the percentage of correct compression depth, not leaning on the thorax and ventilation volumes were suboptimal when evaluated according to the BLS guidelines .To our knowledge, this was the first study specifically designed to investigate the influence of the protective clothing worn by lifeboat-crewmembers on the quality of BLS. Research aimed at improving the quality of resuscitation at sea is of great importance to the KNRM and other maritime rescue organizations which seek to optimize the quality of their efforts.Due to the current COVID-19 pandemic, increasing attention has been focussed on the personal protective equipment (PPE) worn by rescuers. As a consequence, aquatic rescue organizations around the world are re-evaluating their resuscitation protocols, seeking on the one hand to avoid contamination of rescuers as much as possible, while on the other hand assuring high quality resuscitation –27. The Interestingly, the results stand in contrast with the expectation of most volunteers in this study who believed that the protective gear has a negative influence. While there were tendencies toward some parameters being different in volunteers wearing protective gear, these differences did not reach statistical significance . There can be several reasons for this discrepancy. Firstly, the sample size was pragmatically chosen and was small, which may have limited the power of the study to detect significant differences.Secondly, the KNRM volunteers are all used to working in their protective gear, potentially masking negative effects by for example increased heat production when wearing protective-gear. During our study, participants performed BLS for a total of 10 min each, and so it is possible that if BLS is performed for a longer time, then the protective clothing will have a greater effect on performance. On the other hand, in practice it seems that in most cases more than two persons are available to perform BLS. Finally, the measurements were made indoors on land. It is possible that the additional challenges of working on a moving lifeboat, especially during bad weather, might further amplify any possible effect of the clothing on BLS performance. However, especially during bad weather, the conditions at sea itself will probably influence BLS performance as well. This is supported by a recent study by Duncan et al. that demonstrated the negative effects of simulated wave motions on search and rescue tasks and maintaining balance . AlthougPhysical fitness has also been demonstrated to have an effect on BLS performance. As the MET and Borg rating scores were similar in our groups of participants and 2-min BLS intervals are deemed feasible, we do not expect that physical fitness would have influenced the results of our study , 29. NonThese mechanisms, as well as the effects of the influence of protective-gear on patient outcome, need further investigation. It may therefore be useful to include one or more variables about protective gear worn by lifeboat-crewmembers and other aquatic rescuers resuscitation efforts in future updates of the recommended USFD-dataset. This might improve the granularity and applicability of data recorded in future studies .There was a broad variability in the performances of the individual volunteers, despite the fact that they all had passed a recent BLS refresher course. Prior studies have shown that decay of skills after BLS courses occurs within 3 to 6 months . TherefoA recent report of a study by Seesink et al. describes the performance of BLS and the use of automated external defibrillators (AED) in real-life situations by KNRM lifeboat-crews by using the data recorded electronically by AED's . Their rVolunteers in our study group leaned more on the thorax and compressed the thorax slightly deeper than the volunteers of the control group, which may have been caused by the extra weight of the protective gear. Although these differences did not reach statistical significance, the higher incidence of leaning on the thorax may well be clinically relevant as it impairs the extent of thoracic recoil which may influence the effectiveness of BLS performance. It is also possible that the control group had poorer BLS skills than the study group, since the volunteers in this group administered more excessively high ventilation volumes in combination with more superficial chest compressions. This is however unlikely as participants were randomized to the control and study group, the control group participants scored better on not leaning on the thorax, and there were no differences between the groups in their demographics and in the time since the last BLS training. It is therefore more likely that these differences were caused by the absence of the protective gear in this group.As no previous studies have investigated the influence of the protective gear worn by lifeboat-crewmembers, the sample size of this study was pragmatically chosen. As explained in the Methods section, a larger sample size for this study was also not feasible. Should it be possible for other groups to access a larger sample size, then our data could inform the calculation of sample size.Although the use of mannequins helps to create a safe study environment, avoids interference with rescue and resuscitation operations and eliminates potential negative effects on patients in cardiac arrest, it's not the same as a real resuscitation. However, since the results in part correspond with real life circumstances and most crewmembers considered the setting in which the measurements were done to be realistic or had a neutral opinion about it , we expeWe did not collect temperature-related data during this study, even though some of the volunteers commented on the warmth when wearing the protective gear. As both groups performed BLS under the same conditions, we do not expect this to have influenced the results of this study.This study shows that the survival suits with integrated life jackets worn by KNRM lifeboat-crewmembers do not have a significant influence on the quality of BLS performance under controlled study conditions. The impact and significance on outcome in real life situations needs to be studied further. The results provide valuable input for optimizing the BLS skills of KNRM lifeboat-crewmembers and other organizations involved in nautical search and rescue.The datasets presented in this article are not readily available. We are happy to share the data with the editor and reviewers. However, as the participants only consented to their data being used for the purposes of this study, we cannot make it publicly available.Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. The local ethics committee of the University Medical Center Groningen waived the requirement for a full consideration of the study on the basis that it does not fall within the remit of the Dutch law on medical research (Wet Medisch Onderzoek) (METc-reference number 2018/166). The study was registered with the Netherlands Trial Register (NL7324) according to ICMJE-requirements. Participants provided written informed consent prior to participation in this randomized controlled trial.AV, AA, and JW were involved in the conception and design of the study. AV, AR, YR, and MS were involved in the data collection. AV, AA, JW, and MS were involved in the analysis and interpretation of the data. All authors have been involved in drafting the manuscript or revising it critically for important intellectual content. All authors have given final approval of the version to be published.AV reports non-financial support from Laerdal Medical , during the conduct of the study; and AV is a volunteer at one of the KNRM lifeboat stations. AA reports personal fees and other from The Medicines Company, personal fees and other from Philips, personal fees from Janssen Pharma (Johnson and Johnson), grants and personal fees from Carefusion (BD), personal fees from Ever Pharma, personal fees from Orion, personal fees from PAION, other from Rigel Inc., outside the submitted work. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The handling editor declared a shared committee/research group with one of the authors AV at time of review.


