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Public health policies are decisions and practices that shape the accessibility of individuals to health services and the quality and financing of these services. These policies define the mode of delivery of health services, who will provide these services and how they will be financed. Proposals on Public Health Policies have been updated from the past to the present. This study aims to evaluate the suggestions of Dr Milaslı İsmail Hakkı, who has many works in the fields of language, religion and medicine, who has been among the scientists who have transferred his suggestions for the benefit of all humanity and included these suggestions in his ongoing studies until today, about public health policies. The study was subjected to content analysis, one of the qualitative research methods. Document analysis was conducted in order to evaluate the research. In this context, Dr Milaslı İsmail Hakkı's medical works were examined, and his suggestions on Public Health Policies were evaluated. [ABSTRACT FROM AUTHOR]

Background. The article discusses the problem of the impact of epidemic diseases on ethnogenesis. As an ancient agricultural ethnic group in contact with nomadic and Mediterranean populations, the Adyghe were formed during periods of frequent epidemics of dangerous diseases caused by the process of animal domestication and subsequent impact of anthropogenic factors. The devastating effect of smallpox and measles pandemics was felt following the establishment of intensive trade exchange during the 2nd century CE by the Roman and the Han empires. The first plague pandemic (beginning with the “Justinianic Plague”, 542–543) mostly affected the Balkans and the Eastern Mediterranean. The second plague pandemic, the so-called “Black Death” (1346–1353), which appeared in the center of the Mongol Empire, quickly spread via caravan routes across most of Eurasia. The scale of the epidemic was influenced by the pathogenic complex that had developed by the 14th century, which included humans, the bacterial pathogenic agent itself, and its carriers (large rodents especially black rats, fleas). Factors such as famine caused by overpopulation, low standards of personal and public hygiene, the prevalence of wooden buildings in cities and towns, the fur trade, etc., also had contributory effects. Objectives. The study set out to identify the natural and the socio-cultural factors that influenced the spread of the plague and the smallpox epidemics in the North-West Caucasus. Methods. The data on which this study is based are formed from collections of narrative sources (Russian, Arabic-Persian, European), archaeological and historical-anthropological works, medical and genetic studies (electronic platforms: eLIBRARY.RU, Academia.edu, CyberLeninka, J-STAGE [Japan Science Technology Information Aggregator, Electronic]). Research methods: historical and comparative, on which basis a comparative analysis of the impact of the plague pandemic of the mid-14th century centering on contact between on nomadic and sedentary ethnic groups (Mongols and Russians, Mongols and Circassians) was carried out; the individualizing comparison method, which was used to identify the specific means by which the Circassian ethnic group overcame the Black Death pandemic; the systems analysis method, on which basis the roles of pathogenic complex and anthropogenic factors in the course of epidemics were compared. Results. The results of the analysis show that the population of the North-West Caucasus were affected by the Black Death plague of the mid-14th century to a significantly lesser extent than the urban centers of the Golden Horde, Rus’ and Europe. The serendipitous failure of the pathogenic complex in the former region was due to the following natural and anthropogenic factors: (1) the North-West Caucasus (more precisely, the entire area to the west of the Teberda River) was naturally bereft of effective carriers of the plague bacillus (marmots, gophers, etc.); (2) Matrega and Copa, the two contemporaneous Circassian towns serving as ports for the shipment of grain and other agricultural products, were located on the periphery of the Adyghe settlement area; (3) the dispersed, farmstead way of life among the estuaries and the complex network of peninsulas of the Kuban Delta, mountains and forests of the NWC contributed to the rapid adoption of quarantine measures; (4) a reliable vitamin-rich nutrition system (sturgeon and their caviar, goat meat and cheese, fruit, honey, etc.); (5) a well-developed system of hygiene and disease prevention (smallpox, malaria), whose existence was retrospectively established based on sources from the 17th–19th centuries. Conclusion. The plague pandemic that took place within the unified space of Mongol imperial communications had a huge impact on the historical fate of the Adyghe ethnic group, which was manifested in their paradoxical numerical growth against the backdrop of general depopulation. Against the backdrop of frequent epidemics, Adyghe developed strict and quite effective rules for preserving life and health, covering disease prevention, personal and public hygiene. Some particularly impressive practices, if not created by Adyghe folk medicine, then preserved there and later transmitted to Ottoman Turkey and Western Europe, was manifested in the original practice of smallpox inoculation.

Abstract Background Somali Red Crescent Society (SRCS), supported by Norwegian Red Cross, has implemented community-based surveillance (CBS) in Somaliland. This methodology aims to reduce the high risk of epidemics by strengthening early warning and response from and at community level, particularly where there is a weak public health surveillance system. CBS is implemented through SRCS community volunteers, who report signals from the community via SMS to the software platform Nyss. This paper presents key findings from the CBS programme evaluation. Methods A retrospective observational mixed-methods approach to evaluate the CBS programme was conducted, using routine CBS data from 2021 for Awdal and Togdheer regions and qualitative interviews with stakeholders’ representatives. Results The usefulness of the CBS programme in preventing, detecting, and responding to disease outbreaks was acknowledged by the stakeholders’ representatives. 83% of the signals in Awdal region matched a Community Case Definition (CCD) and were escalated to the Ministry of Health and Development (MoHD)). For Togdheer region, 97% were escalated. Verification of signals by supervisors and escalation to the authorities was done timely.Alert outcome and response action was not well recorded, therefore there is limited evidence on sensitivity. The programme was shown to be simple and can be flexibly adjusted for new diseases and changing CCDs.Stakeholders appreciated being engaged, the good collaboration, their participation throughout the implementation and expressed high acceptance of the programme. Conclusion CBS can support early warning and response for a variety of public health risks. Improved documentation for alert outcomes could help to better evaluate the sensitivity of CBS. A participatory approach is vital to achieve successful community volunteer engagement. Software tools, such as the Nyss platform, can be useful to support effective and efficient CBS implementation.

