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Brucella is a gram-negative, nonmotile bacterium without a capsule. The infection scope of Brucella is wide. The major source of infection is mammals such as cattle, sheep, goats, pigs, and dogs. Currently, human beings do not transmit Brucella to each other. When humans eat Brucella-contaminated food or contact animals or animal secretions and excretions infected with Brucella, they may develop brucellosis. Although brucellosis does not originate in humans, its diagnosis and cure are very difficult; thus, it has a huge impact on humans. Even with the rapid development of medical science, brucellosis is still a major problem for Chinese people. Currently, the number of patients with brucellosis in China is 100,000 per year. In addition, due to the ongoing improvement in the living standards of Chinese people, the demand for meat products has gradually increased, and increased meat transactions have greatly promoted the spread of brucellosis. Therefore, many researchers are concerned with investigating the transmission of Brucella as well as the diagnosis and treatment of brucellosis. Mathematical models have become an important tool for the study of infectious diseases. Mathematical models can reflect the spread of infectious diseases and be used to study the effect of different inhibition methods on infectious diseases. The effect of control measures to obtain effective suppression can provide theoretical support for the suppression of infectious diseases. Therefore, it is the objective of this study to build a suitable mathematical model for brucellosis infection.

We aimed to study the optimized precontrol methods of brucellosis using a dynamic threshold–based microcomputer model and to provide critical theoretical support for the prevention and control of brucellosis.

By studying the transmission characteristics of Brucella and building a Brucella transmission model, the precontrol methods were designed and presented to the key populations (Brucella-susceptible populations). We investigated the utilization of protective tools by the key populations before and after precontrol methods.

An improvement in the amount of glove-wearing was evident and significant (

By demonstrating the optimized precontrol methods for a brucellosis model built on a dynamic threshold–based microcomputer model, this study provides theoretical support for the suppression of Brucella and the improved usage of protective measures by key populations.

Infectious diseases enter the human body through pathogens such as bacteria, fungi, or viruses, causing bodily damage or even death. In serious cases, infectious diseases cause large-scale transmission of diseases among the population [

In terms of transmission,

At present, most of the studies on the transmission of brucellosis are in single populations. When studying the process of infection, transmission between humans is ignored, and only direct infection is considered. However, the transmission process of brucellosis is complicated, and its transmission form is not the same in different regions and between populations. Due to its asymptomatic characteristics, an outbreak of brucellosis is often synchronized between humans and animals. Investigating the spread of brucellosis would help researchers understand and prevent the onset and large-scale transmission of brucellosis. Researchers have built a model for the transmission of brucellosis between humans and flocks. The effects of different control methods on the transmission of brucellosis are known, which aids researchers in designing more effective methods to inhibit the spread of brucellosis [

By understanding the effect that control measures have on transmission suppression, we can create theoretical support for the suppression of infectious diseases [

Based on the characteristics of

In _{a}, V_{a}, E_{a}, and I_{a} represent ordinary ewes that are susceptible, vaccinated, inapparently infected, and isolated positive infections, respectively,. S_{b}, V_{b}, E_{b}, and I_{b} represent susceptible, vaccinated, inapparently infected, and isolated positive infections of other goats, respectively. W represents _{c}, I_{c}, and Y_{c} represent susceptible, acute, and chronic human populations, respectively. As shown in

In these equations, A is the input constant value, b is the probability of each infection, m is the conversion rate of the young to mature goats, d is the production rate, t is the ratio of the adult and young infection rates in the flock, which should be between 0 and 1 combining with the actual situation, a is the mortality rate of infected sheep due to brucellosis, k is the release rate of infected bacteria per unit time, and d is the Brucella mortality rate in the entire goat flock.