Disaster Management Act 57 of 2002 (DMA) and the National Disaster Management Framework of 2005 (NDMF). Risk reduction, in particular risk communication, remained unexploited until the occurrence of coronavirus disease 2019 (COVID-19) pandemic. The legislation and policy mandate a proactive approach for disaster management, requiring a focus on disaster risk reduction. Therefore, this study aimed to assess the significance of risk communication as a critical prevention and mitigatory strategy in disaster risk management, focusing on the COVID-19 pandemic. Key to risk communication success is ensuring adequate comprehension, accurate perception of the disseminated information, and compliance with regulations. Questions of trustworthiness, acceptability, effectiveness, and usefulness of messages and strategies communicated sought answers from the Bloemfontein population. Furthermore, the Agenda-setting Theory provided the grounding for the study. The study sample was picked in a stratified random sampling manner, using the confidence level and margin of error equation. A questionnaire survey was used to collect the data required to achieve the research objectives. Risk communication as a disaster risk reduction strategy implemented concurrently with imposed regulations was found to have played a vital role in mitigating the virus spread. However, the respondents were not aware of the local disaster management centre, which is supposed to be engaged in COVID-19 disaster management activities.The South African disaster response activities surpass risk reduction since the implementation of the  Amidst the pandemonium of developing a coronavirus vaccine, preventing and mitigating the spread of coronavirus disease 2019 (COVID-19), risk communication proved by far to be one of the profound methods of containment  variables were explored to understand the aspects under investigation.The deadly effects of a disaster can be reduced by implementing the following vital strategies: anticipating, educating, and informing those at risk  and the Department of Health. They also had to ensure compliance with the COVID-19 regulations at public gatherings such as funerals or church gatherings. Other roles were the monitoring of infection progression, development of reports, public awareness and serving on various COVID-19 committees. Since the disaster management institution is a primary stakeholder in responding to COVID-19, their visibility at the local level in ensuring adherence to risk communication is important. Ther were also entrusted with improving the trustworthiness and credibility of the risk communication initiativesGiven the nature and the living dynamics of some South African citizens such as culture, beliefs, norms, living environment, and economic status, these may hinder the consumption of the risk messages communicated and regulations imposed. In turn, the circumstances for example the lack of running water instil receptivity resistance or challenges among many community members. Receptivity resistance to COVID-19 is when an individual is not able and willing to take in information or an idea. Based on the National Hand Hygiene Behaviour Change Strategy 2016–2020, a 2012 General Household Survey found that about 11% of South African households have inadequate sanitation services. It was also found that 26% of households have deteriorated sanitation infrastructure DoH . AdditioIn Bloemfontein, high-density suburbs such as Batho and Bochabela have blocked and broken sewerage pipes. The water quality is poor and in most informal settlements there is limited access to water  were females, and the rest (37%), males. Their age groups ranged from 19–29 (29%), 30–41 (50%), 42–53 (14%), and over 54 (7%). Most of the participants (83%) had tertiary education, 10% had vocational training, and the rest (7%) had high school education. In January 2021, Mangaung Metropolitan Municipality (MMM) had 29 069 COVID-19 cases, the highest in the Free State Province. Out of the eight towns in MMM, Bloemfontein had the highest number (22 672) number of reported COVID-19 cases . The respondents who indicated that they do not trust the Ministers believed that their messages were pessimistic instead of encouraging. The respondents were also of the opinion that the untrusted Ministers are political with hidden interests, contradict each other, and are corrupt. Therefore, the respondents view the officials as untrustworthy and uninformed. And that they were not advised by experts, therefore, unknowledgeable. They also miscommunicated and were driven by fear and not facts, and that they lie and lack concern for the country’s citizens. ‘Dr Zweli Mkhize, the Health Minister, tested positive, so that says a lot’, said one respondent. In this case, the respondent is probably doubting what the Minister of Health communicates since he contracted the virus.As shown in The disaster management centre under the Ministry of CoGTA is leading the response and management of COVID-19. Hence, this study investigated the respondents’ knowledge of the Mangaung Metropolitan Disaster Management Centre. It also investigated if the respondents received information about COVID-19 from the local disaster management centre. At the local level, the disaster management centre is supposed to monitor the implementation of regulations and ensure compliance like the number of people allowed at funerals. They are also supposed to make available resources to help fight the virus and many other activities. Just more than a third of the respondents (37%) said they knew that there is a local disaster management centre, while more than half (63%) are unaware of the institution. Less than 8% of the respondents indicated that they received information about COVID-19 from the local disaster management centre. It seems that the disaster management centre is silent and inactive regarding COVID-19 risk communication at the local level. As noted in the AST, policy agenda-setting is when the government takes action in response to a certain matter. However in this case, it seems the Mangaung Metropolitan Disaster Management Centre was not visible in its response to COVID-19 at a local level. Perhaps the public and the media did not place much influence on the disaster management centre for it and its work to be noticeable.The final question was whether the respondents perceive the Bloemfontein population as compliant with the COVID-19 regulations.Disaster Management Act, 57 of 2002 was used to declare the virus a disaster and the fact that disaster management is the coordinating function. Research by Wiggill .Also, the visibility of the disaster management centre, leading the dissemination of information at the local level as a recognised COVID-19 response organisation might reduce some receptivity barriers. The respondents argued that the government should inform the communities about the institutions that play a significant role in their lives, with specific reference to the disaster management centre. The reason for this request could be because they will be able to report issues of non-compliance or requests for resources. Some of the responsibilities of the disaster management centre