Copyright of Mersin University School of Medicine Lokman Hekim Journal of History of Medicine & Folk Medicine is the property of Mersin University School of Medicine and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background: Somali Red Crescent Society (SRCS), supported by Norwegian Red Cross, has implemented community-based surveillance (CBS) in Somaliland. This methodology aims to reduce the high risk of epidemics by strengthening early warning and response from and at community level, particularly where there is a weak public health surveillance system. CBS is implemented through SRCS community volunteers, who report signals from the community via SMS to the software platform Nyss. This paper presents key findings from the CBS programme evaluation. Methods: A retrospective observational mixed-methods approach to evaluate the CBS programme was conducted, using routine CBS data from 2021 for Awdal and Togdheer regions and qualitative interviews with stakeholders' representatives. Results: The usefulness of the CBS programme in preventing, detecting, and responding to disease outbreaks was acknowledged by the stakeholders' representatives. 83% of the signals in Awdal region matched a Community Case Definition (CCD) and were escalated to the Ministry of Health and Development (MoHD)). For Togdheer region, 97% were escalated. Verification of signals by supervisors and escalation to the authorities was done timely.Alert outcome and response action was not well recorded, therefore there is limited evidence on sensitivity. The programme was shown to be simple and can be flexibly adjusted for new diseases and changing CCDs.Stakeholders appreciated being engaged, the good collaboration, their participation throughout the implementation and expressed high acceptance of the programme. Conclusion: CBS can support early warning and response for a variety of public health risks. Improved documentation for alert outcomes could help to better evaluate the sensitivity of CBS. A participatory approach is vital to achieve successful community volunteer engagement. Software tools, such as the Nyss platform, can be useful to support effective and efficient CBS implementation. [ABSTRACT FROM AUTHOR]

While the origins of syphilis remain a subject of debate, it is widely acknowledged that it emerged following the discovery of the American continent. This virus, originating in America, rapidly spread across Europe during the 15th and 16th centuries. The impact of syphilis, a contagious disease, became significantly pronounced in the Ottoman State during the 19th century. During this period, it is evident that the Ottoman Government undertook various initiatives and implemented measures to combat the epidemic. In the latter part of the 19th century, syphilis escalated in the Ottoman territories, and economic challenges hindered the necessary efforts to combat the disease. Another challenge was the perception of syphilis as a simple ailment, leading to widespread ignorance. The stigma associated with the disease, often transmitted through sexual contact, prompted individuals to conceal their illness. Those seeking to address syphilis, considered a shameful ailment, often resorted to individuals posing as physicians or healers. Unfortunately, these charlatans, lacking any medical credentials, exploited people financially and contributed to the further spread of the disease, exacerbating public health concerns. Celaleddin Muhtar, a graduate of the School of Medicine, emerged as a prominent Ottoman physician in the battle against syphilis. He conducted extensive studies aimed at educating the public about syphilis, publishing his work titled "Syphilis Articles" in 1899 and 1907, presenting it to the public. In this comprehensive work, Celaleddin Muhtar emphasized that his commitment to writing about the disease was a debt of loyalty to his state and nation. [ABSTRACT FROM AUTHOR]

Introduction: Anxiety, depression, and poor sleep quality are the most common mental health issues in post-acute Coronavirus Disease-2019 (COVID-19) syndrome. Healthcare professionals (HCPs) were overburdened and unable to focus on their own health issues. Previous epidemic diseases, such as Severe Acute Respiratory Syndrome (SARS) in 2003, also accompanied psychiatric issues in HCPs. In light of this, the purpose of this study was to identify the symptoms of COVID19 that continue to affect HCPs and their associations with fatigue, poor sleep, anxiety, depression, and activity levels, in order to provide better care and treatment for them. Aim: To analyse the impact of post-acute COVID-19 syndrome on the mental health of HCPs and compare the results with non COVID-19 infected participants. Materials and Methods: A cross-sectional study was conducted at PGIMS, Rohtak in Haryana, India from October 2021 to September 2022. A total of 280 participants between 25-45 years of age, including doctors, nurses, and dentists involved in direct COVID-19 patient care, were included in the survey. They were divided equally into a non COVID-19 infected group and a post-acute COVID-19 syndrome group, with atleast three months having passed since infection. The survey included scales addressing anxiety, depression, and sleep quality, including the Zung Self-rating Anxiety Scale (SAS), Zung Self-rating Depression Scale (SDS), and Pittsburgh Sleep Quality Index (PSQI). The mean scores were compared between both groups using an Independent sample t-test. Results: The data of 240 participants were analysed and compared between both groups. The mean age was 32.41 years in the post-acute COVID-19 syndrome group and 31.24 years in the non COVID-19 infected group. The post-acute COVID-19 syndrome group consisted of a large proportion of doctors (60, 42.9%) and nurses (53, 37.9%), whereas the non COVID-19 infected group included doctors (47, 33.5%) and nurses (56, 40%). The results showed statistically significant differences for anxiety (p-value=0.001) and sleep quality (p-value =0.001), while no significant differences were found for depression (p-value>0.05). Conclusion: Anxiety and poorer sleep quality occur in healthcare workers suffering from post-acute COVID-19 syndrome. Creating multidisciplinary rehabilitation health teams in hospitals with clinician psychologists, physiotherapists, nurses, and psychiatrists is essential to address mental health issues in the post-COVID-19 population.

