Royal Society of Medicine, Colt Foundation Research & Clinical Innovation Meeting 2022

Correspondence to Wg Cdr Robert M Barker-Davies, Academic Department of Military Rehabilitation, Defence Medical Rehabilitation Centre Stanford Hall, Loughborough, LE12 5BR, UK;Robert.barker-davies@nhs.net The Royal Society of Medicine (RSM–Military Medicine Section), Colt Foundation Research & Clinical Innovation Meeting has been at the core of Defence Medical Services (DMS) academic activity since its inception in 2007. Initially, the meeting sought submissions from medical officers, typically undertaking higher degrees, but in recent years the scope of the meeting has been broadened to include all healthcare professional disciplines.1 2 This reflects the strategic directions of both UK Healthcare and the DMS towards greater interdisciplinary healthcare delivery models and integrated research3 in which shared role understanding and supportive team structures have been identified as themes that promote collaborative success in military medical teams.4 The top six s for the Colt Research prize, published here in BMJ Military Health, reflect this diversity as they span several disciplines and both academic and Defence institutions. [...]we heard from Captain Kat Matthews, army physiotherapist and professional triathlete.

Indoor Airflow Dynamics in Compartmentalized Pneumology Units Equipped with Variable-Thickness MERV-13 Filters

Infection containment in the post-pandemic scenario became a top priority for healthcare engineering control staffers, especially in pneumology sectors, where the treatment of airborne infectious diseases is frequent. In Brazil, where COVID-19 left a long record of casualties, there is a lack of information on the influence of filtration systems on the maintenance of regulated operational conditions for indoor comfort in hospital environments. This paper has the following objectives: to study arrangements of filtering systems in hospital acclimatization ducts;to verify how filtering characteristics could compromise safety regulations for airflow in hospital environments;and to identify airflow stagnation points that might favor suspended viral concentrations and increase contamination risks. We used the computational fluid dynamics STAR-CCM+© software to perform numerical simulations of different cases of indoor airflow in a model corresponding to a sector of the Lauro Wanderley University Hospital (João Pessoa city, Brazil). We concluded that standards for maximum velocity are reachable despite thinner or thicker filters affecting the spread of the air. In this way, acclimatization systems are limited by a tradeoff between regulation and protection. Our findings are relevant to future technological development, interventions, safety strategies amidst contamination scenarios, and new filtration arrangements in hospital environments.

A literature review of dual disaster challenges for resilient office building toward reducing disaster risks

Flood is acknowledged as the most common natural disaster in several parts of the globe. For Malaysia, flood is considered as the most frequent natural disaster, with the frequency of at least once a year. The risks of flood can be seen through property loss and damages, infrastructure casualties, and disruption to socio-economic activities. Adding to that, Malaysia also faced the flood during the pandemic when it hit several states, namely Pahang, Johor, and Kelantan, during the monsoon season in 2020 and 2021, amid the rising cases of Covid-19. The ongoing COVID-19 pandemic has posed significant challenges for disaster response, calling for the “new norms” to be quickly established for better disaster risk reduction. Several mitigation strategies have been taken by the government to reduce the risks of floods in Malaysia. As office buildings possess important roles in delivering effective services to the public while maintaining their critical documents, this paper focuses on providing related literature on building resilience. Having an early understanding of the core elements of office building resilience in dual disaster challenges will provide the basis for further investigation in the later stage of data collection.

Fusing XGBoost and SHAP Models for Maritime Accident Prediction and Causality Interpretability Analysis

In order to prevent safety risks, control marine accidents and improve the overall safety of marine navigation, this study established a marine accident prediction model. The influences of management characteristics, environmental characteristics, personnel characteristics, ship characteristics, pilotage characteristics, wharf characteristics and other factors on the safety risk of maritime navigation are discussed. Based on the official data of Zhejiang Maritime Bureau, the extreme gradient boosting (XGBoost) algorithm was used to construct a maritime accident classification prediction model, and the explainable machine learning framework SHAP was used to analyze the causal factors of accident risk and the contribution of each feature to the occurrence of maritime accidents. The results show that the XGBoost algorithm can accurately predict the accident types of maritime accidents with an accuracy, precision and recall rate of 97.14%. The crew factor is an important factor affecting the safety risk of maritime navigation, whereas maintaining the equipment and facilities in good condition and improving the management level of shipping companies have positive effects on improving maritime safety. By explaining the correlation between maritime accident characteristics and maritime accidents, this study can provide scientific guidance for maritime management departments and ship companies regarding the control or management of maritime accident prevention.