are to obtain resources during a disaster and monitor compliance to regulations imposed (Section 27(2)(a) of the The respondents feel that although people might adhere to the regulations, they will still be infected. The respondents feel like this because adherence to the regulations is not possible 100%. People tend to relax when they are with people they trust , thereby disregarding social distancing, or perhaps certain situations does not allow adherence to the regulations, like when grocery shopping, inter alia.Furthermore, the respondents feel that the government should enforce more stringent laws on crowd gathering, that the wearing of a face shield alone without a mask should be prohibited. Medical experts believe that a plastic face shield is inadequate for protecting oneself from the virus Cesak . The resCoronavirus disease 2019 risk communication included the dissemination of risk reduction messages and risk reduction regulations. To investigate the mitigatory and preventative role COVID-19 risk communication played, the study assessed, inter alia, the Bloemfonteins’ population attitude, beliefs and behaviours towards the risk messages and regulations. The study found that although most participants indicated that they use sanitiser and stay clear of sick people, problematic aspects are that they struggle to keep the social distance of 1.5 m and do not wash their hands regularly with soap and water. Besides some other barriers to receptivity, most respondents’ social-economic status can be an indication of why they struggle with these aspects. This is especially in densely populated informal settlements and high-density residential areas like Bergman Square, Freedom Square, Phase 6, Mangaung, etc. see , where tFrom this study, it can be deduced that receptivity barriers remain a challenge wherever there is risk communication on or about COVID-19. However, imposing regulations and access to resources that accompany the dissemination of the risk messages might be the best approach. At least based on the significant number of the respondents who adhere to the regulations communicated, the study deduces that COVID-19 risk communication is successful. The media agenda and policy agenda are playing a positive role in influencing behaviour change. However, the public agenda especially the spread of fake news seems to affect the government’s efforts in mitigating and preventing the spread of the virus.The South African Government through the influence of the media should also encourage people to start adapting to the COVID-19 risk communication messages. And to take them as a new way of life. Through risk communication, the media must preach the theme ‘living with COVID-19’. This theme comes with the permanent enforcement of the currently communicated COVID-19 protection measures , which can become the new norm and global culture. This can be until a certain time that the virus has ended.Regardless of the role that the disaster management centres are supposed to play in responding to COVID-19, the study found that the local Mangaung Metropolitan Disaster Management Centre was not known by the respondents. It is therefore imperative for the disaster management centre to be more proactive, visible and assertive in their risk reduction activities and communication with the public. Knowledge of the local disaster management centre will assist people to know where to report non-compliance, and request for resources required to mitigate the spread of the virus. At the local level, the centre can further advocate through influential stakeholders the adherence to risk communication strategies imposed by the government. Risk communication should be pivotal in all disaster management operations and deserves the bigger part of the disaster management operations’ financial investment.


The development of custom cellular materials has been driven by recent advances in additive manufacturing and structural topological optimization. These contemporary materials with complex topologies have better structural efficiency than traditional materials. Particularly, truss-like cellular structures exhibit considerable potential for application in lightweight structures owing to their excellent strength-to-mass ratio. Along with being light, these materials can exhibit unprecedented vibration properties, such as the phononic bandgap, which prohibits the propagation of mechanical waves over certain frequency ranges. Consequently, they have been extensively investigated over the last few years, being the cores for sandwich panels among the most important potential applications of lattice-based cellular structures. This study aims to develop a methodology for optimizing the topology of sandwich panels using cellular truss cores for bandgap maximization. In particular, a methodology is developed for designing lightweight composite panels with vibration absorption properties, which would bring significant benefits in applications such as satellites, spacecraft, aircraft, ships, automobiles, etc. The phononic bandgap of a periodic sandwich structure with a square core topology is maximized by varying the material and the geometrical properties of the core under different configurations. The proposed optimization methodology considers smooth approximations of the objective function to avoid non-differentiability problems and implements an optimization approach based on the globally convergent method of moving asymptotes. The results show that it is feasible to design a sandwich panel using a cellular core with large phononic bandgaps. Various natural materials exhibit properties that cannot be realized in conventional materials employed in structural engineering. This is the result of the evolution of these natural materials over millions of years, resulting in an optimized architecture. As an example, the internal architecture of bones provides them with exceptional structural efficiency that has not be replicated with artificial materials.Despite the fact that the concept of elastic tailoring of materials is not new, the fabrication of cellular materials with increasingly complex topologies has been driven by the development of additive manufacturing technologies . MoreoveAmong the potential applications of lattice-based cellular materials, cores for sandwich panels are included . SandwicAdditive manufacturing techniques offer the possibility of manufacturing tailored cores for sandwich structures with superior properties than conventional foam or honeycomb cores. For example, it widens the range of possible core architectures; in addition, the cores can be designed to achieve multiple objectives . In addition to their attractive static properties, architected cellular materials can also be design to suppress the propagation of mechanical waves, which may be achieved via phononic bandgaps . PhononiJensen  was amonThe mentioned investigations show that by modifying the material or geometrical properties of the struts, the corresponding bandgaps of a lattice material can be manipulated. Accordingly, it becomes reasonable to implement topology optimization techniques to design lattice structures to meet desired bandgap properties. Several