Introduction: Anxiety, depression, and poor sleep quality are the most common mental health issues in post-acute Coronavirus Disease-2019 (COVID-19) syndrome. Healthcare professionals (HCPs) were overburdened and unable to focus on their own health issues. Previous epidemic diseases, such as Severe Acute Respiratory Syndrome (SARS) in 2003, also accompanied psychiatric issues in HCPs. In light of this, the purpose of this study was to identify the symptoms of COVID-19 that continue to affect HCPs and their associations with fatigue, poor sleep, anxiety, depression, and activity levels, in order to provide better care and treatment for them. Aim: To analyse the impact of post-acute COVID-19 syndrome on the mental health of HCPs and compare the results with non COVID-19 infected participants. Materials and Methods: A cross-sectional study was conducted at PGIMS, Rohtak in Haryana, India from October 2021 to September 2022. A total of 280 participants between 25-45 years of age, including doctors, nurses, and dentists involved in direct COVID-19 patient care, were included in the survey. They were divided equally into a non COVID-19 infected group and a post-acute COVID-19 syndrome group, with atleast three months having passed since infection. The survey included scales addressing anxiety, depression, and sleep quality, including the Zung Self-rating Anxiety Scale (SAS), Zung Self-rating Depression Scale (SDS), and Pittsburgh Sleep Quality Index (PSQI). The mean scores were compared between both groups using an Independent sample t-test. Results: The data of 240 participants were analysed and compared between both groups. The mean age was 32.41 years in the post-acute COVID-19 syndrome group and 31.24 years in the non COVID-19 infected group. The post-acute COVID-19 syndrome group consisted of a large proportion of doctors (60, 42.9%) and nurses (53, 37.9%), whereas the non COVID-19 infected group included doctors (47, 33.5%) and nurses (56, 40%). The results showed statistically significant differences for anxiety (p-value=0.001) and sleep quality (p-value =0.001), while no significant differences were found for depression (p-value>0.05). Conclusion: Anxiety and poorer sleep quality occur in healthcare workers suffering from post-acute COVID-19 syndrome. Creating multidisciplinary rehabilitation health teams in hospitals with clinician psychologists, physiotherapists, nurses, and psychiatrists is essential to address mental health issues in the post-COVID-19 population. [ABSTRACT FROM AUTHOR]

The Coronavirus disease, which emerged in Wuhan, China in December 2019 and spread rapidly all over the world, infected healthy people by being transmitted by small droplets. Medical experts have stated that the most effective fight against the Coronavirus disease is the need for people in contact to wear masks. Despite this, some people violated the obligation to wear masks. In this study, mask detection performances of pre-trained Convolutional Neural Network (CNN) models such as NasNetMobile, MobileNetV3Small, ResNet50, DenseNet121 and EfficientNetV2B0, which were previously trained, were evaluated in order to automatically detect people who violate the mask wearing obligation. At the end of this evaluation, DenseNet121 architechture has become the most successful model. This model has been tested with the image obtained from the camera on a robotic system with six Degrees of Freedom (6-DOF). The human face images taken from the camera were processed using the Jetson Xavier NX development board. As a result, this study will help the officers who carry out mask inspections in public areas and will significantly reduce the spread of new outbreaks similar to the Coronavirus. [ABSTRACT FROM AUTHOR]

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Asymptomatic carriers serve as a potential source of transmission of epidemic diseases. Exposed people who develop symptoms only get tested and remain isolated in their homes or sometimes in hospitals when needed. In contrast, the asymptomatic individuals go untested and spread the disease silently as they roam freely throughout their entire infectious lifetime. The work intends to explore the role of asymptomatic carriers in the transmission of epidemic diseases and investigate suitable optimal control strategies. We propose a SEIAQR compartmental model subdividing the total population into six different compartments. To illustrate the model’s implication, we estimate the number of asymptomatic individuals using COVID-19 data during June 9–July 18, 2021 from Bangladesh. We then analyze the model to explore whether the epidemic subsides if the asymptomatic individuals are tested randomly and isolated. Finally, to gain a better understanding of the potential of this unidentified transmission route, we propose an optimal control model considering two different control strategies: personal protective measures and isolation of asymptomatic carriers through random testing. Our results show that simultaneous implementation of both control strategies can reduce the epidemic early. Most importantly, sustained effort in identifying and isolation of asymptotic individuals allows relaxation in personal protective measures.

One of the important applications of simulation modeling methods is forecasting the spread of epidemic dis- eases. System-dynamic and agent-based approaches are used in this area. The goal of agent-based modeling is to obtain an understanding of these global rules, the general behavior of the system, based on assumptions about the individual, private behavior of its individual active objects and the interaction of these objects in the system. Sys- tem-dynamic and agent-based models of the spread of epidemic diseases have complementary properties, and therefore, the task of jointly using these classes of models to predict the dynamics of epidemic systems is relevant and requires a comprehensive comparison of their characteristics and properties. [ABSTRACT FROM AUTHOR]

This paper proposes an impulsive control scheme for a complex network that helps reduce the spread of two epidemic diseases: influenza type A and COVID-19. Both are respiratory infections; thus, they have a similar form of transmission, and it is possible to use the same control scheme in both study cases. The objective of this work is to use neural impulsive inverse optimal pinning control for complex networks to reduce the effects of propagation. The dynamic model is considered unknown, for which we design a neural identifier that, through training using the extended Kalman filter algorithm, provides the appropriate nonlinear model for this complex network. The dynamics of the network nodes are represented by the Susceptible-Infected-Removed (SIR) compartmental model in their discrete form. The results of the simulations are presented and addressed, applying the same control scheme but with different parameter values for each case study. [ABSTRACT FROM AUTHOR]