Pandemic covers up setbacks in Brazilian environmental laws

Speed read Legislative changes in Brazil have ‘weakened environmental regulations’ Legal deregulation peaked at the height of the COVID-19 pandemic, study found Brazilian society, scientists and media may have mitigated the damage Environmental progress in Brazil looks set to be one of the casualties of COVID-19, with harmful changes to the law being pushed through while attention is diverted by the global pandemic, a study warns. Since January 2019, the Brazilian government approved 57 pieces of legislation that effectively weaken national environmental laws, the study published in Biological Conservation found. Researchers also analysed monthly deforestation rates provided by the National Institute for Space Research (INPE) of Brazil, and fines associated with illegal deforestation, issued by the Brazilian Institute of Environment and Renewable Natural Resources (IBAMA). According to INPE, deforestation in the Amazon was 9.5 per cent higher between August 2019 and July 2020 than between August 2018 and July 2019 – making it the highest level of annual deforestation since 2008.

Public Acceptability of Environmentally Linked Congestion and Parking Charging Policies in Greek Urban Centers

Nowadays, urban centers face the challenge to upgrade life quality by reducing traffic congestion, air pollution emissions and road casualties. Transport charging policies applied in cities at a quick pace are a key tool for sustainable mobility. However, public acceptability is an important precondition to be adopted for such policies. In this context, the scope of this paper is the investigation of the public acceptability of environmentally linked urban charging policies in Greek urban centers. Specifically, the paper’s objective is the investigation of Greek drivers’ acceptability of the implementation of a congestion charging policy and a parking charging policy with the charging being adjusted according to the Euro class and technology of the vehicle in favor of less polluting cars. A structural equation model (SEM) was developed using data from a questionnaire survey which provided a sample encompassing 733 respondent drivers from three main urban centers of Greece: Athens, Thessaloniki and Volos. Several statistical relationships were detected and quantified correlating the two examined urban environmental charging policies with five latent unobserved variables. Based on the results, public acceptability of environmental congestion charging policies and the public acceptability of environmental parking charging policies were found to be positively correlated with each other, meaning that a driver who supports one environmentally linked transport charging policy is more likely to support the other one as well. The environmental sensitivity and high commuting profiles of drivers are influential factors that positively affect the acceptability of the two examined transport charging policies’ implementation in Greek urban centers. Analysis has also shown that younger, higher-educated respondents and females are more likely to accept the environmental charging policies under consideration.

The Sahel: Challenges and opportunities

Since 2015, over 2000 people have been killed and 1.5 million displaced due to violence attributed to extremist groups in Burkina Faso. In the first half of 2021 alone, over 540 conflict-related civilian casualties were reported in Niger.9 The armed conflict has had a devastating impact on children in Niger: of the 3.8 million people in need of humanitarian aid in Niger, 2.1 million are children and 1.6 million children suffer from malnutrition.10 More than eighty children between the ages of 15 and 17 years living in towns on the Niger–Burkina Faso border have reportedly been recruited as child soldiers.11 Over sixty children were killed in conflict-related violence in Niger in 2021 alone.12 Chad has also witnessed its fair share of violence and intercommunal tensions. In their efforts to coerce the Sahelian people and government decision-makers for ransom or political concessions, these groups have employed various terrorist activities, including launching deadly attacks against civilians and military targets alike, attacking public and private property, kidnapping individuals, and more.18 Weak State institutions around the inter-State borders have enabled such groups to flourish in the peripheries and border towns, targeting people in multiple countries at once. Burkina Faso, Mali and Niger saw over 4000 casualties of terrorist attacks in 2019;19 these attacks led, in turn, to the displacement of over half a million people.20 The growing presence of “terrorist” groups in the Sahel has also intensified organized crime and criminal networks that have served as routes for lucrative criminal activities such as drugs, arms trade, human trafficking and the kidnapping of persons for ransom.

Behavioral Compliance Effect of Covid-19 Cases and Casualties and Perceived Community Risk

Besides the physical distancing, other policy that can be applied is targeted lockdown. [...]the government should take more consideration in implementing the non-pharmaceutical intervention. The method used in this study is linear regression utilizes data on COVID-19 cases and deaths from KawalCovid (kawal.covid19.id), a national COVID-19 data with province-level detail data, COVID-19 Beliefs, Behaviors & Norms Survey published by Massachusetts Institute of Technology. Xi = logarithm of 2-week lag moving average (MA) new cases X2 = logarithm of 2-week lag moving average (MA) new deaths X3 = dummy variable when people perceived their community has high risk of transmission X4 = dummy variable when people perceived their community has low risk of transmission 4.RESULTS We performed a correlation test on people's behavior by utilizing MIT beliefs and behavior survey which shown in Table 1.