studies have investigated the use of topology optimization algorithms to obtain optimal configurations that yield the largest bandgap. However, these studies have focused mainly on the design of two-dimensional structures ,12.During topology optimization, the geometrical and/or material properties of each element in the design domain are designated as design variables. The optimization then finds the microstructure that maximizes the bandgap (distance) between two bands in the phononic band diagram. The first application of topology optimization for bandgap maximization was presented by Sigmund and Jensen , designiSome investigations have implemented multi-objective optimization strategies for phononic materials with mass or stiffness restrictions. Dong et al.  implemenEvolutionary optimization algorithms such as GAs are more eficient for problems with few design variables. As the computational cost of these algorithms largely depends on the number of design variables, the objective function must be evaluated several times. Therefore, in problems with a large amount of design variables, gradient-based optimization approaches are desirable, even with the possibility of obtaining local optima . GradienSandwich panels with lattice-based cellular cores are extremely desirable for applications that require multi-functional materials, such as lightweight ultra-stiff structural materials. The optimal cell that maximizes the stiffness/weight ratio in these type of panels has been investigated by ,34. HoweThe aim of this investigation is to develop an efficient methodology for designing ultralight sandwich panels with cellular truss cores and large phononic bandgaps. The phononic bandgap of a periodic sandwich structure with a regular core topology was maximized by varying both the material and the geometrical properties under different core configurations. The proposed optimization methodology considers smooth approximations of the objective function to avoid non-differentiability problems and uses the globally convergent method of moving asymptotes (GCMMA) optimization algorithm. The remainder of this article is organized as follows. L has been chosen to tile an infinite 2D space. The structure does not necessarily need to be a square, and it could possess a complex 2D [To understand periodic structure theory (PST), it is necessary to establish the equations used to describe the phenomenon of mechanical wave propagation in periodic materials. Let us consider a continuum two-dimensional (2D) structure, as shown in mplex 2D  or 3D [3mplex 2D  geometryThe periodic square geometry extracted from see ref. ). FigureX and M, and returns to The last important concept is the wave vector The path conducted by the vector is also referred to as the irreducible Brillouin zone (IBZ) . Accordiu(X+r)=u per node, which consist of three displacements in the axis directions u and ϕv . The uniD of a given bar r are then associated to the design variables The accuracy of the model is evaluated through a verification process based on the mesh refinement. Owing to geometrical considerations, this refinement will be achieved using a variable denoted as The PST is implemented to infinitely replicate the sandwich panel unit cells shown in j-th, the boundary conditions are defined asj-th layer and L represents all the nodes in the left edge, R is used for the right edge nodes, B for the bottom edge nodes, and T for the top edge nodes. The degrees of freedom over the corners are denoted by the subscripts j layer are arranged in the vector According to the methodology proposed by Langlet et al. , the infj-th layer is given byOwing to the Bloch relation, the number of independent degrees of freedom can be reduced by using the relationship Considering the assemblage of the entire sandwich cell structure using the finite element method, the matrix ith eigenvalue  and Poisson’s ratio , the optimization process may now be considered. The objective is to determine the unit cell that maximizes the bandgap, which is calculated by the distance between two consecutive bands An issue that may occur in eigenvalue optimization problems is the non-differentiability related to repeated eigenvalues . Other pj refers to the j-band.In both expressions, the first sum to n represents the maximum value for bands 1 to n. This indicates that all the eigenvalues in this range are considered. On the other hand, Equation  or M . The eigenvalue sensitivities are given by [In this study, a gradient-based solver is used to optimize the bandgap. The derivatives of Equations  and 16)16) with given by ,(20)dωie are denoted using r are given byHere, the matrices In this section, the optimization parameters and results are provided. The material and geometrical considerations are presented in The convergence of the optimization algorithm is investigated by using different combinations of The plots presented in A poor refinement quations –11), th, thη=3 wThe best bandgaps were obtained with the configurations First, the computation of the band diagrams for non-optimized cores is performed for reference. A topology with The best four normalized bandgaps obtained in the previous section are selected for calculating the band diagram and core topology. The bandgaps were selected using the refinement The band diagrams are shown in n is different (It should be noted that both bandgaps shown in The wider bandgap presented in All of the structures show a reinforcement on their corners, which use tungsten for those elements. A similar behavior was reported in previous literature for 2D structures ,25,26,32The design of sandwich structures with consideration of vibration reduction was examined in this study. In this regard, the core was optimized using phononic materials to obtain a wide phononic bandgap. Two design variables, namely the area and material properties, were selected for optimization. Different configurations were investigated to study how the number of cells affects the resulting bandgaps, as well as to identify which configuration provides the best results.The use of a normalized objective function in this problem allows the bandgap to appear at lower frequencies, because the absolute width of the bandgap is not crucial the location at which the bandgap is tuned is more important. The use of simplified structures with a low number of cells is desirable because a simplified manufacturing process may be used. If bandgaps with lower mean frequencies are required, the structure may need to be scaled. However, complex cores may be also required when the structure length is scaled rapidly.For further investigations, a study of these structure’s scalability is needed in order to consider the range of frequencies in which the bandgaps may be tuned. Moreover, in this study, only two materials with high contrast properties were used; research focused on improving bandgap properties in designs with two materials could be promising in future applications of these structures. This problems does not include structural performance in the formulation, so a multi-objective optimization including the strength, weight, thermal conductivity, among others aspects is a natural continuation of this work.