The massive contagion diseases such as COVID-19 amongst others, has affected nearly all the economies and business concerns that leads to substantial decline in the cashflows and returns dynamics. Considering the same, this paper intends to investigate the impact of outbreak of epidemic diseases on the stock return (observed through cumulative average abnormal return (CAAR)) for the listed banks in Pakistan from 2011 to 2020. Event study method was employed and five days pre and ten days post event of each disease were observed as an event window. The results confirm that none of the epidemic disease outbreak significantly determines the CAAR for all listed banks, except COVID-19 and Dengue Fever during the event day. There is a negative and significant impact of COVID-19 on the stock returns for all the banks in Pakistan from the event day to day eight except day seven. More specifically, COVID-19 is found to be a significant indicator for the stock returns of private banks. However, in case of public listed banks, only the outbreak of HIV cases possesses significant and positive impact on CAAR at the day of event. These findings would guide all stakeholders such as investors, financial analysts, regulators, and chief risk officers specifically in banks to make strategic decisions while analyzing the relationship between epidemic outbreaks and stock returns. [ABSTRACT FROM AUTHOR]

BackgroundThe resources available to fight an epidemic are typically limited, and the time and effort required to control it grow as the start date of the containment effort are delayed. When the population is afflicted in various regions, scheduling a fair and acceptable distribution of limited available resources stored in multiple emergency resource centers to each epidemic area has become a serious problem that requires immediate resolution.MethodsThis study presents an emergency medical logistics model for rapid response to public health emergencies. The proposed methodology consists of two recursive mechanisms: (1) time-varying forecasting of medical resources and (2) emergency medical resource allocation. Considering the epidemic's features and the heterogeneity of existing medical treatment capabilities in different epidemic areas, we provide the modified susceptible-exposed-infected-recovered (SEIR) model to predict the early stage emergency medical resource demand for epidemics. Then we define emergency indicators for each epidemic area based on this. By maximizing the weighted demand satisfaction rate and minimizing the total vehicle travel distance, we develop a bi-objective optimization model to determine the optimal medical resource allocation plan.ResultsDecision-makers should assign appropriate values to parameters at various stages of the emergency process based on the actual situation, to ensure that the results obtained are feasible and effective. It is necessary to set up an appropriate number of supply points in the epidemic emergency medical logistics supply to effectively reduce rescue costs and improve the level of emergency services.ConclusionsOverall, this work provides managerial insights to improve decisions made on medical distribution as per demand forecasting for quick response to public health emergencies.

Introduction. This article examines the health care sector in Kalmykia after the return of the Kalmyk people from the places of deportation in 1957. The chronological framework covers several periods: the state of health care in the abolished Kalmyk ASSR after the illegal deportation of the Kalmyks; the first years since the restoration of the Kalmyk Autonomous Region in 1957 and the return of the rehabilitated people. The article aims to study the state of health care in Kalmykia after the deportation of the Kalmyk people and after their return in 1957. The article also examines the state of the health sector in the region in the post-deportation period, including staffing and material problems, and the state and development of the sector upon the return of the Kalmyks. Materials and methods. The problem-chronological principle became the basis of this article. In accordance with it, the transformations in the sector under study were examined during the historical periods in question. A quantitative (statistical) method was also used, which made it possible to trace the dynamics of changes in the health care network and personnel. The sources were archival documents of the State Archives of the Russian Federation and the National Archives of the Republic of Kalmykia. Results. The article shows the state and changes in the health care sector of Kalmykia in the time periods under study. The regional health care institutions managed to organize their work in a difficult historical period, after the people’s return from exile. Conclusions. Despite heavy losses and difficulties, health workers in the Republic managed to resume their work, begin the restoration of medical institutions, the health care system after the deportation. Thanks to the dedication of medical workers, the republic’s health care system was restored after its complete destruction.

Today, healthcare system models should have high accuracy and sensitivity so that patients do not have a misdiagnosis. For this reason, sufficient knowledge of the area is required, with the medical staff being able to validate the correctness of their decisions. Therefore, artificial intelligence (AI) in combination with other emerging technologies could provide many benefits in the medical sector. In this paper, we demonstrate the combination of Internet of Things (IoT) and cloud computing (CC) with AI-related techniques such as artificial intelligence (AI), machine learning (ML), deep learning (DL), and neural networks (NN) in order to provide a useful approach for scientists and doctors. Our proposed model makes use of these immersive technologies so as to provide epidemic forecasting and help accelerate drug and antibiotic discovery. [ABSTRACT FROM AUTHOR]

Healthy qi plays an extremely important role in the occurrence, development and changes of epidemics. The battle between exogenous pathogens and healthy qi determines the occurrence and outcome of epidemics. As a tonic medicinal, Renshen (ginseng, Radix et Rhizoma Ginseng) has played an important role in the prevention and treatment of epidemic diseases. However, it needs to be carefully considered the timing of using tonic medicinals in treatment and how to maximize the merits of tonic medicinals while minimizing its demerits. In Wen Yi Lun (Treatise on Warm Pestilence), Wu Youke has made a detailed exposition on the method of tonification and the application of Renshen (ginseng, Radix et Rhizoma Ginseng) in the treatment of epidemics, indicating that the method of tonification should be used with caution rather than prohibited, and it should be used urgently but not excessively; besides, in the application of Renshen (ginseng, Radix et Rhizoma Ginseng), the following items are discussed: the differentiation of deficiency and excess patterns, the true or false nature of the pathogenesis, the suitable patterns for tonification, as well as the timing and taboos of using it, thus providing reference and inspiration for the application of tonifying method and the use of Renshen (ginseng, Radix et Rhizoma Ginseng) in the treatment of epidemic diseases in later generations. [ABSTRACT FROM AUTHOR]