Efficient Cloaked Face Recognition Methodology throughout The Covid-19 Pandemic

The COVID-19 is a partner in Nursing unequaled disaster resulting in a huge range of casualties and protection issues. to cut back the unfold of coronavirus, individuals typically wear masks to guard themselves. This makes face popularity a truly tough project because bound components of the face rectangular measure hidden. A primary awareness of researchers for the duration of the continuing coronavirus pandemic is to come back up with hints to handle this downside thru fast and reasonably-priced solutions. during this paper, we tend to endorse a dependable technique supported by discard cloaked region and deep learning-based options to deal with the matter of the cloaked face recognition technique. the number one step is to discard the cloaked face vicinity. next, we tend to apply pre-trained deep Convolutional neural networks (CNN) to extract the only options from the received areas (in general eyes and forehead regions). in the end, the Bag-of-features paradigm is carried out on the function maps of the last convolutional layer to quantize them and to induce small illustration scrutiny to the simply related layer of classical CNN. in the end, Multilayer Perceptron (MLP) is implemented for the class approach. Experimental effects on real-global-Masked-Face-Dataset display high popularity overall performance.

Motorcycle Accident Casualties During the COVID-19 Epidemic in Taiwan

A descriptive time series study of casualties from motorcycle accidents in Taiwan between 2016 and 2020. The data on casualties were obtained from the road safety information system provided by the Ministry of Transportation and Communications. Between 2016 and 2020 the casualties increased from 2,571 to 3,191 (an increase of 241% in casualty rates during the period studied). High casualty rates in 2020 were observed in Taiwan. There was a significant increase in motorcycle accident casualty rates for the country as a whole during the studied period. © 2022 IEEE.

Assessing Rehabilitation Outcomes after Severe Neuromusculoskeletal Injury: Development of Patient Reported Outcomes Assessment Instruments

Currently, there is a paucity of patient reported outcomes (PRO) measures of secondary health effects and complications that result from neuromusculoskeletal injuries, which greatly limits the clinical care and successful rehabilitation, reintegration, and return to duty/work of injured individuals. This study will create valid, standardized, psychometrically robust, and clinically useful PRO measures for traits and symptoms relevant to understanding quality of life and the health and rehabilitation outcomes of Wounded Warriors and civilians with neuromusculoskeletal trauma. Furthermore, this study will develop clinical score reports in an actionable format to improve the clinical workflow and standard of care for individuals with traumatic limb injuries. During year three, we successfully prepared for and launched large-scale field testing. To date, 159 participants have begun baseline and 154 have completed the baseline interviews. Work to plan for data analyses is underway. We have also worked this year amid challenges from the COVID-19 pandemic, which affected our daily operations and caused the pace of recruitment to slow. However, we have established processes for remote recruitment, consenting, and data collection. We have identified several additional sites that have eligible participants and are willing to recruit them, to help increase recruitment throughput.

Autologous Hematopoietic Stem Cell Transplantation to Prevent Antibody Mediated Rejection After Vascularized Composite Allotransplantation

Close to 40 of combat injuries sustained in Operation Iraqi Freedom and Operation Enduring Freedom involve severe extremity and craniofacial trauma. For many devastating injuries where conventional reconstruction is not possible, vascularized composite allotransplantation (VCA) has become a viable alternative, providing new, exciting options for Wounded Warriors that could better restore the appearance, anatomy, and function. However, clinical management of these injuries prior to reconstruction frequently requires multiple blood transfusion or skin allografts resulting in the formation of alloantibodies (anti-HLA IgG Abs, donor specific antibodies or DSA) and a high degree of sensitization. The role of DSA and mechanisms of antibody mediated rejection (AMR) in VCA are still largely unknown. To date, there is only one single experimental study published that has recently attempted to define the role of DSA in a rat model of vascularized osteomyocutaneous flap allotransplantation. As such, this project aims to comprehensively investigate the mechanisms and impact of pre-existing and de-novo DSA and AMR in in VCA. The goal is to develop a clinically translatable desensitization protocol that will subsequently broaden the eligible population for reconstructive transplantation to include those patients who have become sensitized to foreign antibodies

Risk of investment losses from operations and casualties and insurance coverage decisions