Most post-stroke patients experience varying degrees of impairment in upper limb function and fine motor skills. Occupational therapy (OT) with other rehabilitation trainings is beneficial in improving the strength and dexterity of the impaired upper limb. An accurate upper limb assessment should be conducted before prescribing upper limb OT programs. In this paper, we present a novel multisensor method for the assessment of upper limb movements that uses kinematics and physiological sensors to capture the movement of the limbs and the surface electromyogram (sEMG). These sensors are Kinect, inertial measurement unit (IMU), Xsens, and sEMG. The key assessment features of the proposed model are as follows: (1) classification of OT exercises into four classes, (2) evaluation of the quality and completion of the OT exercises, and (3) evaluation of the relationship between upper limb mobility and muscle strength in patients. According to experimental results, the overall accuracy for OT-based motion classification is 82.2%. In addition, the fusing of Kinect and Xsens data reveals that muscle strength is highly correlated with the data with a correlation coefficient (CC) of 0.88. As a result of this research, occupational therapy specialists will be able to provide early support discharge, which could alleviate the problem of the great stress that the healthcare system is experiencing today. Cerebrovascular accident also known as stroke has become the second most common cause of death and the leading cause of ongoing disabilities worldwide , 2. ChinIt has become increasingly important to assist stroke patients to become more independent through post-stroke rehabilitation. Occupational therapy (OT) is one of the important treatment modalities for post-stroke rehabilitation. OT refers to the application of focused and selected occupational activities to prevent or recover the loss of various degrees due to physical, mental, and developmental dysfunction or disability . It aimsIn previous studies, it has been proved that patients' motor function and self-care abilities are improved after they undergo OT. In , researcRichards et al. in their study pointed out that OT has positive impact on the health and QOL of people with disabilities; however, the demand for OT services globally is difficult to fulfill . OT currResearchers have widely used wearable sensors for upper and lower limb assessments –22. TablAn in-home upper limb rehabilitation system using two IMU sensors was proposed in , 16. TheResearch in  combinedIn previous research, the classification of OT actions overlooked the quality and completeness of the exercises. In addition, previous research did not investigate and correlate muscle strength with upper limb motion. An upper limb evaluation method that is capable of both assessment tasks is required. The findings of multisensor assessment methods motivate the investigation in this paper where a Kinect, IMU, and sEMG are used to assess upper extremities. In this research, we choose patients' kinematic data and also physiological data to assess upper limb motion during OT sessions. Kinect and Xsens are used to obtain the kinematic data whereas Biopac Multichannel Physiometer are used to capture an sEMG data. In this paper, Kinect and Xsens/IMU data are combined using a correlation measurement technique to determine the completion of specific OT action whereas sEMG is used for the classification of the OT actions. Using the three sensors' data, the proposed model can classify the OT action types and also to determine the completion of the actions during OT sessions.A multisensor assessment method for upper limb will be presented in this section. The proposed method used kinematic information and physiological data to classify the OT rehabilitation actions and also to determine the completion and quality of the actions during OT sessions. Azure Kinect is a depth camera sensor launched by Microsoft™. It provides human tracking software development kit (SDK), which can conveniently provide the spatial coordinates of human joints.The IMU sensor uses the Xsens Dot package developed by Xsens, which can provide acceleration and angular information with sampling frequency at 800 Hz. A precise time alignment between Kinect and Xsens data during OT exercises is crucial. Data from Kinect and Xsens include timestamps for each sample. The synchronization is performed by aligning the two sensors' timestamps. Data from both sensors are examined, synchronized, and fine-tuned manually. To prevent data loss during synchronization, extra data were collected before and after the OT exercises. The fusion of the two synchronized sensors can reduce the measurement error caused by the two sensors, thus improving the quality of kinematic data. Generally, sensor measurement inherits errors. To reduce the error, the idea of fusing two sensors' data is proposed. Xsens Dot uses related derivative software, Kinexyz, to provide the spatial coordinate data whereas Kinect directly calls the joint coordinate data using the method and data collection process shown in Kalman filter, is an algorithm, uses for optimal system's parameter estimation . It achiPatients' joint spatial movement data are acquired by the kinematic sensors including the Kinect and the Xsens for upper limb motion completion assessment analysis. The motion data are segmented into different motion group types for each patient and subject. The analysis in this method is based on the correlation between healthy subjects' standard motion and patients' motion for each OT action type. The CC represents the motion completion level for specific motion type performed by the patient. Dynamic time wrapping (DTW)  is used x1 is the healthy subject's fused kinematic movement data and x2 is the patient's fused kinematic movement data.where the variable The OT movement completion assessment is based on the significance of the Pearson CC. The scale of the CC is divided into multiple ranges to represent the correlation between the patient's spatial data and the healthy subject's spatial data. These ranges are used to score the completion of the limb's joint movement. The ranges and the corresponding scores are shown in Surface electromyogram (sEMG) is used for the classification of the OT actions. The classification process includes automatic sEMG segmentation, filtration, and classification. Automatic sEMG segmentation and classification will be presented in the next sections.via the Biopac MP150. The sEMG's electrodes were placed at the upper forelimb muscle group, at biceps brachii region and at the deltoid region. The preprocessed sEMG is used as input to the classification model, TextCNN model  and nine patients (mean age: 58.2 ± 4.8 years) were recruited in this study. Healthy participants were part of this investigation to help in setting an upper limb mobility features benchmark. Particularly, their sEMGs were used to determine the reliability of the modified TextCNN model, whereas their motion data were used to determine the completion and quality of the actions during OT sessions as part of the post-stroke patient assessment. The patients were recruited from the Second Hospital of Jiaxing, Zhejiang province, China. The data collection was approved by the ethics committees of the hospital, and the procedures were strictly followed to ensure the investigation complied with the Declaration of Helsinki. The patients were verified to be within the phase of stroke recovery using computer tomography (CT) or magnetic resonance imaging (MRI). The criteria for including the patients were as follows: (1) no hemodynamic instability; (2) no severe cognitive impairments; (3) no dementia; (4) no major post-stroke complication; and (5) able and willing to give consent. These criteria are necessary to minimize the effects of a slight age difference within the patients' group. The age difference among the recruited patients is small in comparison with other studies in the literature . For exaFour upper limb OT movement sets were performed by patients and healthy subjects. These OT movement actions aim to imitate various OT actions. The first set of action involves grabbing an object whose weight is fixed during all experiments. To perform this action, the patient must first stretch his/her arm while holding the object, then raise it over his/ her head at an angle of more than 45 degrees and slowly put it down. The second set of actions, pick up the object tremblingly, raise it