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Jan 2021 DOI 10.14302/issn.2766-8681.jcsr-21-3701
Zhao BinCorresponding author
School of Science, Hubei University of Technology, Wuhan, Hubei, China.
Background The novel coronavirus (COVID-19) suddenly appeared in Wuhan, Hubei since December 2019, and quickly swept across China, then the whole world. Today, after more than 100 days of fighting against the virus, China's epidemic has been effectively controlled, but when we looking at the entire world, the novel coronavirus has rampaged globally, especially in the United States and many European countries. This paper mainly studies the impact of COVID-19 outbreaks at Hubei Province and the United States, fits the given data and predicts future trends. Methods Based on the theoretical basis of traditional differential equations and SIR infectious disease model1, and combined with the actual situation to improve the model. Hubei Province is modeled in different time periods, and the effects of birth rate and natural mortality on the model are analyzed. Since the birth rate and natural mortality in the United States in recent years cannot be found, the epidemic situation in the United States can only be analyzed based on the absence of births and natural deaths. Finally, we used Netlogo2 to establish a closed environment (Small World), and combined with known data to conduct simulation experiments on COVID-19 infection. Findings Through the analysis of given data through the SIR model, it is found that before the Chinese government has taken comprehensive measures to cure patients (before 10 February), the number of patients in Hubei Province will reach the peak at the end of February, and will gradually decline thereafter, and on 20 March, the epidemic will be effectively controlled in the future, which coincides with the fact that Wuhan closed the last mobile cabin hospital on 10 March. On the other hand, after the Chinese government tried its best to cure the patients (after 21 February), the number of patients continued to decline over time and will reach 0 in mid-April, which is also consistent with the actual data. According to the factors of birth and natural death, the sensitivity analysis of the above model found that when the epidemic situation is at its peak, it has little effect on the curve, but when the epidemic situation gradually flattens, it still has a certain effect on the trend of the curve. Finally, looking at the situation in the United States, due to the high transmission rate, the number of patients in the United States continues to rise and is expected to reach its maximum in mid-June. We also use Netlogo to simulate the environment in which the virus spread, and find that the general trend of the curves is also consistent with the actual curves. Interpretation The Chinese government has taken various measures to deal with the novel coronavirus pneumonia, including the establishment of two temporary hospitals and dozens of sheltered hospitals, the temporary transformation of university dormitories into isolation rooms345, the closure of Wuhan, the ban on the movement of people and so on. These measures have helped to reduce the spread of the virus and greatly increased the patient's cure rate. But the US government ’s actions are not as effective as China’s, not only because the government ’s actions are inappropriate and untimely, and the people’s opposition to isolation has not subsided. As a result, the virus has spread widely in the United States. More than one million people have been infected with the virus, and tens of thousands of people have died from COVID-196.
Mar 2020 DOI 10.14302/issn.2766-8681.jcsr-21-3719
Zhao BinCorresponding author
School of Science, Hubei University of Technology, Wuhan, Hubei, China.
Background Since receiving unexplained pneumonia patients at the Jinyintan Hospital in Wuhan, China in December 2019, the new coronavirus (COVID-19) has rapidly spread in Wuhan, China and spread to the entire China and some neighboring countries. We establish the dynamics model of infectious diseases and time series model to predict the trend and short-term prediction of the transmission of COVID-19, which will be conducive to the intervention and prevention of COVID-19 by departments at all levels in mainland China and buy more time for clinical trials. Methods Based on the transmission mechanism of COVID-19 in the population and the implemented prevention and control measures, we establish the dynamic models of the six chambers, and establish the time series models based on different mathematical formulas according to the variation law of the original data. Findings The results based on time series analysis and kinetic model analysis show that the cumulative diagnosis of pneumonia of COVID-19 in mainland China can reach 36,343 after one week (February 8, 2020), and the number of basic regenerations can reach 4.01. The cumulative number of confirmed diagnoses will reach a peak of 87,701 on March 15, 2020; the number of basic regenerations in Wuhan will reach 4.3, and the cumulative number of confirmed cases in Wuhan will reach peak at 76,982 on March 20. Whether in Mainland China or Wuhan, both the infection rate and the basic regeneration number of COVID-19 continue to decline, and the results of the sensitivity analysis show that the time it takes for a suspected population to be diagnosed as a confirmed population can have a significant impact on the peak size and duration of the cumulative number of diagnoses. Increased mortality leads to additional cases of pneumonia, while increased cure rates are not sensitive to the cumulative number of confirmed cases. Interpretation Chinese governments at various levels have intervened in many ways to control the epidemic. According to the results of the model analysis, we believe that the emergency intervention measures adopted in the early stage of the epidemic, such as blocking Wuhan, restricting the flow of people in Hubei province, and increasing the support to Wuhan, had a crucial restraining effect on the original spread of the epidemic. It is a very effective prevention and treatment method to continue to increase investment in various medical resources to ensure that suspected patients can be diagnosed and treated in a timely manner. Based on the results of the sensitivity analysis, we believe that enhanced treatment of the bodies of deceased patients can be effective in ensuring that the bodies themselves and the process do not result in additional viral infections, and once the pneumonia patients with the COVID-19 are cured, the antibodies left in their bodies may prevent them from reinfection COVID-19 for a longer period of time.