Family business firms, like other firms, face internal and external risks such as accidents, errors, omissions, fire, unexpected casualties, natural disasters, the COVID-19 pandemic, etc., that can lead to investment losses. This study aimed to investigate the impact of the perceived risk of investment losses from operations (RISK_ILFO) and the perceived risk of investment losses from casualties (RISK_ILFC) on the commercial insurance coverage (CIC) decision of family business owners in India. This study utilized a survey research design to collect data from the owners of micro, small, and medium-sized family business firms located in India. The research participants were asked about their perceptions of the impact of RISK ILFO and RISKILFC on CIC. Findings show that perceived RISK_ILFO and RISK_ILFC positively impact CIC decision of family business owners. The results also show that the impact of perceived RISK_ILFO on CIC is higher than the impact of perceived RISK_ILFC on CIC. This study contributes to the literature on the relationship between the risk of investment losses and insurance coverage decisions. Insurance planners may find results useful to provide suggestions to family business owners on commercial insurance coverage. Research scholars may find empirical results useful to develop further studies in risk management and insurance areas. Family business owners may find the results useful to mitigate the risk of investment losses.

Multi-Disaster Management Strategy in Indonesia

Managing natural disasters amid the Covid-19 pandemic needs a special strategic approach. Indonesia, as a natural disaster-prone country, is now facing a problem due to areas with high vulnerability and a lack of disaster literacy. A multi-disaster e.g., the occurrence of natural disasters in the middle of Covid-19) management strategy is badly needed to face up to this situation. This paper offers a multi-disaster management strategy based on empirical experience as a reference for stakeholders, especially government at all levels. Five strategies are offered to overcome the impacts of a multi-disaster event, especially in natural disaster-prone areas with high Covid-19 case rates. Strengthening government institutions is the first priority to be addressed as coordination among government at all levels (from central to regency and city governments) will minimize the casualties. Logistic and infrastructure arrangements should be well managed to avoid any panic and chaos during multi-disasters. Recruitment of volunteers could be very helpful during the emergency response stage. Provision of evacuation sites and temporary shelters that meet the Covid-19 requirements is a must to prevent virus spread during evacuation and emergency response stages. Last but not least, increasing individual disaster literacy should be considered an essential component of a multi-disaster management strategy. Finally, well-managed coordination between all stakeholders is paramount, especially in natural disaster-prone areas with a high level of Covid-19 spread.

Linear and non-linear dynamics of the epidemics: System identification based parametric prediction models for the pandemic outbreaks.

Coronavirus disease 2019 (COVID-19) has endured constituting formidable economic, social, educational, and phycological challenges for the societies. Moreover, during pandemic outbreaks, the hospitals are overwhelmed with patients requiring more intensive care units and intubation equipment. Therein, to cope with these urgent healthcare demands, the state authorities seek ways to develop policies based on the estimated future casualties. These policies are mainly non-pharmacological policies including the restrictions, curfews, closures, and lockdowns. In this paper, we construct three model structures of the SpInItIbD-N (suspicious Sp, infected In, intensive care It, intubated Ib, and dead D together with the non-pharmacological policies N) holding two key targets. The first one is to predict the future COVID-19 casualties including the intensive care and intubated ones, which directly determine the need for urgent healthcare facilities, and the second one is to analyse the linear and non-linear dynamics of the COVID-19 pandemic under the non-pharmacological policies. In this respect, we have modified the non-pharmacological policies and incorporated them within the models whose parameters are learned from the available data. The trained models with the data released by the Turkish Health Ministry confirmed that the linear SpInItIbD-N model yields more accurate results under the imposed non-pharmacological policies. It is important to note that the non-pharmacological policies have a damping effect on the pandemic casualties and this can dominate the non-linear dynamics. Herein, a model without pharmacological or non-pharmacological policies might have more dominant non-linear dynamics. In addition, the paper considers two machine learning approaches to optimize the unknown parameters of the constructed models. The results show that the recursive neural network has superior performance for learning nonlinear dynamics. However, the batch least squares outperforms in the presence of linear dynamics and stochastic data. The estimated future pandemic casualties with the linear SpInItIbD-N model confirm that the suspicious, infected, and dead casualties converge to zero from 200000, 1400, 200 casualties, respectively. The convergences occur in 120 days under the current conditions.

Management of mass casualties due to COVID-19: handling the dead.

A high number of fatalities can occur during major disasters or during events like the COVID-19 pandemic. In a natural disaster, the dead must be removed from disaster sites while rescue work is in progress; otherwise, the health and safety of the community are threatened. The COVID-19 pandemic is analogous to a natural disaster with mass casualties where the disaster sites are hospitals with morgues that are overwhelmed. As the number of the deceased rise rapidly and hospital morgues are at their full capacity, hospitals use what is called a Body Collection Point (BCP). BCP is defined as a temporary refrigeration unit used to store decedents until transport is arranged. Decedents should always be handled in a manner denoting respect, and provisions and management of resources should be properly mobilized to ensure this. Contingency plans must be created to prepare for worsening of the disaster that further overwhelms the capacity of the health care systems.

