General public health research covers two areas: epidemiology and clinical trials. Epidemiology is a systematic study to learn observational data gathered from a study population that is not influenced by experimental settings, about the cause and the origin (amorosis) of the disease. It is essentially an interdisciplinary academic discipline. It covers areas such as clinical epidemiology, behavioral epidemiology, occupational epidemiology, chronic disease epidemiology, infectious disease epidemiology, environmental epidemiology and so on. Nelder and Wedderburn learned such a study, for example, casual relationships between smoking and lung cancer, air pollution and respiratory disease, heart disease and diet, childhood leukemia and water pollution, and the prevalence of HIV and Incidence infection, AIDS, etc. [14] [18]. Its function is mainly aimed at improving the overall health condition of the population.
Clinical trials, on the other hand, are specially designed in a controlled laboratory setting to assess the specific type of treatment or intervention. Examples of clinical trial trials include the effect of comparing placebo with the effect of applying HIV medication to the survival of patients suffering from AIDS, learning the effectiveness of new drugs for fungal development of athlete's foot, hormone Evaluating corrections, and so on.
Modeling is an act of scientific research that enables accurate and rigorous analysis and quantitative prediction without claiming full certainty. It is mathematically to express ideas to clarify thinking. Cliff & Murray and Spicer argue that modeling the dynamics of infectious diseases is directly related to curiosity selection, optimal allocation of resources, and development of the best medical intervention technology [10] [20]. Epidemiology of the scientific community has reached that era. This has not happened by chance. It is happening due to the demanding requirements and advanced methods of public health research for epidemiologists. As discussed [3] By black, epidemiological methods can deal with sophisticated methods of assessing public health risk indicators attributable to many exposures and environmental pollutants in modern society. Factors and dynamics of epidemiological methods have emerged as strong as ever. Advances in information technology in the 21st century, including super powered microcomputers, the Internet, software development, and prosperous prospects have paved the way to conduct broader research. The way healthcare is provided today, and in particular the emergence and growth of organized medical systems in the digital world, has created opportunities and opportunities for epidemiology and epidemiologists to appreciate evidence-based public health and medical management . excellence. Even though there is no analysis or reasoning of healthy epidemiological data, decisions and policies formed in public health surveillance are in the past. Public citizens and healthcare professionals benefit from the consciousness purchased as a result of evidence-based epidemiological research on important diseases [9] [17]. Disease incidence and morbidity rate, morbidity rate and mortality rate, its importance (risk fraction resulting from population and burden of worldwide diseases, etc.), time (rising tendency and downward trend of incidence), place (population ( All of the most dangerous people) and prevention (primary, secondary and third), in terms of demographics, lifestyle, health and workplace, [8] [12].
Epidemiology modeling has the root when Daniel Bernoulli designed a model to investigate the efficacy of healthy person vaccination against smallpox virus in 1760 in the 1860's [26]. Hamer also studied the recurrence of measles epidemic in 1906 and analyzed the analysis of the discrete-time model [7]. A universally accepted mathematical epidemiological model was developed by Ronald Ross in 1911 [22] We developed a differential equation model of malaria (Ronald Ross, 1857-1932). Since then, in 1927 a mathematical model was developed as an extension of the model of Ross by Kermack and Mckendrick, and an epic threshold result was derived [26]. The research that Ross did was not only to eliminate all pathogenic insects, but also to show that the disease could be extinct by satisfying certain conditions. Hethcote, 1976 and Fred, 2008 discussed such models in detail, and modeled without life mechanics as SIR model [25] [27].
Past studies synthesized and applied the results of traditional epidemiology focusing only on methods to determine the pathogenesis of diseases such as research design, source of bias, and extraordinary reasoning, and intervention Set priority, prioritize public health interventions and policies, measure the quality and outcomes of health care, effectively communicate epidemiological knowledge to healthcare professionals, the public is the most important [2] [15] [21].