Diagram of the transmission of Brucellosis. S_{a}: susceptible ewe; V_{a}: immunized ewe; E_{a}: recessive infected ewe; I_{a}: isolated infected ewe; S_{b}: sheep with hepatitis B; V_{b}: immunized sheep; E_{b}: recessive infected sheep; I_{b}: isolated infected sheep; W: environmental Brucella; S_{c}: susceptible human population; I_{c}: acutely infected human population; Y_{c}: chronically infected human population.

Since the last 3 equations are independent of the previous 9, only the first 9 equations are included when considering model dynamics. Within equations 1-9, an equilibrium point free of brucellosis can be found, represented by the equation below:

The resulting positive invariant set of the research system is as follows:

Based on the number of brucellosis cases reported on the internet, a hypothetical estimation and numerical simulation process were performed. The values used in the numerical simulation are described here. First, 2-3 years is the inventory time of ordinary ewes, and the number of ewes is about 4.2 million; therefore, the average removal rate of ordinary ewes d_{b} is 0.4, and the supplement amount of ordinary ewes A_{b} is 1.68 million. According to the actual data, the production rate of the flock is about 60%. Therefore, the removal rate of other goats is 0.6, and the supplement amount is 1.976 million. Second, according to population data, the natural death rate in the human population is 5.68%; thus, the supplement rate of the human population is estimated, and the supplement amount is about 9,150. Third, according to the existing data, the culling rate of infected goats can reach 0.15; therefore, the positive detection rate of ordinary ewes and other goats was set to 0.15 for this model. Additionally, according to the average 1-month survival time of affected goats, the culling rate of infected ewes and other goats is set to 12.

Studies have shown that the daily behavior of susceptible populations (that is, those with high frequency of contact with animals) is the key to whether brucellosis can be effectively transmitted, and that the irregular daily behaviors of susceptible populations greatly increase the rate of

The precontrol methods in this study included giving lectures on prevention, control, and health education to the experimental population, so that they would have a comprehensive understanding of

Follow-up visits or phone calls were made to infected patients to understand whether they are used to the corresponding prevention and control behaviors, and whether their prevention and control behaviors are correct. Additionally, any questions that susceptible populations and infected patients have about brucellosis should be answered promptly to reduce panic and strengthen the effect of precontrol methods.

SPSS 22.0 software (IBM) was used to statistically analyze the data obtained in this study and make corresponding statistical descriptions. The Chi-square test was used to statistically analyze the clinical characteristics of the diseased populations with different disease forms and different

The numerical simulation process was performed according to the actual data, which enabled us to generate the data shown in

Fitting and short-term prediction of Brucellosis cases. This figure shows the relationship between the number of brucellosis cases and time in the region, and the appropriate number of measures required to obtain a forecast of the number of future brucellosis cases.

The mathematical model of this study suggests that humans are infected with

The distribution of brucellosis in the study population is shown in

The number of people with brucellosis in different stages by gender (A) and age (B).

Clinical symptoms of patients at different stages.

Utilization of protective tools by the key populations.

In this study, we first designed a transmission model of

After the precontrol methods in this study, the study populations improved their use of protective tools compared to before the precontrol methods. The utilization of several protective tools improved to varying degrees. In particular, the improvement in glove-wearing was the most striking, as it increased from 51.01% before the precontrol methods to 66.22% after the precontrol methods, an increase of 15.21%. The difference was statistically significant (

This study researched brucellosis transmission and the effects of precontrol methods on protective equipment usage by key populations. The results of our numerical simulation indicated that the incidence of brucellosis is projected to become stable, without a major increase or decrease. After the precontrol methods, the utilization of protective tools by the key populations improved significantly. Most people in the key populations wear gloves, masks, rubber shoes, hats, and work clothes; wash their hands frequently; and disinfect animal shelters. This study achieved our objectives, but there are still some deficiencies in the research process. Due to the limitation of time, this study failed to analyze the prevalence of brucellosis in key populations after the precontrol methods. In a future study, we aim to research precontrol methods and the subsequent number of brucellosis infections.

None declared.