to a certain height and lower it a little, then raise to a certain height and lower it again, and then raise it to the horizontal plane with an angle of >45 degrees and put it down. The third set of action is to grab the object straight with the arm lifted to the same horizontal plane for an angle of >20 degrees and then put it down, while the fourth set of action is to lift the object to a certain height, then move it horizontally to the right and then put it down. The four sets of OT actions are intended to simulate some daily activities to test a patient's motor skills. Characteristics of each group of OT actions are shown in The experiment protocol requested patients and subjects to perform at least 15 movements on each set of four OT actions; the overall number of actions per formed for the four groups is 60 times (15 × 4). Patients requested to rest for 2 s between each movement within the same OT action, then rest for 5 min after completing the 15 actions within the same OT action group. A total of 536 movements were recorded from nine patients, whereas 586 were recorded from healthy subjects.For evaluating the correlation between patients' muscle strength and their fused motion data, three senior rehabilitation specialists each with more than 20 years of clinical practice evaluated and graded the nine patients' muscle strength scores. Lovett muscle strength grading standard was used . In thisIn this paper, we proposed a multisensor-based upper limb movement assessment method. The main assessment features of the proposed method are as follows:A classification of patient's OT-based actions into four classes. Patients' sEMG data from the upper extremity were used in the OT-based multiclassification task.A measurement of quality of the patient's OT-based actions, OT action completion score. The assessment was conducted using Kinect and Xsens kinematic sensors.Evaluation of the relationship between upper limb mobility and muscle strength in patients.In the proposed kinematic fusion technique, the Kinect and Xsens data were fused to obtain the most accurate estimation of the upper limb's joint movements. The fused motion data are then segmented and grouped in four OT actions and scored for action completion. The automatically segmented and filtered sEMG is used to classify and label relevant OT actions. The results for the completion of OT actions and its classification are shown in the next subsection.A sEMG signal is used as input to the classification model, modified-improved TextCNN. The parameters of the modified-improved TextCNN were set to produce an optimum classification performance. For patient's data classification, the sEMG is preprocessed to obtain a 536*200*3 dataset for three sEMG channels. In the movement classification model, sEMG of patients was used during training and testing. A train-test split method was used to estimate the TextCNN's performance. In this study, 80% of the data were used for training and 20% for testing. The data for the tests were randomly selected. Due to the small number of patients, each patient repeated OT exercises multiple times. The size of the data in each OT action increases using this method, thus improving classification model performance and reliability. From In the proposed upper limb motion completion, we fused IMUs and Kinect data with KF to effectively mitigate errors of IMU and overcome the instability of Kinect. The fusion results agreed with the finding in . HealthyFrom As a result of the correlation between the average coefficient of each OT action and patients' muscle strength grade, it was found that action 2 has a high correlation with muscle strength grade. Actions 1 and 3 have a moderate correlation, whereas action 4 has a low correlation. A low correlation between action 4 and muscle strength is caused by the difficulty of this action and the impairment severity of the patient. Through this action, the horizontal movement can be aided to some degree by the waist offset. It is a result of the frequent vertical movement in the air that can determine the movement capability of the upper limbs. Since the CCs for patients 3, 7, and 9 are similar, we used a DTW algorithm to calculate the distance between their movement dataset and the corresponding dataset from the healthy subjects. In this paper, a multisensor-based upper limb movement assessment method during OT has been proposed. Two kinematic sensors were fused to evaluate the completion of OT actions. The preprocessed sEMG and the modified improved TextCNN classifier have been used to classify the four OT actions.The proposed upper limb OT actions' classification model has overall accuracy of 82.2 %. Kinect and Xsens fused data show a high correlation with muscle strength giving a CC of 0.88. Moreover, the experimental results show a high correlation between the patients' CC and their muscle strength grades.The proposed upper limb movement assessment method can provide feedback to post-stroke patients about their upper limb motor improvement during the OT procedure, without a rehabilitation specialist intervention, thus improving the effectiveness of in-home OT. In the future, the improvement of the classification accuracy will be investigated. Large dataset for various upper extremities' severity levels is required to improve the accuracy.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The experimental protocol was approved by the Ethics Committee of the Second Hospital of Jiaxing and was conducted in accordance with the Declaration of Helsinki. All participants signed a written informed consent before the experiment started. The patients/participants provided their written informed consent to participate in this study.SM, ZC, and QF conceived and designed the experiments. ZC and JF performed the experiments. SM, ZC, JF, XG, and QF analyzed the data. All authors participated in writing the paper.This research was supported by a Li Ka-shing Foundation Cross-Disciplinary Research Grant (2020LKSFG01C).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


To address the current COVID-19 and any future pandemic, we need robust, real-time, and population-scale collection and analysis of data. Rapid and comprehensive knowledge on the trends in reported symptoms in populations provides an earlier window into the progression of viral spread, and helps to predict the needs and timing of professional health care.The objective of this study was to use a Conformité Européenne (CE)-marked medical online symptom checker service, Omaolo, and validate the data against the national demand for COVID-19–related care to predict the pandemic progression in Finland.Our data comprised real-time Omaolo COVID-19 symptom checker responses  and daily admission counts in nationwide inpatient and outpatient registers provided by the Finnish Institute for Health and Welfare from March 16 to June 15, 2020 (the first wave of the pandemic in Finland). The symptom checker responses provide self-triage information input to a medically qualified algorithm that produces a personalized probability of having COVID-19, and provides graded recommendations for further actions. We trained linear regression and extreme gradient boosting (XGBoost) models together with F-score and mutual information feature preselectors to predict the admissions once a week, 1 week in advance.Our models reached a mean absolute percentage error between 24.2% and 36.4% in predicting the national daily patient admissions. The best result was achieved by combining both Omaolo and historical patient admission counts. Our best predictor was linear regression with mutual information as the feature preselector.Accurate short-term predictions of COVID-19 patient admissions can be made, and both symptom check questionnaires and daily admissions data contribute to the accuracy of the predictions. Thus, symptom checkers can be used to estimate the progression of the pandemic, which can be considered when predicting the health care burden in a future pandemic. The rapid spread of the SARS-CoV-2 virus leading to a pandemic presented challenges for nationwide assessment of the progression of the COVID-19 pandemic . In FinlDigital health technology tools such as symptom checkers have been used in different countries  as self-triage tools for possible SARS-CoV-2 infections -11. In FTo study if the data collected by the Omaolo service and the national care notification registers could be used to predict pandemic progression in Finland, we used