Standard World Health Organization Guidelines [4] We emphasize that the basic principles of epidemiological research are based on three basic values. These are finding cases, increasing knowledge of public health diseases, identifying important diseases.
Case discovery is a strategy that targets individuals or groups of resources that appear to be at a particular risk of disease. It involves proactively searching for high-risk people, rather than waiting for symptoms to appear, or symptoms of active disease occur, they will pay medical attention. Please note the similarities between case discovery and screening: to achieve better results by exploring the risk of hierarchizing groups in a simple and inexpensive procedure, identifying the early stages of disease and providing prompt treatment What you can do. By way of example, case finding can be used as part of an investigation into the outbreak of infectious diseases (eg syphilis) to identify potential causes of infection. It can also be used to identify individuals with as many risks as possible during a foodborne outbreak. Advantages of case finding include low cost and low labor costs and case finding improves positive predictive value of diagnostic tests by targeting high risk patients with higher basis. The case finding tool helps improve personal care and state cost by targeting preventive care. The main drawback is the possibility of expanding the health disparity because it is difficult to reach high risk groups (homeless, refugees etc.).
The knowledge of epidemiological research explains to define the clinical features, distribution, causes, behavioral characteristics and determinants of diseases that have a serious effect on the health of the local population, especially those potentially preventable Or at the individual, community and structural level, or in other respects special public concerns, such as mental health. The World Health Organization (WHO) worldwide disease burden project evaluates the relative importance of all infectious diseases and non-communicable diseases along with intentional hormones (suicide, war etc.). The world burden of disease does not explain the degree to which disease prevention or treatment is possible, but it is important for public health to provide a useful guide that disease has the world's greatest influence [24].
- Characterization of infectious diseases
The rate and onset of infectious diseases can be qualitatively defined for the cause of the disease. The cause of the infection is a microscopic or macroscopic pathogen that can potentially replicate itself and invade the tissues of the human body. Furthermore, it produces toxins and poisons cells. The interaction of these pathogens, the rate of proliferation in the human body and the immune response of the human body are indispensable for determining the progress of the infection. The conclusion of this study is that, [6] And [16] The basic principle in epidemiology of infectious diseases is to understand the whole process and to gain insight as to how specific interventions at different stages control how to prevent disease spread.
Illness occurs when an infectious agent finds its way and enters the human body through what is known as an invasive route. Potential pathways for successful disease transmission are the respiratory tract, the gastrointestinal tract and the skin. Infectious agents such as Mycobacterium tuberculosis invade the human body through the air inhaled into the lungs. For example, a pathogen causing diarrhea invades the human body by contaminated food, water ingested in the mouth, or non hygienic hands. Naturally human skin can function as a barrier against many infectious agents, but if infected mosquitoes suck blood through the skin, infectious agents may invade the human body.
In the early stages, hosts are more susceptible to infection. This is a stage where no pathogen exists in anybody's system, but there are low-level unidentified suspected host immunity. Examples of people entering this stage include, for example, those who are shaking hands with a cold and those who live in the same room as adults with tuberculosis.
The host is then exposed to infection. The parasites multiply multiply and enter the host, but the host shows no clear signs of infection and the number of pathogens is small enough to cause further infection. At this stage, we place individuals on the exposed stages. Exposure is the stage immediately after infectious pathogens invade and evaluate proliferation. An example is said to be exposed when a person ingests bacterial contaminated food that causes typhoid fever (Salmonella typhii). However, when the bacteria reach the inner surface of the intestine and start proliferation, it is said that the person has entered an infected stage. However, at this stage there are not necessarily clinical symptoms of this disease. Clinical symptoms are trained in cases where coincidence is found between disease symptoms (human complaints such as headache, vomiting, dizziness) and disease symptoms (high temperature, high pulse rate, swelling of body organs, etc.) It is only detected by medical staff. Entering this stage, the pathogen spreads sufficiently to acquire the possibility of spreading to other unacceptable individuals, and the pathogen enters the infectious stage. Infected people can become carriers, but infections themselves are not. If they are infectious, they are called active cases. After the pathogen is removed from the afflicted individuals and pathogens, the individual has reached a restored stage after the host has been cleared from its infectious disease stage. The recovered stage is a general term to infer the complete recovery from disease, ineffective or dead.
