Mathematical Models of Infectious Diseases in Multiple Populations

1989 ◽  
pp. 102-108 ◽  
Author(s):  
Wei-min Liu
Keyword(s):  
Infectious Diseases ◽  
Mathematical Models ◽  
Multiple Populations

scholarly journals Influencing public health policy with data-informed mathematical models of infectious diseases: Recent developments and new challenges

Epidemics ◽  
2020 ◽  
Vol 32 ◽  
pp. 100393 ◽  
Author(s):  
Amani Alahmadi ◽  
Sarah Belet ◽  
Andrew Black ◽  
Deborah Cromer ◽  
Jennifer A. Flegg ◽  
...  
Keyword(s):  
Public Health ◽  
Health Policy ◽  
Infectious Diseases ◽  
Mathematical Models ◽  
Public Health Policy ◽  
Recent Developments ◽  
New Challenges ◽  
Data Informed

Mathematics for Infectious Diseases; Deterministic Models: A Key

2015 ◽  
Vol 7 (1) ◽  
Author(s):  
Manindra Kumar Srivastava ◽  
Purnima Srivastava
Keyword(s):  
Infectious Diseases ◽  
Mathematical Models ◽  
Whooping Cough ◽  
Deterministic Models ◽  
Ancient India ◽  
Current Information ◽  
Clear Description ◽  
Methods Of Control

The occurrence of infectious diseases was the principle reason for the demise of the ancient India. The main infectious diseases were smallpox, measles, influenza and typhus. There were also other diseases such as whooping cough, the mumps and diphtheria. It would be very difficult to obtain current information regarding important diseases, methods of transmission, methods of control and the likes. Since the wrong theories or knowledge have hindered advances in understanding. Therefore, this paper seeks to give a simple and clear description of mathematical models for infectious diseases. It has become important tools in understanding the fundamental mechanisms that drive the spread of infectious diseases.

Practical models for the care of HIV and AIDS patients

1996 ◽  
Vol 7 (2) ◽  
pp. 91-97 ◽  
Author(s):  
S. C. Brailsford ◽  
R. Basu Roy ◽  
A. K. Shahani ◽  
S. Sivapalan
Keyword(s):  
Decision Making ◽  
Infectious Diseases ◽  
Mathematical Models ◽  
Resource Use ◽  
Health Professionals ◽  
Operational Research ◽  
Resource Planning ◽  
Hiv And Aids ◽  
Operational Modelling ◽  
Aid Decision

Mathematical models for infectious diseases are of lim ited use in providing practical help to health professionals. In this paper we discuss computer models developed jointly by operational research mathematicians and clinicians to meet this need. W e use the term 'operational modelling' to describe this pragm atic approach. The models can aid decision-making at a resource planning level and can also be used by clinicians to monitor and im prove patient care. The models incorporate uncertainty and variability and are therefore mathematically complex, but are easy to use and provide a great deal of useful information about morbidity and resource use.

scholarly journals Disease Detectives: Using Mathematics to Forecast the Spread of Infectious Diseases

2021 ◽  
Vol 8 ◽  
Author(s):  
Heather Z. Brooks ◽  
Unchitta Kanjanasaratool ◽  
Yacoub H. Kureh ◽  
Mason A. Porter
Keyword(s):  
Mathematical Model ◽  
Infectious Diseases ◽  
Mathematical Models ◽  
Human Behavior ◽  
Government Policies ◽  
Disease Spread

The COVID-19 pandemic has led to significant changes in how people are currently living their lives. To determine how to best reduce the effects of the pandemic and start reopening communities, governments have used mathematical models of the spread of infectious diseases. In this article, we introduce a popular type of mathematical model of disease spread. We discuss how the results of analyzing mathematical models can influence government policies and human behavior, such as encouraging mask wearing and physical distancing to help slow the spread of a disease.

scholarly journals Optimization of Precontrol Methods and Analysis of a Dynamic Model for Brucellosis: Model Development and Validation (Preprint)

2020 ◽  
Author(s):  
Yihao Huang ◽  
Mingtao Li
Keyword(s):  
Infectious Diseases ◽  
Mathematical Models ◽  
Rapid Development ◽  
Transmission Model ◽  
Dynamic Threshold ◽  
Key Populations ◽  
Human Beings ◽  
Chinese People ◽  
Theoretical Support ◽  
Number Of Patients

BACKGROUND 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. OBJECTIVE 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. METHODS 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. RESULTS An improvement in the amount of glove-wearing was evident and significant (<i>P</i>&lt;.001), increasing from 51.01% before the precontrol methods to 66.22% after the precontrol methods, an increase of 15.21%. However, the amount of hat-wearing did not improve significantly (<i>P</i>=.95). Hat-wearing among the key populations increased from 57.3% before the precontrol methods to 58.6% after the precontrol methods, an increase of 1.3%. CONCLUSIONS 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.

Analysis with Mathematical Models Provides Insights into Infectious Diseases

2009 ◽  
Vol 4 (4) ◽  
pp. 176-182
Author(s):  
Olivier Restif ◽  
Juilee Thakar ◽  
Eric T. Harvill
Keyword(s):  
Infectious Diseases ◽  
Mathematical Models
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