Unknown uncertainties in the COVID-19 pandemic: Multi-dimensional identification and mathematical modelling for the analysis and estimation of the casualties.

Insights about the dominant dynamics, coupled structures and the unknown uncertainties of the pandemic diseases play an important role in determining the future characteristics of the pandemic diseases. To enhance the prediction capabilities of the models, properties of the unknown uncertainties in the pandemic disease, which can be utterly random, or function of the system dynamics, or it can be correlated with an unknown function, should be determined. The known structures and amount of the uncertainties can also help the state authorities to improve the policies based on the recognized source of the uncertainties. For instance, the uncertainties correlated with an unknown function imply existence of an undetected factor in the casualties. In this paper, we extend the SpID-N (Suspicious-Infected-Death with non-pharmacological policies) model as in the form of MIMO (Multi-Input-Multi-Output) structure by adding the multi-dimensional unknown uncertainties. The results confirm that the infected and death sub-models mostly have random uncertainties (due undetected casualties) whereas the suspicious sub-model has uncertainties correlated with the internal dynamics (governmental policy of increasing the number of the daily tests) for Turkey. However, since the developed MIMO model parameters are learned from the data (daily reported casualties), it can be easily adapted for other countries. Obtained model with the corresponding uncertainties predicts a distinctive second peak where the number of deaths, infected and suspicious casualties disappear in 240, 290, and more than 300 days, respectively, for Turkey.

Development of a Multi-Dimensional Parametric Model With Non-Pharmacological Policies for Predicting the COVID-19 Pandemic Casualties.

Coronavirus Disease 2019 (COVID-19) has spread the world resulting in detrimental effects on human health, lives, societies, and economies. The state authorities mostly take non-pharmacological actions against the outbreak since there are no confirmed vaccines or treatments yet. In this paper, we developed Suspicious-Infected-Death with Non-Pharmacological policies (SpID-N) model to analyze the properties of the COVID-19 casualties and also estimate the future behavior of the outbreak. We can state the key contributions of the paper with three folds. Firstly, we propose the SpID-N model covering the higher-order internal dynamics which cause the peaks in the casualties. Secondly, we parametrize the non-pharmacological policies such as the curfews on people with chronic disease, people age over 65, people age under 20, restrictions on the weekends and holidays, and closure of the schools and universities. Thirdly, we explicitly incorporate the internal and coupled dynamics of the model with these multi-dimensional non-pharmacological policies. The corresponding higher-order and strongly coupled model has utterly unknown parameters and we construct a batch type Least Square (LS) based optimization algorithm to learn these unknown parameters from the available data. The parametric model and the predicted future casualties are analyzed extensively.

A Novel Parametric Model for the Prediction and Analysis of the COVID-19 Casualties.

Coronavirus disease (COVID-19) outbreak has affected billions of people, where millions of them have been infected and thousands of them have lost their lives. In addition, to constraint the spread of the virus, economies have been shut down, curfews and restrictions have interrupted the social lives. Currently, the key question in minds is the future impacts of the virus on the people. It is a fact that the parametric modelling and analyses of the pandemic viruses are able to provide crucial information about the character and also future behaviour of the viruses. This paper initially reviews and analyses the Susceptible-Infected-Recovered (SIR) model, which is extensively considered for the estimation of the COVID-19 casualties. Then, this paper introduces a novel comprehensive higher-order, multi-dimensional, strongly coupled, and parametric Suspicious-Infected-Death (SpID) model. The mathematical analysis results performed by using the casualties in Turkey show that the COVID-19 dynamics are inside the slightly oscillatory, stable (bounded) region, although some of the dynamics are close to the instability region (unbounded). However, analysis with the data just after lifting the restrictions reveals that the dynamics of the COVID-19 are moderately unstable, which would blow up if no actions are taken. The developed model estimates that the number of the infected and death individuals will converge zero around 300 days whereas the number of the suspicious individuals will require about a thousand days to be minimized under the current conditions. Even though the developed model is used to estimate the casualties in Turkey, it can be easily trained with the data from the other countries and used for the estimation of the corresponding COVID-19 casualties.

Preparing for Mass Casualty Events.

There are many forms of mass casualty events, including mass trauma, exposure to radiation and chemical agents, biological exposure (pandemics and bioterrorism), or a combination of insults. The underlying theme is that the number and severity of patients outstrip the resources available. These events are more common than thought. Planning and preparation allow institutions to better respond to the patient surge and thus mitigate the consequences-ultimately saving lives and reducing morbidity.