This absolute classification of infection (susceptibility, exposure, infectivity, or recoverability) depends only on the ability of the disease to pass or transmit the pathogen (in this case the host). The gathering here is that the status of the host regarding the illness is irrelevant. In other words, individuals who actually have a complete sense of health without symptoms may release large amounts of pathogens. The boundary between exposure and infection (and infection and recovery) is somehow gray and the tendency of transmission is not as simple as turning a button on or off. This is in addition to understanding the complex nature of infectious diseases as well as the variability responsive to the individual's disease and pathogen levels over the infection period. It is important to note that during symptom onset, it does not necessarily correlate with a specific stage of infection.
2. Method
Research from [11] [13] [19] [23] We show that adequately crafted public health research methods are needed to achieve the symbolic purpose of epidemiological studies.
a. Disclose the cause, origin and environmental factors that affect health to provide scientific basis for prevention of illness and injury and promotion of health.
b. Determine the relative importance of disease, disability, and cause of death to establish research and behavior priorities.
c. Identify the part of the population that has the greatest risk due to the specific cause of the illness so that the indicated behavior is properly indicated.
d. Evaluate the effectiveness of health programs and services to improve population health.
People in epidemiological research are primarily concerned with finding the dominant features in determining the pattern of illnesses and how to propagate or diffuse them.
We have a certain population N and we assume that the population is divided into three states: susceptibility S, infection I, and recovery or immunity R. Most specifically, this model covers the grandest SIR model of its simplest form.
The first group is an individual capable of infecting a specific disease. The second group consists of individuals that are infected and able to infect others. Sometimes these models may include class E of infected individuals who are infected, but are not yet ill. Finally, Class R will recover from the disease and present people who have escaped infection. Most viral diseases such as measles and varicella cause an immune response in the body [5]. If the body sees a specific disease, future infections are not very reliable. After the hosts become infected, they develop permanent immunity to the disease.
2.1 Modeling considerations
To model epidemic diseases, it is necessary to consider the population structure and population statistics (stratification by age, sex, place etc.), the natural course of infection (incubation period, infection period, immunity etc.) and intervention (stage of disease transmission) .
2.1.1 Transmission Rate
Consider an individual susceptible to sickness.
· Contact rate applied to all individuals irrespective of other individual & c & # 39; infection status.
· The proportion of infectious individuals who need to contact infected people and contact them is I / N is the proportion of infectious population, not infected, and N is the total population , I / N.
The infection rate from infectious individuals is given by "pcI / N". It is usually called infectivity and p is the probability of transmission when an infectious individual comes in contact with the suspect.
Considering all susceptible individuals, the total transmission of the population is pcSI / N, where S is the number of susceptible individuals. In most cases, "Personal computer" is written as "b # 39;
2.2 Simulation of epidemic model (SIR)
Derivative methods for calculating the time derivatives of S, I and R are implemented. Given the values of S, I and R at time t, the derivative calculates the time derivatives of S, I and R. Contains model parameters such as recovery phase and transmission speed.
The population size N is always S + I + R because there are no births or deaths in the model.
dS / dt = - bSI / N + gR,
dI / dt = bSI / N-aI,
dR / dt = aI - gR
As with many processes related to living organisms, the spread of diseases caused by microorganisms through the population can be mathematically modeled using differential equations. Numerous models of diverse complexity have been developed to describe the dynamics of population disease spread, but the SIR model presented here spreads from person to person, with relatively simple things, Measles, smallpox, influenza.