Copyright of AAU Journal of Business & Law is the property of Association of Arab Universities and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Gümüshane Üniversitesi Saglik Bilimleri Dergisi is the property of Gumushane University, Faculty of Health Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Introduction. The article examines the understudied issues of how and to what extent epidemic diseases used to spread across Kalmyk uluses (‘districts’) in the late 19th – early 20th centuries, with special attention be paid to employed control and monitoring methods. The problem was covered in a number of published reports delivered at the First Congress of Astrakhan physicians to have worked in Kalmyk-inhabited lands during the period under study, and the former contain their shared their experiences and valuable findings. Historians hardly ever approached the topic in just a few papers. Goals. So, the work aims at a detailed survey of epidemic diseases in the Kalmyk Steppe of Astrakhan Governorate in the late 19th – early 20th centuries. Materials and Methods. The study employs a set of general scientific and specific historical research methods. The observance of the historicism principle made it possible to avoid modern misinterpretations of the century-old events examined, while system-analysis techniques and interdisciplinary approaches resulted in that certain specific events of Kalmyk life were analyzed as parts of an overall picture. The article mainly explores and newly introduces materials of the Medical Department — a healthcare agency within the Kalmyk People’s Administration — currently stored at the National Archive of Kalmykia. Results. Despite the remoteness of Kalmyk nomadic settlements (Kalm. khoton) from administrative centers and first-aid stations, healthcare practitioners still were efficient enough to promptly respond in case of epidemic outbreaks. Besides treatment proper, the medical, administrative and police personnel were largely responsible for quarantine and disinfection activities, medical examination and supervision of people living around the periphery of the effective disease area. Conclusions. The analysis of materials dealing with the issue reveals Kalmyk districts were widely affected by epidemic diseases, such as typhus, smallpox, measles, diphtheria and others, while cholera and plague were not that often. It should be admitted that the frequent occurrence of those diseases in medical records across the Kalmyk Steppe was determined by their endemicity to have resulted from a number of reasons.

This article shows how French doctors based in Saint-Louis-du-Sénégal, the capital of colonial Senegal, conceptualised the Senegambian region as a diseased environment and Africans as carriers of infectious agents. It explains how perceptions of the hot tropical climate, combined with outbreaks of epidemic diseases and seasonal allergies, were instrumental in the processes of urban transformation through hygienic measures such as waste removal, the closing of cemeteries, and the imposition of new building codes. The article also shows how the stigmatisation of Africans was implicated in the forced removal of the urban poor – firstly from the city centre, and later from the entire city-island. Colonial medical knowledge in Senegal was initially based on the miasma theory, however, germ theory was adopted in the aftermath of the 1900 yellow fever epidemic. Both theories, in relation with racialism, impacted the urban landscape in Saint-Louis, Senegal.

The experience of epidemic diseases and responding to them in certain environments, in particular, educational at different times, is interesting and didactic. Therefore, the study examines the experience of antiepidemic and sanitary measures used in gymnasiums, pro-gymnasiums and schools of the Kyiv Educational District at the end of the 19th and the beginning of the 20th centuries. The purpose of the publication is to clarify the specifics of the putting these measures into practice by means of analysis of selected sources and their systematization. During the study of available materials, the methods of detecting infectious diseases in the past at that time’s level of knowledge were investigated; it was demonstrated which diseases bothered students the most, how their emergence was tried to be prevented; bureaucratic component of the process of informing and reporting the emergence and the spread of diseases was highlighted. It was investigated that as of the beginning of the 20th century the management of educational institutions obtained more independence in the sphere of decision-making regarding activities at the time of epidemics. It was also found out that social problems and natural conditions were considered to be the causes of diseases among schoolchildren at that time’s level of knowledge. It was determined that contemporaries considered scarlet fever, measles, diphtheria, smallpox, etc. to be the most dangerous diseases. The documents show that infectious diseases caused changes in educational process, which was suspended for periods specified in instructional materials, or optimized by shortening the exams. Such restrictions were of a local nature. To prevent the emergence and spread of diseases, quarantine was used for sick people, school premises were disinfected, mostly with a formaldehyde solution, vaccinations against smallpox, which were mandatory for students. Students were provided with recommendations on body hygiene as well as with assistance in treatment. The author clarified that deaths from epidemic diseases occurred, although relatively infrequently, and that the set of measures implemented in the past is quite applicable nowadays. [ABSTRACT FROM AUTHOR]

This study was carried out to determine the views of science teachers about the inclusion of epidemic diseases in science curriculum. Phenomenology, one of the qualitative research designs, was used as the research design in the study. The study group consists of six science teachers who have at least five years of experience and have participated in the development of science curriculum in previous years. A semi-structured interview form consisting of five questions, whose content validity was provided by taking expert opinion, was used as a data collection tool. Code, category and themes were created by analyzing the content of the teachers' opinions. As a result of the study, inclusion of epidemic diseases, which has become a current topic all around the world, into the science curriculum; reasons, advantages and disadvantages, class level, achievements that can be included in the program were evaluated according to the views of science teachers in terms of teaching methods-techniques and materials also it was emphasized that the subject of epidemic diseases should be included in the science course curriculum. Suggestions that in line with the findings of the research were made to the developers of the science course curriculum and researchers. [ABSTRACT FROM AUTHOR]

Adaptive Neuro-fuzzy Inference System (ANFIS) remains one of the promising AI techniques to handle data over-fitting and as well, improves generalization. Presently, many ANFIS optimization techniques have been synergized and found effective at some points through trial and error procedures. In this work, we tune ANFIS using Grid partition algorithm to handle unseen data effectively with fast convergence. This model is initialized using a careful selection of effective parameters that discriminate climate conditions; minimum temperature, maximum temperature, average temperature, wind speed and relative humidity. These parameters are used as inputs for ANFIS, whereas confirmed cases of COVID-19 is chosen as dependent values for two consecutive months and first ten days of December for new COVID-19 confirmed cases according to the Department of disease control (DDC) Thailand. The proposed ANFIS model provides outstanding achievement to predict confirmed cases of COVID-19 with $R^{2}$ of 0.99. Furthermore, data set trend analysis is done to compare fluctuations of daily climatic parameters, to satisfy our proposition, and illustrates the serious effect of these parameters on COVID-19 epidemic virus spread.