Dec 2025 DOI 10.14302/issn.2643-2811.jmbr-25-5731
Mukeredzi InnocentCorresponding author
Background Typhoid fever remains a significant public health issue in Harare City, Zimbabwe, exacerbated by recurrent outbreaks between 2018 and 2020. Key challenges, including inadequate water supply and sanitation infrastructure, high population density, and limited healthcare access, have intensified the disease burden. Understanding the key transmission drivers and assessing the impact of various interventions are essential for informing policy and health strategies. Objectives This study aimed to: 1: To predict future trends in typhoid fever cases Harare City typhoid hot areas. 2: To develop a mathematical model to simulate the spread of typhoid fever incidence under different intervention scenarios and recommend evidence-based strategies for reducing the disease burden in Harare City. Methods A dynamic compartmental SIR-based model, adapted from the Pitzer Vaccine Effectiveness (VE) framework, was employed to simulate disease transmission. This model accounted for both short-cycle (human-to-human) and long-cycle (environmental) transmission pathways. Data from Harare City (2018–2020) were used for model calibration and forecasting, and sensitivity analysis was performed to assess the impact of different intervention levels. Findings The model identified inadequate sanitation, contaminated water sources, and low health- seeking behaviors as primary drivers of typhoid transmission. In the absence of interventions, the model projected a sustained high rate of transmission. However, treatment and WASH interventions could reduce the disease burden by 50–60%, while combined strategies incorporating vaccination and education led to an 80% reduction in cases. Sensitivity analysis indicated that treatment and WASH interventions were particularly impactful at moderate coverage levels. Conclusion Mathematical modeling effectively demonstrated the multifactorial drivers of typhoid fever transmission in Harare. Integrated interventions that combine WASH, vaccination, treatment, and education present the most promising approach for long-term control of the disease. The findings offer a solid, data-driven foundation for public health decision-making and resource allocation.
Jan 2021 DOI 10.14302/issn.2692-1537.ijcv-20-3383
Zhao BinCorresponding author
School of Science, Hubei University of Technology, Wuhan, Hubei, China.
With the spread of the new coronavirus around the world, governments of various countries have begun to use the mathematical modeling method to construct some virus transmission models assessing the risks of spatial spread of the new coronavirus COVID-19, while carrying out epidemic prevention work, and then calculate the inflection point for better prevention and control of epidemic transmission. This work analyzes the spread of the new coronavirus in China, Italy, Germany, Spain, and France, and explores the quantitative relationship between the growth rate of the number of new coronavirus infections and time. In investigating the dynamics of a disease such as COVID-19, its mathematical representation can be constructed at many levels of details, guided by the questions the model tries to help answer. Mathematical sophistication may have to yield to a more pragmatic approach closer to the ability to make predictions that inform public health policies. Background In December 2019 , the first Chinese patients with pneumonia of unknown cause is China admitted to hospital in Wuhan, Hubei Jinyintan , since then, COVID-19 in the rapid expansion of China Wuhan, Hubei, in a few months time, COVID-19 is Soon it spread to a total of 34 provincial-level administrative regions in China and neighboring countries, and Hubei Province immediately became the hardest hit by the new coronavirus. In an emergency situation, we strive to establish an accurate infectious disease retardation growth model to predict the development and propagation of COVID-19, and on this basis, make some short-term effective predictions. The construction of this model has Relevant departments are helpful for the prevention and monitoring of the new coronavirus, and also strive for more time for the clinical trials of Chinese researchers and the research on vaccines against the virus to eliminate the new corona virus as soon as possible. Methods According to the original data change law, Establish a Logistic growth model, we collect and compare and integrate the spread of COVID-19 in China, Italy, France, Spain and Germany, record the virus transmission trend among people in each country and the protest measures of relevant government departments. Findings Based on the analysis results of the Logistic model model, the Logistic model has a good fitting effect on the actual cumulative number of confirmed cases, which can bring a better effect to the prediction of the epidemic situation and the prevention and control of the epidemic situation. Interpretation In the early stage of the epidemic, due to inadequate anti-epidemic measures in various countries, the epidemic situation in various countries spread rapidly. However, with the gradual understanding of COVI D -19, the epidemic situation began to be gradually controlled, thereby retarding growth
Oct 2019 DOI 10.14302/issn.2642-3146.jec-19-3049
A.A BerezinCorresponding author
Independent Researcher
As a result of mathematical modeling it has been shown that any closed electrical line can be interpreted as a ring waveguide where the Fermi-Pasta-Ulam recurrences of the electron and phonon currents interact with each other on the transversal and longitudinal periodical structures of the line conductor’s crystalline lattice as well as on the structures of the wire insulation. An electronic circuit simulating the mathematical model through the dynamics of magnons and phonons in a closed ferrite core with two different coils switched into the shoulders of a multivibrator has been developed. It has been demonstrated that the interacting ferromagnetic and ferroacoustic resonances excited simultaneously in a ferrite core qualitatively correspond to the dynamics of the electron and phonon currents interaction process in a closed electrical line.
Jul 2019 DOI 10.14302/issn.2694-1198.jge-19-2756
Volobuev A.N.Corresponding author
Samara State Medical University, Department of Medical Physics, Samara, Russia
Processes of genetic-mathematical modeling of a population development are considered. A basic distinction in the mathematical description of a family tree and a population is shown. In a family tree alternation of generations has discrete character. In a population there is a continuous alternation of generations. The method of the differential equations is applied for the description of a population. It is shown that mutational process in a population can be described with use of a Green’s function. For radiating influence on a population the universal evolutionary law is found.
Mar 2019 DOI 10.14302/issn.2643-2811.jmbr-19-2652
Zhao BinCorresponding author
School of Science, Hubei University of Technology, Wuhan, Hubei, China.
This methods paper combines molecular docking and biomathematical modeling to construct a virtual neuron framework for studying sleep‑related memory consolidation. It outlines model components and validation approach.