In the SIR model, members of a group are classified into one of three groups: those who are susceptible to infection, those who can infect susceptible people, and those who recover from disease, eventually You can escape infection. The individual's movements are unidirectional and the two basic parameters of the model, a (day infection rate) and b (recovery rate), act as rate constants to control the rate of advance of members to I and R groups, Each. A complex parameter, g = a / b, is often used and is called a contact number. The SIR model is a differential equation
Solving such an equation is algebraically difficult, so integration techniques are used. By doing so, you can see different rate changes in each stage of the model over time. When differentiating an equation, it shows how the inclination (change in velocity) is related to the model at what time and at what point.
Initially, S (0) = 1.
dI / dt = bsi - ai = (bs / a - 1) ai, I = I / N, s = S / N
Trends will now occur as the number of infections increases.
dI / dt> 0.
This is true when b / a> 1.
Conversely, if the number of infections decreases, the disease disappears.
dI / dt
This is true when b / a <1.
b / a = R 0 is the base playback number. It is the average number of secondary infections caused by a single infectious case with a completely suspicious population.
When the initial conditions of these groups are specified, the change in size of these groups over time is plotted.
- simulation result
Whether an epidemic occurs under certain initial conditions can be discussed from the point of view of contact numbers and the transition between epidemiological and non epidemiological states is based on the initial percentage of susceptible populations of infected numbers It is equal to reciprocal. Recovery rate b can also be introduced indirectly as a more accessible period of disease 1 / b .
Discussing popular trends from the viewpoint of these easier understandable parameters, R To convert to the actual model parameters behind the scenes, it is possible to tailor the discussion of important topics in general. The dynamic nature of the output makes it easy to discuss the influence of different parameters on the nature of the disease propagation within the population, not necessarily depending on the equation governing the model. In particular, the importance of the number of infections and the influence of the artificial migration members of the population, the influence on the group recovered (and immune) directly from the susceptibility group via immunity can be controlled by manipulating the appropriate speed of the model Can be easily investigated.
- Debate
Careful examination of the SIR model reveals insight into the dynamics of population diseases. For example, if the proportion of the population of infected groups is increasing first (ie, dI / dt> 0 = t = 0), which means that the epidemic has started. The transition between epidemic diseases and non- dI / dt = 0, and the examination of the differential equations shows that this transition point is b / a . Likewise, the peak of the epidemic, s = b / a The rate of change of the infected group will no longer increase and will begin to decline. In the contact number, the interpretation of the "real world" can be easily understood: the average number of suspect members of the population. The infected individual spreads the disease while the individual is in the infected group. The epidemic anatomical structure is initially a small number according to stochastic properties, the number of infections is not extreme. After that infections will increase and speed will increase. As infection depletes the number of problems, the spreading factor decreases over time.
4.1 Restrictions
The classical SIR model presented here asserts that the total size of the population is constant and the populations are homogeneous and homogeneously mixed. Mixing depends on many factors, including age, gender, geographic location, etc. Different geographical and socio-economic groups have different contact rates. Also, the model ignores random effects. Random effect, s Or Me It's small.
- Conclusion
In order to control the spread of the disease, it is necessary to choose the optimal solution for maximum public health benefit. Mathematical models will help you better understand the transmission of infections and test control strategies. In this paper, we can solve fashion trends by simulating trendy problems using SIR model and R statistics package program. Different deterministic models can be constructed by selecting different numbers and types of epidemic models. The analysis approach is based on dynamic system theory. Since it is reasonably enough to justify the modeling method, we clarify what underwriting hypothesis is. For optimal results, model analysis and simulation prediction show important data to collect and control executable strategies. Estimate R 0 For various diseases, it is useful for comparing diseases. If R When 0> 1, R 0 S (0) <1. Therefore, if the initial susceptible fraction is 1 / R 0, for example by immunizing, prevention of epidemics can be prevented.
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