Epidemic risk has great uncertainty and harmfulness, which poses a potential threat to public health in a certain region. Establishing a special risk assessment system to assess and predict the potential epidemic risk of a region can effectively avoid or reduce the impact of epidemic risk. Therefore, this paper combs the related factors that affect the epidemic risk, and proposes an epidemic risk assessment model based on 12 indicators by combining Markov chain and AHP. The model can assess the epidemic situation in a certain region from four aspects: the probability of risk occurrence, the probability of loss, the possibility of risk disappearance and risk duration, so as to provide detailed data for the risk management and control of epidemic in the region, and help the epidemic prevention work to be carried out in a targeted way. Finally, the case analysis and method comparison are carried out,and the results show that the model proposed in this paper is reasonable and feasible.

In the second half of the 19th century, Crimea between the Ottoman and Russia experienced a significant intense migration to the Ottoman lands as a result of the 1877-78 and 1897 Ottoman-Greek Wars. During the migration, migrants, education, food, etc. Facing problems, reasons such as starvation, misery, malnutrition and lack of hygienic environment caused by this migration event caused the spread of epidemic diseases among the immigrants. In this study, the scope of health services, measures taken and health services implemented by the state against the epidemic diseases suffered by refugees during the settlement will be emphasized. In the context, it has been determined that the state primarily transferred the refugees who came in crowded groups in the settlements to different places, prevented the spread of the disease, applied quarantines to the migrants who were sick in port cities such as Samsun and Trabzon in the face of outbreaks, and treatments were applied to those who got ill in the settlements. Thus, efforts were made to reveal the efforts made by the state in the period concerned to prevent epidemic diseases and to reduce death cases. From here, the movements were tried to be revealed in the line with the data about the health problems of the immigrants who were determined to be not included in the studies published on the migrations in the second half of the 19th century to the Ottoman State and the health services that the state was trying to fulfill. [ABSTRACT FROM AUTHOR]

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Saudi Arabia is one of the key countries in the Middle East and the Arab-Muslim world. The processes developing in this country, determined by both internal and external factors, are directly related to the development of the surrounding geopolitical space. In this regard, there arises the need to study the essence of such processes, their circumstances as well as the driving forces behind them. One of these factors is the epidemic of such diseases as A/ H1N1, MERS, and the coronavirus COVID-19. The author views this factor as an accelerator of socio-political transformations taking place in Saudi Arabia after 2009. These transformations were based on the experience of previous years already accumulated by the Saudi authorities. They took the form of implementing “national projects” aimed at restructuring the national education and health system. The results of the implementation of these projects were manifested in the country’s ever-wider opening to the outside world, consistent reduction of the influential religious establishment, and increasing role of the new “educated class,” which has been turned into a supporting pillar to the authorities. The government took on new forms of legitimation and won in the struggle for the minds of people, pushing the ulema away from influencing their political decision. The post-traditional Saudi society, as it was in the early 2000s, acquired features of modernity under the influence of the “educated class,” while Saudi Arabia itself began to be considered as an integral and a full-fledged element of the international community.

Epidemic diseases (EDs) present a significant but challenging risk endangering public health, evidenced by the outbreak of COVID-19. Compared to other risks affecting public health such as flooding, EDs attract little attention in terms of risk assessment in the current literature. It does not well respond to the high practical demand for advanced techniques capable of tackling ED risks. To bridge this gap, an adapted fuzzy evidence reasoning method is proposed to realize the quantitative analysis of ED outbreak risk assessment (EDRA) with high uncertainty in risk data. The novelty of this article lies in (1) taking the lead to establish the outbreak risk evaluation system of epidemics covering the whole epidemic developing process, (2) combining quantitative and qualitative analysis in the fields of epidemic risk evaluation, (3) collecting substantial first-hand data by reviewing transaction data and interviewing the frontier experts and policymakers from Chinese Centers for Disease Control and Chinese National Medical Products Administration. This work provides useful insights for the regulatory bodies to (1) understand the risk levels of different EDs in a quantitative manner and (2) the sensitivity of different EDs to the identified risk factors for their effective control. For instance, in the case study, we use real data to disclose that influenza has the highest breakout risk level in Beijing. The proposed method also provides a potential tool for evaluating the outbreak risk of COVID-19. [ABSTRACT FROM AUTHOR]

The aim of the current research is to know the level of health awareness about epidemic diseases in light of the corona virus epidemic Covid-19, among students of the basic education stage (primary - preparatory) in some Egyptian governorates, and the research also aimed to identify the level of health awareness they have in three dimensions (cognitive - emotional - behavioral), as well as for each of the four health awareness axes (personal health - healthy nutrition - prevention of epidemic diseases - prevention of the corona virus epidemic Covid-19). The research also aimed to know the effect of the variables (educational stage - type of education - gender - student governorate) on the level of health awareness among students. To achieve these goals, the researcher prepared a health awareness scale that was applied through Google Forms on a sample of (262) male and female students from the basic education stage in grades from the fourth primary to the third preparatory level in governmental, private and international education, and they were distributed in four Egyptian governorates (Cairo – Alexandria- Beheira - Sohag). The results of the research concluded that the general level of health awareness as a whole, its three dimensions and its four axes specified in the research were available with a degree higher than the required sufficiency (75%) of the research sample. The results also indicated that there are statistically significant differences at the level of (0.01) in the variable of the educational stage in favor of the preparatory stage students. And the presence of statistically significant differences at the level of (0.01) in the education type variable in favor of public education students. And the presence of statistically significant differences at the level of (0.01) in the variable of the student governorate in favor of the students of Beheira Governorate, then Alexandria, then Sohag, and finally Cairo. While the results indicated that there were no statistically significant differences in the sex variable of the research sample. The researcher recommended implementing programs and approaches to increase health awareness among students and benefiting from the high level of health awareness among students in spreading and acquiring sound health habits related to personal health, healthy nutrition and disease resistance in general and epidemic diseases in particular. [ABSTRACT FROM AUTHOR]