the methods of machine learning. Perhaps the best potential of machine learning over more traditional methods lies in its ability to better adapt to the data, and thus to the evolution of the underlying phenomenon. With large data sets such as the Omaolo COVID-19 symptom checker responses, machine learning may also uncover more complex associations between the factors contributing to the predicted outcomes. Machine learning models can also be trained and retrained along the way to reveal how the significance of the individual input variables for making the predictions will change over time and to become a more accurate predictor as more data are collected.The study objective was to assess if a nationwide symptom checker can be used as a predictive tool in estimating the national progression of the COVID-19 pandemic and health care admissions by utilizing machine learning methods.The COVID-19 epidemic in Finland started in mid-March 2020. On March 16, 2020, the Finnish government announced a state of emergency due to the COVID-19 epidemic, and consequently implemented several physical distancing measures aimed at slowing the spread and protecting risk groups ,12. OmaoIn the symptom checker, the user answers a set of predefined, expert-validated questions. As a result, it returns self-triage information on how to proceed with one’s situation. The progress of filling in the questionnaire from start to finish is recorded to the log files of the service. The respondent has a choice to answer anonymously without including one’s personal information in the process. The questionnaire itself includes several background questions such as age, postal code, gender, and reason for filling in the questionnaire; existing medical conditions; whether the respondent has had close contact with a COVID-19–positive person; whether the respondent or close contacts have been ordered to quarantine by a physician; where the respondent thinks they may have caught the virus; and what kind of work the respondent does in regard to contacts with others  to give 2 full days to prepare for the weekend, for example by reassessing the need for extra resources and personnel.For training, testing, and validation of the models, we used time-series nested cross-validation . This stWe also chose to train the classifiers with Hilmo and Omaolo data first separately and then combined. This was to test how much, if any, the results would improve if the data from both sources were used.The Omaolo COVID-19 symptom checker achieved considerable popularity immediately after its release. Tens of thousands of responses per day were submitted during the first week. The submission activity showed clear peaks during infomercials and other major media mentions. To distinguish the users that were truly suspecting a COVID-19 exposure from the users that were visiting Omaolo just out of curiosity, a question about this matter was added to the questionnaire in the form of a simple tick-box on March 28, 2020. After this update, it was found that approximately 40% of the responses had the out-of-curiosity option checked. We then investigated whether it was possible to distinguish the two response profiles (out-of-curiosity or not) and which questions were the best predictors of this behavior. We used a naive Bayes classifier , and XGBAll three models were tested with 5-fold random cross-validation, and the sensitivity and specificity for each fold were computed and finally averaged over all folds. The size of the majority set (not out-of-curiosity) was balanced by undersampling to the size of the minority prior to cross-validation.All analyses, both the predictions and profiling, were performed using Python version 3.6  with theOur study data are based on statistical register data at the national level. These register data contain no personalizing identifiers. Therefore, this study does not fall under the purview of laws regarding medical research. The study protocol does not violate any ethical considerations or standards, according to a statement from the Medical Ethics Committee of the Hospital District of Helsinki and Uusimaa in Finland (June 2013).Women used the web-based symptom checker more often than men; approximately two-thirds of the filled-in forms were completed by women . People For the analyses, we chose two regressors, linear regression and XGBoost, with three feature preselection strategies for each, and compared their performance. To predict the COVID-19–related admissions for each day, 7 days ahead of the prediction point, the features given to the model were extracted from the responses and the Hilmo register on the 7 and 14 days prior (lag variables). The use of lag variables essentially means that two sets of the time-dependent features were formed, with the first delayed 7 days and the second delayed 14 days. This was to ensure that no data from any of the sources, Omaolo or Hilmo, were leaked past the point of prediction during feature selection, model training, or testing. The regressors were first trained with 5 expert-selected features: how many of the questionnaires were filled-in by people over 60 years old, how many reported lengths of symptoms were greater than 10 days, and how many were assigned the urgency code P1 (the most urgent) in the care recommendations, in addition to the number of COVID-19–related admissions. The feature preselectors F-score and mutual information were added later.The predictions made by both models for data gathered between March 16, 2020, and June 15, 2020, were compared to the true admission count . The firTo check that the Omaolo questionnaire data are relevant for the predictions, we compared the error of both models with and without the questionnaire data, and only with the questionnaire data . Both moIn addition to expert-selected features, we tested automated feature selection methods that select the top 8 features based on the F-score or mutual information of the feature with the predicted variable (number of admissions on a given day) . The F-sOlder age groups, who are more likely to have a severe form of COVID-19 and hence be admitted to hospital, are underrepresented in the Omaolo questionnaire data. This could affect the performance of the models, as it is more difficult for the models to learn from imbalanced training data. To assess the problem, oversampling of the underrepresented age groups was performed to see if it would decrease the error of the models . ResamplThe three different models produced the following sensitivity and specificity for detecting users that answered they were using the service out of curiosity: 0.622 and 0.367, respectively, for the naive Bayes classifier; 0.665 and 0.332, respectively, for logistic regression; and 0.607 and 0.388, respectively, for the XGBoost binary classifier. These results were acquired by maximizing the number of correct classifications.In this study, we examined whether it is possible to predict the national epidemic progression and the burden of health care using machine learning methods with real-life data on symptoms and usage of health care. The main finding of this study is that it is possible to predict national health care admissions related to COVID-19 using a symptom checker combined with register data by using machine learning methods with considerable accuracy . These methods were tested in a scenario where the predictions were made 1 week ahead, once per week. The best model was achieved using the symptom checker data combined with register data (MAPE 24.23%). This result was reached by using linear regression with mutual information as the feature preselector. All tested models and combinations of feature preselectors and models were able to produce predictions that followed the true epidemic progression . OverallFinally, predictions that can follow the progression can be made using either Omaolo symptom checks or historic Hilmo counts separately. However, the best results were achieved by combining both. Adding Omaolo to Hilmo counts reduced the MAPE of linear regression from 34.98% to 31.33% and that of XGBoost from 46.63% to 31.78%. These results suggest that Omaolo contains information of the pandemic progression that is not present in Hilmo alone.Oversampling the data to balance the regional differences between the Omaolo users and general population seemed to produce conflicting results: marginal gain with linear regression but loss of accuracy with XGBoost. Oversampling leads to added complexity in the