Existing compartmental models in epidemiology are limited in terms of optimizing the resource allocation to control an epidemic outbreak under disease growth uncertainty. In this study, we address this core limitation by presenting a multi-stage stochastic programming compartmental model, which integrates the uncertain disease progression and resource allocation to control an infectious disease outbreak. The proposed multi-stage stochastic program involves various disease growth scenarios and optimizes the distribution of treatment centers and resources while minimizing the total expected number of new infections and funerals. We define two new equity metrics, namely infection and capacity equity, and explicitly consider equity for allocating treatment funds and facilities over multiple time stages. We also study the multi-stage value of the stochastic solution (VSS), which demonstrates the superiority of the proposed stochastic programming model over its deterministic counterpart. We apply the proposed formulation to control the Ebola Virus Disease (EVD) in Guinea, Sierra Leone, and Liberia of West Africa to determine the optimal and fair resource-allocation strategies. Our model balances the proportion of infections over all regions, even without including the infection equity or prevalence equity constraints. Model results also show that allocating treatment resources proportional to population is sub-optimal, and enforcing such a resource allocation policy might adversely impact the total number of infections and deaths, and thus resulting in a high cost that we have to pay for the fairness. Our multi-stage stochastic epidemic-logistics model is practical and can be adapted to control other infectious diseases in meta-populations and dynamically evolving situations. [ABSTRACT FROM AUTHOR]

This paper proposes an impulsive control scheme for a complex network that helps reduce the spread of two epidemic diseases: influenza type A and COVID-19. Both are respiratory infections; thus, they have a similar form of transmission, and it is possible to use the same control scheme in both study cases. The objective of this work is to use neural impulsive inverse optimal pinning control for complex networks to reduce the effects of propagation. The dynamic model is considered unknown, for which we design a neural identifier that, through training using the extended Kalman filter algorithm, provides the appropriate nonlinear model for this complex network. The dynamics of the network nodes are represented by the Susceptible-Infected-Removed (SIR) compartmental model in their discrete form. The results of the simulations are presented and addressed, applying the same control scheme but with different parameter values for each case study.

Today, healthcare system models should have high accuracy and sensitivity so that patients do not have a misdiagnosis. For this reason, sufficient knowledge of the area is required, with the medical staff being able to validate the correctness of their decisions. Therefore, artificial intelligence (AI) in combination with other emerging technologies could provide many benefits in the medical sector. In this paper, we demonstrate the combination of Internet of Things (IoT) and cloud computing (CC) with AI-related techniques such as artificial intelligence (AI), machine learning (ML), deep learning (DL), and neural networks (NN) in order to provide a useful approach for scientists and doctors. Our proposed model makes use of these immersive technologies so as to provide epidemic forecasting and help accelerate drug and antibiotic discovery.

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This manuscript proposes a model of $n$ distinct populations interaction structured SEIR to describe the spread of COVID-19 pandemic diseases. The proposed model has the flexibility to include geographically separated communities as well as taking into account aging population groups and their interactions. We show that distinct assumptions on the dynamics of the proposed model lead to a plateau-like curve of the infected population, reflecting collected data from large countries like Brazil. Such observations aim to the following conjecture: " Covid-19 diseased diffusion from the capitals to the Brazil interior, as reflected by the proposed model, is responsible for plateau-like reported cases in the country". We present numerical simulations of some scenarios and comparison with reported data from Brazil that corroborate with the aforementioned conclusions.

Emerald ash borer (EAB), a wood-boring insect native to Asia and invading North America, has killed untold millions of high-value ash trees that shade streets, homes, and parks and caused significant economic damage in cities of the United States. Local actions to reduce damage include surveillance to find EAB and control to slow its spread. We present a multistage stochastic mixed-integer programming (M-SMIP) model for the optimization of surveillance, treatment, and removal of ash trees in cities. Decision-dependent uncertainty is modeled by representing surveillance decisions and the realizations of the uncertain infestation parameter contingent on surveillance as branches in the M-SMIP scenario tree. The objective is to allocate resources to surveillance and control over space and time to maximize public benefits. We develop a new cutting-plane algorithm to strengthen the M-SMIP formulation and facilitate an optimal solution. We calibrate and validate our model of ash dynamics using seven years of observational data and apply the optimization model to a possible infestation in Burnsville, Minnesota. Proposed cutting planes improve the solution time by an average of seven times over solving the original M-SMIP model without cutting planes. Our comparative analysis shows that the M-SMIP model outperforms six different heuristic approaches proposed for the management of EAB. Results from optimally solving our M-SMIP model imply that under a belief of infestation, it is critical to apply surveillance immediately to locate EAB and then prioritize treatment of minimally infested trees followed by removal of highly infested trees. Summary of Contributions: Emerald ash borer (EAB) is one of the most damaging invasive species ever to reach the United States, damaging millions of ash trees. Much of the economic impact of EAB occurs in cities, where high-value ash trees grow in abundance along streets and in yards and parks. This paper addresses the joint optimization of surveillance and control of the emerald ash borer invasion, which is a novel application for the INFORMS society because, to our knowledge, this specific problem of EAB management has not been published before in any OR/MS journals. We develop a new multi-stage stochastic mixed-integer programming (MSS-MIP) formulation, and we apply our model to surveillance and control of EAB in cities. Our MSS-MIP model aims to help city managers maximize the net benefits of their healthy ash trees by determining the optimal timing and target population for surveying, treating, and removing infested ash trees while taking into account the spatio-temporal stochastic growth of the EAB infestation. We develop a new cutting plane methodology motivated by our problem, which could also be applied to other stochastic MIPs. Our cutting plane approach provides significant computational benefit in solving the problem. Specifically, proposed cutting planes improve the solution time by an average of seven times over solving the original M-SMIP model without cutting planes. We calibrate and validate our model using seven years of ash infestation observations in forests near Toledo, Ohio. We then apply our model to an urban forest in Burnsville, Minnesota, that is threatened by EAB. Our results provide insights into the optimal timing and location of EAB surveillance and control strategies. [ABSTRACT FROM AUTHOR]