analysis pipeline, and without a clear benefit, its use is hard to justify.The answer profiles of those using the COVID-19 symptom checker out of curiosity were very similar to those of the other users, and no reliable classification between the groups could be made by any of the tested models. Neither of the groups reported longer or more serious symptoms over the other. The only striking difference between the groups was that there appeared to be more out-of-curiosity responses during high service utilization such as after television infomercials.We also considered using the daily number of phone calls received at the 116117 Medical Helpline service. The 116117 Medical Helpline provides professional assistance on health care–related topics in urgent, but nonemergency cases to over 4 million Finns in extended business hours. However, since it was not possible to extract the topic of the call, whether they were COVID-19–related or not, and since the calls could be localized with much poorer resolution than with the rest of the sources, this data set was eventually dropped from the analyses.The Google Trends  of populThis study had several potential limitations. A considerable number of responses  were not saved during the study period. Nearly all of these were submitted during the first 2 weeks of the study, complicating the analysis for the first month. This may have contributed to the relatively poor prediction accuracy for the related weeks by delaying the convergence of the regressors to the true admission count.The true admission count showed a strong diminishing trend toward the end of our study period. During the last weeks of the period, there were days when only a few dozen new admissions were recorded nationally. Because our error metric, MAPE, is relative to the true values, if these values are small, error values will appear high even though the absolute error between true and predicted counts remains low. Despite this, we decided to use MAPE for its intuitiveness, wide use, and easy comparability of the error between the days, weeks, and methods.At the beginning, the survey did not include an item about the motive to fill in the survey . This adds some additional forms to our data that do not reflect the situation at hand. The proportion of responses filled-in out of curiosity remained remarkably stable at around 40% throughout the study period. Moreover, the results of trying to separate the responses filled-in out of curiosity from the rest with binary classifiers  failed to reveal any meaningful differences between the answer profiles of these groups. Thus, we did not find a justification to remove these responses from the analyses or handle them differently than the rest of responses (not filled-in out of curiosity).The data available for this study contained COVID-19–related admissions data with a steady downward trend, and it would have been interesting to see if the models could predict a reversal of the trend before it occurs. However, there is weekly variability in the admissions and the models learned this pattern well. An interesting deviation from the weekly pattern was Ascension Day on Thursday, May 21, which was a public holiday in Finland. The models did not have enough training data on admissions on public holidays on a weekday to predict a similar dip in the admissions as on weekends, although longer data sets could allow the models to learn this pattern as well.Much of the prediction errors took place during days that showed sharp peaks of increased or decreased demand that were not immediately explainable with the data available. Some of these errors may be due to technical reasons such as a major care provider suffering an error on one day and reporting higher counts on the following day. Naturally, these kinds of special events cannot be learned from the admission count data alone. On a positive note, the developed models appear robust and thus not susceptible to these kinds of anomalies.One could also question if biological tests make other monitoring redundant. While in many countries biological tests are performed to follow the pandemic, it is important to note that online symptom checking does not replace the need for biological testing but provides a different and valuable perspective. In many countries, including Finland, the testing services are saturated, and only some population groups get tested, making our picture of the pandemic progression biased. Biological testing further comes with an immense cost, particularly with an exponential rise in cases.The symptom checker, in turn, can be filled in anytime and by large numbers. Furthermore, it can be continuously updated to include the most relevant questions. It is possible that the health care burden is going to change even quite rapidly, and we can see that using Omaolo surveys, even without an indication from the time-series data. Additionally, the machine learning models are trained every week, allowing adaptability to changes in the statistical relationship between the predictors and the predicted variable as the pandemic progresses.Furthermore, while our data only cover the first wave of the pandemic, the results remain important from the perspective of early decision-making against a new threat, and overcoming the challenge on modeling a novel phenomenon from the start with no history. Additionally, we do not only validate the data, but our approach further enables the prediction and modeling of the pandemic. Emergence of vaccinations, new variants, new policies, and restrictions will all affect the progression of the pandemic. These changes have also affected Omaolo, which has been continuously updated . Our prediction models are also upgradeable continuously; thus, the drift in data and concept can be mitigated on the go, and features that will drop or rise in importance can be monitored along with the actual result of the prediction.Finally, the lack of reported anosmia could suggest a lack of specificity. In our data, anosmia is a rarely reported symptom. It is also a known problem that without a specific test, COVID-19 is notoriously hard to distinguish from a common flu. Despite the relatively small number of reported anosmia cases, we have other features that have been shown to be highly significant in predicting International Classification of Diseases-10– and International Classification of Primary Care-2–coded admissions. These include features such as how many of the questionnaires were filled-in by people over 60 years old, how many reported lengths of symptoms were greater than 10 days, and how many were assigned an urgency code P1 (the most urgent) in the care recommendations.Alternatively, if anosmia is important for specificity, it is likely that we have more false positives and our final models are less accurate. In other words, with the key symptoms, the precision of the model is improved and the error is smaller. The consequence of such a lack depends on how the results are used. If the burden of health care were solely estimated based on these models, we might underestimate the need for health care. However, it is unlikely that the model would be used to define any absolute health care need, whereas it can provide an indication to prepare for an increased health care burden., which is a CE-marked medical device complying with the Medical Device Directive, used by health care and social service professionals [The study also has several strengths. We had access to a nationwide online symptom checker data source, Omaolossionals . We alsoIn addition, a clear advantage of machine learning methods is that both the model section and fit are automatized. This means that the prediction method adapts in the face of any new data and attempts to make as accurate predictions as possible with the data in use at any given time. However, in case the phenomenon in question changes notably, changes in data use and sources and machine learning methods are also required.Our study shows that COVID-19–related health care admissions in the short term can be predicted with considerable accuracy using symptom checker data combined with register data based on machine learning methods. This type of approach could help health care providers better assess the burden of the health care system in advance, which would make resource allocation more predictable. Furthermore, we consider that this type of approach could also be implemented in different stages of the pandemic and in future pandemics as well.