• Determinants extracted from open data of dengue, enterovirus, and influenza epidemics are presented. • Location, time, age, gender, climate data, and Google Trends data are considered. • Comparison between Google Trends excluded and included is conducted. • Accuracy and simplicity are used to validate the knowledge extraction technique. The research reveals the determinants associated with the epidemic diseases (dengue, influenza, and enterovirus) in Taiwan. It demonstrates the value of open data in prediction model development to support policymaking in the domain of public health care. A knowledge discovery technique was employed to extract determinants from open data on epidemic diseases. The open dataset collected and integrated from Taiwan's Center for Disease Control, the Center Weather Bureau, and Google Trends includes 70,915 dengue, 34,062 enterovirus, and 52,908 influenza cases. A prediction model using the classification-oriented extraction mechanism was applied to open epidemic data, climate data, and Google Trends data. Prediction models that either included or did not include Google Trends data were compared. Prediction accuracy and simplicity of the decision rules are presented. Prediction accuracy and simplicity of three diseases is acceptable when Google Trends is excluded but is slightly different when Google Trends is considered. Location (county) holds the main predictor of the three epidemic diseases. Time (month) presents the second-highest determinant for dengue, and age shows remarkable determinant for enterovirus and influenza. Mean temperature exhibits the highest entropy for dengue, time for enterovirus, and humidity for influenza. The number of confirmed cases for all three epidemic diseases cannot be predicted by a single variable. Knowledge extraction using the classification-oriented technique can be successfully applied in prediction model development. Google Trends data reveal a remarkable but inconsistent role in predicting three epidemic diseases with respect to prediction accuracy and simplicity of the generated decision tree. [ABSTRACT FROM AUTHOR]

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Copyright of Journal of Al-Frahids Arts is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Introduction: Given, widespread COVID-19 across the world a comprehensive literature review can be used to forecast COVID-19 peak in the countries. The present protocol study aimed to explore epidemic peak prediction models in communicable diseases. Material and Methods: This protocol study was conducted based on Arksey and O'Malley's. This framework encompasses purpose and hypothesis, modeling, model achievements aspects. A systematic search of English in PubMed was conducted to identify relevant studies. In the pilot step, two reviewers independently extracted the variables from 10 eligible studies to develop a primary list of variables and a data extraction form. In the second step, all eligible studies were assessed by researchers. In the third step, two data extraction forms were combined. The data were extracted and categories were created based on frequency. Qualitative and quantitative methods were used to synthesize the extracted data. Results: The current study were focused on forecasting the epidemic peak time that is a worlds’ concern issue. The results of current scoping review on prediction methods for epidemic disease can provide foundational knowledge, and have important value for the prediction model studies of COVID-19. Conclusion: Our findings will help researchers by a summary of evidence to present new ideas and further research especially for studies were focused on COVID-19. Our results can improve the understanding of prediction methods for COVID-19. [ABSTRACT FROM AUTHOR]

At the end of 2019, the world faced the novel coronavirus, and with it fear of economic collapse and mass fatalities. Simulation systems can be used to monitor the behavior of the virus. Simulation provides an abstract representation of reality by conveying details and characteristics of reality in a simple application. One of the most important ways to simulate is agent-based modeling. The health information professional plays an important role in developing these models. In this research, we simulate the spread of COVID-19 in a region restricted to a population with specific demographic characteristics and social relationships. This study aims to clarify the effects of preventative techniques that suppress the spread of epidemics, such as quarantines, social distancing, and reduced mass transit. [ABSTRACT FROM AUTHOR]

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During the current pandemic of COVID-19, the medical fraternity, pharmaceutical giants, governments and policy makers are figuring out ways to diagnose and control its spread in the absence of any known regular treatment protocol. All orthodox medicine can offer at this point is supportive care and mechanical ventilation in severe, life threatening disease (except possibly for remdesivir).1 The escalating medical cost for providing medical care to an ever increasing number of patients is proving to be a challenge. Furthermore, to date, there is no documented proof of any vaccine preventing an epidemic from happening, nor is there any data supporting its efficacy in recovering from one.2 In fact, a leading world health expert has recently suggested that there may never be an effective vaccine against COVID-19.3 Homoeopathy, however, has withstood the test of time as an efficacious approach in controlling morbidity as well as mortality in epidemics with its cost-effective medications. Individualised homoeopathic therapy and/or finding the genius epidemicus for this particular pandemic, based on the characteristics of this epidemic, can provide both therapeutic and prophylactic support for the public. Unfortunately, a lack of cohesiveness amongst the homoeopathic fraternity and failure to adhere to our fundamental principles, creates diverse approaches which confuses the public and invites ridicule from conventional medical practitioners. This article highlights the twin tragedies: the first caused by the epidemic, and the second caused to the public by denying them access to safe and effective homoeopathic treatment, despite a substantive pool of published data supporting its use in epidemics. (See Table 1 below.) Homoeopathy should not be banished from the battlefield of fighting the COVID-19 epidemic by policy makers and conventional medical specialists, especially now, in this time of need. [ABSTRACT FROM AUTHOR]